## What changes were proposed in this pull request?
SQL hint syntax:
* support expressions such as strings, numbers, etc. instead of only identifiers as it is currently.
* support multiple hints, which was missing compared to the DataFrame syntax.
DataFrame API:
* support any parameters in DataFrame.hint instead of just strings
## How was this patch tested?
Existing tests. New tests in PlanParserSuite. New suite DataFrameHintSuite.
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#18086 from bogdanrdc/SPARK-20854.
## What changes were proposed in this pull request?
Add build-int SQL function - UUID.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18136 from wangyum/SPARK-20910.
## What changes were proposed in this pull request?
We changed the parser to reject unaliased subqueries in the FROM clause in SPARK-20690. However, the error message that we now give isn't very helpful:
scala> sql("""SELECT x FROM (SELECT 1 AS x)""")
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input 'FROM' expecting {<EOF>, 'WHERE', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'LATERAL', 'WINDOW', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'SORT', 'CLUSTER', 'DISTRIBUTE'}(line 1, pos 9)
We should modify the parser to throw a more clear error for such queries:
scala> sql("""SELECT x FROM (SELECT 1 AS x)""")
org.apache.spark.sql.catalyst.parser.ParseException:
The unaliased subqueries in the FROM clause are not supported.(line 1, pos 14)
## How was this patch tested?
Modified existing tests to reflect this change.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18141 from viirya/SPARK-20916.
## What changes were proposed in this pull request?
Add build-int SQL function - DAYOFWEEK
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18134 from wangyum/SPARK-20909.
## What changes were proposed in this pull request?
This PR adds built-in SQL function `(REPLACE(<string_expression>, <search_string> [, <replacement_string>])`
`REPLACE()` return that string that is replaced all occurrences with given string.
## How was this patch tested?
added new test suites
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18047 from kiszk/SPARK-20750.
## What changes were proposed in this pull request?
See class doc of `ConstantPropagation` for the approach used.
## How was this patch tested?
- Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17993 from tejasapatil/SPARK-20758_const_propagation.
## What changes were proposed in this pull request?
This pr added parsing rules to support table column aliases in FROM clause.
## How was this patch tested?
Added tests in `PlanParserSuite`, `SQLQueryTestSuite`, and `PlanParserSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18079 from maropu/SPARK-20841.
### What changes were proposed in this pull request?
In Cache manager, the plan matching should ignore Hint.
```Scala
val df1 = spark.range(10).join(broadcast(spark.range(10)))
df1.cache()
spark.range(10).join(spark.range(10)).explain()
```
The output plan of the above query shows that the second query is not using the cached data of the first query.
```
BroadcastNestedLoopJoin BuildRight, Inner
:- *Range (0, 10, step=1, splits=2)
+- BroadcastExchange IdentityBroadcastMode
+- *Range (0, 10, step=1, splits=2)
```
After the fix, the plan becomes
```
InMemoryTableScan [id#20L, id#23L]
+- InMemoryRelation [id#20L, id#23L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- BroadcastNestedLoopJoin BuildRight, Inner
:- *Range (0, 10, step=1, splits=2)
+- BroadcastExchange IdentityBroadcastMode
+- *Range (0, 10, step=1, splits=2)
```
### How was this patch tested?
Added a test.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18131 from gatorsmile/HintCache.
## What changes were proposed in this pull request?
spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12345
For this case, the result we expected is `12346`
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12345
For this case, the result we expected is `-12346`
Because in `Ceil` or `Floor`, `inputTypes` has no FloatType, so it is converted to LongType.
## How was this patch tested?
After the modification:
spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12346
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12346
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18103 from 10110346/wip-lx-0525-1.
## What changes were proposed in this pull request?
Add built-in SQL function `CH[A]R`:
For `CHR(bigint|double n)`, returns the ASCII character having the binary equivalent to `n`. If n is larger than 256 the result is equivalent to CHR(n % 256)
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18019 from wangyum/SPARK-20748.
## What changes were proposed in this pull request?
It is reported that there is performance downgrade when applying ML pipeline for dataset with many columns but few rows.
A big part of the performance downgrade comes from some operations (e.g., `select`) on DataFrame/Dataset which re-create new DataFrame/Dataset with a new `LogicalPlan`. The cost can be ignored in the usage of SQL, normally.
However, it's not rare to chain dozens of pipeline stages in ML. When the query plan grows incrementally during running those stages, the total cost spent on re-creation of DataFrame grows too. In particular, the `Analyzer` will go through the big query plan even most part of it is analyzed.
By eliminating part of the cost, the time to run the example code locally is reduced from about 1min to about 30 secs.
In particular, the time applying the pipeline locally is mostly spent on calling transform of the 137 `Bucketizer`s. Before the change, each call of `Bucketizer`'s transform can cost about 0.4 sec. So the total time spent on all `Bucketizer`s' transform is about 50 secs. After the change, each call only costs about 0.1 sec.
<del>We also make `boundEnc` as lazy variable to reduce unnecessary running time.</del>
### Performance improvement
The codes and datasets provided by Barry Becker to re-produce this issue and benchmark can be found on the JIRA.
Before this patch: about 1 min
After this patch: about 20 secs
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17770 from viirya/SPARK-20392.
## What changes were proposed in this pull request?
This is a follow-up to SPARK-20857 to move the broadcast hint from Statistics into a new HintInfo class, so we can be more flexible in adding new hints in the future.
## How was this patch tested?
Updated test cases to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#18087 from rxin/SPARK-20867.
## What changes were proposed in this pull request?
This patch renames BroadcastHint to ResolvedHint (and Hint to UnresolvedHint) so the hint framework is more generic and would allow us to introduce other hint types in the future without introducing new hint nodes.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#18072 from rxin/SPARK-20857.
## What changes were proposed in this pull request?
As srowen pointed in 609ba5f2b9 (commitcomment-22221259), the previous tests are not proper.
This follow-up is going to fix the tests.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18048 from viirya/SPARK-20399-follow-up.
### What changes were proposed in this pull request?
After we adding a new field `stats` into `CatalogTable`, we should not expose Hive-specific Stats metadata to `MetastoreRelation`. It complicates all the related codes. It also introduces a bug in `SHOW CREATE TABLE`. The statistics-related table properties should be skipped by `SHOW CREATE TABLE`, since it could be incorrect in the newly created table. See the Hive JIRA: https://issues.apache.org/jira/browse/HIVE-13792
Also fix the issue to fill Hive-generated RowCounts to our stats.
This PR is to handle Hive-specific Stats metadata in `HiveClientImpl`.
### How was this patch tested?
Added a few test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#14971 from gatorsmile/showCreateTableNew.
### What changes were proposed in this pull request?
Currently, we have a bug when we specify `IF NOT EXISTS` in `INSERT OVERWRITE` data source tables. For example, given a query:
```SQL
INSERT OVERWRITE TABLE $tableName partition (b=2, c=3) IF NOT EXISTS SELECT 9, 10
```
we will get the following error:
```
unresolved operator 'InsertIntoTable Relation[a#425,d#426,b#427,c#428] parquet, Map(b -> Some(2), c -> Some(3)), true, true;;
'InsertIntoTable Relation[a#425,d#426,b#427,c#428] parquet, Map(b -> Some(2), c -> Some(3)), true, true
+- Project [cast(9#423 as int) AS a#429, cast(10#424 as int) AS d#430]
+- Project [9 AS 9#423, 10 AS 10#424]
+- OneRowRelation$
```
This PR is to fix the issue to follow the behavior of Hive serde tables
> INSERT OVERWRITE will overwrite any existing data in the table or partition unless IF NOT EXISTS is provided for a partition
### How was this patch tested?
Modified an existing test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18050 from gatorsmile/insertPartitionIfNotExists.
## What changes were proposed in this pull request?
spark-sql>SELECT ceil(1234567890123456);
1234567890123456
spark-sql>SELECT ceil(12345678901234567);
12345678901234568
spark-sql>SELECT ceil(123456789012345678);
123456789012345680
when the length of the getText is greater than 16. long to double will be precision loss.
but mysql handle the value is ok.
mysql> SELECT ceil(1234567890123456);
+------------------------+
| ceil(1234567890123456) |
+------------------------+
| 1234567890123456 |
+------------------------+
1 row in set (0.00 sec)
mysql> SELECT ceil(12345678901234567);
+-------------------------+
| ceil(12345678901234567) |
+-------------------------+
| 12345678901234567 |
+-------------------------+
1 row in set (0.00 sec)
mysql> SELECT ceil(123456789012345678);
+--------------------------+
| ceil(123456789012345678) |
+--------------------------+
| 123456789012345678 |
+--------------------------+
1 row in set (0.00 sec)
## How was this patch tested?
Supplement the unit test.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#18016 from heary-cao/ceil_long.
## What changes were proposed in this pull request?
spark-sql>select month("1582-09-28");
spark-sql>10
For this case, the expected result is 9, but it is 10.
spark-sql>select day("1582-04-18");
spark-sql>28
For this case, the expected result is 18, but it is 28.
when the date before "1582-10-04", the function of `month` and `day` return the value which is not we expected.
## How was this patch tested?
unit tests
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17997 from 10110346/wip_lx_0516.
## What changes were proposed in this pull request?
GenerateUnsafeProjection.writeStructToBuffer() did not honor the assumption that the caller must make sure that a value is not null before using the getter. This could lead to various errors. This change fixes that behavior.
Example of code generated before:
```scala
/* 059 */ final UTF8String fieldName = value.getUTF8String(0);
/* 060 */ if (value.isNullAt(0)) {
/* 061 */ rowWriter1.setNullAt(0);
/* 062 */ } else {
/* 063 */ rowWriter1.write(0, fieldName);
/* 064 */ }
```
Example of code generated now:
```scala
/* 060 */ boolean isNull1 = value.isNullAt(0);
/* 061 */ UTF8String value1 = isNull1 ? null : value.getUTF8String(0);
/* 062 */ if (isNull1) {
/* 063 */ rowWriter1.setNullAt(0);
/* 064 */ } else {
/* 065 */ rowWriter1.write(0, value1);
/* 066 */ }
```
## How was this patch tested?
Adds GenerateUnsafeProjectionSuite.
Author: Ala Luszczak <ala@databricks.com>
Closes#18030 from ala/fix-generate-unsafe-projection.
## What changes were proposed in this pull request?
In the previous approach we used `aliasMap` to link an `Attribute` to the expression with potentially the form `f(a, b)`, but we only searched the `expressions` and `children.expressions` for this, which is not enough when an `Alias` may lies deep in the logical plan. In that case, we can't generate the valid equivalent constraint classes and thus we fail at preventing the recursive deductions.
We fix this problem by collecting all `Alias`s from the logical plan.
## How was this patch tested?
No additional test case is added, but do modified one test case to cover this situation.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18020 from jiangxb1987/inferConstrants.
## What changes were proposed in this pull request?
We add missing attributes into Filter in Analyzer. But we shouldn't do it through subqueries like this:
select 1 from (select 1 from onerow t1 LIMIT 1) where t1.c1=1
This query works in current codebase. However, the outside where clause shouldn't be able to refer `t1.c1` attribute.
The root cause is we allow subqueries in FROM have no alias names previously, it is confusing and isn't supported by various databases such as MySQL, Postgres, Oracle. We shouldn't support it too.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17935 from viirya/SPARK-20690.
## What changes were proposed in this pull request?
Hive allows inserting data to bucketed table without guaranteeing bucketed and sorted-ness based on these two configs : `hive.enforce.bucketing` and `hive.enforce.sorting`.
What does this PR achieve ?
- Spark will disallow users from writing outputs to hive bucketed tables by default (given that output won't adhere with Hive's semantics).
- IF user still wants to write to hive bucketed table, the only resort is to use `hive.enforce.bucketing=false` and `hive.enforce.sorting=false` which means user does NOT care about bucketing guarantees.
Changes done in this PR:
- Extract table's bucketing information in `HiveClientImpl`
- While writing table info to metastore, `HiveClientImpl` now populates the bucketing information in the hive `Table` object
- `InsertIntoHiveTable` allows inserts to bucketed table only if both `hive.enforce.bucketing` and `hive.enforce.sorting` are `false`
Ability to create bucketed tables will enable adding test cases to Spark while I add more changes related to hive bucketing support. Design doc for hive hive bucketing support : https://docs.google.com/document/d/1a8IDh23RAkrkg9YYAeO51F4aGO8-xAlupKwdshve2fc/edit#
## How was this patch tested?
- Added test for creating bucketed and sorted table.
- Added test to ensure that INSERTs fail if strict bucket / sort is enforced
- Added test to ensure that INSERTs can go through if strict bucket / sort is NOT enforced
- Added test to validate that bucketing information shows up in output of DESC FORMATTED
- Added test to ensure that `SHOW CREATE TABLE` works for hive bucketed tables
Author: Tejas Patil <tejasp@fb.com>
Closes#17644 from tejasapatil/SPARK-17729_create_bucketed_table.
## What changes were proposed in this pull request?
This pr added a new Optimizer rule to combine nested Concat. The master supports a pipeline operator '||' to concatenate strings in #17711 (This pr is follow-up). Since the parser currently generates nested Concat expressions, the optimizer needs to combine the nested expressions.
## How was this patch tested?
Added tests in `CombineConcatSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17970 from maropu/SPARK-20730.
## What changes were proposed in this pull request?
This PR proposes three things as below:
- Use casting rules to a timestamp in `to_timestamp` by default (it was `yyyy-MM-dd HH:mm:ss`).
- Support single argument for `to_timestamp` similarly with APIs in other languages.
For example, the one below works
```
import org.apache.spark.sql.functions._
Seq("2016-12-31 00:12:00.00").toDF("a").select(to_timestamp(col("a"))).show()
```
prints
```
+----------------------------------------+
|to_timestamp(`a`, 'yyyy-MM-dd HH:mm:ss')|
+----------------------------------------+
| 2016-12-31 00:12:00|
+----------------------------------------+
```
whereas this does not work in SQL.
**Before**
```
spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
Error in query: Invalid number of arguments for function to_timestamp; line 1 pos 7
```
**After**
```
spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
2016-12-31 00:12:00
```
- Related document improvement for SQL function descriptions and other API descriptions accordingly.
**Before**
```
spark-sql> DESCRIBE FUNCTION extended to_date;
...
Usage: to_date(date_str, fmt) - Parses the `left` expression with the `fmt` expression. Returns null with invalid input.
Extended Usage:
Examples:
> SELECT to_date('2016-12-31', 'yyyy-MM-dd');
2016-12-31
```
```
spark-sql> DESCRIBE FUNCTION extended to_timestamp;
...
Usage: to_timestamp(timestamp, fmt) - Parses the `left` expression with the `format` expression to a timestamp. Returns null with invalid input.
Extended Usage:
Examples:
> SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
2016-12-31 00:00:00.0
```
**After**
```
spark-sql> DESCRIBE FUNCTION extended to_date;
...
Usage:
to_date(date_str[, fmt]) - Parses the `date_str` expression with the `fmt` expression to
a date. Returns null with invalid input. By default, it follows casting rules to a date if
the `fmt` is omitted.
Extended Usage:
Examples:
> SELECT to_date('2009-07-30 04:17:52');
2009-07-30
> SELECT to_date('2016-12-31', 'yyyy-MM-dd');
2016-12-31
```
```
spark-sql> DESCRIBE FUNCTION extended to_timestamp;
...
Usage:
to_timestamp(timestamp[, fmt]) - Parses the `timestamp` expression with the `fmt` expression to
a timestamp. Returns null with invalid input. By default, it follows casting rules to
a timestamp if the `fmt` is omitted.
Extended Usage:
Examples:
> SELECT to_timestamp('2016-12-31 00:12:00');
2016-12-31 00:12:00
> SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
2016-12-31 00:00:00
```
## How was this patch tested?
Added tests in `datetime.sql`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17901 from HyukjinKwon/to_timestamp_arg.
## What changes were proposed in this pull request?
spark-sql>select bround(12.3, 2);
spark-sql>NULL
For this case, the expected result is 12.3, but it is null.
So ,when the second parameter is bigger than "decimal.scala", the result is not we expected.
"round" function has the same problem. This PR can solve the problem for both of them.
## How was this patch tested?
unit test cases in MathExpressionsSuite and MathFunctionsSuite
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17906 from 10110346/wip_lx_0509.
## What changes were proposed in this pull request?
The new SQL parser is introduced into Spark 2.0. All string literals are unescaped in parser. Seems it bring an issue regarding the regex pattern string.
The following codes can reproduce it:
val data = Seq("\u0020\u0021\u0023", "abc")
val df = data.toDF()
// 1st usage: works in 1.6
// Let parser parse pattern string
val rlike1 = df.filter("value rlike '^\\x20[\\x20-\\x23]+$'")
// 2nd usage: works in 1.6, 2.x
// Call Column.rlike so the pattern string is a literal which doesn't go through parser
val rlike2 = df.filter($"value".rlike("^\\x20[\\x20-\\x23]+$"))
// In 2.x, we need add backslashes to make regex pattern parsed correctly
val rlike3 = df.filter("value rlike '^\\\\x20[\\\\x20-\\\\x23]+$'")
Follow the discussion in #17736, this patch adds a config to fallback to 1.6 string literal parsing and mitigate migration issue.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17887 from viirya/add-config-fallback-string-parsing.
## What changes were proposed in this pull request?
This pr added parsing rules to support aliases in table value functions.
The previous pr (#17666) has been reverted because of the regression. This new pr fixed the regression and add tests in `SQLQueryTestSuite`.
## How was this patch tested?
Added tests in `PlanParserSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17928 from maropu/SPARK-20311-3.
## What changes were proposed in this pull request?
In filter estimation, we update column stats for those columns in filter condition. However, if the number of rows decreases after the filter (i.e. the overall selectivity is less than 1), we need to update (scale down) the number of distinct values (NDV) for all columns, no matter they are in filter conditions or not.
This pr also fixes the inconsistency of rounding mode for ndv and rowCount.
## How was this patch tested?
Added new tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17918 from wzhfy/scaleDownNdvAfterFilter.
## What changes were proposed in this pull request?
The query
```
SELECT 1 FROM (SELECT COUNT(*) WHERE FALSE) t1
```
should return a single row of output because the subquery is an aggregate without a group-by and thus should return a single row. However, Spark incorrectly returns zero rows.
This is caused by SPARK-16208 / #13906, a patch which added an optimizer rule to propagate EmptyRelation through operators. The logic for handling aggregates is wrong: it checks whether aggregate expressions are non-empty for deciding whether the output should be empty, whereas it should be checking grouping expressions instead:
An aggregate with non-empty grouping expression will return one output row per group. If the input to the grouped aggregate is empty then all groups will be empty and thus the output will be empty. It doesn't matter whether the aggregation output columns include aggregate expressions since that won't affect the number of output rows.
If the grouping expressions are empty, however, then the aggregate will always produce a single output row and thus we cannot propagate the EmptyRelation.
The current implementation is incorrect and also misses an optimization opportunity by not propagating EmptyRelation in the case where a grouped aggregate has aggregate expressions (in other words, `SELECT COUNT(*) from emptyRelation GROUP BY x` would _not_ be optimized to `EmptyRelation` in the old code, even though it safely could be).
This patch resolves this issue by modifying `PropagateEmptyRelation` to consider only the presence/absence of grouping expressions, not the aggregate functions themselves, when deciding whether to propagate EmptyRelation.
## How was this patch tested?
- Added end-to-end regression tests in `SQLQueryTest`'s `group-by.sql` file.
- Updated unit tests in `PropagateEmptyRelationSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#17929 from JoshRosen/fix-PropagateEmptyRelation.
## What changes were proposed in this pull request?
This pr added parsing rules to support aliases in table value functions.
## How was this patch tested?
Added tests in `PlanParserSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17666 from maropu/SPARK-20311.
## What changes were proposed in this pull request?
Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17803 from srowen/SPARK-20523.
## What changes were proposed in this pull request?
A fix for the same problem was made in #17693 but ignored `JsonToStructs`. This PR uses the same fix for `JsonToStructs`.
## How was this patch tested?
Regression test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#17826 from brkyvz/SPARK-20549.
## What changes were proposed in this pull request?
Add support for the SQL standard distinct predicate to SPARK SQL.
```
<expression> IS [NOT] DISTINCT FROM <expression>
```
## How was this patch tested?
Tested using unit tests, integration tests, manual tests.
Author: ptkool <michael.styles@shopify.com>
Closes#17764 from ptkool/is_not_distinct_from.
## What changes were proposed in this pull request?
Currently, when the type string is invalid, it looks printing empty parentheses. This PR proposes a small improvement in an error message by removing it in the parse as below:
```scala
spark.range(1).select($"col".cast("aa"))
```
**Before**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa() is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
**After**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
## How was this patch tested?
Unit tests in `DataTypeParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17784 from HyukjinKwon/SPARK-20492.
## What changes were proposed in this pull request?
Relax the requirement that a `TimeZoneAwareExpression` has to have its `timeZoneId` set to be considered resolved.
With this change, a `Cast` (which is a `TimeZoneAwareExpression`) can be considered resolved if the `(fromType, toType)` combination doesn't require time zone information.
Also de-relaxed test cases in `CastSuite` so Casts in that test suite don't get a default`timeZoneId = Option("GMT")`.
## How was this patch tested?
Ran the de-relaxed`CastSuite` and it's passing. Also ran the SQL unit tests and they're passing too.
Author: Kris Mok <kris.mok@databricks.com>
Closes#17777 from rednaxelafx/fix-catalyst-cast-timezone.
## What changes were proposed in this pull request?
change to using Jackson's `com.fasterxml.jackson.core.JsonFactory`
public JsonParser createParser(String content)
## How was this patch tested?
existing unit tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Eric Wasserman <ericw@sgn.com>
Closes#17693 from ewasserman/SPARK-20314.
## What changes were proposed in this pull request?
This PR avoids an exception in the case where `scala.math.BigInt` has a value that does not fit into long value range (e.g. `Long.MAX_VALUE+1`). When we run the following code by using the current Spark, the following exception is thrown.
This PR keeps the value using `BigDecimal` if we detect such an overflow case by catching `ArithmeticException`.
Sample program:
```
case class BigIntWrapper(value:scala.math.BigInt)```
spark.createDataset(BigIntWrapper(scala.math.BigInt("10000000000000000002"))::Nil).show
```
Exception:
```
Error while encoding: java.lang.ArithmeticException: BigInteger out of long range
staticinvoke(class org.apache.spark.sql.types.Decimal$, DecimalType(38,0), apply, assertnotnull(assertnotnull(input[0, org.apache.spark.sql.BigIntWrapper, true])).value, true) AS value#0
java.lang.RuntimeException: Error while encoding: java.lang.ArithmeticException: BigInteger out of long range
staticinvoke(class org.apache.spark.sql.types.Decimal$, DecimalType(38,0), apply, assertnotnull(assertnotnull(input[0, org.apache.spark.sql.BigIntWrapper, true])).value, true) AS value#0
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:290)
at org.apache.spark.sql.SparkSession$$anonfun$2.apply(SparkSession.scala:454)
at org.apache.spark.sql.SparkSession$$anonfun$2.apply(SparkSession.scala:454)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.SparkSession.createDataset(SparkSession.scala:454)
at org.apache.spark.sql.Agg$$anonfun$18.apply$mcV$sp(MySuite.scala:192)
at org.apache.spark.sql.Agg$$anonfun$18.apply(MySuite.scala:192)
at org.apache.spark.sql.Agg$$anonfun$18.apply(MySuite.scala:192)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68)
at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
...
Caused by: java.lang.ArithmeticException: BigInteger out of long range
at java.math.BigInteger.longValueExact(BigInteger.java:4531)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:140)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:434)
at org.apache.spark.sql.types.Decimal.apply(Decimal.scala)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:287)
... 59 more
```
## How was this patch tested?
Add new test suite into `DecimalSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#17684 from kiszk/SPARK-20341.
## What changes were proposed in this pull request?
It is often useful to be able to track changes to the `ExternalCatalog`. This PR makes the `ExternalCatalog` emit events when a catalog object is changed. Events are fired before and after the change.
The following events are fired per object:
- Database
- CreateDatabasePreEvent: event fired before the database is created.
- CreateDatabaseEvent: event fired after the database has been created.
- DropDatabasePreEvent: event fired before the database is dropped.
- DropDatabaseEvent: event fired after the database has been dropped.
- Table
- CreateTablePreEvent: event fired before the table is created.
- CreateTableEvent: event fired after the table has been created.
- RenameTablePreEvent: event fired before the table is renamed.
- RenameTableEvent: event fired after the table has been renamed.
- DropTablePreEvent: event fired before the table is dropped.
- DropTableEvent: event fired after the table has been dropped.
- Function
- CreateFunctionPreEvent: event fired before the function is created.
- CreateFunctionEvent: event fired after the function has been created.
- RenameFunctionPreEvent: event fired before the function is renamed.
- RenameFunctionEvent: event fired after the function has been renamed.
- DropFunctionPreEvent: event fired before the function is dropped.
- DropFunctionPreEvent: event fired after the function has been dropped.
The current events currently only contain the names of the object modified. We add more events, and more details at a later point.
A user can monitor changes to the external catalog by adding a listener to the Spark listener bus checking for `ExternalCatalogEvent`s using the `SparkListener.onOtherEvent` hook. A more direct approach is add listener directly to the `ExternalCatalog`.
## How was this patch tested?
Added the `ExternalCatalogEventSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17710 from hvanhovell/SPARK-20420.
## What changes were proposed in this pull request?
A cast expression with a resolved time zone is not equal to a cast expression without a resolved time zone. The `ResolveAggregateFunction` assumed that these expression were the same, and would fail to resolve `HAVING` clauses which contain a `Cast` expression.
This is in essence caused by the fact that a `TimeZoneAwareExpression` can be resolved without a set time zone. This PR fixes this, and makes a `TimeZoneAwareExpression` unresolved as long as it has no TimeZone set.
## How was this patch tested?
Added a regression test to the `SQLQueryTestSuite.having` file.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17641 from hvanhovell/SPARK-20329.
## What changes were proposed in this pull request?
Apply Complementation Laws during boolean expression simplification.
## How was this patch tested?
Tested using unit tests, integration tests, and manual tests.
Author: ptkool <michael.styles@shopify.com>
Author: Michael Styles <michael.styles@shopify.com>
Closes#17650 from ptkool/apply_complementation_laws.
## What changes were proposed in this pull request?
If a plan has multi-level successive joins, e.g.:
```
Join
/ \
Union t5
/ \
Join t4
/ \
Join t3
/ \
t1 t2
```
Currently we fail to reorder the inside joins, i.e. t1, t2, t3.
In join reorder, we use `OrderedJoin` to indicate a join has been ordered, such that when transforming down the plan, these joins don't need to be rerodered again.
But there's a problem in the definition of `OrderedJoin`:
The real join node is a parameter, but not a child. This breaks the transform procedure because `mapChildren` applies transform function on parameters which should be children.
In this patch, we change `OrderedJoin` to a class having the same structure as a join node.
## How was this patch tested?
Add a corresponding test case.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17668 from wzhfy/recursiveReorder.
## What changes were proposed in this pull request?
Replace non-existent `repartitionBy` with `distribute` in `CollapseRepartitionSuite`.
## How was this patch tested?
local build and `catalyst/testOnly *CollapseRepartitionSuite`
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17657 from jaceklaskowski/CollapseRepartitionSuite.
## What changes were proposed in this pull request?
This patch fixes a bug in the way LIKE patterns are translated to Java regexes. The bug causes any character following an escaped backslash to be escaped, i.e. there is double-escaping.
A concrete example is the following pattern:`'%\\%'`. The expected Java regex that this pattern should correspond to (according to the behavior described below) is `'.*\\.*'`, however the current situation leads to `'.*\\%'` instead.
---
Update: in light of the discussion that ensued, we should explicitly define the expected behaviour of LIKE expressions, especially in certain edge cases. With the help of gatorsmile, we put together a list of different RDBMS and their variations wrt to certain standard features.
| RDBMS\Features | Wildcards | Default escape [1] | Case sensitivity |
| --- | --- | --- | --- |
| [MS SQL Server](https://msdn.microsoft.com/en-us/library/ms179859.aspx) | _, %, [], [^] | none | no |
| [Oracle](https://docs.oracle.com/cd/B12037_01/server.101/b10759/conditions016.htm) | _, % | none | yes |
| [DB2 z/OS](http://www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/sqlref/src/tpc/db2z_likepredicate.html) | _, % | none | yes |
| [MySQL](http://dev.mysql.com/doc/refman/5.7/en/string-comparison-functions.html) | _, % | none | no |
| [PostreSQL](https://www.postgresql.org/docs/9.0/static/functions-matching.html) | _, % | \ | yes |
| [Hive](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF) | _, % | none | yes |
| Current Spark | _, % | \ | yes |
[1] Default escape character: most systems do not have a default escape character, instead the user can specify one by calling a like expression with an escape argument [A] LIKE [B] ESCAPE [C]. This syntax is currently not supported by Spark, however I would volunteer to implement this feature in a separate ticket.
The specifications are often quite terse and certain scenarios are undocumented, so here is a list of scenarios that I am uncertain about and would appreciate any input. Specifically I am looking for feedback on whether or not Spark's current behavior should be changed.
1. [x] Ending a pattern with the escape sequence, e.g. `like 'a\'`.
PostreSQL gives an error: 'LIKE pattern must not end with escape character', which I personally find logical. Currently, Spark allows "non-terminated" escapes and simply ignores them as part of the pattern.
According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), ending a pattern in an escape character is invalid.
_Proposed new behaviour in Spark: throw AnalysisException_
2. [x] Empty input, e.g. `'' like ''`
Postgres and DB2 will match empty input only if the pattern is empty as well, any other combination of empty input will not match. Spark currently follows this rule.
3. [x] Escape before a non-special character, e.g. `'a' like '\a'`.
Escaping a non-wildcard character is not really documented but PostgreSQL just treats it verbatim, which I also find the least surprising behavior. Spark does the same.
According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), it is invalid to follow an escape character with anything other than an escape character, an underscore or a percent sign.
_Proposed new behaviour in Spark: throw AnalysisException_
The current specification is also described in the operator's source code in this patch.
## How was this patch tested?
Extra case in regex unit tests.
Author: Jakob Odersky <jakob@odersky.com>
This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>
Closes#15398 from jodersky/SPARK-17647.
## What changes were proposed in this pull request?
Currently when estimating predicates like col > literal or col = literal, we will update min or max in column stats based on literal value. However, literal value is of Catalyst type (internal type), while min/max is of external type. Then for the next predicate, we again need to do type conversion to compare and update column stats. This is awkward and causes many unnecessary conversions in estimation.
To solve this, we use Catalyst type for min/max in `ColumnStat`. Note that the persistent format in metastore is still of external type, so there's no inconsistency for statistics in metastore.
This pr also fixes a bug for boolean type in `IN` condition.
## How was this patch tested?
The changes for ColumnStat are covered by existing tests.
For bug fix, a new test for boolean type in IN condition is added
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17630 from wzhfy/refactorColumnStat.
### What changes were proposed in this pull request?
Session catalog API `createTempFunction` is being used by Hive build-in functions, persistent functions, and temporary functions. Thus, the name is confusing. This PR is to rename it by `registerFunction`. Also we can move construction of `FunctionBuilder` and `ExpressionInfo` into the new `registerFunction`, instead of duplicating the logics everywhere.
In the next PRs, the remaining Function-related APIs also need cleanups.
### How was this patch tested?
Existing test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17615 from gatorsmile/cleanupCreateTempFunction.
## What changes were proposed in this pull request?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17477 from HyukjinKwon/SPARK-18692.
## What changes were proposed in this pull request?
When we perform a cast expression and the from and to types are structurally the same (having the same structure but different field names), we should be able to skip the actual cast.
## How was this patch tested?
Added unit tests for the newly introduced functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#17614 from rxin/SPARK-20302.
## What changes were proposed in this pull request?
`NaNvl(float value, null)` will be converted into `NaNvl(float value, Cast(null, DoubleType))` and finally `NaNvl(Cast(float value, DoubleType), Cast(null, DoubleType))`.
This will cause mismatching in the output type when the input type is float.
By adding extra rule in TypeCoercion can resolve this issue.
## How was this patch tested?
unite tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: DB Tsai <dbt@netflix.com>
Closes#17606 from dbtsai/fixNaNvl.
## What changes were proposed in this pull request?
This is a regression caused by SPARK-19716.
Before SPARK-19716, we will cast an array field to the expected array type. However, after SPARK-19716, the cast is removed, but we forgot to push the cast to the element level.
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17587 from cloud-fan/array.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
```
sql("SELECT t1.b, rand(0) as r FROM cachedData, cachedData t1 GROUP BY t1.b having r > 0.5").show()
```
We will get the following error:
```
Job aborted due to stage failure: Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 8, localhost, executor driver): java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
```
Filters could be pushed down to the join conditions by the optimizer rule `PushPredicateThroughJoin`. However, Analyzer [blocks users to add non-deterministics conditions](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala#L386-L395) (For details, see the PR https://github.com/apache/spark/pull/7535).
We should not push down non-deterministic conditions; otherwise, we need to explicitly initialize the non-deterministic expressions. This PR is to simply block it.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17585 from gatorsmile/joinRandCondition.
## What changes were proposed in this pull request?
This PR proposes to add `IGNORE NULLS` keyword in `first`/`last` in Spark's parser likewise http://docs.oracle.com/cd/B19306_01/server.102/b14200/functions057.htm. This simply maps the keywords to existing `ignoreNullsExpr`.
**Before**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input 'NULLS' expecting {')', ','}(line 1, pos 24)
== SQL ==
select first('a' IGNORE NULLS)
------------------------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:210)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:112)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:46)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:66)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:622)
... 48 elided
```
**After**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
+--------------+
|first(a, true)|
+--------------+
| a|
+--------------+
```
## How was this patch tested?
Unit tests in `ExpressionParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17566 from HyukjinKwon/SPARK-19518.
## What changes were proposed in this pull request?
Similar to `Project`, when `Aggregate` has non-deterministic expressions, we should not push predicate down through it, as it will change the number of input rows and thus change the evaluation result of non-deterministic expressions in `Aggregate`.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17562 from cloud-fan/filter.
## What changes were proposed in this pull request?
This commit moves star schema code from ```join.scala``` to ```StarSchemaDetection.scala```. It also applies some minor fixes in ```StarJoinReorderSuite.scala```.
## How was this patch tested?
Run existing ```StarJoinReorderSuite.scala```.
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#17544 from ioana-delaney/starSchemaCBOv2.
## What changes were proposed in this pull request?
Previously when we construct deserializer expression for array type, we will first cast the corresponding field to expected array type and then apply `MapObjects`.
However, by doing that, we lose the opportunity to do by-name resolution for struct type inside array type. In this PR, I introduce a `UnresolvedMapObjects` to hold the lambda function and the input array expression. Then during analysis, after the input array expression is resolved, we get the actual array element type and apply by-name resolution. Then we don't need to add `Cast` for array type when constructing the deserializer expression, as the element type is determined later at analyzer.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17398 from cloud-fan/dataset.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/17285 .
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17521 from cloud-fan/conf.
## What changes were proposed in this pull request?
In SQL queries, we also see predicate expressions involving two columns such as "column-1 (op) column-2" where column-1 and column-2 belong to same table. Note that, if column-1 and column-2 belong to different tables, then it is a join operator's work, NOT a filter operator's work.
This PR estimates filter selectivity on two columns of same table. For example, multiple tpc-h queries have this predicate "WHERE l_commitdate < l_receiptdate"
## How was this patch tested?
We added 6 new test cases to test various logical predicates involving two columns of same table.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ron Hu <ron.hu@huawei.com>
Author: U-CHINA\r00754707 <r00754707@R00754707-SC04.china.huawei.com>
Closes#17415 from ron8hu/filterTwoColumns.
## What changes were proposed in this pull request?
This patch implements `listPartitionsByFilter()` for `InMemoryCatalog` and thus resolves an outstanding TODO causing the `PruneFileSourcePartitions` optimizer rule not to apply when "spark.sql.catalogImplementation" is set to "in-memory" (which is the default).
The change is straightforward: it extracts the code for further filtering of the list of partitions returned by the metastore's `getPartitionsByFilter()` out from `HiveExternalCatalog` into `ExternalCatalogUtils` and calls this new function from `InMemoryCatalog` on the whole list of partitions.
Now that this method is implemented we can always pass the `CatalogTable` to the `DataSource` in `FindDataSourceTable`, so that the latter is resolved to a relation with a `CatalogFileIndex`, which is what the `PruneFileSourcePartitions` rule matches for.
## How was this patch tested?
Ran existing tests and added new test for `listPartitionsByFilter` in `ExternalCatalogSuite`, which is subclassed by both `InMemoryCatalogSuite` and `HiveExternalCatalogSuite`.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#17510 from adrian-ionescu/InMemoryCatalog.
## What changes were proposed in this pull request?
Currently, `DataType.fromJson` throws `scala.MatchError` or `java.util.NoSuchElementException` in some cases when the JSON input is invalid as below:
```scala
DataType.fromJson(""""abcd"""")
```
```
java.util.NoSuchElementException: key not found: abcd
at ...
```
```scala
DataType.fromJson("""{"abcd":"a"}""")
```
```
scala.MatchError: JObject(List((abcd,JString(a)))) (of class org.json4s.JsonAST$JObject)
at ...
```
```scala
DataType.fromJson("""{"fields": [{"a":123}], "type": "struct"}""")
```
```
scala.MatchError: JObject(List((a,JInt(123)))) (of class org.json4s.JsonAST$JObject)
at ...
```
After this PR,
```scala
DataType.fromJson(""""abcd"""")
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string 'abcd' to a data type.
at ...
```
```scala
DataType.fromJson("""{"abcd":"a"}""")
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string '{"abcd":"a"}' to a data type.
at ...
```
```scala
DataType.fromJson("""{"fields": [{"a":123}], "type": "struct"}""")
at ...
