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…nsistent with old sql parser behavior
## 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? 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.
…ould be the same after canonicalization ## What changes were proposed in this pull request? Since `constraints` in `QueryPlan` is a set, the order of filters can differ. Usually this is ok because of canonicalization. However, in `FileSourceScanExec`, its data filters and partition filters are sequences, and their orders are not canonicalized. So `def sameResult` returns different results for different orders of data/partition filters. This leads to, e.g. different decision for `ReuseExchange`, and thus results in unstable performance. ## How was this patch tested? Added a new test for `FileSourceScanExec.sameResult`. Author: wangzhenhua <wangzhenhua@huawei.com> Closes #17959 from wzhfy/canonicalizeFileSourceScanExec.
## What changes were proposed in this pull request? - [x] need to test by running R CMD check --as-cran - [x] sanity check vignettes ## How was this patch tested? Jenkins Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #17945 from felixcheung/rchangesforpackage.
## What changes were proposed in this pull request? StringIndexer maps labels to numbers according to the descending order of label frequency. Other types of ordering (e.g., alphabetical) may be needed in feature ETL. For example, the ordering will affect the result in one-hot encoding and RFormula. This PR proposes to support other ordering methods and we add a parameter `stringOrderType` that supports the following four options: - 'frequencyDesc': descending order by label frequency (most frequent label assigned 0) - 'frequencyAsc': ascending order by label frequency (least frequent label assigned 0) - 'alphabetDesc': descending alphabetical order - 'alphabetAsc': ascending alphabetical order The default is still descending order of label frequency, so there should be no impact to existing programs. ## How was this patch tested? new test Author: Wayne Zhang <actuaryzhang@uber.com> Closes #17879 from actuaryzhang/stringIndexer.
…L with documentation improvement
## 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? Remove uses of scala.language.reflectiveCalls that are either unnecessary or probably resulting in more complex code. This turned out to be less significant than I thought, but, still worth a touch-up. ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #17949 from srowen/SPARK-20554.
## 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? 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 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? 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.
… the original error ## What changes were proposed in this pull request? This PR adds an `error` parameter to `TaskContextImpl.markTaskCompleted` to propagate the original error. It also fixes an issue that `TaskCompletionListenerException.getMessage` doesn't include `previousError`. ## How was this patch tested? New unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #17942 from zsxwing/SPARK-20702.
…= no timeout / processing timeout
## What changes were proposed in this pull request?
When watermark is set, and timeout conf is NoTimeout or ProcessingTimeTimeout (both do not need the watermark), the query fails at runtime with the following exception.
```
MatchException: Some(org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate1a9b798e) (of class scala.Some)
org.apache.spark.sql.execution.streaming.FlatMapGroupsWithStateExec$$anonfun$doExecute$1.apply(FlatMapGroupsWithStateExec.scala:120)
org.apache.spark.sql.execution.streaming.FlatMapGroupsWithStateExec$$anonfun$doExecute$1.apply(FlatMapGroupsWithStateExec.scala:116)
org.apache.spark.sql.execution.streaming.state.package$StateStoreOps$$anonfun$1.apply(package.scala:70)
org.apache.spark.sql.execution.streaming.state.package$StateStoreOps$$anonfun$1.apply(package.scala:65)
org.apache.spark.sql.execution.streaming.state.StateStoreRDD.compute(StateStoreRDD.scala:64)
```
The match did not correctly handle cases where watermark was defined by the timeout was different from EventTimeTimeout.
## How was this patch tested?
New unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes #17954 from tdas/SPARK-20714.
…starts with "." to avoid being deleted if we set hive.exec.stagingdir under the table directory. JIRA Issue: https://issues.apache.org/jira/browse/SPARK-20594 ## What changes were proposed in this pull request? The staging directory should be a child directory starts with "." to avoid being deleted before moving staging directory to table directory if we set hive.exec.stagingdir under the table directory. ## How was this patch tested? Added unit tests Author: zuotingbing <zuo.tingbing9@zte.com.cn> Closes #17858 from zuotingbing/spark-stagingdir.
### 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.
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### What changes were proposed in this pull request? Currently, Spark DS V2 aggregate push-down doesn't supports project with alias. Refer https://github.com/apache/spark/blob/c91c2e9afec0d5d5bbbd2e155057fe409c5bb928/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala#L96 This PR let it works good with alias. **The first example:** the origin plan show below: ``` Aggregate [DEPT#0], [DEPT#0, sum(mySalary#8) AS total#14] +- Project [DEPT#0, SALARY#2 AS mySalary#8] +- ScanBuilderHolder [DEPT#0, NAME#1, SALARY#2, BONUS#3], RelationV2[DEPT#0, NAME#1, SALARY#2, BONUS#3] test.employee, JDBCScanBuilder(org.apache.spark.sql.test.TestSparkSession77978658,StructType(StructField(DEPT,IntegerType,true),StructField(NAME,StringType,true),StructField(SALARY,DecimalType(20,2),true),StructField(BONUS,DoubleType,true)),org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions5f8da82) ``` If we can complete push down the aggregate, then the plan will be: ``` Project [DEPT#0, SUM(SALARY)#18 AS sum(SALARY#2)#13 AS total#14] +- RelationV2[DEPT#0, SUM(SALARY)#18] test.employee ``` If we can partial push down the aggregate, then the plan will be: ``` Aggregate [DEPT#0], [DEPT#0, sum(cast(SUM(SALARY)#18 as decimal(20,2))) AS total#14] +- RelationV2[DEPT#0, SUM(SALARY)#18] test.employee ``` **The second example:** the origin plan show below: ``` Aggregate [myDept#33], [myDept#33, sum(mySalary#34) AS total#40] +- Project [DEPT#25 AS myDept#33, SALARY#27 AS mySalary#34] +- ScanBuilderHolder [DEPT#25, NAME#26, SALARY#27, BONUS#28], RelationV2[DEPT#25, NAME#26, SALARY#27, BONUS#28] test.employee, JDBCScanBuilder(org.apache.spark.sql.test.TestSparkSession25c4f621,StructType(StructField(DEPT,IntegerType,true),StructField(NAME,StringType,true),StructField(SALARY,DecimalType(20,2),true),StructField(BONUS,DoubleType,true)),org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions345d641e) ``` If we can complete push down the aggregate, then the plan will be: ``` Project [DEPT#25 AS myDept#33, SUM(SALARY)#44 AS sum(SALARY#27)#39 AS total#40] +- RelationV2[DEPT#25, SUM(SALARY)#44] test.employee ``` If we can partial push down the aggregate, then the plan will be: ``` Aggregate [myDept#33], [DEPT#25 AS myDept#33, sum(cast(SUM(SALARY)#56 as decimal(20,2))) AS total#52] +- RelationV2[DEPT#25, SUM(SALARY)#56] test.employee ``` ### Why are the changes needed? Alias is more useful. ### Does this PR introduce _any_ user-facing change? 'Yes'. Users could see DS V2 aggregate push-down supports project with alias. ### How was this patch tested? New tests. Closes apache#35932 from beliefer/SPARK-38533_new. Authored-by: Jiaan Geng <beliefer@163.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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Please review http://spark.apache.org/contributing.html before opening a pull request.