### What changes were proposed in this pull request?
in `spark-daemon.sh`, `spark_rotate_log()` accepts `$2` as a custom setting for the number of maximum rotate log files, but this part of code is actually never used.
This PR adds `SPARK_LOG_MAX_FILES` environment variable to represent the maximum log files of Spark daemons can rotate to.
### Why are the changes needed?
the logs files that all spark daemons are hardcoded as 5, but it supposed to be configurable
### Does this PR introduce _any_ user-facing change?
yes, SPARK_LOG_MAX_FILES is added to represent the maximum log files of Spark daemons can rotate to.
### How was this patch tested?
verify locally for the added shell script:
```shell
kentyaohulk ~ SPARK_LOG_MAX_FILES=1 sh test.sh
1
kentyaohulk ~ SPARK_LOG_MAX_FILES=a sh test.sh
Error: SPARK_LOG_MAX_FILES must be a postive number
✘ kentyaohulk ~ SPARK_LOG_MAX_FILES=b sh test.sh
Error: SPARK_LOG_MAX_FILES must be a postive number
✘ kentyaohulk ~ SPARK_LOG_MAX_FILES=-1 sh test.sh
Error: SPARK_LOG_MAX_FILES must be a postive number
✘ kentyaohulk ~ sh test.sh
5
✘ kentyaohulk ~ cat test.sh
#!/bin/bash
if [[ -z ${SPARK_LOG_MAX_FILES} ]] ; then
num=5
elif [[ ${SPARK_LOG_MAX_FILES} -gt 0 ]]; then
num=${SPARK_LOG_MAX_FILES}
else
echo "Error: SPARK_LOG_MAX_FILES must be a postive number"
exit -1
fi
```
Closes#28580 from yaooqinn/SPARK-31759.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added MDC support in all thread pools.
ThreaddUtils create new pools that pass over MDC.
### Why are the changes needed?
In many cases, it is very hard to understand from which actions the logs in the executor come from.
when you are doing multi-thread work in the driver and send actions in parallel.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
No test added because no new functionality added it is thread pull change and all current tests pass.
Closes#26624 from igreenfield/master.
Authored-by: Izek Greenfield <igreenfield@axiomsl.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to add Spark version prefix during generating test image tag for K8s integration testing.
### Why are the changes needed?
This helps to distinguish the images by version.
**BEFORE**
```
$ docker images | grep kubespark
kubespark/spark-py F7188CBD-AE08-4705-9C8A-D0DD3DC8B86F ...
kubespark/spark F7188CBD-AE08-4705-9C8A-D0DD3DC8B86F ...
```
**AFTER**
```
$ docker images | grep kubespark
kubespark/spark-py 3.1.0-SNAPSHOT_F7188CBD-AE08-4705-9C8A-D0DD3DC8B86F ...
kubespark/spark 3.1.0-SNAPSHOT_F7188CBD-AE08-4705-9C8A-D0DD3DC8B86F ...
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the K8s integration test.
```
...
Successfully tagged kubespark/spark:3.1.0-SNAPSHOT_688b46c8-c119-404d-aadb-d05a14262db7
...
Successfully tagged kubespark/spark-py:3.1.0-SNAPSHOT_688b46c8-c119-404d-aadb-d05a14262db7
...
Successfully tagged kubespark/spark-r:3.1.0-SNAPSHOT_688b46c8-c119-404d-aadb-d05a14262db7
```
Closes#28587 from dongjoon-hyun/SPARK-31766.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to only allow the import of `ResourceInformation` as below:
```
pyspark.resource.ResourceInformation
```
instead of
```
pyspark.ResourceInformation
pyspark.resource.ResourceInformation
```
because `pyspark.resource` is a separate module, and it is documented so.
The constructor of `ResourceInformation` isn't supposed to directly call anyway.
### Why are the changes needed?
To keep the code structure coherent.
### Does this PR introduce _any_ user-facing change?
No, it will be in the unreleased branches.
### How was this patch tested?
Manually tested via importing:
Before:
```python
>>> import pyspark
>>> pyspark.ResourceInformation
<class 'pyspark.resource.information.ResourceInformation'>
>>> pyspark.resource.ResourceInformation
<class 'pyspark.resource.information.ResourceInformation'>
```
After:
```python
>>> import pyspark
>>> pyspark.ResourceInformation
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'pyspark' has no attribute 'ResourceInformation'
>>> pyspark.resource.ResourceInformation
<class 'pyspark.resource.information.ResourceInformation'>
```
Also tested via
```bash
cd python
./run-tests --python-executables=python3 --modules=pyspark-core,pyspark-resource
```
Jenkins will test and existing tests should cover.
Closes#28589 from HyukjinKwon/SPARK-31767.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR adds a private `WriteBuilder` mixin trait: `SupportsStreamingUpdate`, so that the builtin v2 streaming sinks can still support the update mode.
Note: it's private because we don't have a proper design yet. I didn't take the proposal in https://github.com/apache/spark/pull/23702#discussion_r258593059 because we may want something more general, like updating by an expression `key1 = key2 + 10`.
### Why are the changes needed?
In Spark 2.4, all builtin v2 streaming sinks support all streaming output modes, and v2 sinks are enabled by default, see https://issues.apache.org/jira/browse/SPARK-22911
It's too risky for 3.0 to go back to v1 sinks, so I propose to add a private trait to fix builtin v2 sinks, to keep backward compatibility.
### Does this PR introduce _any_ user-facing change?
Yes, now all the builtin v2 streaming sinks support all streaming output modes, which is the same as 2.4
### How was this patch tested?
existing tests.
Closes#28523 from cloud-fan/update.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Eliminate the `UpCast` if it's child data type is already decimal type.
### Why are the changes needed?
While deserializing internal `Decimal` value to external `BigDecimal`(Java/Scala) value, Spark should also respect `Decimal`'s precision and scale, otherwise it will cause precision lost and look weird in some cases, e.g.:
```
sql("select cast(11111111111111111111111111111111111111 as decimal(38, 0)) as d")
.write.mode("overwrite")
.parquet(f.getAbsolutePath)
// can fail
spark.read.parquet(f.getAbsolutePath).as[BigDecimal]
```
```
[info] org.apache.spark.sql.AnalysisException: Cannot up cast `d` from decimal(38,0) to decimal(38,18).
[info] The type path of the target object is:
[info] - root class: "scala.math.BigDecimal"
[info] You can either add an explicit cast to the input data or choose a higher precision type of the field in the target object;
[info] at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveUpCast$$fail(Analyzer.scala:3060)
[info] at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$33$$anonfun$applyOrElse$174.applyOrElse(Analyzer.scala:3087)
[info] at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$33$$anonfun$applyOrElse$174.applyOrElse(Analyzer.scala:3071)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:309)
[info] at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:309)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:314)
```
### Does this PR introduce _any_ user-facing change?
Yes, for cases(cause precision lost) mentioned above will fail before this change but run successfully after this change.
### How was this patch tested?
Added tests.
Closes#28572 from Ngone51/fix_encoder.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is kind of a followup for SPARK-29641 and SPARK-28234. This PR proposes:
1.. Document the new `pyspark.resource` module introduced at 95aec091e4, in PySpark API docs.
