### What changes were proposed in this pull request?
`LIMIT ALL` is the same as omitting the `LIMIT` clause. It is supported by both PrestgreSQL and Presto. This PR is to support it by adding it in the parser.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17960 from gatorsmile/LimitAll.
## What changes were proposed in this pull request?
This pr added code to support `||` for string concatenation. This string operation is supported in PostgreSQL and MySQL.
## How was this patch tested?
Added tests in `SparkSqlParserSuite`
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17711 from maropu/SPARK-19951.
## What changes were proposed in this pull request?
This pr added `Analyzer` code for supporting aliases in CUBE/ROLLUP/GROUPING SETS (This is follow-up of #17191).
## How was this patch tested?
Added tests in `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17948 from maropu/SPARK-20710.
## What changes were proposed in this pull request?
Fix canonicalization for different filter orders in `HiveTableScanExec`.
## How was this patch tested?
Added a new test case.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17962 from wzhfy/canonicalizeHiveTableScanExec.
## What changes were proposed in this pull request?
This method gets a type's primary constructor and fills in type parameters with concrete types. For example, `MapPartitions[T, U] -> MapPartitions[Int, String]`. This Substitution fails when the actual type args are empty because they are still unknown. Instead, when there are no resolved types to subsitute, this returns the original args with unresolved type parameters.
## How was this patch tested?
This doesn't affect substitutions where the type args are determined. This fixes our case where the actual type args are empty and our job runs successfully.
Author: Ryan Blue <blue@apache.org>
Closes#15062 from rdblue/SPARK-17424-fix-unsound-reflect-substitution.
## 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?
`RuntimeReplaceable` always has a constructor with the expression to replace with, and this constructor should not be the function builder.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17876 from cloud-fan/minor.
## 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?
In `CheckAnalysis`, we should call `checkAnalysis` for `ScalarSubquery` at the beginning, as later we will call `plan.output` which is invalid if `plan` is not resolved.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17930 from cloud-fan/tmp.
## What changes were proposed in this pull request?
When registering Scala UDF, we can know if the udf will return nullable value or not. `ScalaUDF` and related classes should handle the nullability.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17911 from ueshin/issues/SPARK-20668.
## 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?
Any Dataset/DataFrame batch query with the operation `withWatermark` does not execute because the batch planner does not have any rule to explicitly handle the EventTimeWatermark logical plan.
The right solution is to simply remove the plan node, as the watermark should not affect any batch query in any way.
Changes:
- In this PR, we add a new rule `EliminateEventTimeWatermark` to check if we need to ignore the event time watermark. We will ignore watermark in any batch query.
Depends upon:
- [SPARK-20672](https://issues.apache.org/jira/browse/SPARK-20672). We can not add this rule into analyzer directly, because streaming query will be copied to `triggerLogicalPlan ` in every trigger, and the rule will be applied to `triggerLogicalPlan` mistakenly.
Others:
- A typo fix in example.
## How was this patch tested?
add new unit test.
Author: uncleGen <hustyugm@gmail.com>
Closes#17896 from uncleGen/SPARK-20373.
## 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?
So far, we do not drop all the cataloged objects after each package. Sometimes, we might hit strange test case errors because the previous test suite did not drop the cataloged/temporary objects (tables/functions/database). At least, we can first clean up the environment when completing the package of `sql/core` and `sql/hive`.
## How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17908 from gatorsmile/reset.
### What changes were proposed in this pull request?
Table comment was not getting set/unset using **ALTER TABLE SET/UNSET TBLPROPERTIES** query
eg: ALTER TABLE table_with_comment SET TBLPROPERTIES("comment"= "modified comment)
when user alter the table properties and adds/updates table comment,table comment which is a field of **CatalogTable** instance is not getting updated and old table comment if exists was shown to user, inorder to handle this issue, update the comment field value in **CatalogTable** with the newly added/modified comment along with other table level properties when user executes **ALTER TABLE SET TBLPROPERTIES** query.
This pr has also taken care of unsetting the table comment when user executes query **ALTER TABLE UNSET TBLPROPERTIES** inorder to unset or remove table comment.
eg: ALTER TABLE table_comment UNSET TBLPROPERTIES IF EXISTS ('comment')
### How was this patch tested?
Added test cases as part of **SQLQueryTestSuite** for verifying table comment using desc formatted table query after adding/modifying table comment as part of **AlterTableSetPropertiesCommand** and unsetting the table comment using **AlterTableUnsetPropertiesCommand**.
