# What changes were proposed in this pull request?
UDF to_json only supports converting `StructType` or `ArrayType` of `StructType`s to a json output string now.
According to the discussion of JIRA SPARK-21513, I allow to `to_json` support converting `MapType` and `ArrayType` of `MapType`s to a json output string.
This PR is for SQL and Scala API only.
# How was this patch tested?
Adding unit test case.
cc viirya HyukjinKwon
Author: goldmedal <liugs963@gmail.com>
Author: Jia-Xuan Liu <liugs963@gmail.com>
Closes#18875 from goldmedal/SPARK-21513.
## What changes were proposed in this pull request?
Improve QueryPlanConstraints framework, make it robust and simple.
In https://github.com/apache/spark/pull/15319, constraints for expressions like `a = f(b, c)` is resolved.
However, for expressions like
```scala
a = f(b, c) && c = g(a, b)
```
The current QueryPlanConstraints framework will produce non-converging constraints.
Essentially, the problem is caused by having both the name and child of aliases in the same constraint set. We infer constraints, and push down constraints as predicates in filters, later on these predicates are propagated as constraints, etc..
Simply using the alias names only can resolve these problems. The size of constraints is reduced without losing any information. We can always get these inferred constraints on child of aliases when pushing down filters.
Also, the EqualNullSafe between name and child in propagating alias is meaningless
```scala
allConstraints += EqualNullSafe(e, a.toAttribute)
```
It just produces redundant constraints.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19201 from gengliangwang/QueryPlanConstraints.
## What changes were proposed in this pull request?
TPCDSQueryBenchmark packaged into a jar doesn't work with spark-submit.
It's because of the failure of reference query files in the jar file.
## How was this patch tested?
Ran the benchmark.
Author: sarutak <sarutak@oss.nttdata.co.jp>
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#18592 from sarutak/fix-tpcds-benchmark.
## What changes were proposed in this pull request?
Support DESC (EXTENDED | FORMATTED) ? TABLE COLUMN command.
Support DESC EXTENDED | FORMATTED TABLE COLUMN command to show column-level statistics.
Do NOT support describe nested columns.
## How was this patch tested?
Added test cases.
Author: Zhenhua Wang <wzh_zju@163.com>
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16422 from wzhfy/descColumn.
## What changes were proposed in this pull request?
When the `requiredSchema` only contains `_corrupt_record`, the derived `actualSchema` is empty and the `_corrupt_record` are all null for all rows. This PR captures above situation and raise an exception with a reasonable workaround messag so that users can know what happened and how to fix the query.
## How was this patch tested?
Added unit test in `CSVSuite`.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#19199 from jmchung/SPARK-21610-FOLLOWUP.
## What changes were proposed in this pull request?
this PR describe remove the import class that are unused.
## How was this patch tested?
N/A
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#19131 from heary-cao/unuse_import.
## What changes were proposed in this pull request?
```
echo '{"field": 1}
{"field": 2}
{"field": "3"}' >/tmp/sample.json
```
```scala
import org.apache.spark.sql.types._
val schema = new StructType()
.add("field", ByteType)
.add("_corrupt_record", StringType)
val file = "/tmp/sample.json"
val dfFromFile = spark.read.schema(schema).json(file)
scala> dfFromFile.show(false)
+-----+---------------+
|field|_corrupt_record|
+-----+---------------+
|1 |null |
|2 |null |
|null |{"field": "3"} |
+-----+---------------+
scala> dfFromFile.filter($"_corrupt_record".isNotNull).count()
res1: Long = 0
scala> dfFromFile.filter($"_corrupt_record".isNull).count()
res2: Long = 3
```
When the `requiredSchema` only contains `_corrupt_record`, the derived `actualSchema` is empty and the `_corrupt_record` are all null for all rows. This PR captures above situation and raise an exception with a reasonable workaround messag so that users can know what happened and how to fix the query.
## How was this patch tested?
Added test case.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#18865 from jmchung/SPARK-21610.
## What changes were proposed in this pull request?
This PR implements the sql feature:
INSERT OVERWRITE [LOCAL] DIRECTORY directory1
[ROW FORMAT row_format] [STORED AS file_format]
SELECT ... FROM ...
## How was this patch tested?
Added new unittests and also pulled the code to fb-spark so that we could test writing to hdfs directory.
Author: Jane Wang <janewang@fb.com>
Closes#18975 from janewangfb/port_local_directory.
## What changes were proposed in this pull request?
Correct DataFrame doc.
## How was this patch tested?
Only doc change, no tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19173 from yanboliang/df-doc.
## What changes were proposed in this pull request?
`JacksonUtils.verifySchema` verifies if a data type can be converted to JSON. For `MapType`, it now verifies the key type. However, in `JacksonGenerator`, when converting a map to JSON, we only care about its values and create a writer for the values. The keys in a map are treated as strings by calling `toString` on the keys.
Thus, we should change `JacksonUtils.verifySchema` to verify the value type of `MapType`.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19167 from viirya/test-jacksonutils.
## What changes were proposed in this pull request?
In a driver heap dump containing 390,105 instances of SQLTaskMetrics this
would have saved me approximately 3.2MB of memory.
Since we're not getting any benefit from storing this unused value, let's
eliminate it until a future PR makes use of it.
## How was this patch tested?
Existing unit tests
Author: Andrew Ash <andrew@andrewash.com>
Closes#19153 from ash211/aash/trim-sql-listener.
## What changes were proposed in this pull request?
This PR fixes flaky test `InMemoryCatalogedDDLSuite "alter table: rename cached table"`.
Since this test validates distributed DataFrame, the result should be checked by using `checkAnswer`. The original version used `df.collect().Seq` method that does not guaranty an order of each element of the result.
## How was this patch tested?
Use existing test case
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19159 from kiszk/SPARK-21946.
## What changes were proposed in this pull request?
The condition in `Optimizer.isPlanIntegral` is wrong. We should always return `true` if not in test mode.
## How was this patch tested?
Manually test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19161 from viirya/SPARK-21726-followup.
## What changes were proposed in this pull request?
`HiveExternalCatalog` is a semi-public interface. When creating tables, `HiveExternalCatalog` converts the table metadata to hive table format and save into hive metastore. It's very import to guarantee backward compatibility here, i.e., tables created by previous Spark versions should still be readable in newer Spark versions.
Previously we find backward compatibility issues manually, which is really easy to miss bugs. This PR introduces a test framework to automatically test `HiveExternalCatalog` backward compatibility, by downloading Spark binaries with different versions, and create tables with these Spark versions, and read these tables with current Spark version.
## How was this patch tested?
test-only change
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19148 from cloud-fan/test.
## What changes were proposed in this pull request?
We have many optimization rules now in `Optimzer`. Right now we don't have any checks in the optimizer to check for the structural integrity of the plan (e.g. resolved). When debugging, it is difficult to identify which rules return invalid plans.
It would be great if in test mode, we can check whether a plan is still resolved after the execution of each rule, so we can catch rules that return invalid plans.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18956 from viirya/SPARK-21726.
## What changes were proposed in this pull request?
Tables should be dropped after use in unit tests.
## How was this patch tested?
N/A
Author: liuxian <liu.xian3@zte.com.cn>
Closes#19155 from 10110346/droptable.
Since ScalaTest 3.0.0, `org.scalatest.concurrent.Timeouts` is deprecated.
This PR replaces the deprecated one with `org.scalatest.concurrent.TimeLimits`.
```scala
-import org.scalatest.concurrent.Timeouts._
+import org.scalatest.concurrent.TimeLimits._
```
Pass the existing test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19150 from dongjoon-hyun/SPARK-21939.
