### What changes were proposed in this pull request?
This PR proposes migration of Core to NumPy documentation style.
### Why are the changes needed?
To improve documentation style.
### Does this PR introduce _any_ user-facing change?
Yes, this changes both rendered HTML docs and console representation (SPARK-33243).
### How was this patch tested?
dev/lint-python and manual inspection.
Closes#30320 from zero323/SPARK-33254.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Exit Spark SQL CLI processing loop if one of the commands (sub sql statement) process failed
This is a regression at Apache Spark 3.0.0.
```
$ cat 1.sql
select * from nonexistent_table;
select 2;
```
**Apache Spark 2.4.7**
```
spark-2.4.7-bin-hadoop2.7:$ bin/spark-sql -f 1.sql
20/11/15 16:14:38 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Error in query: Table or view not found: nonexistent_table; line 1 pos 14
```
**Apache Spark 3.0.1**
```
$ bin/spark-sql -f 1.sql
Error in query: Table or view not found: nonexistent_table; line 1 pos 14;
'Project [*]
+- 'UnresolvedRelation [nonexistent_table]
2
Time taken: 2.786 seconds, Fetched 1 row(s)
```
**Apache Hive 1.2.2**
```
apache-hive-1.2.2-bin:$ bin/hive -f 1.sql
Logging initialized using configuration in jar:file:/Users/dongjoon/APACHE/hive-release/apache-hive-1.2.2-bin/lib/hive-common-1.2.2.jar!/hive-log4j.properties
FAILED: SemanticException [Error 10001]: Line 1:14 Table not found 'nonexistent_table'
```
Yes. This is a fix of regression.
Pass the UT.
Closes#30263 from artiship/SPARK-33358.
Authored-by: artiship <meilziner@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This patch adds a benchmark `SubExprEliminationBenchmark` for benchmarking subexpression elimination feature.
### Why are the changes needed?
We need a benchmark for subexpression elimination feature for change such as #30341.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Unit test.
Closes#30379 from viirya/SPARK-33455.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR makes SQL parser using active SQLConf instead of the one in ctor-parameters.
### Why are the changes needed?
In ANSI mode, schema string parsing should fail if the schema uses ANSI reserved keyword as attribute name:
```scala
spark.conf.set("spark.sql.ansi.enabled", "true")
spark.sql("""select from_json('{"time":"26/10/2015"}', 'time Timestamp', map('timestampFormat', 'dd/MM/yyyy'));""").show
```
output:
> Cannot parse the data type:
> no viable alternative at input 'time'(line 1, pos 0)
>
> == SQL ==
> time Timestamp
> ^^^
But this query may accidentally succeed in certain cases cause the DataType parser sticks to the configs of the first created session in the current thread:
```scala
DataType.fromDDL("time Timestamp")
val newSpark = spark.newSession()
newSpark.conf.set("spark.sql.ansi.enabled", "true")
newSpark.sql("""select from_json('{"time":"26/10/2015"}', 'time Timestamp', map('timestampFormat', 'dd/MM/yyyy'));""").show
```
output:
> +--------------------------------+
> |from_json({"time":"26/10/2015"})|
> +--------------------------------+
> | {2015-10-26 00:00...|
> +--------------------------------+
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Newly and updated UTs
Closes#30357 from luluorta/SPARK-33432.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Modify SparkSQLCLIDriver.scala to move ahead calling the cli.printMasterAndAppId method before process file.
### Why are the changes needed?
Even though in SPARK-25043 it has already brought in the printing application id feature. But the process file situation seems have not been included. This small change is to make spark-sql will also print out application id when process file.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
env
```
spark version: 3.0.1
os: centos 7
```
/tmp/tmp.sql
```sql
select 1;
```
submit command:
```sh
export HADOOP_USER_NAME=my-hadoop-user
bin/spark-sql \
--master yarn \
--deploy-mode client \
--queue my.queue.name \
--conf spark.driver.host=$(hostname -i) \
--conf spark.app.name=spark-test \
--name "spark-test" \
-f /tmp/tmp.sql
```
execution log:
```sh
20/11/09 23:18:39 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
20/11/09 23:18:40 WARN HiveConf: HiveConf of name hive.spark.client.rpc.server.address.use.ip does not exist
20/11/09 23:18:40 WARN HiveConf: HiveConf of name hive.spark.client.submit.timeout.interval does not exist
20/11/09 23:18:40 WARN HiveConf: HiveConf of name hive.enforce.bucketing does not exist
20/11/09 23:18:40 WARN HiveConf: HiveConf of name hive.server2.enable.impersonation does not exist
20/11/09 23:18:40 WARN HiveConf: HiveConf of name hive.run.timeout.seconds does not exist
20/11/09 23:18:40 WARN HiveConf: HiveConf of name hive.support.sql11.reserved.keywords does not exist
20/11/09 23:18:40 WARN DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
20/11/09 23:18:41 WARN SparkConf: Note that spark.local.dir will be overridden by the value set by the cluster manager (via SPARK_LOCAL_DIRS in mesos/standalone/kubernetes and LOCAL_DIRS in YARN).
20/11/09 23:18:42 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
20/11/09 23:18:52 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
Spark master: yarn, Application Id: application_1567136266901_27355775
1
1
Time taken: 4.974 seconds, Fetched 1 row(s)
```
Closes#30301 from artiship/SPARK-33396.
Authored-by: artiship <meilziner@gmail.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
This is a simple followup to prevent test flakyness in SubexpressionEliminationSuite. If `getAllEquivalentExprs` returns more than 1 sequences, due to HashMap, we should use `contains` instead of assuming the order of results.
