### What changes were proposed in this pull request?
Fix the migration guide document for `spark.sql.legacy.ctePrecedence.enabled`, which is introduced in #27579.
### Why are the changes needed?
The config value changed.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Document only.
Closes#27782 from xuanyuanking/SPARK-30829-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
ForEachBatch Java example was incorrect
### Why are the changes needed?
Example did not compile
### Does this PR introduce any user-facing change?
Yes, to docs.
### How was this patch tested?
In IDE.
Closes#27740 from roland1982/foreachwriter_java_example_fix.
Authored-by: roland-ondeviceresearch <roland@ondeviceresearch.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Remove automatically resource coordination support from Standalone.
### Why are the changes needed?
Resource coordination is mainly designed for the scenario where multiple workers launched on the same host. However, it's, actually, a non-existed scenario for today's Spark. Because, Spark now can start multiple executors in a single Worker, while it only allow one executor per Worker at very beginning. So, now, it really help nothing for user to launch multiple workers on the same host. Thus, it's not worth for us to bring over complicated implementation and potential high maintain cost for such an impossible scenario.
### Does this PR introduce any user-facing change?
No, it's Spark 3.0 feature.
### How was this patch tested?
Pass Jenkins.
Closes#27722 from Ngone51/abandon_coordination.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
1.Add version information to the configuration of `Kryo`.
2.Update the docs of `Kryo`.
I sorted out some information show below.
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.kryo.registrationRequired | 1.1.0 | SPARK-2102 | efdaeb111917dd0314f1d00ee8524bed1e2e21ca#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryo.registrator | 0.5.0 | None | 91c07a33d90ab0357e8713507134ecef5c14e28a#diff-792ed56b3398163fa14e8578549d0d98 | This is not a release version, do we need to record it?
spark.kryo.classesToRegister | 1.2.0 | SPARK-1813 | 6bb56faea8d238ea22c2de33db93b1b39f492b3a#diff-529fc5c06b9731c1fbda6f3db60b16aa |
spark.kryo.unsafe | 2.1.0 | SPARK-928 | bc167a2a53f5a795d089e8a884569b1b3e2cd439#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryo.pool | 3.0.0 | SPARK-26466 | 38f030725c561979ca98b2a6cc7ca6c02a1f80ed#diff-a3c6b992784f9abeb9f3047d3dcf3ed9 |
spark.kryo.referenceTracking | 0.8.0 | None | 0a8cc309211c62f8824d76618705c817edcf2424#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryoserializer.buffer | 1.4.0 | SPARK-5932 | 2d222fb39dd978e5a33cde6ceb59307cbdf7b171#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryoserializer.buffer.max | 1.4.0 | SPARK-5932 | 2d222fb39dd978e5a33cde6ceb59307cbdf7b171#diff-1f81c62dad0e2dfc387a974bb08c497c |
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27734 from beliefer/add-version-to-kryo-config.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Rename `spark.sql.legacy.addDirectory.recursive.enabled` to `spark.sql.legacy.addSingleFileInAddFile`
### Why are the changes needed?
To follow the naming convention
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UTs.
Closes#27725 from iRakson/SPARK-30234_CONFIG.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Renamed configuration from `spark.sql.legacy.useHashOnMapType` to `spark.sql.legacy.allowHashOnMapType`.
### Why are the changes needed?
Better readability of configuration.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UTs.
Closes#27719 from iRakson/SPARK-27619_FOLLOWUP.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR groups all hive upgrade related migration guides inside Spark 3.0 together.
Also add another behavior change of `ScriptTransform` in the new Hive section.
### Why are the changes needed?
Make the doc more clearly to user.
### Does this PR introduce any user-facing change?
No, new doc for Spark 3.0.
### How was this patch tested?
N/A.
Closes#27670 from Ngone51/hive_migration.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1.Add version information to the configuration of `Python`.
2.Update the docs of `Python`.
I sorted out some information show below.
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.python.worker.reuse | 1.2.0 | SPARK-3030 | 2aea0da84c58a179917311290083456dfa043db7#diff-0a67bc4d171abe4df8eb305b0f4123a2 |
spark.python.task.killTimeout | 2.2.2 | SPARK-22535 | be68f86e11d64209d9e325ce807025318f383bea#diff-0a67bc4d171abe4df8eb305b0f4123a2 |
spark.python.use.daemon | 2.3.0 | SPARK-22554 | 57c5514de9dba1c14e296f85fb13fef23ce8c73f#diff-9008ad45db34a7eee2e265a50626841b |
spark.python.daemon.module | 2.4.0 | SPARK-22959 | afae8f2bc82597593595af68d1aa2d802210ea8b#diff-9008ad45db34a7eee2e265a50626841b |
spark.python.worker.module | 2.4.0 | SPARK-22959 | afae8f2bc82597593595af68d1aa2d802210ea8b#diff-9008ad45db34a7eee2e265a50626841b |
spark.executor.pyspark.memory | 2.4.0 | SPARK-25004 | 7ad18ee9f26e75dbe038c6034700f9cd4c0e2baa#diff-6bdad48cfc34314e89599655442ff210 |
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27704 from beliefer/add-version-to-python-config.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1.Add version information to the configuration of `R`.
2.Update the docs of `R`.
I sorted out some information show below.
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.r.backendConnectionTimeout | 2.1.0 | SPARK-17919 | 2881a2d1d1a650a91df2c6a01275eba14a43b42a#diff-025470e1b7094d7cf4a78ea353fb3981 |
spark.r.numRBackendThreads | 1.4.0 | SPARK-8282 | 28e8a6ea65fd08ab9cefc4d179d5c66ffefd3eb4#diff-697f7f2fc89808e0113efc71ed235db2 |
spark.r.heartBeatInterval | 2.1.0 | SPARK-17919 | 2881a2d1d1a650a91df2c6a01275eba14a43b42a#diff-fe903bf14db371aa320b7cc516f2463c |
spark.sparkr.r.command | 1.5.3 | SPARK-10971 | 9695f452e86a88bef3bcbd1f3c0b00ad9e9ac6e1#diff-025470e1b7094d7cf4a78ea353fb3981 |
spark.r.command | 1.5.3 | SPARK-10971 | 9695f452e86a88bef3bcbd1f3c0b00ad9e9ac6e1#diff-025470e1b7094d7cf4a78ea353fb3981 |
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27708 from beliefer/add-version-to-R-config.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
`hash()` and `xxhash64()` cannot be used on elements of `Maptype`. A new configuration `spark.sql.legacy.useHashOnMapType` is introduced to allow users to restore the previous behaviour.
When `spark.sql.legacy.useHashOnMapType` is set to false:
```
scala> spark.sql("select hash(map())");
org.apache.spark.sql.AnalysisException: cannot resolve 'hash(map())' due to data type mismatch: input to function hash cannot contain elements of MapType; line 1 pos 7;
'Project [unresolvedalias(hash(map(), 42), None)]
+- OneRowRelation
```
when `spark.sql.legacy.useHashOnMapType` is set to true :
```
scala> spark.sql("set spark.sql.legacy.useHashOnMapType=true");
res3: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.sql("select hash(map())").first()
res4: org.apache.spark.sql.Row = [42]
```
### Why are the changes needed?
As discussed in Jira, SparkSql's map hashcodes depends on their order of insertion which is not consistent with the normal scala behaviour which might confuse users.
Code snippet from JIRA :
```
val a = spark.createDataset(Map(1->1, 2->2) :: Nil)
val b = spark.createDataset(Map(2->2, 1->1) :: Nil)
// Demonstration of how Scala Map equality is unaffected by insertion order:
assert(Map(1->1, 2->2).hashCode() == Map(2->2, 1->1).hashCode())
assert(Map(1->1, 2->2) == Map(2->2, 1->1))
assert(a.first() == b.first())
// In contrast, this will print two different hashcodes:
println(Seq(a, b).map(_.selectExpr("hash(*)").first()))
```
Also `MapType` is prohibited for aggregation / joins / equality comparisons #7819 and set operations #17236.
### Does this PR introduce any user-facing change?
Yes. Now users cannot use hash functions on elements of `mapType`. To restore the previous behaviour set `spark.sql.legacy.useHashOnMapType` to true.
### How was this patch tested?
UT added.
Closes#27580 from iRakson/SPARK-27619.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.1.0-SNAPSHOT.
### Why are the changes needed?
N/A
### Does this PR introduce any user-facing change?
N/A
### How was this patch tested?
N/A
Closes#27698 from gatorsmile/updateVersion.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Rename config `spark.resources.discovery.plugin` to `spark.resources.discoveryPlugin`.
Also, as a side minor change: labeled `ResourceDiscoveryScriptPlugin` as `DeveloperApi` since it's not for end user.
### Why are the changes needed?
Discovery plugin doesn't need to reserve the "discovery" namespace here and it's more consistent with the interface name `ResourceDiscoveryPlugin` if we use `discoveryPlugin` instead.
### Does this PR introduce any user-facing change?
No, it's newly added in Spark3.0.
### How was this patch tested?
Pass Jenkins.
Closes#27689 from Ngone51/spark_30689_followup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a FOLLOW-UP PR for review comment on #27208 : https://github.com/apache/spark/pull/27208#pullrequestreview-347451714
This PR documents a new feature `Eventlog Compaction` into the new section of `monitoring.md`, as it only has one configuration on the SHS side and it's hard to explain everything on the description on the single configuration.
### Why are the changes needed?
Event log compaction lacks the documentation for what it is and how it helps. This PR will explain it.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Built docs via jekyll.
> change on the new section
<img width="951" alt="Screen Shot 2020-02-16 at 2 23 18 PM" src="https://user-images.githubusercontent.com/1317309/74599587-eb9efa80-50c7-11ea-942c-f7744268e40b.png">
> change on the table
<img width="1126" alt="Screen Shot 2020-01-30 at 5 08 12 PM" src="https://user-images.githubusercontent.com/1317309/73431190-2e9c6680-4383-11ea-8ce0-815f10917ddd.png">
Closes#27398 from HeartSaVioR/SPARK-30481-FOLLOWUP-document-new-feature.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
1.Add version information to the configuration of `Deploy`.
2.Update the docs of `Deploy`.
I sorted out some information show below.
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.deploy.recoveryMode | 0.8.1 | None | d66c01f2b6defb3db6c1be99523b734a4d960532#diff-29dffdccd5a7f4c8b496c293e87c8668 |
spark.deploy.recoveryMode.factory | 1.2.0 | SPARK-1830 | deefd9d7377a8091a1d184b99066febd0e9f6afd#diff-29dffdccd5a7f4c8b496c293e87c8668 | This configuration appears in branch-1.3, but the version number in the pom.xml file corresponding to the commit is 1.2.0-SNAPSHOT
spark.deploy.recoveryDirectory | 0.8.1 | None | d66c01f2b6defb3db6c1be99523b734a4d960532#diff-29dffdccd5a7f4c8b496c293e87c8668 |
spark.deploy.zookeeper.url | 0.8.1 | None | d66c01f2b6defb3db6c1be99523b734a4d960532#diff-4457313ca662a1cd60197122d924585c |
spark.deploy.zookeeper.dir | 0.8.1 | None | d66c01f2b6defb3db6c1be99523b734a4d960532#diff-a84228cb45c7d5bd93305a1f5bf720b6 |
spark.deploy.retainedApplications | 0.8.0 | None | 46eecd110a4017ea0c86cbb1010d0ccd6a5eb2ef#diff-29dffdccd5a7f4c8b496c293e87c8668 |
spark.deploy.retainedDrivers | 1.1.0 | None | 7446f5ff93142d2dd5c79c63fa947f47a1d4db8b#diff-29dffdccd5a7f4c8b496c293e87c8668 |
spark.dead.worker.persistence | 0.8.0 | None | 46eecd110a4017ea0c86cbb1010d0ccd6a5eb2ef#diff-29dffdccd5a7f4c8b496c293e87c8668 |
spark.deploy.maxExecutorRetries | 1.6.3 | SPARK-16956 | ace458f0330f22463ecf7cbee7c0465e10fba8a8#diff-29dffdccd5a7f4c8b496c293e87c8668 |
spark.deploy.spreadOut | 0.6.1 | None | bb2b9ff37cd2503cc6ea82c5dd395187b0910af0#diff-0e7ae91819fc8f7b47b0f97be7116325 |
spark.deploy.defaultCores | 0.9.0 | None | d8bcc8e9a095c1b20dd7a17b6535800d39bff80e#diff-29dffdccd5a7f4c8b496c293e87c8668 |
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27668 from beliefer/add-version-to-deploy-config.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Previous exemple given for spark-streaming-kinesis was true for Apache Spark < 2.3.0. After that the method used in exemple became deprecated:
deprecated("use initialPosition(initialPosition: KinesisInitialPosition)", "2.3.0")
def initialPositionInStream(initialPosition: InitialPositionInStream)
This PR updates the doc on rewriting exemple in Scala/Java (remain unchanged in Python) to adapt Apache Spark 2.4.0 + releases.
### Why are the changes needed?
It introduces some confusion for developers to test their spark-streaming-kinesis exemple.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
In my opinion, the change is only about the documentation level, so I did not add any special test.
Closes#27652 from supaggregator/SPARK-30901.