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string '{"a":123}' to a field.
```
## How was this patch tested?
Unit test added in `DataTypeSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17468 from HyukjinKwon/fromjson_exception.
## What changes were proposed in this pull request?
`BroadcastHint` should use child's statistics and set `isBroadcastable` to true.
## How was this patch tested?
Added a new stats estimation test for `BroadcastHint`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17504 from wzhfy/broadcastHintEstimation.
…adoc
## What changes were proposed in this pull request?
Use recommended values for row boundaries in Window's scaladoc, i.e. `Window.unboundedPreceding`, `Window.unboundedFollowing`, and `Window.currentRow` (that were introduced in 2.1.0).
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17417 from jaceklaskowski/window-expression-scaladoc.
### What changes were proposed in this pull request?
`FalseLiteral` and `TrueLiteral` should have been eliminated by optimizer rule `BooleanSimplification`, but null literals might be added by optimizer rule `NullPropagation`. For safety, our filter estimation should handle all the eligible literal cases.
Our optimizer rule BooleanSimplification is unable to remove the null literal in many cases. For example, `a < 0 or null`. Thus, we need to handle null literal in filter estimation.
`Not` can be pushed down below `And` and `Or`. Then, we could see two consecutive `Not`, which need to be collapsed into one. Because of the limited expression support for filter estimation, we just need to handle the case `Not(null)` for avoiding incorrect error due to the boolean operation on null. For details, see below matrix.
```
not NULL = NULL
NULL or false = NULL
NULL or true = true
NULL or NULL = NULL
NULL and false = false
NULL and true = NULL
NULL and NULL = NULL
```
### How was this patch tested?
Added the test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17446 from gatorsmile/constantFilterEstimation.
## What changes were proposed in this pull request?
This pr added `StructType.fromDDL` to convert a DDL format string into `StructType` for defining schemas in `functions.from_json`.
## How was this patch tested?
Added tests in `JsonFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17406 from maropu/SPARK-20009.
## What changes were proposed in this pull request?
Join reorder algorithm should keep exactly the same order of output attributes in the top project.
For example, if user want to select a, b, c, after reordering, we should output a, b, c in the same order as specified by user, instead of b, a, c or other orders.
## How was this patch tested?
A new test case is added in `JoinReorderSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17453 from wzhfy/keepOrderInProject.
## What changes were proposed in this pull request?
TPCDS q45 fails becuase:
`ReorderJoin` collects all predicates and try to put them into join condition when creating ordered join. If a predicate with an IN subquery (`ListQuery`) is in a join condition instead of a filter condition, `RewritePredicateSubquery.rewriteExistentialExpr` would fail to convert the subquery to an `ExistenceJoin`, and thus result in error.
We should prevent push down of IN subquery to Join operator.
## How was this patch tested?
Add a new test case in `FilterPushdownSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17428 from wzhfy/noSubqueryInJoinCond.
## What changes were proposed in this pull request?
The current SessionState initialization code path is quite complex. A part of the creation is done in the SessionState companion objects, a part of the creation is one inside the SessionState class, and a part is done by passing functions.
This PR refactors this code path, and consolidates SessionState initialization into a builder class. This SessionState will not do any initialization and just becomes a place holder for the various Spark SQL internals. This also lays the ground work for two future improvements:
1. This provides us with a start for removing the `HiveSessionState`. Removing the `HiveSessionState` would also require us to move resource loading into a separate class, and to (re)move metadata hive.
2. This makes it easier to customize the Spark Session. Currently you will need to create a custom version of the builder. I have added hooks to facilitate this. A future step will be to create a semi stable API on top of this.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17433 from hvanhovell/SPARK-20100.
## What changes were proposed in this pull request?
In current stage, we don't have advanced statistics such as sketches or histograms. As a result, some operator can't estimate `nullCount` accurately. E.g. left outer join estimation does not accurately update `nullCount` currently. So for `IsNull` and `IsNotNull` predicates, we only estimate them when the child is a leaf node, whose `nullCount` is accurate.
## How was this patch tested?
A new test case is added in `FilterEstimationSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17438 from wzhfy/nullEstimation.
## What changes were proposed in this pull request?
The `CollapseWindow` is currently to aggressive when collapsing adjacent windows. It also collapses windows in the which the parent produces a column that is consumed by the child; this creates an invalid window which will fail at runtime.
This PR fixes this by adding a check for dependent adjacent windows to the `CollapseWindow` rule.
## How was this patch tested?
Added a new test case to `CollapseWindowSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17432 from hvanhovell/SPARK-20086.
## What changes were proposed in this pull request?
Constraint propagation can be computation expensive and block the driver execution for long time. For example, the below benchmark needs 30mins.
Compared with previous PRs #16998, #16785, this is a much simpler option: add a flag to disable constraint propagation.
### Benchmark
Run the following codes locally.
import org.apache.spark.ml.{Pipeline, PipelineStage}
import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer, VectorAssembler}
import org.apache.spark.sql.internal.SQLConf
spark.conf.set(SQLConf.CONSTRAINT_PROPAGATION_ENABLED.key, false)
val df = (1 to 40).foldLeft(Seq((1, "foo"), (2, "bar"), (3, "baz")).toDF("id", "x0"))((df, i) => df.withColumn(s"x$i", $"x0"))
val indexers = df.columns.tail.map(c => new StringIndexer()
.setInputCol(c)
.setOutputCol(s"${c}_indexed")
.setHandleInvalid("skip"))
val encoders = indexers.map(indexer => new OneHotEncoder()
.setInputCol(indexer.getOutputCol)
.setOutputCol(s"${indexer.getOutputCol}_encoded")
.setDropLast(true))
val stages: Array[PipelineStage] = indexers ++ encoders
val pipeline = new Pipeline().setStages(stages)
val startTime = System.nanoTime
pipeline.fit(df).transform(df).show
val runningTime = System.nanoTime - startTime
Before this patch: 1786001 ms ~= 30 mins
After this patch: 26392 ms = less than half of a minute
Related PRs: #16998, #16785.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17186 from viirya/add-flag-disable-constraint-propagation.
## What changes were proposed in this pull request?
Since the state is tied a "group" in the "mapGroupsWithState" operations, its better to call the state "GroupState" instead of a key. This would make it more general if you extends this operation to RelationGroupedDataset and python APIs.
## How was this patch tested?
Existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17385 from tdas/SPARK-20057.
## What changes were proposed in this pull request?
This PR proposes to make `mode` options in both CSV and JSON to use `cass object` and fix some related comments related previous fix.
Also, this PR modifies some tests related parse modes.
## How was this patch tested?
Modified unit tests in both `CSVSuite.scala` and `JsonSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17377 from HyukjinKwon/SPARK-19949.
## What changes were proposed in this pull request?
Adding event time based timeout. The user sets the timeout timestamp directly using `KeyedState.setTimeoutTimestamp`. The keys times out when the watermark crosses the timeout timestamp.
## How was this patch tested?
Unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17361 from tdas/SPARK-20030.
## What changes were proposed in this pull request?
Change the nullability of function `StringToMap` from `false` to `true`.
Author: zhaorongsheng <334362872@qq.com>
Closes#17350 from zhaorongsheng/bug-fix_strToMap_NPE.
## What changes were proposed in this pull request?
Support` ALTER TABLE ADD COLUMNS (...) `syntax for Hive serde and some datasource tables.
In this PR, we consider a few aspects:
1. View is not supported for `ALTER ADD COLUMNS`
2. Since tables created in SparkSQL with Hive DDL syntax will populate table properties with schema information, we need make sure the consistency of the schema before and after ALTER operation in order for future use.
3. For embedded-schema type of format, such as `parquet`, we need to make sure that the predicate on the newly-added columns can be evaluated properly, or pushed down properly. In case of the data file does not have the columns for the newly-added columns, such predicates should return as if the column values are NULLs.
4. For datasource table, this feature does not support the following:
4.1 TEXT format, since there is only one default column `value` is inferred for text format data.
4.2 ORC format, since SparkSQL native ORC reader does not support the difference between user-specified-schema and inferred schema from ORC files.
4.3 Third party datasource types that implements RelationProvider, including the built-in JDBC format, since different implementations by the vendors may have different ways to dealing with schema.
4.4 Other datasource types, such as `parquet`, `json`, `csv`, `hive` are supported.
5. Column names being added can not be duplicate of any existing data column or partition column names. Case sensitivity is taken into consideration according to the sql configuration.
6. This feature also supports In-Memory catalog, while Hive support is turned off.
## How was this patch tested?
Add new test cases
Author: Xin Wu <xinwu@us.ibm.com>
Closes#16626 from xwu0226/alter_add_columns.
### What changes were proposed in this pull request?
SessionCatalog API setCurrentDatabase does not set the current database of the underlying ExternalCatalog. Thus, weird errors could come in the test suites after we call reset. We need to fix it.
So far, have not found the direct impact in the other code paths because we expect all the SessionCatalog APIs should always use the current database value we managed, unless some of code paths skip it. Thus, we fix it in the test-only function reset().
### How was this patch tested?
Multiple test case failures are observed in mvn and add a test case in SessionCatalogSuite.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17354 from gatorsmile/useDB.
## What changes were proposed in this pull request?
update `StatFunctions.multipleApproxQuantiles` to handle NaN/null
## How was this patch tested?
existing tests and added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16971 from zhengruifeng/quantiles_nan.
## What changes were proposed in this pull request?
Star schema consists of one or more fact tables referencing a number of dimension tables. In general, queries against star schema are expected to run fast because of the established RI constraints among the tables. This design proposes a join reordering based on natural, generally accepted heuristics for star schema queries:
- Finds the star join with the largest fact table and places it on the driving arm of the left-deep join. This plan avoids large tables on the inner, and thus favors hash joins.
- Applies the most selective dimensions early in the plan to reduce the amount of data flow.
The design document was included in SPARK-17791.
Link to the google doc: [StarSchemaDetection](https://docs.google.com/document/d/1UAfwbm_A6wo7goHlVZfYK99pqDMEZUumi7pubJXETEA/edit?usp=sharing)
## How was this patch tested?
A new test suite StarJoinSuite.scala was implemented.
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#15363 from ioana-delaney/starJoinReord2.
## What changes were proposed in this pull request?
This PR proposes to support an array of struct type in `to_json` as below:
```scala
import org.apache.spark.sql.functions._
val df = Seq(Tuple1(Tuple1(1) :: Nil)).toDF("a")
df.select(to_json($"a").as("json")).show()
```
```
+----------+
| json|
+----------+
|[{"_1":1}]|
+----------+
```
Currently, it throws an exception as below (a newline manually inserted for readability):
```
org.apache.spark.sql.AnalysisException: cannot resolve 'structtojson(`array`)' due to data type
mismatch: structtojson requires that the expression is a struct expression.;;
```
This allows the roundtrip with `from_json` as below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
val df = Seq("""[{"a":1}, {"a":2}]""").toDF("json").select(from_json($"json", schema).as("array"))
df.show()
// Read back.
df.select(to_json($"array").as("json")).show()
```
```
+----------+
| array|
+----------+
|[[1], [2]]|
+----------+
+-----------------+
| json|
+-----------------+
|[{"a":1},{"a":2}]|
+-----------------+
```
Also, this PR proposes to rename from `StructToJson` to `StructsToJson ` and `JsonToStruct` to `JsonToStructs`.
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite` for Scala, doctest for Python and test in `test_sparkSQL.R` for R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17192 from HyukjinKwon/SPARK-19849.
## What changes were proposed in this pull request?
When a key does not get any new data in `mapGroupsWithState`, the mapping function is never called on it. So we need a timeout feature that calls the function again in such cases, so that the user can decide whether to continue waiting or clean up (remove state, save stuff externally, etc.).
Timeouts can be either based on processing time or event time. This JIRA is for processing time, but defines the high level API design for both. The usage would look like this.
```
def stateFunction(key: K, value: Iterator[V], state: KeyedState[S]): U = {
...
state.setTimeoutDuration(10000)
...
}
dataset // type is Dataset[T]
.groupByKey[K](keyingFunc) // generates KeyValueGroupedDataset[K, T]
.mapGroupsWithState[S, U](
func = stateFunction,
timeout = KeyedStateTimeout.withProcessingTime) // returns Dataset[U]
```
Note the following design aspects.
- The timeout type is provided as a param in mapGroupsWithState as a parameter global to all the keys. This is so that the planner knows this at planning time, and accordingly optimize the execution based on whether to saves extra info in state or not (e.g. timeout durations or timestamps).
- The exact timeout duration is provided inside the function call so that it can be customized on a per key basis.
- When the timeout occurs for a key, the function is called with no values, and KeyedState.isTimingOut() set to true.
- The timeout is reset for key every time the function is called on the key, that is, when the key has new data, or the key has timed out. So the user has to set the timeout duration everytime the function is called, otherwise there will not be any timeout set.
Guarantees provided on timeout of key, when timeout duration is D ms:
- Timeout will never be called before real clock time has advanced by D ms
- Timeout will be called eventually when there is a trigger with any data in it (i.e. after D ms). So there is a no strict upper bound on when the timeout would occur. For example, if there is no data in the stream (for any key) for a while, then the timeout will not be hit.
Implementation details:
- Added new param to `mapGroupsWithState` for timeout
- Added new method to `StateStore` to filter data based on timeout timestamp
- Changed the internal map type of `HDFSBackedStateStore` from Java's `HashMap` to `ConcurrentHashMap` as the latter allows weakly-consistent fail-safe iterators on the map data. See comments in code for more details.
- Refactored logic of `MapGroupsWithStateExec` to
- Save timeout info to state store for each key that has data.
- Then, filter states that should be timed out based on the current batch processing timestamp.
- Moved KeyedState for `o.a.s.sql` to `o.a.s.sql.streaming`. I remember that this was a feedback in the MapGroupsWithState PR that I had forgotten to address.
## How was this patch tested?
New unit tests in
- MapGroupsWithStateSuite for timeouts.
- StateStoreSuite for new APIs in StateStore.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17179 from tdas/mapgroupwithstate-timeout.
## What changes were proposed in this pull request?
We have some concerns about removing size in the cost model [in the previous pr](https://github.com/apache/spark/pull/17240). It's a tradeoff between code structure and algorithm completeness. I tend to keep the size and thus create this new pr without changing cost model.
What this pr does:
1. We only consider consecutive inner joinable items, thus excluding cartesian products in reordering procedure. This significantly reduces the search space and memory overhead of memo. Otherwise every combination of items will exist in the memo.
2. This pr also includes a bug fix: if a leaf item is a project(_, child), current solution will miss the project.
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17286 from wzhfy/joinReorder3.
## What changes were proposed in this pull request?
Currently `SessionCatalogSuite` is only for `InMemoryCatalog`, there is no suite for `HiveExternalCatalog`.
And there are some ddl function is not proper to test in `ExternalCatalogSuite`, because some logic are not full implement in `ExternalCatalog`, these ddl functions are full implement in `SessionCatalog`(e.g. merge the same logic from `ExternalCatalog` up to `SessionCatalog` ).
It is better to test it in `SessionCatalogSuite` for this situation.
So we should add a test suite for `SessionCatalog` with `HiveExternalCatalog`
The main change is that in `SessionCatalogSuite` add two functions:
`withBasicCatalog` and `withEmptyCatalog`
And replace the code like `val catalog = new SessionCatalog(newBasicCatalog)` with above two functions
## How was this patch tested?
add `HiveExternalSessionCatalogSuite`
Author: windpiger <songjun@outlook.com>
Closes#17287 from windpiger/sessioncatalogsuit.
### What changes were proposed in this pull request?
Specifying the table schema in DDL formats is needed for different scenarios. For example,
- [specifying the schema in SQL function `from_json` using DDL formats](https://issues.apache.org/jira/browse/SPARK-19637), which is suggested by marmbrus ,
- [specifying the customized JDBC data types](https://github.com/apache/spark/pull/16209).
These two PRs need users to use the JSON format to specify the table schema. This is not user friendly.
This PR is to provide a `parseTableSchema` API in `ParserInterface`.
### How was this patch tested?
Added a test suite `TableSchemaParserSuite`
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17171 from gatorsmile/parseDDLStmt.
## What changes were proposed in this pull request?
As timezone setting can also affect partition values, it works for all formats, we should make it clear.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17281 from ueshin/issues/SPARK-19817.
## What changes were proposed in this pull request?
The `RemoveRedundantAlias` rule can change the output attributes (the expression id's to be precise) of a query by eliminating the redundant alias producing them. This is no problem for a regular query, but can cause problems for correlated subqueries: The attributes produced by the subquery are used in the parent plan; changing them will break the parent plan.
This PR fixes this by wrapping a subquery in a `Subquery` top level node when it gets optimized. The `RemoveRedundantAlias` rule now recognizes `Subquery` and makes sure that the output attributes of the `Subquery` node are retained.
## How was this patch tested?
Added a test case to `RemoveRedundantAliasAndProjectSuite` and added a regression test to `SubquerySuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17278 from hvanhovell/SPARK-19933.
## What changes were proposed in this pull request?
We currently cannot use aliases in SQL function calls. This is inconvenient when you try to create a struct. This SQL query for example `select struct(1, 2) st`, will create a struct with column names `col1` and `col2`. This is even more problematic when we want to append a field to an existing struct. For example if we want to a field to struct `st` we would issue the following SQL query `select struct(st.*, 1) as st from src`, the result will be struct `st` with an a column with a non descriptive name `col3` (if `st` itself has 2 fields).
This PR proposes to change this by allowing the use of aliased expression in function parameters. For example `select struct(1 as a, 2 as b) st`, will create a struct with columns `a` & `b`.
## How was this patch tested?
Added a test to `ExpressionParserSuite` and added a test file for `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17245 from hvanhovell/SPARK-19850.
## What changes were proposed in this pull request?
- Timestamp hashing is done as per [TimestampWritable.hashCode()](ff67cdda1c/serde/src/java/org/apache/hadoop/hive/serde2/io/TimestampWritable.java (L406)) in Hive
- Interval hashing is done as per [HiveIntervalDayTime.hashCode()](ff67cdda1c/storage-api/src/java/org/apache/hadoop/hive/common/type/HiveIntervalDayTime.java (L178)). Note that there are inherent differences in how Hive and Spark store intervals under the hood which limits the ability to be in completely sync with hive's hashing function. I have explained this in the method doc.
- Date type was already supported. This PR adds test for that.
## How was this patch tested?
Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17062 from tejasapatil/SPARK-17495_time_related_types.
## Summary of changes
Add a new configuration option that allows Spark SQL to infer a case-sensitive schema from a Hive Metastore table's data files when a case-sensitive schema can't be read from the table properties.
- Add spark.sql.hive.caseSensitiveInferenceMode param to SQLConf
- Add schemaPreservesCase field to CatalogTable (set to false when schema can't
successfully be read from Hive table props)
- Perform schema inference in HiveMetastoreCatalog if schemaPreservesCase is
false, depending on spark.sql.hive.caseSensitiveInferenceMode
- Add alterTableSchema() method to the ExternalCatalog interface
- Add HiveSchemaInferenceSuite tests
- Refactor and move ParquetFileForamt.meregeMetastoreParquetSchema() as
HiveMetastoreCatalog.mergeWithMetastoreSchema
- Move schema merging tests from ParquetSchemaSuite to HiveSchemaInferenceSuite
[JIRA for this change](https://issues.apache.org/jira/browse/SPARK-19611)
## How was this patch tested?
The tests in ```HiveSchemaInferenceSuite``` should verify that schema inference is working as expected. ```ExternalCatalogSuite``` has also been extended to cover the new ```alterTableSchema()``` API.
Author: Budde <budde@amazon.com>
Closes#16944 from budde/SPARK-19611.
Forking a newSession() from SparkSession currently makes a new SparkSession that does not retain SessionState (i.e. temporary tables, SQL config, registered functions etc.) This change adds a method cloneSession() which creates a new SparkSession with a copy of the parent's SessionState.
Subsequent changes to base session are not propagated to cloned session, clone is independent after creation.
If the base is changed after clone has been created, say user registers new UDF, then the new UDF will not be available inside the clone. Same goes for configs and temp tables.
Unit tests
Author: Kunal Khamar <kkhamar@outlook.com>
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16826 from kunalkhamar/fork-sparksession.
## What changes were proposed in this pull request?
Add a output mode parameter to `flatMapGroupsWithState` and just define `mapGroupsWithState` as `flatMapGroupsWithState(Update)`.
`UnsupportedOperationChecker` is modified to disallow unsupported cases.
- Batch mapGroupsWithState or flatMapGroupsWithState is always allowed.
- For streaming (map/flatMap)GroupsWithState, see the following table:
| Operators | Supported Query Output Mode |
| ------------- | ------------- |
| flatMapGroupsWithState(Update) without aggregation | Update |
| flatMapGroupsWithState(Update) with aggregation | None |
| flatMapGroupsWithState(Append) without aggregation | Append |
| flatMapGroupsWithState(Append) before aggregation | Append, Update, Complete |
| flatMapGroupsWithState(Append) after aggregation | None |
| Multiple flatMapGroupsWithState(Append)s | Append |
| Multiple mapGroupsWithStates | None |
| Mxing mapGroupsWithStates and flatMapGroupsWithStates | None |
| Other cases of multiple flatMapGroupsWithState | None |
## How was this patch tested?
The added unit tests. Here are the tests related to (map/flatMap)GroupsWithState:
```
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation: supported (1 millisecond)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Append)s on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Update)s on batch relation: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in update mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in append mode: not supported (7 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Append mode: not supported (11 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Update mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in update mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Update mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Complete mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Update mode: not supported (4 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation in complete mode: not supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Append output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Update output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Append output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Update output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on streaming relation and all are in append mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on s streaming relation but some are not in append mode: not supported (7 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in append mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in complete mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Update mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Complete mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - multiple mapGroupsWithStates on streaming relation and all are in append mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - mixing mapGroupsWithStates and flatMapGroupsWithStates on streaming relation: not supported (4 milliseconds)
```
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17197 from zsxwing/mapgroups-check.
## What changes were proposed in this pull request?
Fix for SQL round function that modifies original column when underlying data frame is created from a local product.
import org.apache.spark.sql.functions._
case class NumericRow(value: BigDecimal)
val df = spark.createDataFrame(Seq(NumericRow(BigDecimal("1.23456789"))))
df.show()
+--------------------+
| value|
+--------------------+
|1.234567890000000000|
+--------------------+
df.withColumn("value_rounded", round('value)).show()
// before
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.000000000000000000| 1|
+--------------------+-------------+
// after
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.234567890000000000| 1|
+--------------------+-------------+
## How was this patch tested?
New unit test added to existing suite `org.apache.spark.sql.MathFunctionsSuite`
Author: Wojtek Szymanski <wk.szymanski@gmail.com>
Closes#17075 from wojtek-szymanski/SPARK-19727.
### What changes were proposed in this pull request?
Observed by felixcheung in https://github.com/apache/spark/pull/16739, when users use the shuffle-enabled `repartition` API, they expect the partition they got should be the exact number they provided, even if they call shuffle-disabled `coalesce` later.
Currently, `CollapseRepartition` rule does not consider whether shuffle is enabled or not. Thus, we got the following unexpected result.
```Scala
val df = spark.range(0, 10000, 1, 5)
val df2 = df.repartition(10)
assert(df2.coalesce(13).rdd.getNumPartitions == 5)
assert(df2.coalesce(7).rdd.getNumPartitions == 5)
assert(df2.coalesce(3).rdd.getNumPartitions == 3)
```
This PR is to fix the issue. We preserve shuffle-enabled Repartition.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#16933 from gatorsmile/CollapseRepartition.
## What changes were proposed in this pull request?
Since we have a `View` node now, we can remove the view identifier in `SubqueryAlias`, which was used to indicate a view node before.
## How was this patch tested?
Update the related test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17210 from jiangxb1987/SubqueryAlias.
## What changes were proposed in this pull request?
Reorder the joins using a dynamic programming algorithm (Selinger paper):
First we put all items (basic joined nodes) into level 1, then we build all two-way joins at level 2 from plans at level 1 (single items), then build all 3-way joins from plans at previous levels (two-way joins and single items), then 4-way joins ... etc, until we build all n-way joins and pick the best plan among them.
When building m-way joins, we only keep the best plan (with the lowest cost) for the same set of m items. E.g., for 3-way joins, we keep only the best plan for items {A, B, C} among plans (A J B) J C, (A J C) J B and (B J C) J A. Thus, the plans maintained for each level when reordering four items A, B, C, D are as follows:
```
level 1: p({A}), p({B}), p({C}), p({D})
level 2: p({A, B}), p({A, C}), p({A, D}), p({B, C}), p({B, D}), p({C, D})
level 3: p({A, B, C}), p({A, B, D}), p({A, C, D}), p({B, C, D})
level 4: p({A, B, C, D})
```
where p({A, B, C, D}) is the final output plan.
For cost evaluation, since physical costs for operators are not available currently, we use cardinalities and sizes to compute costs.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#17138 from wzhfy/joinReorder.
## What changes were proposed in this pull request?
1. support boolean type in binary expression estimation.
2. deal with compound Not conditions.
3. avoid convert BigInt/BigDecimal directly to double unless it's within range (0, 1).
4. reorganize test code.
## How was this patch tested?
modify related test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#17148 from wzhfy/fixFilter.
## What changes were proposed in this pull request?
Before this pr, LocalLimit/GlobalLimit/Sample propagates the same row count and column stats from its child, which is incorrect.
We can get the correct rowCount in Statistics for GlobalLimit/Sample whether cbo is enabled or not.
We don't know the rowCount for LocalLimit because we don't know the partition number at that time. Column stats should not be propagated because we don't know the distribution of columns after Limit or Sample.
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16696 from wzhfy/limitEstimation.
## What changes were proposed in this pull request?
Currently we treat the location of table/partition/database as URI string.
It will be safer if we can make the type of location as java.net.URI.
In this PR, there are following classes changes:
**1. CatalogDatabase**
```
case class CatalogDatabase(
name: String,
description: String,
locationUri: String,
properties: Map[String, String])
--->
case class CatalogDatabase(
name: String,
description: String,
locationUri: URI,
properties: Map[String, String])
```
**2. CatalogStorageFormat**
```
case class CatalogStorageFormat(
locationUri: Option[String],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
---->
case class CatalogStorageFormat(
locationUri: Option[URI],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
```
Before and After this PR, it is transparent for user, there is no change that the user should concern. The `String` to `URI` just happened in SparkSQL internally.
Here list some operation related location:
**1. whitespace in the location**
e.g. `/a/b c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b c/d`,
and the real path in the FileSystem also show `/a/b c/d`
**2. colon(:) in the location**
e.g. `/a/b:c/d`
For both table location and partition location,
when `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b:c/d'` ,
**In linux file system**
`DESC EXTENDED t ` show the location is `/a/b:c/d`,
and the real path in the FileSystem also show `/a/b:c/d`
**in HDFS** throw exception:
`java.lang.IllegalArgumentException: Pathname /a/b:c/d from hdfs://iZbp1151s8hbnnwriekxdeZ:9000/a/b:c/d is not a valid DFS filename.`
**while** After `INSERT INTO TABLE t PARTITION(a="a:b") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=a%3Ab`,
and the real path in the FileSystem also show `/xxx/a=a%3Ab`
**3. percent sign(%) in the location**
e.g. `/a/b%c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%c/d`,
and the real path in the FileSystem also show `/a/b%c/d`
**4. encoded(%25) in the location**
e.g. `/a/b%25c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%25c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%25c/d`,
and the real path in the FileSystem also show `/a/b%25c/d`
**while** After `INSERT INTO TABLE t PARTITION(a="%25") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=%2525`,
and the real path in the FileSystem also show `/xxx/a=%2525`
**Additionally**, except the location, there are two other factors will affect the location of the table/partition. one is the table name which does not allowed to have special characters, and the other is `partition name` which have the same actions with `partition value`, and `partition name` with special character situation has add some testcase and resolve a bug in [PR](https://github.com/apache/spark/pull/17173)
### Summary:
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION path`,
the path which we get from `DESC TABLE` and `real path in FileSystem` are all the same with the `CREATE TABLE` command(different filesystem has different action that allow what kind of special character to create the path, e.g. HDFS does not allow colon, but linux filesystem allow it ).
`DataBase` also have the same logic with `CREATE TABLE`
while if the `partition value` has some special character like `%` `:` `#` etc, then we will get the path with encoded `partition value` like `/xxx/a=A%25B` from `DESC TABLE` and `real path in FileSystem`
In this PR, the core change code is using `new Path(str).toUri` and `new Path(uri).toString`
which transfrom `str to uri `or `uri to str`.
for example:
```
val str = '/a/b c/d'
val uri = new Path(str).toUri --> '/a/b%20c/d'
val strFromUri = new Path(uri).toString -> '/a/b c/d'
```
when we restore table/partition from metastore, or get the location from `CREATE TABLE` command, we can use it as above to change string to uri `new Path(str).toUri `
## How was this patch tested?
unit test added.
The `current master branch` also `passed all the test cases` added in this PR by a litter change.
https://github.com/apache/spark/pull/17149/files#diff-b7094baa12601424a5d19cb930e3402fR1764
here `toURI` -> `toString` when test in master branch.
This can show that this PR is transparent for user.
Author: windpiger <songjun@outlook.com>
Closes#17149 from windpiger/changeStringToURI.
## What changes were proposed in this pull request?
This PR adds a new `Once` analysis rule batch consists of a single analysis rule `LookupFunctions` that performs simple existence check over `UnresolvedFunctions` without actually resolving them.
The benefit of this rule is that it doesn't require function arguments to be resolved first and therefore doesn't rely on relation resolution, which may incur potentially expensive partition/schema discovery cost.
Please refer to [SPARK-19737][1] for more details about the motivation.
## How was this patch tested?
New test case added in `AnalysisErrorSuite`.
[1]: https://issues.apache.org/jira/browse/SPARK-19737
Author: Cheng Lian <lian@databricks.com>
Closes#17168 from liancheng/spark-19737-lookup-functions.
## What changes were proposed in this pull request?
Hive hash to support Decimal datatype. [Hive internally normalises decimals](4ba713ccd8/storage-api/src/java/org/apache/hadoop/hive/common/type/HiveDecimalV1.java (L307)) and I have ported that logic as-is to HiveHash.
## How was this patch tested?
Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17056 from tejasapatil/SPARK-17495_decimal.
## What changes were proposed in this pull request?
This PR proposes to both,
**Do not allow json arrays with multiple elements and return null in `from_json` with `StructType` as the schema.**
Currently, it only reads the single row when the input is a json array. So, the codes below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = StructType(StructField("a", IntegerType) :: Nil)
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("struct").select(from_json(col("struct"), schema)).show()
```
prints
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| [1]|
+--------------------+
```
This PR simply suggests to print this as `null` if the schema is `StructType` and input is json array.with multiple elements
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| null|
+--------------------+
```
**Support json arrays in `from_json` with `ArrayType` as the schema.**
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("array").select(from_json(col("array"), schema)).show()
```
prints
```
+-------------------+
|jsontostruct(array)|
+-------------------+
| [[1], [2]]|
+-------------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite`, `JsonFunctionsSuite`, Python doctests and manual test.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16929 from HyukjinKwon/disallow-array.
## What changes were proposed in this pull request?
This pr is to support Seq, Map, and Struct in functions.lit; it adds a new IF named `lit2` with `TypeTag` for avoiding type erasure.
## How was this patch tested?
Added tests in `LiteralExpressionSuite`
Author: Takeshi Yamamuro <yamamuro@apache.org>
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#16610 from maropu/SPARK-19254.
## What changes were proposed in this pull request?
When we resolve inline tables in analyzer, we will evaluate the expressions of inline tables.
When it evaluates a `TimeZoneAwareExpression` expression, an error will happen because the `TimeZoneAwareExpression` is not associated with timezone yet.
So we need to resolve these `TimeZoneAwareExpression`s with time zone when resolving inline tables.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17114 from viirya/resolve-timeawareexpr-inline-table.
## What changes were proposed in this pull request?
`MetastoreRelation` is used to represent table relation for hive tables, and provides some hive related information. We will resolve `SimpleCatalogRelation` to `MetastoreRelation` for hive tables, which is unnecessary as these 2 are the same essentially. This PR merges `SimpleCatalogRelation` and `MetastoreRelation`
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17015 from cloud-fan/table-relation.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/16395. It fixes some code style issues, naming issues, some missing cases in pattern match, etc.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17065 from cloud-fan/follow-up.
## What changes were proposed in this pull request?
Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users.
In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan.
E.g.
```
spark-sql> EXPLAIN COST select count(1) from store_returns;
...
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false)
+- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false)
+- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false)
...
```
## How was this patch tested?
Add test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16594 from wzhfy/showStats.
## What changes were proposed in this pull request?
Fixed the line ending of `FilterEstimation.scala` (It's still using `\n\r`). Also improved the tests to cover the cases where the literals are on the left side of a binary operator.
## How was this patch tested?
Existing unit tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#17051 from lins05/fix-cbo-filter-file-encoding.
## What changes were proposed in this pull request?
This PR adds tests hive-hash by comparing the outputs generated against Hive 1.2.1. Following datatypes are covered by this PR:
- null
- boolean
- byte
- short
- int
- long
- float
- double
- string
- array
- map
- struct
Datatypes that I have _NOT_ covered but I will work on separately are:
- Decimal (handled separately in https://github.com/apache/spark/pull/17056)
- TimestampType
- DateType
- CalendarIntervalType
## How was this patch tested?
NA
Author: Tejas Patil <tejasp@fb.com>
Closes#17049 from tejasapatil/SPARK-17495_remaining_types.
## What changes were proposed in this pull request?
We traverse predicate and evaluate the logical expressions to compute the selectivity of a FILTER operator.
## How was this patch tested?
We add a new test suite to test various logical operators.
Author: Ron Hu <ron.hu@huawei.com>
Closes#16395 from ron8hu/filterSelectivity.
## What changes were proposed in this pull request?
This PR adds a special streaming deduplication operator to support `dropDuplicates` with `aggregation` and watermark. It reuses the `dropDuplicates` API but creates new logical plan `Deduplication` and new physical plan `DeduplicationExec`.
The following cases are supported:
- one or multiple `dropDuplicates()` without aggregation (with or without watermark)
- `dropDuplicates` before aggregation
Not supported cases:
- `dropDuplicates` after aggregation
Breaking changes:
- `dropDuplicates` without aggregation doesn't work with `complete` or `update` mode.
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16970 from zsxwing/dedup.
## What changes were proposed in this pull request?
This pr fixed a class-cast exception below;
```
scala> spark.range(10).selectExpr("cast (id as decimal) as x").selectExpr("percentile(x, 0.5)").collect()
java.lang.ClassCastException: org.apache.spark.sql.types.Decimal cannot be cast to java.lang.Number
at org.apache.spark.sql.catalyst.expressions.aggregate.Percentile.update(Percentile.scala:141)
at org.apache.spark.sql.catalyst.expressions.aggregate.Percentile.update(Percentile.scala:58)
at org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate.update(interfaces.scala:514)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$1$$anonfun$applyOrElse$1.apply(AggregationIterator.scala:171)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$1$$anonfun$applyOrElse$1.apply(AggregationIterator.scala:171)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$generateProcessRow$1.apply(AggregationIterator.scala:187)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$generateProcessRow$1.apply(AggregationIterator.scala:181)
at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.processInputs(ObjectAggregationIterator.scala:151)
at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.<init>(ObjectAggregationIterator.scala:78)
at org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:109)
at
```
This fix simply converts catalyst values (i.e., `Decimal`) into scala ones by using `CatalystTypeConverters`.
## How was this patch tested?
Added a test in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17028 from maropu/SPARK-19691.
### What changes were proposed in this pull request?
Currently, if `NumPartitions` is not set in RepartitionByExpression, we will set it using `spark.sql.shuffle.partitions` during Planner. However, this is not following the general resolution process. This PR is to set it in `Parser` and then `Optimizer` can use the value for plan optimization.
### How was this patch tested?
Added a test case.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#16988 from gatorsmile/resolveRepartition.
## What changes were proposed in this pull request?
This is a follow-up pr of #16308.
This pr enables timezone support in CSV/JSON parsing.
We should introduce `timeZone` option for CSV/JSON datasources (the default value of the option is session local timezone).
The datasources should use the `timeZone` option to format/parse to write/read timestamp values.
Notice that while reading, if the timestampFormat has the timezone info, the timezone will not be used because we should respect the timezone in the values.
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values written with the default timezone option, which is `"GMT"` because session local timezone is `"GMT"` here, are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "GMT")
scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts")
df: org.apache.spark.sql.DataFrame = [ts: timestamp]
scala> df.show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> df.write.json("/path/to/gmtjson")
```
```sh
$ cat /path/to/gmtjson/part-*
{"ts":"2016-01-01T00:00:00.000Z"}
```
whereas setting the option to `"PST"`, they are:
```scala
scala> df.write.option("timeZone", "PST").json("/path/to/pstjson")
```
```sh
$ cat /path/to/pstjson/part-*
{"ts":"2015-12-31T16:00:00.000-08:00"}
```
We can properly read these files even if the timezone option is wrong because the timestamp values have timezone info:
```scala
scala> val schema = new StructType().add("ts", TimestampType)
schema: org.apache.spark.sql.types.StructType = StructType(StructField(ts,TimestampType,true))
scala> spark.read.schema(schema).json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> spark.read.schema(schema).option("timeZone", "PST").json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
And even if `timezoneFormat` doesn't contain timezone info, we can properly read the values with setting correct timezone option:
```scala
scala> df.write.option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson")
```
```sh
$ cat /path/to/jstjson/part-*
{"ts":"2016-01-01T09:00:00"}
```
```scala
// wrong result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 09:00:00|
+-------------------+
// correct result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
This pr also makes `JsonToStruct` and `StructToJson` `TimeZoneAwareExpression` to be able to evaluate values with timezone option.