### Why are the changes needed?
Prevent test flakyness in SubexpressionEliminationSuite.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Unit test.
Closes#30371 from viirya/SPARK-33337-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is a follow-up fix for the failing tests in `YarnShuffleServiceSuite.java`. This java class was introduced in https://github.com/apache/spark/pull/30062. The tests in the class fail when run with hadoop-2.7 profile:
```
[ERROR] testCreateDefaultMergedShuffleFileManagerInstance(org.apache.spark.network.yarn.YarnShuffleServiceSuite) Time elapsed: 0.627 s <<< ERROR!
java.lang.NoClassDefFoundError: org/apache/commons/logging/LogFactory
at org.apache.spark.network.yarn.YarnShuffleServiceSuite.testCreateDefaultMergedShuffleFileManagerInstance(YarnShuffleServiceSuite.java:37)
Caused by: java.lang.ClassNotFoundException: org.apache.commons.logging.LogFactory
at org.apache.spark.network.yarn.YarnShuffleServiceSuite.testCreateDefaultMergedShuffleFileManagerInstance(YarnShuffleServiceSuite.java:37)
[ERROR] testCreateRemoteBlockPushResolverInstance(org.apache.spark.network.yarn.YarnShuffleServiceSuite) Time elapsed: 0 s <<< ERROR!
java.lang.NoClassDefFoundError: Could not initialize class org.apache.spark.network.yarn.YarnShuffleService
at org.apache.spark.network.yarn.YarnShuffleServiceSuite.testCreateRemoteBlockPushResolverInstance(YarnShuffleServiceSuite.java:47)
[ERROR] testInvalidClassNameOfMergeManagerWillUseNoOpInstance(org.apache.spark.network.yarn.YarnShuffleServiceSuite) Time elapsed: 0.001 s <<< ERROR!
java.lang.NoClassDefFoundError: Could not initialize class org.apache.spark.network.yarn.YarnShuffleService
at org.apache.spark.network.yarn.YarnShuffleServiceSuite.testInvalidClassNameOfMergeManagerWillUseNoOpInstance(YarnShuffleServiceSuite.java:57)
```
A test suit for `YarnShuffleService` did exist here:
`resource-managers/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala`
I missed this when I created `common/network-yarn/src/test/java/org/apache/spark/network/yarn/YarnShuffleServiceSuite.java`. Moving all the new tests to the earlier test suite fixes the failures with hadoop-2.7 even though why this happened is not clear.
### Why are the changes needed?
The newly added tests are failing when run with hadoop profile 2.7
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Ran the unit tests with the default profile as well as hadoop 2.7 profile.
`build/mvn test -Dtest=none -DwildcardSuites=org.apache.spark.network.yarn.YarnShuffleServiceSuite -Phadoop-2.7 -Pyarn`
```
Run starting. Expected test count is: 11
YarnShuffleServiceSuite:
- executor state kept across NM restart
- removed applications should not be in registered executor file
- shuffle service should be robust to corrupt registered executor file
- get correct recovery path
- moving recovery file from NM local dir to recovery path
- service throws error if cannot start
- recovery db should not be created if NM recovery is not enabled
- SPARK-31646: metrics should be registered into Node Manager's metrics system
- create default merged shuffle file manager instance
- create remote block push resolver instance
- invalid class name of merge manager will use noop instance
Run completed in 2 seconds, 572 milliseconds.
Total number of tests run: 11
Suites: completed 2, aborted 0
Tests: succeeded 11, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Closes#30349 from otterc/SPARK-32916-followup.
Authored-by: Chandni Singh <singh.chandni@gmail.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This adds support for Stage level scheduling to kubernetes. Kubernetes can support dynamic allocation via the shuffle tracking option which means we can support stage level scheduling by getting new executors.
The main changes here are having the k8s cluster manager pass the resource profile id into the executors and then the ExecutorsPodsAllocator has to request executors based on the individual resource profiles. I tried to keep code changes here to a minimum. I specifically choose to leave the ExecutorPodsSnapshot the way it was and construct the resource profile to pod states on the fly, with a fast path when not using other resource profiles, to keep the impact to a minimum. This results in the main changes required are just wrapping the allocation logic in a for loop over each profile. The other main change is in the basic feature step we have to look at the resources in the ResourceProfile to request pods with the correct resources. Much of the other logic like in the executor life cycle manager doesn't need to be resource profile.
This also adds support for [SPARK-32661]Spark executors on K8S should request extra memory for off-heap allocations because the stage level scheduling api has support for this and it made sense to make consistent with YARN. This was started with PR https://github.com/apache/spark/pull/29477 but never updated so I just did it here. To do this I moved a few functions around that were now used by both YARN and kubernetes so you will see some changes in Utils.
### Why are the changes needed?
Add the feature to Kubernetes based on customer feedback.
### Does this PR introduce _any_ user-facing change?
Yes the feature now works with K8s, but not underlying API changes.
### How was this patch tested?
Tested manually on kubernetes cluster and with unit tests.
Closes#30204 from tgravescs/stagek8sOrigSnapshotsRebase.
Lead-authored-by: Thomas Graves <tgraves@apache.org>
Co-authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
In [SPARK-33139] we defined `setActionSession` and `clearActiveSession` as deprecated API, it turns out it is widely used, and after discussion, even if without this PR, it should work with unify view feature, it might only be a risk if user really abuse using these two API. So revert the PR is needed.
[SPARK-33139] has two commit, include a follow up. Revert them both.