Change-Id: I1a1b07f1b97e51e2263dfb34b7eaaa099b2ded5e
## What changes were proposed in this pull request?
Since [SPARK-15639](https://github.com/apache/spark/pull/13701), `spark.sql.parquet.cacheMetadata` and `PARQUET_CACHE_METADATA` is not used. This PR removes from SQLConf and docs.
## How was this patch tested?
Pass the existing Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19129 from dongjoon-hyun/SPARK-13656.
## What changes were proposed in this pull request?
Currently, users meet job abortions while creating or altering ORC/Parquet tables with invalid column names. We had better prevent this by raising **AnalysisException** with a guide to use aliases instead like Paquet data source tables.
**BEFORE**
```scala
scala> sql("CREATE TABLE orc1 USING ORC AS SELECT 1 `a b`")
17/09/04 13:28:21 ERROR Utils: Aborting task
java.lang.IllegalArgumentException: Error: : expected at the position 8 of 'struct<a b:int>' but ' ' is found.
17/09/04 13:28:21 ERROR FileFormatWriter: Job job_20170904132821_0001 aborted.
17/09/04 13:28:21 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
org.apache.spark.SparkException: Task failed while writing rows.
```
**AFTER**
```scala
scala> sql("CREATE TABLE orc1 USING ORC AS SELECT 1 `a b`")
17/09/04 13:27:40 ERROR CreateDataSourceTableAsSelectCommand: Failed to write to table orc1
org.apache.spark.sql.AnalysisException: Attribute name "a b" contains invalid character(s) among " ,;{}()\n\t=". Please use alias to rename it.;
```
## How was this patch tested?
Pass the Jenkins with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19124 from dongjoon-hyun/SPARK-21912.
## What changes were proposed in this pull request?
This is a follow-up of #19050 to deal with `ExistenceJoin` case.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19151 from viirya/SPARK-21835-followup.
## What changes were proposed in this pull request?
Just `StateOperatorProgress.toString` + few formatting fixes
## How was this patch tested?
Local build. Waiting for OK from Jenkins.
Author: Jacek Laskowski <jacek@japila.pl>
Closes#19112 from jaceklaskowski/SPARK-21901-StateOperatorProgress-toString.
## What changes were proposed in this pull request?
Add an assert in logical plan optimization that the isStreaming bit stays the same, and fix empty relation rules where that wasn't happening.
## How was this patch tested?
new and existing unit tests
Author: Jose Torres <joseph.torres@databricks.com>
Author: Jose Torres <joseph-torres@databricks.com>
Closes#19056 from joseph-torres/SPARK-21765-followup.
## What changes were proposed in this pull request?
Correlated predicate subqueries are rewritten into `Join` by the rule `RewritePredicateSubquery` during optimization.
It is possibly that the two sides of the `Join` have conflicting attributes. The query plans produced by `RewritePredicateSubquery` become unresolved and break structural integrity.
We should check if there are conflicting attributes in the `Join` and de-duplicate them by adding a `Project`.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19050 from viirya/SPARK-21835.
Spark ThriftServer doesn't support spnego auth for thrift/http protocol, this mainly used for knox+thriftserver scenario. Since in HiveServer2 CLIService there already has existing codes to support it. So here copy it to Spark ThriftServer to make it support.
Related Hive JIRA HIVE-6697.
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#18628 from jerryshao/SPARK-21407.
Change-Id: I61ef0c09f6972bba982475084a6b0ae3a74e385e
## What changes were proposed in this pull request?
For the given example below, the predicate added by `InferFiltersFromConstraints` is folded by `ConstantPropagation` later, this leads to unconverged optimize iteration:
```
Seq((1, 1)).toDF("col1", "col2").createOrReplaceTempView("t1")
Seq(1, 2).toDF("col").createOrReplaceTempView("t2")
sql("SELECT * FROM t1, t2 WHERE t1.col1 = 1 AND 1 = t1.col2 AND t1.col1 = t2.col AND t1.col2 = t2.col")
```
We can fix this by adjusting the indent of the optimize rules.
## How was this patch tested?
Add test case that would have failed in `SQLQuerySuite`.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#19099 from jiangxb1987/unconverge-optimization.
## What changes were proposed in this pull request?
We should make codegen fallback of expressions configurable. So far, it is always on. We might hide it when our codegen have compilation bugs. Thus, we should also disable the codegen fallback when running test cases.
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19119 from gatorsmile/fallbackCodegen.
## What changes were proposed in this pull request?
There was a bug in Univocity Parser that causes the issue in SPARK-20978. This was fixed as below:
```scala
val df = spark.read.schema("a string, b string, unparsed string").option("columnNameOfCorruptRecord", "unparsed").csv(Seq("a").toDS())
df.show()
```
**Before**
```
java.lang.NullPointerException
at scala.collection.immutable.StringLike$class.stripLineEnd(StringLike.scala:89)
at scala.collection.immutable.StringOps.stripLineEnd(StringOps.scala:29)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser.org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$getCurrentInput(UnivocityParser.scala:56)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
...
```
**After**
```
+---+----+--------+
| a| b|unparsed|
+---+----+--------+
| a|null| a|
+---+----+--------+
```
It was fixed in 2.5.0 and 2.5.4 was released. I guess it'd be safe to upgrade this.
## How was this patch tested?
Unit test added in `CSVSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19113 from HyukjinKwon/bump-up-univocity.
## What changes were proposed in this pull request?
Currently, `withDatabase` fails if the database is not empty. It would be great if we drop cleanly with CASCADE.
## How was this patch tested?
This is a change on test util. Pass the existing Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19125 from dongjoon-hyun/SPARK-21913.
## What changes were proposed in this pull request?
If no SparkConf is available to Utils.redact, simply don't redact.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19123 from srowen/SPARK-21418.
## What changes were proposed in this pull request?
SQL predicates don't have complete expression description. This patch goes to complement the description by adding arguments, examples.
This change also adds related test cases for the SQL predicate expressions.
## How was this patch tested?
Existing tests. And added predicate test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18869 from viirya/SPARK-21654.
## What changes were proposed in this pull request?
Add `TBLPROPERTIES` to the DDL statement `CREATE TABLE USING`.
After this change, the DDL becomes
```
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db_name.]table_name
USING table_provider
[OPTIONS table_property_list]
[PARTITIONED BY (col_name, col_name, ...)]
[CLUSTERED BY (col_name, col_name, ...)
[SORTED BY (col_name [ASC|DESC], ...)]
INTO num_buckets BUCKETS
]
[LOCATION path]
[COMMENT table_comment]
[TBLPROPERTIES (property_name=property_value, ...)]
[[AS] select_statement];
```
## How was this patch tested?
Add a few tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19100 from gatorsmile/addTablePropsToCreateTableUsing.
## What changes were proposed in this pull request?
Supporting moving tables across different database in HiveClient `alterTable`
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19104 from gatorsmile/alterTable.
…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure
## What changes were proposed in this pull request?
This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.
In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.
It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.
- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)
This change does _not_ fully enable a Scala 2.12 build:
- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.
What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.
## How was this patch tested?
Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.
Author: Sean Owen <sowen@cloudera.com>
Closes#18645 from srowen/SPARK-14280.
## What changes were proposed in this pull request?
As shown below, for example, When the job 5 is running, It was a mistake to think that five jobs were running, So I think it would be more appropriate to change jobs to job id.
## How was this patch tested?
no need
Author: he.qiao <he.qiao17@zte.com.cn>
Closes#19093 from Geek-He/08_31_sqltable.