## How was this patch tested?
Existing tests and added some tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#16750 from ueshin/issues/SPARK-18937.
## What changes were proposed in this pull request?
A follow-up to disallow space as the delimiter in broadcast hint.
## How was this patch tested?
Jenkins test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16941 from viirya/disallow-space-delimiter.
## What changes were proposed in this pull request?
Support cardinality estimation and stats propagation for all join types.
Limitations:
- For inner/outer joins without any equal condition, we estimate it like cartesian product.
- For left semi/anti joins, since we can't apply the heuristics for inner join to it, for now we just propagate the statistics from left side. We should support them when other advanced stats (e.g. histograms) are available in spark.
## How was this patch tested?
Add a new test suite.
Author: Zhenhua Wang <wzh_zju@163.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16228 from wzhfy/joinEstimate.
## What changes were proposed in this pull request?
A small update to https://github.com/apache/spark/pull/16925
1. Rename SubstituteHints -> ResolveHints to be more consistent with rest of the rules.
2. Added more documentation in the rule and be more defensive / future proof to skip views as well as CTEs.
## How was this patch tested?
This pull request contains no real logic change and all behavior should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#16939 from rxin/SPARK-16475.
## What changes were proposed in this pull request?
This pull request introduces a simple hint infrastructure to SQL and implements broadcast join hint using the infrastructure.
The hint syntax looks like the following:
```
SELECT /*+ BROADCAST(t) */ * FROM t
```
For broadcast hint, we accept "BROADCAST", "BROADCASTJOIN", and "MAPJOIN", and a sequence of relation aliases can be specified in the hint. A broadcast hint plan node will be inserted on top of any relation (that is not aliased differently), subquery, or common table expression that match the specified name.
The hint resolution works by recursively traversing down the query plan to find a relation or subquery that matches one of the specified broadcast aliases. The traversal does not go past beyond any existing broadcast hints, subquery aliases. This rule happens before common table expressions.
Note that there was an earlier patch in https://github.com/apache/spark/pull/14426. This is a rewrite of that patch, with different semantics and simpler test cases.
## How was this patch tested?
Added a new unit test suite for the broadcast hint rule (SubstituteHintsSuite) and new test cases for parser change (in PlanParserSuite). Also added end-to-end test case in BroadcastSuite.
Author: Reynold Xin <rxin@databricks.com>
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16925 from rxin/SPARK-16475-broadcast-hint.
## What changes were proposed in this pull request?
This PR proposes to support type coercion between `ArrayType`s where the element types are compatible.
**Before**
```
Seq(Array(1)).toDF("a").selectExpr("greatest(a, array(1D))")
org.apache.spark.sql.AnalysisException: cannot resolve 'greatest(`a`, array(1.0D))' due to data type mismatch: The expressions should all have the same type, got GREATEST(array<int>, array<double>).; line 1 pos 0;
Seq(Array(1)).toDF("a").selectExpr("least(a, array(1D))")
org.apache.spark.sql.AnalysisException: cannot resolve 'least(`a`, array(1.0D))' due to data type mismatch: The expressions should all have the same type, got LEAST(array<int>, array<double>).; line 1 pos 0;
sql("SELECT * FROM values (array(0)), (array(1D)) as data(a)")
org.apache.spark.sql.AnalysisException: incompatible types found in column a for inline table; line 1 pos 14
Seq(Array(1)).toDF("a").union(Seq(Array(1D)).toDF("b"))
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. ArrayType(DoubleType,false) <> ArrayType(IntegerType,false) at the first column of the second table;;
sql("SELECT IF(1=1, array(1), array(1D))")
org.apache.spark.sql.AnalysisException: cannot resolve '(IF((1 = 1), array(1), array(1.0D)))' due to data type mismatch: differing types in '(IF((1 = 1), array(1), array(1.0D)))' (array<int> and array<double>).; line 1 pos 7;
```
**After**
```scala
Seq(Array(1)).toDF("a").selectExpr("greatest(a, array(1D))")
res5: org.apache.spark.sql.DataFrame = [greatest(a, array(1.0)): array<double>]
Seq(Array(1)).toDF("a").selectExpr("least(a, array(1D))")
res6: org.apache.spark.sql.DataFrame = [least(a, array(1.0)): array<double>]
sql("SELECT * FROM values (array(0)), (array(1D)) as data(a)")
res8: org.apache.spark.sql.DataFrame = [a: array<double>]
Seq(Array(1)).toDF("a").union(Seq(Array(1D)).toDF("b"))
res10: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: array<double>]
sql("SELECT IF(1=1, array(1), array(1D))")
res15: org.apache.spark.sql.DataFrame = [(IF((1 = 1), array(1), array(1.0))): array<double>]
```
## How was this patch tested?
Unit tests in `TypeCoercion` and Jenkins tests and
building with scala 2.10
```scala
./dev/change-scala-version.sh 2.10
./build/mvn -Pyarn -Phadoop-2.4 -Dscala-2.10 -DskipTests clean package
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16777 from HyukjinKwon/SPARK-19435.
## What changes were proposed in this pull request?
This PR proposes to fix the error message when some data types are compatible and others are not in set/union operation.
Currently, the code below:
```scala
Seq((1,("a", 1))).toDF.union(Seq((1L,("a", "b"))).toDF)
```
throws an exception saying `LongType` and `IntegerType` are incompatible types. It should say something about `StructType`s with more readable format as below:
**Before**
```
Union can only be performed on tables with the compatible column types.
LongType <> IntegerType at the first column of the second table;;
```
**After**
```
Union can only be performed on tables with the compatible column types.
struct<_1:string,_2:string> <> struct<_1:string,_2:int> at the second column of the second table;;
```
*I manually inserted a newline in the messages above for readability only in this PR description.
## How was this patch tested?
Unit tests in `AnalysisErrorSuite`, manual tests and build wth Scala 2.10.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16882 from HyukjinKwon/SPARK-19544.
## What changes were proposed in this pull request?
Using from_json on a column with an empty string results in: java.util.NoSuchElementException: head of empty list.
This is because `parser.parse(input)` may return `Nil` when `input.trim.isEmpty`
## How was this patch tested?
Regression test in `JsonExpressionsSuite`
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#16881 from brkyvz/json-fix.
## What changes were proposed in this pull request?
`mapGroupsWithState` is a new API for arbitrary stateful operations in Structured Streaming, similar to `DStream.mapWithState`
*Requirements*
- Users should be able to specify a function that can do the following
- Access the input row corresponding to a key
- Access the previous state corresponding to a key
- Optionally, update or remove the state
- Output any number of new rows (or none at all)
*Proposed API*
```
// ------------ New methods on KeyValueGroupedDataset ------------
class KeyValueGroupedDataset[K, V] {
// Scala friendly
def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V], KeyedState[S]) => U)
def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K, Iterator[V], KeyedState[S]) => Iterator[U])
// Java friendly
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K, V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
}
// ------------------- New Java-friendly function classes -------------------
public interface MapGroupsWithStateFunction<K, V, S, R> extends Serializable {
R call(K key, Iterator<V> values, state: KeyedState<S>) throws Exception;
}
public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends Serializable {
Iterator<R> call(K key, Iterator<V> values, state: KeyedState<S>) throws Exception;
}
// ---------------------- Wrapper class for state data ----------------------
trait State[S] {
def exists(): Boolean
def get(): S // throws Exception is state does not exist
def getOption(): Option[S]
def update(newState: S): Unit
def remove(): Unit // exists() will be false after this
}
```
Key Semantics of the State class
- The state can be null.
- If the state.remove() is called, then state.exists() will return false, and getOption will returm None.
- After that state.update(newState) is called, then state.exists() will return true, and getOption will return Some(...).
- None of the operations are thread-safe. This is to avoid memory barriers.
*Usage*
```
val stateFunc = (word: String, words: Iterator[String, runningCount: KeyedState[Long]) => {
val newCount = words.size + runningCount.getOption.getOrElse(0L)
runningCount.update(newCount)
(word, newCount)
}
dataset // type is Dataset[String]
.groupByKey[String](w => w) // generates KeyValueGroupedDataset[String, String]
.mapGroupsWithState[Long, (String, Long)](stateFunc) // returns Dataset[(String, Long)]
```
## How was this patch tested?
New unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16758 from tdas/mapWithState.
## What changes were proposed in this pull request?
The optimizer tries to remove redundant alias only projections from the query plan using the `RemoveAliasOnlyProject` rule. The current rule identifies removes such a project and rewrites the project's attributes in the **entire** tree. This causes problems when parts of the tree are duplicated (for instance a self join on a temporary view/CTE) and the duplicated part contains the alias only project, in this case the rewrite will break the tree.
This PR fixes these problems by using a blacklist for attributes that are not to be moved, and by making sure that attribute remapping is only done for the parent tree, and not for unrelated parts of the query plan.
The current tree transformation infrastructure works very well if the transformation at hand requires little or a global contextual information. In this case we need to know both the attributes that were not to be moved, and we also needed to know which child attributes were modified. This cannot be done easily using the current infrastructure, and solutions typically involves transversing the query plan multiple times (which is super slow). I have moved around some code in `TreeNode`, `QueryPlan` and `LogicalPlan`to make this much more straightforward; this basically allows you to manually traverse the tree.
This PR subsumes the following PRs by windpiger:
Closes https://github.com/apache/spark/pull/16267
Closes https://github.com/apache/spark/pull/16255
## How was this patch tested?
I have added unit tests to `RemoveRedundantAliasAndProjectSuite` and I have added integration tests to the `SQLQueryTestSuite.union` and `SQLQueryTestSuite.cte` test cases.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16757 from hvanhovell/SPARK-18609.
## What changes were proposed in this pull request?
I have a frequency distribution table with following entries
Age, No of person
21, 10
22, 15
23, 18
..
..
30, 14
Moreover it is common to have data in frequency distribution format to further calculate Percentile, Median. With current implementation
It would be very difficult and complex to find the percentile.
Therefore i am proposing enhancement to current Percentile and Approx Percentile implementation to take frequency distribution column into consideration
## How was this patch tested?
1) Enhanced /sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/PercentileSuite.scala to cover the additional functionality
2) Run some performance benchmark test with 20 million row in local environment and did not see any performance degradation
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: gagan taneja <tanejagagan@gagans-MacBook-Pro.local>
Closes#16497 from tanejagagan/branch-18940.
## What changes were proposed in this pull request?
It often happens that a complex object (struct/map/array) is created only to get elements from it in an subsequent expression. We can add an optimizer rule for this.
## How was this patch tested?
unit-tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Eyal Farago <eyal@nrgene.com>
Author: eyal farago <eyal.farago@gmail.com>
Closes#16043 from eyalfa/SPARK-18601.
## What changes were proposed in this pull request?
The SQL parser can mistake a `WHEN (...)` used in `CASE` for a function call. This happens in cases like the following:
```sql
select case when (1) + case when 1 > 0 then 1 else 0 end = 2 then 1 else 0 end
from tb
```
This PR fixes this by re-organizing the case related parsing rules.
## How was this patch tested?
Added a regression test to the `ExpressionParserSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16821 from hvanhovell/SPARK-19472.
## What changes were proposed in this pull request?
DataFrame.except doesn't work for UDT columns. It is because `ExtractEquiJoinKeys` will run `Literal.default` against UDT. However, we don't handle UDT in `Literal.default` and an exception will throw like:
java.lang.RuntimeException: no default for type
org.apache.spark.ml.linalg.VectorUDT3bfc3ba7
at org.apache.spark.sql.catalyst.expressions.Literal$.default(literals.scala:179)
at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:117)
at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:110)
More simple fix is just let `Literal.default` handle UDT by its sql type. So we can use more efficient join type on UDT.
Besides `except`, this also fixes other similar scenarios, so in summary this fixes:
* `except` on two Datasets with UDT
* `intersect` on two Datasets with UDT
* `Join` with the join conditions using `<=>` on UDT columns
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16765 from viirya/df-except-for-udt.
## What changes were proposed in this pull request?
This PR proposes to
- remove unused `findTightestCommonType` in `TypeCoercion` as suggested in https://github.com/apache/spark/pull/16777#discussion_r99283834
- rename `findTightestCommonTypeOfTwo ` to `findTightestCommonType`.
- fix comments accordingly
The usage was removed while refactoring/fixing in several JIRAs such as SPARK-16714, SPARK-16735 and SPARK-16646
## How was this patch tested?
Existing tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16786 from HyukjinKwon/SPARK-19446.
## What changes were proposed in this pull request?
There is a metadata introduced before to mark the optional columns in merged Parquet schema for filter predicate pushdown. As we upgrade to Parquet 1.8.2 which includes the fix for the pushdown of optional columns, we don't need this metadata now.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16756 from viirya/remove-optional-metadata.
## What changes were proposed in this pull request?
During canonicalization, `NOT(...(l, r))` should not expect such cases that `l.hashcode > r.hashcode`.
Take the rule `case NOT(GreaterThan(l, r)) if l.hashcode > r.hashcode` for example, it should never be matched since `GreaterThan(l, r)` itself would be re-written as `GreaterThan(r, l)` given `l.hashcode > r.hashcode` after canonicalization.
This patch consolidates rules like `case NOT(GreaterThan(l, r)) if l.hashcode > r.hashcode` and `case NOT(GreaterThan(l, r))`.
## How was this patch tested?
This patch expanded the `NOT` test case to cover both cases where:
- `l.hashcode > r.hashcode`
- `l.hashcode < r.hashcode`
Author: Liwei Lin <lwlin7@gmail.com>
Closes#16719 from lw-lin/canonicalize.
## What changes were proposed in this pull request?
As of Spark 2.1, Spark SQL assumes the machine timezone for datetime manipulation, which is bad if users are not in the same timezones as the machines, or if different users have different timezones.
We should introduce a session local timezone setting that is used for execution.
An explicit non-goal is locale handling.
### Semantics
Setting the session local timezone means that the timezone-aware expressions listed below should use the timezone to evaluate values, and also it should be used to convert (cast) between string and timestamp or between timestamp and date.
- `CurrentDate`
- `CurrentBatchTimestamp`
- `Hour`
- `Minute`
- `Second`
- `DateFormatClass`
- `ToUnixTimestamp`
- `UnixTimestamp`
- `FromUnixTime`
and below are implicitly timezone-aware through cast from timestamp to date:
- `DayOfYear`
- `Year`
- `Quarter`
- `Month`
- `DayOfMonth`
- `WeekOfYear`
- `LastDay`
- `NextDay`
- `TruncDate`
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values evaluated by some of timezone-aware expressions are:
```scala
scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts")
df: org.apache.spark.sql.DataFrame = [ts: timestamp]
scala> df.selectExpr("cast(ts as string)", "year(ts)", "month(ts)", "dayofmonth(ts)", "hour(ts)", "minute(ts)", "second(ts)").show(truncate = false)
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|ts |year(CAST(ts AS DATE))|month(CAST(ts AS DATE))|dayofmonth(CAST(ts AS DATE))|hour(ts)|minute(ts)|second(ts)|
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|2016-01-01 00:00:00|2016 |1 |1 |0 |0 |0 |
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
```
whereas setting the session local timezone to `"PST"`, they are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "PST")
scala> df.selectExpr("cast(ts as string)", "year(ts)", "month(ts)", "dayofmonth(ts)", "hour(ts)", "minute(ts)", "second(ts)").show(truncate = false)
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|ts |year(CAST(ts AS DATE))|month(CAST(ts AS DATE))|dayofmonth(CAST(ts AS DATE))|hour(ts)|minute(ts)|second(ts)|
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|2015-12-31 16:00:00|2015 |12 |31 |16 |0 |0 |
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
```
Notice that even if you set the session local timezone, it affects only in `DataFrame` operations, neither in `Dataset` operations, `RDD` operations nor in `ScalaUDF`s. You need to properly handle timezone by yourself.
### Design of the fix
I introduced an analyzer to pass session local timezone to timezone-aware expressions and modified DateTimeUtils to take the timezone argument.
## How was this patch tested?
Existing tests and added tests for timezone aware expressions.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#16308 from ueshin/issues/SPARK-18350.
## What changes were proposed in this pull request?
Spark SQL follows MySQL to do the implicit type conversion for binary comparison: http://dev.mysql.com/doc/refman/5.7/en/type-conversion.html
However, this may return confusing result, e.g. `1 = 'true'` will return true, `19157170390056973L = '19157170390056971'` will return true.
I think it's more reasonable to follow postgres in this case, i.e. cast string to the type of the other side, but return null if the string is not castable to keep hive compatibility.
## How was this patch tested?
newly added tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15880 from cloud-fan/compare.
## What changes were proposed in this pull request?
Hive will expand the view text, so it needs 2 fields: originalText and viewText. Since we don't expand the view text, but only add table properties, perhaps only a single field `viewText` is enough in CatalogTable.
This PR brought in the following changes:
1. Remove the param `viewOriginalText` from `CatalogTable`;
2. Update the output of command `DescribeTableCommand`.
## How was this patch tested?
Tested by exsiting test cases, also updated the failed test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16679 from jiangxb1987/catalogTable.
## What changes were proposed in this pull request?
As I pointed out in https://github.com/apache/spark/pull/15807#issuecomment-259143655 , the current subexpression elimination framework has a problem, it always evaluates all common subexpressions at the beginning, even they are inside conditional expressions and may not be accessed.
Ideally we should implement it like scala lazy val, so we only evaluate it when it gets accessed at lease once. https://github.com/apache/spark/issues/15837 tries this approach, but it seems too complicated and may introduce performance regression.
This PR simply stops common subexpression elimination for conditional expressions, with some cleanup.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16659 from cloud-fan/codegen.
### What changes were proposed in this pull request?
It is weird to create Hive source tables when using InMemoryCatalog. We are unable to operate it. This PR is to block users to create Hive source tables.
### How was this patch tested?
Fixed the test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16587 from gatorsmile/blockHiveTable.
## What changes were proposed in this pull request?
Sort in a streaming plan should be allowed only after a aggregation in complete mode. Currently it is incorrectly allowed when present anywhere in the plan. It gives unpredictable potentially incorrect results.
## How was this patch tested?
New test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16662 from tdas/SPARK-19314.
## What changes were proposed in this pull request?
Change non-cbo estimation behavior of aggregate:
- If groupExpression is empty, we can know row count (=1) and the corresponding size;
- otherwise, estimation falls back to UnaryNode's computeStats method, which should not propagate rowCount and attributeStats in Statistics because they are not estimated in that method.
## How was this patch tested?
Added test case
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16631 from wzhfy/aggNoCbo.
## What changes were proposed in this pull request?
We have a table relation plan cache in `HiveMetastoreCatalog`, which caches a lot of things: file status, resolved data source, inferred schema, etc.
However, it doesn't make sense to limit this cache with hive support, we should move it to SQL core module so that users can use this cache without hive support.
It can also reduce the size of `HiveMetastoreCatalog`, so that it's easier to remove it eventually.
main changes:
1. move the table relation cache to `SessionCatalog`
2. `SessionCatalog.lookupRelation` will return `SimpleCatalogRelation` and the analyzer will convert it to `LogicalRelation` or `MetastoreRelation` later, then `HiveSessionCatalog` doesn't need to override `lookupRelation` anymore
3. `FindDataSourceTable` will read/write the table relation cache.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16621 from cloud-fan/plan-cache.
### What changes were proposed in this pull request?
Empty partition column values are not valid for partition specification. Before this PR, we accept users to do it; however, Hive metastore does not detect and disallow it too. Thus, users hit the following strange error.
```Scala
val df = spark.createDataFrame(Seq((0, "a"), (1, "b"))).toDF("partCol1", "name")
df.write.mode("overwrite").partitionBy("partCol1").saveAsTable("partitionedTable")
spark.sql("alter table partitionedTable drop partition(partCol1='')")
spark.table("partitionedTable").show()
```
In the above example, the WHOLE table is DROPPED when users specify a partition spec containing only one partition column with empty values.
When the partition columns contains more than one, Hive metastore APIs simply ignore the columns with empty values and treat it as partial spec. This is also not expected. This does not follow the actual Hive behaviors. This PR is to disallow users to specify such an invalid partition spec in the `SessionCatalog` APIs.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16583 from gatorsmile/disallowEmptyPartColValue.
## What changes were proposed in this pull request?
In this pr, we add more test cases for project and aggregate estimation.
## How was this patch tested?
Add test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16551 from wzhfy/addTests.
## What changes were proposed in this pull request?
We should be able to resolve a nested view. The main advantage is that if you update an underlying view, the current view also gets updated.
The new approach should be compatible with older versions of SPARK/HIVE, that means:
1. The new approach should be able to resolve the views that created by older versions of SPARK/HIVE;
2. The new approach should be able to resolve the views that are currently supported by SPARK SQL.
The new approach mainly brings in the following changes:
1. Add a new operator called `View` to keep track of the CatalogTable that describes the view, and the output attributes as well as the child of the view;
2. Update the `ResolveRelations` rule to resolve the relations and views, note that a nested view should be resolved correctly;
3. Add `viewDefaultDatabase` variable to `CatalogTable` to keep track of the default database name used to resolve a view, if the `CatalogTable` is not a view, then the variable should be `None`;
4. Add `AnalysisContext` to enable us to still support a view created with CTE/Windows query;
5. Enables the view support without enabling Hive support (i.e., enableHiveSupport);
6. Fix a weird behavior: the result of a view query may have different schema if the referenced table has been changed. After this PR, we try to cast the child output attributes to that from the view schema, throw an AnalysisException if cast is not allowed.
Note this is compatible with the views defined by older versions of Spark(before 2.2), which have empty `defaultDatabase` and all the relations in `viewText` have database part defined.
## How was this patch tested?
1. Add new tests in `SessionCatalogSuite` to test the function `lookupRelation`;
2. Add new test case in `SQLViewSuite` to test resolve a nested view.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16233 from jiangxb1987/resolve-view.
## What changes were proposed in this pull request?
Currently we have two sets of statistics in LogicalPlan: a simple stats and a stats estimated by cbo, but the computing logic and naming are quite confusing, we need to unify these two sets of stats.
## How was this patch tested?
Just modify existing tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16529 from wzhfy/unifyStats.
## What changes were proposed in this pull request?
This PR allow update mode for non-aggregation streaming queries. It will be same as the append mode if a query has no aggregations.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16520 from zsxwing/update-without-agg.
## What changes were proposed in this pull request?
Support cardinality estimation of aggregate operator
## How was this patch tested?
Add test cases
Author: Zhenhua Wang <wzh_zju@163.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16431 from wzhfy/aggEstimation.
## What changes were proposed in this pull request?
Support cardinality estimation for project operator.
## How was this patch tested?
Add a test suite and a base class in the catalyst package.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16430 from wzhfy/projectEstimation.
## What changes were proposed in this pull request?
Added a `to` call at the end of the code generated by `ScalaReflection.deserializerFor` if the requested type is not a supertype of `WrappedArray[_]` that uses `CanBuildFrom[_, _, _]` to convert result into an arbitrary subtype of `Seq[_]`.
Care was taken to preserve the original deserialization where it is possible to avoid the overhead of conversion in cases where it is not needed
`ScalaReflection.serializerFor` could already be used to serialize any `Seq[_]` so it was not altered
`SQLImplicits` had to be altered and new implicit encoders added to permit serialization of other sequence types
Also fixes [SPARK-16815] Dataset[List[T]] leads to ArrayStoreException
## How was this patch tested?
```bash
./build/mvn -DskipTests clean package && ./dev/run-tests
```
Also manual execution of the following sets of commands in the Spark shell:
```scala
case class TestCC(key: Int, letters: List[String])
val ds1 = sc.makeRDD(Seq(
(List("D")),
(List("S","H")),
(List("F","H")),
(List("D","L","L"))
)).map(x=>(x.length,x)).toDF("key","letters").as[TestCC]
val test1=ds1.map{_.key}
test1.show
```
```scala
case class X(l: List[String])
spark.createDataset(Seq(List("A"))).map(X).show
```
```scala
spark.sqlContext.createDataset(sc.parallelize(List(1) :: Nil)).collect
```
After adding arbitrary sequence support also tested with the following commands:
```scala
case class QueueClass(q: scala.collection.immutable.Queue[Int])
spark.createDataset(Seq(List(1,2,3))).map(x => QueueClass(scala.collection.immutable.Queue(x: _*))).map(_.q.dequeue).collect
```
Author: Michal Senkyr <mike.senkyr@gmail.com>
Closes#16240 from michalsenkyr/sql-caseclass-list-fix.
## What changes were proposed in this pull request?
There are many locations in the Spark repo where the same word occurs consecutively. Sometimes they are appropriately placed, but many times they are not. This PR removes the inappropriately duplicated words.
## How was this patch tested?
N/A since only docs or comments were updated.
Author: Niranjan Padmanabhan <niranjan.padmanabhan@gmail.com>
Closes#16455 from neurons/np.structure_streaming_doc.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/16402 we made a mistake that, when double/float is infinity, the `Literal` codegen will output boxed value and cause wrong result.
This PR fixes this by special handling infinity to not output boxed value.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16469 from cloud-fan/literal.
### What changes were proposed in this pull request?
The data in the managed table should be deleted after table is dropped. However, if the partition location is not under the location of the partitioned table, it is not deleted as expected. Users can specify any location for the partition when they adding a partition.
This PR is to delete partition location when dropping managed partitioned tables stored in `InMemoryCatalog`.
### How was this patch tested?
Added test cases for both HiveExternalCatalog and InMemoryCatalog
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16448 from gatorsmile/unsetSerdeProp.
## What changes were proposed in this pull request?
Currently collect_set/collect_list aggregation expression don't support partial aggregation. This patch is to enable partial aggregation for them.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16371 from viirya/collect-partial-support.
## What changes were proposed in this pull request?
We add a cbo configuration to switch between default stats and estimated stats.
We also define a new statistics method `planStats` in LogicalPlan with conf as its parameter, in order to pass the cbo switch and other estimation related configurations in the future. `planStats` is used on the caller sides (i.e. in Optimizer and Strategies) to make transformation decisions based on stats.
## How was this patch tested?
Add a test case using a dummy LogicalPlan.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16401 from wzhfy/cboSwitch.
### What changes were proposed in this pull request?
Remove useless `databaseName ` from `SimpleCatalogRelation`.
### How was this patch tested?
Existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16438 from gatorsmile/removeDBFromSimpleCatalogRelation.
## What changes were proposed in this pull request?
This PR proposes to fix the test failures due to different format of paths on Windows.
Failed tests are as below:
```
ColumnExpressionSuite:
- input_file_name, input_file_block_start, input_file_block_length - FileScanRDD *** FAILED *** (187 milliseconds)
"file:///C:/projects/spark/target/tmp/spark-0b21b963-6cfa-411c-8d6f-e6a5e1e73bce/part-00001-c083a03a-e55e-4b05-9073-451de352d006.snappy.parquet" did not contain "C:\projects\spark\target\tmp\spark-0b21b963-6cfa-411c-8d6f-e6a5e1e73bce" (ColumnExpressionSuite.scala:545)
- input_file_name, input_file_block_start, input_file_block_length - HadoopRDD *** FAILED *** (172 milliseconds)
"file:/C:/projects/spark/target/tmp/spark-5d0afa94-7c2f-463b-9db9-2e8403e2bc5f/part-00000-f6530138-9ad3-466d-ab46-0eeb6f85ed0b.txt" did not contain "C:\projects\spark\target\tmp\spark-5d0afa94-7c2f-463b-9db9-2e8403e2bc5f" (ColumnExpressionSuite.scala:569)
- input_file_name, input_file_block_start, input_file_block_length - NewHadoopRDD *** FAILED *** (156 milliseconds)
"file:/C:/projects/spark/target/tmp/spark-a894c7df-c74d-4d19-82a2-a04744cb3766/part-00000-29674e3f-3fcf-4327-9b04-4dab1d46338d.txt" did not contain "C:\projects\spark\target\tmp\spark-a894c7df-c74d-4d19-82a2-a04744cb3766" (ColumnExpressionSuite.scala:598)
```
```
DataStreamReaderWriterSuite:
- source metadataPath *** FAILED *** (62 milliseconds)
org.mockito.exceptions.verification.junit.ArgumentsAreDifferent: Argument(s) are different! Wanted:
streamSourceProvider.createSource(
org.apache.spark.sql.SQLContext3b04133b,
"C:\projects\spark\target\tmp\streaming.metadata-b05db6ae-c8dc-4ce4-b0d9-1eb8c84876c0/sources/0",
None,
"org.apache.spark.sql.streaming.test",
Map()
);
-> at org.apache.spark.sql.streaming.test.DataStreamReaderWriterSuite$$anonfun$12.apply$mcV$sp(DataStreamReaderWriterSuite.scala:374)
Actual invocation has different arguments:
streamSourceProvider.createSource(
org.apache.spark.sql.SQLContext3b04133b,
"/C:/projects/spark/target/tmp/streaming.metadata-b05db6ae-c8dc-4ce4-b0d9-1eb8c84876c0/sources/0",
None,
"org.apache.spark.sql.streaming.test",
Map()
);
```
```
GlobalTempViewSuite:
- CREATE GLOBAL TEMP VIEW USING *** FAILED *** (110 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-960398ba-a0a1-45f6-a59a-d98533f9f519;
```
```
CreateTableAsSelectSuite:
- CREATE TABLE USING AS SELECT *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- create a table, drop it and create another one with the same name *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- create table using as select - with partitioned by *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- create table using as select - with non-zero buckets *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
```
```
HiveMetadataCacheSuite:
- partitioned table is cached when partition pruning is true *** FAILED *** (532 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- partitioned table is cached when partition pruning is false *** FAILED *** (297 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
MultiDatabaseSuite:
- createExternalTable() to non-default database - with USE *** FAILED *** (954 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-0839d9a7-5e29-467a-9e3e-3e4cd618ee09;
- createExternalTable() to non-default database - without USE *** FAILED *** (500 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-c7e24d73-1d8f-45e8-ab7d-53a83087aec3;
- invalid database name and table names *** FAILED *** (31 milliseconds)
"Path does not exist: file:/C:projectsspark arget mpspark-15a2a494-3483-4876-80e5-ec396e704b77;" did not contain "`t:a` is not a valid name for tables/databases. Valid names only contain alphabet characters, numbers and _." (MultiDatabaseSuite.scala:296)
```
```
OrcQuerySuite:
- SPARK-8501: Avoids discovery schema from empty ORC files *** FAILED *** (15 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- Verify the ORC conversion parameter: CONVERT_METASTORE_ORC *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- converted ORC table supports resolving mixed case field *** FAILED *** (297 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
HadoopFsRelationTest - JsonHadoopFsRelationSuite, OrcHadoopFsRelationSuite, ParquetHadoopFsRelationSuite, SimpleTextHadoopFsRelationSuite:
- Locality support for FileScanRDD *** FAILED *** (15 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-383d1f13-8783-47fd-964d-9c75e5eec50f, expected: file:///
```
```
HiveQuerySuite:
- CREATE TEMPORARY FUNCTION *** FAILED *** (0 milliseconds)
java.net.MalformedURLException: For input string: "%5Cprojects%5Cspark%5Csql%5Chive%5Ctarget%5Cscala-2.11%5Ctest-classes%5CTestUDTF.jar"
- ADD FILE command *** FAILED *** (500 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\sql\hive\target\scala-2.11\test-classes\data\files\v1.txt
- ADD JAR command 2 *** FAILED *** (110 milliseconds)
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive argetscala-2.11 est-classesdatafilessample.json;
```
```
PruneFileSourcePartitionsSuite:
- PruneFileSourcePartitions should not change the output of LogicalRelation *** FAILED *** (15 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
HiveCommandSuite:
- LOAD DATA LOCAL *** FAILED *** (109 milliseconds)
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive argetscala-2.11 est-classesdatafilesemployee.dat;
- LOAD DATA *** FAILED *** (93 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 15: C:projectsspark arget mpemployee.dat7496657117354281006.tmp
- Truncate Table *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive argetscala-2.11 est-classesdatafilesemployee.dat;
```
```
HiveExternalCatalogBackwardCompatibilitySuite:
- make sure we can read table created by old version of Spark *** FAILED *** (0 milliseconds)
"[/C:/projects/spark/target/tmp/]spark-0554d859-74e1-..." did not equal "[C:\projects\spark\target\tmp\]spark-0554d859-74e1-..." (HiveExternalCatalogBackwardCompatibilitySuite.scala:213)
org.scalatest.exceptions.TestFailedException
- make sure we can alter table location created by old version of Spark *** FAILED *** (110 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 15: C:projectsspark arget mpspark-0e9b2c5f-49a1-4e38-a32a-c0ab1813a79f
```
```
ExternalCatalogSuite:
- create/drop/rename partitions should create/delete/rename the directory *** FAILED *** (610 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-4c24f010-18df-437b-9fed-990c6f9adece
```
```
SQLQuerySuite:
- describe functions - temporary user defined functions *** FAILED *** (16 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 22: C:projectssparksqlhive argetscala-2.11 est-classesTestUDTF.jar
- specifying database name for a temporary table is not allowed *** FAILED *** (125 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-a34c9814-a483-43f2-be29-37f616b6df91;
```
```
PartitionProviderCompatibilitySuite:
- convert partition provider to hive with repair table *** FAILED *** (281 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-ee5fc96d-8c7d-4ebf-8571-a1d62736473e;
- when partition management is enabled, new tables have partition provider hive *** FAILED *** (187 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-803ad4d6-3e8c-498d-9ca5-5cda5d9b2a48;
- when partition management is disabled, new tables have no partition provider *** FAILED *** (172 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-c9fda9e2-4020-465f-8678-52cd72d0a58f;
- when partition management is disabled, we preserve the old behavior even for new tables *** FAILED *** (203 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget
mpspark-f4a518a6-c49d-43d3-b407-0ddd76948e13;
- insert overwrite partition of legacy datasource table *** FAILED *** (188 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-f4a518a6-c49d-43d3-b407-0ddd76948e79;
- insert overwrite partition of new datasource table overwrites just partition *** FAILED *** (219 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-6ba3a88d-6f6c-42c5-a9f4-6d924a0616ff;
- SPARK-18544 append with saveAsTable - partition management true *** FAILED *** (173 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-cd234a6d-9cb4-4d1d-9e51-854ae9543bbd;
- SPARK-18635 special chars in partition values - partition management true *** FAILED *** (2 seconds, 967 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-18635 special chars in partition values - partition management false *** FAILED *** (62 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-18659 insert overwrite table with lowercase - partition management true *** FAILED *** (63 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-18544 append with saveAsTable - partition management false *** FAILED *** (266 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-18659 insert overwrite table files - partition management false *** FAILED *** (63 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-18659 insert overwrite table with lowercase - partition management false *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- sanity check table setup *** FAILED *** (31 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- insert into partial dynamic partitions *** FAILED *** (47 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- insert into fully dynamic partitions *** FAILED *** (62 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- insert into static partition *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- overwrite partial dynamic partitions *** FAILED *** (63 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- overwrite fully dynamic partitions *** FAILED *** (47 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- overwrite static partition *** FAILED *** (63 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
MetastoreDataSourcesSuite:
- check change without refresh *** FAILED *** (203 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-00713fe4-ca04-448c-bfc7-6c5e9a2ad2a1;
- drop, change, recreate *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-2030a21b-7d67-4385-a65b-bb5e2bed4861;
- SPARK-15269 external data source table creation *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-4d50fd4a-14bc-41d6-9232-9554dd233f86;
- CTAS *** FAILED *** (109 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- CTAS with IF NOT EXISTS *** FAILED *** (109 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- CTAS: persisted partitioned bucketed data source table *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- SPARK-15025: create datasource table with path with select *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- CTAS: persisted partitioned data source table *** FAILED *** (47 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
```
```
HiveMetastoreCatalogSuite:
- Persist non-partitioned parquet relation into metastore as managed table using CTAS *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- Persist non-partitioned orc relation into metastore as managed table using CTAS *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
```
```
HiveUDFSuite:
- SPARK-11522 select input_file_name from non-parquet table *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
QueryPartitionSuite:
- SPARK-13709: reading partitioned Avro table with nested schema *** FAILED *** (250 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
```
ParquetHiveCompatibilitySuite:
- simple primitives *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-10177 timestamp *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- array *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- map *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- struct *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-16344: array of struct with a single field named 'array_element' *** FAILED *** (15 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
## How was this patch tested?
Manually tested via AppVeyor.