### Why are the changes needed?
Revert.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#30367 from leanken/leanken-revert-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the last few releases, our Spark documentation https://spark.apache.org/docs/latest/ becomes richer. It would nice to provide a search function to make our users find contents faster.
[DocSearch](https://docsearch.algolia.com/) is entirely free and automated. This PR will use it to provides search function.
The screenshots show below:
### Why are the changes needed?
Let the users of Spark documentation could find the needed information effectively.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
build on my machine and look on brower.
Closes#30292 from beliefer/SPARK-33166.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
SparkSession.sql converts a string value to a DataFrame, and the string value should be one single SQL statement ending up w/ or w/o one or more semicolons. e.g.
```sql
scala> spark.sql(" select 2").show
+---+
| 2|
+---+
| 2|
+---+
scala> spark.sql(" select 2;").show
+---+
| 2|
+---+
| 2|
+---+
scala> spark.sql(" select 2;;;;").show
+---+
| 2|
+---+
| 2|
+---+
```
If we put 2 or more statements in, it fails in the parser as expected, e.g.
```sql
scala> spark.sql(" select 2; select 1;").show
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input 'select' expecting {<EOF>, ';'}(line 1, pos 11)
== SQL ==
select 2; select 1;
-----------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:263)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:130)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
```
As a very generic user scenario, users may want to change some settings before they execute
the queries. They may pass a string value like `set spark.sql.abc=2; select 1;` into this API, which creates a confusing gap between the actual effect and the user's expectations.
The user may want the query to be executed with spark.sql.abc=2, but Spark actually treats the whole part of `2; select 1;` as the value of the property 'spark.sql.abc',
e.g.
```
scala> spark.sql("set spark.sql.abc=2; select 1;").show
+-------------+------------+
| key| value|
+-------------+------------+
|spark.sql.abc|2; select 1;|
+-------------+------------+
```
What's more, the SET symbol could digest everything behind it, which makes it unstable from version to version, e.g.
#### 3.1
```sql
scala> spark.sql("set;").show
org.apache.spark.sql.catalyst.parser.ParseException:
Expected format is 'SET', 'SET key', or 'SET key=value'. If you want to include special characters in key, please use quotes, e.g., SET `ke y`=value.(line 1, pos 0)
== SQL ==
set;
^^^
at org.apache.spark.sql.execution.SparkSqlAstBuilder.$anonfun$visitSetConfiguration$1(SparkSqlParser.scala:83)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:72)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:58)
at org.apache.spark.sql.catalyst.parser.SqlBaseParser$SetConfigurationContext.accept(SqlBaseParser.java:2161)
at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:18)
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitSingleStatement$1(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.$anonfun$parsePlan$1(ParseDriver.scala:82)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:113)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
scala> spark.sql("set a;").show
org.apache.spark.sql.catalyst.parser.ParseException:
Expected format is 'SET', 'SET key', or 'SET key=value'. If you want to include special characters in key, please use quotes, e.g., SET `ke y`=value.(line 1, pos 0)
== SQL ==
set a;
^^^
at org.apache.spark.sql.execution.SparkSqlAstBuilder.$anonfun$visitSetConfiguration$1(SparkSqlParser.scala:83)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:72)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:58)
at org.apache.spark.sql.catalyst.parser.SqlBaseParser$SetConfigurationContext.accept(SqlBaseParser.java:2161)
at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:18)
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitSingleStatement$1(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.$anonfun$parsePlan$1(ParseDriver.scala:82)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:113)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
```
#### 2.4
```sql
scala> spark.sql("set;").show
+---+-----------+
|key| value|
+---+-----------+
| ;|<undefined>|
+---+-----------+
scala> spark.sql("set a;").show
+---+-----------+
|key| value|
+---+-----------+
| a;|<undefined>|
+---+-----------+
```
In this PR,
1. make `set spark.sql.abc=2; select 1;` in `SparkSession.sql` fail directly, user should call `.sql` for each statement separately.
2. make the semicolon as the separator of statements, and if users want to use it as part of the property value, shall use quotes too.
### Why are the changes needed?
1. disambiguation for `SparkSession.sql`
2. make semicolon work same both w/ `SET` and other statements
### Does this PR introduce _any_ user-facing change?
yes,
the semicolon works as a separator of statements now, it will be trimmed if it is at the end of the statement and fail the statement if it is in the middle. you need to use quotes if you want it to be part of the property value
### How was this patch tested?
new tests
Closes#30332 from yaooqinn/SPARK-33419.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `Aggregate` max rows to 1 if grouping is empty.
### Why are the changes needed?
If `Aggregate` grouping is empty, the result is always one row.
Then we don't need push down limit in `LimitPushDown` with such case
```
select count(*) from t1
union
select count(*) from t2
limit 1
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30356 from ulysses-you/SPARK-33433.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to decrease the parallelism of `SQL` module like `Hive` module.
### Why are the changes needed?
GitHub Action `sql - slow tests` become flaky.
- https://github.com/apache/spark/runs/1393670291
- https://github.com/apache/spark/runs/1393088031
### Does this PR introduce _any_ user-facing change?
No. This is dev-only feature.
Although this will increase the running time, but it's better than flakiness.
### How was this patch tested?
Pass the GitHub Action stably.
Closes#30365 from dongjoon-hyun/SPARK-33439.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
1. Create the separate test suite `org.apache.spark.sql.hive.execution.command.ShowTablesSuite`.
2. Re-use V1 SHOW TABLES tests added by https://github.com/apache/spark/pull/30287 in the Hive test suites.
3. Add new test case for the pattern `'table_name_1*|table_name_2*'` in the common test suite.
### Why are the changes needed?
To test V1 + common SHOW TABLES tests in Hive.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running v1/v2 and Hive v1 `ShowTablesSuite`:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *ShowTablesSuite"
```
Closes#30340 from MaxGekk/show-tables-hive-tests.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to disable the streaming query with possible correctness issue in chained stateful operators. The behavior can be controlled by a SQL config, so if users understand the risk and still want to run the query, they can disable the check.