Author: sujith71955 <sujithchacko.2010@gmail.com>
Closes#17649 from sujith71955/alter_table_comment.
## What changes were proposed in this pull request?
This change allows timestamps in parquet-based hive table to behave as a "floating time", without a timezone, as timestamps are for other file formats. If the storage timezone is the same as the session timezone, this conversion is a no-op. When data is read from a hive table, the table property is *always* respected. This allows spark to not change behavior when reading old data, but read newly written data correctly (whatever the source of the data is).
Spark inherited the original behavior from Hive, but Hive is also updating behavior to use the same scheme in HIVE-12767 / HIVE-16231.
The default for Spark remains unchanged; created tables do not include the new table property.
This will only apply to hive tables; nothing is added to parquet metadata to indicate the timezone, so data that is read or written directly from parquet files will never have any conversions applied.
## How was this patch tested?
Added a unit test which creates tables, reads and writes data, under a variety of permutations (different storage timezones, different session timezones, vectorized reading on and off).
Author: Imran Rashid <irashid@cloudera.com>
Closes#16781 from squito/SPARK-12297.
## What changes were proposed in this pull request?
* Docs are consistent (across different `unix_timestamp` variants and their internal expressions)
* typo hunting
## How was this patch tested?
local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17801 from jaceklaskowski/unix_timestamp.
## What changes were proposed in this pull request?
Due to a likely typo, the logDebug msg printing the diff of query plans shows a diff to the initial plan, not diff to the start of batch.
## How was this patch tested?
Now the debug message prints the diff between start and end of batch.
Author: Juliusz Sompolski <julek@databricks.com>
Closes#17875 from juliuszsompolski/SPARK-20616.
## What changes were proposed in this pull request?
We allow users to specify hints (currently only "broadcast" is supported) in SQL and DataFrame. However, while SQL has a standard hint format (/*+ ... */), DataFrame doesn't have one and sometimes users are confused that they can't find how to apply a broadcast hint. This ticket adds a generic hint function on DataFrame that allows using the same hint on DataFrames as well as SQL.
As an example, after this patch, the following will apply a broadcast hint on a DataFrame using the new hint function:
```
df1.join(df2.hint("broadcast"))
```
## How was this patch tested?
Added a test case in DataFrameJoinSuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#17839 from rxin/SPARK-20576.
## 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?
Generate exec does not produce `null` values if the generator for the input row is empty and the generate operates in outer mode without join. This is caused by the fact that the `join=false` code path is different from the `join=true` code path, and that the `join=false` code path did deal with outer properly. This PR addresses this issue.
## How was this patch tested?
Updated `outer*` tests in `GeneratorFunctionSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17810 from hvanhovell/SPARK-20534.
## 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($"col".cast("aa"))
```
**Before**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa() is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
**After**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
## How was this patch tested?
Unit tests in `DataTypeParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17784 from HyukjinKwon/SPARK-20492.
## What changes were proposed in this pull request?
This PR proposes to fill up the documentation with examples for `bitwiseOR`, `bitwiseAND`, `bitwiseXOR`. `contains`, `asc` and `desc` in `Column` API.
Also, this PR fixes minor typos in the documentation and matches some of the contents between Scala doc and Python doc.
Lastly, this PR suggests to use `spark` rather than `sc` in doc tests in `Column` for Python documentation.
## How was this patch tested?
Doc tests were added and manually tested with the commands below:
`./python/run-tests.py --module pyspark-sql`
`./python/run-tests.py --module pyspark-sql --python-executable python3`
`./dev/lint-python`
Output was checked via `make html` under `./python/docs`. The snapshots will be left on the codes with comments.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17737 from HyukjinKwon/SPARK-20442.
## What changes were proposed in this pull request?
This pr added a new rule in `Analyzer` to resolve aliases in `GROUP BY`.
The current master throws an exception if `GROUP BY` clauses have aliases in `SELECT`;
```
scala> spark.sql("select a a1, a1 + 1 as b, count(1) from t group by a1")
org.apache.spark.sql.AnalysisException: cannot resolve '`a1`' given input columns: [a]; line 1 pos 51;
'Aggregate ['a1], [a#83L AS a1#87L, ('a1 + 1) AS b#88, count(1) AS count(1)#90L]
+- SubqueryAlias t
+- Project [id#80L AS a#83L]
+- Range (0, 10, step=1, splits=Some(8))
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
```
## How was this patch tested?
Added tests in `SQLQuerySuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17191 from maropu/SPARK-14471.