## What changes were proposed in this pull request?
This PR make `DataFrame.sample(...)` can omit `withReplacement` defaulting `False`, consistently with equivalent Scala / Java API.
In short, the following examples are allowed:
```python
>>> df = spark.range(10)
>>> df.sample(0.5).count()
7
>>> df.sample(fraction=0.5).count()
3
>>> df.sample(0.5, seed=42).count()
5
>>> df.sample(fraction=0.5, seed=42).count()
5
```
In addition, this PR also adds some type checking logics as below:
```python
>>> df = spark.range(10)
>>> df.sample().count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [].
>>> df.sample(True).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>].
>>> df.sample(42).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'int'>].
>>> df.sample(fraction=False, seed="a").count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>, <type 'str'>].
>>> df.sample(seed=[1]).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'list'>].
>>> df.sample(withReplacement="a", fraction=0.5, seed=1)
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'str'>, <type 'float'>, <type 'int'>].
```
## How was this patch tested?
Manually tested, unit tests added in doc tests and manually checked the built documentation for Python.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18999 from HyukjinKwon/SPARK-21779.
## What changes were proposed in this pull request?
Allows `BinaryComparison` operators to work on any data type that actually supports ordering as verified by `TypeUtils.checkForOrderingExpr` instead of relying on the incomplete list `TypeCollection.Ordered` (which is removed by this PR).
## How was this patch tested?
Updated unit tests to cover structs and arrays.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#18818 from aray/SPARK-21110.
## What changes were proposed in this pull request?
Also remove useless function `partitionByDeterministic` after the changes of https://github.com/apache/spark/pull/14687
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19097 from gatorsmile/followupSPARK-17107.
Removing a check in the ColumnarBatchSuite that depended on a Java assertion. This assertion is being compiled out in the Maven builds causing the test to fail. This part of the test is not specifically from to the functionality that is being tested here.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19098 from BryanCutler/hotfix-ColumnarBatchSuite-assertion.
… Dataset with LogicalRDD logical operator
## What changes were proposed in this pull request?
Reusing `SparkSession.internalCreateDataFrame` wherever possible (to cut dups)
## How was this patch tested?
Local build and waiting for Jenkins
Author: Jacek Laskowski <jacek@japila.pl>
Closes#19095 from jaceklaskowski/SPARK-21886-internalCreateDataFrame.
## What changes were proposed in this pull request?
Creates `SQLMetricsTestUtils` for the utility functions of both Hive-specific and the other SQLMetrics test cases.
Also, move two SQLMetrics test cases from sql/hive to sql/core.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19092 from gatorsmile/rewriteSQLMetrics.
## What changes were proposed in this pull request?
This PR allows the creation of a `ColumnarBatch` from `ReadOnlyColumnVectors` where previously a columnar batch could only allocate vectors internally. This is useful for using `ArrowColumnVectors` in a batch form to do row-based iteration. Also added `ArrowConverter.fromPayloadIterator` which converts `ArrowPayload` iterator to `InternalRow` iterator and uses a `ColumnarBatch` internally.
## How was this patch tested?
Added a new unit test for creating a `ColumnarBatch` with `ReadOnlyColumnVectors` and a test to verify the roundtrip of rows -> ArrowPayload -> rows, using `toPayloadIterator` and `fromPayloadIterator`.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18787 from BryanCutler/arrow-ColumnarBatch-support-SPARK-21583.
## What changes were proposed in this pull request?
Fix Java code style so `./dev/lint-java` succeeds
## How was this patch tested?
Run `./dev/lint-java`
Author: Andrew Ash <andrew@andrewash.com>
Closes#19088 from ash211/spark-21875-lint-java.
## What changes were proposed in this pull request?
This PR aims to support `spark.sql.orc.compression.codec` like Parquet's `spark.sql.parquet.compression.codec`. Users can use SQLConf to control ORC compression, too.
## How was this patch tested?
Pass the Jenkins with new and updated test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19055 from dongjoon-hyun/SPARK-21839.
## What changes were proposed in this pull request?
igore("shuffle hash join") is to shuffle hash join to test _case class ShuffledHashJoinExec_.
But when you 'ignore' -> 'test', the test is _case class BroadcastHashJoinExec_.
Before modified, as a result of：canBroadcast is true.
Print information in _canBroadcast(plan: LogicalPlan)_
```
canBroadcast plan.stats.sizeInBytes:6710880
canBroadcast conf.autoBroadcastJoinThreshold:10000000
```
After modified, plan.stats.sizeInBytes is 11184808.
Print information in _canBuildLocalHashMap(plan: LogicalPlan)_
and _muchSmaller(a: LogicalPlan, b: LogicalPlan)_ :
```
canBuildLocalHashMap plan.stats.sizeInBytes:11184808
canBuildLocalHashMap conf.autoBroadcastJoinThreshold:10000000
canBuildLocalHashMap conf.numShufflePartitions:2
```
```
muchSmaller a.stats.sizeInBytes * 3:33554424
muchSmaller b.stats.sizeInBytes:33554432
```
## How was this patch tested?
existing test case.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#19069 from heary-cao/shuffle_hash_join.
## What changes were proposed in this pull request?
`org.apache.spark.deploy.RPackageUtilsSuite`
```
- jars without manifest return false *** FAILED *** (109 milliseconds)
java.io.IOException: Unable to delete file: C:\projects\spark\target\tmp\1500266936418-0\dep1-c.jar
```
`org.apache.spark.deploy.SparkSubmitSuite`
```
- download one file to local *** FAILED *** (16 milliseconds)
java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2630198944759847458.jar
- download list of files to local *** FAILED *** (0 milliseconds)
java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2783551769392880031.jar
```
`org.apache.spark.scheduler.ReplayListenerSuite`
```
- Replay compressed inprogress log file succeeding on partial read (156 milliseconds)
Exception encountered when attempting to run a suite with class name:
org.apache.spark.scheduler.ReplayListenerSuite *** ABORTED *** (1 second, 391 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-8f3cacd6-faad-4121-b901-ba1bba8025a0
- End-to-end replay *** FAILED *** (62 milliseconds)
java.io.IOException: No FileSystem for scheme: C
- End-to-end replay with compression *** FAILED *** (110 milliseconds)
java.io.IOException: No FileSystem for scheme: C
```
`org.apache.spark.sql.hive.StatisticsSuite`
```
- SPARK-21079 - analyze table with location different than that of individual partitions *** FAILED *** (875 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-21079 - analyze partitioned table with only a subset of partitions visible *** FAILED *** (47 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
**Note:** this PR does not fix:
`org.apache.spark.deploy.SparkSubmitSuite`
```
- launch simple application with spark-submit with redaction *** FAILED *** (172 milliseconds)
java.util.NoSuchElementException: next on empty iterator
```
I can't reproduce this on my Windows machine but looks appearntly consistently failed on AppVeyor. This one is unclear to me yet and hard to debug so I did not include this one for now.
**Note:** it looks there are more instances but it is hard to identify them partly due to flakiness and partly due to swarming logs and errors. Will probably go one more time if it is fine.
## How was this patch tested?
Manually via AppVeyor:
**Before**
- `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/8t8ra3lrljuir7q4
- `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/taquy84yudjjen64
- `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/24omrfn2k0xfa9xq
- `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/2079y1plgj76dc9l
**After**
- `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/3803dbfn89ne1164
- `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/m5l350dp7u9a4xjr
- `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/565vf74pp6bfdk18
- `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/qm78tsk8c37jb6s4
Jenkins tests are required and AppVeyor tests will be triggered.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18971 from HyukjinKwon/windows-fixes.