```
ColumnExpressionSuite:
- input_file_name, input_file_block_start, input_file_block_length - FileScanRDD (234 milliseconds)
- input_file_name, input_file_block_start, input_file_block_length - HadoopRDD (235 milliseconds)
- input_file_name, input_file_block_start, input_file_block_length - NewHadoopRDD (203 milliseconds)
```
```
DataStreamReaderWriterSuite:
- source metadataPath (63 milliseconds)
```
```
GlobalTempViewSuite:
- CREATE GLOBAL TEMP VIEW USING (436 milliseconds)
```
```
CreateTableAsSelectSuite:
- CREATE TABLE USING AS SELECT (171 milliseconds)
- create a table, drop it and create another one with the same name (422 milliseconds)
- create table using as select - with partitioned by (141 milliseconds)
- create table using as select - with non-zero buckets (125 milliseconds)
```
```
HiveMetadataCacheSuite:
- partitioned table is cached when partition pruning is true (3 seconds, 211 milliseconds)
- partitioned table is cached when partition pruning is false (1 second, 781 milliseconds)
```
```
MultiDatabaseSuite:
- createExternalTable() to non-default database - with USE (797 milliseconds)
- createExternalTable() to non-default database - without USE (640 milliseconds)
- invalid database name and table names (62 milliseconds)
```
```
OrcQuerySuite:
- SPARK-8501: Avoids discovery schema from empty ORC files (703 milliseconds)
- Verify the ORC conversion parameter: CONVERT_METASTORE_ORC (750 milliseconds)
- converted ORC table supports resolving mixed case field (625 milliseconds)
```
```
HadoopFsRelationTest - JsonHadoopFsRelationSuite, OrcHadoopFsRelationSuite, ParquetHadoopFsRelationSuite, SimpleTextHadoopFsRelationSuite:
- Locality support for FileScanRDD (296 milliseconds)
```
```
HiveQuerySuite:
- CREATE TEMPORARY FUNCTION (125 milliseconds)
- ADD FILE command (250 milliseconds)
- ADD JAR command 2 (609 milliseconds)
```
```
PruneFileSourcePartitionsSuite:
- PruneFileSourcePartitions should not change the output of LogicalRelation (359 milliseconds)
```
```
HiveCommandSuite:
- LOAD DATA LOCAL (1 second, 829 milliseconds)
- LOAD DATA (1 second, 735 milliseconds)
- Truncate Table (1 second, 641 milliseconds)
```
```
HiveExternalCatalogBackwardCompatibilitySuite:
- make sure we can read table created by old version of Spark (32 milliseconds)
- make sure we can alter table location created by old version of Spark (125 milliseconds)
- make sure we can rename table created by old version of Spark (281 milliseconds)
```
```
ExternalCatalogSuite:
- create/drop/rename partitions should create/delete/rename the directory (625 milliseconds)
```
```
SQLQuerySuite:
- describe functions - temporary user defined functions (31 milliseconds)
- specifying database name for a temporary table is not allowed (390 milliseconds)
```
```
PartitionProviderCompatibilitySuite:
- convert partition provider to hive with repair table (813 milliseconds)
- when partition management is enabled, new tables have partition provider hive (562 milliseconds)
- when partition management is disabled, new tables have no partition provider (344 milliseconds)
- when partition management is disabled, we preserve the old behavior even for new tables (422 milliseconds)
- insert overwrite partition of legacy datasource table (750 milliseconds)
- SPARK-18544 append with saveAsTable - partition management true (985 milliseconds)
- SPARK-18635 special chars in partition values - partition management true (3 seconds, 328 milliseconds)
- SPARK-18635 special chars in partition values - partition management false (2 seconds, 891 milliseconds)
- SPARK-18659 insert overwrite table with lowercase - partition management true (750 milliseconds)
- SPARK-18544 append with saveAsTable - partition management false (656 milliseconds)
- SPARK-18659 insert overwrite table files - partition management false (922 milliseconds)
- SPARK-18659 insert overwrite table with lowercase - partition management false (469 milliseconds)
- sanity check table setup (937 milliseconds)
- insert into partial dynamic partitions (2 seconds, 985 milliseconds)
- insert into fully dynamic partitions (1 second, 937 milliseconds)
- insert into static partition (1 second, 578 milliseconds)
- overwrite partial dynamic partitions (7 seconds, 561 milliseconds)
- overwrite fully dynamic partitions (1 second, 766 milliseconds)
- overwrite static partition (1 second, 797 milliseconds)
```
```
MetastoreDataSourcesSuite:
- check change without refresh (610 milliseconds)
- drop, change, recreate (437 milliseconds)
- SPARK-15269 external data source table creation (297 milliseconds)
- CTAS with IF NOT EXISTS (437 milliseconds)
- CTAS: persisted partitioned bucketed data source table (422 milliseconds)
- SPARK-15025: create datasource table with path with select (265 milliseconds)
- CTAS (438 milliseconds)
- CTAS with IF NOT EXISTS (469 milliseconds)
- CTAS: persisted partitioned bucketed data source table (406 milliseconds)
```
```
HiveMetastoreCatalogSuite:
- Persist non-partitioned parquet relation into metastore as managed table using CTAS (406 milliseconds)
- Persist non-partitioned orc relation into metastore as managed table using CTAS (313 milliseconds)
```
```
HiveUDFSuite:
- SPARK-11522 select input_file_name from non-parquet table (3 seconds, 144 milliseconds)
```
```
QueryPartitionSuite:
- SPARK-13709: reading partitioned Avro table with nested schema (1 second, 67 milliseconds)
```
```
ParquetHiveCompatibilitySuite:
- simple primitives (745 milliseconds)
- SPARK-10177 timestamp (375 milliseconds)
- array (407 milliseconds)
- map (409 milliseconds)
- struct (437 milliseconds)
- SPARK-16344: array of struct with a single field named 'array_element' (391 milliseconds)
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16397 from HyukjinKwon/SPARK-18922-paths.
## What changes were proposed in this pull request?
SortPartitions and RedistributeData logical operators are not actually used and can be removed. Note that we do have a Sort operator (with global flag false) that subsumed SortPartitions.
## How was this patch tested?
Also updated test cases to reflect the removal.
Author: Reynold Xin <rxin@databricks.com>
Closes#16381 from rxin/SPARK-18973.
## What changes were proposed in this pull request?
Made update mode public. As part of that here are the changes.
- Update DatastreamWriter to accept "update"
- Changed package of InternalOutputModes from o.a.s.sql to o.a.s.sql.catalyst
- Added update mode state removing with watermark to StateStoreSaveExec
## How was this patch tested?
Added new tests in changed modules
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16360 from tdas/SPARK-18234.
## What changes were proposed in this pull request?
Currently `ImplicitTypeCasts` doesn't handle casts between `ArrayType`s, this is not convenient, we should add a rule to enable casting from `ArrayType(InternalType)` to `ArrayType(newInternalType)`.
Goals:
1. Add a rule to `ImplicitTypeCasts` to enable casting between `ArrayType`s;
2. Simplify `Percentile` and `ApproximatePercentile`.
## How was this patch tested?
Updated test cases in `TypeCoercionSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16057 from jiangxb1987/implicit-cast-complex-types.
## What changes were proposed in this pull request?
Check whether Aggregation operators on a streaming subplan have aggregate expressions with isDistinct = true.
## How was this patch tested?
Added unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16289 from tdas/SPARK-18870.
## What changes were proposed in this pull request?
This patch reduces the default number element estimation for arrays and maps from 100 to 1. The issue with the 100 number is that when nested (e.g. an array of map), 100 * 100 would be used as the default size. This sounds like just an overestimation which doesn't seem that bad (since it is usually better to overestimate than underestimate). However, due to the way we assume the size output for Project (new estimated column size / old estimated column size), this overestimation can become underestimation. It is actually in general in this case safer to assume 1 default element.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#16274 from rxin/SPARK-18853.
## What changes were proposed in this pull request?
`OverwriteOptions` was introduced in https://github.com/apache/spark/pull/15705, to carry the information of static partitions. However, after further refactor, this information becomes duplicated and we can remove `OverwriteOptions`.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15995 from cloud-fan/overwrite.
## What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/15620 , all of the Maven-based 2.0 Jenkins jobs time out consistently. As I pointed out in https://github.com/apache/spark/pull/15620#discussion_r91829129 , it seems that the regression test is an overkill and may hit constants pool size limitation, which is a known issue and hasn't been fixed yet.
Since #15620 only fix the code size limitation problem, we can simplify the test to avoid hitting constants pool size limitation.
## How was this patch tested?
test only change
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16244 from cloud-fan/minor.
(Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-18572)
## What changes were proposed in this pull request?
Currently Spark answers the `SHOW PARTITIONS` command by fetching all of the table's partition metadata from the external catalog and constructing partition names therefrom. The Hive client has a `getPartitionNames` method which is many times faster for this purpose, with the performance improvement scaling with the number of partitions in a table.
To test the performance impact of this PR, I ran the `SHOW PARTITIONS` command on two Hive tables with large numbers of partitions. One table has ~17,800 partitions, and the other has ~95,000 partitions. For the purposes of this PR, I'll call the former table `table1` and the latter table `table2`. I ran 5 trials for each table with before-and-after versions of this PR. The results are as follows:
Spark at bdc8153, `SHOW PARTITIONS table1`, times in seconds:
7.901
3.983
4.018
4.331
4.261
Spark at bdc8153, `SHOW PARTITIONS table2`
(Timed out after 10 minutes with a `SocketTimeoutException`.)
Spark at this PR, `SHOW PARTITIONS table1`, times in seconds:
3.801
0.449
0.395
0.348
0.336
Spark at this PR, `SHOW PARTITIONS table2`, times in seconds:
5.184
1.63
1.474
1.519
1.41
Taking the best times from each trial, we get a 12x performance improvement for a table with ~17,800 partitions and at least a 426x improvement for a table with ~95,000 partitions. More significantly, the latter command doesn't even complete with the current code in master.
This is actually a patch we've been using in-house at VideoAmp since Spark 1.1. It's made all the difference in the practical usability of our largest tables. Even with tables with about 1,000 partitions there's a performance improvement of about 2-3x.
## How was this patch tested?
I added a unit test to `VersionsSuite` which tests that the Hive client's `getPartitionNames` method returns the correct number of partitions.
Author: Michael Allman <michael@videoamp.com>
Closes#15998 from mallman/spark-18572-list_partition_names.
## What changes were proposed in this pull request?
Fix for SPARK-18091 which is a bug related to large if expressions causing generated SpecificUnsafeProjection code to exceed JVM code size limit.
This PR changes if expression's code generation to place its predicate, true value and false value expressions' generated code in separate methods in context so as to never generate too long combined code.
## How was this patch tested?
Added a unit test and also tested manually with the application (having transformations similar to the unit test) which caused the issue to be identified in the first place.
Author: Kapil Singh <kapsingh@adobe.com>
Closes#15620 from kapilsingh5050/SPARK-18091-IfCodegenFix.
## What changes were proposed in this pull request?
This fix puts an explicit list of operators that Spark supports for correlated subqueries.
## How was this patch tested?
Run sql/test, catalyst/test and add a new test case on Generate.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16046 from nsyca/spark18455.0.
## What changes were proposed in this pull request?
This fixes the parser rule to match named expressions, which doesn't work for two reasons:
1. The name match is not coerced to a regular expression (missing .r)
2. The surrounding literals are incorrect and attempt to escape a single quote, which is unnecessary
## How was this patch tested?
This adds test cases for named expressions using the bracket syntax, including one with quoted spaces.
Author: Ryan Blue <blue@apache.org>
Closes#16107 from rdblue/SPARK-18677-fix-json-path.
### What changes were proposed in this pull request?
Added a test case for using joins with nested fields.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16110 from gatorsmile/followup-18674.
## What changes were proposed in this pull request?
Two bugs are addressed here
1. INSERT OVERWRITE TABLE sometime crashed when catalog partition management was enabled. This was because when dropping partitions after an overwrite operation, the Hive client will attempt to delete the partition files. If the entire partition directory was dropped, this would fail. The PR fixes this by adding a flag to control whether the Hive client should attempt to delete files.
2. The static partition spec for OVERWRITE TABLE was not correctly resolved to the case-sensitive original partition names. This resulted in the entire table being overwritten if you did not correctly capitalize your partition names.
cc yhuai cloud-fan
## How was this patch tested?
Unit tests. Surprisingly, the existing overwrite table tests did not catch these edge cases.
Author: Eric Liang <ekl@databricks.com>
Closes#16088 from ericl/spark-18659.
## What changes were proposed in this pull request?
SPARK-18429 introduced count-min sketch aggregate function for SQL, but the implementation and testing is more complicated than needed. This simplifies the test cases and removes support for data types that don't have clear equality semantics:
1. Removed support for floating point and decimal types.
2. Removed the heavy randomized tests. The underlying CountMinSketch implementation already had pretty good test coverage through randomized tests, and the SPARK-18429 implementation is just to add an aggregate function wrapper around CountMinSketch. There is no need for randomized tests at three different levels of the implementations.
## How was this patch tested?
A lot of the change is to simplify test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#16093 from rxin/SPARK-18663.
## What changes were proposed in this pull request?
This PR makes `ExpressionEncoder.serializer.nullable` for flat encoder for a primitive type `false`. Since it is `true` for now, it is too conservative.
While `ExpressionEncoder.schema` has correct information (e.g. `<IntegerType, false>`), `serializer.head.nullable` of `ExpressionEncoder`, which got from `encoderFor[T]`, is always false. It is too conservative.
This is accomplished by checking whether a type is one of primitive types. If it is `true`, `nullable` should be `false`.
## How was this patch tested?
Added new tests for encoder and dataframe
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#15780 from kiszk/SPARK-18284.
## What changes were proposed in this pull request?
The current error message of USING join is quite confusing, for example:
```
scala> val df1 = List(1,2,3).toDS.withColumnRenamed("value", "c1")
df1: org.apache.spark.sql.DataFrame = [c1: int]
scala> val df2 = List(1,2,3).toDS.withColumnRenamed("value", "c2")
df2: org.apache.spark.sql.DataFrame = [c2: int]
scala> df1.join(df2, usingColumn = "c1")
org.apache.spark.sql.AnalysisException: using columns ['c1] can not be resolved given input columns: [c1, c2] ;;
'Join UsingJoin(Inner,List('c1))
:- Project [value#1 AS c1#3]
: +- LocalRelation [value#1]
+- Project [value#7 AS c2#9]
+- LocalRelation [value#7]
```
after this PR, it becomes:
```
scala> val df1 = List(1,2,3).toDS.withColumnRenamed("value", "c1")
df1: org.apache.spark.sql.DataFrame = [c1: int]
scala> val df2 = List(1,2,3).toDS.withColumnRenamed("value", "c2")
df2: org.apache.spark.sql.DataFrame = [c2: int]
scala> df1.join(df2, usingColumn = "c1")
org.apache.spark.sql.AnalysisException: USING column `c1` can not be resolved with the right join side, the right output is: [c2];
```
## How was this patch tested?
updated tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16100 from cloud-fan/natural.
### What changes were proposed in this pull request?
The `constraints` of an operator is the expressions that evaluate to `true` for all the rows produced. That means, the expression result should be neither `false` nor `unknown` (NULL). Thus, we can conclude that `IsNotNull` on all the constraints, which are generated by its own predicates or propagated from the children. The constraint can be a complex expression. For better usage of these constraints, we try to push down `IsNotNull` to the lowest-level expressions (i.e., `Attribute`). `IsNotNull` can be pushed through an expression when it is null intolerant. (When the input is NULL, the null-intolerant expression always evaluates to NULL.)
Below is the existing code we have for `IsNotNull` pushdown.
```Scala
private def scanNullIntolerantExpr(expr: Expression): Seq[Attribute] = expr match {
case a: Attribute => Seq(a)
case _: NullIntolerant | IsNotNull(_: NullIntolerant) =>
expr.children.flatMap(scanNullIntolerantExpr)
case _ => Seq.empty[Attribute]
}
```
**`IsNotNull` itself is not null-intolerant.** It converts `null` to `false`. If the expression does not include any `Not`-like expression, it works; otherwise, it could generate a wrong result. This PR is to fix the above function by removing the `IsNotNull` from the inference. After the fix, when a constraint has a `IsNotNull` expression, we infer new attribute-specific `IsNotNull` constraints if and only if `IsNotNull` appears in the root.
Without the fix, the following test case will return empty.
```Scala
val data = Seq[java.lang.Integer](1, null).toDF("key")
data.filter("not key is not null").show()
```
Before the fix, the optimized plan is like
```
== Optimized Logical Plan ==
Project [value#1 AS key#3]
+- Filter (isnotnull(value#1) && NOT isnotnull(value#1))
+- LocalRelation [value#1]
```
After the fix, the optimized plan is like
```
== Optimized Logical Plan ==
Project [value#1 AS key#3]
+- Filter NOT isnotnull(value#1)
+- LocalRelation [value#1]
```
### How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16067 from gatorsmile/isNotNull2.
## What changes were proposed in this pull request?
ExistenceJoin should be treated the same as LeftOuter and LeftAnti, not InnerLike and LeftSemi. This is not currently exposed because the rewrite of [NOT] EXISTS OR ... to ExistenceJoin happens in rule RewritePredicateSubquery, which is in a separate rule set and placed after the rule PushPredicateThroughJoin. During the transformation in the rule PushPredicateThroughJoin, an ExistenceJoin never exists.
The semantics of ExistenceJoin says we need to preserve all the rows from the left table through the join operation as if it is a regular LeftOuter join. The ExistenceJoin augments the LeftOuter operation with a new column called exists, set to true when the join condition in the ON clause is true and false otherwise. The filter of any rows will happen in the Filter operation above the ExistenceJoin.
Example:
A(c1, c2): { (1, 1), (1, 2) }
// B can be any value as it is irrelevant in this example
B(c1): { (NULL) }
select A.*
from A
where exists (select 1 from B where A.c1 = A.c2)
or A.c2=2
In this example, the correct result is all the rows from A. If the pattern ExistenceJoin around line 935 in Optimizer.scala is indeed active, the code will push down the predicate A.c1 = A.c2 to be a Filter on relation A, which will incorrectly filter the row (1,2) from A.
## How was this patch tested?
Since this is not an exposed case, no new test cases is added. The scenario is discovered via a code review of another PR and confirmed to be valid with peer.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16044 from nsyca/spark-18614.
## What changes were proposed in this pull request?
This PR implements a new Aggregate to generate count min sketch, which is a wrapper of CountMinSketch.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15877 from wzhfy/cms.
## What changes were proposed in this pull request?
In #15764 we added a mechanism to detect if a function is temporary or not. Hive functions are treated as non-temporary. Of the three hive functions, now "percentile" has been implemented natively, and "hash" has been removed. So we should update the list.
## How was this patch tested?
Unit tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#16049 from lins05/update-temp-function-detect-hive-list.
## What changes were proposed in this pull request?
Implement percentile SQL function. It computes the exact percentile(s) of expr at pc with range in [0, 1].
## How was this patch tested?
Add a new testsuite `PercentileSuite` to test percentile directly.
Updated related testcases in `ExpressionToSQLSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Author: 蒋星博 <jiangxingbo@meituan.com>
Author: jiangxingbo <jiangxingbo@meituan.com>
Closes#14136 from jiangxb1987/percentile.
## What changes were proposed in this pull request?
We currently push down join conditions of a Left Anti join to both sides of the join. This is similar to Inner, Left Semi and Existence (a specialized left semi) join. The problem is that this changes the semantics of the join; a left anti join filters out rows that matches the join condition.
This PR fixes this by only pushing down conditions to the left hand side of the join. This is similar to the behavior of left outer join.
## How was this patch tested?
Added tests to `FilterPushdownSuite.scala` and created a SQLQueryTestSuite file for left anti joins with a regression test.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16026 from hvanhovell/SPARK-18597.
## What changes were proposed in this pull request?
The `CollapseWindow` optimizer rule changes the order of output attributes. This modifies the output of the plan, which the optimizer cannot do. This also breaks things like `collect()` for which we use a `RowEncoder` that assumes that the output attributes of the executed plan are equal to those outputted by the logical plan.
## How was this patch tested?
I have updated an incorrect test in `CollapseWindowSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16027 from hvanhovell/SPARK-18604.
### What changes were proposed in this pull request?
Currently, the name validation checks are limited to table creation. It is enfored by Analyzer rule: `PreWriteCheck`.
However, table renaming and database creation have the same issues. It makes more sense to do the checks in `SessionCatalog`. This PR is to add it into `SessionCatalog`.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16018 from gatorsmile/nameValidate.
## What changes were proposed in this pull request?
Currently, `OuterReference` is not `NamedExpression`. So, it raises 'ClassCastException` when it used in projection lists of IN correlated subqueries. This PR aims to support that by making `OuterReference` as `NamedExpression` to show correct error messages.
```scala
scala> sql("CREATE TEMPORARY VIEW t1 AS SELECT * FROM VALUES 1, 2 AS t1(a)")
scala> sql("CREATE TEMPORARY VIEW t2 AS SELECT * FROM VALUES 1 AS t2(b)")
scala> sql("SELECT a FROM t1 WHERE a IN (SELECT a FROM t2)").show
java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.OuterReference cannot be cast to org.apache.spark.sql.catalyst.expressions.NamedExpression
```
## How was this patch tested?
Pass the Jenkins test with new test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16015 from dongjoon-hyun/SPARK-17251-2.
## What changes were proposed in this pull request?
The expression `in(empty seq)` is invalid in some data source. Since `in(empty seq)` is always false, we should generate `in(empty seq)` to false literal in optimizer.
The sql `SELECT * FROM t WHERE a IN ()` throws a `ParseException` which is consistent with Hive, don't need to change that behavior.
## How was this patch tested?
Add new test case in `OptimizeInSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15977 from jiangxb1987/isin-empty.
## What changes were proposed in this pull request?
In `HyperLogLogPlusPlus`, if the relative error is so small that p >= 19, it will cause ArrayIndexOutOfBoundsException in `THRESHOLDS(p-4)` . We should check `p` and when p >= 19, regress to the original HLL result and use the small range correction they use.
The pr also fixes the upper bound in the log info in `require()`.
The upper bound is computed by:
```
val relativeSD = 1.106d / Math.pow(Math.E, p * Math.log(2.0d) / 2.0d)
```
which is derived from the equation for computing `p`:
```
val p = 2.0d * Math.log(1.106d / relativeSD) / Math.log(2.0d)
```
## How was this patch tested?
add test cases for:
1. checking validity of parameter relatvieSD
2. estimation with smaller relative error so that p >= 19
Author: Zhenhua Wang <wzh_zju@163.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15990 from wzhfy/hllppRsd.
## What changes were proposed in this pull request?
In Spark SQL, some expression may output safe format values, e.g. `CreateArray`, `CreateStruct`, `Cast`, etc. When we compare 2 values, we should be able to compare safe and unsafe formats.
The `GreaterThan`, `LessThan`, etc. in Spark SQL already handles it, but the `EqualTo` doesn't. This PR fixes it.
## How was this patch tested?
new unit test and regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15929 from cloud-fan/type-aware.
## What changes were proposed in this pull request?
This PR proposes throwing an `AnalysisException` with a proper message rather than `NoSuchElementException` with the message ` key not found: TimestampType` when unsupported types are given to `reflect` and `java_method` functions.
```scala
spark.range(1).selectExpr("reflect('java.lang.String', 'valueOf', cast('1990-01-01' as timestamp))")
```
produces
**Before**
```
java.util.NoSuchElementException: key not found: TimestampType
at scala.collection.MapLike$class.default(MapLike.scala:228)
at scala.collection.AbstractMap.default(Map.scala:59)
at scala.collection.MapLike$class.apply(MapLike.scala:141)
at scala.collection.AbstractMap.apply(Map.scala:59)
at org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection$$anonfun$findMethod$1$$anonfun$apply$1.apply(CallMethodViaReflection.scala:159)
...
```
**After**
```
cannot resolve 'reflect('java.lang.String', 'valueOf', CAST('1990-01-01' AS TIMESTAMP))' due to data type mismatch: arguments from the third require boolean, byte, short, integer, long, float, double or string expressions; line 1 pos 0;
'Project [unresolvedalias(reflect(java.lang.String, valueOf, cast(1990-01-01 as timestamp)), Some(<function1>))]
+- Range (0, 1, step=1, splits=Some(2))
...
```
Added message is,
```
arguments from the third require boolean, byte, short, integer, long, float, double or string expressions
```
## How was this patch tested?
Tests added in `CallMethodViaReflection`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15694 from HyukjinKwon/SPARK-18179.
## What changes were proposed in this pull request?
Technically map type is not orderable, but can be used in equality comparison. However, due to the limitation of the current implementation, map type can't be used in equality comparison so that it can't be join key or grouping key.
This PR makes this limitation explicit, to avoid wrong result.
## How was this patch tested?
updated tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15956 from cloud-fan/map-type.
## What changes were proposed in this pull request?
This PR adds code generation to `Generate`. It supports two code paths:
- General `TraversableOnce` based iteration. This used for regular `Generator` (code generation supporting) expressions. This code path expects the expression to return a `TraversableOnce[InternalRow]` and it will iterate over the returned collection. This PR adds code generation for the `stack` generator.
- Specialized `ArrayData/MapData` based iteration. This is used for the `explode`, `posexplode` & `inline` functions and operates directly on the `ArrayData`/`MapData` result that the child of the generator returns.
### Benchmarks
I have added some benchmarks and it seems we can create a nice speedup for explode:
#### Environment
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.11.6
Intel(R) Core(TM) i7-4980HQ CPU 2.80GHz
```
#### Explode Array
##### Before
```
generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode array wholestage off 7377 / 7607 2.3 439.7 1.0X
generate explode array wholestage on 6055 / 6086 2.8 360.9 1.2X
```
##### After
```
generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode array wholestage off 7432 / 7696 2.3 443.0 1.0X
generate explode array wholestage on 631 / 646 26.6 37.6 11.8X
```
#### Explode Map
##### Before
```
generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode map wholestage off 12792 / 12848 1.3 762.5 1.0X
generate explode map wholestage on 11181 / 11237 1.5 666.5 1.1X
```
##### After
```
generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode map wholestage off 10949 / 10972 1.5 652.6 1.0X
generate explode map wholestage on 870 / 913 19.3 51.9 12.6X
```
#### Posexplode
##### Before
```
generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate posexplode array wholestage off 7547 / 7580 2.2 449.8 1.0X
generate posexplode array wholestage on 5786 / 5838 2.9 344.9 1.3X
```
##### After
```
generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate posexplode array wholestage off 7535 / 7548 2.2 449.1 1.0X
generate posexplode array wholestage on 620 / 624 27.1 37.0 12.1X
```
#### Inline
##### Before
```
generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate inline array wholestage off 6935 / 6978 2.4 413.3 1.0X
generate inline array wholestage on 6360 / 6400 2.6 379.1 1.1X
```
##### After
```
generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate inline array wholestage off 6940 / 6966 2.4 413.6 1.0X
generate inline array wholestage on 1002 / 1012 16.7 59.7 6.9X
```
#### Stack
##### Before
```
generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate stack wholestage off 12980 / 13104 1.3 773.7 1.0X
generate stack wholestage on 11566 / 11580 1.5 689.4 1.1X
```
##### After
```
generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate stack wholestage off 12875 / 12949 1.3 767.4 1.0X
generate stack wholestage on 840 / 845 20.0 50.0 15.3X
```
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#13065 from hvanhovell/SPARK-15214.
## What changes were proposed in this pull request?
Small fix, fix the errors caused by lint check in Java
- Clear unused objects and `UnusedImports`.
- Add comments around the method `finalize` of `NioBufferedFileInputStream`to turn off checkstyle.
- Cut the line which is longer than 100 characters into two lines.
## How was this patch tested?
Travis CI.
```
$ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
$ dev/lint-java
```
Before:
```
Checkstyle checks failed at following occurrences:
[ERROR] src/main/java/org/apache/spark/network/util/TransportConf.java:[21,8] (imports) UnusedImports: Unused import - org.apache.commons.crypto.cipher.CryptoCipherFactory.
[ERROR] src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java:[516,5] (modifier) RedundantModifier: Redundant 'public' modifier.
[ERROR] src/main/java/org/apache/spark/io/NioBufferedFileInputStream.java:[133] (coding) NoFinalizer: Avoid using finalizer method.
[ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java:[71] (sizes) LineLength: Line is longer than 100 characters (found 113).
[ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java:[112] (sizes) LineLength: Line is longer than 100 characters (found 110).
[ERROR] src/test/java/org/apache/spark/sql/catalyst/expressions/HiveHasherSuite.java:[31,17] (modifier) ModifierOrder: 'static' modifier out of order with the JLS suggestions.
[ERROR]src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java:[64] (sizes) LineLength: Line is longer than 100 characters (found 103).
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[22,8] (imports) UnusedImports: Unused import - org.apache.spark.ml.linalg.Vectors.
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[51] (regexp) RegexpSingleline: No trailing whitespace allowed.
```
After:
```
$ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
$ dev/lint-java
Using `mvn` from path: /home/travis/build/ConeyLiu/spark/build/apache-maven-3.3.9/bin/mvn
Checkstyle checks passed.
```
Author: Xianyang Liu <xyliu0530@icloud.com>
Closes#15865 from ConeyLiu/master.
## What changes were proposed in this pull request?
The `FoldablePropagation` optimizer rule, pulls foldable values out from under an `Expand`. This breaks the `Expand` in two ways:
- It rewrites the output attributes of the `Expand`. We explicitly define output attributes for `Expand`, these are (unfortunately) considered as part of the expressions of the `Expand` and can be rewritten.
- Expand can actually change the column (it will typically re-use the attributes or the underlying plan). This means that we cannot safely propagate the expressions from under an `Expand`.
This PR fixes this and (hopefully) other issues by explicitly whitelisting allowed operators.
## How was this patch tested?
Added tests to `FoldablePropagationSuite` and to `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15857 from hvanhovell/SPARK-18300.
## What changes were proposed in this pull request?
This removes the serialization test from RegexpExpressionsSuite and
replaces it by serializing all expressions in checkEvaluation.
This also fixes math constant expressions by making LeafMathExpression
Serializable and fixes NumberFormat values that are null or invalid
after serialization.
## How was this patch tested?
This patch is to tests.
Author: Ryan Blue <blue@apache.org>
Closes#15847 from rdblue/SPARK-18387-fix-serializable-expressions.
## What changes were proposed in this pull request?
As of current 2.1, INSERT OVERWRITE with dynamic partitions against a Datasource table will overwrite the entire table instead of only the partitions matching the static keys, as in Hive. It also doesn't respect custom partition locations.
This PR adds support for all these operations to Datasource tables managed by the Hive metastore. It is implemented as follows
- During planning time, the full set of partitions affected by an INSERT or OVERWRITE command is read from the Hive metastore.
- The planner identifies any partitions with custom locations and includes this in the write task metadata.
- FileFormatWriter tasks refer to this custom locations map when determining where to write for dynamic partition output.
- When the write job finishes, the set of written partitions is compared against the initial set of matched partitions, and the Hive metastore is updated to reflect the newly added / removed partitions.
It was necessary to introduce a method for staging files with absolute output paths to `FileCommitProtocol`. These files are not handled by the Hadoop output committer but are moved to their final locations when the job commits.
The overwrite behavior of legacy Datasource tables is also changed: no longer will the entire table be overwritten if a partial partition spec is present.
cc cloud-fan yhuai
## How was this patch tested?
Unit tests, existing tests.
Author: Eric Liang <ekl@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15814 from ericl/sc-5027.
## What changes were proposed in this pull request?
This PR corrects several partition related behaviors of `ExternalCatalog`:
1. default partition location should not always lower case the partition column names in path string(fix `HiveExternalCatalog`)
2. rename partition should not always lower case the partition column names in updated partition path string(fix `HiveExternalCatalog`)
3. rename partition should update the partition location only for managed table(fix `InMemoryCatalog`)
4. create partition with existing directory should be fine(fix `InMemoryCatalog`)
5. create partition with non-existing directory should create that directory(fix `InMemoryCatalog`)
6. drop partition from external table should not delete the directory(fix `InMemoryCatalog`)
## How was this patch tested?
new tests in `ExternalCatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15797 from cloud-fan/partition.
## What changes were proposed in this pull request?
This makes the result value both transient and lazy, so that if the RegExpReplace object is initialized then serialized, `result: StringBuffer` will be correctly initialized.
## How was this patch tested?
* Verified that this patch fixed the query that found the bug.
* Added a test case that fails without the fix.
Author: Ryan Blue <blue@apache.org>
Closes#15834 from rdblue/SPARK-18368-fix-regexp-replace.
## What changes were proposed in this pull request?
This makes the result value both transient and lazy, so that if the RegExpReplace object is initialized then serialized, `result: StringBuffer` will be correctly initialized.
## How was this patch tested?
* Verified that this patch fixed the query that found the bug.
* Added a test case that fails without the fix.
Author: Ryan Blue <blue@apache.org>
Closes#15816 from rdblue/SPARK-18368-fix-regexp-replace.
## What changes were proposed in this pull request?
We generate bitmasks for grouping sets during the parsing process, and use these during analysis. These bitmasks are difficult to work with in practice and have lead to numerous bugs. This PR removes these and use actual sets instead, however we still need to generate these offsets for the grouping_id.
This PR does the following works:
1. Replace bitmasks by actual grouping sets durning Parsing/Analysis stage of CUBE/ROLLUP/GROUPING SETS;
2. Add new testsuite `ResolveGroupingAnalyticsSuite` to test the `Analyzer.ResolveGroupingAnalytics` rule directly;
3. Fix a minor bug in `ResolveGroupingAnalytics`.
## How was this patch tested?
By existing test cases, and add new testsuite `ResolveGroupingAnalyticsSuite` to test directly.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15484 from jiangxb1987/group-set.
### What changes were proposed in this pull request?
Based on the discussion in [SPARK-18209](https://issues.apache.org/jira/browse/SPARK-18209). It doesn't really make sense to create permanent views based on temporary views or temporary UDFs.
To disallow the supports and issue the exceptions, this PR needs to detect whether a temporary view/UDF is being used when defining a permanent view. Basically, this PR can be split to two sub-tasks:
**Task 1:** detecting a temporary view from the query plan of view definition.
When finding an unresolved temporary view, Analyzer replaces it by a `SubqueryAlias` with the corresponding logical plan, which is stored in an in-memory HashMap. After replacement, it is impossible to detect whether the `SubqueryAlias` is added/generated from a temporary view. Thus, to detect the usage of a temporary view in view definition, this PR traverses the unresolved logical plan and uses the name of an `UnresolvedRelation` to detect whether it is a (global) temporary view.
**Task 2:** detecting a temporary UDF from the query plan of view definition.
Detecting usage of a temporary UDF in view definition is not straightfoward.
First, in the analyzed plan, we are having different forms to represent the functions. More importantly, some classes (e.g., `HiveGenericUDF`) are not accessible from `CreateViewCommand`, which is part of `sql/core`. Thus, we used the unanalyzed plan `child` of `CreateViewCommand` to detect the usage of a temporary UDF. Because the plan has already been successfully analyzed, we can assume the functions have been defined/registered.
Second, in Spark, the functions have four forms: Spark built-in functions, built-in hash functions, permanent UDFs and temporary UDFs. We do not have any direct way to determine whether a function is temporary or not. Thus, we introduced a function `isTemporaryFunction` in `SessionCatalog`. This function contains the detailed logics to determine whether a function is temporary or not.
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15764 from gatorsmile/blockTempFromPermViewCreation.
## What changes were proposed in this pull request?
This PR proposes to match up the behaviour of `to_json` to `from_json` function for null-safety.
Currently, it throws `NullPointException` but this PR fixes this to produce `null` instead.
with the data below:
```scala
import spark.implicits._
val df = Seq(Some(Tuple1(Tuple1(1))), None).toDF("a")
df.show()
```
```
+----+
| a|
+----+
| [1]|
|null|
+----+
```
the codes below
```scala
import org.apache.spark.sql.functions._
df.select(to_json($"a")).show()
```
produces..
**Before**
throws `NullPointException` as below:
```
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeFields(JacksonGenerator.scala:138)
at org.apache.spark.sql.catalyst.json.JacksonGenerator$$anonfun$write$1.apply$mcV$sp(JacksonGenerator.scala:194)
at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeObject(JacksonGenerator.scala:131)
at org.apache.spark.sql.catalyst.json.JacksonGenerator.write(JacksonGenerator.scala:193)
at org.apache.spark.sql.catalyst.expressions.StructToJson.eval(jsonExpressions.scala:544)
at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:142)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:48)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:30)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
```
**After**
```
+---------------+
|structtojson(a)|
+---------------+
| {"_1":1}|
| null|
+---------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite.scala` and `JsonFunctionsSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15792 from HyukjinKwon/SPARK-18295.
## What changes were proposed in this pull request?
We have an undocumented naming convention to call expression unit tests ExpressionsSuite, and the end-to-end tests FunctionsSuite. It'd be great to make all test suites consistent with this naming convention.
## How was this patch tested?
This is a test-only naming change.
Author: Reynold Xin <rxin@databricks.com>
Closes#15793 from rxin/SPARK-18296.
## What changes were proposed in this pull request?
Previously `TRUNCATE TABLE ... PARTITION` will always truncate the whole table for data source tables, this PR fixes it and improve `InMemoryCatalog` to make this command work with it.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15688 from cloud-fan/truncate.
## What changes were proposed in this pull request?
This PR proposes `rand`/`randn` accept `null` as input in Scala/SQL and `LongType` as input in SQL. In this case, it treats the values as `0`.
So, this PR includes both changes below:
- `null` support
It seems MySQL also accepts this.
``` sql
mysql> select rand(0);
+---------------------+
| rand(0) |
+---------------------+
| 0.15522042769493574 |
+---------------------+
1 row in set (0.00 sec)
mysql> select rand(NULL);
+---------------------+
| rand(NULL) |
+---------------------+
| 0.15522042769493574 |
+---------------------+
1 row in set (0.00 sec)
```
and also Hive does according to [HIVE-14694](https://issues.apache.org/jira/browse/HIVE-14694)
So the codes below:
``` scala
spark.range(1).selectExpr("rand(null)").show()
```
prints..
**Before**
```
Input argument to rand must be an integer literal.;; line 1 pos 0
org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465)
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:444)
```
**After**
```
+-----------------------+
|rand(CAST(NULL AS INT))|
+-----------------------+
| 0.13385709732307427|
+-----------------------+
```
- `LongType` support in SQL.
In addition, it make the function allows to take `LongType` consistently within Scala/SQL.
In more details, the codes below:
``` scala
spark.range(1).select(rand(1), rand(1L)).show()
spark.range(1).selectExpr("rand(1)", "rand(1L)").show()
```
prints..
**Before**
```
+------------------+------------------+
| rand(1)| rand(1)|
+------------------+------------------+
|0.2630967864682161|0.2630967864682161|
+------------------+------------------+
Input argument to rand must be an integer literal.;; line 1 pos 0
org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465)
at
```
**After**
```
+------------------+------------------+
| rand(1)| rand(1)|
+------------------+------------------+
|0.2630967864682161|0.2630967864682161|
+------------------+------------------+
+------------------+------------------+
| rand(1)| rand(1)|
+------------------+------------------+
|0.2630967864682161|0.2630967864682161|
+------------------+------------------+
```
## How was this patch tested?
Unit tests in `DataFrameSuite.scala` and `RandomSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15432 from HyukjinKwon/SPARK-17854.