2.. Move classes into fewer and simpler modules
Before:
```
pyspark
├── resource
│ ├── executorrequests.py
│ │ ├── class ExecutorResourceRequest
│ │ └── class ExecutorResourceRequests
│ ├── taskrequests.py
│ │ ├── class TaskResourceRequest
│ │ └── class TaskResourceRequests
│ ├── resourceprofilebuilder.py
│ │ └── class ResourceProfileBuilder
│ ├── resourceprofile.py
│ │ └── class ResourceProfile
└── resourceinformation
└── class ResourceInformation
```
After:
```
pyspark
└── resource
├── requests.py
│ ├── class ExecutorResourceRequest
│ ├── class ExecutorResourceRequests
│ ├── class TaskResourceRequest
│ └── class TaskResourceRequests
├── profile.py
│ ├── class ResourceProfileBuilder
│ └── class ResourceProfile
└── information.py
└── class ResourceInformation
```
3.. Minor docstring fix e.g.:
```diff
- param name the name of the resource
- param addresses an array of strings describing the addresses of the resource
+ :param name: the name of the resource
+ :param addresses: an array of strings describing the addresses of the resource
+
+ .. versionadded:: 3.0.0
```
### Why are the changes needed?
To document APIs, and move Python modules to fewer and simpler modules.
### Does this PR introduce _any_ user-facing change?
No, the changes are in unreleased branches.
### How was this patch tested?
Manually tested via:
```bash
cd python
./run-tests --python-executables=python3 --modules=pyspark-core
./run-tests --python-executables=python3 --modules=pyspark-resource
```
Closes#28569 from HyukjinKwon/SPARK-28234-SPARK-29641-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
With SPARK-18107, we will disable the underlying replace(overwrite) and instead do delete in spark side and only do copy in hive side to bypass the performance issue - [HIVE-11940](https://issues.apache.org/jira/browse/HIVE-11940)
Conditionally, if the table location and partition location do not belong to the same `FileSystem`, We should not disable hive overwrite. Otherwise, hive will use the `FileSystem` instance belong to the table location to copy files, which will fail in `FileSystem#checkPath`
https://github.com/apache/hive/blob/rel/release-2.3.7/ql/src/java/org/apache/hadoop/hive/ql/metadata/Hive.java#L1657
In this PR, for Hive 2.0.0 and onwards, as [HIVE-11940](https://issues.apache.org/jira/browse/HIVE-11940) has been fixed, and there is no performance issue anymore. We should leave the overwrite logic to hive to avoid failure in `FileSystem#checkPath`
**NOTE THAT** For Hive 2.2.0 and earlier, if the table and partition locations do not belong together, we will still get the same error thrown by hive encryption check due to [HIVE-14380]( https://issues.apache.org/jira/browse/HIVE-14380) which need to fix in another ticket SPARK-31675.
### Why are the changes needed?
bugfix. a logic table can be decoupled with the storage layer and may contain data from remote storage systems.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Currently verified manually. add benchmark tests
```sql
-INSERT INTO DYNAMIC 7742 7918 248 0.0 756044.0 1.0X
-INSERT INTO HYBRID 1289 1307 26 0.0 125866.3 6.0X
-INSERT INTO STATIC 371 393 38 0.0 36219.4 20.9X
-INSERT OVERWRITE DYNAMIC 8456 8554 138 0.0 825790.3 0.9X
-INSERT OVERWRITE HYBRID 1303 1311 12 0.0 127198.4 5.9X
-INSERT OVERWRITE STATIC 434 447 13 0.0 42373.8 17.8X
+INSERT INTO DYNAMIC 7382 7456 105 0.0 720904.8 1.0X
+INSERT INTO HYBRID 1128 1129 1 0.0 110169.4 6.5X
+INSERT INTO STATIC 349 370 39 0.0 34095.4 21.1X
+INSERT OVERWRITE DYNAMIC 8149 8362 301 0.0 795821.8 0.9X
+INSERT OVERWRITE HYBRID 1317 1318 2 0.0 128616.7 5.6X
+INSERT OVERWRITE STATIC 387 408 37 0.0 37804.1 19.1X
```
+ for master
- for this PR
both using hive 2.3.7
Closes#28511 from yaooqinn/SPARK-31684.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The QueryPlanningTracker in QueryExeuction reports the planning time that also includes the optimization time. This happens because the optimizedPlan in QueryExecution is lazy and only will initialize when first called. When df.queryExecution.executedPlan is called, the the tracker starts recording the planning time, and then calls the optimized plan. This causes the planning time to start before optimization and also include the planning time.
This PR fixes this behavior by introducing a method assertOptimized, similar to assertAnalyzed that explicitly initializes the optimized plan. This method is called before measuring the time for sparkPlan and executedPlan. We call it before sparkPlan because that also counts as planning time.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#28543 from dbaliafroozeh/AddAssertOptimized.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
This PR improves handling the case where different barrier sync types in a single sync:
- use `clear` instead of `cleanupBarrierStage `
- make sure all requesters are failed because of "different barrier sync types"
### Why are the changes needed?
Currently, we use `cleanupBarrierStage` to clean up a barrier stage when we detecting the case of "different barrier sync types". But this leads to a problem that we could create new a `ContextBarrierState` for the same stage again if there're on-way requests from tasks. As a result, those task will fail because of killing instead of "different barrier sync types".
Besides, we don't handle the current request which is being handling properly as it will fail due to epoch mismatch instead of "different barrier sync types".
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Updated a existed test.
Closes#28462 from Ngone51/impr_barrier_req.
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?
SQL Rest API exposes query execution metrics as Public API. This PR aims to apply following improvements on SQL Rest API by aligning Spark-UI.
**Proposed Improvements:**
1- Support Physical Operations and group metrics per physical operation by aligning Spark UI.
2- Support `wholeStageCodegenId` for Physical Operations
3- `nodeId` can be useful for grouping metrics and sorting physical operations (according to execution order) to differentiate same operators (if used multiple times during the same query execution) and their metrics.
4- Filter `empty` metrics by aligning with Spark UI - SQL Tab. Currently, Spark UI does not show empty metrics.
5- Remove line breakers(`\n`) from `metricValue`.
6- `planDescription` can be `optional` Http parameter to avoid network cost where there is specially complex jobs creating big-plans.
7- `metrics` attribute needs to be exposed at the bottom order as `nodes`. Specially, this can be useful for the user where `nodes` array size is high.
8- `edges` attribute is being exposed to show relationship between `nodes`.
9- Reverse order on `metricDetails` aims to match with Spark UI by supporting Physical Operators' execution order.
### Why are the changes needed?
Proposed improvements provides more useful (e.g: physical operations and metrics correlation, grouping) and clear (e.g: filtering blank metrics, removing line breakers) result for the end-user.
### Does this PR introduce any user-facing change?
Yes. Please find both current and improved versions of the results as attached for following SQL Rest Endpoint:
```
curl -X GET http://localhost:4040/api/v1/applications/$appId/sql/$executionId?details=true
```
**Current version:**
https://issues.apache.org/jira/secure/attachment/12999821/current_version.json
**Improved version:**
https://issues.apache.org/jira/secure/attachment/13000621/improved_version.json
### Backward Compatibility
SQL Rest API will be started to expose with `Spark 3.0` and `3.0.0-preview2` (released on 12/23/19) does not cover this API so if PR can catch 3.0 release, this will not have any backward compatibility issue.
### How was this patch tested?
1. New Unit tests are added.
2. Also, patch has been tested manually through both **Spark Core** and **History Server** Rest APIs.
Closes#28208 from erenavsarogullari/SPARK-31440.
Authored-by: Eren Avsarogullari <eren.avsarogullari@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
After changes in SPARK-20628, CoarseGrainedSchedulerBackend can decommission an executor and stop assigning new tasks on it. We should also decommission the corresponding blockmanagers in the same way. i.e. Move the cached RDD blocks from those executors to other active executors.
### Why are the changes needed?
We need to gracefully decommission the block managers so that the underlying RDD cache blocks are not lost in case the executors are taken away forcefully after some timeout (because of spotloss/pre-emptible VM etc). Its good to save as much cache data as possible.