## What changes were proposed in this pull request?
We should make codegen fallback of expressions configurable. So far, it is always on. We might hide it when our codegen have compilation bugs. Thus, we should also disable the codegen fallback when running test cases.
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19062 from gatorsmile/fallbackCodegen.
## What changes were proposed in this pull request?
This is a follow-up for https://github.com/apache/spark/pull/18488, to simplify the code.
The major change is, we should map java enum to string type, instead of a struct type with a single string field.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19066 from cloud-fan/fix.
## What changes were proposed in this pull request?
Add trait UserDefinedExpression to identify user-defined functions.
UDF can be expensive. In optimizer we may need to avoid executing UDF multiple times.
E.g.
```scala
table.select(UDF as 'a).select('a, ('a + 1) as 'b)
```
If UDF is expensive in this case, optimizer should not collapse the project to
```scala
table.select(UDF as 'a, (UDF+1) as 'b)
```
Currently UDF classes like PythonUDF, HiveGenericUDF are not defined in catalyst.
This PR is to add a new trait to make it easier to identify user-defined functions.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19064 from gengliangwang/UDFType.
## What changes were proposed in this pull request?
As mentioned at https://github.com/apache/spark/pull/18680#issuecomment-316820409, when we have more `ColumnVector` implementations, it might (or might not) have huge performance implications because it might disable inlining, or force virtual dispatches.
As for read path, one of the major paths is the one generated by `ColumnBatchScan`. Currently it refers `ColumnVector` so the penalty will be bigger as we have more classes, but we can know the concrete type from its usage, e.g. vectorized Parquet reader uses `OnHeapColumnVector`. We can use the concrete type in the generated code directly to avoid the penalty.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18989 from ueshin/issues/SPARK-21781.
Signed-off-by: iamhumanbeing <iamhumanbeinggmail.com>
## What changes were proposed in this pull request?
testNameNote = "(minNumPostShufflePartitions: 3) is not correct.
it should be "(minNumPostShufflePartitions: " + numPartitions + ")" in ExchangeCoordinatorSuite
## How was this patch tested?
unit tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: iamhumanbeing <iamhumanbeing@gmail.com>
Closes#19058 from iamhumanbeing/testnote.
## What changes were proposed in this pull request?
This PR proposes both:
- Add information about Javadoc, SQL docs and few more information in `docs/README.md` and a comment in `docs/_plugins/copy_api_dirs.rb` related with Javadoc.
- Adds some commands so that the script always runs the SQL docs build under `./sql` directory (for directly running `./sql/create-docs.sh` in the root directory).
## How was this patch tested?
Manual tests with `jekyll build` and `./sql/create-docs.sh` in the root directory.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19019 from HyukjinKwon/minor-doc-build.
## What changes were proposed in this pull request?
[SPARK-19025](https://github.com/apache/spark/pull/16869) removes SQLBuilder, so we don't need the following in HiveCompatibilitySuite.
```scala
// Ensures that the plans generation use metastore relation and not OrcRelation
// Was done because SqlBuilder does not work with plans having logical relation
TestHive.setConf(HiveUtils.CONVERT_METASTORE_ORC, false)
```
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19043 from dongjoon-hyun/SPARK-21831.
## What changes were proposed in this pull request?
Adjust Local UDTs test to assert about results, and fix index of vector column. See JIRA for details.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#19053 from srowen/SPARK-21837.
## What changes were proposed in this pull request?
This patch adds allowUnquotedControlChars option in JSON data source to allow JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters)
## How was this patch tested?
Add new test cases
Author: vinodkc <vinod.kc.in@gmail.com>
Closes#19008 from vinodkc/br_fix_SPARK-21756.
## What changes were proposed in this pull request?
After [SPARK-19025](https://github.com/apache/spark/pull/16869), there is no need to keep SQLBuilderTest.
ExpressionSQLBuilderSuite is the only place to use it.
This PR aims to remove SQLBuilderTest.
## How was this patch tested?
Pass the updated `ExpressionSQLBuilderSuite`.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19044 from dongjoon-hyun/SPARK-21832.
## What changes were proposed in this pull request?
Fix build warnings and Java lint errors. This just helps a bit in evaluating (new) warnings in another PR I have open.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19051 from srowen/JavaWarnings.
## What changes were proposed in this pull request?
Fixed NPE when creating encoder for enum.
When you try to create an encoder for Enum type (or bean with enum property) via Encoders.bean(...), it fails with NullPointerException at TypeToken:495.
I did a little research and it turns out, that in JavaTypeInference following code
```
def getJavaBeanReadableProperties(beanClass: Class[_]): Array[PropertyDescriptor] = {
val beanInfo = Introspector.getBeanInfo(beanClass)
beanInfo.getPropertyDescriptors.filterNot(_.getName == "class")
.filter(_.getReadMethod != null)
}
```
filters out properties named "class", because we wouldn't want to serialize that. But enum types have another property of type Class named "declaringClass", which we are trying to inspect recursively. Eventually we try to inspect ClassLoader class, which has property "defaultAssertionStatus" with no read method, which leads to NPE at TypeToken:495.
I added property name "declaringClass" to filtering to resolve this.
## How was this patch tested?
Unit test in JavaDatasetSuite which creates an encoder for enum
Author: mike <mike0sv@gmail.com>
Author: Mikhail Sveshnikov <mike0sv@gmail.com>
Closes#18488 from mike0sv/enum-support.
## What changes were proposed in this pull request?
This PR bumps the ANTLR version to 4.7, and fixes a number of small parser related issues uncovered by the bump.
The main reason for upgrading is that in some cases the current version of ANTLR (4.5) can exhibit exponential slowdowns if it needs to parse boolean predicates. For example the following query will take forever to parse:
```sql
SELECT *
FROM RANGE(1000)
WHERE
TRUE
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
```
This is caused by a know bug in ANTLR (https://github.com/antlr/antlr4/issues/994), which was fixed in version 4.6.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#19042 from hvanhovell/SPARK-21830.
## What changes were proposed in this pull request?
Add more cases we should view as a normal query stop rather than a failure.
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <zsxwing@gmail.com>
Closes#18997 from zsxwing/SPARK-21788.
## What changes were proposed in this pull request?
With the check for structural integrity proposed in SPARK-21726, it is found that the optimization rule `PullupCorrelatedPredicates` can produce unresolved plans.
For a correlated IN query looks like:
SELECT t1.a FROM t1
WHERE
t1.a IN (SELECT t2.c
FROM t2
WHERE t1.b < t2.d);
The query plan might look like:
Project [a#0]
+- Filter a#0 IN (list#4 [b#1])
: +- Project [c#2]
: +- Filter (outer(b#1) < d#3)
: +- LocalRelation <empty>, [c#2, d#3]
+- LocalRelation <empty>, [a#0, b#1]
After `PullupCorrelatedPredicates`, it produces query plan like:
'Project [a#0]
+- 'Filter a#0 IN (list#4 [(b#1 < d#3)])
: +- Project [c#2, d#3]
: +- LocalRelation <empty>, [c#2, d#3]
+- LocalRelation <empty>, [a#0, b#1]
Because the correlated predicate involves another attribute `d#3` in subquery, it has been pulled out and added into the `Project` on the top of the subquery.
When `list` in `In` contains just one `ListQuery`, `In.checkInputDataTypes` checks if the size of `value` expressions matches the output size of subquery. In the above example, there is only `value` expression and the subquery output has two attributes `c#2, d#3`, so it fails the check and `In.resolved` returns `false`.