Authored-by: XU Duo <Duo.XU@canal-plus.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Structured streaming documentation example fix
### Why are the changes needed?
Currently the java example uses incorrect syntax
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
In IDE
Closes#27671 from roland1982/foreachwriter_java_example_fix.
Authored-by: roland-ondeviceresearch <roland@ondeviceresearch.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to throw exception by default when user use untyped UDF(a.k.a `org.apache.spark.sql.functions.udf(AnyRef, DataType)`).
And user could still use it by setting `spark.sql.legacy.useUnTypedUdf.enabled` to `true`.
### Why are the changes needed?
According to #23498, since Spark 3.0, the untyped UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return 0 in Spark 3.0 but null in Spark 2.4. And the behavior change is introduced due to Spark3.0 is built with Scala 2.12 by default.
As a result, this might change data silently and may cause correctness issue if user still expect `null` in some cases. Thus, we'd better to encourage user to use typed UDF to avoid this problem.
### Does this PR introduce any user-facing change?
Yeah. User will hit exception now when use untyped UDF.
### How was this patch tested?
Added test and updated some tests.
Closes#27488 from Ngone51/spark_26580_followup.
Lead-authored-by: yi.wu <yi.wu@databricks.com>
Co-authored-by: wuyi <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Revise the documentation of `spark.ui.retainedTasks` to make it clear that the configuration is for one stage.
### Why are the changes needed?
There are configurations for the limitation of UI data.
`spark.ui.retainedJobs`, `spark.ui.retainedStages` and `spark.worker.ui.retainedExecutors` are the total max number for one application, while the configuration `spark.ui.retainedTasks` is the max number for one stage.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
None, just doc.
Closes#27660 from gengliangwang/reviseRetainTask.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
mention the workaround if users do want to use map type as key, and add a test to demonstrate it.
### Why are the changes needed?
it's better to provide an alternative when we ban something.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#27621 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Improve the CREATE TABLE document:
1. mention that some clauses can come in as any order.
2. refine the description for some parameters.
3. mention how data source table interacts with data source
4. make the examples consistent between data source and hive serde tables.
### Why are the changes needed?
improve doc
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#27638 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
fix kubernetes-client version doc
### Why are the changes needed?
correct doc
### Does this PR introduce any user-facing change?
nah
### How was this patch tested?
nah
Closes#27605 from yaooqinn/k8s-version-update.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
mention that `INT96` timestamp is still useful for interoperability.
### Why are the changes needed?
Give users more context of the behavior changes.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#27622 from cloud-fan/parquet.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
[HIVE-15167](https://issues.apache.org/jira/browse/HIVE-15167) removed the `SerDe` interface. This may break custom `SerDe` builds for Hive 1.2. This PR update the migration guide for this change.
### Why are the changes needed?
Otherwise:
```
2020-01-27 05:11:20.446 - stderr> 20/01/27 05:11:20 INFO DAGScheduler: ResultStage 2 (main at NativeMethodAccessorImpl.java:0) failed in 1.000 s due to Job aborted due to stage failure: Task 0 in stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID 13, 10.110.21.210, executor 1): java.lang.NoClassDefFoundError: org/apache/hadoop/hive/serde2/SerDe
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.defineClass1(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
2020-01-27 05:11:20.446 - stderr> at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.defineClass(URLClassLoader.java:468)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.access$100(URLClassLoader.java:74)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader$1.run(URLClassLoader.java:369)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader$1.run(URLClassLoader.java:363)
2020-01-27 05:11:20.446 - stderr> at java.security.AccessController.doPrivileged(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.findClass(URLClassLoader.java:362)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
2020-01-27 05:11:20.446 - stderr> at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:405)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
2020-01-27 05:11:20.446 - stderr> at java.lang.Class.forName0(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.lang.Class.forName(Class.java:348)
2020-01-27 05:11:20.446 - stderr> at org.apache.hadoop.hive.ql.plan.TableDesc.getDeserializerClass(TableDesc.java:76)
.....
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual test
Closes#27492 from wangyum/SPARK-30755.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a follow-up for #23124, add a new config `spark.sql.legacy.allowDuplicatedMapKeys` to control the behavior of removing duplicated map keys in build-in functions. With the default value `false`, Spark will throw a RuntimeException while duplicated keys are found.
### Why are the changes needed?
Prevent silent behavior changes.
### Does this PR introduce any user-facing change?
Yes, new config added and the default behavior for duplicated map keys changed to RuntimeException thrown.
### How was this patch tested?
Modify existing UT.
Closes#27478 from xuanyuanking/SPARK-25892-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch addresses the post-hoc review comment linked here - https://github.com/apache/spark/pull/25670#discussion_r373304076
### Why are the changes needed?
We would like to explicitly document the direct relationship before we finish up structuring of configurations.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#27576 from HeartSaVioR/SPARK-28869-FOLLOWUP-doc.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
fix style issue in the k8s document, please go to http://spark.apache.org/docs/3.0.0-preview2/running-on-kubernetes.html and search the keyword`spark.kubernetes.file.upload.path` to jump to the error context
### Why are the changes needed?
doc correctness
### Does this PR introduce any user-facing change?
Nah
### How was this patch tested?
Nah
Closes#27582 from yaooqinn/k8s-doc.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add doc for recommended pandas and pyarrow versions.
### Why are the changes needed?
The recommended versions are those that have been thoroughly tested by Spark CI. Other versions may be used at the discretion of the user.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
NA
Closes#27587 from BryanCutler/python-doc-rec-pandas-pyarrow-SPARK-30834-3.0.
Lead-authored-by: Bryan Cutler <cutlerb@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/27489.
It declares the ANSI SQL compliance options as experimental in the documentation.
### Why are the changes needed?
The options are experimental. There can be new features/behaviors in future releases.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Generating doc
Closes#27590 from gengliangwang/ExperimentalAnsi.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change the link to the Scala API document.
```
$ git grep "#org.apache.spark.package"
docs/_layouts/global.html: <li><a href="api/scala/index.html#org.apache.spark.package">Scala</a></li>
docs/index.md:* [Spark Scala API (Scaladoc)](api/scala/index.html#org.apache.spark.package)
docs/rdd-programming-guide.md:[Scala](api/scala/#org.apache.spark.package), [Java](api/java/), [Python](api/python/) and [R](api/R/).
```
### Why are the changes needed?
The home page link for Scala API document is incorrect after upgrade to 3.0
### Does this PR introduce any user-facing change?
Document UI change only.
### How was this patch tested?
Local test, attach screenshots below:
Before:
After:
Closes#27549 from xuanyuanking/scala-doc.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR address the comment at https://github.com/apache/spark/pull/26496#discussion_r379194091 and improves the migration guide to explicitly note that the legacy environment variable to set in both executor and driver.
### Why are the changes needed?
To clarify this env should be set both in driver and executors.
### Does this PR introduce any user-facing change?
Nope.
### How was this patch tested?
I checked it via md editor.
Closes#27573 from HyukjinKwon/SPARK-29748.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
`spark.sql("select map()")` returns {}.
After these changes it will return map<null,null>
### Why are the changes needed?
After changes introduced due to #27521, it is important to maintain consistency while using map().
### Does this PR introduce any user-facing change?
Yes. Now map() will give map<null,null> instead of {}.
### How was this patch tested?
UT added. Migration guide updated as well
Closes#27542 from iRakson/SPARK-30790.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr is a follow up of https://github.com/apache/spark/pull/26200.
In this PR, I modify the description of spark.sql.files.* in sql-performance-tuning.md to keep consistent with that in SQLConf.
### Why are the changes needed?
To keep consistent with the description in SQLConf.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existed UT.
Closes#27545 from turboFei/SPARK-29542-follow-up.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR targets to document the Pandas UDF redesign with type hints introduced at SPARK-28264.
Mostly self-describing; however, there are few things to note for reviewers.
1. This PR replace the existing documentation of pandas UDFs to the newer redesign to promote the Python type hints. I added some words that Spark 3.0 still keeps the compatibility though.
2. This PR proposes to name non-pandas UDFs as "Pandas Function API"
3. SCALAR_ITER become two separate sections to reduce confusion:
- `Iterator[pd.Series]` -> `Iterator[pd.Series]`
- `Iterator[Tuple[pd.Series, ...]]` -> `Iterator[pd.Series]`
4. I removed some examples that look overkill to me.
5. I also removed some information in the doc, that seems duplicating or too much.
### Why are the changes needed?
To document new redesign in pandas UDF.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests should cover.
Closes#27466 from HyukjinKwon/SPARK-30722.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Document updated for `CACHE TABLE` & `UNCACHE TABLE`
### Why are the changes needed?
Cache table creates a temp view while caching data using `CACHE TABLE name AS query`. `UNCACHE TABLE` does not remove this temp view.
These things were not mentioned in the existing doc for `CACHE TABLE` & `UNCACHE TABLE`.
### Does this PR introduce any user-facing change?
Document updated for `CACHE TABLE` & `UNCACHE TABLE` command.
### How was this patch tested?
Manually
Closes#27090 from iRakson/SPARK-27545.
Lead-authored-by: root1 <raksonrakesh@gmail.com>
Co-authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This brings https://github.com/apache/spark/pull/26324 back. It was reverted basically because, firstly Hive compatibility, and the lack of investigations in other DBMSes and ANSI.
- In case of PostgreSQL seems coercing NULL literal to TEXT type.
- Presto seems coercing `array() + array(1)` -> array of int.
- Hive seems `array() + array(1)` -> array of strings
Given that, the design choices have been differently made for some reasons. If we pick one of both, seems coercing to array of int makes much more sense.
Another investigation was made offline internally. Seems ANSI SQL 2011, section 6.5 "<contextually typed value specification>" states:
> If ES is specified, then let ET be the element type determined by the context in which ES appears. The declared type DT of ES is Case:
>
> a) If ES simply contains ARRAY, then ET ARRAY[0].
>
> b) If ES simply contains MULTISET, then ET MULTISET.
>
> ES is effectively replaced by CAST ( ES AS DT )
From reading other related context, doing it to `NullType`. Given the investigation made, choosing to `null` seems correct, and we have a reference Presto now. Therefore, this PR proposes to bring it back.
### Why are the changes needed?
When empty array is created, it should be declared as array<null>.
### Does this PR introduce any user-facing change?
Yes, `array()` creates `array<null>`. Now `array(1) + array()` can correctly create `array(1)` instead of `array("1")`.
### How was this patch tested?
Tested manually
Closes#27521 from HyukjinKwon/SPARK-29462.
Lead-authored-by: HyukjinKwon <gurwls223@apache.org>
Co-authored-by: Aman Omer <amanomer1996@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up for #24938 to tweak error message and migration doc.
### Why are the changes needed?
Making user know workaround if SHOW CREATE TABLE doesn't work for some Hive tables.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing unit tests.
Closes#27505 from viirya/SPARK-27946-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
Add the new tab `SQL` in the `Data Types` page.
### Why are the changes needed?
New type added in SPARK-29587.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Locally test by Jekyll.
Closes#27447 from xuanyuanking/SPARK-29587-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a follow-up for #25029, in this PR we throw an AnalysisException when name conflict is detected in nested WITH clause. In this way, the config `spark.sql.legacy.ctePrecedence.enabled` should be set explicitly for the expected behavior.
### Why are the changes needed?
The original change might risky to end-users, it changes behavior silently.
### Does this PR introduce any user-facing change?
Yes, change the config `spark.sql.legacy.ctePrecedence.enabled` as optional.
### How was this patch tested?
New UT.
Closes#27454 from xuanyuanking/SPARK-28228-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add new config `spark.network.maxRemoteBlockSizeFetchToMem` fallback to the old config `spark.maxRemoteBlockSizeFetchToMem`.
### Why are the changes needed?
For naming consistency.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#27463 from xuanyuanking/SPARK-26700-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add migration note for removing `org.apache.spark.ml.image.ImageSchema.readImages`
### Why are the changes needed?
### Does this PR introduce any user-facing change?
### How was this patch tested?
Closes#27467 from WeichenXu123/SC-26286.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to partially revert the commit 51a6ba0181, and provide a legacy parser based on `FastDateFormat` which is compatible to `SimpleDateFormat`.
To enable the legacy parser, set `spark.sql.legacy.timeParser.enabled` to `true`.
### Why are the changes needed?
To allow users to restore old behavior in parsing timestamps/dates using `SimpleDateFormat` patterns. The main reason for restoring is `DateTimeFormatter`'s patterns are not fully compatible to `SimpleDateFormat` patterns, see https://issues.apache.org/jira/browse/SPARK-30668
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
- Added new test to `DateFunctionsSuite`
- Restored additional test cases in `JsonInferSchemaSuite`.
Closes#27441 from MaxGekk/support-simpledateformat.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This reverts commit b89c3de1a4.
### Why are the changes needed?
`FIRST_VALUE` is used only for window expression. Please see the discussion on https://github.com/apache/spark/pull/25082 .
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
Pass the Jenkins.
Closes#27458 from dongjoon-hyun/SPARK-28310.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a follow-up for #22787. In #22787 we disallowed empty strings for json parser except for string and binary types. This follow-up adds a legacy config for restoring previous behavior of allowing empty string.
### Why are the changes needed?
Adding a legacy config to make migration easy for Spark users.
### Does this PR introduce any user-facing change?
Yes. If set this legacy config to true, the users can restore previous behavior prior to Spark 3.0.0.