Also In future once the decommissioning signal comes from Cluster Manager (say YARN/Mesos etc), dynamic allocation + this change gives us opportunity to downscale the executors faster by making the executors free of cache data.
Note that this is a best effort approach. We try to move cache blocks from decommissioning executors to active executors. If the active executors don't have free resources available on them for caching, then the decommissioning executors will keep the cache block which it was not able to move and it will still be able to serve them.
Current overall Flow:
1. CoarseGrainedSchedulerBackend receives a signal to decommissionExecutor. On receiving the signal, it do 2 things - Stop assigning new tasks (SPARK-20628), Send another message to BlockManagerMasterEndpoint (via BlockManagerMaster) to decommission the corresponding BlockManager.
2. BlockManagerMasterEndpoint receives "DecommissionBlockManagers" message. On receiving this, it moves the corresponding block managers to "decommissioning" state. All decommissioning BMs are excluded from the getPeers RPC call which is used for replication. All these decommissioning BMs are also sent message from BlockManagerMasterEndpoint to start decommissioning process on themselves.
3. BlockManager on worker (say BM-x) receives the "DecommissionBlockManager" message. Now it will start BlockManagerDecommissionManager thread to offload all the RDD cached blocks. This thread can make multiple reattempts to decommission the existing cache blocks (multiple reattempts might be needed as there might not be sufficient space in other active BMs initially).
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Added UTs.
Closes#28370 from prakharjain09/SPARK-20732-rddcache-1.
Authored-by: Prakhar Jain <prakharjain09@gmail.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
1. Describe standard 'M' and stand-alone 'L' text forms
2. Add examples for all supported number of month letters
<img width="1047" alt="Screenshot 2020-05-18 at 08 57 31" src="https://user-images.githubusercontent.com/1580697/82178856-b16f1000-98e5-11ea-87c0-456ef94dcd43.png">
### Why are the changes needed?
To improve docs and show how to use month patterns.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By building docs and checking by eyes.
Closes#28558 from MaxGekk/describe-L-M-date-pattern.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This commit is published into the public domain.
### What changes were proposed in this pull request?
Some syntax issues in docstrings have been fixed.
### Why are the changes needed?
In some places, the documentation did not render as intended, e.g. parameter documentations were not formatted as such.
### Does this PR introduce any user-facing change?
Slight improvements in documentation.
### How was this patch tested?
Manual testing and `dev/lint-python` run. No new Sphinx warnings arise due to this change.
Closes#28559 from DavidToneian/SPARK-31739.
Authored-by: David Toneian <david@toneian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to show the actual traceback when "handle large number of containers and tasks (SPARK-18750)" test fails in `LocalityPlacementStrategySuite`.
**It does not fully resolve the JIRA SPARK-31746 yet**. I tried to reproduce in my local by controlling the factors in the tests but I couldn't. I double checked the changes in SPARK-18750 are still valid.
### Why are the changes needed?
This test is flaky for an unknown reason (see https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/122768/testReport/org.apache.spark.deploy.yarn/LocalityPlacementStrategySuite/handle_large_number_of_containers_and_tasks__SPARK_18750_/):
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException: java.lang.StackOverflowError did not equal null
at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:530)
at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:529)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1560)
at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:503)
```
After this PR, it will help to investigate the root cause:
**Before**:
```
[info] - handle large number of containers and tasks (SPARK-18750) *** FAILED *** (824 milliseconds)
[info] java.lang.StackOverflowError did not equal null (LocalityPlacementStrategySuite.scala:49)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:530)
[info] at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:529)
[info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1560)
[info] at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:503)
[info] at org.apache.spark.deploy.yarn.LocalityPlacementStrategySuite.$anonfun$new$1(LocalityPlacementStrategySuite.scala:49)
[info] at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
[info] at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
[info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info] at org.scalatest.Transformer.apply(Transformer.scala:22)
[info] at org.scalatest.Transformer.apply(Transformer.scala:20)
[info] at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
[info] at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:157)
[info] at org.scalatest.FunSuiteLike.invokeWithFixture$1(FunSuiteLike.scala:184)
[info] at org.scalatest.FunSuiteLike.$anonfun$runTest$1(FunSuiteLike.scala:196)
[info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:286)
[info] at org.scalatest.FunSuiteLike.runTest(FunSuiteLike.scala:196)
[info] at org.scalatest.FunSuiteLike.runTest$(FunSuiteLike.scala:178)
...
```
**After**:
```
[info] - handle large number of containers and tasks (SPARK-18750) *** FAILED *** (825 milliseconds)
[info] StackOverflowError should not be thrown; however, got:
[info]
[info] java.lang.StackOverflowError
[info] at scala.collection.TraversableLike.$anonfun$filterImpl$1(TraversableLike.scala:256)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info] at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info] at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info] at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info] at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info] at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info] at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info] at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info] at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
...
```
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Manually tested by reverting 76db394f2b locally.
Closes#28566 from HyukjinKwon/SPARK-31746.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to enhance Spark's `ClosureCleaner` to support "indylambda" style of Scala closures to the same level as the existing implementation for the old (inner class) style ones. The goal is to reach feature parity with the support of the old style Scala closures, with as close to bug-for-bug compatibility as possible.
Specifically, this PR addresses one lacking support for indylambda closures vs the inner class closures:
- When a closure is declared in a Scala REPL and captures the enclosing REPL line object, such closure should be cleanable (unreferenced fields on the enclosing REPL line object should be cleaned)
This PR maintains the same limitations in the new indylambda closure support as the old inner class closures, in particular the following two:
- Cleaning is only available for one level of REPL line object. If a closure captures state from a REPL line object further out from the immediate enclosing one, it won't be subject to cleaning. See example below.
- "Sibling" closures are not handled yet. A "sibling" closure is defined here as a closure that is directly or indirectly referenced by the starting closure, but isn't lexically enclosing. e.g.
```scala
{
val siblingClosure = (x: Int) => x + this.fieldA // captures `this`, references `fieldA` on `this`.
val startingClosure = (y: Int) => y + this.fieldB + siblingClosure(y) // captures `this` and `siblingClosure`, references `fieldB` on `this`.
}
```
The changes are intended to be minimal, with further code cleanups planned in separate PRs.
Jargons:
- old, inner class style Scala closures, aka `delambdafy:inline`: default in Scala 2.11 and before
- new, "indylambda" style Scala closures, aka `delambdafy:method`: default in Scala 2.12 and later
### Why are the changes needed?
There had been previous effortsto extend Spark's `ClosureCleaner` to support "indylambda" Scala closures, which is necessary for proper Scala 2.12 support. Most notably the work done for [SPARK-14540](https://issues.apache.org/jira/browse/SPARK-14540).
But the previous efforts had missed one import scenario: a Scala closure declared in a Scala REPL, and it captures the enclosing `this` -- a REPL line object. e.g. in a Spark Shell:
```scala
:pa
class NotSerializableClass(val x: Int)
val ns = new NotSerializableClass(42)
val topLevelValue = "someValue"
val func = (j: Int) => {
(1 to j).flatMap { x =>
(1 to x).map { y => y + topLevelValue }
}
}
<Ctrl+D>
sc.parallelize(0 to 2).map(func).collect
```
In this example, `func` refers to a Scala closure that captures the enclosing `this` because it needs to access `topLevelValue`, which is in turn implemented as a field on the enclosing REPL line object.
The existing `ClosureCleaner` in Spark supports cleaning this case in Scala 2.11-, and this PR brings feature parity to Scala 2.12+.