We should not let `In.checkInputDataTypes` wrongly report unresolved plans to fail the structural integrity check.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18968 from viirya/SPARK-21759.
## What changes were proposed in this pull request?
This is a refactoring of `ColumnVector` hierarchy and related classes.
1. make `ColumnVector` read-only
2. introduce `WritableColumnVector` with write interface
3. remove `ReadOnlyColumnVector`
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18958 from ueshin/issues/SPARK-21745.
## What changes were proposed in this pull request?
When json_tuple in extracting values from JSON it returns null values within repeated columns except the first one as below:
``` scala
scala> spark.sql("""SELECT json_tuple('{"a":1, "b":2}', 'a', 'b', 'a')""").show()
+---+---+----+
| c0| c1| c2|
+---+---+----+
| 1| 2|null|
+---+---+----+
```
I think this should be consistent with Hive's implementation:
```
hive> SELECT json_tuple('{"a": 1, "b": 2}', 'a', 'a');
...
1 1
```
In this PR, we located all the matched indices in `fieldNames` instead of returning the first matched index, i.e., indexOf.
## How was this patch tested?
Added test in JsonExpressionsSuite.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#19017 from jmchung/SPARK-21804.
## What changes were proposed in this pull request?
The given example in the comment of Class ExchangeCoordinator is exist four post-shuffle partitions,but the current comment is “three”.
## How was this patch tested?
Author: lufei <lu.fei80@zte.com.cn>
Closes#19028 from figo77/SPARK-21816.
## What changes were proposed in this pull request?
The getAliasedConstraints fuction in LogicalPlan.scala will clone the expression set when an element added,
and it will take a long time. This PR add a function to add multiple elements at once to reduce the clone time.
Before modified, the cost of getAliasedConstraints is:
100 expressions: 41 seconds
150 expressions: 466 seconds
After modified, the cost of getAliasedConstraints is:
100 expressions: 1.8 seconds
150 expressions: 6.5 seconds
The test is like this:
test("getAliasedConstraints") {
val expressionNum = 150
val aggExpression = (1 to expressionNum).map(i => Alias(Count(Literal(1)), s"cnt$i")())
val aggPlan = Aggregate(Nil, aggExpression, LocalRelation())
val beginTime = System.currentTimeMillis()
val expressions = aggPlan.validConstraints
println(s"validConstraints cost: ${System.currentTimeMillis() - beginTime}ms")
// The size of Aliased expression is n * (n - 1) / 2 + n
assert( expressions.size === expressionNum * (expressionNum - 1) / 2 + expressionNum)
}
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Run new added test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: 10129659 <chen.yanshan@zte.com.cn>
Closes#19022 from eatoncys/getAliasedConstraints.
## What changes were proposed in this pull request?
This pr changed the default value of `maxLinesPerFunction` into `4000`. In #18810, we had this new option to disable code generation for too long functions and I found this option only affected `Q17` and `Q66` in TPC-DS. But, `Q66` had some performance regression:
```
Q17 w/o #18810, 3224ms --> q17 w/#18810, 2627ms (improvement)
Q66 w/o #18810, 1712ms --> q66 w/#18810, 3032ms (regression)
```
To keep the previous performance in TPC-DS, we better set higher value at `maxLinesPerFunction` by default.
## How was this patch tested?
Existing tests.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19021 from maropu/SPARK-21603-FOLLOWUP-1.
## What changes were proposed in this pull request?
All streaming logical plans will now have isStreaming set. This involved adding isStreaming as a case class arg in a few cases, since a node might be logically streaming depending on where it came from.
## How was this patch tested?
Existing unit tests - no functional change is intended in this PR.
Author: Jose Torres <joseph-torres@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18973 from joseph-torres/SPARK-21765.
## What changes were proposed in this pull request?
For Hive-serde tables, we always respect the schema stored in Hive metastore, because the schema could be altered by the other engines that share the same metastore. Thus, we always trust the metastore-controlled schema for Hive-serde tables when the schemas are different (without considering the nullability and cases). However, in some scenarios, Hive metastore also could INCORRECTLY overwrite the schemas when the serde and Hive metastore built-in serde are different.
The proposed solution is to introduce a table-specific option for such scenarios. For a specific table, users can make Spark always respect Spark-inferred/controlled schema instead of trusting metastore-controlled schema. By default, we trust Hive metastore-controlled schema.
## How was this patch tested?
Added a cross-version test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19003 from gatorsmile/respectSparkSchema.
## What changes were proposed in this pull request?
This PR is to enable users to create persistent Scala UDAF (that extends UserDefinedAggregateFunction).
```SQL
CREATE FUNCTION myDoubleAvg AS 'test.org.apache.spark.sql.MyDoubleAvg'
```
Before this PR, Spark UDAF only can be registered through the API `spark.udf.register(...)`
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18700 from gatorsmile/javaUDFinScala.
## What changes were proposed in this pull request?
We do not have any Hive-specific parser. It does not make sense to keep a parser-specific test suite `HiveDDLCommandSuite.scala` in the Hive package. This PR is to remove it.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19015 from gatorsmile/combineDDL.
For Hive tables, the current "replace the schema" code is the correct
path, except that an exception in that path should result in an error, and
not in retrying in a different way.
For data source tables, Spark may generate a non-compatible Hive table;
but for that to work with Hive 2.1, the detection of data source tables needs
to be fixed in the Hive client, to also consider the raw tables used by code
such as `alterTableSchema`.
Tested with existing and added unit tests (plus internal tests with a 2.1 metastore).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18849 from vanzin/SPARK-21617.
## What changes were proposed in this pull request?
Reduce 'Skipping partitions' message to debug
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19010 from srowen/SPARK-21718.
## What changes were proposed in this pull request?
Fix a typo in test.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19005 from viirya/SPARK-21721-followup.
## What changes were proposed in this pull request?
This PR proposes to install `mkdocs` by `pip install` if missing in the path. Mainly to fix Jenkins's documentation build failure in `spark-master-docs`. See https://amplab.cs.berkeley.edu/jenkins/job/spark-master-docs/3580/console.
It also adds `mkdocs` as requirements in `docs/README.md`.
## How was this patch tested?
I manually ran `jekyll build` under `docs` directory after manually removing `mkdocs` via `pip uninstall mkdocs`.
Also, tested this in the same way but on CentOS Linux release 7.3.1611 (Core) where I built Spark few times but never built documentation before and `mkdocs` is not installed.
```
...
Moving back into docs dir.
Moving to SQL directory and building docs.
Missing mkdocs in your path, trying to install mkdocs for SQL documentation generation.