### Why are the changes needed?
The possible correctness in chained stateful operators in streaming query is not straightforward for users. From users perspective, it will be considered as a Spark bug. It is also possible the worse case, users are not aware of the correctness issue and use wrong results.
A better approach should be to disable such queries and let users choose to run the query if they understand there is such risk, instead of implicitly running the query and let users to find out correctness issue by themselves and report this known to Spark community.
### Does this PR introduce _any_ user-facing change?
Yes. Streaming query with possible correctness issue will be blocked to run, except for users explicitly disable the SQL config.
### How was this patch tested?
Unit test.
Closes#30210 from viirya/SPARK-33259.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This changes `RefreshTableExec` in DSv2 to also invalidate caches with references to the target table to be refreshed. The change itself is similar to what's done in #30211. Note that though, since we currently don't support caching a DSv2 table directly, this doesn't add recache logic as in the DSv1 impl. I marked it as a TODO for now.
### Why are the changes needed?
Currently the behavior in DSv1 and DSv2 is inconsistent w.r.t refreshing table: in DSv1 we invalidate both metadata cache as well as all table caches that are related to the table, but in DSv2 we only do the former. This addresses the issue and make the behavior consistent.
### Does this PR introduce _any_ user-facing change?
Yes, now refreshing a v2 table also invalidate all the related caches.
### How was this patch tested?
Added a new UT.
Closes#30359 from sunchao/SPARK-33435.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In #30097, many rules changed from case class to object, but if the rule
is stateful, there will be a problem. For example, if an object rule uses a
`val` to refer to a config, it will be unchanged after initialization even if
other spark session uses a different config value.
### Why are the changes needed?
Avoid potential bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT
Closes#30354 from linhongliu-db/SPARK-33140-followup-2.
Lead-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Co-authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/29800 provides a performance improvement for `NTH_VALUE`.
`FIRST_VALUE` also could use the `UnboundedOffsetWindowFunctionFrame` and `UnboundedPrecedingOffsetWindowFunctionFrame`.
### Why are the changes needed?
Improve the performance for `FIRST_VALUE`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#30178 from beliefer/SPARK-33278.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `Greatest` and `Least` check in `Canonicalize`.
### Why are the changes needed?
The children of both `Greatest` and `Least` are order Irrelevant.
Let's say we have `greatest(1, 2)` and `greatest(2, 1)`. We can get the same canonicalized expression in this case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30330 from ulysses-you/SPARK-33421.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove "in cluster mode" from the description of `spark.executor.memoryOverhead`
### Why are the changes needed?
fix correctness issue in documentaion
### Does this PR introduce _any_ user-facing change?
yes, users may not get confused about the description `spark.executor.memoryOverhead`
### How was this patch tested?
pass GA doc generation
Closes#30311 from yaooqinn/minordoc.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Instead of returning NULL, throws runtime ArrayIndexOutOfBoundsException when ansiMode is enable for `element_at`，`elt`, `GetArrayItem` functions.
### Why are the changes needed?
For ansiMode.
### Does this PR introduce any user-facing change?
When `spark.sql.ansi.enabled` = true, Spark will throw `ArrayIndexOutOfBoundsException` if out-of-range index when accessing array elements
### How was this patch tested?
Added UT and existing UT.
Closes#30297 from leanken/leanken-SPARK-33386.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to use R 3.6.3 in K8s R image and re-enable `RTestsSuite`.
### Why are the changes needed?
Jenkins Server is using `R 3.6.3`.
```
+ SPARK_HOME=/home/jenkins/workspace/SparkPullRequestBuilder-K8s
+ /usr/bin/R CMD check --as-cran --no-tests SparkR_3.1.0.tar.gz
* using log directory ‘/home/jenkins/workspace/SparkPullRequestBuilder-K8s/R/SparkR.Rcheck’
* using R version 3.6.3 (2020-02-29)
```
OpenJDK docker image is using `R 3.5.2 (2018-12-20)` which is old and currently `spark-3.0.1` fails to run SparkR.
```
$ cd spark-3.0.1-bin-hadoop3.2
$ bin/docker-image-tool.sh -R kubernetes/dockerfiles/spark/bindings/R/Dockerfile -n build
...
exit code: 1
termination reason: Error
...
$ bin/spark-submit --master k8s://https://192.168.64.49:8443 --deploy-mode cluster --conf spark.kubernetes.container.image=spark-r:latest local:///opt/spark/examples/src/main/r/dataframe.R
$ k logs dataframe-r-b1c14b75b0c09eeb-driver
...
+ exec /usr/bin/tini -s -- /opt/spark/bin/spark-submit --conf spark.driver.bindAddress=172.17.0.4 --deploy-mode client --properties-file /opt/spark/conf/spark.properties --class org.apache.spark.deploy.RRunner local:///opt/spark/examples/src/main/r/dataframe.R
20/11/10 06:03:58 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
log4j:WARN No appenders could be found for logger (io.netty.util.internal.logging.InternalLoggerFactory).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Error: package or namespace load failed for ‘SparkR’ in rbind(info, getNamespaceInfo(env, "S3methods")):
number of columns of matrices must match (see arg 2)
In addition: Warning message:
package ‘SparkR’ was built under R version 4.0.2
Execution halted
```
In addition, this PR aims to recover the test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass K8S IT Jenkins job.