Note that the existing cleaning logic only supported one level of REPL line object nesting. This PR does not go beyond that. When a closure references state declared a few commands earlier, the cleaning will fail in both Scala 2.11 and Scala 2.12. e.g.
```scala
scala> :pa
// Entering paste mode (ctrl-D to finish)
class NotSerializableClass1(val x: Int)
case class Foo(id: String)
val ns = new NotSerializableClass1(42)
val topLevelValue = "someValue"
// Exiting paste mode, now interpreting.
defined class NotSerializableClass1
defined class Foo
ns: NotSerializableClass1 = NotSerializableClass1615b1baf
topLevelValue: String = someValue
scala> :pa
// Entering paste mode (ctrl-D to finish)
val closure2 = (j: Int) => {
(1 to j).flatMap { x =>
(1 to x).map { y => y + topLevelValue } // 2 levels
}
}
// Exiting paste mode, now interpreting.
closure2: Int => scala.collection.immutable.IndexedSeq[String] = <function1>
scala> sc.parallelize(0 to 2).map(closure2).collect
org.apache.spark.SparkException: Task not serializable
...
```
in the Scala 2.11 / Spark 2.4.x case:
```
Caused by: java.io.NotSerializableException: NotSerializableClass1
Serialization stack:
- object not serializable (class: NotSerializableClass1, value: NotSerializableClass1615b1baf)
- field (class: $iw, name: ns, type: class NotSerializableClass1)
- object (class $iw, $iw64df3f4b)
- field (class: $iw, name: $iw, type: class $iw)
- object (class $iw, $iw66e6e5e9)
- field (class: $line14.$read, name: $iw, type: class $iw)
- object (class $line14.$read, $line14.$readc310aa3)
- field (class: $iw, name: $line14$read, type: class $line14.$read)
- object (class $iw, $iw79224636)
- field (class: $iw, name: $outer, type: class $iw)
- object (class $iw, $iw636d4cdc)
- field (class: $anonfun$1, name: $outer, type: class $iw)
- object (class $anonfun$1, <function1>)
```
in the Scala 2.12 / Spark master case after this PR:
```
Caused by: java.io.NotSerializableException: NotSerializableClass1
Serialization stack:
- object not serializable (class: NotSerializableClass1, value: NotSerializableClass16f3b4c9a)
- field (class: $iw, name: ns, type: class NotSerializableClass1)
- object (class $iw, $iw2945a3c1)
- field (class: $iw, name: $iw, type: class $iw)
- object (class $iw, $iw152705d0)
- field (class: $line14.$read, name: $iw, type: class $iw)
- object (class $line14.$read, $line14.$read7cf311eb)
- field (class: $iw, name: $line14$read, type: class $line14.$read)
- object (class $iw, $iwd980dac)
- field (class: $iw, name: $outer, type: class $iw)
- object (class $iw, $iw557d9532)
- element of array (index: 0)
- array (class [Ljava.lang.Object;, size 1)
- field (class: java.lang.invoke.SerializedLambda, name: capturedArgs, type: class [Ljava.lang.Object;)
- object (class java.lang.invoke.SerializedLambda, SerializedLambda[capturingClass=class $iw, functionalInterfaceMethod=scala/Function1.apply:(Ljava/lang/Object;)Ljava/lang/Object;, implementation=invokeStatic $anonfun$closure2$1$adapted:(L$iw;Ljava/lang/Object;)Lscala/collection/immutable/IndexedSeq;, instantiatedMethodType=(Ljava/lang/Object;)Lscala/collection/immutable/IndexedSeq;, numCaptured=1])
- writeReplace data (class: java.lang.invoke.SerializedLambda)
- object (class $Lambda$2103/815179920, $Lambda$2103/815179920569b57c4)
```
For more background of the new and old ways Scala lowers closures to Java bytecode, please see [A note on how NSC (New Scala Compiler) lowers lambdas](https://gist.github.com/rednaxelafx/e9ecd09bbd1c448dbddad4f4edf25d48#file-notes-md).
For more background on how Spark's `ClosureCleaner` works and what's needed to make it support "indylambda" Scala closures, please refer to [A Note on Apache Spark's ClosureCleaner](https://gist.github.com/rednaxelafx/e9ecd09bbd1c448dbddad4f4edf25d48#file-spark_closurecleaner_notes-md).
#### tl;dr
The `ClosureCleaner` works like a mark-sweep algorithm on fields:
- Finding (a chain of) outer objects referenced by the starting closure;
- Scanning the starting closure and its inner closures and marking the fields on the outer objects accessed;
- Cloning the outer objects, nulling out fields that are not accessed by any closure of concern.
##### Outer Objects
For the old, inner class style Scala closures, the "outer objects" is defined as the lexically enclosing closures of the starting closure, plus an optional enclosing REPL line object if these closures are defined in a Scala REPL. All of them are on a singly-linked `$outer` chain.
For the new, "indylambda" style Scala closures, the capturing implementation changed, so closures no longer refer to their enclosing closures via an `$outer` chain. However, a closure can still capture its enclosing REPL line object, much like the old style closures. The name of the field that captures this reference would be `arg$1` (instead of `$outer`).
So what's missing in the `ClosureCleaner` for the "indylambda" support is find and potentially clone+clean the captured enclosing `this` REPL line object. That's what this PR implements.
##### Inner Closures
The old, inner class style of Scala closures are compiled into separate inner classes, one per lambda body. So in order to discover the implementation (bytecode) of the inner closures, one has to jump over multiple classes. The name of such a class would contain the marker substring `$anonfun$`.
The new, "indylambda" style Scala closures are compiled into **static methods** in the class where the lambdas were declared. So for lexically nested closures, their lambda bodies would all be compiled into static methods **in the same class**. This makes it much easier to discover the implementation (bytecode) of the nested lambda bodies. The name of such a static method would contain the marker substring `$anonfun$`.
Discovery of inner closures involves scanning bytecode for certain patterns that represent the creation of a closure object for the inner closure.
- For inner class style: the closure object creation site is like `new <InnerClassForTheClosure>(captured args)`
- For "indylambda" style: the closure object creation site would be compiled into an `invokedynamic` instruction, with its "bootstrap method" pointing to the same one used by Java 8 for its serializable lambdas, and with the bootstrap method arguments pointing to the implementation method.
### Does this PR introduce _any_ user-facing change?
Yes. Before this PR, Spark 2.4 / 3.0 / master on Scala 2.12 would not support Scala closures declared in a Scala REPL that captures anything from the REPL line objects. After this PR, such scenario is supported.
### How was this patch tested?
Added new unit test case to `org.apache.spark.repl.SingletonReplSuite`. The new test case fails without the fix in this PR, and pases with the fix.
Closes#28463 from rednaxelafx/closure-cleaner-indylambda.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This PR aims to use GitHub URL instead of a broken link in `BasicTestsSuite.scala`.
Currently, K8s integration test is broken:
https://amplab.cs.berkeley.edu/jenkins/view/Spark%20K8s%20Builds/job/spark-master-test-k8s/534/console
```
- Run SparkRemoteFileTest using a remote data file *** FAILED ***
The code passed to eventually never returned normally. Attempted 130 times over 2.00109555135 minutes. Last failure message: false was not true. (KubernetesSuite.scala:370)
```
No.
Pass the K8s integration test.
Closes#28561 from williamhyun/williamhyun-patch-1.
Authored-by: williamhyun <62487364+williamhyun@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Change timestamp casting to int in ANSI and non-codegen mode, and make the error message consistent to the error messages in the codegen mode. In particular, casting to int is implemented in the same way as casting to short and byte.