### How was this patch tested?
Unit test.
Closes#27456 from viirya/SPARK-25040-followup.
Lead-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to update the SQL migration guide, and clarify behavior change of typed `TIMESTAMP` and `DATE` literals for input strings without time zone information - local timestamp and date strings.
### Why are the changes needed?
To inform users that the typed literals may change their behavior in Spark 3.0 because of different sources of the default time zone - JVM system time zone in Spark 2.4 and earlier, and `spark.sql.session.timeZone` in Spark 3.0.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#27435 from MaxGekk/timestamp-lit-migration-guide.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
We have upgraded the built-in Hive from 1.2 to 2.3. This may need to set `spark.sql.hive.metastore.version` and `spark.sql.hive.metastore.jars` according to the version of your Hive metastore. Example:
```
--conf spark.sql.hive.metastore.version=1.2.1 --conf spark.sql.hive.metastore.jars=/root/hive-1.2.1-lib/*
```
Otherwise:
```
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: Unable to fetch table spark_27686. Invalid method name: 'get_table_req';
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:110)
at org.apache.spark.sql.hive.HiveExternalCatalog.tableExists(HiveExternalCatalog.scala:841)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.tableExists(ExternalCatalogWithListener.scala:146)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.tableExists(SessionCatalog.scala:431)
at org.apache.spark.sql.execution.command.CreateDataSourceTableCommand.run(createDataSourceTables.scala:52)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:79)
at org.apache.spark.sql.Dataset.$anonfun$logicalPlan$1(Dataset.scala:226)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3487)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$4(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:87)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3485)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:226)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:96)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Unable to fetch table spark_27686. Invalid method name: 'get_table_req'
at org.apache.hadoop.hive.ql.metadata.Hive.getTable(Hive.java:1282)
at org.apache.spark.sql.hive.client.HiveClientImpl.getRawTableOption(HiveClientImpl.scala:422)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$tableExists$1(HiveClientImpl.scala:436)
at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:322)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:256)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:255)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:305)
at org.apache.spark.sql.hive.client.HiveClientImpl.tableExists(HiveClientImpl.scala:436)
at org.apache.spark.sql.hive.HiveExternalCatalog.$anonfun$tableExists$1(HiveExternalCatalog.scala:841)
at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:100)
... 63 more
Caused by: org.apache.thrift.TApplicationException: Invalid method name: 'get_table_req'
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.recv_get_table_req(ThriftHiveMetastore.java:1567)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.get_table_req(ThriftHiveMetastore.java:1554)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.getTable(HiveMetaStoreClient.java:1350)
at org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.getTable(SessionHiveMetaStoreClient.java:127)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.invoke(RetryingMetaStoreClient.java:173)
at com.sun.proxy.$Proxy38.getTable(Unknown Source)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient$SynchronizedHandler.invoke(HiveMetaStoreClient.java:2336)
at com.sun.proxy.$Proxy38.getTable(Unknown Source)
at org.apache.hadoop.hive.ql.metadata.Hive.getTable(Hive.java:1274)
... 74 more
```
### Why are the changes needed?
Improve documentation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
```SKIP_API=1 jekyll build```:
Closes#27161 from wangyum/SPARK-27686.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This change is to allow custom resource scheduler (GPUs,FPGAs,etc) resource discovery to be more flexible. Users are asking for it to work with hadoop 2.x versions that do not support resource scheduling in YARN and/or also they may not run in an isolated environment.
This change creates a plugin api that users can write their own resource discovery class that allows a lot more flexibility. The user can chain plugins for different resource types. The user specified plugins execute in the order specified and will fall back to use the discovery script plugin if they don't return information for a particular resource.
I had to open up a few of the classes to be public and change them to not be case classes and make them developer api in order for the the plugin to get enough information it needs.
I also relaxed the yarn side so that if yarn isn't configured for resource scheduling we just warn and go on. This helps users that have yarn 3.1 but haven't configured the resource scheduling side on their cluster yet, or aren't running in isolated environment.
The user would configured this like:
--conf spark.resources.discovery.plugin="org.apache.spark.resource.ResourceDiscoveryFPGAPlugin, org.apache.spark.resource.ResourceDiscoveryGPUPlugin"
Note the executor side had to be wrapped with a classloader to make sure we include the user classpath for jars they specified on submission.
Note this is more flexible because the discovery script has limitations such as spawning it in a separate process. This means if you are trying to allocate resources in that process they might be released when the script returns. Other things are the class makes it more flexible to be able to integrate with existing systems and solutions for assigning resources.
### Why are the changes needed?
to more easily use spark resource scheduling with older versions of hadoop or in non-isolated enivronments.
### Does this PR introduce any user-facing change?
Yes a plugin api
### How was this patch tested?
Unit tests added and manual testing done on yarn and standalone modes.
Closes#27410 from tgravescs/hadoop27spark3.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This patch adds a DDL command `SHOW CREATE TABLE AS SERDE`. It is used to generate Hive DDL for a Hive table.
For original `SHOW CREATE TABLE`, it now shows Spark DDL always. If given a Hive table, it tries to generate Spark DDL.
For Hive serde to data source conversion, this uses the existing mapping inside `HiveSerDe`. If can't find a mapping there, throws an analysis exception on unsupported serde configuration.
It is arguably that some Hive fileformat + row serde might be mapped to Spark data source, e.g., CSV. It is not included in this PR. To be conservative, it may not be supported.
For Hive serde properties, for now this doesn't save it to Spark DDL because it may not useful to keep Hive serde properties in Spark table.
## How was this patch tested?
Added test.
Closes#24938 from viirya/SPARK-27946.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Add a section to the Configuration page to document configurations for executor metrics.
At the same time, rename spark.eventLog.logStageExecutorProcessTreeMetrics.enabled to spark.executor.processTreeMetrics.enabled and make it independent of spark.eventLog.logStageExecutorMetrics.enabled.
### Why are the changes needed?
Executor metrics are new in Spark 3.0. They lack documentation.
Memory metrics as a whole are always collected, but the ones obtained from the process tree have to be optionally enabled. Making this depend on a single configuration makes for more intuitive behavior. Given this, the configuration property is renamed to better reflect its meaning.
### Does this PR introduce any user-facing change?
Yes, only in that the configurations are all new to 3.0.
### How was this patch tested?
Not necessary.
Closes#27329 from wypoon/SPARK-27324.
Authored-by: Wing Yew Poon <wypoon@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
### What changes were proposed in this pull request?
- Add `minPartitions` support for Kafka Streaming V1 source.
- Add `minPartitions` support for Kafka batch V1 and V2 source.
- There is lots of refactoring (moving codes to KafkaOffsetReader) to reuse codes.
### Why are the changes needed?
Right now, the "minPartitions" option only works in Kafka streaming source v2. It would be great that we can support it in batch and streaming source v1 (v1 is the fallback mode when a user hits a regression in v2) as well.
### Does this PR introduce any user-facing change?
Yep. The `minPartitions` options is supported in Kafka batch and streaming queries for both data source V1 and V2.
### How was this patch tested?
New unit tests are added to test "minPartitions".
Closes#27388 from zsxwing/kafka-min-partitions.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
This PR removes any dependencies on pypandoc. It also makes related tweaks to the docs README to clarify the dependency on pandoc (not pypandoc).
### Why are the changes needed?
We are using pypandoc to convert the Spark README from Markdown to ReST for PyPI. PyPI now natively supports Markdown, so we don't need pypandoc anymore. The dependency on pypandoc also sometimes causes issues when installing Python packages that depend on PySpark, as described in #18981.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually:
```sh
python -m venv venv
source venv/bin/activate
pip install -U pip
cd python/
python setup.py sdist
pip install dist/pyspark-3.0.0.dev0.tar.gz
pyspark --version
```
I also built the PySpark and R API docs with `jekyll` and reviewed them locally.
It would be good if a maintainer could also test this by creating a PySpark distribution and uploading it to [Test PyPI](https://test.pypi.org) to confirm the README looks as it should.
Closes#27376 from nchammas/SPARK-30665-pypandoc.
Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
add supported hive features
### Why are the changes needed?
update doc
### Does this PR introduce any user-facing change?
Before change UI info:
After this pr:
### How was this patch tested?
For PR about Spark Doc Web UI, we need to show UI format before and after pr.
We can build our local web server about spark docs with reference `$SPARK_PROJECT/docs/README.md`
You should install python and ruby in your env and also install plugin like below
```sh
$ sudo gem install jekyll jekyll-redirect-from rouge
# Following is needed only for generating API docs
$ sudo pip install sphinx pypandoc mkdocs
$ sudo Rscript -e 'install.packages(c("knitr", "devtools", "rmarkdown"), repos="https://cloud.r-project.org/")'
$ sudo Rscript -e 'devtools::install_version("roxygen2", version = "5.0.1", repos="https://cloud.r-project.org/")'
$ sudo Rscript -e 'devtools::install_version("testthat", version = "1.0.2", repos="https://cloud.r-project.org/")'
```
Then we call `jekyll serve --watch` after build we see below message
```
~/Documents/project/AngersZhu/spark/sql
Moving back into docs dir.
Making directory api/sql
cp -r ../sql/site/. api/sql
Source: /Users/angerszhu/Documents/project/AngersZhu/spark/docs
Destination: /Users/angerszhu/Documents/project/AngersZhu/spark/docs/_site
Incremental build: disabled. Enable with --incremental
Generating...
done in 24.717 seconds.
Auto-regeneration: enabled for '/Users/angerszhu/Documents/project/AngersZhu/spark/docs'
Server address: http://127.0.0.1:4000
Server running... press ctrl-c to stop.
```
Visit http://127.0.0.1:4000 to get your newest change in doc web.
Closes#27106 from AngersZhuuuu/SPARK-30435.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds `numpy` to the list of things that need to be installed in order to build the API docs. It doesn't add a new dependency; it just documents an existing dependency.
### Why are the changes needed?
You cannot build the PySpark API docs without numpy installed. Otherwise you get this series of errors:
```
$ SKIP_SCALADOC=1 SKIP_RDOC=1 SKIP_SQLDOC=1 jekyll serve
Configuration file: .../spark/docs/_config.yml
Moving to python/docs directory and building sphinx.
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v2.3.1
loading pickled environment... done
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 0 source files that are out of date
updating environment: 0 added, 2 changed, 0 removed
reading sources... [100%] pyspark.mllib
WARNING: autodoc: failed to import module 'ml' from module 'pyspark'; the following exception was raised:
No module named 'numpy'
WARNING: autodoc: failed to import module 'ml.param' from module 'pyspark'; the following exception was raised:
No module named 'numpy'
...
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually, by building the API docs with and without numpy.
Closes#27390 from nchammas/SPARK-30672-numpy-pyspark-docs.
Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Sets up links between related sections.
- Add "Related sections" for each section.
- Change to the left hand side menu to reflect the current status of the doc.
- Other minor cleanups.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
Tested using jykyll build --serve
Closes#27371 from dilipbiswal/select_finalization.
Authored-by: Dilip Biswal <dkbiswal@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This pr intends to rename `spark.sql.legacy.addDirectory.recursive` into `spark.sql.legacy.addDirectory.recursive.enabled`.
### Why are the changes needed?
For consistent option names.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#27372 from maropu/SPARK-30234-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Update `testthat` to >= 2.0.0
- Replace of `testthat:::run_tests` with `testthat:::test_package_dir`
- Add trivial assertions for tests, without any expectations, to avoid skipping.
- Update related docs.
### Why are the changes needed?
`testthat` version has been frozen by [SPARK-22817](https://issues.apache.org/jira/browse/SPARK-22817) / https://github.com/apache/spark/pull/20003, but 1.0.2 is pretty old, and we shouldn't keep things in this state forever.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
- Existing CI pipeline:
- Windows build on AppVeyor, R 3.6.2, testthtat 2.3.1
- Linux build on Jenkins, R 3.1.x, testthat 1.0.2
- Additional builds with thesthat 2.3.1 using [sparkr-build-sandbox](https://github.com/zero323/sparkr-build-sandbox) on c7ed64af9e697b3619779857dd820832176b3be3
R 3.4.4 (image digest ec9032f8cf98)
```
docker pull zero323/sparkr-build-sandbox:3.4.4
docker run zero323/sparkr-build-sandbox:3.4.4 zero323 --branch SPARK-23435 --commit c7ed64af9e697b3619779857dd820832176b3be3 --public-key https://keybase.io/zero323/pgp_keys.asc
```
3.5.3 (image digest 0b1759ee4d1d)
```
docker pull zero323/sparkr-build-sandbox:3.5.3
docker run zero323/sparkr-build-sandbox:3.5.3 zero323 --branch SPARK-23435 --commit
c7ed64af9e697b3619779857dd820832176b3be3 --public-key https://keybase.io/zero323/pgp_keys.asc
```
and 3.6.2 (image digest 6594c8ceb72f)
```
docker pull zero323/sparkr-build-sandbox:3.6.2
docker run zero323/sparkr-build-sandbox:3.6.2 zero323 --branch SPARK-23435 --commit c7ed64af9e697b3619779857dd820832176b3be3 --public-key https://keybase.io/zero323/pgp_keys.asc
````
Corresponding [asciicast](https://asciinema.org/) are available as 10.5281/zenodo.3629431
[](https://doi.org/10.5281/zenodo.3629431)
(a bit to large to burden asciinema.org, but can run locally via `asciinema play`).