Closes#30130 from dongjoon-hyun/SPARK-32354.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### Why are the changes needed?
Follow the comment: https://github.com/apache/spark/pull/26935#discussion_r514697997
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing test and Mima test.
Closes#30344 from xuanyuanking/SPARK-30294-follow.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
This reverts commit 61ee5d8a4e.
### What changes were proposed in this pull request?
I need to merge https://github.com/apache/spark/pull/30327 to https://github.com/apache/spark/pull/30009,
but I merged it to master by mistake.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Closes#30345 from zhengruifeng/revert-30327-adaptively_blockify_linear_svc_II.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
* resend
* address comments
* directly gen new Iter
* directly gen new Iter
* update blockify strategy
* address comments
* try to fix 2.13
* try to fix scala 2.13
* use 1.0 as the default value for gemv
* update
Co-authored-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This PR intends to fix typos in the sub-modules: graphx, external, and examples.
Split per holdenk https://github.com/apache/spark/pull/30323#issuecomment-725159710
NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No testing was performed
Closes#30326 from jsoref/spelling-graphx.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
1. Applies the SQL changes in SPARK-33230 to SparkHadoopWriter, so that `rdd.saveAsNewAPIHadoopDataset` passes in a unique job UUID in `spark.sql.sources.writeJobUUID`
1. `SparkHadoopWriterUtils.createJobTrackerID` generates a JobID by appending a random long number to the supplied timestamp to ensure the probability of a collision is near-zero.
1. With tests of uniqueness, round trips and negative jobID rejection.
### Why are the changes needed?
Without this, if more than one job is started in the same second *and the committer expects application attempt IDs to be unique* is at risk of clashing with other jobs.
With the fix,
* those committers which use the ID set in `spark.sql.sources.writeJobUUID` as a priority ID will pick that up instead and so be unique.
* committers which use the Hadoop JobID for unique paths and filenames will get the randomly generated jobID. Assuming all clocks in a cluster in sync, the probability of two jobs launched in the same second has dropped from 1 to 1/(2^63)
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests
There's a new test suite SparkHadoopWriterUtilsSuite which creates jobID, verifies they are unique even for the same timestamp and that they can be marshalled to string and parsed back in the hadoop code, which contains some (brittle) assumptions about the format of job IDs.
Functional Integration Tests
1. Hadoop-trunk built with [HADOOP-17318], publishing to local maven repository
1. Spark built with hadoop.version=3.4.0-SNAPSHOT to pick up these JARs.
1. Spark + Object store integration tests at [https://github.com/hortonworks-spark/cloud-integration](https://github.com/hortonworks-spark/cloud-integration) were built against that local spark version
1. And executed against AWS london.
The tests were run with `fs.s3a.committer.require.uuid=true`, so the s3a committers fail fast if they don't get a job ID down. This showed that `rdd.saveAsNewAPIHadoopDataset` wasn't setting the UUID option. It again uses the current Date value for an app attempt -which is not guaranteed to be unique.
With the change applied to spark, the relevant tests work, therefore the committers are getting unique job IDs.
Closes#30319 from steveloughran/BUG/SPARK-33402-jobuuid.
Authored-by: Steve Loughran <stevel@cloudera.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Updated results of `DateTimeBenchmark` in the environment:
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge (spot instance) |
| AMI | ami-06f2f779464715dc5 (ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1) |
| Java | OpenJDK8/11 installed by`sudo add-apt-repository ppa:openjdk-r/ppa` & `sudo apt install openjdk-11-jdk`|
### Why are the changes needed?
The fix https://github.com/apache/spark/pull/30303 slowed down `date_trunc`. This PR updates benchmark results to have actual info about performance of `date_trunc`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By regenerating benchmark results:
```
$ SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.DateTimeBenchmark"
```
Closes#30338 from MaxGekk/fix-trunc_date-benchmark.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Removes encoding of the JVM response in `pyspark.sql.column.Column.__repr__`.
### Why are the changes needed?
API consistency and improved readability of the expressions.
### Does this PR introduce _any_ user-facing change?
Before this change
col("abc")
col("wąż")
result in
Column<b'abc'>
Column<b'w\xc4\x85\xc5\xbc'>
After this change we'll get
Column<'abc'>
Column<'wąż'>
### How was this patch tested?
Existing tests and manual inspection.
Closes#30322 from zero323/SPARK-33415.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch is trying to add `AlterTableAddPartitionExec` and `AlterTableDropPartitionExec` with the new table partition API, defined in #28617.
### Does this PR introduce _any_ user-facing change?
Yes. User can use `alter table add partition` or `alter table drop partition` to create/drop partition in V2Table.
### How was this patch tested?
Run suites and fix old tests.
Closes#29339 from stczwd/SPARK-32512-new.
Lead-authored-by: stczwd <qcsd2011@163.com>
Co-authored-by: Jacky Lee <qcsd2011@163.com>
Co-authored-by: Jackey Lee <qcsd2011@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make a special case in `ResolveReferences`, which resolves `OverwriteByExpression`'s condition expression based on the table relation instead of the input query.
### Why are the changes needed?
The condition expression is passed to the table implementation at the end, so we should resolve it using table schema. Previously it works because we have a hack in `ResolveReferences` to delay the resolution if `outputResolved == false`. However, this hack doesn't work for tables accepting any schema like https://github.com/delta-io/delta/pull/521 . We may wrongly resolve the delete condition using input query's outout columns which don't match the table column names.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests and updated test in v2 write.