### Why are the changes needed?
1. The error message in the non-codegen mode is diversed from the error message in the codegen mode.
2. The error message contains intermediate results that could confuse.
### Does this PR introduce _any_ user-facing change?
Yes. Before the changes, the error message of casting timestamp to int contains intermediate result but after the changes it contains the input values which causes arithmetic overflow.
### How was this patch tested?
By running the modified test suite `AnsiCastSuite`.
Closes#28549 from MaxGekk/fix-error-msg-cast-timestamp.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to add `spark_info` metric into `PrometheusResource`.
### Why are the changes needed?
This exposes Apache Spark version and revision like the following.
### Does this PR introduce _any_ user-facing change?
Yes, but it's exposed as an additional metric.
### How was this patch tested?
Manual.
```
$ bin/spark-shell --driver-memory 4G -c spark.ui.prometheus.enabled=true
$ curl -s http://localhost:4041/metrics/executors/prometheus/ | head -n1
spark_info{version="3.1.0", revision="097d5098cca987e5f7bbb8394783c01517ebed0f"} 1.0
```
Closes#28563 from dongjoon-hyun/SPARK-31743.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR is kind of a follow up of SPARK-31631. In some cases, it only attempts once for ~35 seconds. Seems 10 seconds are not enough to try multiple times - took a quick look and seems difficult to manipulate the socket configuration as well.
It simply proposes to increase the time limit for now. It affects master and branch-3.0.
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedDueToTimeoutException: The code passed to eventually never returned normally. Attempted 1 times over 34.294744142999996 seconds. Last failure message: Address already in use.
at org.scalatest.concurrent.Eventually.tryTryAgain$1(Eventually.scala:432)
at org.scalatest.concurrent.Eventually.eventually(Eventually.scala:439)
at org.scalatest.concurrent.Eventually.eventually$(Eventually.scala:391)
at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:479)
at org.scalatest.concurrent.Eventually.eventually(Eventually.scala:308)
at org.scalatest.concurrent.Eventually.eventually$(Eventually.scala:307)
at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:479)
at org.apache.spark.deploy.security.HadoopDelegationTokenManagerSuite.$anonfun$new$4(HadoopDelegationTokenManagerSuite.scala:106)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:157)
at org.scalatest.FunSuiteLike.invokeWithFixture$1(FunSuiteLike.scala:184)
at org.scalatest.FunSuiteLike.$anonfun$runTest$1(FunSuiteLike.scala:196)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:286)
at org.scalatest.FunSuiteLike.runTest(FunSuiteLike.scala:196)
at org.scalatest.FunSuiteLike.runTest$(FunSuiteLike.scala:178)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:59)
at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:221)
at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:214)
at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:59)
at org.scalatest.FunSuiteLike.$anonfun$runTests$1(FunSuiteLike.scala:229)
at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:393)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:381)
at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:376)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:458)
at org.scalatest.FunSuiteLike.runTests(FunSuiteLike.scala:229)
at org.scalatest.FunSuiteLike.runTests$(FunSuiteLike.scala:228)
at org.scalatest.FunSuite.runTests(FunSuite.scala:1560)
at org.scalatest.Suite.run(Suite.scala:1124)
at org.scalatest.Suite.run$(Suite.scala:1106)
at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1560)
at org.scalatest.FunSuiteLike.$anonfun$run$1(FunSuiteLike.scala:233)
at org.scalatest.SuperEngine.runImpl(Engine.scala:518)
at org.scalatest.FunSuiteLike.run(FunSuiteLike.scala:233)
at org.scalatest.FunSuiteLike.run$(FunSuiteLike.scala:232)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:59)
at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:59)
at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:317)
at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:510)
at sbt.ForkMain$Run$2.call(ForkMain.java:296)
at sbt.ForkMain$Run$2.call(ForkMain.java:286)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
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)
Caused by: sbt.ForkMain$ForkError: java.net.BindException: Address already in use
at sun.nio.ch.Net.bind0(Native Method)
at sun.nio.ch.Net.bind(Net.java:433)
at sun.nio.ch.Net.bind(Net.java:425)
at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:223)
at sun.nio.ch.ServerSocketAdaptor.bind(ServerSocketAdaptor.java:74)
at org.apache.mina.transport.socket.nio.NioSocketAcceptor.open(NioSocketAcceptor.java:198)
at org.apache.mina.transport.socket.nio.NioSocketAcceptor.open(NioSocketAcceptor.java:51)
at org.apache.mina.core.polling.AbstractPollingIoAcceptor.registerHandles(AbstractPollingIoAcceptor.java:547)
at org.apache.mina.core.polling.AbstractPollingIoAcceptor.access$400(AbstractPollingIoAcceptor.java:68)
at org.apache.mina.core.polling.AbstractPollingIoAcceptor$Acceptor.run(AbstractPollingIoAcceptor.java:422)
at org.apache.mina.util.NamePreservingRunnable.run(NamePreservingRunnable.java:64)
... 3 more
```
### Why are the changes needed?
To fix flakiness in the tests.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Jenkins will test it out.
Closes#28562 from HyukjinKwon/SPARK-31742.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR targets to remove Hive profile in SparkR build at AppVeyor in order to:
- Speed up the build. Currently, SparkR build is [reaching the time limit](https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark/builds/32853533).
- Clean up the build profile.
### Why are the changes needed?
We're hitting a time limit issue again and this PR could reduce the build time.
Seems like we're [already skipping Hive related tests in SparkR](https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark/builds/32853533) for some reasons, see below:
```
test_sparkSQL.R:307: skip: create DataFrame from RDD
Reason: Hive is not build with SparkSQL, skipped
test_sparkSQL.R:1341: skip: test HiveContext
Reason: Hive is not build with SparkSQL, skipped
test_sparkSQL.R:2813: skip: read/write ORC files
Reason: Hive is not build with SparkSQL, skipped
test_sparkSQL.R:2834: skip: read/write ORC files - compression option
Reason: Hive is not build with SparkSQL, skipped
test_sparkSQL.R:3727: skip: enableHiveSupport on SparkSession
Reason: Hive is not build with SparkSQL, skipped
```
Although we build with Hive profile. So, the Hive profile is useless here.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
AppVeyor will test it out.
Closes#28564 from HyukjinKwon/SPARK-31744.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
a typo in logging. (just added `: `)
Closes#28505 from sungpeo/typo_fshistoryprovider.
Authored-by: Sungpeo Kook <elixir.kook@kakaocorp.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This patch effectively reverts SPARK-30098 via below changes:
* Removed the config
* Removed the changes done in parser rule
* Removed the usage of config in tests
* Removed tests which depend on the config
* Rolled back some tests to before SPARK-30098 which were affected by SPARK-30098
* Reflect the change into docs (migration doc, create table syntax)
### Why are the changes needed?
SPARK-30098 brought confusion and frustration on using create table DDL query, and we agreed about the bad effect on the change.
Please go through the [discussion thread](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Resolve-ambiguous-parser-rule-between-two-quot-create-table-quot-s-td29051i20.html) to see the details.
### Does this PR introduce _any_ user-facing change?
No, compared to Spark 2.4.x. End users tried to experiment with Spark 3.0.0 previews will see the change that the behavior is going back to Spark 2.4.x, but I believe we won't guarantee compatibility in preview releases.
### How was this patch tested?
Existing UTs.