## What changes were proposed in this pull request?
As the title suggests, this patch moves hash expressions from misc.scala into hash.scala, to make it easier to find the hash functions. I wanted to do this a while ago but decided to wait for the branch-2.1 cut so the chance of conflicts will be smaller.
## How was this patch tested?
Test cases were also moved out of MiscFunctionsSuite into HashExpressionsSuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#15784 from rxin/SPARK-18287.
## What changes were proposed in this pull request?
For data source tables, we will put its table schema, partition columns, etc. to table properties, to work around some hive metastore issues, e.g. not case-preserving, bad decimal type support, etc.
We should also do this for hive serde tables, to reduce the difference between hive serde tables and data source tables, e.g. column names should be case preserving.
## How was this patch tested?
existing tests, and a new test in `HiveExternalCatalog`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14750 from cloud-fan/minor1.
## What changes were proposed in this pull request?
`from_json` is currently not safe against `null` rows. This PR adds a fix and a regression test for it.
## How was this patch tested?
Regression test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15771 from brkyvz/json_fix.
## What changes were proposed in this pull request?
This patch renames partitionProviderIsHive to tracksPartitionsInCatalog, as the old name was too Hive specific.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15750 from rxin/SPARK-18244.
## What changes were proposed in this pull request?
In Spark 1.6 and earlier, we can drop the database we are using. In Spark 2.0, native implementation prevent us from dropping current database, which may break some old queries. This PR would re-enable the feature.
## How was this patch tested?
one new unit test in `SessionCatalogSuite`.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#15011 from adrian-wang/dropcurrent.
### What changes were proposed in this pull request?
So far, we have limited test case coverage about implicit type casting. We need to draw a matrix to find all the possible casting pairs.
- Reorged the existing test cases
- Added all the possible type casting pairs
- Drawed a matrix to show the implicit type casting. The table is very wide. Maybe hard to review. Thus, you also can access the same table via the link to [a google sheet](https://docs.google.com/spreadsheets/d/19PS4ikrs-Yye_mfu-rmIKYGnNe-NmOTt5DDT1fOD3pI/edit?usp=sharing).
SourceType\CastToType | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | BooleanType | StringType | DateType | TimestampType | ArrayType | MapType | StructType | NullType | CalendarIntervalType | DecimalType | NumericType | IntegralType
------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | -----------
**ByteType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(3, 0) | ByteType | ByteType
**ShortType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(5, 0) | ShortType | ShortType
**IntegerType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(10, 0) | IntegerType | IntegerType
**LongType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(20, 0) | LongType | LongType
**DoubleType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(30, 15) | DoubleType | IntegerType
**FloatType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(14, 7) | FloatType | IntegerType
**Dec(10, 2)** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(10, 2) | Dec(10, 2) | IntegerType
**BinaryType** | X | X | X | X | X | X | X | BinaryType | X | StringType | X | X | X | X | X | X | X | X | X | X
**BooleanType** | X | X | X | X | X | X | X | X | BooleanType | StringType | X | X | X | X | X | X | X | X | X | X
**StringType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | X | StringType | DateType | TimestampType | X | X | X | X | X | DecimalType(38, 18) | DoubleType | X
**DateType** | X | X | X | X | X | X | X | X | X | StringType | DateType | TimestampType | X | X | X | X | X | X | X | X
**TimestampType** | X | X | X | X | X | X | X | X | X | StringType | DateType | TimestampType | X | X | X | X | X | X | X | X
**ArrayType** | X | X | X | X | X | X | X | X | X | X | X | X | ArrayType* | X | X | X | X | X | X | X
**MapType** | X | X | X | X | X | X | X | X | X | X | X | X | X | MapType* | X | X | X | X | X | X
**StructType** | X | X | X | X | X | X | X | X | X | X | X | X | X | X | StructType* | X | X | X | X | X
**NullType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | BooleanType | StringType | DateType | TimestampType | ArrayType | MapType | StructType | NullType | CalendarIntervalType | DecimalType(38, 18) | DoubleType | IntegerType
**CalendarIntervalType** | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | CalendarIntervalType | X | X | X
Note: ArrayType\*, MapType\*, StructType\* are castable only when the internal child types also match; otherwise, not castable
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15691 from gatorsmile/implicitTypeCasting.
## What changes were proposed in this pull request?
RuntimeReplaceable is used to create aliases for expressions, but the way it deals with type coercion is pretty weird (each expression is responsible for how to handle type coercion, which does not obey the normal implicit type cast rules).
This patch simplifies its handling by allowing the analyzer to traverse into the actual expression of a RuntimeReplaceable.
## How was this patch tested?
- Correctness should be guaranteed by existing unit tests already
- Removed SQLCompatibilityFunctionSuite and moved it sql-compatibility-functions.sql
- Added a new test case in sql-compatibility-functions.sql for verifying explain behavior.
Author: Reynold Xin <rxin@databricks.com>
Closes#15723 from rxin/SPARK-18214.
## What changes were proposed in this pull request?
When a user appended a column using a "nondeterministic" function to a DataFrame, e.g., `rand`, `randn`, and `monotonically_increasing_id`, the expected semantic is the following:
- The value in each row should remain unchanged, as if we materialize the column immediately, regardless of later DataFrame operations.
However, since we use `TaskContext.getPartitionId` to get the partition index from the current thread, the values from nondeterministic columns might change if we call `union` or `coalesce` after. `TaskContext.getPartitionId` returns the partition index of the current Spark task, which might not be the corresponding partition index of the DataFrame where we defined the column.
See the unit tests below or JIRA for examples.
This PR uses the partition index from `RDD.mapPartitionWithIndex` instead of `TaskContext` and fixes the partition initialization logic in whole-stage codegen, normal codegen, and codegen fallback. `initializeStatesForPartition(partitionIndex: Int)` was added to `Projection`, `Nondeterministic`, and `Predicate` (codegen) and initialized right after object creation in `mapPartitionWithIndex`. `newPredicate` now returns a `Predicate` instance rather than a function for proper initialization.
## How was this patch tested?
Unit tests. (Actually I'm not very confident that this PR fixed all issues without introducing new ones ...)
cc: rxin davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#15567 from mengxr/SPARK-14393.
## What changes were proposed in this pull request?
This pr is to add pattern-matching entries for array data in `Literal.apply`.
## How was this patch tested?
Added tests in `LiteralExpressionSuite`.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#15257 from maropu/SPARK-17683.
## What changes were proposed in this pull request?
Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`.
This PR includes:
1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`).
2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees.
3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`.
4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved.
5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns.
## How was this patch tested?
Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully.
Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`.
Author: eyal farago <eyal farago>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: eyal farago <eyal.farago@gmail.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#15718 from hvanhovell/SPARK-16839-2.
## What changes were proposed in this pull request?
Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat`
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15610 from srowen/SPARK-18076.
## What changes were proposed in this pull request?
There are a couple issues with the current 2.1 behavior when inserting into Datasource tables with partitions managed by Hive.
(1) OVERWRITE TABLE ... PARTITION will actually overwrite the entire table instead of just the specified partition.
(2) INSERT|OVERWRITE does not work with partitions that have custom locations.
This PR fixes both of these issues for Datasource tables managed by Hive. The behavior for legacy tables or when `manageFilesourcePartitions = false` is unchanged.
There is one other issue in that INSERT OVERWRITE with dynamic partitions will overwrite the entire table instead of just the updated partitions, but this behavior is pretty complicated to implement for Datasource tables. We should address that in a future release.
## How was this patch tested?
Unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes#15705 from ericl/sc-4942.
## What changes were proposed in this pull request?
This PR proposes to add `to_json` function in contrast with `from_json` in Scala, Java and Python.
It'd be useful if we can convert a same column from/to json. Also, some datasources do not support nested types. If we are forced to save a dataframe into those data sources, we might be able to work around by this function.
The usage is as below:
``` scala
val df = Seq(Tuple1(Tuple1(1))).toDF("a")
df.select(to_json($"a").as("json")).show()
```
``` bash
+--------+
| json|
+--------+
|{"_1":1}|
+--------+
```
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15354 from HyukjinKwon/SPARK-17764.
## What changes were proposed in this pull request?
Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`.
This PR includes:
1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`).
2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees.
3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`.
4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved.
5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns.
## How was this patch tested?
running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully.
modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`.
Credit goes to hvanhovell for assisting with this PR.
Author: eyal farago <eyal farago>
Author: eyal farago <eyal.farago@gmail.com>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#14444 from eyalfa/SPARK-16839_redundant_aliases_after_cleanupAliases.
## What changes were proposed in this pull request?
We should follow hive table and also store partition spec in metastore for data source table.
This brings 2 benefits:
1. It's more flexible to manage the table data files, as users can use `ADD PARTITION`, `DROP PARTITION` and `RENAME PARTITION`
2. We don't need to cache all file status for data source table anymore.
## How was this patch tested?
existing tests.
Author: Eric Liang <ekl@databricks.com>
Author: Michael Allman <michael@videoamp.com>
Author: Eric Liang <ekhliang@gmail.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15515 from cloud-fan/partition.
## What changes were proposed in this pull request?
The `UnaryNode.getAliasedConstraints` function fails to replace all expressions by their alias where constraints contains more than one expression to be replaced.
For example:
```
val tr = LocalRelation('a.int, 'b.string, 'c.int)
val multiAlias = tr.where('a === 'c + 10).select('a.as('x), 'c.as('y))
multiAlias.analyze.constraints
```
currently outputs:
```
ExpressionSet(Seq(
IsNotNull(resolveColumn(multiAlias.analyze, "x")),
IsNotNull(resolveColumn(multiAlias.analyze, "y"))
)
```
The constraint `resolveColumn(multiAlias.analyze, "x") === resolveColumn(multiAlias.analyze, "y") + 10)` is missing.
## How was this patch tested?
Add new test cases in `ConstraintPropagationSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15597 from jiangxb1987/alias-constraints.
## What changes were proposed in this pull request?
The function `QueryPlan.inferAdditionalConstraints` and `UnaryNode.getAliasedConstraints` can produce a non-converging set of constraints for recursive functions. For instance, if we have two constraints of the form(where a is an alias):
`a = b, a = f(b, c)`
Applying both these rules in the next iteration would infer:
`f(b, c) = f(f(b, c), c)`
This process repeated, the iteration won't converge and the set of constraints will grow larger and larger until OOM.
~~To fix this problem, we collect alias from expressions and skip infer constraints if we are to transform an `Expression` to another which contains it.~~
To fix this problem, we apply additional check in `inferAdditionalConstraints`, when it's possible to generate recursive constraints, we skip generate that.
## How was this patch tested?
Add new testcase in `SQLQuerySuite`/`InferFiltersFromConstraintsSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15319 from jiangxb1987/constraints.
## What changes were proposed in this pull request?
The PR tries to fix [SPARK-18058](https://issues.apache.org/jira/browse/SPARK-18058) which refers to a bug that the column types are compared with the extra care about Nullability in Union and SetOperation.
This PR converts the columns types by setting all fields as nullable before comparison
## How was this patch tested?
regular unit test cases
Author: CodingCat <zhunansjtu@gmail.com>
Closes#15595 from CodingCat/SPARK-18058.
## What changes were proposed in this pull request?
`Array[T]()` -> `Array.empty[T]` to avoid allocating 0-length arrays.
Use regex `find . -name '*.scala' | xargs -i bash -c 'egrep "Array\[[A-Za-z]+\]\(\)" -n {} && echo {}'` to find modification candidates.
cc srowen
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15564 from zhengruifeng/avoid_0_length_array.
## What changes were proposed in this pull request?
Unlike Hive, in Spark SQL, ALTER TABLE RENAME TO cannot move a table from one database to another(e.g. `ALTER TABLE db1.tbl RENAME TO db2.tbl2`), and will report error if the database in source table and destination table is different. So in #14955 , we forbid users to specify database of destination table in ALTER TABLE RENAME TO, to be consistent with other database systems and also make it easier to rename tables in non-current database, e.g. users can write `ALTER TABLE db1.tbl RENAME TO tbl2`, instead of `ALTER TABLE db1.tbl RENAME TO db1.tbl2`.
However, this is a breaking change. Users may already have queries that specify database of destination table in ALTER TABLE RENAME TO.
This PR reverts most of #14955 , and simplify the usage of ALTER TABLE RENAME TO by making database of source table the default database of destination table, instead of current database, so that users can still write `ALTER TABLE db1.tbl RENAME TO tbl2`, which is consistent with other databases like MySQL, Postgres, etc.
## How was this patch tested?
The added back tests and some new tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15434 from cloud-fan/revert.
## What changes were proposed in this pull request?
Value classes were unsupported because catalyst data types were
obtained through reflection on erased types, which would resolve to a
value class' wrapped type and hence lead to unavailable methods during
code generation.
E.g. the following class
```scala
case class Foo(x: Int) extends AnyVal
```
would be seen as an `int` in catalyst and will cause instance cast failures when generated java code tries to treat it as a `Foo`.
This patch simply removes the erasure step when getting data types for
catalyst.
## How was this patch tested?
Additional tests in `ExpressionEncoderSuite`.
Author: Jakob Odersky <jakob@odersky.com>
Closes#15284 from jodersky/value-classes.
## What changes were proposed in this pull request?
This change adds a check in castToInterval method of Cast expression , such that if converted value is null , then isNull variable should be set to true.
Earlier, the expression Cast(Literal(), CalendarIntervalType) was throwing NullPointerException because of the above mentioned reason.
## How was this patch tested?
Added test case in CastSuite.scala
jira entry for detail: https://issues.apache.org/jira/browse/SPARK-17884
Author: prigarg <prigarg@adobe.com>
Closes#15449 from priyankagargnitk/SPARK-17884.
## What changes were proposed in this pull request?
Currently `Canonicalize` object doesn't support `And` and `Or`. So we can compare canonicalized form of predicates consistently. We should add the support.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15388 from viirya/canonicalize-and-or.
## What changes were proposed in this pull request?
Currently we use the same rule to parse top level and nested data fields. For example:
```
create table tbl_x(
id bigint,
nested struct<col1:string,col2:string>
)
```
Shows both syntaxes. In this PR we split this rule in a top-level and nested rule.
Before this PR,
```
sql("CREATE TABLE my_tab(column1: INT)")
```
works fine.
After this PR, it will throw a `ParseException`:
```
scala> sql("CREATE TABLE my_tab(column1: INT)")
org.apache.spark.sql.catalyst.parser.ParseException:
no viable alternative at input 'CREATE TABLE my_tab(column1:'(line 1, pos 27)
```
## How was this patch tested?
Add new testcases in `SparkSqlParserSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15346 from jiangxb1987/cdt.
## What changes were proposed in this pull request?
The `quotedString` method in `TableIdentifier` and `FunctionIdentifier` produce an illegal (un-parseable) name when the name contains a backtick. For example:
```
import org.apache.spark.sql.catalyst.parser.CatalystSqlParser._
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
val complexName = TableIdentifier("`weird`table`name", Some("`d`b`1"))
parseTableIdentifier(complexName.unquotedString) // Does not work
parseTableIdentifier(complexName.quotedString) // Does not work
parseExpression(complexName.unquotedString) // Does not work
parseExpression(complexName.quotedString) // Does not work
```
We should handle the backtick properly to make `quotedString` parseable.
## How was this patch tested?
Add new testcases in `TableIdentifierParserSuite` and `ExpressionParserSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15403 from jiangxb1987/backtick.
## What changes were proposed in this pull request?
In practice we cannot guarantee that an `InternalRow` is immutable. This makes the `MutableRow` almost redundant. This PR folds `MutableRow` into `InternalRow`.
The code below illustrates the immutability issue with InternalRow:
```scala
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
val struct = new GenericMutableRow(1)
val row = InternalRow(struct, 1)
println(row)
scala> [[null], 1]
struct.setInt(0, 42)
println(row)
scala> [[42], 1]
```
This might be somewhat controversial, so feedback is appreciated.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15333 from hvanhovell/SPARK-17761.
## What changes were proposed in this pull request?
The result of the `Last` function can be wrong when the last partition processed is empty. It can return `null` instead of the expected value. For example, this can happen when we process partitions in the following order:
```
- Partition 1 [Row1, Row2]
- Partition 2 [Row3]
- Partition 3 []
```
In this case the `Last` function will currently return a null, instead of the value of `Row3`.
This PR fixes this by adding a `valueSet` flag to the `Last` function.
## How was this patch tested?
We only used end to end tests for `DeclarativeAggregateFunction`s. I have added an evaluator for these functions so we can tests them in catalyst. I have added a `LastTestSuite` to test the `Last` aggregate function.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15348 from hvanhovell/SPARK-17758.
## What changes were proposed in this pull request?
Currently Spark SQL parses regular decimal literals (e.g. `10.00`) as decimals and scientific decimal literals (e.g. `10.0e10`) as doubles. The difference between the two confuses most users. This PR unifies the parsing behavior and also parses scientific decimal literals as decimals.
This implications in tests are limited to a single Hive compatibility test.
## How was this patch tested?
Updated tests in `ExpressionParserSuite` and `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14828 from hvanhovell/SPARK-17258.
## What changes were proposed in this pull request?
Code generation including too many mutable states exceeds JVM size limit to extract values from `references` into fields in the constructor.
We should split the generated extractions in the constructor into smaller functions.
## How was this patch tested?
I added some tests to check if the generated codes for the expressions exceed or not.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15275 from ueshin/issues/SPARK-17702.
## What changes were proposed in this pull request?
We currently only allow relatively simple expressions as the input for a value based case statement. Expressions like `case (a > 1) or (b = 2) when true then 1 when false then 0 end` currently fail. This PR adds support for such expressions.
## How was this patch tested?
Added a test to the ExpressionParserSuite.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15322 from hvanhovell/SPARK-17753.
## What changes were proposed in this pull request?
Currently, Spark does not collapse adjacent windows with the same partitioning and sorting. This PR implements `CollapseWindow` optimizer to do the followings.
1. If the partition specs and order specs are the same, collapse into the parent.
2. If the partition specs are the same and one order spec is a prefix of the other, collapse to the more specific one.
For example:
```scala
val df = spark.range(1000).select($"id" % 100 as "grp", $"id", rand() as "col1", rand() as "col2")
// Add summary statistics for all columns
import org.apache.spark.sql.expressions.Window
val cols = Seq("id", "col1", "col2")
val window = Window.partitionBy($"grp").orderBy($"id")
val result = cols.foldLeft(df) { (base, name) =>
base.withColumn(s"${name}_avg", avg(col(name)).over(window))
.withColumn(s"${name}_stddev", stddev(col(name)).over(window))
.withColumn(s"${name}_min", min(col(name)).over(window))
.withColumn(s"${name}_max", max(col(name)).over(window))
}
```
**Before**
```scala
scala> result.explain
== Physical Plan ==
Window [max(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_max#234], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [min(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_min#216], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [stddev_samp(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_stddev#191], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [avg(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_avg#167], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [max(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_max#152], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [min(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_min#138], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [stddev_samp(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_stddev#117], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [avg(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_avg#97], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [max(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_max#86L], [grp#17L], [id#14L ASC NULLS FIRST]
+- Window [min(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_min#76L], [grp#17L], [id#14L ASC NULLS FIRST]
+- *Project [grp#17L, id#14L, col1#18, col2#19, id_avg#26, id_stddev#42]
+- Window [stddev_samp(_w0#59) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_stddev#42], [grp#17L], [id#14L ASC NULLS FIRST]
+- *Project [grp#17L, id#14L, col1#18, col2#19, id_avg#26, cast(id#14L as double) AS _w0#59]
+- Window [avg(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_avg#26], [grp#17L], [id#14L ASC NULLS FIRST]
+- *Sort [grp#17L ASC NULLS FIRST, id#14L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(grp#17L, 200)
+- *Project [(id#14L % 100) AS grp#17L, id#14L, rand(-6329949029880411066) AS col1#18, rand(-7251358484380073081) AS col2#19]
+- *Range (0, 1000, step=1, splits=Some(8))
```
**After**
```scala
scala> result.explain
== Physical Plan ==
Window [max(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_max#220, min(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_min#202, stddev_samp(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_stddev#177, avg(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_avg#153, max(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_max#138, min(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_min#124, stddev_samp(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_stddev#103, avg(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_avg#83, max(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_max#72L, min(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_min#62L], [grp#3L], [id#0L ASC NULLS FIRST]
+- *Project [grp#3L, id#0L, col1#4, col2#5, id_avg#12, id_stddev#28]
+- Window [stddev_samp(_w0#45) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_stddev#28], [grp#3L], [id#0L ASC NULLS FIRST]
+- *Project [grp#3L, id#0L, col1#4, col2#5, id_avg#12, cast(id#0L as double) AS _w0#45]
+- Window [avg(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_avg#12], [grp#3L], [id#0L ASC NULLS FIRST]
+- *Sort [grp#3L ASC NULLS FIRST, id#0L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(grp#3L, 200)
+- *Project [(id#0L % 100) AS grp#3L, id#0L, rand(6537478539664068821) AS col1#4, rand(-8961093871295252795) AS col2#5]
+- *Range (0, 1000, step=1, splits=Some(8))
```
## How was this patch tested?
Pass the Jenkins tests with a newly added testsuite.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15317 from dongjoon-hyun/SPARK-17739.
## What changes were proposed in this pull request?
Currently for `Union [Distinct]`, a `Distinct` operator is necessary to be on the top of `Union`. Once there are adjacent `Union [Distinct]`, there will be multiple `Distinct` in the query plan.
E.g.,
For a query like: select 1 a union select 2 b union select 3 c
Before this patch, its physical plan looks like:
*HashAggregate(keys=[a#13], functions=[])
+- Exchange hashpartitioning(a#13, 200)
+- *HashAggregate(keys=[a#13], functions=[])
+- Union
:- *HashAggregate(keys=[a#13], functions=[])
: +- Exchange hashpartitioning(a#13, 200)
: +- *HashAggregate(keys=[a#13], functions=[])
: +- Union
: :- *Project [1 AS a#13]
: : +- Scan OneRowRelation[]
: +- *Project [2 AS b#14]
: +- Scan OneRowRelation[]
+- *Project [3 AS c#15]
+- Scan OneRowRelation[]
Only the top distinct should be necessary.
After this patch, the physical plan looks like:
*HashAggregate(keys=[a#221], functions=[], output=[a#221])
+- Exchange hashpartitioning(a#221, 5)
+- *HashAggregate(keys=[a#221], functions=[], output=[a#221])
+- Union
:- *Project [1 AS a#221]
: +- Scan OneRowRelation[]
:- *Project [2 AS b#222]
: +- Scan OneRowRelation[]
+- *Project [3 AS c#223]
+- Scan OneRowRelation[]
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15238 from viirya/remove-extra-distinct-union.
Spark SQL has great support for reading text files that contain JSON data. However, in many cases the JSON data is just one column amongst others. This is particularly true when reading from sources such as Kafka. This PR adds a new functions `from_json` that converts a string column into a nested `StructType` with a user specified schema.
Example usage:
```scala
val df = Seq("""{"a": 1}""").toDS()
val schema = new StructType().add("a", IntegerType)
df.select(from_json($"value", schema) as 'json) // => [json: <a: int>]
```
This PR adds support for java, scala and python. I leveraged our existing JSON parsing support by moving it into catalyst (so that we could define expressions using it). I left SQL out for now, because I'm not sure how users would specify a schema.
Author: Michael Armbrust <michael@databricks.com>
Closes#15274 from marmbrus/jsonParser.
## What changes were proposed in this pull request?
This patch fixes a minor correctness issue impacting the pushdown of filters beneath aggregates. Specifically, if a filter condition references no grouping or aggregate columns (e.g. `WHERE false`) then it would be incorrectly pushed beneath an aggregate.
Intuitively, the only case where you can push a filter beneath an aggregate is when that filter is deterministic and is defined over the grouping columns / expressions, since in that case the filter is acting to exclude entire groups from the query (like a `HAVING` clause). The existing code would only push deterministic filters beneath aggregates when all of the filter's references were grouping columns, but this logic missed the case where a filter has no references. For example, `WHERE false` is deterministic but is independent of the actual data.
This patch fixes this minor bug by adding a new check to ensure that we don't push filters beneath aggregates when those filters don't reference any columns.
## How was this patch tested?
New regression test in FilterPushdownSuite.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15289 from JoshRosen/SPARK-17712.
## What changes were proposed in this pull request?
This PR introduces more compact representation for ```UnsafeArrayData```.
```UnsafeArrayData``` needs to accept ```null``` value in each entry of an array. In the current version, it has three parts
```
[numElements] [offsets] [values]
```
`Offsets` has the number of `numElements`, and represents `null` if its value is negative. It may increase memory footprint, and introduces an indirection for accessing each of `values`.
This PR uses bitvectors to represent nullability for each element like `UnsafeRow`, and eliminates an indirection for accessing each element. The new ```UnsafeArrayData``` has four parts.
```
[numElements][null bits][values or offset&length][variable length portion]
```
In the `null bits` region, we store 1 bit per element, represents whether an element is null. Its total size is ceil(numElements / 8) bytes, and it is aligned to 8-byte boundaries.
In the `values or offset&length` region, we store the content of elements. For fields that hold fixed-length primitive types, such as long, double, or int, we store the value directly in the field. For fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the base address of the array) that points to the beginning of the variable-length field and length (they are combined into a long). Each is word-aligned. For `variable length portion`, each is aligned to 8-byte boundaries.
The new format can reduce memory footprint and improve performance of accessing each element. An example of memory foot comparison:
1024x1024 elements integer array
Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024 + 1024x1024 = 2M bytes
Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024/8 + 1024x1024 = 1.25M bytes
In summary, we got 1.0-2.6x performance improvements over the code before applying this PR.
Here are performance results of [benchmark programs](04d2e4b6db/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/UnsafeArrayDataBenchmark.scala):
**Read UnsafeArrayData**: 1.7x and 1.6x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 430 / 436 390.0 2.6 1.0X
Double 456 / 485 367.8 2.7 0.9X
With SPARK-15962
Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 252 / 260 666.1 1.5 1.0X
Double 281 / 292 597.7 1.7 0.9X
````
**Write UnsafeArrayData**: 1.0x and 1.1x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 203 / 273 103.4 9.7 1.0X
Double 239 / 356 87.9 11.4 0.8X
With SPARK-15962
Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 196 / 249 107.0 9.3 1.0X
Double 227 / 367 92.3 10.8 0.9X
````
**Get primitive array from UnsafeArrayData**: 2.6x and 1.6x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 207 / 217 304.2 3.3 1.0X
Double 257 / 363 245.2 4.1 0.8X
With SPARK-15962
Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 151 / 198 415.8 2.4 1.0X
Double 214 / 394 293.6 3.4 0.7X
````
**Create UnsafeArrayData from primitive array**: 1.7x and 2.1x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 340 / 385 185.1 5.4 1.0X
Double 479 / 705 131.3 7.6 0.7X
With SPARK-15962
Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 206 / 211 306.0 3.3 1.0X
Double 232 / 406 271.6 3.7 0.9X
````
1.7x and 1.4x performance improvements in [```UDTSerializationBenchmark```](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/mllib/linalg/UDTSerializationBenchmark.scala) over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
serialize 442 / 533 0.0 441927.1 1.0X
deserialize 217 / 274 0.0 217087.6 2.0X
With SPARK-15962
VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
serialize 265 / 318 0.0 265138.5 1.0X
deserialize 155 / 197 0.0 154611.4 1.7X
````
## How was this patch tested?
Added unit tests into ```UnsafeArraySuite```
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#13680 from kiszk/SPARK-15962.
## What changes were proposed in this pull request?
We currently cannot execute an aggregate that contains a single distinct aggregate function and an one or more non-partially plannable aggregate functions, for example:
```sql
select grp,
collect_list(col1),
count(distinct col2)
from tbl_a
group by 1
```
This is a regression from Spark 1.6. This is caused by the fact that the single distinct aggregation code path assumes that all aggregates can be planned in two phases (is partially aggregatable). This PR works around this issue by triggering the `RewriteDistinctAggregates` in such cases (this is similar to the approach taken in 1.6).
## How was this patch tested?
Created `RewriteDistinctAggregatesSuite` which checks if the aggregates with distinct aggregate functions get rewritten into two `Aggregates` and an `Expand`. Added a regression test to `DataFrameAggregateSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15187 from hvanhovell/SPARK-17616.
## What changes were proposed in this pull request?
After #15054 , there is no place in Spark SQL that need `SessionCatalog.tableExists` to check temp views, so this PR makes `SessionCatalog.tableExists` only check permanent table/view and removes some hacks.
This PR also improves the `getTempViewOrPermanentTableMetadata` that is introduced in #15054 , to make the code simpler.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15160 from cloud-fan/exists.
## What changes were proposed in this pull request?
Floor()/Ceil() of decimal is implemented using changePrecision() by passing a rounding mode, but the rounding mode is not respected when the decimal is in compact mode (could fit within a Long).
This Update the changePrecision() to respect rounding mode, which could be ROUND_FLOOR, ROUND_CEIL, ROUND_HALF_UP, ROUND_HALF_EVEN.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#15154 from davies/decimal_round.
## What changes were proposed in this pull request?
Remainder(%) expression's `eval()` returns incorrect result when the dividend is a big double. The reason is that Remainder converts the double dividend to decimal to do "%", and that lose precision.
This bug only affects the `eval()` that is used by constant folding, the codegen path is not impacted.
### Before change
```
scala> -5083676433652386516D % 10
res2: Double = -6.0
scala> spark.sql("select -5083676433652386516D % 10 as a").show
+---+
| a|
+---+
|0.0|
+---+
```
### After change
```
scala> spark.sql("select -5083676433652386516D % 10 as a").show
+----+
| a|
+----+
|-6.0|
+----+
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#15171 from clockfly/SPARK-17617.
### What changes were proposed in this pull request?
- When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for partition-related ALTER TABLE commands. However, it always reports a confusing error message. For example,
```
Partition spec is invalid. The spec (a, b) must match the partition spec () defined in table '`testview`';
```
- When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for `ALTER TABLE ... UNSET TBLPROPERTIES`. However, it reports a missing table property. For example,
```
Attempted to unset non-existent property 'p' in table '`testView`';
```
- When `ANALYZE TABLE` is called on a view or a temporary view, we should issue an error message. However, it reports a strange error:
```
ANALYZE TABLE is not supported for Project
```
- When inserting into a temporary view that is generated from `Range`, we will get the following error message:
```
assertion failed: No plan for 'InsertIntoTable Range (0, 10, step=1, splits=Some(1)), false, false
+- Project [1 AS 1#20]
+- OneRowRelation$
```
This PR is to fix the above four issues.
### How was this patch tested?
Added multiple test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15054 from gatorsmile/tempViewDDL.
This patch addresses a corner-case escaping bug where field names which contain special characters were unsafely interpolated into error message string literals in generated Java code, leading to compilation errors.
This patch addresses these issues by using `addReferenceObj` to store the error messages as string fields rather than inline string constants.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15156 from JoshRosen/SPARK-17160.
## What changes were proposed in this pull request?
In `ExpressionEvalHelper`, we check the equality between two double values by comparing whether the expected value is within the range [target - tolerance, target + tolerance], but this can cause a negative false when the compared numerics are very large.
Before：
```
val1 = 1.6358558070241E306
val2 = 1.6358558070240974E306
ExpressionEvalHelper.compareResults(val1, val2)
false
```
In fact, `val1` and `val2` are but with different precisions, we should tolerant this case by comparing with percentage range, eg.,expected is within range [target - target * tolerance_percentage, target + target * tolerance_percentage].
After:
```
val1 = 1.6358558070241E306
val2 = 1.6358558070240974E306
ExpressionEvalHelper.compareResults(val1, val2)
true
```
## How was this patch tested?
Exsiting testcases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15059 from jiangxb1987/deq.
## What changes were proposed in this pull request?
In `SessionCatalog`, we have several operations(`tableExists`, `dropTable`, `loopupRelation`, etc) that handle both temp views and metastore tables/views. This brings some bugs to DDL commands that want to handle temp view only or metastore table/view only. These bugs are:
1. `CREATE TABLE USING` will fail if a same-name temp view exists
2. `Catalog.dropTempView`will un-cache and drop metastore table if a same-name table exists
3. `saveAsTable` will fail or have unexpected behaviour if a same-name temp view exists.
These bug fixes are pulled out from https://github.com/apache/spark/pull/14962 and targets both master and 2.0 branch
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15099 from cloud-fan/fix-view.
## What changes were proposed in this pull request?
This PR is a follow up of SPARK-17356. Current implementation of `TreeNode.toJSON` recursively converts all fields of TreeNode to JSON, even if the field is of type `Seq` or type Map. This may trigger out of memory exception in cases like:
1. the Seq or Map can be very big. Converting them to JSON may take huge memory, which may trigger out of memory error.
2. Some user space input may also be propagated to the Plan. The user space input can be of arbitrary type, and may also be self-referencing. Trying to print user space input to JSON may trigger out of memory error or stack overflow error.
For a code example, please check the Jira description of SPARK-17426.
In this PR, we refactor the `TreeNode.toJSON` so that we only convert a field to JSON string if the field is a safe type.
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14990 from clockfly/json_oom2.
## What changes were proposed in this pull request?
The Antlr lexer we use to tokenize a SQL string may wrongly tokenize a fully qualified identifier as a decimal number token. For example, table identifier `default.123_table` is wrongly tokenized as
```
default // Matches lexer rule IDENTIFIER
.123 // Matches lexer rule DECIMAL_VALUE
_TABLE // Matches lexer rule IDENTIFIER
```
The correct tokenization for `default.123_table` should be:
```
default // Matches lexer rule IDENTIFIER,
. // Matches a single dot
123_TABLE // Matches lexer rule IDENTIFIER
```
This PR fix the Antlr grammar so that it can tokenize fully qualified identifier correctly:
1. Fully qualified table name can be parsed correctly. For example, `select * from database.123_suffix`.
2. Fully qualified column name can be parsed correctly, for example `select a.123_suffix from a`.
### Before change
#### Case 1: Failed to parse fully qualified column name
```
scala> spark.sql("select a.123_column from a").show
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input '.123' expecting {<EOF>,
...
, IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 8)
== SQL ==
select a.123_column from a
--------^^^
```
#### Case 2: Failed to parse fully qualified table name
```
scala> spark.sql("select * from default.123_table")
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input '.123' expecting {<EOF>,
...
IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 21)
== SQL ==
select * from default.123_table
---------------------^^^
```
### After Change
#### Case 1: fully qualified column name, no ParseException thrown
```
scala> spark.sql("select a.123_column from a").show
```
#### Case 2: fully qualified table name, no ParseException thrown
```
scala> spark.sql("select * from default.123_table")
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#15006 from clockfly/SPARK-17364.
## What changes were proposed in this pull request?
This PR fixes an issue with aggregates that have an empty input, and use a literals as their grouping keys. These aggregates are currently interpreted as aggregates **without** grouping keys, this triggers the ungrouped code path (which aways returns a single row).
This PR fixes the `RemoveLiteralFromGroupExpressions` optimizer rule, which changes the semantics of the Aggregate by eliminating all literal grouping keys.
## How was this patch tested?
Added tests to `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15101 from hvanhovell/SPARK-17114-3.
## What changes were proposed in this pull request?
This PR has the appendRowUntilExceedingPageSize test in RowBasedKeyValueBatchSuite use whatever spark.buffer.pageSize value a user has specified to prevent a test failure for anyone testing Apache Spark on a box with a reduced page size. The test is currently hardcoded to use the default page size which is 64 MB so this minor PR is a test improvement
## How was this patch tested?
Existing unit tests with 1 MB page size and with 64 MB (the default) page size
Author: Adam Roberts <aroberts@uk.ibm.com>
Closes#15079 from a-roberts/patch-5.
### What changes were proposed in this pull request?
As explained in https://github.com/apache/spark/pull/14797:
>Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs.
For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result.
We should not optimize the query in CTAS more than once. For example,
```Scala
spark.range(99, 101).createOrReplaceTempView("tab1")
val sqlStmt = "SELECT id, cast(id as long) * cast('1.0' as decimal(38, 18)) as num FROM tab1"
sql(s"CREATE TABLE tab2 USING PARQUET AS $sqlStmt")
checkAnswer(spark.table("tab2"), sql(sqlStmt))
```
Before this PR, the results do not match
```
== Results ==
!== Correct Answer - 2 == == Spark Answer - 2 ==
![100,100.000000000000000000] [100,null]
[99,99.000000000000000000] [99,99.000000000000000000]
```
After this PR, the results match.
```
+---+----------------------+
|id |num |
+---+----------------------+
|99 |99.000000000000000000 |
|100|100.000000000000000000|
+---+----------------------+
```
In this PR, we do not treat the `query` in CTAS as a child. Thus, the `query` will not be optimized when optimizing CTAS statement. However, we still need to analyze it for normalizing and verifying the CTAS in the Analyzer. Thus, we do it in the analyzer rule `PreprocessDDL`, because so far only this rule needs the analyzed plan of the `query`.
### How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15048 from gatorsmile/ctasOptimized.
## What changes were proposed in this pull request?
In `ReorderAssociativeOperator` rule, we extract foldable expressions with Add/Multiply arithmetics, and replace with eval literal. For example, `(a + 1) + (b + 2)` is optimized to `(a + b + 3)` by this rule.
For aggregate operator, output expressions should be derived from groupingExpressions, current implemenation of `ReorderAssociativeOperator` rule may break this promise. A instance could be:
```
SELECT
((t1.a + 1) + (t2.a + 2)) AS out_col
FROM
testdata2 AS t1
INNER JOIN
testdata2 AS t2
ON
(t1.a = t2.a)
GROUP BY (t1.a + 1), (t2.a + 2)
```
`((t1.a + 1) + (t2.a + 2))` is optimized to `(t1.a + t2.a + 3)`, which could not be derived from `ExpressionSet((t1.a +1), (t2.a + 2))`.