Collecting mkdocs
Downloading mkdocs-0.16.3-py2.py3-none-any.whl (1.2MB)
100% |████████████████████████████████| 1.2MB 574kB/s
Requirement already satisfied: PyYAML>=3.10 in /usr/lib64/python2.7/site-packages (from mkdocs)
Collecting livereload>=2.5.1 (from mkdocs)
Downloading livereload-2.5.1-py2-none-any.whl
Collecting tornado>=4.1 (from mkdocs)
Downloading tornado-4.5.1.tar.gz (483kB)
100% |████████████████████████████████| 491kB 1.4MB/s
Collecting Markdown>=2.3.1 (from mkdocs)
Downloading Markdown-2.6.9.tar.gz (271kB)
100% |████████████████████████████████| 276kB 2.4MB/s
Collecting click>=3.3 (from mkdocs)
Downloading click-6.7-py2.py3-none-any.whl (71kB)
100% |████████████████████████████████| 71kB 2.8MB/s
Requirement already satisfied: Jinja2>=2.7.1 in /usr/lib/python2.7/site-packages (from mkdocs)
Requirement already satisfied: six in /usr/lib/python2.7/site-packages (from livereload>=2.5.1->mkdocs)
Requirement already satisfied: backports.ssl_match_hostname in /usr/lib/python2.7/site-packages (from tornado>=4.1->mkdocs)
Collecting singledispatch (from tornado>=4.1->mkdocs)
Downloading singledispatch-3.4.0.3-py2.py3-none-any.whl
Collecting certifi (from tornado>=4.1->mkdocs)
Downloading certifi-2017.7.27.1-py2.py3-none-any.whl (349kB)
100% |████████████████████████████████| 358kB 2.1MB/s
Collecting backports_abc>=0.4 (from tornado>=4.1->mkdocs)
Downloading backports_abc-0.5-py2.py3-none-any.whl
Requirement already satisfied: MarkupSafe>=0.23 in /usr/lib/python2.7/site-packages (from Jinja2>=2.7.1->mkdocs)
Building wheels for collected packages: tornado, Markdown
Running setup.py bdist_wheel for tornado ... done
Stored in directory: /root/.cache/pip/wheels/84/83/cd/6a04602633457269d161344755e6766d24307189b7a67ff4b7
Running setup.py bdist_wheel for Markdown ... done
Stored in directory: /root/.cache/pip/wheels/bf/46/10/c93e17ae86ae3b3a919c7b39dad3b5ccf09aeb066419e5c1e5
Successfully built tornado Markdown
Installing collected packages: singledispatch, certifi, backports-abc, tornado, livereload, Markdown, click, mkdocs
Successfully installed Markdown-2.6.9 backports-abc-0.5 certifi-2017.7.27.1 click-6.7 livereload-2.5.1 mkdocs-0.16.3 singledispatch-3.4.0.3 tornado-4.5.1
Generating markdown files for SQL documentation.
Generating HTML files for SQL documentation.
INFO - Cleaning site directory
INFO - Building documentation to directory: .../spark/sql/site
Moving back into docs dir.
Making directory api/sql
cp -r ../sql/site/. api/sql
Source: .../spark/docs
Destination: .../spark/docs/_site
Generating...
done.
Auto-regeneration: disabled. Use --watch to enable.
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18984 from HyukjinKwon/sql-doc-mkdocs.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/18955 , to fix a bug that we break whole stage codegen for `Limit`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18993 from cloud-fan/bug.
## What changes were proposed in this pull request?
Added support for ANALYZE TABLE [db_name].tablename PARTITION (partcol1[=val1], partcol2[=val2], ...) COMPUTE STATISTICS [NOSCAN] SQL command to calculate total number of rows and size in bytes for a subset of partitions. Calculated statistics are stored in Hive Metastore as user-defined properties attached to partition objects. Property names are the same as the ones used to store table-level statistics: spark.sql.statistics.totalSize and spark.sql.statistics.numRows.
When partition specification contains all partition columns with values, the command collects statistics for a single partition that matches the specification. When some partition columns are missing or listed without their values, the command collects statistics for all partitions which match a subset of partition column values specified.
For example, table t has 4 partitions with the following specs:
* Partition1: (ds='2008-04-08', hr=11)
* Partition2: (ds='2008-04-08', hr=12)
* Partition3: (ds='2008-04-09', hr=11)
* Partition4: (ds='2008-04-09', hr=12)
'ANALYZE TABLE t PARTITION (ds='2008-04-09', hr=11)' command will collect statistics only for partition 3.
'ANALYZE TABLE t PARTITION (ds='2008-04-09')' command will collect statistics for partitions 3 and 4.
'ANALYZE TABLE t PARTITION (ds, hr)' command will collect statistics for all four partitions.
When the optional parameter NOSCAN is specified, the command doesn't count number of rows and only gathers size in bytes.
The statistics gathered by ANALYZE TABLE command can be fetched using DESC EXTENDED [db_name.]tablename PARTITION command.
## How was this patch tested?
Added tests.
Author: Masha Basmanova <mbasmanova@fb.com>
Closes#18421 from mbasmanova/mbasmanova-analyze-partition.
## What changes were proposed in this pull request?
Dataset.sample requires a boolean flag withReplacement as the first argument. However, most of the time users simply want to sample some records without replacement. This ticket introduces a new sample function that simply takes in the fraction and seed.
## How was this patch tested?
Tested manually. Not sure yet if we should add a test case for just this wrapper ...
Author: Reynold Xin <rxin@databricks.com>
Closes#18988 from rxin/SPARK-21778.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/projects/SPARK/issues/SPARK-21739
This issue is caused by introducing TimeZoneAwareExpression.
When the **Cast** expression converts something into TimestampType, it should be resolved with setting `timezoneId`. In general, it is resolved in LogicalPlan phase.
However, there are still some places that use Cast expression statically to convert datatypes without setting `timezoneId`. In such cases, `NoSuchElementException: None.get` will be thrown for TimestampType.
This PR is proposed to fix the issue. We have checked the whole project and found two such usages(i.e., in`TableReader` and `HiveTableScanExec`).
## How was this patch tested?
unit test
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18960 from DonnyZone/spark-21739.
## What changes were proposed in this pull request?
Decimal is a logical type of AVRO. We need to ensure the support of Hive's AVRO serde works well in Spark
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18977 from gatorsmile/addAvroTest.
## What changes were proposed in this pull request?
``` scala
scala> Seq(("""{"Hyukjin": 224, "John": 1225}""")).toDS.selectExpr("json_tuple(value, trim(null))").show()
...
java.lang.NullPointerException
at ...
```
Currently the `null` field name will throw NullPointException. As a given field name null can't be matched with any field names in json, we just output null as its column value. This PR achieves it by returning a very unlikely column name `__NullFieldName` in evaluation of the field names.
## How was this patch tested?
Added unit test.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#18930 from jmchung/SPARK-21677.
## What changes were proposed in this pull request?
This pr sorted output attributes on their name and exprId in `AttributeSet.toSeq` to make the order consistent. If the order is different, spark possibly generates different code and then misses cache in `CodeGenerator`, e.g., `GenerateColumnAccessor` generates code depending on an input attribute order.
## How was this patch tested?
Added tests in `AttributeSetSuite` and manually checked if the cache worked well in the given query of the JIRA.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18959 from maropu/SPARK-18394.
## What changes were proposed in this pull request?
When running IntelliJ, we are unable to capture the exception of memory leak detection.
> org.apache.spark.executor.Executor: Managed memory leak detected
Explicitly setting `spark.unsafe.exceptionOnMemoryLeak` in SparkConf when building the SparkSession, instead of reading it from system properties.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18967 from gatorsmile/setExceptionOnMemoryLeak.
## What changes were proposed in this pull request?
Set isolated to false while using builtin hive jars and `SessionState.get` returns a `CliSessionState` instance.