Closes#28517 from HeartSaVioR/revert-SPARK-30098.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set default time zone and locale in the default constructor of `SparkFunSuite`:
- Default time zone to `America/Los_Angeles`
- Default locale to `Locale.US`
### Why are the changes needed?
1. To deduplicate code by moving common time zone and locale settings to one place SparkFunSuite
2. To have the same default time zone and locale in all tests. This should prevent errors like https://github.com/apache/spark/pull/28538
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running all affected test suites
Closes#28548 from MaxGekk/timezone-settings-SparkFunSuite.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to recover Hadoop-3.2 profile jobs on `master` branch by disabling a UT added by SPARK-31235 in Hadoop 3.2 temporarily. The target UT is not a flaky test. It always fail on Hadoop-3.2 profile currently although it works in Hadoop 2.7 profile. So, in this PR, we keep the test coverage in Hadoop 2.7 and ignore the test in Hadoop 3.2 temporarily to unblock the other PRs.
### Why are the changes needed?
SPARK-31235 added a test case which is breaking Hadoop 3.2 and there are two follow-up to fix it. Although two follow-ups can fix the UT in Hadoop 3.2 environment. The side-effect on Hadoop classes cause some random UT failures in the other suites.
- https://github.com/apache/spark/pull/28456
- https://github.com/apache/spark/pull/28550
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with SBT/Maven.
Closes#28552 from dongjoon-hyun/SPARK-31235-2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
It's quite annoying to be blocked by flaky tests in several PRs. This PR disables them. The tests come from 3 PRs I'm recently watching:
https://github.com/apache/spark/pull/28526https://github.com/apache/spark/pull/28463https://github.com/apache/spark/pull/28517
### Why are the changes needed?
To make PR builder more stable
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#28547 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
CliSuite seems to be flaky while using metastoreDir per test.
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120470/testReport/org.apache.spark.sql.hive.thriftserver/CliSuite/https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120470/testReport/junit/org.apache.spark.sql.hive.thriftserver/CliSuite/history/
According to the error stack trace in the failed test, the test failed to instantiate a hive metastore client because of derby requirements.
```scala
Caused by: ERROR XBM0A:
The database directory '/home/jenkins/workspace/SparkPullRequestBuilder/target/tmp/spark-9249ce52-0a06-42b6-a3df-e6295e880df0' exists.
However, it does not contain the expected 'service.properties' file.
Perhaps Derby was brought down in the middle of creating this database.
You may want to delete this directory and try creating the database again.
```
The derby requires the metastore dir does not exist, but it does exist probably due to the test case before it failed to clear the metastore dir
In this PR, the metastore is shared across the tests of CliSuite except those explicitly asked a separated metastore env itself
### Why are the changes needed?
CliSuite seems to be flaky while using metastoreDir per test.
To eliminate test flakiness
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
modified test
Closes#28055 from yaooqinn/clisuite.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Resolve the havingcondition with expanding the GROUPING SETS/CUBE/ROLLUP expressions together in `ResolveGroupingAnalytics`:
- Change the operations resolving directions to top-down.
- Try resolving the condition of the filter as though it is in the aggregate clause by reusing the function in `ResolveAggregateFunctions`
- Push the aggregate expressions into the aggregate which contains the expanded operations.
- Use UnresolvedHaving for all having clause.
### Why are the changes needed?
Correctness bug fix. See the demo and analysis in SPARK-31663.
### Does this PR introduce _any_ user-facing change?
Yes, correctness bug fix for HAVING with GROUPING SETS.
### How was this patch tested?
New UTs added.
Closes#28501 from xuanyuanking/SPARK-31663.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Instead of using `child.output` directly, we should use `inputAggBufferAttributes` from the current agg expression for `Final` and `PartialMerge` aggregates to bind references for their `mergeExpression`.
### Why are the changes needed?
When planning aggregates, the partial aggregate uses agg fucs' `inputAggBufferAttributes` as its output, see https://github.com/apache/spark/blob/v3.0.0-rc1/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggUtils.scala#L105
For final `HashAggregateExec`, we need to bind the `DeclarativeAggregate.mergeExpressions` with the output of the partial aggregate operator, see https://github.com/apache/spark/blob/v3.0.0-rc1/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala#L348
This is usually fine. However, if we copy the agg func somehow after agg planning, like `PlanSubqueries`, the `DeclarativeAggregate` will be replaced by a new instance with new `inputAggBufferAttributes` and `mergeExpressions`. Then we can't bind the `mergeExpressions` with the output of the partial aggregate operator, as it uses the `inputAggBufferAttributes` of the original `DeclarativeAggregate` before copy.
Note that, `ImperativeAggregate` doesn't have this problem, as we don't need to bind its `mergeExpressions`. It has a different mechanism to access buffer values, via `mutableAggBufferOffset` and `inputAggBufferOffset`.
### Does this PR introduce _any_ user-facing change?
Yes, user hit error previously but run query successfully after this change.
### How was this patch tested?
Added a regression test.
Closes#28496 from Ngone51/spark-31620.
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 provide a fallback version instead of `Nil` in `HiveExternalCatalogVersionsSuite`. The provided fallback Spark versions recovers Jenkins jobs instead of failing.
### Why are the changes needed?
Currently, `HiveExternalCatalogVersionsSuite` is aborted in all Jenkins jobs except JDK11 Jenkins jobs which don't have old Spark releases supporting JDK11.
```
HiveExternalCatalogVersionsSuite:
org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite *** ABORTED ***
Exception encountered when invoking run on a nested suite - Fail to get the lates Spark versions to test. (HiveExternalCatalogVersionsSuite.scala:180)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the Jenkins
Closes#28536 from dongjoon-hyun/SPARK-HiveExternalCatalogVersionsSuite.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/122622/testReport/junit/org.apache.spark.sql.hive.thriftserver/SparkSQLEnvSuite/SPARK_29604_external_listeners_should_be_initialized_with_Spark_classloader/history/?start=25
According to the test report history of SparkSQLEnvSuite，this test fails frequently which is caused by single derby instance restriction.
```java
Caused by: sbt.ForkMain$ForkError: org.apache.derby.iapi.error.StandardException: Another instance of Derby may have already booted the database /home/jenkins/workspace/SparkPullRequestBuilder/sql/hive-thriftserver/metastore_db.
at org.apache.derby.iapi.error.StandardException.newException(Unknown Source)
at org.apache.derby.impl.store.raw.data.BaseDataFileFactory.privGetJBMSLockOnDB(Unknown Source)
at org.apache.derby.impl.store.raw.data.BaseDataFileFactory.run(Unknown Source)
at java.security.AccessController.doPrivileged(Native Method)
at org.apache.derby.impl.store.raw.data.BaseDataFileFactory.getJBMSLockOnDB(Unknown Source)
at org.apache.derby.impl.store.raw.data.BaseDataFileFactory.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)
at org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)
at org.apache.derby.impl.store.raw.RawStore.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)
at org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)
at org.apache.derby.impl.store.access.RAMAccessManager.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.startModule(Unknown Source)
at org.apache.derby.iapi.services.monitor.Monitor.bootServiceModule(Unknown Source)
at org.apache.derby.impl.db.BasicDatabase.bootStore(Unknown Source)
at org.apache.derby.impl.db.BasicDatabase.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.boot(Unknown Source)
at org.apache.derby.impl.services.monitor.TopService.bootModule(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.bootService(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.startProviderService(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.findProviderAndStartService(Unknown Source)
at org.apache.derby.impl.services.monitor.BaseMonitor.startPersistentService(Unknown Source)
at org.apache.derby.iapi.services.monitor.Monitor.startPersistentService(Unknown Source)
... 138 more
```
This PR adds a separate directory to locate the metastore_db for this test which runs in a dedicated JVM.
Besides, diable the UI for the potential race on `spark.ui.port` which may also let the test case become flaky.
### Why are the changes needed?
test fix
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
SparkSQLEnvSuite itself.