Closes#30318 from cloud-fan/v2-write.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to fix the behaviour of query filters in `TPCDSQueryBenchmark`. We can use an option `--query-filter` for selecting TPCDS queries to run, e.g., `--query-filter q6,q8,q13`. But, the current master has a weird behaviour about the option. For example, if we pass `--query-filter q6` so as to run the TPCDS q6 only, `TPCDSQueryBenchmark` runs `q6` and `q6-v2.7` because the `filterQueries` method does not respect the name suffix. So, there is no way now to run the TPCDS q6 only.
### Why are the changes needed?
Bugfix.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually checked.
Closes#30324 from maropu/FilterBugInTPCDSQueryBenchmark.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR proposes to migrate `SHOW CREATE TABLE` to use `UnresolvedTableOrView` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `SHOW CREATE TABLE` works only with a v1 table and a permanent view, and not supported for v2 tables.
### Why are the changes needed?
The changes allow consistent resolution behavior when resolving the table identifier. For example, the following is the current behavior:
```scala
sql("CREATE TEMPORARY VIEW t AS SELECT 1")
sql("CREATE DATABASE db")
sql("CREATE TABLE t (key INT, value STRING) USING hive")
sql("USE db")
sql("SHOW CREATE TABLE t AS SERDE") // Succeeds
```
With this change, `SHOW CREATE TABLE ... AS SERDE` above fails with the following:
```
org.apache.spark.sql.AnalysisException: t is a temp view not table or permanent view.; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.$anonfun$applyOrElse$43(Analyzer.scala:883)
at scala.Option.map(Option.scala:230)
```
, which is expected since temporary view is resolved first and `SHOW CREATE TABLE ... AS SERDE` doesn't support a temporary view.
Note that there is no behavior change for `SHOW CREATE TABLE` without `AS SERDE` since it was already resolving to a temporary view first. See below for more detail.
### Does this PR introduce _any_ user-facing change?
After this PR, `SHOW CREATE TABLE t AS SERDE` is resolved to a temp view `t` instead of table `db.t` in the above scenario.
Note that there is no behavior change for `SHOW CREATE TABLE` without `AS SERDE`, but the exception message changes from `SHOW CREATE TABLE is not supported on a temporary view` to `t is a temp view not table or permanent view`.
### How was this patch tested?
Updated existing tests.
Closes#30321 from imback82/show_create_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to gather common `SHOW TABLES` tests into one trait `org.apache.spark.sql.execution.command.ShowTablesSuite`, and put datasource specific tests to the `v1.ShowTablesSuite` and `v2.ShowTablesSuite`. Also tests for parsing `SHOW TABLES` are extracted to `ShowTablesParserSuite`.
### Why are the changes needed?
- The unification will allow to run common `SHOW TABLES` tests for both DSv1 and DSv2
- We can detect missing features and differences between DSv1 and DSv2 implementations.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running new test suites:
- `org.apache.spark.sql.execution.command.v1.ShowTablesSuite`
- `org.apache.spark.sql.execution.command.v2.ShowTablesSuite`
- `ShowTablesParserSuite`
Closes#30287 from MaxGekk/unify-dsv1_v2-tests.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make Literal support char array.
### Why are the changes needed?
We always use `Literal()` to create foldable value, and `char[]` is a usual data type. We can make it easy that support create String Literal with `char[]`.
### Does this PR introduce _any_ user-facing change?
Yes, user can call `Literal()` with `char[]`.
### How was this patch tested?
Add test.
Closes#30295 from ulysses-you/SPARK-33390.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The following query produces incorrect results:
```
SELECT date_trunc('minute', '1769-10-17 17:10:02')
```
Spark currently incorrectly returns
```
1769-10-17 17:10:02
```
against the expected return value of
```
1769-10-17 17:10:00
```
**Steps to repro**
Run the following commands in spark-shell:
```
spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
spark.sql("SELECT date_trunc('minute', '1769-10-17 17:10:02')").show()
```
This happens as `truncTimestamp` in package `org.apache.spark.sql.catalyst.util.DateTimeUtils` incorrectly assumes that time zone offsets can never have the granularity of a second and thus does not account for time zone adjustment when truncating the given timestamp to `minute`.
This assumption is currently used when truncating the timestamps to `microsecond, millisecond, second, or minute`.
This PR fixes this issue and always uses time zone knowledge when truncating timestamps regardless of the truncation unit.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added new tests to `DateTimeUtilsSuite` which previously failed and pass now.
Closes#30303 from utkarsh39/trunc-timestamp-fix.
Authored-by: Utkarsh <utkarsh.agarwal@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Currently we skip subexpression elimination in branches of conditional expressions including `If`, `CaseWhen`, and `Coalesce`. Actually we can do subexpression elimination for such branches if the subexpression is common across all branches. This patch proposes to support subexpression elimination in branches of conditional expressions.
### Why are the changes needed?
We may miss subexpression elimination chances in branches of conditional expressions. This kind of subexpression is frequently seen. It may be written manually by users or come from query optimizer. For example, project collapsing could embed expressions between two `Project`s and produces conditional expression like:
```
CASE WHEN jsonToStruct(json).a = '1' THEN 1.0 WHEN jsonToStruct(json).a = '2' THEN 2.0 ... ELSE 1.2 END
```
If `jsonToStruct(json)` is time-expensive expression, we don't eliminate the duplication and waste time on running it repeatedly now.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#30245 from viirya/SPARK-33337.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Changes
pyspark.sql.dataframe.DataFrame
to
:py:class:`pyspark.sql.DataFrame`
### Why are the changes needed?
Consistency (see https://github.com/apache/spark/pull/30285#pullrequestreview-526764104).