----------------------------
Continued from #27328Closes#27359 from zero323/SPARK-23435.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch addresses remaining functionality on event log compaction: integrate compaction into FsHistoryProvider.
This patch is next task of SPARK-30479 (#27164), please refer the description of PR #27085 to see overall rationalization of this patch.
### Why are the changes needed?
One of major goal of SPARK-28594 is to prevent the event logs to become too huge, and SPARK-29779 achieves the goal. We've got another approach in prior, but the old approach required models in both KVStore and live entities to guarantee compatibility, while they're not designed to do so.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT.
Closes#27208 from HeartSaVioR/SPARK-30481.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@apache.org>
### What changes were proposed in this pull request?
Document CLUSTER BY clause of SELECT statement in SQL Reference Guide.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
There was no documentation for this.
**After.**
<img width="972" alt="Screen Shot 2020-01-20 at 2 59 05 PM" src="https://user-images.githubusercontent.com/14225158/72762704-7528de80-3b95-11ea-9d34-8fa0ab63d4c0.png">
<img width="972" alt="Screen Shot 2020-01-20 at 2 59 19 PM" src="https://user-images.githubusercontent.com/14225158/72762710-78bc6580-3b95-11ea-8279-2848d3b9e619.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#27297 from dilipbiswal/sql-ref-select-clusterby.
Authored-by: Dilip Biswal <dkbiswal@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Document DISTRIBUTE BY clause of SELECT statement in SQL Reference Guide.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
There was no documentation for this.
**After.**
<img width="972" alt="Screen Shot 2020-01-20 at 3 08 24 PM" src="https://user-images.githubusercontent.com/14225158/72763045-c08fbc80-3b96-11ea-8fb6-023cba5eb96a.png">
<img width="972" alt="Screen Shot 2020-01-20 at 3 08 34 PM" src="https://user-images.githubusercontent.com/14225158/72763047-c38aad00-3b96-11ea-80d8-cd3d2d4257c8.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#27298 from dilipbiswal/sql-ref-select-distributeby.
Authored-by: Dilip Biswal <dkbiswal@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Document CREATE TABLE statement in SQL Reference Guide.
### Why are the changes needed?
Adding documentation for SQL reference.
### Does this PR introduce any user-facing change?
yes
Before:
There was no documentation for this.
### How was this patch tested?
Used jekyll build and serve to verify.
Closes#26759 from PavithraRamachandran/create_doc.
Authored-by: Pavithra Ramachandran <pavi.rams@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fix a few super nit problems
### Why are the changes needed?
To make doc look better
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Tested using jykyll build --serve
Closes#27332 from huaxingao/spark-30575-followup.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Document SELECT statement in SQL Reference Guide. In this PR includes the main
entry page for SELECT. I will open follow-up PRs for different clauses.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
There was no documentation for this.
**After.**
<img width="972" alt="Screen Shot 2020-01-19 at 11 20 41 PM" src="https://user-images.githubusercontent.com/14225158/72706257-6c42f900-3b12-11ea-821a-171ff035443f.png">
<img width="972" alt="Screen Shot 2020-01-19 at 11 21 55 PM" src="https://user-images.githubusercontent.com/14225158/72706313-91d00280-3b12-11ea-90e4-be7174b4593d.png">
<img width="972" alt="Screen Shot 2020-01-19 at 11 22 16 PM" src="https://user-images.githubusercontent.com/14225158/72706323-97c5e380-3b12-11ea-99e5-e7aaa3b4df68.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#27216 from dilipbiswal/sql_ref_select_hook.
Authored-by: Dilip Biswal <dkbiswal@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Document LIMIT clause of SELECT statement in SQL Reference Guide.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
There was no documentation for this.
**After.**
<img width="972" alt="Screen Shot 2020-01-20 at 1 37 28 AM" src="https://user-images.githubusercontent.com/14225158/72715533-7e7a6280-3b25-11ea-98fc-ed68b5d5024a.png">
<img width="972" alt="Screen Shot 2020-01-20 at 1 37 43 AM" src="https://user-images.githubusercontent.com/14225158/72715549-83d7ad00-3b25-11ea-98b3-610eca2628f6.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#27290 from dilipbiswal/sql-ref-select-limit.
Authored-by: Dilip Biswal <dkbiswal@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This pr removes the nonstandard `SET OWNER` syntax for namespaces and changes the owner reserved properties from `ownerName` and `ownerType` to `owner`.
### Why are the changes needed?
the `SET OWNER` syntax for namespaces is hive-specific and non-sql standard, we need a more future-proofing design before we implement user-facing changes for SQL security issues
### Does this PR introduce any user-facing change?
no, just revert an unpublic syntax
### How was this patch tested?
modified uts
Closes#27300 from yaooqinn/SPARK-30591.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR propose to disallow negative `scale` of `Decimal` in Spark. And this PR brings two behavior changes:
1) for literals like `1.23E4BD` or `1.23E4`(with `spark.sql.legacy.exponentLiteralAsDecimal.enabled`=true, see [SPARK-29956](https://issues.apache.org/jira/browse/SPARK-29956)), we set its `(precision, scale)` to (5, 0) rather than (3, -2);
2) add negative `scale` check inside the decimal method if it exposes to set `scale` explicitly. If check fails, `AnalysisException` throws.
And user could still use `spark.sql.legacy.allowNegativeScaleOfDecimal.enabled` to restore the previous behavior.
### Why are the changes needed?
According to SQL standard,
> 4.4.2 Characteristics of numbers
An exact numeric type has a precision P and a scale S. P is a positive integer that determines the number of significant digits in a particular radix R, where R is either 2 or 10. S is a non-negative integer.
scale of Decimal should always be non-negative. And other mainstream databases, like Presto, PostgreSQL, also don't allow negative scale.
Presto:
```
presto:default> create table t (i decimal(2, -1));
Query 20191213_081238_00017_i448h failed: line 1:30: mismatched input '-'. Expecting: <integer>, <type>
create table t (i decimal(2, -1))
```
PostgrelSQL:
```
postgres=# create table t(i decimal(2, -1));
ERROR: NUMERIC scale -1 must be between 0 and precision 2
LINE 1: create table t(i decimal(2, -1));
^
```
And, actually, Spark itself already doesn't allow to create table with negative decimal types using SQL:
```
scala> spark.sql("create table t(i decimal(2, -1))");
org.apache.spark.sql.catalyst.parser.ParseException:
no viable alternative at input 'create table t(i decimal(2, -'(line 1, pos 28)
== SQL ==
create table t(i decimal(2, -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:48)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:76)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:605)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:605)
... 35 elided
```
However, it is still possible to create such table or `DatFrame` using Spark SQL programming API:
```
scala> val tb =
CatalogTable(
TableIdentifier("test", None),
CatalogTableType.MANAGED,
CatalogStorageFormat.empty,
StructType(StructField("i", DecimalType(2, -1) ) :: Nil))
```
```
scala> spark.sql("SELECT 1.23E4BD")
res2: org.apache.spark.sql.DataFrame = [1.23E+4: decimal(3,-2)]
```
while, these two different behavior could make user confused.
On the other side, even if user creates such table or `DataFrame` with negative scale decimal type, it can't write data out if using format, like `parquet` or `orc`. Because these formats have their own check for negative scale and fail on it.
```
scala> spark.sql("SELECT 1.23E4BD").write.saveAsTable("parquet")
19/12/13 17:37:04 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.IllegalArgumentException: Invalid DECIMAL scale: -2
at org.apache.parquet.Preconditions.checkArgument(Preconditions.java:53)
at org.apache.parquet.schema.Types$BasePrimitiveBuilder.decimalMetadata(Types.java:495)
at org.apache.parquet.schema.Types$BasePrimitiveBuilder.build(Types.java:403)
at org.apache.parquet.schema.Types$BasePrimitiveBuilder.build(Types.java:309)
at org.apache.parquet.schema.Types$Builder.named(Types.java:290)
at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.convertField(ParquetSchemaConverter.scala:428)
at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.convertField(ParquetSchemaConverter.scala:334)
at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.$anonfun$convert$2(ParquetSchemaConverter.scala:326)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at org.apache.spark.sql.types.StructType.foreach(StructType.scala:99)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at org.apache.spark.sql.types.StructType.map(StructType.scala:99)
at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.convert(ParquetSchemaConverter.scala:326)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.init(ParquetWriteSupport.scala:97)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:388)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:349)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:150)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:124)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:109)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:264)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$write$15(FileFormatWriter.scala:205)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
```
So, I think it would be better to disallow negative scale totally and make behaviors above be consistent.
### Does this PR introduce any user-facing change?
Yes, if `spark.sql.legacy.allowNegativeScaleOfDecimal.enabled=false`, user couldn't create Decimal value with negative scale anymore.
### How was this patch tested?
Added new tests in `ExpressionParserSuite` and `DecimalSuite`;
Updated `SQLQueryTestSuite`.
Closes#26881 from Ngone51/nonnegative-scale.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `owner` property to v2 table, it is reversed by `TableCatalog`, indicates the table's owner.
### Why are the changes needed?
enhance ownership management of catalog API
### Does this PR introduce any user-facing change?
yes, add 1 reserved property - `owner` , and it is not allowed to use in OPTIONS/TBLPROPERTIES anymore, only if legacy on
### How was this patch tested?
add uts
Closes#27249 from yaooqinn/SPARK-30019.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds a migration guide for `SHOW TBLPROPERTIES` for Apache Spark 3.0.0.
### Why are the changes needed?
The behavior of `SHOW TBLPROPERTIES` changed when the table does not exist. The migration guide reflects this user facing change.
### Does this PR introduce any user-facing change?
Yes. This is a documentation change.
### How was this patch tested?
No tests were added because this is a doc change.
Closes#27276 from imback82/SPARK-30282-followup.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
make KUBERNETES_MASTER_INTERNAL_URL configurable
### Why are the changes needed?
we do not always use the default port number 443 to access our kube-apiserver, and even in some mulit-tenant cluster, people do not use the service `kubernetes.default.svc` to access the kube-apiserver, so make the internal master configurable is necessary。
### Does this PR introduce any user-facing change?
user can configure the internal master url by
```
--conf spark.kubernetes.internal.master=https://kubernetes.default.svc:6443
```
### How was this patch tested?
run in multi-cluster that do not use the https://kubernetes.default.svc to access the kube-apiserver
Closes#27029 from wackxu/internalmaster.
Authored-by: xushiwei 00425595 <xushiwei5@huawei.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds a migration guide for MsSQLServer JDBC dialect for Apache Spark 2.4.4 and 2.4.5.
### Why are the changes needed?
Apache Spark 2.4.4 updates the type mapping correctly according to MS SQL Server, but missed to mention that in the migration guide. In addition, 2.4.4 adds a configuration for the legacy behavior.
### Does this PR introduce any user-facing change?
Yes. This is a documentation change.
### How was this patch tested?
Manually generate and see the doc.
Closes#27270 from dongjoon-hyun/SPARK-28152-DOC.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/26956 to add a migration document for 2.4.5.
### Why are the changes needed?
New legacy configuration will restore the previous behavior safely.
### Does this PR introduce any user-facing change?
This PR updates the doc.
<img width="763" alt="screenshot" src="https://user-images.githubusercontent.com/9700541/72639939-9da5a400-391b-11ea-87b1-14bca15db5a6.png">
### How was this patch tested?
Build the document and see the change manually.
Closes#27269 from dongjoon-hyun/SPARK-30312.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
[SPARK-20568](https://issues.apache.org/jira/browse/SPARK-20568) added the possibility to clean up completed files in streaming query. Deleting/archiving uses the main thread which can slow down processing. In this PR I've created thread pool to handle file delete/archival. The number of threads can be configured with `spark.sql.streaming.fileSource.cleaner.numThreads`.
### Why are the changes needed?
Do file delete/archival in separate thread.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#26502 from gaborgsomogyi/SPARK-29876.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
The default value for backLog set back to -1, as any other value may break existing configuration by overriding Netty's default io.netty.util.NetUtil#SOMAXCONN. The documentation accordingly adjusted.
See discussion thread: https://github.com/apache/spark/pull/24732
### What changes were proposed in this pull request?
Partial rollback of https://github.com/apache/spark/pull/24732 (default for backLog set back to -1).
### Why are the changes needed?
Previous change introduces backward incompatibility by overriding default of Netty's `io.netty.util.NetUtil#SOMAXCONN`
Closes#27230 from xCASx/master.
Authored-by: Maxim Kolesnikov <swe.kolesnikov@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
SPARK-29894 provides information on the Codegen Stage Id in WEBUI for SQL Plan graphs. Similarly, this proposes to add Codegen Stage Id in the DAG visualization for Stage execution. DAGs for Stage execution are available in the WEBUI under the Jobs and Stages tabs.
### Why are the changes needed?
This is proposed as an aid for drill-down analysis of complex SQL statement execution, as it is not always easy to match parts of the SQL Plan graph with the corresponding Stage DAG execution graph. Adding Codegen Stage Id for WholeStageCodegen operations makes this task easier.
### Does this PR introduce any user-facing change?
Stage DAG visualization in the WEBUI will show codegen stage id for WholeStageCodegen operations, as in the example snippet from the WEBUI, Jobs tab (the query used in the example is TPCDS 2.4 q14a):
### How was this patch tested?
Manually tested, see also example snippet.