Closes#28537 from yaooqinn/SPARK-31715.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added tests to check casting timestamps before 1970-01-01 00:00:00Z to ByteType, ShortType, IntegerType and LongType in ansi and non-ansi modes.
### Why are the changes needed?
To improve test coverage and prevent errors while modifying the CAST expression code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the modified test suites:
```
$ ./build/sbt "test:testOnly *CastSuite"
```
Closes#28531 from MaxGekk/test-cast-timestamp-to-byte.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes `test-dependencies.sh` detect the version string correctly by ignoring all the other lines.
### Why are the changes needed?
Currently, all SBT jobs are broken like the following.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-3.0-test-sbt-hadoop-3.2-hive-2.3/476/console
```
[error] running /home/jenkins/workspace/spark-branch-3.0-test-sbt-hadoop-3.2-hive-2.3/dev/test-dependencies.sh ; received return code 1
Build step 'Execute shell' marked build as failure
```
The reason is that the script detects the old version like `Falling back to archive.apache.org to download Maven 3.1.0-SNAPSHOT` when `build/mvn` did fallback.
Specifically, in the script, `OLD_VERSION` became `Falling back to archive.apache.org to download Maven 3.1.0-SNAPSHOT` instead of `3.1.0-SNAPSHOT` if build/mvn did fallback. Then, `pom.xml` file is corrupted like the following at the end and the exit code become `1` instead of `0`. It causes Jenkins jobs fails
```
- <version>3.1.0-SNAPSHOT</version>
+ <version>Falling</version>
```
**NO FALLBACK**
```
$ build/mvn -q -Dexec.executable="echo" -Dexec.args='${project.version}' --non-recursive org.codehaus.mojo:exec-maven-plugin:1.6.0:exec
Using `mvn` from path: /Users/dongjoon/APACHE/spark-merge/build/apache-maven-3.6.3/bin/mvn
3.1.0-SNAPSHOT
```
**FALLBACK**
```
$ build/mvn -q -Dexec.executable="echo" -Dexec.args='${project.version}' --non-recursive org.codehaus.mojo:exec-maven-plugin:1.6.0:exec
Falling back to archive.apache.org to download Maven
Using `mvn` from path: /Users/dongjoon/APACHE/spark-merge/build/apache-maven-3.6.3/bin/mvn
3.1.0-SNAPSHOT
```
**In the script**
```
$ echo $(build/mvn -q -Dexec.executable="echo" -Dexec.args='${project.version}' --non-recursive org.codehaus.mojo:exec-maven-plugin:1.6.0:exec)
Using `mvn` from path: /Users/dongjoon/APACHE/spark-merge/build/apache-maven-3.6.3/bin/mvn
Falling back to archive.apache.org to download Maven 3.1.0-SNAPSHOT
```
This PR will prevent irrelevant logs like `Falling back to archive.apache.org to download Maven`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the PR Builder.
Closes#28532 from dongjoon-hyun/SPARK-31713.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
# What changes were proposed in this pull request?
This PR is a follow-up to fix a version of configuration document.
### Why are the changes needed?
The original PR is backported to branch-3.0.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
Manual.
Closes#28530 from dongjoon-hyun/SPARK-31696-2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Return LogisticRegressionSummary for multiclass logistic regression evaluate in PySpark
### Why are the changes needed?
Currently we have
```
since("2.0.0")
def evaluate(self, dataset):
if not isinstance(dataset, DataFrame):
raise ValueError("dataset must be a DataFrame but got %s." % type(dataset))
java_blr_summary = self._call_java("evaluate", dataset)
return BinaryLogisticRegressionSummary(java_blr_summary)
```
we should return LogisticRegressionSummary for multiclass logistic regression
### Does this PR introduce _any_ user-facing change?
Yes
return LogisticRegressionSummary instead of BinaryLogisticRegressionSummary for multiclass logistic regression in Python
### How was this patch tested?
unit test
Closes#28503 from huaxingao/lr_summary.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In QuantileDiscretizer.getDistinctSplits, before invoking distinct, normalize all -0.0 and 0.0 to be 0.0
```
for (i <- 0 until splits.length) {
if (splits(i) == -0.0) {
splits(i) = 0.0
}
}
```
### Why are the changes needed?
Fix bug.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
#### Manually test:
~~~scala
import scala.util.Random
val rng = new Random(3)
val a1 = Array.tabulate(200)(_=>rng.nextDouble * 2.0 - 1.0) ++ Array.fill(20)(0.0) ++ Array.fill(20)(-0.0)
import spark.implicits._
val df1 = sc.parallelize(a1, 2).toDF("id")
import org.apache.spark.ml.feature.QuantileDiscretizer
val qd = new QuantileDiscretizer().setInputCol("id").setOutputCol("out").setNumBuckets(200).setRelativeError(0.0)
val model = qd.fit(df1) // will raise error in spark master.
~~~
### Explain
scala `0.0 == -0.0` is True but `0.0.hashCode == -0.0.hashCode()` is False. This break the contract between equals() and hashCode() If two objects are equal, then they must have the same hash code.
And array.distinct will rely on elem.hashCode so it leads to this error.
Test code on distinct
```
import scala.util.Random
val rng = new Random(3)
val a1 = Array.tabulate(200)(_=>rng.nextDouble * 2.0 - 1.0) ++ Array.fill(20)(0.0) ++ Array.fill(20)(-0.0)
a1.distinct.sorted.foreach(x => print(x.toString + "\n"))
```
Then you will see output like:
```
...
-0.009292684662246975
-0.0033280686465135823
-0.0
0.0
0.0022219556032221366
0.02217419561977274
...
```
Closes#28498 from WeichenXu123/SPARK-31676.
Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR try to fix a bug in `org.apache.spark.sql.hive.execution.ScriptTransformationExec`. This bug appears in our online cluster. `ScriptTransformationExec` should throw an exception, when user uses a python script which contains parse error. But current implementation may miss this case of failure.
### Why are the changes needed?
When user uses a python script which contains a parse error, there will be no output. So `scriptOutputReader.next(scriptOutputWritable) <= 0` matches, then we use `checkFailureAndPropagate()` to check the `proc`. But the `proc` may still be alive and `writerThread.exception` is not defined, `checkFailureAndPropagate` cannot check this case of failure. In the end, the Spark SQL job runs successfully and returns no result. In fact, the SparK SQL job should fails and shows the exception properly.
For example, the error python script is blow.
``` python
# encoding: utf8
import unknow_module
import sys
for line in sys.stdin:
print line
```
The bug can be reproduced by running the following code in our cluter.
```
spark.range(100*100).toDF("index").createOrReplaceTempView("test")
spark.sql("select TRANSFORM(index) USING 'python error_python.py' as new_index from test").collect.foreach(println)
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UT
Closes#27724 from slamke/transformation.