### Does this PR introduce _any_ user-facing change?
User will see shorter reference with a link.
### How was this patch tested?
`dev/lint-python` and manual check of the rendered docs.
Closes#30313 from zero323/SPARK-33251-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
### What changes were proposed in this pull request?
This removes the `sharesHadoopClasses` flag from `IsolatedClientLoader` in Hive module.
### Why are the changes needed?
Currently, when initializing `IsolatedClientLoader`, users can set the `sharesHadoopClasses` flag to decide whether the `HiveClient` created should share Hadoop classes with Spark itself or not. In the latter case, the client will only load Hadoop classes from the Hive dependencies.
There are two reasons to remove this:
1. this feature is currently used in two cases: 1) unit tests, 2) when the Hadoop version defined in Maven can not be found when `spark.sql.hive.metastore.jars` is equal to "maven", which could be very rare.
2. when `sharesHadoopClasses` is false, Spark doesn't really only use Hadoop classes from Hive jars: we also download `hadoop-client` jar and put all the sub-module jars (e.g., `hadoop-common`, `hadoop-hdfs`) together with the Hive jars, and the Hadoop version used by `hadoop-client` is the same version used by Spark itself. As result, we're mixing two versions of Hadoop jars in the classpath, which could potentially cause issues, especially considering that the default Hadoop version is already 3.2.0 while most Hive versions supported by the `IsolatedClientLoader` is still using Hadoop 2.x or even lower.
### Does this PR introduce _any_ user-facing change?
This affects Spark users in one scenario: when `spark.sql.hive.metastore.jars` is set to `maven` AND the Hadoop version specified in pom file cannot be downloaded, currently the behavior is to switch to _not_ share Hadoop classes, but with the PR it will share Hadoop classes with Spark.
### How was this patch tested?
Existing UTs.
Closes#30284 from sunchao/SPARK-33376.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Push down filter through expand. For case below:
```
create table t1(pid int, uid int, sid int, dt date, suid int) using parquet;
create table t2(pid int, vs int, uid int, csid int) using parquet;
SELECT
years,
appversion,
SUM(uusers) AS users
FROM (SELECT
Date_trunc('year', dt) AS years,
CASE
WHEN h.pid = 3 THEN 'iOS'
WHEN h.pid = 4 THEN 'Android'
ELSE 'Other'
END AS viewport,
h.vs AS appversion,
Count(DISTINCT u.uid) AS uusers
,Count(DISTINCT u.suid) AS srcusers
FROM t1 u
join t2 h
ON h.uid = u.uid
GROUP BY 1,
2,
3) AS a
WHERE viewport = 'iOS'
GROUP BY 1,
2
```
Plan. before this pr:
```
== Physical Plan ==
*(5) HashAggregate(keys=[years#30, appversion#32], functions=[sum(uusers#33L)])
+- Exchange hashpartitioning(years#30, appversion#32, 200), true, [id=#251]
+- *(4) HashAggregate(keys=[years#30, appversion#32], functions=[partial_sum(uusers#33L)])
+- *(4) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[count(if ((gid#44 = 1)) u.`uid`#47 else null)])
+- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, 200), true, [id=#246]
+- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[partial_count(if ((gid#44 = 1)) u.`uid`#47 else null)])
+- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
+- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44, 200), true, [id=#241]
+- *(2) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
+- *(2) Filter (CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46 = iOS)
+- *(2) Expand [ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, uid#7, null, 1), ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, null, suid#10, 2)], [date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44]
+- *(2) Project [uid#7, dt#9, suid#10, pid#11, vs#12]
+- *(2) BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight
:- *(2) Project [uid#7, dt#9, suid#10]
: +- *(2) Filter isnotnull(uid#7)
: +- *(2) ColumnarToRow
: +- FileScan parquet default.t1[uid#7,dt#9,suid#10] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t1], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date,suid:int>
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, true] as bigint))), [id=#233]
+- *(1) Project [pid#11, vs#12, uid#13]
+- *(1) Filter isnotnull(uid#13)
+- *(1) ColumnarToRow
+- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [isnotnull(uid#13)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t2], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>
```
Plan. after. this pr. :
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[years#0, appversion#2], functions=[sum(uusers#3L)], output=[years#0, appversion#2, users#5L])
+- Exchange hashpartitioning(years#0, appversion#2, 5), true, [id=#71]
+- HashAggregate(keys=[years#0, appversion#2], functions=[partial_sum(uusers#3L)], output=[years#0, appversion#2, sum#22L])
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[count(distinct uid#7)], output=[years#0, appversion#2, uusers#3L])
+- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, 5), true, [id=#67]
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[partial_count(distinct uid#7)], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, count#27L])
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
+- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7, 5), true, [id=#63]
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles)) AS date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END AS CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
+- Project [uid#7, dt#9, pid#11, vs#12]
+- BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight, false
:- Filter isnotnull(uid#7)
: +- FileScan parquet default.t1[uid#7,dt#9] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date>
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, false] as bigint)),false), [id=#58]
+- Filter ((CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS) AND isnotnull(uid#13))
+- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [(CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS), isnotnull..., Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>
```
### Why are the changes needed?
Improve performance, filter more data.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#30278 from AngersZhuuuu/SPARK-33302.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This changes `DropTableExec` to also invalidate caches referencing the table to be dropped, in a cascading manner.