Closes#26675 from LucaCanali/addCodegenStageIdtoStageGraph.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use the new framework to resolve the SHOW TBLPROPERTIES command. This PR along with #27243 should update all the existing V2 commands with `UnresolvedV2Relation`.
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-2990](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce any user-facing change?
Yes `SHOW TBLPROPERTIES temp_view` now fails with `AnalysisException` will be thrown with a message `temp_view is a temp view not table`. Previously, it was returning empty row.
### How was this patch tested?
Existing tests
Closes#26921 from imback82/consistnet_v2command.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
TableCatalog reserves some properties, e,g `provider`, `location` for internal usage. Some of them are static once create, some of them need specific syntax to modify. Instead of using `OPTIONS (k='v')` or TBLPROPERTIES (k='v'), if k is a reserved TableCatalog property, we should use its specific syntax to add/modify/delete it. e.g. `provider` is a reserved property, we should use the `USING` clause to specify it, and should not allow `ALTER TABLE ... UNSET TBLPROPERTIES('provider')` to delete it. Also, there are two paths for v1/v2 catalog tables to resolve these properties, e.g. the v1 session catalog tables will only use the `USING` clause to decide `provider` but v2 tables will also lookup OPTION/TBLPROPERTIES(although there is a bug prohibit it).
Additionally, 'path' is not reserved but holds special meaning for `LOCATION` and it is used in `CREATE/REPLACE TABLE`'s `OPTIONS` sub-clause. Now for the session catalog tables, the `path` is case-insensitive, but for the non-session catalog tables, it is case-sensitive, we should make it both case insensitive for disambiguation.
### Why are the changes needed?
prevent reserved properties from being modified unexpectedly
unify the property resolution for v1/v2.
fix some bugs.
### Does this PR introduce any user-facing change?
yes
1 . `location` and `provider` (case sensitive) cannot be used in `CREATE/REPLACE TABLE ... OPTIONS/TBLPROPETIES` and `ALTER TABLE ... SET TBLPROPERTIES (...)`, if legacy on, they will be ignored to let the command success without having side effects
3. Once `path` in `CREATE/REPLACE TABLE ... OPTIONS` is case insensitive for v1 but sensitive for v2, but now we change it case insensitive for both kinds of tables, then v2 tables will also fail if `LOCATION` and `OPTIONS('PaTh' ='abc')` are both specified or will pick `PaTh`'s value as table location if `LOCATION` is missing.
4. Now we will detect if there are two different case `path` keys or more in `CREATE/REPLACE TABLE ... OPTIONS`, once it is a kind of unexpected last-win policy for v1, and v2 is case sensitive.
### How was this patch tested?
add ut
Closes#27197 from yaooqinn/SPARK-30507.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Updated `docs/sql-data-sources-avro.md`, and added a few sentences about already deprecated in code Avro option `ignoreExtension`.
<img width="968" alt="Screen Shot 2020-01-15 at 10 24 14" src="https://user-images.githubusercontent.com/1580697/72413684-64d1c780-3781-11ea-948a-d3cccf4c72df.png">
Closes#27174
### Why are the changes needed?
To make users doc consistent to the code where `ignoreExtension` has been already deprecated, see 3663dbe541/external/avro/src/main/scala/org/apache/spark/sql/avro/AvroUtils.scala (L46-L47)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
by building docs
Closes#27194 from MaxGekk/avro-doc-deprecation-ignoreExtension.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
* Annotate UserDefinedAggregateFunction as deprecated by SPARK-27296
* Update user doc examples to reflect new ability to register typed Aggregator[IN, BUF, OUT] as an untyped aggregating UDF
### Why are the changes needed?
UserDefinedAggregateFunction is being deprecated
### Does this PR introduce any user-facing change?
Changes are to user documentation, and deprecation annotations.
### How was this patch tested?
Testing was via package build to verify doc generation, deprecation warnings, and successful example compilation.
Closes#27193 from erikerlandson/spark-30423.
Authored-by: Erik Erlandson <eerlands@redhat.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Updated the doc for ADD FILE and LIST FILE
### Why are the changes needed?
Due to changes made in #26863 , it is necessary to update ADD FILE and LIST FILE doc.
### Does this PR introduce any user-facing change?
Yeah. Document updated.
### How was this patch tested?
Manually
Closes#27188 from iRakson/SPARK-30234_FOLLOWUP.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This patch documents the configuration for the Kafka producer pool, newly revised via SPARK-21869 (#26845)
### Why are the changes needed?
The explanation of new Kafka producer pool configuration is missing, whereas the doc has Kafka
consumer pool configuration.
### Does this PR introduce any user-facing change?
Yes. This is a documentation change.
### How was this patch tested?
N/A
Closes#27146 from HeartSaVioR/SPARK-21869-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Removing the sorting of PySpark SQL Row fields that were previously sorted by name alphabetically for Python versions 3.6 and above. Field order will now match that as entered. Rows will be used like tuples and are applied to schema by position. For Python versions < 3.6, the order of kwargs is not guaranteed and therefore will be sorted automatically as in previous versions of Spark.
### Why are the changes needed?
This caused inconsistent behavior in that local Rows could be applied to a schema by matching names, but once serialized the Row could only be used by position and the fields were possibly in a different order.
### Does this PR introduce any user-facing change?
Yes, Row fields are no longer sorted alphabetically but will be in the order entered. For Python < 3.6 `kwargs` can not guarantee the order as entered, so `Row`s will be automatically sorted.
An environment variable "PYSPARK_ROW_FIELD_SORTING_ENABLED" can be set that will override construction of `Row` to maintain compatibility with Spark 2.x.
### How was this patch tested?
Existing tests are run with PYSPARK_ROW_FIELD_SORTING_ENABLED=true and added new test with unsorted fields for Python 3.6+
Closes#26496 from BryanCutler/pyspark-remove-Row-sorting-SPARK-29748.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
### What changes were proposed in this pull request?
Now users can add directories from sql CLI as well using ADD FILE command and setting spark.sql.addDirectory.recursive to true.
### Why are the changes needed?
In SPARK-4687, support was added for adding directories as resources. But sql users cannot use that feature from CLI.
`ADD FILE /path/to/folder` gives the following error:
`org.apache.spark.SparkException: Added file /path/to/folder is a directory and recursive is not turned on.`
Users need to turn on `recursive` for adding directories. Thus a configuration was required which will allow users to turn on `recursive`.
Also Hive allow users to add directories from their shell.
### Does this PR introduce any user-facing change?
Yes. Users can set recursive using `spark.sql.addDirectory.recursive`.
### How was this patch tested?
Manually.
Will add test cases soon.
SPARK SCREENSHOTS
When `spark.sql.addDirectory.recursive` is not turned on.
After setting `spark.sql.addDirectory.recursive` to true.
HIVE SCREENSHOT
`RELEASE_NOTES.txt` is text file while `dummy` is a directory.
Closes#26863 from iRakson/SPARK-30234.
Lead-authored-by: root1 <raksonrakesh@gmail.com>
Co-authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In this pull request, we are going to support `SET OWNER` syntax for databases and namespaces,
```sql
ALTER (DATABASE|SCHEME|NAMESPACE) database_name SET OWNER [USER|ROLE|GROUP] user_or_role_group;
```
Before this commit 332e252a14, we didn't care much about ownerships for the catalog objects. In 332e252a14, we determined to use properties to store ownership staff, and temporarily used `alter database ... set dbproperties ...` to support switch ownership of a database. This PR aims to use the formal syntax to replace it.
In hive, `ownerName/Type` are fields of the database objects, also they can be normal properties.
```
create schema test1 with dbproperties('ownerName'='yaooqinn')
```
The create/alter database syntax will not change the owner to `yaooqinn` but store it in parameters. e.g.
```
+----------+----------+---------------------------------------------------------------+-------------+-------------+-----------------------+--+
| db_name | comment | location | owner_name | owner_type | parameters |
+----------+----------+---------------------------------------------------------------+-------------+-------------+-----------------------+--+
| test1 | | hdfs://quickstart.cloudera:8020/user/hive/warehouse/test1.db | anonymous | USER | {ownerName=yaooqinn} |
+----------+----------+---------------------------------------------------------------+-------------+-------------+-----------------------+--+
```
In this pull request, because we let the `ownerName` become reversed, so it will neither change the owner nor store in dbproperties, just be omitted silently.
## Why are the changes needed?
Formal syntax support for changing database ownership
### Does this PR introduce any user-facing change?
yes, add a new syntax
### How was this patch tested?
add unit tests
Closes#26775 from yaooqinn/SPARK-30018.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, COMMENT and LOCATION are reserved properties for Datasource v2 namespaces. They can be set via specific clauses and via properties. And the ones specified in clauses take precede of properties. Since they are reserved, which means they are not able to visit directly. They should be used in COMMENT/LOCATION clauses ONLY.
### Why are the changes needed?
make reserved properties be reserved.
### Does this PR introduce any user-facing change?
yes, 'location', 'comment' are not allowed use in db properties
### How was this patch tested?
UNIT tests.
Closes#26806 from yaooqinn/SPARK-30183.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Previously in https://github.com/apache/spark/pull/26614/files#diff-bad3987c83bd22d46416d3dd9d208e76R90, we compare the number of tasks with `(conf.get(EXECUTOR_CORES) / sched.CPUS_PER_TASK)`. In standalone mode if the value is not explicitly set by default, the conf value would be 1 but the executor would actually use all the cores of the worker. So it is allowed to have `CPUS_PER_TASK` greater than `EXECUTOR_CORES`. To handle this case, we change the condition to be `numTasks <= Math.max(conf.get(EXECUTOR_CORES) / sched.CPUS_PER_TASK, 1)`
### Why are the changes needed?
For standalone mode if the user set the `spark.task.cpus` to be greater than 1 but didn't set the `spark.executor.cores`. Even though there is only 1 task in the stage it would not be speculative run.
### Does this PR introduce any user-facing change?
Solve the problem above by allowing speculative run when there is only 1 task in the stage.
### How was this patch tested?
Existing tests and one more test in TaskSetManagerSuite
Closes#27126 from yuchenhuo/SPARK-30417.
Authored-by: Yuchen Huo <yuchen.huo@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
This patch renews the verification logic of archive path for FileStreamSource, as we found the logic doesn't take partitioned/recursive options into account.
Before the patch, it only requires the archive path to have depth more than 2 (two subdirectories from root), leveraging the fact FileStreamSource normally reads the files where the parent directory matches the pattern or the file itself matches the pattern. Given 'archive' operation moves the files to the base archive path with retaining the full path, archive path is tend to be safe if the depth is more than 2, meaning FileStreamSource doesn't re-read archived files as new source files.
WIth partitioned/recursive options, the fact is invalid, as FileStreamSource can read any files in any depth of subdirectories for source pattern. To deal with this correctly, we have to renew the verification logic, which may not intuitive and simple but works for all cases.
The new verification logic prevents both cases:
1) archive path matches with source pattern as "prefix" (the depth of archive path > the depth of source pattern)
e.g.
* source pattern: `/hello*/spar?`
* archive path: `/hello/spark/structured/streaming`
Any files in archive path will match with source pattern when recursive option is enabled.
2) source pattern matches with archive path as "prefix" (the depth of source pattern > the depth of archive path)
e.g.
* source pattern: `/hello*/spar?/structured/hello2*`
* archive path: `/hello/spark/structured`
Some archive files will not match with source pattern, e.g. file path: `/hello/spark/structured/hello2`, then final archived path: `/hello/spark/structured/hello/spark/structured/hello2`.
But some other archive files will still match with source pattern, e.g. file path: `/hello2/spark/structured/hello2`, then final archived path: `/hello/spark/structured/hello2/spark/structured/hello2` which matches with source pattern when recursive is enabled.
Implicitly it also prevents archive path matches with source pattern as full match (same depth).
We would want to prevent any source files to be archived and added to new source files again, so the patch takes most restrictive approach to prevent the possible cases.
### Why are the changes needed?
Without this patch, there's a chance archived files are included as new source files when partitioned/recursive option is enabled, as current condition doesn't take these options into account.
### Does this PR introduce any user-facing change?
Only for Spark 3.0.0-preview (only preview 1 for now, but possibly preview 2 as well) - end users are required to provide archive path with ensuring a bit complicated conditions, instead of simply higher than 2 depths.
### How was this patch tested?
New UT.
Closes#26920 from HeartSaVioR/SPARK-30281.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Revert https://github.com/apache/spark/pull/20433 .
### Why are the changes needed?
According to the SQL standard, the INTERVAL prefix is required:
```
<interval literal> ::=
INTERVAL [ <sign> ] <interval string> <interval qualifier>
<interval string> ::=
<quote> <unquoted interval string> <quote>
```
### Does this PR introduce any user-facing change?
yes, but omitting the INTERVAL prefix is a new feature in 3.0
### How was this patch tested?
existing tests
Closes#27080 from cloud-fan/interval.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Change config name from `spark.sql.legacy.typeCoercion.datetimeToString` to `spark.sql.legacy.typeCoercion.datetimeToString.enabled`.
### Why are the changes needed?
To follow the other boolean conf naming convention.
### Does this PR introduce any user-facing change?
No, it's newly added in Spark 3.0.