## 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 patch adds support for customizing the spark session by injecting user-defined custom extensions. This allows a user to add custom analyzer rules/checks, optimizer rules, planning strategies or even a customized parser.
## How was this patch tested?
Unit Tests in SparkSessionExtensionSuite
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#17724 from sameeragarwal/session-extensions.
## 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?
If a partitionSpec is supposed to not contain optional values, a ParseException should be thrown, and not nulls returned.
The nulls can later cause NullPointerExceptions in places not expecting them.
## How was this patch tested?
A query like "SHOW PARTITIONS tbl PARTITION(col1='val1', col2)" used to throw a NullPointerException.
Now it throws a ParseException.
Author: Juliusz Sompolski <julek@databricks.com>
Closes#17707 from juliuszsompolski/SPARK-20412.
## 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?
Address a follow up in [comment](https://github.com/apache/spark/pull/16954#discussion_r105718880)
Currently subqueries with correlated predicates containing aggregate expression having mixture of outer references and local references generate a codegen error like following :
```SQL
SELECT t1a
FROM t1
GROUP BY 1
HAVING EXISTS (SELECT 1
FROM t2
WHERE t2a < min(t1a + t2a));
```
Exception snippet.
```
Cannot evaluate expression: min((input[0, int, false] + input[4, int, false]))
at org.apache.spark.sql.catalyst.expressions.Unevaluable$class.doGenCode(Expression.scala:226)
at org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression.doGenCode(interfaces.scala:87)
at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:106)
at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:103)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:103)
```
After this PR, a better error message is issued.
```
org.apache.spark.sql.AnalysisException
Error in query: Found an aggregate expression in a correlated
predicate that has both outer and local references, which is not supported yet.
Aggregate expression: min((t1.`t1a` + t2.`t2a`)),
Outer references: t1.`t1a`,
Local references: t2.`t2a`.;
```
## How was this patch tested?
Added tests in SQLQueryTestSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17636 from dilipbiswal/subquery_followup1.
## What changes were proposed in this pull request?
It's illegal to have aggregate function in GROUP BY, and we should fail at analysis phase, if this happens.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17704 from cloud-fan/minor.
### What changes were proposed in this pull request?
Database and Table names conform the Hive standard ("[a-zA-z_0-9]+"), i.e. if this name only contains characters, numbers, and _.
When calling `toLowerCase` on the names, we should add `Locale.ROOT` to the `toLowerCase`for avoiding inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17655 from gatorsmile/locale.
## What changes were proposed in this pull request?
Also went through the same file to ensure other string concatenation are correct.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17691 from zsxwing/fix-error-message.
## 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.
Avoid necessary execution that can lead to NPE in EliminateOuterJoin and add test in DataFrameSuite to confirm NPE is no longer thrown
## What changes were proposed in this pull request?
Change leftHasNonNullPredicate and rightHasNonNullPredicate to lazy so they are only executed when needed.
## How was this patch tested?
Added test in DataFrameSuite that failed before this fix and now succeeds. Note that a test in catalyst project would be better but i am unsure how to do this.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Koert Kuipers <koert@tresata.com>
Closes#17660 from koertkuipers/feat-catch-npe-in-eliminate-outer-join.
## 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?
fix typo
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17663 from felixcheung/likedoctypo.
## 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?
The session catalog caches some persistent functions in the `FunctionRegistry`, so there can be duplicates. Our Catalog API `listFunctions` does not handle it.
It would be better if `SessionCatalog` API can de-duplciate the records, instead of doing it by each API caller. In `FunctionRegistry`, our functions are identified by the unquoted string. Thus, this PR is try to parse it using our parser interface and then de-duplicate the names.
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17646 from gatorsmile/showFunctions.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/17398 we introduced `UnresolvedMapObjects` as a placeholder of `MapObjects`. Unfortunately `UnresolvedMapObjects` is not serializable as its `function` may reference Scala `Type` which is not serializable.
Ideally this is fine, as we will never serialize and send unresolved expressions to executors. However users may accidentally do this, e.g. mistakenly reference an encoder instance when implementing `Aggregator`, we should fix it so that it's just a performance issue(more network traffic) and should not fail the query.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17639 from cloud-fan/minor.
## 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?
AssertNotNull's toString/simpleString dumps the entire walkedTypePath. walkedTypePath is used for error message reporting and shouldn't be part of the output.
## How was this patch tested?
Manually tested.
