## 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?
Add built-in SQL Function - COT.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#17999 from wangyum/SPARK-20751.
## 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?
Currently the parser logs the query it is parsing at `info` level. This is too high, this PR lowers the log level to `debug`.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#18006 from hvanhovell/lower_parser_log_level.
## What changes were proposed in this pull request?
When an expression for `df.filter()` has many nodes (e.g. 400), the size of Java bytecode for the generated Java code is more than 64KB. It produces an Java exception. As a result, the execution fails.
This PR continues to execute by calling `Expression.eval()` disabling code generation if an exception has been caught.
## How was this patch tested?
Add a test suite into `DataFrameSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#17087 from kiszk/SPARK-19372.
## What changes were proposed in this pull request?
Because the method `TimeZone.getTimeZone(String ID)` is synchronized on the TimeZone class, concurrent call of this method will become a bottleneck.
This especially happens when casting from string value containing timezone info to timestamp value, which uses `DateTimeUtils.stringToTimestamp()` and gets TimeZone instance on the site.
This pr makes a cache of the generated TimeZone instances to avoid the synchronization.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17933 from ueshin/issues/SPARK-20588.
## 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?
For aggregate function with `PartialMerge` or `Final` mode, the input is aggregate buffers instead of the actual children expressions. So the actual children expressions won't affect the result, we should normalize the expr id for them.
## How was this patch tested?
a new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17964 from cloud-fan/tmp.
## What changes were proposed in this pull request?
This PR is based on https://github.com/apache/spark/pull/16199 and extracts the valid change from https://github.com/apache/spark/pull/9759 to resolve SPARK-18772
This avoids additional conversion try with `toFloat` and `toDouble`.
For avoiding additional conversions, please refer the codes below:
**Before**
```scala
scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._
scala> spark.read.schema(StructType(Seq(StructField("a", DoubleType)))).option("mode", "FAILFAST").json(Seq("""{"a": "nan"}""").toDS).show()
17/05/12 11:30:41 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)
java.lang.NumberFormatException: For input string: "nan"
...
```
**After**
```scala
scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._
scala> spark.read.schema(StructType(Seq(StructField("a", DoubleType)))).option("mode", "FAILFAST").json(Seq("""{"a": "nan"}""").toDS).show()
17/05/12 11:44:30 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.RuntimeException: Cannot parse nan as DoubleType.
...
```
## How was this patch tested?
Unit tests added in `JsonSuite`.
Closes#16199
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Nathan Howell <nhowell@godaddy.com>
Closes#17956 from HyukjinKwon/SPARK-18772.
### 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.