### How was this patch tested?
Pass Jenkins
Closes#27065 from Ngone51/SPARK-27638-FOLLOWUP.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Documentation added for refresh resources command in spark-sql.
### Why are the changes needed?
Previously, only refresh table command was documented.
### Does this PR introduce any user-facing change?
Yes. Now users can access documentation for refresh resources command.
### How was this patch tested?
Manually.
Closes#27023 from iRakson/SPARK-30363.
Authored-by: root1 <raksonrakesh@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/26780
In https://github.com/apache/spark/pull/26780, a new Avro data source option `actualSchema` is introduced for setting the original Avro schema in function `from_avro`, while the expected schema is supposed to be set in the parameter `jsonFormatSchema` of `from_avro`.
However, there is another Avro data source option `avroSchema`. It is used for setting the expected schema in readiong and writing.
This PR is to use the option `avroSchema` option for reading Avro data with an evolved schema and remove the new one `actualSchema`
### Why are the changes needed?
Unify and simplify the Avro data source options.
### Does this PR introduce any user-facing change?
Yes.
To deserialize Avro data with an evolved schema, before changes:
```
from_avro('col, expectedSchema, ("actualSchema" -> actualSchema))
```
After changes:
```
from_avro('col, actualSchema, ("avroSchema" -> expectedSchema))
```
The second parameter is always the actual Avro schema after changes.
### How was this patch tested?
Update the existing tests in https://github.com/apache/spark/pull/26780Closes#27045 from gengliangwang/renameAvroOption.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Updated the document for LIST FILE/JAR command.
### Why are the changes needed?
LIST FILE/JAR can take multiple filenames as argument and it returns the files which were added as resources.
### Does this PR introduce any user-facing change?
Yes. Documentation updated for LIST FILE/JAR command
### How was this patch tested?
Manually
Closes#26996 from iRakson/SPARK-30342.
Authored-by: root1 <raksonrakesh@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
Create temporary or permanent function it should throw AnalysisException if the resource is not found. Need to keep behavior consistent across permanent and temporary functions.
## How was this patch tested?
Added UT and also tested manually
**Before Fix**
If the UDF resource is not present then on creation of temporary function it throws AnalysisException where as for permanent function it does not throw. Permanent funtcion throws AnalysisException only after select operation is performed.
**After Fix**
For temporary and permanent function check for the resource, if the UDF resource is not found then throw AnalysisException
Closes#25399 from sandeep-katta/funcIssue.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Update the Spark SQL document menu and join strategy hints.
### Why are the changes needed?
- Several new changes in the Spark SQL document didn't change the menu-sql.yaml correspondingly.
- Update the demo code for join strategy hints.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Document change only.
Closes#26917 from xuanyuanking/SPARK-30278.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
1. loss function supports: logloss, mse
2. optimizer: GD, adamW
### Why are the changes needed?
Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.
References:
1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#27000 from mob-ai/ml/fm.
Authored-by: zhanjf <zhanjf@mob.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
The filter predicate for aggregate expression is an `ANSI SQL`.
```
<aggregate function> ::=
COUNT <left paren> <asterisk> <right paren> [ <filter clause> ]
| <general set function> [ <filter clause> ]
| <binary set function> [ <filter clause> ]
| <ordered set function> [ <filter clause> ]
| <array aggregate function> [ <filter clause> ]
| <row pattern count function> [ <filter clause> ]
```
There are some mainstream database support this syntax.
**PostgreSQL:**
https://www.postgresql.org/docs/current/sql-expressions.html#SYNTAX-AGGREGATES
For example:
```
SELECT
year,
count(*) FILTER (WHERE gdp_per_capita >= 40000)
FROM
countries
GROUP BY
year
```
```
SELECT
year,
code,
gdp_per_capita,
count(*)
FILTER (WHERE gdp_per_capita >= 40000)
OVER (PARTITION BY year)
FROM
countries
```
**jOOQ:**
https://blog.jooq.org/2014/12/30/the-awesome-postgresql-9-4-sql2003-filter-clause-for-aggregate-functions/
**Notice:**
1.This PR only supports FILTER predicate without codegen. maropu will create another PR is related to SPARK-30027 to support codegen.
2.This PR only supports FILTER predicate without DISTINCT. I will create another PR is related to SPARK-30276 to support this.
3.This PR only supports FILTER predicate that can't reference the outer query. I created ticket SPARK-30219 to support it.
4.This PR only supports FILTER predicate that can't use IN/EXISTS predicate sub-queries. I created ticket SPARK-30220 to support it.
5.Spark SQL cannot supports a SQL with nested aggregate. I created ticket SPARK-30182 to support it.
There are some show of the PR on my production environment.
```
spark-sql> desc gja_test_partition;
key string NULL
value string NULL
other string NULL
col2 int NULL
# Partition Information
# col_name data_type comment
col2 int NULL
Time taken: 0.79 s
```
```
spark-sql> select * from gja_test_partition;
a A ao 1
b B bo 1
c C co 1
d D do 1
e E eo 2
g G go 2
h H ho 2
j J jo 2
f F fo 3
k K ko 3
l L lo 4
i I io 4
Time taken: 1.75 s
```
```
spark-sql> select count(key), sum(col2) from gja_test_partition;
12 26
Time taken: 1.848 s
```
```
spark-sql> select count(key) filter (where col2 > 1) from gja_test_partition;
8
Time taken: 2.926 s
```
```
spark-sql> select sum(col2) filter (where col2 > 2) from gja_test_partition;
14
Time taken: 2.087 s
```
```
spark-sql> select count(key) filter (where col2 > 1), sum(col2) filter (where col2 > 2) from gja_test_partition;
8 14
Time taken: 2.847 s
```
```
spark-sql> select count(key), count(key) filter (where col2 > 1), sum(col2), sum(col2) filter (where col2 > 2) from gja_test_partition;
12 8 26 14
Time taken: 1.787 s
```
```
spark-sql> desc student;
id int NULL
name string NULL
sex string NULL
class_id int NULL
Time taken: 0.206 s
```
```
spark-sql> select * from student;
1 张三 man 1
2 李四 man 1
3 王五 man 2
4 赵六 man 2
5 钱小花 woman 1
6 赵九红 woman 2
7 郭丽丽 woman 2
Time taken: 0.786 s
```
```
spark-sql> select class_id, count(id), sum(id) from student group by class_id;
1 3 8
2 4 20
Time taken: 18.783 s
```
```
spark-sql> select class_id, count(id) filter (where sex = 'man'), sum(id) filter (where sex = 'woman') from student group by class_id;
1 2 5
2 2 13
Time taken: 3.887 s
```
### Why are the changes needed?
Add new SQL feature.
### Does this PR introduce any user-facing change?
'No'.
### How was this patch tested?
Exists UT and new UT.
Closes#26656 from beliefer/support-aggregate-clause.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
accuracyExpression can accept Long which may cause overflow error.
accuracyExpression can accept fractions which are implicitly floored.
accuracyExpression can accept null which is implicitly changed to 0.
percentageExpression can accept null but cause MatchError.
percentageExpression can accept ArrayType(_, nullable=true) in which the nulls are implicitly changed to zeros.
##### cases
```sql
select percentile_approx(10.0, 0.5, 2147483648); -- overflow and fail
select percentile_approx(10.0, 0.5, 4294967297); -- overflow but success
select percentile_approx(10.0, 0.5, null); -- null cast to 0
select percentile_approx(10.0, 0.5, 1.2); -- 1.2 cast to 1
select percentile_approx(10.0, null, 1); -- scala.MatchError
select percentile_approx(10.0, array(0.2, 0.4, null), 1); -- null cast to zero.
```
##### behavior before
```sql
+select percentile_approx(10.0, 0.5, 2147483648)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, CAST(0.5BD AS DOUBLE), CAST(2147483648L AS INT))' due to data type mismatch: The accuracy provided must be a positive integer literal (current value = -2147483648); line 1 pos 7
+
+select percentile_approx(10.0, 0.5, 4294967297)
+10.0
+
+select percentile_approx(10.0, 0.5, null)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, CAST(0.5BD AS DOUBLE), CAST(NULL AS INT))' due to data type mismatch: The accuracy provided must be a positive integer literal (current value = 0); line 1 pos 7
+
+select percentile_approx(10.0, 0.5, 1.2)
+10.0
+
+select percentile_approx(10.0, null, 1)
+scala.MatchError
+null
+
+
+select percentile_approx(10.0, array(0.2, 0.4, null), 1)
+[10.0,10.0,10.0]
```
##### behavior after
```sql
+select percentile_approx(10.0, 0.5, 2147483648)
+10.0
+
+select percentile_approx(10.0, 0.5, 4294967297)
+10.0
+
+select percentile_approx(10.0, 0.5, null)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, 0.5BD, NULL)' due to data type mismatch: argument 3 requires integral type, however, 'NULL' is of null type.; line 1 pos 7
+
+select percentile_approx(10.0, 0.5, 1.2)
+org.apache.spark.sql.AnalysisException
+cannot resolve 'percentile_approx(10.0BD, 0.5BD, 1.2BD)' due to data type mismatch: argument 3 requires integral type, however, '1.2BD' is of decimal(2,1) type.; line 1 pos 7
+
+select percentile_approx(10.0, null, 1)
+java.lang.IllegalArgumentException
+The value of percentage must be be between 0.0 and 1.0, but got null
+
+select percentile_approx(10.0, array(0.2, 0.4, null), 1)
+java.lang.IllegalArgumentException
+Each value of the percentage array must be be between 0.0 and 1.0, but got [0.2,0.4,null]
```
### Why are the changes needed?
bug fix
### Does this PR introduce any user-facing change?
yes, fix some improper usages of percentile_approx as cases list above
### How was this patch tested?
add ut
Closes#26905 from yaooqinn/SPARK-30266.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change config name from `spark.eventLog.allowErasureCoding` to `spark.eventLog.allowErasureCoding.enabled`.
### Why are the changes needed?
To follow the other boolean conf naming convention.
### Does this PR introduce any user-facing change?
No, it's newly added in Spark 3.0.
### How was this patch tested?
Tested manually and pass Jenkins.
Closes#26998 from Ngone51/SPARK-25855-FOLLOWUP.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to update the SQL migration guide and clarify semantic of string conversion to typed `TIMESTAMP` and `DATE` literals.
### Why are the changes needed?
This is a follow-up of the PR https://github.com/apache/spark/pull/23541 which changed the behavior of `TIMESTAMP`/`DATE` literals, and can impact on results of user's queries.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
It should be checked by jenkins build.
Closes#26985 from MaxGekk/timestamp-date-constructors-followup.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
1. loss function supports: logloss, mse
2. optimizer: GD, adamW
### Why are the changes needed?
Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.
References:
1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#26124 from mob-ai/ml/fm.
Authored-by: zhanjf <zhanjf@mob.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fixed typo in `docs` directory and in other directories
1. Find typo in `docs` and apply fixes to files in all directories
2. Fix `the the` -> `the`
### Why are the changes needed?
Better readability of documents
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
No test needed
Closes#26976 from kiszk/typo_20191221.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR update document for make Hive 2.3 dependency by default.
### Why are the changes needed?
The documentation is incorrect.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#26919 from wangyum/SPARK-30280.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
add a migration guide for date_add and date_sub to indicates their behavior change. It a followup for #26412
### Why are the changes needed?
add a migration guide
### Does this PR introduce any user-facing change?
yes, doc change
### How was this patch tested?
no
Closes#26932 from yaooqinn/SPARK-29774-f.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When `spark.shuffle.useOldFetchProtocol` is enabled then switching off the direct disk reading of host-local shuffle blocks and falling back to remote block fetching (and this way avoiding the `GetLocalDirsForExecutors` block transfer message which is introduced from Spark 3.0.0).
### Why are the changes needed?
In `[SPARK-27651][Core] Avoid the network when shuffle blocks are fetched from the same host` a new block transfer message is introduced, `GetLocalDirsForExecutors`. This new message could be sent to the external shuffle service and as it is not supported by the previous version of external shuffle service it should be avoided when `spark.shuffle.useOldFetchProtocol` is true.
In the migration guide I changed the exception type as `org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Decoder#fromByteBuffer`
throws a IllegalArgumentException with the given text and uses the message type which is just a simple number (byte). I have checked and this is true for version 2.4.4 too.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This specific case (considering one extra boolean to switch off host local disk reading feature) is not tested but existing tests were run.
Closes#26869 from attilapiros/SPARK-30235.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
1. Revert "Preparing development version 3.0.1-SNAPSHOT": 56dcd79
2. Revert "Preparing Spark release v3.0.0-preview2-rc2": c216ef1
### Why are the changes needed?
Shouldn't change master.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test:
https://github.com/apache/spark/compare/5de5e46..wangyum:revert-masterCloses#26915 from wangyum/revert-master.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Include `$SPARK_DIST_CLASSPATH` in class path when launching `CoarseGrainedExecutorBackend` on Kubernetes executors using the provided `entrypoint.sh`
### Why are the changes needed?
For user provided Hadoop, `$SPARK_DIST_CLASSPATH` contains the required jars.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
Kubernetes 1.14, Spark 2.4.4, Hadoop 3.2.1. Adding $SPARK_DIST_CLASSPATH to `-cp ` param of entrypoint.sh enables launching the executors correctly.