### Why are the changes needed?
In DSv1, `DROP TABLE` command also invalidate caches as described in [SPARK-19765](https://issues.apache.org/jira/browse/SPARK-19765). However in DSv2 the same command only drops the table but doesn't handle the caches. This could lead to correctness issue.
### Does this PR introduce _any_ user-facing change?
Yes. Now DSv2 `DROP TABLE` command also invalidates cache.
### How was this patch tested?
Added a new UT
Closes#30211 from sunchao/SPARK-33305.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When a system.exit exception occurs during the process, the python worker exits abnormally, and then the executor task is still waiting for the worker for reading from socket, causing it to hang.
The system.exit exception may be caused by the user's error code, but spark should at least throw an error to remind the user, not get stuck
we can run a simple test to reproduce this case:
```
from pyspark.sql import SparkSession
def err(line):
raise SystemExit
spark = SparkSession.builder.appName("test").getOrCreate()
spark.sparkContext.parallelize(range(1,2), 2).map(err).collect()
spark.stop()
```
### Why are the changes needed?
to make sure pyspark application won't hang if there's non-Exception error in python worker
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
added a new test and also manually tested the case above
Closes#30248 from li36909/pyspark.
Lead-authored-by: lrz <lrz@lrzdeMacBook-Pro.local>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
element_at with CreateArray not respect one based index.
repo step:
```
var df = spark.sql("select element_at(array(3, 2, 1), 0)")
df.printSchema()
df = spark.sql("select element_at(array(3, 2, 1), 1)")
df.printSchema()
df = spark.sql("select element_at(array(3, 2, 1), 2)")
df.printSchema()
df = spark.sql("select element_at(array(3, 2, 1), 3)")
df.printSchema()
root
– element_at(array(3, 2, 1), 0): integer (nullable = false)
root
– element_at(array(3, 2, 1), 1): integer (nullable = false)
root
– element_at(array(3, 2, 1), 2): integer (nullable = false)
root
– element_at(array(3, 2, 1), 3): integer (nullable = true)
correct answer should be
0 true which is outOfBounds return default true.
1 false
2 false
3 false
```
For expression eval, it respect the oneBasedIndex, but within checking the nullable, it calculates with zeroBasedIndex using `computeNullabilityFromArray`.
### Why are the changes needed?
Correctness issue.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and existing UT.
Closes#30296 from leanken/leanken-SPARK-33391.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Call `spark.read.table` in `spark.table`.
- Add comments for `spark.table` to emphasize it also support streaming temp view reading.
### Why are the changes needed?
The code paths of `spark.table` and `spark.read.table` should be the same. This behavior is broke in SPARK-32592 since we need to respect options in `spark.read.table` API.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#30148 from xuanyuanking/SPARK-33244.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `LOAD DATA` to use `UnresolvedTable` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `LOAD DATA` is not supported for v2 tables.
### Why are the changes needed?
The changes allow consistent resolution behavior when resolving the table identifier. For example, the following is the current behavior:
```scala
sql("CREATE TEMPORARY VIEW t AS SELECT 1")
sql("CREATE DATABASE db")
sql("CREATE TABLE t (key INT, value STRING) USING hive")
sql("USE db")
sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE t") // Succeeds
```
With this change, `LOAD DATA` above fails with the following:
```
org.apache.spark.sql.AnalysisException: t is a temp view not table.; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.$anonfun$applyOrElse$39(Analyzer.scala:865)
at scala.Option.foreach(Option.scala:407)
```
, which is expected since temporary view is resolved first and `LOAD DATA` doesn't support a temporary view.
### Does this PR introduce _any_ user-facing change?
After this PR, `LOAD DATA ... t` is resolved to a temp view `t` instead of table `db.t` in the above scenario.
### How was this patch tested?
Updated existing tests.
Closes#30270 from imback82/load_data_cmd.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When TableProvider.supportsExternalMetadata() is true, Spark will use the input Dataframe's schema in `DataframeWriter.save()`/`DataStreamWriter.start()` and skip schema/partitioning inference.
### Why are the changes needed?
For all the v2 data sources which are not FileDataSourceV2, Spark always infers the table schema/partitioning on `DataframeWriter.save()`/`DataStreamWriter.start()`.
The inference of table schema/partitioning can be expensive. However, there is no such trait or flag for indicating a V2 source can use the input DataFrame's schema on `DataframeWriter.save()`/`DataStreamWriter.start()`. We can resolve the problem by adding a new expected behavior for the method `TableProvider.supportsExternalMetadata()`.
### Does this PR introduce _any_ user-facing change?
Yes, a new behavior for the data source v2 API `TableProvider.supportsExternalMetadata()` when it returns true.
### How was this patch tested?
Unit test
Closes#30273 from gengliangwang/supportsExternalMetadata.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This upgrade Apache Arrow version from 1.0.1 to 2.0.0
### Why are the changes needed?
Apache Arrow 2.0.0 was released with some improvements from Java side, so it's better to upgrade Spark to the new version.
Note that the format version in Arrow 2.0.0 is still 1.0.0 so API should still be compatible between 1.0.1 and 2.0.0.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#30306 from sunchao/SPARK-33213.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Structured Streaming UI is not containing state information. In this PR I've added it.
### Why are the changes needed?
Missing state information.
### Does this PR introduce _any_ user-facing change?
Additional UI elements appear.
### How was this patch tested?
Existing unit tests + manual test.
<img width="1044" alt="Screenshot 2020-10-30 at 15 14 21" src="https://user-images.githubusercontent.com/18561820/97715405-a1797000-1ac2-11eb-886a-e3e6efa3af3e.png">
Closes#30151 from gaborgsomogyi/SPARK-33223.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