Author: Reynold Xin <rxin@databricks.com>
Closes#17616 from rxin/SPARK-20304.
### 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?
Update count distinct error message for streaming datasets/dataframes to match current behavior. These aggregations are not yet supported, regardless of whether the dataset/dataframe is aggregated.
Author: jtoka <jason.tokayer@gmail.com>
Closes#17609 from jtoka/master.
## 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?
Dataset typed API currently uses NewInstance to box primitive types (i.e. calling the constructor). Instead, it'd be slightly more idiomatic in Java to use PrimitiveType.valueOf, which can be invoked using StaticInvoke expression.
## How was this patch tested?
The change should be covered by existing tests for Dataset encoders.
Author: Reynold Xin <rxin@databricks.com>
Closes#17604 from rxin/SPARK-20289.
## What changes were proposed in this pull request?
Similar to `ListQuery`, `Exists` should not be evaluated in `Join` operator 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#17491 from viirya/dont-push-exists-to-join.
## 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?
Like `Expression`, `QueryPlan` should also have a `semanticHash` method, then we can put plans to a hash map and look it up fast. This PR refactors `QueryPlan` to follow `Expression` and put all the normalization logic in `QueryPlan.canonicalized`, so that it's very natural to implement `semanticHash`.
follow-up: improve `CacheManager` to leverage this `semanticHash` and speed up plan lookup, instead of iterating all cached plans.
## How was this patch tested?
existing tests. Note that we don't need to test the `semanticHash` method, once the existing tests prove `sameResult` is correct, we are good.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17541 from cloud-fan/plan-semantic.
## What changes were proposed in this pull request?
AssertNotNull currently throws RuntimeException. It should throw NullPointerException, which is more specific.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#17573 from rxin/SPARK-20262.
## 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?
Currently `LogicalRelation` has a `expectedOutputAttributes` parameter, which makes it hard to reason about what the actual output is. Like other leaf nodes, `LogicalRelation` should also take `output` as a parameter, to simplify the logic
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17552 from cloud-fan/minor.
## What changes were proposed in this pull request?
This is a tiny addendum to SPARK-19495 to remove the private visibility for copy, which is the only package private method in the entire file.
## How was this patch tested?
N/A - no semantic change.
Author: Reynold Xin <rxin@databricks.com>
Closes#17555 from rxin/SPARK-19495-2.
## 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?
Make sure SESSION_LOCAL_TIMEZONE reflects the change in JVM's default timezone setting. Currently several timezone related tests fail as the change to default timezone is not picked up by SQLConf.
## How was this patch tested?
Added an unit test in ConfigEntrySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17537 from dilipbiswal/timezone_debug.
## 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?
Observed by felixcheung , in `SparkSession`.`Catalog` APIs, we have different conventions/rules for table/function identifiers/names. Most APIs accept the qualified name (i.e., `databaseName`.`tableName` or `databaseName`.`functionName`). However, the following five APIs do not accept it.
- def listColumns(tableName: String): Dataset[Column]
- def getTable(tableName: String): Table
- def getFunction(functionName: String): Function
- def tableExists(tableName: String): Boolean
- def functionExists(functionName: String): Boolean
To make them consistent with the other Catalog APIs, this PR does the changes, updates the function/API comments and adds the `params` to clarify the inputs we allow.
### How was this patch tested?
Added the test cases .
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17518 from gatorsmile/tableIdentifier.
### What changes were proposed in this pull request?
This PR is to unify and clean up the outputs of `DESC EXTENDED/FORMATTED` and `SHOW TABLE EXTENDED` by moving the logics into the Catalog interface. The output formats are improved. We also add the missing attributes. It impacts the DDL commands like `SHOW TABLE EXTENDED`, `DESC EXTENDED` and `DESC FORMATTED`.
In addition, by following what we did in Dataset API `printSchema`, we can use `treeString` to show the schema in the more readable way.
Below is the current way:
```
Schema: STRUCT<`a`: STRING (nullable = true), `b`: INT (nullable = true), `c`: STRING (nullable = true), `d`: STRING (nullable = true)>
```
After the change, it should look like
```
Schema: root
|-- a: string (nullable = true)
|-- b: integer (nullable = true)
|-- c: string (nullable = true)
|-- d: string (nullable = true)
```
### How was this patch tested?
`describe.sql` and `show-tables.sql`
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17394 from gatorsmile/descFollowUp.