Closes#26493 from sshakeri/master.
Authored-by: Shahin Shakeri <shahin.shakeri@pwc.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
The PR adds a new config option to configure an address for the
proxy server, and a new handler that intercepts redirects and replaces
the URL with one pointing at the proxy server. This is needed on top
of the "proxy base path" support because redirects use full URLs, not
just absolute paths from the server's root.
### Why are the changes needed?
Spark's web UI has support for generating links to paths with a
prefix, to support a proxy server, but those do not apply when
the UI is responding with redirects. In that case, Spark is sending
its own URL back to the client, and if it's behind a dumb proxy
server that doesn't do rewriting (like when using stunnel for HTTPS
support) then the client will see the wrong URL and may fail.
### Does this PR introduce any user-facing change?
Yes. It's a new UI option.
### How was this patch tested?
Tested with added unit test, with Spark behind stunnel, and in a
more complicated app using a different HTTPS proxy.
Closes#26873 from vanzin/SPARK-30240.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
- Reverts commit 1f94bf4 and d6be46e
- Switches python to python3 in Docker release image.
### Why are the changes needed?
`dev/make-distribution.sh` and `python/setup.py` are use python3.
https://github.com/apache/spark/pull/26844/files#diff-ba2c046d92a1d2b5b417788bfb5cb5f8L236https://github.com/apache/spark/pull/26330/files#diff-8cf6167d58ce775a08acafcfe6f40966
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test:
```
yumwangubuntu-3513086:~/spark$ dev/create-release/do-release-docker.sh -n -d /home/yumwang/spark-release
Output directory already exists. Overwrite and continue? [y/n] y
Branch [branch-2.4]: master
Current branch version is 3.0.0-SNAPSHOT.
Release [3.0.0]: 3.0.0-preview2
RC # [1]:
This is a dry run. Please confirm the ref that will be built for testing.
Ref [master]:
ASF user [yumwang]:
Full name [Yuming Wang]:
GPG key [yumwangapache.org]: DBD447010C1B4F7DAD3F7DFD6E1B4122F6A3A338
================
Release details:
BRANCH: master
VERSION: 3.0.0-preview2
TAG: v3.0.0-preview2-rc1
NEXT: 3.0.1-SNAPSHOT
ASF USER: yumwang
GPG KEY: DBD447010C1B4F7DAD3F7DFD6E1B4122F6A3A338
FULL NAME: Yuming Wang
E-MAIL: yumwangapache.org
================
Is this info correct [y/n]? y
GPG passphrase:
========================
= Building spark-rm image with tag latest...
Command: docker build -t spark-rm:latest --build-arg UID=110302528 /home/yumwang/spark/dev/create-release/spark-rm
Log file: docker-build.log
Building v3.0.0-preview2-rc1; output will be at /home/yumwang/spark-release/output
gpg: directory '/home/spark-rm/.gnupg' created
gpg: keybox '/home/spark-rm/.gnupg/pubring.kbx' created
gpg: /home/spark-rm/.gnupg/trustdb.gpg: trustdb created
gpg: key 6E1B4122F6A3A338: public key "Yuming Wang <yumwangapache.org>" imported
gpg: key 6E1B4122F6A3A338: secret key imported
gpg: Total number processed: 1
gpg: imported: 1
gpg: secret keys read: 1
gpg: secret keys imported: 1
========================
= Creating release tag v3.0.0-preview2-rc1...
Command: /opt/spark-rm/release-tag.sh
Log file: tag.log
It may take some time for the tag to be synchronized to github.
Press enter when you've verified that the new tag (v3.0.0-preview2-rc1) is available.
========================
= Building Spark...
Command: /opt/spark-rm/release-build.sh package
Log file: build.log
========================
= Building documentation...
Command: /opt/spark-rm/release-build.sh docs
Log file: docs.log
========================
= Publishing release
Command: /opt/spark-rm/release-build.sh publish-release
Log file: publish.log
```
Generated doc:
Closes#26848 from wangyum/SPARK-30216.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch fixes the availability of `minPartitions` option for Kafka source, as it is only supported by micro-batch for now. There's a WIP PR for batch (#25436) as well but there's no progress on the PR so far, so safer to fix the doc first, and let it be added later when we address it with batch case as well.
### Why are the changes needed?
The doc is wrong and misleading.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Just a doc change.
Closes#26849 from HeartSaVioR/MINOR-FIX-minPartition-availability-doc.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose new implementation of `fromDayTimeString` which strictly parses strings in day-time formats to intervals. New implementation accepts only strings that match to a pattern defined by the `from` and `to`. Here is the mapping of user's bounds and patterns:
- `[+|-]D+ H[H]:m[m]:s[s][.SSSSSSSSS]` for **DAY TO SECOND**
- `[+|-]D+ H[H]:m[m]` for **DAY TO MINUTE**
- `[+|-]D+ H[H]` for **DAY TO HOUR**
- `[+|-]H[H]:m[m]s[s][.SSSSSSSSS]` for **HOUR TO SECOND**
- `[+|-]H[H]:m[m]` for **HOUR TO MINUTE**
- `[+|-]m[m]:s[s][.SSSSSSSSS]` for **MINUTE TO SECOND**
Closes#26327Closes#26358
### Why are the changes needed?
- Improve user experience with Spark SQL, and respect to the bound specified by users.
- Behave the same as other broadly used DBMS - Oracle and MySQL.
### Does this PR introduce any user-facing change?
Yes, before:
```sql
spark-sql> SELECT INTERVAL '10 11:12:13.123' HOUR TO MINUTE;
interval 1 weeks 3 days 11 hours 12 minutes
```
After:
```sql
spark-sql> SELECT INTERVAL '10 11:12:13.123' HOUR TO MINUTE;
Error in query:
requirement failed: Interval string must match day-time format of '^(?<sign>[+|-])?(?<hour>\d{1,2}):(?<minute>\d{1,2})$': 10 11:12:13.123(line 1, pos 16)
== SQL ==
SELECT INTERVAL '10 11:12:13.123' HOUR TO MINUTE
----------------^^^
```
### How was this patch tested?
- Added tests to `IntervalUtilsSuite`
- By `ExpressionParserSuite`
- Updated `literals.sql`
Closes#26473 from MaxGekk/strict-from-daytime-string.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Enhancement of the SQL NULL Semantics document: sql-ref-null-semantics.html.
### Why are the changes needed?
Clarify the behavior of `UNKNOWN` for both `EXIST` and `IN` operation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Doc changes only.
Closes#26837 from xuanyuanking/SPARK-30207.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Follow up of https://github.com/apache/spark/pull/24405
### What changes were proposed in this pull request?
The current implementation of _from_avro_ and _AvroDataToCatalyst_ doesn't allow doing schema evolution since it requires the deserialization of an Avro record with the exact same schema with which it was serialized.
The proposed change is to add a new option `actualSchema` to allow passing the schema used to serialize the records. This allows using a different compatible schema for reading by passing both schemas to _GenericDatumReader_. If no writer's schema is provided, nothing changes from before.
### Why are the changes needed?
Consider the following example.
```
// schema ID: 1
val schema1 = """
{
"type": "record",
"name": "MySchema",
"fields": [
{"name": "col1", "type": "int"},
{"name": "col2", "type": "string"}
]
}
"""
// schema ID: 2
val schema2 = """
{
"type": "record",
"name": "MySchema",
"fields": [
{"name": "col1", "type": "int"},
{"name": "col2", "type": "string"},
{"name": "col3", "type": "string", "default": ""}
]
}
"""
```
The two schemas are compatible - i.e. you can use `schema2` to deserialize events serialized with `schema1`, in which case there will be the field `col3` with the default value.
Now imagine that you have two dataframes (read from batch or streaming), one with Avro events from schema1 and the other with events from schema2. **We want to combine them into one dataframe** for storing or further processing.
With the current `from_avro` function we can only decode each of them with the corresponding schema:
```
scalaval df1 = ... // Avro events created with schema1
df1: org.apache.spark.sql.DataFrame = [eventBytes: binary]
scalaval decodedDf1 = df1.select(from_avro('eventBytes, schema1) as "decoded")
decodedDf1: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string>]
scalaval df2= ... // Avro events created with schema2
df2: org.apache.spark.sql.DataFrame = [eventBytes: binary]
scalaval decodedDf2 = df2.select(from_avro('eventBytes, schema2) as "decoded")
decodedDf2: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string, col3: string>]
```
but then `decodedDf1` and `decodedDf2` have different Spark schemas and we can't union them. Instead, with the proposed change we can decode `df1` in the following way:
```
scalaimport scala.collection.JavaConverters._
scalaval decodedDf1 = df1.select(from_avro(data = 'eventBytes, jsonFormatSchema = schema2, options = Map("actualSchema" -> schema1).asJava) as "decoded")
decodedDf1: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string, col3: string>]
```
so that both dataframes have the same schemas and can be merged.
### Does this PR introduce any user-facing change?
This PR allows users to pass a new configuration but it doesn't affect current code.
### How was this patch tested?
A new unit test was added.
Closes#26780 from Fokko/SPARK-27506.
Lead-authored-by: Fokko Driesprong <fokko@apache.org>
Co-authored-by: Gianluca Amori <gianluca.amori@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This PR switches python to python3 in `make-distribution.sh`.
### Why are the changes needed?
SPARK-29672 changed this
- https://github.com/apache/spark/pull/26330/files#diff-8cf6167d58ce775a08acafcfe6f40966
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26844 from wangyum/SPARK-30211.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR add an optional spark conf for speculation to allow speculative runs for stages where there are only a few tasks.
```
spark.speculation.task.duration.threshold
```
If provided, tasks would be speculatively run if the TaskSet contains less tasks than the number of slots on a single executor and the task is taking longer time than the threshold.
### Why are the changes needed?
This change helps avoid scenarios where there is single executor that could hang forever due to disk issue and we unfortunately assigned the single task in a TaskSet to that executor and cause the whole job to hang forever.
### Does this PR introduce any user-facing change?
yes. If the new config `spark.speculation.task.duration.threshold` is provided and the TaskSet contains less tasks than the number of slots on a single executor and the task is taking longer time than the threshold, then speculative tasks would be submitted for the running tasks in the TaskSet.
### How was this patch tested?
Unit tests are added to TaskSetManagerSuite.
Closes#26614 from yuchenhuo/SPARK-29976.
Authored-by: Yuchen Huo <yuchen.huo@databricks.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Reprocess all PostgreSQL dialect related PRs, listing in order:
- #25158: PostgreSQL integral division support [revert]
- #25170: UT changes for the integral division support [revert]
- #25458: Accept "true", "yes", "1", "false", "no", "0", and unique prefixes as input and trim input for the boolean data type. [revert]
- #25697: Combine below 2 feature tags into "spark.sql.dialect" [revert]
- #26112: Date substraction support [keep the ANSI-compliant part]
- #26444: Rename config "spark.sql.ansi.enabled" to "spark.sql.dialect.spark.ansi.enabled" [revert]
- #26463: Cast to boolean support for PostgreSQL dialect [revert]
- #26584: Make the behavior of Postgre dialect independent of ansi mode config [keep the ANSI-compliant part]
### Why are the changes needed?
As the discussion in http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-PostgreSQL-dialect-td28417.html, we need to remove PostgreSQL dialect form code base for several reasons:
1. The current approach makes the codebase complicated and hard to maintain.
2. Fully migrating PostgreSQL workloads to Spark SQL is not our focus for now.
### Does this PR introduce any user-facing change?
Yes, the config `spark.sql.dialect` will be removed.
### How was this patch tested?
Existing UT.
Closes#26763 from xuanyuanking/SPARK-30125.
Lead-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to add instrumentation of memory usage via the Spark Dropwizard/Codahale metrics system. Memory usage metrics are available via the Executor metrics, recently implemented as detailed in https://issues.apache.org/jira/browse/SPARK-23206.
Additional notes: This takes advantage of the metrics poller introduced in #23767.
## Why are the changes needed?
Executor metrics bring have many useful insights on memory usage, in particular on the usage of storage memory and executor memory. This is useful for troubleshooting. Having the information in the metrics systems allows to add those metrics to Spark performance dashboards and study memory usage as a function of time, as in the example graph https://issues.apache.org/jira/secure/attachment/12962810/Example_dashboard_Spark_Memory_Metrics.PNG
## Does this PR introduce any user-facing change?
Adds `ExecutorMetrics` source to publish executor metrics via the Dropwizard metrics system. Details of the available metrics in docs/monitoring.md
Adds configuration parameter `spark.metrics.executormetrics.source.enabled`
## How was this patch tested?
Tested on YARN cluster and with an existing setup for a Spark dashboard based on InfluxDB and Grafana.
Closes#24132 from LucaCanali/memoryMetricsSource.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
### What changes were proposed in this pull request?
Now, we trim the string when casting string value to those `canCast` types values, e.g. int, double, decimal, interval, date, timestamps, except for boolean.
This behavior makes type cast and coercion inconsistency in Spark.
Not fitting ANSI SQL standard either.
```
If TD is boolean, then
Case:
a) If SD is character string, then SV is replaced by
TRIM ( BOTH ' ' FROM VE )
Case:
i) If the rules for literal in Subclause 5.3, “literal”, can be applied to SV to determine a valid
value of the data type TD, then let TV be that value.
ii) Otherwise, an exception condition is raised: data exception — invalid character value for cast.
b) If SD is boolean, then TV is SV
```
In this pull request, we trim all the whitespaces from both ends of the string before converting it to a bool value. This behavior is as same as others, but a bit different from sql standard, which trim only spaces.