## How was this patch tested?
1 Unit Tests
2 Manually verified: `hive.exec.strachdir` was only created once because of reusing cliSessionState
```java
➜ spark git:(SPARK-21428) ✗ bin/spark-sql --conf spark.sql.hive.metastore.jars=builtin
log4j:WARN No appenders could be found for logger (org.apache.hadoop.util.Shell).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/07/16 23:59:27 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/07/16 23:59:27 INFO HiveMetaStore: 0: Opening raw store with implemenation class:org.apache.hadoop.hive.metastore.ObjectStore
17/07/16 23:59:27 INFO ObjectStore: ObjectStore, initialize called
17/07/16 23:59:28 INFO Persistence: Property hive.metastore.integral.jdo.pushdown unknown - will be ignored
17/07/16 23:59:28 INFO Persistence: Property datanucleus.cache.level2 unknown - will be ignored
17/07/16 23:59:29 INFO ObjectStore: Setting MetaStore object pin classes with hive.metastore.cache.pinobjtypes="Table,StorageDescriptor,SerDeInfo,Partition,Database,Type,FieldSchema,Order"
17/07/16 23:59:30 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:30 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO MetaStoreDirectSql: Using direct SQL, underlying DB is DERBY
17/07/16 23:59:31 INFO ObjectStore: Initialized ObjectStore
17/07/16 23:59:31 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
17/07/16 23:59:31 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
17/07/16 23:59:32 INFO HiveMetaStore: Added admin role in metastore
17/07/16 23:59:32 INFO HiveMetaStore: Added public role in metastore
17/07/16 23:59:32 INFO HiveMetaStore: No user is added in admin role, since config is empty
17/07/16 23:59:32 INFO HiveMetaStore: 0: get_all_databases
17/07/16 23:59:32 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_all_databases
17/07/16 23:59:32 INFO HiveMetaStore: 0: get_functions: db=default pat=*
17/07/16 23:59:32 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_functions: db=default pat=*
17/07/16 23:59:32 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MResourceUri" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:32 INFO SessionState: Created local directory: /var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/beea7261-221a-4711-89e8-8b12a9d37370_resources
17/07/16 23:59:32 INFO SessionState: Created HDFS directory: /tmp/hive/Kent/beea7261-221a-4711-89e8-8b12a9d37370
17/07/16 23:59:32 INFO SessionState: Created local directory: /var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/Kent/beea7261-221a-4711-89e8-8b12a9d37370
17/07/16 23:59:32 INFO SessionState: Created HDFS directory: /tmp/hive/Kent/beea7261-221a-4711-89e8-8b12a9d37370/_tmp_space.db
17/07/16 23:59:32 INFO SparkContext: Running Spark version 2.3.0-SNAPSHOT
17/07/16 23:59:32 INFO SparkContext: Submitted application: SparkSQL::10.0.0.8
17/07/16 23:59:32 INFO SecurityManager: Changing view acls to: Kent
17/07/16 23:59:32 INFO SecurityManager: Changing modify acls to: Kent
17/07/16 23:59:32 INFO SecurityManager: Changing view acls groups to:
17/07/16 23:59:32 INFO SecurityManager: Changing modify acls groups to:
17/07/16 23:59:32 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(Kent); groups with view permissions: Set(); users with modify permissions: Set(Kent); groups with modify permissions: Set()
17/07/16 23:59:33 INFO Utils: Successfully started service 'sparkDriver' on port 51889.
17/07/16 23:59:33 INFO SparkEnv: Registering MapOutputTracker
17/07/16 23:59:33 INFO SparkEnv: Registering BlockManagerMaster
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
17/07/16 23:59:33 INFO DiskBlockManager: Created local directory at /private/var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/blockmgr-9cfae28a-01e9-4c73-a1f1-f76fa52fc7a5
17/07/16 23:59:33 INFO MemoryStore: MemoryStore started with capacity 366.3 MB
17/07/16 23:59:33 INFO SparkEnv: Registering OutputCommitCoordinator
17/07/16 23:59:33 INFO Utils: Successfully started service 'SparkUI' on port 4040.
17/07/16 23:59:33 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://10.0.0.8:4040
17/07/16 23:59:33 INFO Executor: Starting executor ID driver on host localhost
17/07/16 23:59:33 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 51890.
17/07/16 23:59:33 INFO NettyBlockTransferService: Server created on 10.0.0.8:51890
17/07/16 23:59:33 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
17/07/16 23:59:33 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: Registering block manager 10.0.0.8:51890 with 366.3 MB RAM, BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:34 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('file:/Users/Kent/Documents/spark/spark-warehouse').
17/07/16 23:59:34 INFO SharedState: Warehouse path is 'file:/Users/Kent/Documents/spark/spark-warehouse'.
17/07/16 23:59:34 INFO HiveUtils: Initializing HiveMetastoreConnection version 1.2.1 using Spark classes.
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO HiveMetaStore: 0: get_database: default
17/07/16 23:59:34 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_database: default
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO HiveMetaStore: 0: get_database: global_temp
17/07/16 23:59:34 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_database: global_temp
17/07/16 23:59:34 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO StateStoreCoordinatorRef: Registered StateStoreCoordinator endpoint
spark-sql>
```
cc cloud-fan gatorsmile
Author: Kent Yao <yaooqinn@hotmail.com>
Author: hzyaoqin <hzyaoqin@corp.netease.com>
Closes#18648 from yaooqinn/SPARK-21428.
## What changes were proposed in this pull request?
For top-most limit, we will use a special operator to execute it: `CollectLimitExec`.
`CollectLimitExec` will retrieve `n`(which is the limit) rows from each partition of the child plan output, see https://github.com/apache/spark/blob/v2.2.0/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala#L311. It's very likely that we don't exhaust the child plan output.
This is fine when whole-stage-codegen is off, as child plan will release the resource via task completion listener. However, when whole-stage codegen is on, the resource can only be released if all output is consumed.
To fix this memory leak, one simple approach is, when `CollectLimitExec` retrieve `n` rows from child plan output, child plan output should only have `n` rows, then the output is exhausted and resource is released. This can be done by wrapping child plan with `LocalLimit`
## How was this patch tested?
a regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18955 from cloud-fan/leak.
## What changes were proposed in this pull request?
When a session is closed the Thriftserver doesn't cancel the jobs which may still be running. This is a huge waste of resources.
This PR address the problem canceling the pending jobs when a session is closed.
## How was this patch tested?
The patch was tested manually.
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#18951 from mgaido91/SPARK-21738.
## What changes were proposed in this pull request?
Close the whole stage codegen when the function lines is longer than the maxlines which will be setted by
spark.sql.codegen.MaxFunctionLength parameter, because when the function is too long , it will not get the JIT optimizing.
A benchmark test result is 10x slower when the generated function is too long :
ignore("max function length of wholestagecodegen") {
val N = 20 << 15
val benchmark = new Benchmark("max function length of wholestagecodegen", N)
def f(): Unit = sparkSession.range(N)
.selectExpr(
"id",
"(id & 1023) as k1",
"cast(id & 1023 as double) as k2",
"cast(id & 1023 as int) as k3",
"case when id > 100 and id <= 200 then 1 else 0 end as v1",
"case when id > 200 and id <= 300 then 1 else 0 end as v2",
"case when id > 300 and id <= 400 then 1 else 0 end as v3",
"case when id > 400 and id <= 500 then 1 else 0 end as v4",
"case when id > 500 and id <= 600 then 1 else 0 end as v5",
"case when id > 600 and id <= 700 then 1 else 0 end as v6",
"case when id > 700 and id <= 800 then 1 else 0 end as v7",
"case when id > 800 and id <= 900 then 1 else 0 end as v8",
"case when id > 900 and id <= 1000 then 1 else 0 end as v9",
"case when id > 1000 and id <= 1100 then 1 else 0 end as v10",
"case when id > 1100 and id <= 1200 then 1 else 0 end as v11",
"case when id > 1200 and id <= 1300 then 1 else 0 end as v12",
"case when id > 1300 and id <= 1400 then 1 else 0 end as v13",
"case when id > 1400 and id <= 1500 then 1 else 0 end as v14",
"case when id > 1500 and id <= 1600 then 1 else 0 end as v15",
"case when id > 1600 and id <= 1700 then 1 else 0 end as v16",
"case when id > 1700 and id <= 1800 then 1 else 0 end as v17",
"case when id > 1800 and id <= 1900 then 1 else 0 end as v18")
.groupBy("k1", "k2", "k3")
.sum()
.collect()
benchmark.addCase(s"codegen = F") { iter =>
sparkSession.conf.set("spark.sql.codegen.wholeStage", "false")
f()
}
benchmark.addCase(s"codegen = T") { iter =>
sparkSession.conf.set("spark.sql.codegen.wholeStage", "true")
sparkSession.conf.set("spark.sql.codegen.MaxFunctionLength", "10000")
f()
}
benchmark.run()
/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_111-b14 on Windows 7 6.1
Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
max function length of wholestagecodegen: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
codegen = F 443 / 507 1.5 676.0 1.0X
codegen = T 3279 / 3283 0.2 5002.6 0.1X
*/
}
## How was this patch tested?
Run the unit test
Author: 10129659 <chen.yanshan@zte.com.cn>
Closes#18810 from eatoncys/codegen.