Maybe we should improve the rule of `ReorderAssociativeOperator` by adding a GroupingExpressionSet to keep Aggregate.groupingExpressions, and respect these expressions during the optimize stage.
## How was this patch tested?
Add new test case in `ReorderAssociativeOperatorSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#14917 from jiangxb1987/rao.
## What changes were proposed in this pull request?
This PR build on #14976 and fixes a correctness bug that would cause the wrong quantile to be returned for small target errors.
## How was this patch tested?
This PR adds 8 unit tests that were failing without the fix.
Author: Timothy Hunter <timhunter@databricks.com>
Author: Sean Owen <sowen@cloudera.com>
Closes#15002 from thunterdb/ml-1783.
## What changes were proposed in this pull request?
Fixing the typo in the unit test of CodeGenerationSuite.scala
## How was this patch tested?
Ran the unit test after fixing the typo and it passes
Author: Srinivasa Reddy Vundela <vsr@cloudera.com>
Closes#14989 from vundela/typo_fix.
## What changes were proposed in this pull request?
`select size(null)` returns -1 in Hive. In order to be compatible, we should return `-1`.
## How was this patch tested?
unit test in `CollectionFunctionsSuite` and `DataFrameFunctionsSuite`.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#14991 from adrian-wang/size.
## What changes were proposed in this pull request?
We should generally use `ArrayBuffer.+=(A)` rather than `ArrayBuffer.append(A)`, because `append(A)` would involve extra boxing / unboxing.
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14914 from lw-lin/append_to_plus_eq_v2.
## What changes were proposed in this pull request?
Join processing in the parser relies on the fact that the grammar produces a right nested trees, for instance the parse tree for `select * from a join b join c` is expected to produce a tree similar to `JOIN(a, JOIN(b, c))`. However there are cases in which this (invariant) is violated, like:
```sql
SELECT COUNT(1)
FROM test T1
CROSS JOIN test T2
JOIN test T3
ON T3.col = T1.col
JOIN test T4
ON T4.col = T1.col
```
In this case the parser returns a tree in which Joins are located on both the left and the right sides of the parent join node.
This PR introduces a different grammar rule which does not make this assumption. The new rule takes a relation and searches for zero or more joined relations. As a bonus processing is much easier.
## How was this patch tested?
Existing tests and I have added a regression test to the plan parser suite.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14867 from hvanhovell/SPARK-17296.
## What changes were proposed in this pull request?
If `ScalaUDF` throws exceptions during executing user code, sometimes it's hard for users to figure out what's wrong, especially when they use Spark shell. An example
```
org.apache.spark.SparkException: Job aborted due to stage failure: Task 12 in stage 325.0 failed 4 times, most recent failure: Lost task 12.3 in stage 325.0 (TID 35622, 10.0.207.202): java.lang.NullPointerException
at line8414e872fb8b42aba390efc153d1611a12.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:40)
at line8414e872fb8b42aba390efc153d1611a12.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:40)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
...
```
We should catch these exceptions and rethrow them with better error message, to say that the exception is happened in scala udf.
This PR also does some clean up for `ScalaUDF` and add a unit test suite for it.
## How was this patch tested?
the new test suite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14850 from cloud-fan/npe.
## What changes were proposed in this pull request?
It's really weird that we allow users to specify database in both from table name and to table name
in `ALTER TABLE RENAME TO`, while logically we can't support rename a table to a different database.
Both postgres and MySQL disallow this syntax, it's reasonable to follow them and simply our code.
## How was this patch tested?
new test in `DDLCommandSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14955 from cloud-fan/rename.
## What changes were proposed in this pull request?
Improved the code quality of spark by replacing all pattern match on boolean value by if/else block.
## How was this patch tested?
By running the tests
Author: Shivansh <shiv4nsh@gmail.com>
Closes#14873 from shiv4nsh/SPARK-17308.
## What changes were proposed in this pull request?
the `catalogString` for `ArrayType` and `MapType` currently calls the `simpleString` method on its children. This is a problem when the child is a struct, the `struct.simpleString` implementation truncates the number of fields it shows (25 at max). This breaks the generation of a proper `catalogString`, and has shown to cause errors while writing to Hive.
This PR fixes this by providing proper `catalogString` implementations for `ArrayData` or `MapData`.
## How was this patch tested?
Added testing for `catalogString` to `DataTypeSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14938 from hvanhovell/SPARK-17335.
## What changes were proposed in this pull request?
Require the use of CROSS join syntax in SQL (and a new crossJoin
DataFrame API) to specify explicit cartesian products between relations.
By cartesian product we mean a join between relations R and S where
there is no join condition involving columns from both R and S.
If a cartesian product is detected in the absence of an explicit CROSS
join, an error must be thrown. Turning on the
"spark.sql.crossJoin.enabled" configuration flag will disable this check
and allow cartesian products without an explicit CROSS join.
The new crossJoin DataFrame API must be used to specify explicit cross
joins. The existing join(DataFrame) method will produce a INNER join
that will require a subsequent join condition.
That is df1.join(df2) is equivalent to select * from df1, df2.
## How was this patch tested?
Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used.
Author: Srinath Shankar <srinath@databricks.com>
Closes#14866 from srinathshankar/crossjoin.
### What changes were proposed in this pull request?
Function-related `HiveExternalCatalog` APIs do not have enough verification logics. After the PR, `HiveExternalCatalog` and `InMemoryCatalog` become consistent in the error handling.
For example, below is the exception we got when calling `renameFunction`.
```
15:13:40.369 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database db1, returning NoSuchObjectException
15:13:40.377 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database db2, returning NoSuchObjectException
15:13:40.739 ERROR DataNucleus.Datastore.Persist: Update of object "org.apache.hadoop.hive.metastore.model.MFunction205629e9" using statement "UPDATE FUNCS SET FUNC_NAME=? WHERE FUNC_ID=?" failed : org.apache.derby.shared.common.error.DerbySQLIntegrityConstraintViolationException: The statement was aborted because it would have caused a duplicate key value in a unique or primary key constraint or unique index identified by 'UNIQUEFUNCTION' defined on 'FUNCS'.
at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
at org.apache.derby.impl.jdbc.Util.generateCsSQLException(Unknown Source)
at org.apache.derby.impl.jdbc.TransactionResourceImpl.wrapInSQLException(Unknown Source)
at org.apache.derby.impl.jdbc.TransactionResourceImpl.handleException(Unknown Source)
```
### How was this patch tested?
Improved the existing test cases to check whether the messages are right.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14521 from gatorsmile/functionChecking.
## What changes were proposed in this pull request?
This PR adds the ability to parse SQL (hexadecimal) binary literals (AKA bit strings). It follows the following syntax `X'[Hexadecimal Characters]+'`, for example: `X'01AB'` would create a binary the following binary array `0x01AB`.
If an uneven number of hexadecimal characters is passed, then the upper 4 bits of the initial byte are kept empty, and the lower 4 bits are filled using the first character. For example `X'1C7'` would create the following binary array `0x01C7`.
Binary data (Array[Byte]) does not have a proper `hashCode` and `equals` functions. This meant that comparing `Literal`s containing binary data was a pain. I have updated Literal.hashCode and Literal.equals to deal properly with binary data.
## How was this patch tested?
Added tests to the `ExpressionParserSuite`, `SQLQueryTestSuite` and `ExpressionSQLBuilderSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14832 from hvanhovell/SPARK-17263.
## What changes were proposed in this pull request?
This PR implements aggregation function `percentile_approx`. Function `percentile_approx` returns the approximate percentile(s) of a column at the given percentage(s). A percentile is a watermark value below which a given percentage of the column values fall. For example, the percentile of column `col` at percentage 50% is the median value of column `col`.
### Syntax:
```
# Returns percentile at a given percentage value. The approximation error can be reduced by increasing parameter accuracy, at the cost of memory.
percentile_approx(col, percentage [, accuracy])
# Returns percentile value array at given percentage value array
percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy])
```
### Features:
1. This function supports partial aggregation.
2. The memory consumption is bounded. The larger `accuracy` parameter we choose, we smaller error we get. The default accuracy value is 10000, to match with Hive default setting. Choose a smaller value for smaller memory footprint.
3. This function supports window function aggregation.
### Example usages:
```
## Returns the 25th percentile value, with default accuracy
SELECT percentile_approx(col, 0.25) FROM table
## Returns an array of percentile value (25th, 50th, 75th), with default accuracy
SELECT percentile_approx(col, array(0.25, 0.5, 0.75)) FROM table
## Returns 25th percentile value, with custom accuracy value 100, larger accuracy parameter yields smaller approximation error
SELECT percentile_approx(col, 0.25, 100) FROM table
## Returns the 25th, and 50th percentile values, with custom accuracy value 100
SELECT percentile_approx(col, array(0.25, 0.5), 100) FROM table
```
### NOTE:
1. The `percentile_approx` implementation is different from Hive, so the result returned on same query maybe slightly different with Hive. This implementation uses `QuantileSummaries` as the underlying probabilistic data structure, and mainly follows paper `Space-efficient Online Computation of Quantile Summaries` by Greenwald, Michael and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670)`
2. The current implementation of `QuantileSummaries` doesn't support automatic compression. This PR has a rule to do compression automatically at the caller side, but it may not be optimal.
## How was this patch tested?
Unit test, and Sql query test.
## Acknowledgement
1. This PR's work in based on lw-lin's PR https://github.com/apache/spark/pull/14298, with improvements like supporting partial aggregation, fixing out of memory issue.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14868 from clockfly/appro_percentile_try_2.
## What changes were proposed in this pull request?
This PR eliminates redundant cast from an `ArrayType` with `containsNull = false` or a `MapType` with `containsNull = false`.
For example, in `ArrayType` case, current implementation leaves a cast `cast(value#63 as array<double>).toDoubleArray`. However, we can eliminate `cast(value#63 as array<double>)` if we know `value#63` does not include `null`. This PR apply this elimination for `ArrayType` and `MapType` in `SimplifyCasts` at a plan optimization phase.
In summary, we got 1.2-1.3x performance improvements over the code before applying this PR.
Here are performance results of benchmark programs:
```
test("Read array in Dataset") {
import sparkSession.implicits._
val iters = 5
val n = 1024 * 1024
val rows = 15
val benchmark = new Benchmark("Read primnitive array", n)
val rand = new Random(511)
val intDS = sparkSession.sparkContext.parallelize(0 until rows, 1)
.map(i => Array.tabulate(n)(i => i)).toDS()
intDS.count() // force to create ds
val lastElement = n - 1
val randElement = rand.nextInt(lastElement)
benchmark.addCase(s"Read int array in Dataset", numIters = iters)(iter => {
val idx0 = randElement
val idx1 = lastElement
intDS.map(a => a(0) + a(idx0) + a(idx1)).collect
})
val doubleDS = sparkSession.sparkContext.parallelize(0 until rows, 1)
.map(i => Array.tabulate(n)(i => i.toDouble)).toDS()
doubleDS.count() // force to create ds
benchmark.addCase(s"Read double array in Dataset", numIters = iters)(iter => {
val idx0 = randElement
val idx1 = lastElement
doubleDS.map(a => a(0) + a(idx0) + a(idx1)).collect
})
benchmark.run()
}
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.4
Intel(R) Core(TM) i5-5257U CPU 2.70GHz
without this PR
Read primnitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Read int array in Dataset 525 / 690 2.0 500.9 1.0X
Read double array in Dataset 947 / 1209 1.1 902.7 0.6X
with this PR
Read primnitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Read int array in Dataset 400 / 492 2.6 381.5 1.0X
Read double array in Dataset 788 / 870 1.3 751.4 0.5X
```
An example program that originally caused this performance issue.
```
val ds = Seq(Array(1.0, 2.0, 3.0), Array(4.0, 5.0, 6.0)).toDS()
val ds2 = ds.map(p => {
var s = 0.0
for (i <- 0 to 2) { s += p(i) }
s
})
ds2.show
ds2.explain(true)
```
Plans before this PR
```
== Parsed Logical Plan ==
'SerializeFromObject [input[0, double, true] AS value#68]
+- 'MapElements <function1>, obj#67: double
+- 'DeserializeToObject unresolveddeserializer(upcast(getcolumnbyordinal(0, ArrayType(DoubleType,false)), ArrayType(DoubleType,false), - root class: "scala.Array").toDoubleArray), obj#66: [D
+- LocalRelation [value#63]
== Analyzed Logical Plan ==
value: double
SerializeFromObject [input[0, double, true] AS value#68]
+- MapElements <function1>, obj#67: double
+- DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D
+- LocalRelation [value#63]
== Optimized Logical Plan ==
SerializeFromObject [input[0, double, true] AS value#68]
+- MapElements <function1>, obj#67: double
+- DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D
+- LocalRelation [value#63]
== Physical Plan ==
*SerializeFromObject [input[0, double, true] AS value#68]
+- *MapElements <function1>, obj#67: double
+- *DeserializeToObject cast(value#63 as array<double>).toDoubleArray, obj#66: [D
+- LocalTableScan [value#63]
```
Plans after this PR
```
== Parsed Logical Plan ==
'SerializeFromObject [input[0, double, true] AS value#6]
+- 'MapElements <function1>, obj#5: double
+- 'DeserializeToObject unresolveddeserializer(upcast(getcolumnbyordinal(0, ArrayType(DoubleType,false)), ArrayType(DoubleType,false), - root class: "scala.Array").toDoubleArray), obj#4: [D
+- LocalRelation [value#1]
== Analyzed Logical Plan ==
value: double
SerializeFromObject [input[0, double, true] AS value#6]
+- MapElements <function1>, obj#5: double
+- DeserializeToObject cast(value#1 as array<double>).toDoubleArray, obj#4: [D
+- LocalRelation [value#1]
== Optimized Logical Plan ==
SerializeFromObject [input[0, double, true] AS value#6]
+- MapElements <function1>, obj#5: double
+- DeserializeToObject value#1.toDoubleArray, obj#4: [D
+- LocalRelation [value#1]
== Physical Plan ==
*SerializeFromObject [input[0, double, true] AS value#6]
+- *MapElements <function1>, obj#5: double
+- *DeserializeToObject value#1.toDoubleArray, obj#4: [D
+- LocalTableScan [value#1]
```
## How was this patch tested?
Tested by new test cases in `SimplifyCastsSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#13704 from kiszk/SPARK-15985.
### What changes were proposed in this pull request?
Hive Index tables are not supported by Spark SQL. Thus, we issue an exception when users try to access Hive Index tables. When the internal function `tableExists` tries to access Hive Index tables, it always gets the same error message: ```Hive index table is not supported```. This message could be confusing to users, since their SQL operations could be completely unrelated to Hive Index tables. For example, when users try to alter a table to a new name and there exists an index table with the same name, the expected exception should be a `TableAlreadyExistsException`.
This PR made the following changes:
- Introduced a new `AnalysisException` type: `SQLFeatureNotSupportedException`. When users try to access an `Index Table`, we will issue a `SQLFeatureNotSupportedException`.
- `tableExists` returns `true` when hitting a `SQLFeatureNotSupportedException` and the feature is `Hive index table`.
- Add a checking `requireTableNotExists` for `SessionCatalog`'s `createTable` API; otherwise, the current implementation relies on the Hive's internal checking.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14801 from gatorsmile/tableExists.
## What changes were proposed in this pull request?
Given that non-deterministic expressions can be stateful, pushing them down the query plan during the optimization phase can cause incorrect behavior. This patch fixes that issue by explicitly disabling that.
## How was this patch tested?
A new test in `FilterPushdownSuite` that checks catalyst behavior for both deterministic and non-deterministic join conditions.
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#14815 from sameeragarwal/constraint-inputfile.
## What changes were proposed in this pull request?
This PR adds parser support for `BigDecimal` literals. If you append the suffix `BD` to a valid number then this will be interpreted as a `BigDecimal`, for example `12.0E10BD` will interpreted into a BigDecimal with scale -9 and precision 3. This is useful in situations where you need exact values.
## How was this patch tested?
Added tests to `ExpressionParserSuite`, `ExpressionSQLBuilderSuite` and `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14819 from hvanhovell/SPARK-17246.
## What changes were proposed in this pull request?
Currently, type-widening does not work between `TimestampType` and `DateType`.
This applies to `SetOperation`, `Union`, `In`, `CaseWhen`, `Greatest`, `Leatest`, `CreateArray`, `CreateMap`, `Coalesce`, `NullIf`, `IfNull`, `Nvl` and `Nvl2`, .
This PR adds the support for widening `DateType` to `TimestampType` for them.
For a simple example,
**Before**
```scala
Seq(Tuple2(new Timestamp(0), new Date(0))).toDF("a", "b").selectExpr("greatest(a, b)").show()
```
shows below:
```
cannot resolve 'greatest(`a`, `b`)' due to data type mismatch: The expressions should all have the same type, got GREATEST(timestamp, date)
```
or union as below:
```scala
val a = Seq(Tuple1(new Timestamp(0))).toDF()
val b = Seq(Tuple1(new Date(0))).toDF()
a.union(b).show()
```
shows below:
```
Union can only be performed on tables with the compatible column types. DateType <> TimestampType at the first column of the second table;
```
**After**
```scala
Seq(Tuple2(new Timestamp(0), new Date(0))).toDF("a", "b").selectExpr("greatest(a, b)").show()
```
shows below:
```
+----------------------------------------------------+
|greatest(CAST(a AS TIMESTAMP), CAST(b AS TIMESTAMP))|
+----------------------------------------------------+
| 1969-12-31 16:00:...|
+----------------------------------------------------+
```
or union as below:
```scala
val a = Seq(Tuple1(new Timestamp(0))).toDF()
val b = Seq(Tuple1(new Date(0))).toDF()
a.union(b).show()
```
shows below:
```
+--------------------+
| _1|
+--------------------+
|1969-12-31 16:00:...|
|1969-12-31 00:00:...|
+--------------------+
```
## How was this patch tested?
Unit tests in `TypeCoercionSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: HyukjinKwon <gurwls223@gmail.com>
Closes#14786 from HyukjinKwon/SPARK-17212.
### What changes were proposed in this pull request?
This PR is to fix an incorrect outer join elimination when filter's `isNotNull` constraints is unable to filter out all null-supplying rows. For example, `isnotnull(coalesce(b#227, c#238))`.
Users can hit this error when they try to use `using/natural outer join`, which is converted to a normal outer join with a `coalesce` expression on the `using columns`. For example,
```Scala
val a = Seq((1, 2), (2, 3)).toDF("a", "b")
val b = Seq((2, 5), (3, 4)).toDF("a", "c")
val c = Seq((3, 1)).toDF("a", "d")
val ab = a.join(b, Seq("a"), "fullouter")
ab.join(c, "a").explain(true)
```
The dataframe `ab` is doing `using full-outer join`, which is converted to a normal outer join with a `coalesce` expression. Constraints inference generates a `Filter` with constraints `isnotnull(coalesce(b#227, c#238))`. Then, it triggers a wrong outer join elimination and generates a wrong result.
```
Project [a#251, b#227, c#237, d#247]
+- Join Inner, (a#251 = a#246)
:- Project [coalesce(a#226, a#236) AS a#251, b#227, c#237]
: +- Join FullOuter, (a#226 = a#236)
: :- Project [_1#223 AS a#226, _2#224 AS b#227]
: : +- LocalRelation [_1#223, _2#224]
: +- Project [_1#233 AS a#236, _2#234 AS c#237]
: +- LocalRelation [_1#233, _2#234]
+- Project [_1#243 AS a#246, _2#244 AS d#247]
+- LocalRelation [_1#243, _2#244]
== Optimized Logical Plan ==
Project [a#251, b#227, c#237, d#247]
+- Join Inner, (a#251 = a#246)
:- Project [coalesce(a#226, a#236) AS a#251, b#227, c#237]
: +- Filter isnotnull(coalesce(a#226, a#236))
: +- Join FullOuter, (a#226 = a#236)
: :- LocalRelation [a#226, b#227]
: +- LocalRelation [a#236, c#237]
+- LocalRelation [a#246, d#247]
```
**A note to the `Committer`**, please also give the credit to dongjoon-hyun who submitted another PR for fixing this issue. https://github.com/apache/spark/pull/14580
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14661 from gatorsmile/fixOuterJoinElimination.
## What changes were proposed in this pull request?
Currently `MapObjects` does not make copies of unsafe-backed data, leading to problems like [SPARK-17061](https://issues.apache.org/jira/browse/SPARK-17061) [SPARK-17093](https://issues.apache.org/jira/browse/SPARK-17093).
This patch makes `MapObjects` make copies of unsafe-backed data.
Generated code - prior to this patch:
```java
...
/* 295 */ if (isNull12) {
/* 296 */ convertedArray1[loopIndex1] = null;
/* 297 */ } else {
/* 298 */ convertedArray1[loopIndex1] = value12;
/* 299 */ }
...
```
Generated code - after this patch:
```java
...
/* 295 */ if (isNull12) {
/* 296 */ convertedArray1[loopIndex1] = null;
/* 297 */ } else {
/* 298 */ convertedArray1[loopIndex1] = value12 instanceof UnsafeRow? value12.copy() : value12;
/* 299 */ }
...
```
## How was this patch tested?
Add a new test case which would fail without this patch.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14698 from lw-lin/mapobjects-copy.
## What changes were proposed in this pull request?
Given that filters based on non-deterministic constraints shouldn't be pushed down in the query plan, unnecessarily inferring them is confusing and a source of potential bugs. This patch simplifies the inferring logic by simply ignoring them.
## How was this patch tested?
Added a new test in `ConstraintPropagationSuite`.
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#14795 from sameeragarwal/deterministic-constraints.
## What changes were proposed in this pull request?
This is a sub-task of [SPARK-16283](https://issues.apache.org/jira/browse/SPARK-16283) (Implement percentile_approx SQL function), which moves class QuantileSummaries to project catalyst so that it can be reused when implementing aggregation function `percentile_approx`.
## How was this patch tested?
This PR only does class relocation, class implementation is not changed.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14754 from clockfly/move_QuantileSummaries_to_catalyst.
## What changes were proposed in this pull request?
Modifies error message for numeric literals to
Numeric literal <literal> does not fit in range [min, max] for type <T>
## How was this patch tested?
Fixed up the error messages for literals.sql in SqlQueryTestSuite and re-ran via sbt. Also fixed up error messages in ExpressionParserSuite
Author: Srinath Shankar <srinath@databricks.com>
Closes#14721 from srinathshankar/sc4296.
## What changes were proposed in this pull request?
This patch changes predicate pushdown optimization rule (PushDownPredicate) from using a blacklist to a whitelist. That is to say, operators must be explicitly allowed. This approach is more future-proof: previously it was possible for us to introduce a new operator and then render the optimization rule incorrect.
This also fixes the bug that previously we allowed pushing filter beneath limit, which was incorrect. That is to say, before this patch, the optimizer would rewrite
```
select * from (select * from range(10) limit 5) where id > 3
to
select * from range(10) where id > 3 limit 5
```
## How was this patch tested?
- a unit test case in FilterPushdownSuite
- an end-to-end test in limit.sql
Author: Reynold Xin <rxin@databricks.com>
Closes#14713 from rxin/SPARK-16994.
## What changes were proposed in this pull request?
This patch improves inline table support with the following:
1. Support type coercion.
2. Support using foldable expressions. Previously only literals were supported.
3. Improve error message handling.
4. Improve test coverage.
## How was this patch tested?
Added a new unit test suite ResolveInlineTablesSuite and a new file-based end-to-end test inline-table.sql.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14676 from petermaxlee/SPARK-16947.
## What changes were proposed in this pull request?
This patch fixes the problem described in SPARK-17117, i.e. "SELECT 1 / NULL" throws an analysis exception:
```
org.apache.spark.sql.AnalysisException: cannot resolve '(1 / NULL)' due to data type mismatch: differing types in '(1 / NULL)' (int and null).
```
The problem is that division type coercion did not take null type into account.
## How was this patch tested?
A unit test for the type coercion, and a few end-to-end test cases using SQLQueryTestSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14695 from petermaxlee/SPARK-17117.
## What changes were proposed in this pull request?
This adds analyzer rules for resolving table-valued functions, and adds one builtin implementation for range(). The arguments for range() are the same as those of `spark.range()`.
## How was this patch tested?
Unit tests.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#14656 from ericl/sc-4309.
## What changes were proposed in this pull request?
The `Optimizer` rules `PushThroughSetOperations` and `PushDownPredicate` have a redundant rule to push down `Filter` through `Union`. We should remove it.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14687 from viirya/remove-extra-pushdown.
## What changes were proposed in this pull request?
I was looking at the code for UnresolvedOrdinal and made a few small changes to make it slightly more clear:
1. Rename the rule to SubstituteUnresolvedOrdinals which is more consistent with other rules that start with verbs. Note that this is still inconsistent with CTESubstitution and WindowsSubstitution.
2. Broke the test suite down from a single test case to three test cases.
## How was this patch tested?
This is a minor cleanup.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14672 from petermaxlee/SPARK-17034.
## What changes were proposed in this pull request?
Currently methods in `ParserUtils` are tested indirectly, we should add test cases in `ParserUtilsSuite` to verify their integrity directly.
## How was this patch tested?
New test cases in `ParserUtilsSuite`
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#14620 from jiangxb1987/parserUtils.
## What changes were proposed in this pull request?
This PR adds a field to subquery alias in order to make the usage of views in a resolved `LogicalPlan` more visible (and more understandable).
For example, the following view and query:
```sql
create view constants as select 1 as id union all select 1 union all select 42
select * from constants;
```
...now yields the following analyzed plan:
```
Project [id#39]
+- SubqueryAlias c, `default`.`constants`
+- Project [gen_attr_0#36 AS id#39]
+- SubqueryAlias gen_subquery_0
+- Union
:- Union
: :- Project [1 AS gen_attr_0#36]
: : +- OneRowRelation$
: +- Project [1 AS gen_attr_1#37]
: +- OneRowRelation$
+- Project [42 AS gen_attr_2#38]
+- OneRowRelation$
```
## How was this patch tested?
Added tests for the two code paths in `SessionCatalogSuite` (sql/core) and `HiveMetastoreCatalogSuite` (sql/hive)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14657 from hvanhovell/SPARK-17068.
## What changes were proposed in this pull request?
This PR adds expression `UnresolvedOrdinal` to represent the ordinal in GROUP BY or ORDER BY, and fixes the rules when resolving ordinals.
Ordinals in GROUP BY or ORDER BY like `1` in `order by 1` or `group by 1` should be considered as unresolved before analysis. But in current code, it uses `Literal` expression to store the ordinal. This is inappropriate as `Literal` itself is a resolved expression, it gives the user a wrong message that the ordinals has already been resolved.
### Before this change
Ordinal is stored as `Literal` expression
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [1 ASC], true
+- 'Aggregate [1], ['a]
+- 'UnresolvedRelation `t
```
For query:
```
scala> Seq(1).toDF("a").createOrReplaceTempView("t")
scala> sql("select count(a), a from t group by 2 having a > 0").show
```
During analysis, the intermediate plan before applying rule `ResolveAggregateFunctions` is:
```
'Filter ('a > 0)
+- Aggregate [2], [count(1) AS count(1)#83L, a#81]
+- LocalRelation [value#7 AS a#9]
```
Before this PR, rule `ResolveAggregateFunctions` believes all expressions of `Aggregate` have already been resolved, and tries to resolve the expressions in `Filter` directly. But this is wrong, as ordinal `2` in Aggregate is not really resolved!
### After this change
Ordinals are stored as `UnresolvedOrdinal`.
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [unresolvedordinal(1) ASC], true
+- 'Aggregate [unresolvedordinal(1)], ['a]
+- 'UnresolvedRelation `t`
```
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14616 from clockfly/spark-16955.
## What changes were proposed in this pull request?
This PR changes the CTE resolving rule to use only **forward-declared** tables in order to prevent infinite loops. More specifically, new logic is like the following.
* Resolve CTEs in `WITH` clauses first before replacing the main SQL body.
* When resolving CTEs, only forward-declared CTEs or base tables are referenced.
- Self-referencing is not allowed any more.
- Cross-referencing is not allowed any more.
**Reported Error Scenarios**
```scala
scala> sql("WITH t AS (SELECT 1 FROM t) SELECT * FROM t")
java.lang.StackOverflowError
...
scala> sql("WITH t1 AS (SELECT * FROM t2), t2 AS (SELECT 2 FROM t1) SELECT * FROM t1, t2")
java.lang.StackOverflowError
...
```
Note that `t`, `t1`, and `t2` are not declared in database. Spark falls into infinite loops before resolving table names.
## How was this patch tested?
Pass the Jenkins tests with new two testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14397 from dongjoon-hyun/SPARK-16771-TREENODE.
#### What changes were proposed in this pull request?
So far, the test cases of `TableIdentifierParserSuite` do not cover the quoted cases. We should add one for avoiding regression.
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14244 from gatorsmile/quotedIdentifiers.
## What changes were proposed in this pull request?
This PR adds `MINUS` set operator which is equivalent `EXCEPT DISTINCT`. This will slightly improve the compatibility with Oracle.
## How was this patch tested?
Pass the Jenkins with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14570 from dongjoon-hyun/SPARK-10601.
## What changes were proposed in this pull request?
Avoid using postfix operation for command execution in SQLQuerySuite where it wasn't whitelisted and audit existing whitelistings removing postfix operators from most places. Some notable places where postfix operation remains is in the XML parsing & time units (seconds, millis, etc.) where it arguably can improve readability.
## How was this patch tested?
Existing tests.
Author: Holden Karau <holden@us.ibm.com>
Closes#14407 from holdenk/SPARK-16779.
## What changes were proposed in this pull request?
This patch fixes the incorrect results in the rule ResolveSubquery in Catalyst's Analysis phase by returning an error message when the LIMIT is found in the path from the parent table to the correlated predicate in the subquery.
## How was this patch tested?
./dev/run-tests
a new unit test on the problematic pattern.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#14411 from nsyca/master.
## What changes were proposed in this pull request?
we have various logical plans for CREATE TABLE and CTAS: `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateHiveTableAsSelectLogicalPlan`. This PR unifies them to reduce the complexity and centralize the error handling.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14482 from cloud-fan/table.
## What changes were proposed in this pull request?
These 2 methods take `CatalogTable` as parameter, which already have the database information.
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14476 from cloud-fan/minor5.
## What changes were proposed in this pull request?
Implements `eval()` method for expression `AssertNotNull` so that we can convert local projection on LocalRelation to another LocalRelation.
### Before change:
```
scala> import org.apache.spark.sql.catalyst.dsl.expressions._
scala> import org.apache.spark.sql.catalyst.expressions.objects.AssertNotNull
scala> import org.apache.spark.sql.Column
scala> case class A(a: Int)
scala> Seq((A(1),2)).toDS().select(new Column(AssertNotNull("_1".attr, Nil))).explain
java.lang.UnsupportedOperationException: Only code-generated evaluation is supported.
at org.apache.spark.sql.catalyst.expressions.objects.AssertNotNull.eval(objects.scala:850)
...
```
### After the change:
```
scala> Seq((A(1),2)).toDS().select(new Column(AssertNotNull("_1".attr, Nil))).explain(true)
== Parsed Logical Plan ==
'Project [assertnotnull('_1) AS assertnotnull(_1)#5]
+- LocalRelation [_1#2, _2#3]
== Analyzed Logical Plan ==
assertnotnull(_1): struct<a:int>
Project [assertnotnull(_1#2) AS assertnotnull(_1)#5]
+- LocalRelation [_1#2, _2#3]
== Optimized Logical Plan ==
LocalRelation [assertnotnull(_1)#5]
== Physical Plan ==
LocalTableScan [assertnotnull(_1)#5]
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14486 from clockfly/assertnotnull_eval.
## What changes were proposed in this pull request?
Here is a table about the behaviours of `array`/`map` and `greatest`/`least` in Hive, MySQL and Postgres:
| |Hive|MySQL|Postgres|
|---|---|---|---|---|
|`array`/`map`|can find a wider type with decimal type arguments, and will truncate the wider decimal type if necessary|can find a wider type with decimal type arguments, no truncation problem|can find a wider type with decimal type arguments, no truncation problem|
|`greatest`/`least`|can find a wider type with decimal type arguments, and truncate if necessary, but can't do string promotion|can find a wider type with decimal type arguments, no truncation problem, but can't do string promotion|can find a wider type with decimal type arguments, no truncation problem, but can't do string promotion|
I think these behaviours makes sense and Spark SQL should follow them.
This PR fixes `array` and `map` by using `findWiderCommonType` to get the wider type.
This PR fixes `greatest` and `least` by add a `findWiderTypeWithoutStringPromotion`, which provides similar semantic of `findWiderCommonType`, but without string promotion.
## How was this patch tested?
new tests in `TypeCoersionSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#14439 from cloud-fan/bug.
## What changes were proposed in this pull request?
`Greatest` and `Least` are not conditional expressions, but arithmetic expressions.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14460 from cloud-fan/move.
## What changes were proposed in this pull request?
In Spark 1.6 (with Hive support) we could use `CURRENT_DATE` and `CURRENT_TIMESTAMP` functions as literals (without adding braces), for example:
```SQL
select /* Spark 1.6: */ current_date, /* Spark 1.6 & Spark 2.0: */ current_date()
```
This was accidentally dropped in Spark 2.0. This PR reinstates this functionality.
## How was this patch tested?
Added a case to ExpressionParserSuite.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14442 from hvanhovell/SPARK-16836.
## What changes were proposed in this pull request?
Fix of incorrect arguments (dropping slideDuration and using windowDuration) in constructors for TimeWindow.
The JIRA this addresses is here: https://issues.apache.org/jira/browse/SPARK-16837
## How was this patch tested?
Added a test to TimeWindowSuite to check that the results of TimeWindow object apply and TimeWindow class constructor are equivalent.
Author: Tom Magrino <tmagrino@fb.com>
Closes#14441 from tmagrino/windowing-fix.
## What changes were proposed in this pull request?
Greatest/least function does not have the most friendly error message for data types. This patch improves the error message to not show the Seq type, and use more human readable data types.
Before:
```
org.apache.spark.sql.AnalysisException: cannot resolve 'greatest(CAST(1.0 AS DECIMAL(2,1)), "1.0")' due to data type mismatch: The expressions should all have the same type, got GREATEST (ArrayBuffer(DecimalType(2,1), StringType)).; line 1 pos 7
```
After:
```
org.apache.spark.sql.AnalysisException: cannot resolve 'greatest(CAST(1.0 AS DECIMAL(2,1)), "1.0")' due to data type mismatch: The expressions should all have the same type, got GREATEST(decimal(2,1), string).; line 1 pos 7
```
## How was this patch tested?
Manually verified the output and also added unit tests to ConditionalExpressionSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14453 from petermaxlee/SPARK-16850.
## What changes were proposed in this pull request?
These 2 expressions are not needed anymore after we have `Greatest` and `Least`. This PR removes them and related tests.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14434 from cloud-fan/minor1.
## What changes were proposed in this pull request?
Removes the deprecated timestamp constructor and incidentally fixes the use which was using system timezone rather than the one specified when working near DST.
This change also causes the roundtrip tests to fail since it now actually uses all the timezones near DST boundaries where it didn't before.
Note: this is only a partial the solution, longer term we should follow up with https://issues.apache.org/jira/browse/SPARK-16788 to avoid this problem & simplify our timezone handling code.
## How was this patch tested?
New tests for two timezones added so even if user timezone happens to coincided with one, the other tests should still fail. Important note: this (temporarily) disables the round trip tests until we can fix the issue more thoroughly.
Author: Holden Karau <holden@us.ibm.com>
Closes#14398 from holdenk/SPARK-16774-fix-use-of-deprecated-timestamp-constructor.
## What changes were proposed in this pull request?
a failing test case + fix to SPARK-16791 (https://issues.apache.org/jira/browse/SPARK-16791)
## How was this patch tested?
added a failing test case to CastSuit, then fixed the Cast code and rerun the entire CastSuit
Author: eyal farago <eyal farago>
Author: Eyal Farago <eyal.farago@actimize.com>
Closes#14400 from eyalfa/SPARK-16791_cast_struct_with_timestamp_field_fails.
## What changes were proposed in this pull request?
Currently, `UNION` queries on incompatible types show misleading error messages, i.e., `unresolved operator Union`. We had better show a more correct message. This will help users in the situation of [SPARK-16704](https://issues.apache.org/jira/browse/SPARK-16704).
**Before**
```scala
scala> sql("select 1,2,3 union (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: unresolved operator 'Union;
scala> sql("select 1,2,3 intersect (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: unresolved operator 'Intersect;
scala> sql("select 1,2,3 except (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: unresolved operator 'Except;
```
**After**
```scala
scala> sql("select 1,2,3 union (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the second column of the second table;
scala> sql("select 1,2,3 intersect (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: Intersect can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the second column of the second table;
scala> sql("select 1,2,3 except (select array(1),array(2),3)")
org.apache.spark.sql.AnalysisException: Except can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the first column of the second table;
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14355 from dongjoon-hyun/SPARK-16726.
## What changes were proposed in this pull request?
`StructField` has very similar semantic with `CatalogColumn`, except that `CatalogColumn` use string to express data type. I think it's reasonable to use `StructType` as the `CatalogTable.schema` and remove `CatalogColumn`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14363 from cloud-fan/column.
## What changes were proposed in this pull request?
Use foreach/for instead of map where operation requires execution of body, not actually defining a transformation
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#14332 from srowen/SPARK-16694.
## What changes were proposed in this pull request?
Spark currently throws exceptions for invalid casts for all other data types except date type. Somehow date type returns null. It should be consistent and throws analysis exception as well.
## How was this patch tested?
Added a unit test case in CastSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14358 from petermaxlee/SPARK-16729.