### Why are the changes needed?
Type cast/coercion consistency
### Does this PR introduce any user-facing change?
yes, string with whitespaces in both ends will be trimmed before converted to booleans.
e.g. `select cast('\t true' as boolean)` results `true` now, before this pr it's `null`
### How was this patch tested?
add unit tests
Closes#26776 from yaooqinn/SPARK-30147.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In this PR, we propose to use the value of `spark.sql.source.default` as the provider for `CREATE TABLE` syntax instead of `hive` in Spark 3.0.
And to help the migration, we introduce a legacy conf `spark.sql.legacy.respectHiveDefaultProvider.enabled` and set its default to `false`.
### Why are the changes needed?
1. Currently, `CREATE TABLE` syntax use hive provider to create table while `DataFrameWriter.saveAsTable` API using the value of `spark.sql.source.default` as a provider to create table. It would be better to make them consistent.
2. User may gets confused in some cases. For example:
```
CREATE TABLE t1 (c1 INT) USING PARQUET;
CREATE TABLE t2 (c1 INT);
```
In these two DDLs, use may think that `t2` should also use parquet as default provider since Spark always advertise parquet as the default format. However, it's hive in this case.
On the other hand, if we omit the USING clause in a CTAS statement, we do pick parquet by default if `spark.sql.hive.convertCATS=true`:
```
CREATE TABLE t3 USING PARQUET AS SELECT 1 AS VALUE;
CREATE TABLE t4 AS SELECT 1 AS VALUE;
```
And these two cases together can be really confusing.
3. Now, Spark SQL is very independent and popular. We do not need to be fully consistent with Hive's behavior.
### Does this PR introduce any user-facing change?
Yes, before this PR, using `CREATE TABLE` syntax will use hive provider. But now, it use the value of `spark.sql.source.default` as its provider.
### How was this patch tested?
Added tests in `DDLParserSuite` and `HiveDDlSuite`.
Closes#26736 from Ngone51/dev-create-table-using-parquet-by-default.
Lead-authored-by: wuyi <yi.wu@databricks.com>
Co-authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to upgrade Maven from 3.6.2 to 3.6.3.
### Why are the changes needed?
This will bring bug fixes like the following.
- MNG-6759 Maven fails to use <repositories> section from dependency when resolving transitive dependencies in some cases
- MNG-6760 ExclusionArtifactFilter result invalid when wildcard exclusion is followed by other exclusions
The following is the full release note.
- https://maven.apache.org/docs/3.6.3/release-notes.html
### Does this PR introduce any user-facing change?
No. (This is a dev-environment change.)
### How was this patch tested?
Pass the Jenkins with both SBT and Maven.
Closes#26770 from dongjoon-hyun/SPARK-30142.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The syntax 'LIKE predicate: ESCAPE clause' is a ANSI SQL.
For example:
```
select 'abcSpark_13sd' LIKE '%Spark\\_%'; //true
select 'abcSpark_13sd' LIKE '%Spark/_%'; //false
select 'abcSpark_13sd' LIKE '%Spark"_%'; //false
select 'abcSpark_13sd' LIKE '%Spark/_%' ESCAPE '/'; //true
select 'abcSpark_13sd' LIKE '%Spark"_%' ESCAPE '"'; //true
select 'abcSpark%13sd' LIKE '%Spark\\%%'; //true
select 'abcSpark%13sd' LIKE '%Spark/%%'; //false
select 'abcSpark%13sd' LIKE '%Spark"%%'; //false
select 'abcSpark%13sd' LIKE '%Spark/%%' ESCAPE '/'; //true
select 'abcSpark%13sd' LIKE '%Spark"%%' ESCAPE '"'; //true
select 'abcSpark\\13sd' LIKE '%Spark\\\\_%'; //true
select 'abcSpark/13sd' LIKE '%Spark//_%'; //false
select 'abcSpark"13sd' LIKE '%Spark""_%'; //false
select 'abcSpark/13sd' LIKE '%Spark//_%' ESCAPE '/'; //true
select 'abcSpark"13sd' LIKE '%Spark""_%' ESCAPE '"'; //true
```
But Spark SQL only supports 'LIKE predicate'.
Note: If the input string or pattern string is null, then the result is null too.
There are some mainstream database support the syntax.
**PostgreSQL:**
https://www.postgresql.org/docs/11/functions-matching.html
**Vertica:**
https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/SQLReferenceManual/LanguageElements/Predicates/LIKE-predicate.htm?zoom_highlight=like%20escape
**MySQL:**
https://dev.mysql.com/doc/refman/5.6/en/string-comparison-functions.html
**Oracle:**
https://docs.oracle.com/en/database/oracle/oracle-database/19/jjdbc/JDBC-reference-information.html#GUID-5D371A5B-D7F6-42EB-8C0D-D317F3C53708https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/Pattern-matching-Conditions.html#GUID-0779657B-06A8-441F-90C5-044B47862A0A
## How was this patch tested?
Exists UT and new UT.
This PR merged to my production environment and runs above sql:
```
spark-sql> select 'abcSpark_13sd' LIKE '%Spark\\_%';
true
Time taken: 0.119 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark/_%';
false
Time taken: 0.103 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark"_%';
false
Time taken: 0.096 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark/_%' ESCAPE '/';
true
Time taken: 0.096 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark"_%' ESCAPE '"';
true
Time taken: 0.092 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark\\%%';
true
Time taken: 0.109 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark/%%';
false
Time taken: 0.1 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark"%%';
false
Time taken: 0.081 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark/%%' ESCAPE '/';
true
Time taken: 0.095 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark"%%' ESCAPE '"';
true
Time taken: 0.113 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark\\13sd' LIKE '%Spark\\\\_%';
true
Time taken: 0.078 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark/13sd' LIKE '%Spark//_%';
false
Time taken: 0.067 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark"13sd' LIKE '%Spark""_%';
false
Time taken: 0.084 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark/13sd' LIKE '%Spark//_%' ESCAPE '/';
true
Time taken: 0.091 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark"13sd' LIKE '%Spark""_%' ESCAPE '"';
true
Time taken: 0.091 seconds, Fetched 1 row(s)
```
I create a table and its schema is:
```
spark-sql> desc formatted gja_test;
key string NULL
value string NULL
other string NULL
# Detailed Table Information
Database test
Table gja_test
Owner test
Created Time Wed Apr 10 11:06:15 CST 2019
Last Access Thu Jan 01 08:00:00 CST 1970
Created By Spark 2.4.1-SNAPSHOT
Type MANAGED
Provider hive
Table Properties [transient_lastDdlTime=1563443838]
Statistics 26 bytes
Location hdfs://namenode.xxx:9000/home/test/hive/warehouse/test.db/gja_test
Serde Library org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat org.apache.hadoop.mapred.TextInputFormat
OutputFormat org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Storage Properties [field.delim= , serialization.format= ]
Partition Provider Catalog
Time taken: 0.642 seconds, Fetched 21 row(s)
```
Table `gja_test` exists three rows of data.
```
spark-sql> select * from gja_test;
a A ao
b B bo
"__ """__ "
Time taken: 0.665 seconds, Fetched 3 row(s)
```
At finally, I test this function:
```
spark-sql> select * from gja_test where key like value escape '"';
"__ """__ "
Time taken: 0.687 seconds, Fetched 1 row(s)
```
Closes#25001 from beliefer/ansi-sql-like.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This patch prevents the cleanup operation in FileStreamSource if the source files belong to the FileStreamSink. This is needed because the output of FileStreamSink can be read with multiple Spark queries and queries will read the files based on the metadata log, which won't reflect the cleanup.
To simplify the logic, the patch only takes care of the case of when the source path without glob pattern refers to the output directory of FileStreamSink, via checking FileStreamSource to see whether it leverages metadata directory or not to list the source files.
### Why are the changes needed?
Without this patch, if end users turn on cleanup option with the path which is the output of FileStreamSink, there may be out of sync between metadata and available files which may break other queries reading the path.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added UT.
Closes#26590 from HeartSaVioR/SPARK-29953.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
This PR adds a note to the docs README showing how to get Jekyll to automatically pick up changes to the Python API docs.
### Why are the changes needed?
`jekyll serve --watch` doesn't watch for changes to the API docs. Without the technique documented in this note, or something equivalent, developers have to manually retrigger a Jekyll build any time they update the Python API docs.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I tested this PR manually by making changes to Python docstrings and confirming that Jekyll automatically picks them up and serves them locally.
Closes#26719 from nchammas/SPARK-30084-watch-api-docs.
Authored-by: Nicholas Chammas <nicholas.chammas@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This proposes to introduce a naming convention for Spark metrics configuration parameters used to enable/disable metrics source reporting using the Dropwizard metrics library: `spark.metrics.sourceNameCamelCase.enabled` and update 2 parameters to use this naming convention.
### Why are the changes needed?
Currently Spark has a few parameters to enable/disable metrics reporting. Their naming pattern is not uniform and this can create confusion. Currently we have:
`spark.metrics.static.sources.enabled`
`spark.app.status.metrics.enabled`
`spark.sql.streaming.metricsEnabled`
### Does this PR introduce any user-facing change?
Update parameters for enabling/disabling metrics reporting new in Spark 3.0: `spark.metrics.static.sources.enabled` -> `spark.metrics.staticSources.enabled`, `spark.app.status.metrics.enabled` -> `spark.metrics.appStatusSource.enabled`.
Note: `spark.sql.streaming.metricsEnabled` is left unchanged as it is already in use in Spark 2.x.
### How was this patch tested?
Manually tested
Closes#26692 from LucaCanali/uniformNamingMetricsEnableParameters.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
`UnaryPositive` only accepts numeric and interval as we defined, but what we do for this in `AstBuider.visitArithmeticUnary` is just bypassing it.
This should not be omitted for the type checking requirement.
### Why are the changes needed?
bug fix, you can find a pre-discussion here https://github.com/apache/spark/pull/26578#discussion_r347350398
### Does this PR introduce any user-facing change?
yes, +non-numeric-or-interval is now invalid.
```
-- !query 14
select +date '1900-01-01'
-- !query 14 schema
struct<DATE '1900-01-01':date>
-- !query 14 output
1900-01-01
-- !query 15
select +timestamp '1900-01-01'
-- !query 15 schema
struct<TIMESTAMP '1900-01-01 00:00:00':timestamp>
-- !query 15 output
1900-01-01 00:00:00
-- !query 16
select +map(1, 2)
-- !query 16 schema
struct<map(1, 2):map<int,int>>
-- !query 16 output
{1:2}
-- !query 17
select +array(1,2)
-- !query 17 schema
struct<array(1, 2):array<int>>
-- !query 17 output
[1,2]
-- !query 18
select -'1'
-- !query 18 schema
struct<(- CAST(1 AS DOUBLE)):double>
-- !query 18 output
-1.0
-- !query 19
select -X'1'
-- !query 19 schema
struct<>
-- !query 19 output
org.apache.spark.sql.AnalysisException
cannot resolve '(- X'01')' due to data type mismatch: argument 1 requires (numeric or interval) type, however, 'X'01'' is of binary type.; line 1 pos 7
-- !query 20
select +X'1'
-- !query 20 schema
struct<X'01':binary>
-- !query 20 output
```
### How was this patch tested?
add ut check
Closes#26716 from yaooqinn/SPARK-30083.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Standardize sql reference
### Why are the changes needed?
To have consistent docs
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Tested using jykyll build --serve
Closes#26721 from huaxingao/spark-30085.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
add `.enabled` postfix to `spark.sql.analyzer.failAmbiguousSelfJoin`.
### Why are the changes needed?
to follow the existing naming style
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
not needed
Closes#26694 from cloud-fan/conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In SPARK-29421 (#26097) , we can specify a different table provider for `CREATE TABLE LIKE` via `USING provider`.
Hive support `STORED AS` new file format syntax:
```sql
CREATE TABLE tbl(a int) STORED AS TEXTFILE;
CREATE TABLE tbl2 LIKE tbl STORED AS PARQUET;
```
For Hive compatibility, we should also support `STORED AS` in `CREATE TABLE LIKE`.
### Why are the changes needed?
See https://github.com/apache/spark/pull/26097#issue-327424759
### Does this PR introduce any user-facing change?
Add a new syntax based on current CTL:
CREATE TABLE tbl2 LIKE tbl [STORED AS hiveFormat];
### How was this patch tested?
Add UTs.
Closes#26466 from LantaoJin/SPARK-29839.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Summarizer support more metrics: sum, std
### Why are the changes needed?
Those metrics are widely used, it will be convenient to directly obtain them other than a conversion.
in `NaiveBayes`: we want the sum of vectors, mean & weightSum need to computed then multiplied
in `StandardScaler`,`AFTSurvivalRegression`,`LinearRegression`,`LinearSVC`,`LogisticRegression`: we need to obtain `variance` and then sqrt it to get std
### Does this PR introduce any user-facing change?
yes, new metrics are exposed to end users
### How was this patch tested?
added testsuites
Closes#26596 from zhengruifeng/summarizer_add_metrics.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