## What changes were proposed in this pull request?
**Description** from JIRA
The TimestampType in Spark SQL is of microsecond precision. Ideally, we should convert Spark SQL timestamp values into Parquet TIMESTAMP_MICROS. But unfortunately parquet-mr hasn't supported it yet.
For the read path, we should be able to read TIMESTAMP_MILLIS Parquet values and pad a 0 microsecond part to read values.
For the write path, currently we are writing timestamps as INT96, similar to Impala and Hive. One alternative is that, we can have a separate SQL option to let users be able to write Spark SQL timestamp values as TIMESTAMP_MILLIS. Of course, in this way the microsecond part will be truncated.
## How was this patch tested?
Added new tests in ParquetQuerySuite and ParquetIOSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15332 from dilipbiswal/parquet-time-millis.
## 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?
Range in SQL should be case insensitive
## How was this patch tested?
unit test
Author: samelamin <hussam.elamin@gmail.com>
Author: samelamin <sam_elamin@discovery.com>
Closes#17487 from samelamin/SPARK-20145.
## 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?
This PR proposes to use `XXX` format instead of `ZZ`. `ZZ` seems a `FastDateFormat` specific.
`ZZ` supports "ISO 8601 extended format time zones" but it seems `FastDateFormat` specific option.
I misunderstood this is compatible format with `SimpleDateFormat` when this change is introduced.
Please see [SimpleDateFormat documentation]( https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html#iso8601timezone) and [FastDateFormat documentation](https://commons.apache.org/proper/commons-lang/apidocs/org/apache/commons/lang3/time/FastDateFormat.html).
It seems we better replace `ZZ` to `XXX` because they look using the same strategy - [FastDateParser.java#L930](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L930)), [FastDateParser.java#L932-L951 ](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L932-L951)) and [FastDateParser.java#L596-L601](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L596-L601)).
I also checked the codes and manually debugged it for sure. It seems both cases use the same pattern `( Z|(?:[+-]\\d{2}(?::)\\d{2}))`.
_Note that this should be rather a fix about documentation and not the behaviour change because `ZZ` seems invalid date format in `SimpleDateFormat` as documented in `DataFrameReader` and etc, and both `ZZ` and `XXX` look identically working with `FastDateFormat`_
Current documentation is as below:
```
* <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSZZ`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* `java.text.SimpleDateFormat`. This applies to timestamp type.</li>
```
## How was this patch tested?
Existing tests should cover this. Also, manually tested as below (BTW, I don't think these are worth being added as tests within Spark):
**Parse**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res4: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res10: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000-11:00"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000Z"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
```
```scala
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res7: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res1: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
res8: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
res2: java.util.Date = Tue Mar 21 09:00:00 KST 2017
```
**Format**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").format(new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00"))
res6: String = 2017-03-21T20:00:00.000+09:00
```
```scala
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSZZ,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res1: String = 2017-03-21T20:00:00.000+09:00
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSXXX,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res2: String = 2017-03-21T20:00:00.000+09:00
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17489 from HyukjinKwon/SPARK-20166.
## 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.
## What changes were proposed in this pull request?
The query plan in an `AnalysisException` may be `null` when an `AnalysisException` object is serialized and then deserialized, since `plan` is marked `transient`. Or when someone throws an `AnalysisException` with a null query plan (which should not happen).
`def getMessage` is not tolerant of this and throws a `NullPointerException`, leading to loss of information about the original exception.
The fix is to add a `null` check in `getMessage`.
## How was this patch tested?
- Unit test
Author: Kunal Khamar <kkhamar@outlook.com>
Closes#17486 from kunalkhamar/spark-20164.
## What changes were proposed in this pull request?
Instead of iterating all expressions that can return null for null inputs, we can just check `NullIntolerant`.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17450 from cloud-fan/null.
…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?
Commit 6c70a38 broke the build for scala 2.10. The commit uses some reflections which are not available in Scala 2.10. This PR fixes them.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17473 from ueshin/issues/SPARK-19088.
### 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?
When we build the deserializer expression for map type, we will use `StaticInvoke` to call `ArrayBasedMapData.toScalaMap`, and declare the return type as `scala.collection.immutable.Map`. If the map is inside an Option, we will wrap this `StaticInvoke` with `WrapOption`, which requires the input to be `scala.collect.Map`. Ideally this should be fine, as `scala.collection.immutable.Map` extends `scala.collect.Map`, but our `ObjectType` is too strict about this, this PR fixes it.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17454 from cloud-fan/map.
## 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.