## What changes were proposed in this pull request?
This PR is the first step for the following feature:
For hash aggregation in Spark SQL, we use a fast aggregation hashmap to act as a "cache" in order to boost aggregation performance. Previously, the hashmap is backed by a `ColumnarBatch`. This has performance issues when we have wide schema for the aggregation table (large number of key fields or value fields).
In this JIRA, we support another implementation of fast hashmap, which is backed by a `RowBasedKeyValueBatch`. We then automatically pick between the two implementations based on certain knobs.
In this first-step PR, implementations for `RowBasedKeyValueBatch` and `RowBasedHashMapGenerator` are added.
## How was this patch tested?
Unit tests: `RowBasedKeyValueBatchSuite`
Author: Qifan Pu <qifan.pu@gmail.com>
Closes#14349 from ooq/SPARK-16524.
## What changes were proposed in this pull request?
finish the TODO, create a new expression `ExternalMapToCatalyst` to iterate the map directly.
## How was this patch tested?
new test in `JavaDatasetSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14344 from cloud-fan/java-map.
## What changes were proposed in this pull request?
We push down `Project` through `Sample` in `Optimizer` by the rule `PushProjectThroughSample`. However, if the projected columns produce new output, they will encounter whole data instead of sampled data. It will bring some inconsistency between original plan (Sample then Project) and optimized plan (Project then Sample). In the extreme case such as attached in the JIRA, if the projected column is an UDF which is supposed to not see the sampled out data, the result of UDF will be incorrect.
Since the rule `ColumnPruning` already handles general `Project` pushdown. We don't need `PushProjectThroughSample` anymore. The rule `ColumnPruning` also avoids the described issue.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14327 from viirya/fix-sample-pushdown.
## What changes were proposed in this pull request?
SubexpressionEliminationSuite."Semantic equals and hash" assumes the default AttributeReference's exprId wont' be "ExprId(1)". However, that depends on when this test runs. It may happen to use "ExprId(1)".
This PR detects the conflict and makes sure we create a different ExprId when the conflict happens.
## How was this patch tested?
Jenkins unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#14350 from zsxwing/SPARK-16715.
## What changes were proposed in this pull request?
It's weird that we have `BucketSpec` to abstract bucket info, but don't use it in `CatalogTable`. This PR moves `BucketSpec` into catalyst module.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14331 from cloud-fan/check.
## What changes were proposed in this pull request?
we also store data source table options in this field, it's unreasonable to call it `serdeProperties`.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14283 from cloud-fan/minor1.
## What changes were proposed in this pull request?
Currently we don't check the value returned by called method in `Invoke`. When the returned value is null and is assigned to a variable of primitive type, `NullPointerException` will be thrown.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14259 from viirya/agg-empty-ds.
## What changes were proposed in this pull request?
This PR adds `str_to_map` SQL function in order to remove Hive fallback.
## How was this patch tested?
Pass the Jenkins tests with newly added.
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13990 from techaddict/SPARK-16287.
## What changes were proposed in this pull request?
aggregate expressions can only be executed inside `Aggregate`, if we propagate it up with constraints, the parent operator can not execute it and will fail at runtime.
## How was this patch tested?
new test in SQLQuerySuite
Author: Wenchen Fan <wenchen@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#14281 from cloud-fan/bug.
## What changes were proposed in this pull request?
`Nvl` function should support numeric-straing cases like Hive/Spark1.6. Currently, `Nvl` finds the tightest common types among numeric types. This PR extends that to consider `String` type, too.
```scala
- TypeCoercion.findTightestCommonTypeOfTwo(left.dataType, right.dataType).map { dtype =>
+ TypeCoercion.findTightestCommonTypeToString(left.dataType, right.dataType).map { dtype =>
```
**Before**
```scala
scala> sql("select nvl('0', 1)").collect()
org.apache.spark.sql.AnalysisException: cannot resolve `nvl("0", 1)` due to data type mismatch:
input to function coalesce should all be the same type, but it's [string, int]; line 1 pos 7
```
**After**
```scala
scala> sql("select nvl('0', 1)").collect()
res0: Array[org.apache.spark.sql.Row] = Array([0])
```
## How was this patch tested?
Pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14251 from dongjoon-hyun/SPARK-16602.
## What changes were proposed in this pull request?
This patch moves regexp related unit tests from StringExpressionsSuite to RegexpExpressionsSuite to match the file name for regexp expressions.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#14230 from rxin/SPARK-16584.
## What changes were proposed in this pull request?
`Alias` with metadata is not a no-op and we should not strip it in `RemoveAliasOnlyProject` rule.
This PR also did some improvement for this rule:
1. extend the semantic of `alias-only`. Now we allow the project list to be partially aliased.
2. add unit test for this rule.
## How was this patch tested?
new `RemoveAliasOnlyProjectSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14106 from cloud-fan/bug.
## What changes were proposed in this pull request?
Currently our Optimizer may reorder the predicates to run them more efficient, but in non-deterministic condition, change the order between deterministic parts and non-deterministic parts may change the number of input rows. For example:
```SELECT a FROM t WHERE rand() < 0.1 AND a = 1```
And
```SELECT a FROM t WHERE a = 1 AND rand() < 0.1```
may call rand() for different times and therefore the output rows differ.
This PR improved this condition by checking whether the predicate is placed before any non-deterministic predicates.
## How was this patch tested?
Expanded related testcases in FilterPushdownSuite.
Author: 蒋星博 <jiangxingbo@meituan.com>
Closes#14012 from jiangxb1987/ppd.
## What changes were proposed in this pull request?
RegexExtract and RegexReplace currently crash on non-nullable input due use of a hard-coded local variable name (e.g. compiles fail with `java.lang.Exception: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 85, Column 26: Redefinition of local variable "m" `).
This changes those variables to use fresh names, and also in a few other places.
## How was this patch tested?
Unit tests. rxin
Author: Eric Liang <ekl@databricks.com>
Closes#14168 from ericl/sc-3906.
## What changes were proposed in this pull request?
This patch implements reflect SQL function, which can be used to invoke a Java method in SQL. Slightly different from Hive, this implementation requires the class name and the method name to be literals. This implementation also supports only a smaller number of data types, and requires the function to be static, as suggested by rxin in #13969.
java_method is an alias for reflect, so this should also resolve SPARK-16277.
## How was this patch tested?
Added expression unit tests and an end-to-end test.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14138 from petermaxlee/reflect-static.
This option is used by Hive to directly delete the files instead of
moving them to the trash. This is needed in certain configurations
where moving the files does not work. For non-Hive tables and partitions,
Spark already behaves as if the PURGE option was set, so there's no
need to do anything.
Hive support for PURGE was added in 0.14 (for tables) and 1.2 (for
partitions), so the code reflects that: trying to use the option with
older versions of Hive will cause an exception to be thrown.
The change is a little noisier than I would like, because of the code
to propagate the new flag through all the interfaces and implementations;
the main changes are in the parser and in HiveShim, aside from the tests
(DDLCommandSuite, VersionsSuite).
Tested by running sql and catalyst unit tests, plus VersionsSuite which
has been updated to test the version-specific behavior. I also ran an
internal test suite that uses PURGE and would not pass previously.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#13831 from vanzin/SPARK-16119.
## What changes were proposed in this pull request?
In code generation, it is incorrect for expressions to reuse variable names across different instances of itself. As an example, SPARK-16488 reports a bug in which pmod expression reuses variable name "r".
This patch updates ExpressionEvalHelper test harness to always project two instances of the same expression, which will help us catch variable reuse problems in expression unit tests. This patch also fixes the bug in crc32 expression.
## How was this patch tested?
This is a test harness change, but I also created a new test suite for testing the test harness.
Author: Reynold Xin <rxin@databricks.com>
Closes#14146 from rxin/SPARK-16489.
## What changes were proposed in this pull request?
Temporary tables are used frequently, but `spark.catalog.listColumns` does not support those tables. This PR make `SessionCatalog` supports temporary table column listing.
**Before**
```scala
scala> spark.range(10).createOrReplaceTempView("t1")
scala> spark.catalog.listTables().collect()
res1: Array[org.apache.spark.sql.catalog.Table] = Array(Table[name=`t1`, tableType=`TEMPORARY`, isTemporary=`true`])
scala> spark.catalog.listColumns("t1").collect()
org.apache.spark.sql.AnalysisException: Table `t1` does not exist in database `default`.;
```
**After**
```
scala> spark.catalog.listColumns("t1").collect()
res2: Array[org.apache.spark.sql.catalog.Column] = Array(Column[name='id', description='id', dataType='bigint', nullable='false', isPartition='false', isBucket='false'])
```
## How was this patch tested?
Pass the Jenkins tests including a new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14114 from dongjoon-hyun/SPARK-16458.
#### What changes were proposed in this pull request?
**Issue 1:** When a query containing LIMIT/TABLESAMPLE 0, the statistics could be zero. Results are correct but it could cause a huge performance regression. For example,
```Scala
Seq(("one", 1), ("two", 2), ("three", 3), ("four", 4)).toDF("k", "v")
.createOrReplaceTempView("test")
val df1 = spark.table("test")
val df2 = spark.table("test").limit(0)
val df = df1.join(df2, Seq("k"), "left")
```
The statistics of both `df` and `df2` are zero. The statistics values should never be zero; otherwise `sizeInBytes` of `BinaryNode` will also be zero (product of children). This PR is to increase it to `1` when the num of rows is equal to 0.
**Issue 2:** When a query containing negative LIMIT/TABLESAMPLE, we should issue exceptions. Negative values could break the implementation assumption of multiple parts. For example, statistics calculation. Below is the example query.
```SQL
SELECT * FROM testData TABLESAMPLE (-1 rows)
SELECT * FROM testData LIMIT -1
```
This PR is to issue an appropriate exception in this case.
**Issue 3:** Spark SQL follows the restriction of LIMIT clause in Hive. The argument to the LIMIT clause must evaluate to a constant value. It can be a numeric literal, or another kind of numeric expression involving operators, casts, and function return values. You cannot refer to a column or use a subquery. Currently, we do not detect whether the expression in LIMIT clause is foldable or not. If non-foldable, we might issue a strange error message. For example,
```SQL
SELECT * FROM testData LIMIT rand() > 0.2
```
Then, a misleading error message is issued, like
```
assertion failed: No plan for GlobalLimit (_nondeterministic#203 > 0.2)
+- Project [key#11, value#12, rand(-1441968339187861415) AS _nondeterministic#203]
+- LocalLimit (_nondeterministic#202 > 0.2)
+- Project [key#11, value#12, rand(-1308350387169017676) AS _nondeterministic#202]
+- LogicalRDD [key#11, value#12]
java.lang.AssertionError: assertion failed: No plan for GlobalLimit (_nondeterministic#203 > 0.2)
+- Project [key#11, value#12, rand(-1441968339187861415) AS _nondeterministic#203]
+- LocalLimit (_nondeterministic#202 > 0.2)
+- Project [key#11, value#12, rand(-1308350387169017676) AS _nondeterministic#202]
+- LogicalRDD [key#11, value#12]
```
This PR detects it and then issues a meaningful error message.
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14034 from gatorsmile/limit.
## What changes were proposed in this pull request?
This patch implements all remaining xpath functions that Hive supports and not natively supported in Spark: xpath_int, xpath_short, xpath_long, xpath_float, xpath_double, xpath_string, and xpath.
## How was this patch tested?
Added unit tests and end-to-end tests.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13991 from petermaxlee/SPARK-16318.
## What changes were proposed in this pull request?
This PR adds parse_url SQL functions in order to remove Hive fallback.
A new implementation of #13999
## How was this patch tested?
Pass the exist tests including new testcases.
Author: wujian <jan.chou.wu@gmail.com>
Closes#14008 from janplus/SPARK-16281.
## What changes were proposed in this pull request?
This PR implements `sentences` SQL function.
## How was this patch tested?
Pass the Jenkins tests with a new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14004 from dongjoon-hyun/SPARK_16285.
## What changes were proposed in this pull request?
This small patch modifies ExpressionEvalHelper. checkEvaluation to support comparing NaN values for floating point comparisons.
## How was this patch tested?
This is a test harness change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14103 from petermaxlee/SPARK-16436.
## What changes were proposed in this pull request?
This PR improves `OptimizeIn` optimizer to remove the literal repetitions from SQL `IN` predicates. This optimizer prevents user mistakes and also can optimize some queries like [TPCDS-36](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q36.sql#L19).
**Before**
```scala
scala> sql("select state from (select explode(array('CA','TN')) state) where state in ('TN','TN','TN','TN','TN','TN','TN')").explain
== Physical Plan ==
*Filter state#6 IN (TN,TN,TN,TN,TN,TN,TN)
+- Generate explode([CA,TN]), false, false, [state#6]
+- Scan OneRowRelation[]
```
**After**
```scala
scala> sql("select state from (select explode(array('CA','TN')) state) where state in ('TN','TN','TN','TN','TN','TN','TN')").explain
== Physical Plan ==
*Filter state#6 IN (TN)
+- Generate explode([CA,TN]), false, false, [state#6]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests (including a new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13876 from dongjoon-hyun/SPARK-16174.
#### What changes were proposed in this pull request?
Different from the other leaf nodes, `MetastoreRelation` and `SimpleCatalogRelation` have a pre-defined `alias`, which is used to change the qualifier of the node. However, based on the existing alias handling, alias should be put in `SubqueryAlias`.
This PR is to separate alias handling from `MetastoreRelation` and `SimpleCatalogRelation` to make it consistent with the other nodes. It simplifies the signature and conversion to a `BaseRelation`.
For example, below is an example query for `MetastoreRelation`, which is converted to a `LogicalRelation`:
```SQL
SELECT tmp.a + 1 FROM test_parquet_ctas tmp WHERE tmp.a > 2
```
Before changes, the analyzed plan is
```
== Analyzed Logical Plan ==
(a + 1): int
Project [(a#951 + 1) AS (a + 1)#952]
+- Filter (a#951 > 2)
+- SubqueryAlias tmp
+- Relation[a#951] parquet
```
After changes, the analyzed plan becomes
```
== Analyzed Logical Plan ==
(a + 1): int
Project [(a#951 + 1) AS (a + 1)#952]
+- Filter (a#951 > 2)
+- SubqueryAlias tmp
+- SubqueryAlias test_parquet_ctas
+- Relation[a#951] parquet
```
**Note: the optimized plans are the same.**
For `SimpleCatalogRelation`, the existing code always generates two Subqueries. Thus, no change is needed.
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14053 from gatorsmile/removeAliasFromMetastoreRelation.
## What changes were proposed in this pull request?
This PR implements `stack` table generating function.
## How was this patch tested?
Pass the Jenkins tests including new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14033 from dongjoon-hyun/SPARK-16286.
## What changes were proposed in this pull request?
This PR implements `inline` table generating function.
## How was this patch tested?
Pass the Jenkins tests with new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13976 from dongjoon-hyun/SPARK-16288.
## What changes were proposed in this pull request?
This PR adds `map_keys` and `map_values` SQL functions in order to remove Hive fallback.
## How was this patch tested?
Pass the Jenkins tests including new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13967 from dongjoon-hyun/SPARK-16278.
## What changes were proposed in this pull request?
This PR adds a new logical optimizer, `PropagateEmptyRelation`, to collapse a logical plans consisting of only empty LocalRelations.
**Optimizer Targets**
1. Binary(or Higher)-node Logical Plans
- Union with all empty children.
- Join with one or two empty children (including Intersect/Except).
2. Unary-node Logical Plans
- Project/Filter/Sample/Join/Limit/Repartition with all empty children.
- Aggregate with all empty children and without AggregateFunction expressions, COUNT.
- Generate with Explode because other UserDefinedGenerators like Hive UDTF returns results.
**Sample Query**
```sql
WITH t1 AS (SELECT a FROM VALUES 1 t(a)),
t2 AS (SELECT b FROM VALUES 1 t(b) WHERE 1=2)
SELECT a,b
FROM t1, t2
WHERE a=b
GROUP BY a,b
HAVING a>1
ORDER BY a,b
```
**Before**
```scala
scala> sql("with t1 as (select a from values 1 t(a)), t2 as (select b from values 1 t(b) where 1=2) select a,b from t1, t2 where a=b group by a,b having a>1 order by a,b").explain
== Physical Plan ==
*Sort [a#0 ASC, b#1 ASC], true, 0
+- Exchange rangepartitioning(a#0 ASC, b#1 ASC, 200)
+- *HashAggregate(keys=[a#0, b#1], functions=[])
+- Exchange hashpartitioning(a#0, b#1, 200)
+- *HashAggregate(keys=[a#0, b#1], functions=[])
+- *BroadcastHashJoin [a#0], [b#1], Inner, BuildRight
:- *Filter (isnotnull(a#0) && (a#0 > 1))
: +- LocalTableScan [a#0]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
+- *Filter (isnotnull(b#1) && (b#1 > 1))
+- LocalTableScan <empty>, [b#1]
```
**After**
```scala
scala> sql("with t1 as (select a from values 1 t(a)), t2 as (select b from values 1 t(b) where 1=2) select a,b from t1, t2 where a=b group by a,b having a>1 order by a,b").explain
== Physical Plan ==
LocalTableScan <empty>, [a#0, b#1]
```
## How was this patch tested?
Pass the Jenkins tests (including a new testsuite).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13906 from dongjoon-hyun/SPARK-16208.
## What changes were proposed in this pull request?
This patch implements the elt function, as it is implemented in Hive.
## How was this patch tested?
Added expression unit test in StringExpressionsSuite and end-to-end test in StringFunctionsSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13966 from petermaxlee/SPARK-16276.
## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
## What changes were proposed in this pull request?
This PR Checks the size limit when doubling the array size in BufferHolder to avoid integer overflow.
## How was this patch tested?
Manual test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13829 from clockfly/SPARK-16071_2.
## What changes were proposed in this pull request?
This patch implements xpath_boolean expression for Spark SQL, a xpath function that returns true or false. The implementation is modelled after Hive's xpath_boolean, except that how the expression handles null inputs. Hive throws a NullPointerException at runtime if either of the input is null. This implementation returns null if either of the input is null.
## How was this patch tested?
Created two new test suites. One for unit tests covering the expression, and the other for end-to-end test in SQL.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13964 from petermaxlee/SPARK-16274.
## What changes were proposed in this pull request?
This PR adds 3 optimizer rules for typed filter:
1. push typed filter down through `SerializeFromObject` and eliminate the deserialization in filter condition.
2. pull typed filter up through `SerializeFromObject` and eliminate the deserialization in filter condition.
3. combine adjacent typed filters and share the deserialized object among all the condition expressions.
This PR also adds `TypedFilter` logical plan, to separate it from normal filter, so that the concept is more clear and it's easier to write optimizer rules.
## How was this patch tested?
`TypedFilterOptimizationSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13846 from cloud-fan/filter.
## What changes were proposed in this pull request?
This extends SPARK-15860 to include metrics for the actual bytecode size of janino-generated methods. They can be accessed in the same way as any other codahale metric, e.g.
```
scala> org.apache.spark.metrics.source.CodegenMetrics.METRIC_GENERATED_CLASS_BYTECODE_SIZE.getSnapshot().getValues()
res7: Array[Long] = Array(532, 532, 532, 542, 1479, 2670, 3585, 3585)
scala> org.apache.spark.metrics.source.CodegenMetrics.METRIC_GENERATED_METHOD_BYTECODE_SIZE.getSnapshot().getValues()
res8: Array[Long] = Array(5, 5, 5, 5, 10, 10, 10, 10, 15, 15, 15, 38, 63, 79, 88, 94, 94, 94, 132, 132, 165, 165, 220, 220)
```
## How was this patch tested?
Small unit test, also verified manually that the performance impact is minimal (<10%). hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#13934 from ericl/spark-16238.
## What changes were proposed in this pull request?
The analyzer rule for resolving using joins should respect the case sensitivity setting.
## How was this patch tested?
New tests in ResolveNaturalJoinSuite
Author: Yin Huai <yhuai@databricks.com>
Closes#13977 from yhuai/SPARK-16301.
#### What changes were proposed in this pull request?
Based on the previous discussion with cloud-fan hvanhovell in another related PR https://github.com/apache/spark/pull/13764#discussion_r67994276, it looks reasonable to add convenience methods for users to add `comment` when defining `StructField`.
Currently, the column-related `comment` attribute is stored in `Metadata` of `StructField`. For example, users can add the `comment` attribute using the following way:
```Scala
StructType(
StructField(
"cl1",
IntegerType,
nullable = false,
new MetadataBuilder().putString("comment", "test").build()) :: Nil)
```
This PR is to add more user friendly methods for the `comment` attribute when defining a `StructField`. After the changes, users are provided three different ways to do it:
```Scala
val struct = (new StructType)
.add("a", "int", true, "test1")
val struct = (new StructType)
.add("c", StringType, true, "test3")
val struct = (new StructType)
.add(StructField("d", StringType).withComment("test4"))
```
#### How was this patch tested?
Added test cases:
- `DataTypeSuite` is for testing three types of API changes,
- `DataFrameReaderWriterSuite` is for parquet, json and csv formats - using in-memory catalog
- `OrcQuerySuite.scala` is for orc format using Hive-metastore
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13860 from gatorsmile/newMethodForComment.
## What changes were proposed in this pull request?
`MAX(COUNT(*))` is invalid since aggregate expression can't be nested within another aggregate expression. This case should be captured at analysis phase, but somehow sneaks off to runtime.
The reason is that when checking aggregate expressions in `CheckAnalysis`, a checking branch treats all expressions that reference no input attributes as valid ones. However, `MAX(COUNT(*))` is translated into `MAX(COUNT(1))` at analysis phase and also references no input attribute.
This PR fixes this issue by removing the aforementioned branch.
## How was this patch tested?
New test case added in `AnalysisErrorSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#13968 from liancheng/spark-16291-nested-agg-functions.
## What changes were proposed in this pull request?
This patch ports Hive's UDFXPathUtil over to Spark, which can be used to implement xpath functionality in Spark in the near future.
## How was this patch tested?
Added two new test suites UDFXPathUtilSuite and ReusableStringReaderSuite. They have been ported over from Hive (but rewritten in Scala in order to leverage ScalaTest).
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13961 from petermaxlee/xpath.
## What changes were proposed in this pull request?
Fixes a couple old references to `DataFrameWriter.startStream` to `DataStreamWriter.start
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#13952 from brkyvz/minor-doc-fix.
## What changes were proposed in this pull request?
Spark currently shows all functions when issue a `SHOW FUNCTIONS` command. This PR refines the `SHOW FUNCTIONS` command by allowing users to select all functions, user defined function or system functions. The following syntax can be used:
**ALL** (default)
```SHOW FUNCTIONS```
```SHOW ALL FUNCTIONS```
**SYSTEM**
```SHOW SYSTEM FUNCTIONS```
**USER**
```SHOW USER FUNCTIONS```
## How was this patch tested?
Updated tests and added tests to the DDLSuite
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13929 from hvanhovell/SPARK-16220.
## What changes were proposed in this pull request?
This pr is to remove `hashCode` and `equals` in `ArrayBasedMapData` because the type cannot be used as join keys, grouping keys, or in equality tests.
## How was this patch tested?
Add a new test suite `MapDataSuite` for comparison tests.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13847 from maropu/UnsafeMapTest.
## What changes were proposed in this pull request?
This PR changes `CombineFilters` to compose the final predicate condition by using (`child predicate` AND `parent predicate`) instead of (`parent predicate` AND `child predicate`). This is a best effort approach. Some other optimization rules may destroy this order by reorganizing conjunctive predicates.
**Reported Error Scenario**
Chris McCubbin reported a bug when he used StringIndexer in an ML pipeline with additional filters. It seems that during filter pushdown, we changed the ordering in the logical plan.
```scala
import org.apache.spark.ml.feature._
val df1 = (0 until 3).map(_.toString).toDF
val indexer = new StringIndexer()
.setInputCol("value")
.setOutputCol("idx")
.setHandleInvalid("skip")
.fit(df1)
val df2 = (0 until 5).map(_.toString).toDF
val predictions = indexer.transform(df2)
predictions.show() // this is okay
predictions.where('idx > 2).show() // this will throw an exception
```
Please see the notebook at https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1233855/2159162931615821/588180/latest.html for error messages.
## How was this patch tested?
Pass the Jenkins tests (including a new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13872 from dongjoon-hyun/SPARK-16164.
## What changes were proposed in this pull request?
Currently, we use local timezone to parse or format a timestamp (TimestampType), then use Long as the microseconds since epoch UTC.
In from_utc_timestamp() and to_utc_timestamp(), we did not consider the local timezone, they could return different results with different local timezone.
This PR will do the conversion based on human time (in local timezone), it should return same result in whatever timezone. But because the mapping from absolute timestamp to human time is not exactly one-to-one mapping, it will still return wrong result in some timezone (also in the begging or ending of DST).
This PR is kind of the best effort fix. In long term, we should make the TimestampType be timezone aware to fix this totally.
## How was this patch tested?
Tested these function in all timezone.
Author: Davies Liu <davies@databricks.com>
Closes#13784 from davies/convert_tz.
## What changes were proposed in this pull request?
Internally, we use Int to represent a date (the days since 1970-01-01), when we convert that into unix timestamp (milli-seconds since epoch in UTC), we get the offset of a timezone using local millis (the milli-seconds since 1970-01-01 in a timezone), but TimeZone.getOffset() expect unix timestamp, the result could be off by one hour (in Daylight Saving Time (DST) or not).
This PR change to use best effort approximate of posix timestamp to lookup the offset. In the event of changing of DST, Some time is not defined (for example, 2016-03-13 02:00:00 PST), or could lead to multiple valid result in UTC (for example, 2016-11-06 01:00:00), this best effort approximate should be enough in practice.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13652 from davies/fix_timezone.
## What changes were proposed in this pull request?
This small patch renames a few optimizer rules to make the naming more consistent, e.g. class name start with a verb. The main important "fix" is probably SamplePushDown -> PushProjectThroughSample. SamplePushDown is actually the wrong name, since the rule is not about pushing Sample down.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#13732 from rxin/SPARK-16014.
#### What changes were proposed in this pull request?
`IF NOT EXISTS` in `INSERT OVERWRITE` should not support dynamic partitions. If we specify `IF NOT EXISTS`, the inserted statement is not shown in the table.
This PR is to issue an exception in this case, just like what Hive does. Also issue an exception if users specify `IF NOT EXISTS` if users do not specify any `PARTITION` specification.
#### How was this patch tested?
Added test cases into `PlanParserSuite` and `InsertIntoHiveTableSuite`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13447 from gatorsmile/insertIfNotExist.
## What changes were proposed in this pull request?
This PR fixes the problem that Divide Expression inside Aggregation function is casted to wrong type, which cause `select 1/2` and `select sum(1/2)`returning different result.
**Before the change:**
```
scala> sql("select 1/2 as a").show()
+---+
| a|
+---+
|0.5|
+---+
scala> sql("select sum(1/2) as a").show()
+---+
| a|
+---+
|0 |
+---+
scala> sql("select sum(1 / 2) as a").schema
res4: org.apache.spark.sql.types.StructType = StructType(StructField(a,LongType,true))
```
**After the change:**
```
scala> sql("select 1/2 as a").show()
+---+
| a|
+---+
|0.5|
+---+
scala> sql("select sum(1/2) as a").show()
+---+
| a|
+---+
|0.5|
+---+
scala> sql("select sum(1/2) as a").schema
res4: org.apache.spark.sql.types.StructType = StructType(StructField(a,DoubleType,true))
```
## How was this patch tested?
Unit test.
This PR is based on https://github.com/apache/spark/pull/13524 by Sephiroth-Lin
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13651 from clockfly/SPARK-15776.
## What changes were proposed in this pull request?
Adds codahale metrics for the codegen source text size and how long it takes to compile. The size is particularly interesting, since the JVM does have hard limits on how large methods can get.
To simplify, I added the metrics under a statically-initialized source that is always registered with SparkEnv.
## How was this patch tested?
Unit tests
Author: Eric Liang <ekl@databricks.com>
Closes#13586 from ericl/spark-15860.
## What changes were proposed in this pull request?
When the output mode is complete, then the output of a streaming aggregation essentially will contain the complete aggregates every time. So this is not different from a batch dataset within an incremental execution. Other non-streaming operations should be supported on this dataset. In this PR, I am just adding support for sorting, as it is a common useful functionality. Support for other operations will come later.
## How was this patch tested?
Additional unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13549 from tdas/SPARK-15812.
## What changes were proposed in this pull request?
The parser currently does not allow the use of some SQL keywords as table or field names. This PR adds supports for all keywords as identifier. The exception to this are table aliases, in this case most keywords are allowed except for join keywords (```anti, full, inner, left, semi, right, natural, on, join, cross```) and set-operator keywords (```union, intersect, except```).
## How was this patch tested?
I have added/move/renamed test in the catalyst `*ParserSuite`s.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13534 from hvanhovell/SPARK-15789.
## What changes were proposed in this pull request?
This PR makes sure the typed Filter doesn't change the Dataset schema.
**Before the change:**
```
scala> val df = spark.range(0,9)
scala> df.schema
res12: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false))
scala> val afterFilter = df.filter(_=>true)
scala> afterFilter.schema // !!! schema is CHANGED!!! Column name is changed from id to value, nullable is changed from false to true.
res13: org.apache.spark.sql.types.StructType = StructType(StructField(value,LongType,true))
```
SerializeFromObject and DeserializeToObject are inserted to wrap the Filter, and these two can possibly change the schema of Dataset.
**After the change:**
```
scala> afterFilter.schema // schema is NOT changed.
res47: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false))
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13529 from clockfly/spark-15632.
## What changes were proposed in this pull request?
This PR improves the error handling of `RowEncoder`. When we create a `RowEncoder` with a given schema, we should validate the data type of input object. e.g. we should throw an exception when a field is boolean but is declared as a string column.
This PR also removes the support to use `Product` as a valid external type of struct type. This support is added at https://github.com/apache/spark/pull/9712, but is incomplete, e.g. nested product, product in array are both not working. However, we never officially support this feature and I think it's ok to ban it.
## How was this patch tested?
new tests in `RowEncoderSuite`.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13401 from cloud-fan/bug.
## What changes were proposed in this pull request?
In forType function of object RandomDataGenerator, the code following:
if (maybeSqlTypeGenerator.isDefined){
....
Some(generator)
} else{
None
}
will be changed. Instead, maybeSqlTypeGenerator.map will be used.
## How was this patch tested?
All of the current unit tests passed.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#13448 from Sherry302/master.
## What changes were proposed in this pull request?
For input object of non-flat type, we can't encode it to row if it's null, as Spark SQL doesn't allow row to be null, only its columns can be null.
This PR explicitly add this constraint and throw exception if users break it.
## How was this patch tested?
several new tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13469 from cloud-fan/null-object.
## What changes were proposed in this pull request?
There are 2 kinds of `GetStructField`:
1. resolved from `UnresolvedExtractValue`, and it will have a `name` property.
2. created when we build deserializer expression for nested tuple, no `name` property.
When we want to validate the ordinals of nested tuple, we should only catch `GetStructField` without the name property.
## How was this patch tested?
new test in `EncoderResolutionSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13474 from cloud-fan/ordinal-check.
## What changes were proposed in this pull request?
Our encoder framework has been evolved a lot, this PR tries to clean up the code to make it more readable and emphasise the concept that encoder should be used as a container of serde expressions.
1. move validation logic to analyzer instead of encoder
2. only have a `resolveAndBind` method in encoder instead of `resolve` and `bind`, as we don't have the encoder life cycle concept anymore.
3. `Dataset` don't need to keep a resolved encoder, as there is no such concept anymore. bound encoder is still needed to do serialization outside of query framework.
4. Using `BoundReference` to represent an unresolved field in deserializer expression is kind of weird, this PR adds a `GetColumnByOrdinal` for this purpose. (serializer expression still use `BoundReference`, we can replace it with `GetColumnByOrdinal` in follow-ups)
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#13269 from cloud-fan/clean-encoder.
## What changes were proposed in this pull request?
This command didn't work for Hive tables. Now it does:
```
ALTER TABLE boxes PARTITION (width=3)
SET SERDE 'com.sparkbricks.serde.ColumnarSerDe'
WITH SERDEPROPERTIES ('compress'='true')
```
## How was this patch tested?
`HiveExternalCatalogSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#13453 from andrewor14/alter-partition-storage.
## What changes were proposed in this pull request?
This patch fixes a number of `com.esotericsoftware.kryo.KryoException: java.lang.NullPointerException` exceptions reported in [SPARK-15604], [SPARK-14752] etc. (while executing sparkSQL queries with the kryo serializer) by explicitly implementing `KryoSerialization` for `LazilyGenerateOrdering`.
## How was this patch tested?
1. Modified `OrderingSuite` so that all tests in the suite also test kryo serialization (for both interpreted and generated ordering).
2. Manually verified TPC-DS q1.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13466 from sameeragarwal/kryo.
## What changes were proposed in this pull request?
This PR add a rule at the end of analyzer to correct nullable fields of attributes in a logical plan by using nullable fields of the corresponding attributes in its children logical plans (these plans generate the input rows).
This is another approach for addressing SPARK-13484 (the first approach is https://github.com/apache/spark/pull/11371).
Close#113711
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#13290 from yhuai/SPARK-13484.
## What changes were proposed in this pull request?
This patch moves all user-facing structured streaming classes into sql.streaming. As part of this, I also added some since version annotation to methods and classes that don't have them.
## How was this patch tested?
Updated tests to reflect the moves.
Author: Reynold Xin <rxin@databricks.com>
Closes#13429 from rxin/SPARK-15686.
## What changes were proposed in this pull request?
Currently structured streaming only supports append output mode. This PR adds the following.
- Added support for Complete output mode in the internal state store, analyzer and planner.
- Added public API in Scala and Python for users to specify output mode
- Added checks for unsupported combinations of output mode and DF operations
- Plans with no aggregation should support only Append mode
- Plans with aggregation should support only Update and Complete modes
- Default output mode is Append mode (**Question: should we change this to automatically set to Complete mode when there is aggregation?**)
- Added support for Complete output mode in Memory Sink. So Memory Sink internally supports append and complete, update. But from public API only Complete and Append output modes are supported.
## How was this patch tested?
Unit tests in various test suites
- StreamingAggregationSuite: tests for complete mode
- MemorySinkSuite: tests for checking behavior in Append and Complete modes.
- UnsupportedOperationSuite: tests for checking unsupported combinations of DF ops and output modes
- DataFrameReaderWriterSuite: tests for checking that output mode cannot be called on static DFs
- Python doc test and existing unit tests modified to call write.outputMode.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13286 from tdas/complete-mode.
In this case, the result type of the expression becomes DECIMAL(38, 36) as we promote the individual string literals to DECIMAL(38, 18) when we handle string promotions for `BinaryArthmaticExpression`.
I think we need to cast the string literals to Double type instead. I looked at the history and found that this was changed to use decimal instead of double to avoid potential loss of precision when we cast decimal to double.
To double check i ran the query against hive, mysql. This query returns non NULL result for both the databases and both promote the expression to use double.
Here is the output.
- Hive
```SQL
hive> create table l2 as select (cast(99 as decimal(19,6)) + '2') from l1;
OK
hive> describe l2;
OK
_c0 double
```
- MySQL
```SQL
mysql> create table foo2 as select (cast(99 as decimal(19,6)) + '2') from test;
Query OK, 1 row affected (0.01 sec)
Records: 1 Duplicates: 0 Warnings: 0
mysql> describe foo2;
+-----------------------------------+--------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-----------------------------------+--------+------+-----+---------+-------+
| (cast(99 as decimal(19,6)) + '2') | double | NO | | 0 | |
+-----------------------------------+--------+------+-----+---------+-------+
```
## How was this patch tested?
Added a new test in SQLQuerySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#13368 from dilipbiswal/spark-15557.
## What changes were proposed in this pull request?
This change resolves a number of build warnings that have accumulated, before 2.x. It does not address a large number of deprecation warnings, especially related to the Accumulator API. That will happen separately.
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#13377 from srowen/BuildWarnings.
## What changes were proposed in this pull request?
This patch removes the last two commands defined in the catalyst module: DescribeFunction and ShowFunctions. They were unnecessary since the parser could just generate DescribeFunctionCommand and ShowFunctionsCommand directly.
## How was this patch tested?
Created a new SparkSqlParserSuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#13292 from rxin/SPARK-15436.
## What changes were proposed in this pull request?
This PR fixes 3 slow tests:
1. `ParquetQuerySuite.read/write wide table`: This is not a good unit test as it runs more than 5 minutes. This PR removes it and add a new regression test in `CodeGenerationSuite`, which is more "unit".
2. `ParquetQuerySuite.returning batch for wide table`: reduce the threshold and use smaller data size.
3. `DatasetSuite.SPARK-14554: Dataset.map may generate wrong java code for wide table`: Improve `CodeFormatter.format`(introduced at https://github.com/apache/spark/pull/12979) can dramatically speed this it up.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13273 from cloud-fan/test.
## What changes were proposed in this pull request?
in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0.
## How was this patch tested?
tested with modified `StringExpressionsSuite` and `StringFunctionsSuite`
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#13186 from adrian-wang/locate.
## What changes were proposed in this pull request?
When invalid date string like "2015-02-29 00:00:00" are cast as date or timestamp using spark sql, it used to not return null but another valid date (2015-03-01 in this case).
In this pr, invalid date string like "2016-02-29" and "2016-04-31" are returned as null when cast as date or timestamp.
## How was this patch tested?
Unit tests are added.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: wangyang <wangyang@haizhi.com>
Closes#13169 from wangyang1992/invalid_date.
## What changes were proposed in this pull request?
Incrementalizing plans of with multiple streaming aggregation is tricky and we dont have the necessary support for "delta" to implement correctly. So disabling the support for multiple streaming aggregations.
## How was this patch tested?
Additional unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13210 from tdas/SPARK-15428.
## What changes were proposed in this pull request?
This patch simplifies the implementation of Range operator and make the explain string consistent between logical plan and physical plan. To do this, I changed RangeExec to embed a Range logical plan in it.