## What changes were proposed in this pull request?
Like Parquet, this PR aims to depend on the latest Apache ORC 1.4 for Apache Spark 2.3. There are key benefits for Apache ORC 1.4.
- Stability: Apache ORC 1.4.0 has many fixes and we can depend on ORC community more.
- Maintainability: Reduce the Hive dependency and can remove old legacy code later.
Later, we can get the following two key benefits by adding new ORCFileFormat in SPARK-20728 (#17980), too.
- Usability: User can use ORC data sources without hive module, i.e, -Phive.
- Speed: Use both Spark ColumnarBatch and ORC RowBatch together. This will be faster than the current implementation in Spark.
## How was this patch tested?
Pass the jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18640 from dongjoon-hyun/SPARK-21422.
## What changes were proposed in this pull request?
This patch adds the DataFrames API to the multivariate summarizer (mean, variance, etc.). In addition to all the features of MultivariateOnlineSummarizer, it also allows the user to select a subset of the metrics.
## How was this patch tested?
Testcases added.
## Performance
Resolve several performance issues in #17419, further optimization pending on SQL team's work. One of the SQL layer performance issue related to these feature has been resolved in #18712, thanks liancheng and cloud-fan
### Performance data
(test on my laptop, use 2 partitions. tries out = 20, warm up = 10)
The unit of test results is records/milliseconds (higher is better)
Vector size/records number | 1/10000000 | 10/1000000 | 100/1000000 | 1000/100000 | 10000/10000
----|------|----|---|----|----
Dataframe | 15149 | 7441 | 2118 | 224 | 21
RDD from Dataframe | 4992 | 4440 | 2328 | 320 | 33
raw RDD | 53931 | 20683 | 3966 | 528 | 53
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#18798 from WeichenXu123/SPARK-19634-dataframe-summarizer.
## What changes were proposed in this pull request?
The method name `asNonNullabe` should be `asNonNullable`.
## How was this patch tested?
N/A
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18952 from jiangxb1987/typo.
This version fixes a few issues in the import order checker; it provides
better error messages, and detects more improper ordering (thus the need
to change a lot of files in this patch). The main fix is that it correctly
complains about the order of packages vs. classes.
As part of the above, I moved some "SparkSession" import in ML examples
inside the "$example on$" blocks; that didn't seem consistent across
different source files to start with, and avoids having to add more on/off blocks
around specific imports.
The new scalastyle also seems to have a better header detector, so a few
license headers had to be updated to match the expected indentation.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18943 from vanzin/SPARK-21731.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/15900 , to fix one more bug:
When table schema is empty and need to be inferred at runtime, we should not resolve parent plans before the schema has been inferred, or the parent plans will be resolved against an empty schema and may get wrong result for something like `select *`
The fix logic is: introduce `UnresolvedCatalogRelation` as a placeholder. Then we replace it with `LogicalRelation` or `HiveTableRelation` during analysis, so that it's guaranteed that we won't resolve parent plans until the schema has been inferred.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18907 from cloud-fan/bug.
## What changes were proposed in this pull request?
This is a follow-up PR that moves the test case in PR-18920 (https://github.com/apache/spark/pull/18920) to DataFrameAggregateSuit.
## How was this patch tested?
unit test
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18946 from DonnyZone/branch-19471-followingPR.
## What changes were proposed in this pull request?
This PR changes the codes to lazily init hive metastore client so that we can create SparkSession without talking to the hive metastore sever.
It's pretty helpful when you set a hive metastore server but it's down. You can still start the Spark shell to debug.
## How was this patch tested?
The new unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18944 from zsxwing/hive-lazy-init.
## What changes were proposed in this pull request?
This PR adds `since` annotation in documentation so that this can be rendered as below:
<img width="290" alt="2017-08-14 6 54 26" src="https://user-images.githubusercontent.com/6477701/29267050-034c1f64-8122-11e7-862b-7dfc38e292bf.png">
## How was this patch tested?
Manually checked the documentation by `cd sql && ./create-docs.sh`.
Also, Jenkins tests are required.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18939 from HyukjinKwon/add-sinces-date-functions.
## What changes were proposed in this pull request?
We put staging path to delete into the deleteOnExit cache of `FileSystem` in case of the path can't be successfully removed. But when we successfully remove the path, we don't remove it from the cache. We should do it to avoid continuing grow the cache size.
## How was this patch tested?
Added a test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18934 from viirya/SPARK-21721.
## What changes were proposed in this pull request?
Directly writing a snapshot file may generate a partial file. This PR changes it to write to a temp file then rename to the target file.
## How was this patch tested?
Jenkins.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18928 from zsxwing/SPARK-21696.
## What changes were proposed in this pull request?
Recently, we have also encountered such NPE issues in our production environment as described in:
https://issues.apache.org/jira/browse/SPARK-19471
This issue can be reproduced by the following examples:
` val df = spark.createDataFrame(Seq(("1", 1), ("1", 2), ("2", 3), ("2", 4))).toDF("x", "y")
//HashAggregate, SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key=false
df.groupBy("x").agg(rand(),sum("y")).show()
//ObjectHashAggregate, SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key=false
df.groupBy("x").agg(rand(),collect_list("y")).show()
//SortAggregate, SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key=false &&SQLConf.USE_OBJECT_HASH_AGG.key=false
df.groupBy("x").agg(rand(),collect_list("y")).show()`
`
This PR is based on PR-16820(https://github.com/apache/spark/pull/16820) with test cases for all aggregation paths. We want to push it forward.
> When AggregationIterator generates result projection, it does not call the initialize method of the Projection class. This will cause a runtime NullPointerException when the projection involves nondeterministic expressions.
## How was this patch tested?
unit test
verified in production environment
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18920 from DonnyZone/Branch-spark-19471.
## What changes were proposed in this pull request?
At present, in test("broadcasted hash outer join operator selection") case, set the testData2 to _CACHE TABLE_, but no _uncache table_ testData2. It can make people confused.
In addition, in the joinsuite test cases, clear the cache table of work by SharedSQLContext _spark.sharedState.cacheManager.clearCache_ to do, so we do not need to uncache table
let's fix it. thanks.
## How was this patch tested?
Existing test cases.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#18914 from heary-cao/uncache_table.
## What changes were proposed in this pull request?
While discovering optimization rules and their test coverage, I did not find any tests for `CheckCartesianProducts` in the Catalyst folder. So, I decided to create a new test suite. Once I finished, I found a test in `JoinSuite` for this functionality so feel free to discard this change if it does not make much sense. The proposed test suite covers a few additional use cases.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18909 from aokolnychyi/check-cartesian-join-tests.