Before this patch (note that the logical Range and physical Range actually output different information):
```
== Optimized Logical Plan ==
Range 0, 100, 2, 2, [id#8L]
== Physical Plan ==
*Range 0, 2, 2, 50, [id#8L]
```
After this patch:
If step size is 1:
```
== Optimized Logical Plan ==
Range(0, 100, splits=2)
== Physical Plan ==
*Range(0, 100, splits=2)
```
If step size is not 1:
```
== Optimized Logical Plan ==
Range (0, 100, step=2, splits=2)
== Physical Plan ==
*Range (0, 100, step=2, splits=2)
```
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13239 from rxin/SPARK-15459.
#### What changes were proposed in this pull request?
When there are duplicate keys in the partition specs or table properties, we always use the last value and ignore all the previous values. This is caused by the function call `toMap`.
partition specs or table properties are widely used in multiple DDL statements.
This PR is to detect the duplicates and issue an exception if found.
#### How was this patch tested?
Added test cases in DDLSuite
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13095 from gatorsmile/detectDuplicate.
## What changes were proposed in this pull request?
In only `catalyst` module, there exists 8 evaluation test cases on unresolved expressions. But, in real-world situation, those cases doesn't happen since they occurs exceptions before evaluations.
```scala
scala> sql("select format_number(null, 3)")
res0: org.apache.spark.sql.DataFrame = [format_number(CAST(NULL AS DOUBLE), 3): string]
scala> sql("select format_number(cast(null as NULL), 3)")
org.apache.spark.sql.catalyst.parser.ParseException:
DataType null() is not supported.(line 1, pos 34)
```
This PR makes those testcases more realistic.
```scala
- checkEvaluation(FormatNumber(Literal.create(null, NullType), Literal(3)), null)
+ assert(FormatNumber(Literal.create(null, NullType), Literal(3)).resolved === false)
```
Also, this PR also removes redundant `resolved` checking in `FoldablePropagation` optimizer.
## How was this patch tested?
Pass the modified Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13241 from dongjoon-hyun/SPARK-15462.
## What changes were proposed in this pull request?
Right now inferring the schema for case classes happens before searching the SQLUserDefinedType annotation, so the SQLUserDefinedType annotation for case classes doesn't work.
This PR simply changes the inferring order to resolve it. I also reenabled the java.math.BigDecimal test and added two tests for `List`.
## How was this patch tested?
`encodeDecodeTest(UDTCaseClass(new java.net.URI("http://spark.apache.org/")), "udt with case class")`
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12965 from zsxwing/SPARK-15190.
## What changes were proposed in this pull request?
This PR introduce place holder for comment in generated code and the purpose is same for #12939 but much safer.
Generated code to be compiled doesn't include actual comments but includes place holder instead.
Place holders in generated code will be replaced with actual comments only at the time of logging.
Also, this PR can resolve SPARK-15205.
## How was this patch tested?
Existing tests.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#12979 from sarutak/SPARK-15205.
## What changes were proposed in this pull request?
`CreateNamedStruct` and `CreateNamedStructUnsafe` should preserve metadata of value expressions if it is `NamedExpression` like `CreateStruct` or `CreateStructUnsafe` are doing.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13193 from ueshin/issues/SPARK-15400.
## What changes were proposed in this pull request?
The following code:
```
val ds = Seq(("a", 1), ("b", 2), ("c", 3)).toDS()
ds.filter(_._1 == "b").select(expr("_1").as[String]).foreach(println(_))
```
throws an Exception:
```
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: _1#420
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
...
Cause: java.lang.RuntimeException: Couldn't find _1#420 in [_1#416,_2#417]
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
...
```
This is because `EmbedSerializerInFilter` rule drops the `exprId`s of output of surrounded `SerializeFromObject`.
The analyzed and optimized plans of the above example are as follows:
```
== Analyzed Logical Plan ==
_1: string
Project [_1#420]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, scala.Tuple2]._1, true) AS _1#420,input[0, scala.Tuple2]._2 AS _2#421]
+- Filter <function1>.apply
+- DeserializeToObject newInstance(class scala.Tuple2), obj#419: scala.Tuple2
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
== Optimized Logical Plan ==
!Project [_1#420]
+- Filter <function1>.apply
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
```
This PR fixes `EmbedSerializerInFilter` rule to keep `exprId`s of output of surrounded `SerializeFromObject`.
The plans after this patch are as follows:
```
== Analyzed Logical Plan ==
_1: string
Project [_1#420]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, scala.Tuple2]._1, true) AS _1#420,input[0, scala.Tuple2]._2 AS _2#421]
+- Filter <function1>.apply
+- DeserializeToObject newInstance(class scala.Tuple2), obj#419: scala.Tuple2
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
== Optimized Logical Plan ==
Project [_1#416]
+- Filter <function1>.apply
+- LocalRelation [_1#416,_2#417], [[0,1800000001,1,61],[0,1800000001,2,62],[0,1800000001,3,63]]
```
## How was this patch tested?
Existing tests and I added a test to check if `filter and then select` works.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13096 from ueshin/issues/SPARK-15313.
## What changes were proposed in this pull request?
This patch fixes a bug in TypeUtils.checkForSameTypeInputExpr. Previously the code was testing on strict equality, which does not taking nullability into account.
This is based on https://github.com/apache/spark/pull/12768. This patch fixed a bug there (with empty expression) and added a test case.
## How was this patch tested?
Added a new test suite and test case.
Closes#12768.
Author: Reynold Xin <rxin@databricks.com>
Author: Oleg Danilov <oleg.danilov@wandisco.com>
Closes#13208 from rxin/SPARK-14990.
Hello : Can you help check this PR? I am adding support for the java.math.BigInteger for java bean code path. I saw internally spark is converting the BigInteger to BigDecimal in ColumnType.scala and CatalystRowConverter.scala. I use the similar way and convert the BigInteger to the BigDecimal. .
Author: Kevin Yu <qyu@us.ibm.com>
Closes#10125 from kevinyu98/working_on_spark-11827.
## What changes were proposed in this pull request?
Fix `MapObjects.itemAccessorMethod` to handle `TimestampType`. Without this fix, `Array[Timestamp]` cannot be properly encoded or decoded. To reproduce this, in `ExpressionEncoderSuite`, if you add the following test case:
`encodeDecodeTest(Array(Timestamp.valueOf("2016-01-29 10:00:00")), "array of timestamp")
`
... you will see that (without this fix) it fails with the following output:
```
- encode/decode for array of timestamp: [Ljava.sql.Timestamp;fd9ebde *** FAILED ***
Exception thrown while decoding
Converted: [0,1000000010,800000001,52a7ccdc36800]
Schema: value#61615
root
-- value: array (nullable = true)
|-- element: timestamp (containsNull = true)
Encoder:
class[value[0]: array<timestamp>] (ExpressionEncoderSuite.scala:312)
```
## How was this patch tested?
Existing tests
Author: Sumedh Mungee <smungee@gmail.com>
Closes#13108 from smungee/fix-itemAccessorMethod.
## What changes were proposed in this pull request?
This PR aims to add new **FoldablePropagation** optimizer that propagates foldable expressions by replacing all attributes with the aliases of original foldable expression. Other optimizations will take advantage of the propagated foldable expressions: e.g. `EliminateSorts` optimizer now can handle the following Case 2 and 3. (Case 1 is the previous implementation.)
1. Literals and foldable expression, e.g. "ORDER BY 1.0, 'abc', Now()"
2. Foldable ordinals, e.g. "SELECT 1.0, 'abc', Now() ORDER BY 1, 2, 3"
3. Foldable aliases, e.g. "SELECT 1.0 x, 'abc' y, Now() z ORDER BY x, y, z"
This PR has been generalized based on cloud-fan 's key ideas many times; he should be credited for the work he did.
**Before**
```
scala> sql("SELECT 1.0, Now() x ORDER BY 1, x").explain
== Physical Plan ==
WholeStageCodegen
: +- Sort [1.0#5 ASC,x#0 ASC], true, 0
: +- INPUT
+- Exchange rangepartitioning(1.0#5 ASC, x#0 ASC, 200), None
+- WholeStageCodegen
: +- Project [1.0 AS 1.0#5,1461873043577000 AS x#0]
: +- INPUT
+- Scan OneRowRelation[]
```
**After**
```
scala> sql("SELECT 1.0, Now() x ORDER BY 1, x").explain
== Physical Plan ==
WholeStageCodegen
: +- Project [1.0 AS 1.0#5,1461873079484000 AS x#0]
: +- INPUT
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests including a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12719 from dongjoon-hyun/SPARK-14939.
## What changes were proposed in this pull request?
toCommentSafeString method replaces "\u" with "\\\\u" to avoid codegen breaking.
But if the even number of "\" is put before "u", like "\\\\u", in the string literal in the query, codegen can break.
Following code causes compilation error.
```
val df = Seq(...).toDF
df.select("'\\\\\\\\u002A/'").show
```
The reason of the compilation error is because "\\\\\\\\\\\\\\\\u002A/" is translated into "*/" (the end of comment).
Due to this unsafety, arbitrary code can be injected like as follows.
```
val df = Seq(...).toDF
// Inject "System.exit(1)"
df.select("'\\\\\\\\u002A/{System.exit(1);}/*'").show
```
## How was this patch tested?
Added new test cases.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Author: sarutak <sarutak@oss.nttdata.co.jp>
Closes#12939 from sarutak/SPARK-15165.
## What changes were proposed in this pull request?
This PR improves `RowEncoder` and `MapObjects`, to support array as the external type for `ArrayType`. The idea is straightforward, we use `Object` as the external input type for `ArrayType`, and determine its type at runtime in `MapObjects`.
## How was this patch tested?
new test in `RowEncoderSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13138 from cloud-fan/map-object.
## What changes were proposed in this pull request?
We originally designed the type coercion rules to match Hive, but over time we have diverged. It does not make sense to call it HiveTypeCoercion anymore. This patch renames it TypeCoercion.
## How was this patch tested?
Updated unit tests to reflect the rename.
Author: Reynold Xin <rxin@databricks.com>
Closes#13091 from rxin/SPARK-15310.
## What changes were proposed in this pull request?
This patch moves all the object related expressions into expressions.objects package, for better code organization.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13085 from rxin/SPARK-15306.
#### What changes were proposed in this pull request?
~~Currently, multiple partitions are allowed to drop by using a single DDL command: Alter Table Drop Partition. However, the internal implementation could break atomicity. That means, we could just drop a subset of qualified partitions, if hitting an exception when dropping one of qualified partitions~~
~~This PR contains the following behavior changes:~~
~~- disallow dropping multiple partitions by a single command ~~
~~- allow users to input predicates in partition specification and issue a nicer error message if the predicate's comparison operator is not `=`.~~
~~- verify the partition spec in SessionCatalog. This can ensure each partition spec in `Drop Partition` does not correspond to multiple partitions.~~
This PR has two major parts:
- Verify the partition spec in SessionCatalog for fixing the following issue:
```scala
sql(s"ALTER TABLE $externalTab DROP PARTITION (ds='2008-04-09', unknownCol='12')")
```
Above example uses an invalid partition spec. Without this PR, we will drop all the partitions. The reason is Hive megastores getPartitions API returns all the partitions if we provide an invalid spec.
- Re-implemented the `dropPartitions` in `HiveClientImpl`. Now, we always check if all the user-specified partition specs exist before attempting to drop the partitions. Previously, we start drop the partition before completing checking the existence of all the partition specs. If any failure happened after we start to drop the partitions, we will log an error message to indicate which partitions have been dropped and which partitions have not been dropped.
#### How was this patch tested?
Modified the existing test cases and added new test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12801 from gatorsmile/banDropMultiPart.
## What changes were proposed in this pull request?
Deprecates registerTempTable and add dataset.createTempView, dataset.createOrReplaceTempView.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#12945 from clockfly/spark-15171.
## What changes were proposed in this pull request?
SPARK-15241: We now support java decimal and catalyst decimal in external row, it makes sense to also support scala decimal.
SPARK-15242: This is a long-standing bug, and is exposed after https://github.com/apache/spark/pull/12364, which eliminate the `If` expression if the field is not nullable:
```
val fieldValue = serializerFor(
GetExternalRowField(inputObject, i, externalDataTypeForInput(f.dataType)),
f.dataType)
if (f.nullable) {
If(
Invoke(inputObject, "isNullAt", BooleanType, Literal(i) :: Nil),
Literal.create(null, f.dataType),
fieldValue)
} else {
fieldValue
}
```
Previously, we always use `DecimalType.SYSTEM_DEFAULT` as the output type of converted decimal field, which is wrong as it doesn't match the real decimal type. However, it works well because we always put converted field into `If` expression to do the null check, and `If` use its `trueValue`'s data type as its output type.
Now if we have a not nullable decimal field, then the converted field's output type will be `DecimalType.SYSTEM_DEFAULT`, and we will write wrong data into unsafe row.
The fix is simple, just use the given decimal type as the output type of converted decimal field.
These 2 issues was found at https://github.com/apache/spark/pull/13008
## How was this patch tested?
new tests in RowEncoderSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13019 from cloud-fan/encoder-decimal.
Since we cannot really trust if the underlying external catalog can throw exceptions when there is an invalid metadata operation, let's do it in SessionCatalog.
- [X] The first step is to unify the error messages issued in Hive-specific Session Catalog and general Session Catalog.
- [X] The second step is to verify the inputs of metadata operations for partitioning-related operations. This is moved to a separate PR: https://github.com/apache/spark/pull/12801
- [X] The third step is to add database existence verification in `SessionCatalog`
- [X] The fourth step is to add table existence verification in `SessionCatalog`
- [X] The fifth step is to add function existence verification in `SessionCatalog`
Add test cases and verify the error messages we issued
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12385 from gatorsmile/verifySessionAPIs.
#### What changes were proposed in this pull request?
This PR is to address a few existing issues in `EXPLAIN`:
- The `EXPLAIN` options `LOGICAL | FORMATTED | EXTENDED | CODEGEN` should not be 0 or more match. It should 0 or one match. Parser does not allow users to use more than one option in a single command.
- The option `LOGICAL` is not supported. Issue an exception when users specify this option in the command.
- The output of `EXPLAIN ` contains a weird empty line when the output of analyzed plan is empty. We should remove it. For example:
```
== Parsed Logical Plan ==
CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io. HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false
== Analyzed Logical Plan ==
CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io. HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false
== Optimized Logical Plan ==
CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io. HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false
...
```
#### How was this patch tested?
Added and modified a few test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12991 from gatorsmile/explainCreateTable.
## What changes were proposed in this pull request?
following operations have file system operation now:
1. CREATE DATABASE: create a dir
2. DROP DATABASE: delete the dir
3. CREATE TABLE: create a dir
4. DROP TABLE: delete the dir
5. RENAME TABLE: rename the dir
6. CREATE PARTITIONS: create a dir
7. RENAME PARTITIONS: rename the dir
8. DROP PARTITIONS: drop the dir
## How was this patch tested?
new tests in `ExternalCatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12871 from cloud-fan/catalog.
#### What changes were proposed in this pull request?
So far, in the implementation of InMemoryCatalog, we do not check if the new/destination table/function/partition exists or not. Thus, we just silently remove the existent table/function/partition.
This PR is to detect them and issue an appropriate exception.
#### How was this patch tested?
Added the related test cases. They also verify if HiveExternalCatalog also detects these errors.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12960 from gatorsmile/renameInMemoryCatalog.
#### What changes were proposed in this pull request?
When Describe a UDTF, the command returns a wrong result. The command is unable to find the function, which has been created and cataloged in the catalog but not in the functionRegistry.
This PR is to correct it. If the function is not in the functionRegistry, we will check the catalog for collecting the information of the UDTF function.
#### How was this patch tested?
Added test cases to verify the results
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12885 from gatorsmile/showFunction.
## What changes were proposed in this pull request?
The problem is: In `RowEncoder`, we use `Invoke` to get the field of an external row, which lose the nullability information. This PR creates a `GetExternalRowField` expression, so that we can preserve the nullability info.
TODO: simplify the null handling logic in `RowEncoder`, to remove so many if branches, in follow-up PR.
## How was this patch tested?
new tests in `RowEncoderSuite`
Note that, This PR takes over https://github.com/apache/spark/pull/11980, with a little simplification, so all credits should go to koertkuipers
Author: Wenchen Fan <wenchen@databricks.com>
Author: Koert Kuipers <koert@tresata.com>
Closes#12364 from cloud-fan/nullable.
## What changes were proposed in this pull request?
This PR improve the error message for `Generate` in 3 cases:
1. generator is nested in expressions, e.g. `SELECT explode(list) + 1 FROM tbl`
2. generator appears more than one time in SELECT, e.g. `SELECT explode(list), explode(list) FROM tbl`
3. generator appears in other operator which is not project, e.g. `SELECT * FROM tbl SORT BY explode(list)`
## How was this patch tested?
new tests in `AnalysisErrorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12810 from cloud-fan/bug.
## What changes were proposed in this pull request?
Just a bunch of small tweaks on DDL exception messages.
## How was this patch tested?
`DDLCommandSuite` et al.
Author: Andrew Or <andrew@databricks.com>
Closes#12853 from andrewor14/make-exceptions-consistent.
#### What changes were proposed in this pull request?
Compared with the current Spark parser, there are two extra syntax are supported in Hive for sampling
- In `On` clauses, `rand()` is used for indicating sampling on the entire row instead of an individual column. For example,
```SQL
SELECT * FROM source TABLESAMPLE(BUCKET 3 OUT OF 32 ON rand()) s;
```
- Users can specify the total length to be read. For example,
```SQL
SELECT * FROM source TABLESAMPLE(100M) s;
```
Below is the link for references:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Sampling
This PR is to parse and capture these two extra syntax, and issue a better error message.
#### How was this patch tested?
Added test cases to verify the thrown exceptions
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12838 from gatorsmile/bucketOnRand.
## What changes were proposed in this pull request?
Make serializer correctly inferred if the input type is `List[_]`, since `List[_]` is type of `Seq[_]`, before it was matched to different case (`case t if definedByConstructorParams(t)`).
## How was this patch tested?
New test case was added.
Author: bomeng <bmeng@us.ibm.com>
Closes#12849 from bomeng/SPARK-15062.
## What changes were proposed in this pull request?
This PR addresses a few minor issues in SQL parser:
- Removes some unused rules and keywords in the grammar.
- Removes code path for fallback SQL parsing (was needed for Hive native parsing).
- Use `UnresolvedGenerator` instead of hard-coding `Explode` & `JsonTuple`.
- Adds a more generic way of creating error messages for unsupported Hive features.
- Use `visitFunctionName` as much as possible.
- Interpret a `CatalogColumn`'s `DataType` directly instead of parsing it again.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12826 from hvanhovell/SPARK-15047.
## What changes were proposed in this pull request?
In this PR we add support for correlated scalar subqueries. An example of such a query is:
```SQL
select * from tbl1 a where a.value > (select max(value) from tbl2 b where b.key = a.key)
```
The implementation adds the `RewriteCorrelatedScalarSubquery` rule to the Optimizer. This rule plans these subqueries using `LEFT OUTER` joins. It currently supports rewrites for `Project`, `Aggregate` & `Filter` logical plans.
I could not find a well defined semantics for the use of scalar subqueries in an `Aggregate`. The current implementation currently evaluates the scalar subquery *before* aggregation. This means that you either have to make scalar subquery part of the grouping expression, or that you have to aggregate it further on. I am open to suggestions on this.
The implementation currently forces the uniqueness of a scalar subquery by enforcing that it is aggregated and that the resulting column is wrapped in an `AggregateExpression`.
## How was this patch tested?
Added tests to `SubquerySuite`.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12822 from hvanhovell/SPARK-14785.
## What changes were proposed in this pull request?
In order to support nested predicate subquery, this PR introduce an internal join type ExistenceJoin, which will emit all the rows from left, plus an additional column, which presents there are any rows matched from right or not (it's not null-aware right now). This additional column could be used to replace the subquery in Filter.
In theory, all the predicate subquery could use this join type, but it's slower than LeftSemi and LeftAnti, so it's only used for nested subquery (subquery inside OR).
For example, the following SQL:
```sql
SELECT a FROM t WHERE EXISTS (select 0) OR EXISTS (select 1)
```
This PR also fix a bug in predicate subquery push down through join (they should not).
Nested null-aware subquery is still not supported. For example, `a > 3 OR b NOT IN (select bb from t)`
After this, we could run TPCDS query Q10, Q35, Q45
## How was this patch tested?
Added unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#12820 from davies/or_exists.
## What changes were proposed in this pull request?
This PR adds `fromPrimitiveArray` and `toPrimitiveArray` in `UnsafeArrayData`, so that we can do the conversion much faster in VectorUDT/MatrixUDT.
## How was this patch tested?
existing tests and new test suite `UnsafeArraySuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12640 from cloud-fan/ml.
## What changes were proposed in this pull request?
CatalystSqlParser can parse data types. So, we do not need to have an individual DataTypeParser.
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12796 from yhuai/removeDataTypeParser.
## What changes were proposed in this pull request?
This patch fixes a null handling bug in EqualNullSafe's code generation.
## How was this patch tested?
Updated unit test so they would fail without the fix.
Closes#12628.
Author: Reynold Xin <rxin@databricks.com>
Author: Arash Nabili <arash@levyx.com>
Closes#12799 from rxin/equalnullsafe.
The previous subquery PRs did not include support for pushing subqueries used in filters (`WHERE`/`HAVING`) down. This PR adds this support. For example :
```scala
range(0, 10).registerTempTable("a")
range(5, 15).registerTempTable("b")
range(7, 25).registerTempTable("c")
range(3, 12).registerTempTable("d")
val plan = sql("select * from a join b on a.id = b.id left join c on c.id = b.id where a.id in (select id from d)")
plan.explain(true)
```
Leads to the following Analyzed & Optimized plans:
```
== Parsed Logical Plan ==
...
== Analyzed Logical Plan ==
id: bigint, id: bigint, id: bigint
Project [id#0L,id#4L,id#8L]
+- Filter predicate-subquery#16 [(id#0L = id#12L)]
: +- SubqueryAlias predicate-subquery#16 [(id#0L = id#12L)]
: +- Project [id#12L]
: +- SubqueryAlias d
: +- Range 3, 12, 1, 8, [id#12L]
+- Join LeftOuter, Some((id#8L = id#4L))
:- Join Inner, Some((id#0L = id#4L))
: :- SubqueryAlias a
: : +- Range 0, 10, 1, 8, [id#0L]
: +- SubqueryAlias b
: +- Range 5, 15, 1, 8, [id#4L]
+- SubqueryAlias c
+- Range 7, 25, 1, 8, [id#8L]
== Optimized Logical Plan ==
Join LeftOuter, Some((id#8L = id#4L))
:- Join Inner, Some((id#0L = id#4L))
: :- Join LeftSemi, Some((id#0L = id#12L))
: : :- Range 0, 10, 1, 8, [id#0L]
: : +- Range 3, 12, 1, 8, [id#12L]
: +- Range 5, 15, 1, 8, [id#4L]
+- Range 7, 25, 1, 8, [id#8L]
== Physical Plan ==
...
```
I have also taken the opportunity to move quite a bit of code around:
- Rewriting subqueris and pulling out correlated predicated from subqueries has been moved into the analyzer. The analyzer transforms `Exists` and `InSubQuery` into `PredicateSubquery` expressions. A PredicateSubquery exposes the 'join' expressions and the proper references. This makes things like type coercion, optimization and planning easier to do.
- I have added support for `Aggregate` plans in subqueries. Any correlated expressions will be added to the grouping expressions. I have removed support for `Union` plans, since pulling in an outer reference from beneath a Union has no value (a filtered value could easily be part of another Union child).
- Resolution of subqueries is now done using `OuterReference`s. These are used to wrap any outer reference; this makes the identification of these references easier, and also makes dealing with duplicate attributes in the outer and inner plans easier. The resolution of subqueries initially used a resolution loop which would alternate between calling the analyzer and trying to resolve the outer references. We now use a dedicated analyzer which uses a special rule for outer reference resolution.
These changes are a stepping stone for enabling correlated scalar subqueries, enabling all Hive tests & allowing us to use predicate subqueries anywhere.
Current tests and added test cases in FilterPushdownSuite.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12720 from hvanhovell/SPARK-14858.
#### What changes were proposed in this pull request?
Replaces a logical `Except` operator with a `Left-anti Join` operator. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins).
```SQL
SELECT a1, a2 FROM Tab1 EXCEPT SELECT b1, b2 FROM Tab2
==> SELECT DISTINCT a1, a2 FROM Tab1 LEFT ANTI JOIN Tab2 ON a1<=>b1 AND a2<=>b2
```
Note:
1. This rule is only applicable to EXCEPT DISTINCT. Do not use it for EXCEPT ALL.
2. This rule has to be done after de-duplicating the attributes; otherwise, the enerated
join conditions will be incorrect.
This PR also corrects the existing behavior in Spark. Before this PR, the behavior is like
```SQL
test("except") {
val df_left = Seq(1, 2, 2, 3, 3, 4).toDF("id")
val df_right = Seq(1, 3).toDF("id")
checkAnswer(
df_left.except(df_right),
Row(2) :: Row(2) :: Row(4) :: Nil
)
}
```
After this PR, the result is corrected. We strictly follow the SQL compliance of `Except Distinct`.
#### How was this patch tested?
Modified and added a few test cases to verify the optimization rule and the results of operators.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12736 from gatorsmile/exceptByAntiJoin.
## What changes were proposed in this pull request?
This PR aims to implement decimal aggregation optimization for window queries by improving existing `DecimalAggregates`. Historically, `DecimalAggregates` optimizer is designed to transform general `sum/avg(decimal)`, but it breaks recently added windows queries like the followings. The following queries work well without the current `DecimalAggregates` optimizer.
**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: MakeDecimal((sum(UnscaledValue(a#31)),mode=Complete,isDistinct=false),12,1)
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
: +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#21)),mode=Complete,isDistinct=false),12,1) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#21]
+- Scan OneRowRelation[]
```
**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: cast(((avg(UnscaledValue(a#40)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5))
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
: +- INPUT
+- Window [cast(((avg(UnscaledValue(a#42)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#42]
+- Scan OneRowRelation[]
```
After this PR, those queries work fine and new optimized physical plans look like the followings.
**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
: +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#33)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING),12,1) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#33]
+- Scan OneRowRelation[]
```
**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
: +- INPUT
+- Window [cast(((avg(UnscaledValue(a#45)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) / 10.0) as decimal(6,5)) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#45]
+- Scan OneRowRelation[]
```
In this PR, *SUM over window* pattern matching is based on the code of hvanhovell ; he should be credited for the work he did.
## How was this patch tested?
Pass the Jenkins tests (with newly added testcases)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12421 from dongjoon-hyun/SPARK-14664.
## What changes were proposed in this pull request?
#12625 exposed a new user-facing conf interface in `SparkSession`. This patch adds a catalog interface.
## How was this patch tested?
See `CatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#12713 from andrewor14/user-facing-catalog.
## What changes were proposed in this pull request?
This patch changes UnresolvedFunction and UnresolvedGenerator to use a FunctionIdentifier rather than just a String for function name. Also changed SessionCatalog to accept FunctionIdentifier in lookupFunction.
## How was this patch tested?
Updated related unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12659 from rxin/SPARK-14888.
#### What changes were proposed in this pull request?
For performance, predicates can be pushed through Window if and only if the following conditions are satisfied:
1. All the expressions are part of window partitioning key. The expressions can be compound.
2. Deterministic
#### How was this patch tested?
TODO:
- [X] DSL needs to be modified for window
- [X] more tests will be added.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11635 from gatorsmile/pushPredicateThroughWindow.
!< means not less than which is equivalent to >=
!> means not greater than which is equivalent to <=
I'd to create a PR to support these two operators.
I've added new test cases in: DataFrameSuite, ExpressionParserSuite, JDBCSuite, PlanParserSuite, SQLQuerySuite
dilipbiswal viirya gatorsmile
Author: jliwork <jiali@us.ibm.com>
Closes#12316 from jliwork/SPARK-14548.
## What changes were proposed in this pull request?
Currently, `OptimizeIn` optimizer replaces `In` expression into `InSet` expression if the size of set is greater than a constant, 10.
This issue aims to make a configuration `spark.sql.optimizer.inSetConversionThreshold` for that.
After this PR, `OptimizerIn` is configurable.
```scala
scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [a#7 IN (1,2,3) AS (a IN (1, 2, 3))#8]
: +- INPUT
+- Generate explode([1,2]), false, false, [a#7]
+- Scan OneRowRelation[]
scala> sqlContext.setConf("spark.sql.optimizer.inSetConversionThreshold", "2")
scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [a#16 INSET (1,2,3) AS (a IN (1, 2, 3))#17]
: +- INPUT
+- Generate explode([1,2]), false, false, [a#16]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests (with a new testcase)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12562 from dongjoon-hyun/SPARK-14796.
## What changes were proposed in this pull request?
Currently, a column could be resolved wrongly if there are columns from both outer table and subquery have the same name, we should only resolve the attributes that can't be resolved within subquery. They may have same exprId than other attributes in subquery, so we should create alias for them.
Also, the column in IN subquery could have same exprId, we should create alias for them.
## How was this patch tested?
Added regression tests. Manually tests TPCDS Q70 and Q95, work well after this patch.
Author: Davies Liu <davies@databricks.com>
Closes#12539 from davies/fix_subquery.
### What changes were proposed in this pull request?
TPCDS Q90 fails to parse because it uses a reserved keyword as an Identifier; `AT` was used as an alias for one of the subqueries. `AT` is not a reserved keyword and should have been registerd as a in the `nonReserved` rule.
In order to prevent this from happening again I have added tests for all keywords that are non-reserved in Hive. See the `nonReserved`, `sql11ReservedKeywordsUsedAsCastFunctionName` & `sql11ReservedKeywordsUsedAsIdentifier` rules in https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/parse/IdentifiersParser.g.
### How was this patch tested?
Added tests to for all Hive non reserved keywords to `TableIdentifierParserSuite`.
cc davies
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12537 from hvanhovell/SPARK-14762.
## What changes were proposed in this pull request?
Implement some `hashCode` and `equals` together in order to enable the scalastyle.
This is a first batch, I will continue to implement them but I wanted to know your thoughts.
Author: Joan <joan@goyeau.com>
Closes#12157 from joan38/SPARK-6429-HashCode-Equals.
## What changes were proposed in this pull request?
Code generation for complex type, `CreateArray`, `CreateMap`, `CreateStruct`, `CreateNamedStruct`, exceeds JVM size limit for large elements.
We should split generated code into multiple `apply` functions if the complex types have large elements, like `UnsafeProjection` or others for large expressions.
## How was this patch tested?
I added some tests to check if the generated codes for the expressions exceed or not.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#12559 from ueshin/issues/SPARK-14793.
## What changes were proposed in this pull request?
This patch moves analyze table parsing into SparkSqlAstBuilder and removes HiveSqlAstBuilder.
In order to avoid extensive refactoring, I created a common trait for CatalogRelation and MetastoreRelation, and match on that. In the future we should probably just consolidate the two into a single thing so we don't need this common trait.
## How was this patch tested?
Updated unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12584 from rxin/SPARK-14821.
`MutableProjection` is not thread-safe and we won't use it in multiple threads. I think the reason that we return `() => MutableProjection` is not about thread safety, but to save the costs of generating code when we need same but individual mutable projections.
However, I only found one place that use this [feature](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala#L122-L123), and comparing to the troubles it brings, I think we should generate `MutableProjection` directly instead of return a function.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#7373 from cloud-fan/project.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14600
This PR makes `Expand.output` have different attributes from the grouping attributes produced by the underlying `Project`, as they have different meaning, so that we can safely push down filter through `Expand`
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12496 from cloud-fan/expand.
## What changes were proposed in this pull request?
Enable ScalaReflection and User Defined Types for plain Scala classes.
This involves the move of `schemaFor` from `ScalaReflection` trait (which is Runtime and Compile time (macros) reflection) to the `ScalaReflection` object (runtime reflection only) as I believe this code wouldn't work at compile time anyway as it manipulates `Class`'s that are not compiled yet.
## How was this patch tested?
Unit test
Author: Joan <joan@goyeau.com>
Closes#12149 from joan38/SPARK-13929-Scala-reflection.
### What changes were proposed in this pull request?
This PR adds support for in/exists predicate subqueries to Spark. Predicate sub-queries are used as a filtering condition in a query (this is the only supported use case). A predicate sub-query comes in two forms:
- `[NOT] EXISTS(subquery)`
- `[NOT] IN (subquery)`
This PR is (loosely) based on the work of davies (https://github.com/apache/spark/pull/10706) and chenghao-intel (https://github.com/apache/spark/pull/9055). They should be credited for the work they did.
### How was this patch tested?
Modified parsing unit tests.
Added tests to `org.apache.spark.sql.SQLQuerySuite`
cc rxin, davies & chenghao-intel
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12306 from hvanhovell/SPARK-4226.
When `Await.result` throws an exception which originated from a different thread, the resulting stacktrace doesn't include the path leading to the `Await.result` call itself, making it difficult to identify the impact of these exceptions. For example, I've seen cases where broadcast cleaning errors propagate to the main thread and crash it but the resulting stacktrace doesn't include any of the main thread's code, making it difficult to pinpoint which exception crashed that thread.
This patch addresses this issue by explicitly catching, wrapping, and re-throwing exceptions that are thrown by `Await.result`.
I tested this manually using 16b31c8251, a patch which reproduces an issue where an RPC exception which occurs while unpersisting RDDs manages to crash the main thread without any useful stacktrace, and verified that informative, full stacktraces were generated after applying the fix in this PR.
/cc rxin nongli yhuai anabranch
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12433 from JoshRosen/wrap-and-rethrow-await-exceptions.
## What changes were proposed in this pull request?
This PR tries to separate the serialization and deserialization logic from object operators, so that it's easier to eliminate unnecessary serializations in optimizer.
Typed aggregate related operators are special, they will deserialize the input row to multiple objects and it's difficult to simply use a deserializer operator to abstract it, so we still mix the deserialization logic there.
## How was this patch tested?
existing tests and new test in `EliminateSerializationSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12260 from cloud-fan/encoder.
## What changes were proposed in this pull request?
We currently disable codegen for `CaseWhen` if the number of branches is greater than 20 (in CaseWhen.MAX_NUM_CASES_FOR_CODEGEN). It would be better if this value is a non-public config defined in SQLConf.
## How was this patch tested?
Pass the Jenkins tests (including a new testcase `Support spark.sql.codegen.maxCaseBranches option`)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12353 from dongjoon-hyun/SPARK-14577.
## What changes were proposed in this pull request?
The `doGenCode` method currently takes in an `ExprCode`, mutates it and returns the java code to evaluate the given expression. It should instead just return a new `ExprCode` to avoid passing around mutable objects during code generation.
## How was this patch tested?
Existing Tests
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12483 from sameeragarwal/new-exprcode-2.
## What changes were proposed in this pull request?
Per rxin's suggestions, this patch renames `s/gen/genCode` and `s/genCode/doGenCode` to better reflect the semantics of these 2 function calls.
## How was this patch tested?
N/A (refactoring only)
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12475 from sameeragarwal/gencode.
## What changes were proposed in this pull request?
Currently, `HiveTypeCoercion.IfCoercion` removes all predicates whose return-type are null. However, some UDFs need evaluations because they are designed to throw exceptions. This PR fixes that to preserve the predicates. Also, `assert_true` is implemented as Spark SQL function.
**Before**
```
scala> sql("select if(assert_true(false),2,3)").head
res2: org.apache.spark.sql.Row = [3]
```
**After**
```
scala> sql("select if(assert_true(false),2,3)").head
... ASSERT_TRUE ...
```
**Hive**
```
hive> select if(assert_true(false),2,3);
OK
Failed with exception java.io.IOException:org.apache.hadoop.hive.ql.metadata.HiveException: ASSERT_TRUE(): assertion failed.
```
## How was this patch tested?
Pass the Jenkins tests (including a new testcase in `HivePlanTest`)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12340 from dongjoon-hyun/SPARK-14580.
## What changes were proposed in this pull request?
There are many operations that are currently not supported in the streaming execution. For example:
- joining two streams
- unioning a stream and a batch source
- sorting
- window functions (not time windows)
- distinct aggregates
Furthermore, executing a query with a stream source as a batch query should also fail.
This patch add an additional step after analysis in the QueryExecution which will check that all the operations in the analyzed logical plan is supported or not.
## How was this patch tested?
unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12246 from tdas/SPARK-14473.
## What changes were proposed in this pull request?
This PR aims to add `bound` function (aka Banker's round) by extending current `round` implementation. [Hive supports `bround` since 1.3.0.](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF)
**Hive (1.3 ~ 2.0)**
```
hive> select round(2.5), bround(2.5);
OK
3.0 2.0
```
**After this PR**
```scala
scala> sql("select round(2.5), bround(2.5)").head
res0: org.apache.spark.sql.Row = [3,2]
```
## How was this patch tested?
Pass the Jenkins tests (with extended tests).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12376 from dongjoon-hyun/SPARK-14614.
## What changes were proposed in this pull request?
We currently hard code the max number of optimizer/analyzer iterations to 100. This patch makes it configurable. While I'm at it, I also added the SessionCatalog to the optimizer, so we can use information there in optimization.
## How was this patch tested?
Updated unit tests to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12434 from rxin/SPARK-14677.
## What changes were proposed in this pull request?
This PR moves `CurrentDatabase` from sql/hive package to sql/catalyst. It also adds the function description, which looks like the following.
```
scala> sqlContext.sql("describe function extended current_database").collect.foreach(println)
[Function: current_database]
[Class: org.apache.spark.sql.execution.command.CurrentDatabase]
[Usage: current_database() - Returns the current database.]
[Extended Usage:
> SELECT current_database()]
```
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12424 from yhuai/SPARK-14668.