Authored-by: sunke.03 <sunke.03@bytedance.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Pass hadoop confs specifed via Spark confs to URLStreamHandlerfactory
### Why are the changes needed?
**BEFORE**
```
➜ spark git:(SPARK-31692) ✗ ./bin/spark-shell --conf spark.hadoop.fs.file.impl=org.apache.hadoop.fs.RawLocalFileSystem
scala> spark.sharedState
res0: org.apache.spark.sql.internal.SharedState = org.apache.spark.sql.internal.SharedState5793cd84
scala> new java.net.URL("file:///tmp/1.txt").openConnection.getInputStream
res1: java.io.InputStream = org.apache.hadoop.fs.ChecksumFileSystem$FSDataBoundedInputStream22846025
scala> import org.apache.hadoop.fs._
import org.apache.hadoop.fs._
scala> FileSystem.get(new Path("file:///tmp/1.txt").toUri, spark.sparkContext.hadoopConfiguration)
res2: org.apache.hadoop.fs.FileSystem = org.apache.hadoop.fs.LocalFileSystem5a930c03
```
**AFTER**
```
➜ spark git:(SPARK-31692) ✗ ./bin/spark-shell --conf spark.hadoop.fs.file.impl=org.apache.hadoop.fs.RawLocalFileSystem
scala> spark.sharedState
res0: org.apache.spark.sql.internal.SharedState = org.apache.spark.sql.internal.SharedState5c24a636
scala> new java.net.URL("file:///tmp/1.txt").openConnection.getInputStream
res1: java.io.InputStream = org.apache.hadoop.fs.FSDataInputStream2ba8f528
scala> import org.apache.hadoop.fs._
import org.apache.hadoop.fs._
scala> FileSystem.get(new Path("file:///tmp/1.txt").toUri, spark.sparkContext.hadoopConfiguration)
res2: org.apache.hadoop.fs.FileSystem = LocalFS
scala> FileSystem.get(new Path("file:///tmp/1.txt").toUri, spark.sparkContext.hadoopConfiguration).getClass
res3: Class[_ <: org.apache.hadoop.fs.FileSystem] = class org.apache.hadoop.fs.RawLocalFileSystem
```
The type of FileSystem object created(you can check the last statement in the above snippets) in the above two cases are different, which should not have been the case
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Tested locally.
Added Unit test
Closes#28516 from karuppayya/SPARK-31692.
Authored-by: Karuppayya Rajendran <karuppayya1990@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
When reading/writing datetime values that before the rebase switch day, from/to Avro/Parquet files, fail by default and ask users to set a config to explicitly do rebase or not.
### Why are the changes needed?
Rebase or not rebase have different behaviors and we should let users decide it explicitly. In most cases, users won't hit this exception as it only affects ancient datetime values.
### Does this PR introduce _any_ user-facing change?
Yes, now users will see an error when reading/writing dates before 1582-10-15 or timestamps before 1900-01-01 from/to Parquet/Avro files, with an error message to ask setting a config.
### How was this patch tested?
updated tests
Closes#28477 from cloud-fan/rebase.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR caught the `NoSuchElementException` and enriched the error message for `AppStatusStore.applicationInfo()` when Spark is starting up and the application information is unavailable.
### Why are the changes needed?
During the initialization of `SparkContext`, it first starts the Web UI and then set up the `LiveListenerBus` thread for dispatching the `SparkListenerApplicationStart` event (which will trigger writing the requested `ApplicationInfo` to `InMemoryStore`). If the Web UI is accessed before this info's being written to `InMemoryStore`, the following `NoSuchElementException` will be thrown.
```
WARN org.eclipse.jetty.server.HttpChannel: /jobs/
java.util.NoSuchElementException
at java.util.Collections$EmptyIterator.next(Collections.java:4191)
at org.apache.spark.util.kvstore.InMemoryStore$InMemoryIterator.next(InMemoryStore.java:467)
at org.apache.spark.status.AppStatusStore.applicationInfo(AppStatusStore.scala:39)
at org.apache.spark.ui.jobs.AllJobsPage.render(AllJobsPage.scala:266)
at org.apache.spark.ui.WebUI.$anonfun$attachPage$1(WebUI.scala:89)
at org.apache.spark.ui.JettyUtils$$anon$1.doGet(JettyUtils.scala:80)
at javax.servlet.http.HttpServlet.service(HttpServlet.java:687)
at javax.servlet.http.HttpServlet.service(HttpServlet.java:790)
at org.eclipse.jetty.servlet.ServletHolder.handle(ServletHolder.java:873)
at org.eclipse.jetty.servlet.ServletHandler$CachedChain.doFilter(ServletHandler.java:1623)
at org.apache.spark.ui.HttpSecurityFilter.doFilter(HttpSecurityFilter.scala:95)
at org.eclipse.jetty.servlet.ServletHandler$CachedChain.doFilter(ServletHandler.java:1610)
at org.eclipse.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:540)
at org.eclipse.jetty.server.handler.ScopedHandler.nextHandle(ScopedHandler.java:255)
at org.eclipse.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1345)
at org.eclipse.jetty.server.handler.ScopedHandler.nextScope(ScopedHandler.java:203)
at org.eclipse.jetty.servlet.ServletHandler.doScope(ServletHandler.java:480)
at org.eclipse.jetty.server.handler.ScopedHandler.nextScope(ScopedHandler.java:201)
at org.eclipse.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1247)
at org.eclipse.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:144)
at org.eclipse.jetty.server.handler.gzip.GzipHandler.handle(GzipHandler.java:753)
at org.eclipse.jetty.server.handler.ContextHandlerCollection.handle(ContextHandlerCollection.java:220)
at org.eclipse.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:132)
at org.eclipse.jetty.server.Server.handle(Server.java:505)
at org.eclipse.jetty.server.HttpChannel.handle(HttpChannel.java:370)
at org.eclipse.jetty.server.HttpConnection.onFillable(HttpConnection.java:267)
at org.eclipse.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:305)
at org.eclipse.jetty.io.FillInterest.fillable(FillInterest.java:103)
at org.eclipse.jetty.io.ChannelEndPoint$2.run(ChannelEndPoint.java:117)
at org.eclipse.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:698)
at org.eclipse.jetty.util.thread.QueuedThreadPool$Runner.run(QueuedThreadPool.java:804)
at java.lang.Thread.run(Thread.java:748)
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually tested.
This can be reproduced:
1. `./bin/spark-shell`
2. at the same time, open `http://localhost:4040/jobs/` in your browser with quickly refreshing.
Closes#28444 from xccui/SPARK-31632.
Authored-by: Xingcan Cui <xccui@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to add `spark.kubernetes.driver.service.annotation` like `spark.kubernetes.driver.service.annotation`.
### Why are the changes needed?
Annotations are used in many ways. One example is that Prometheus monitoring system search metric endpoint via annotation.
- https://github.com/helm/charts/tree/master/stable/prometheus#scraping-pod-metrics-via-annotations
### Does this PR introduce _any_ user-facing change?
Yes. The documentation is added.
### How was this patch tested?
Pass Jenkins with the updated unit tests.
Closes#28518 from dongjoon-hyun/SPARK-31696.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to set the minimum Arrow version as 0.15.1 to be consistent with PySpark side at.
### Why are the changes needed?
It will reduce the maintenance overhead to match the Arrow versions, and minimize the supported range. SparkR Arrow optimization is experimental yet.
### Does this PR introduce _any_ user-facing change?
No, it's the change in unreleased branches only.
### How was this patch tested?
0.15.x was already tested at SPARK-29378, and we're testing the latest version of SparkR currently in AppVeyor. I already manually tested too.
Closes#28520 from HyukjinKwon/SPARK-31701.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR changes HistoryServer to set Content-Type.
I noticed that we will get html as plain text when we access to wrong URLs which represent non-existence appId on HistoryServer.
```
<html>
<head>
<meta http-equiv="Content-type" content="text/html; charset=utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="/static/bootstrap.min.css" type="text/css"/><link rel="stylesheet" href="/static/vis-timeline-graph2d.min.css" type="text/css"/><link rel="stylesheet" href="/static/webui.css" type="text/css"/><link rel="stylesheet" href="/static/timeline-view.css" type="text/css"/><script src="/static/sorttable.js"></script><script src="/static/jquery-3.4.1.min.js"></script><script src="/static/vis-timeline-graph2d.min.js"></script><script src="/static/bootstrap.bundle.min.js"></script><script src="/static/initialize-tooltips.js"></script><script src="/static/table.js"></script><script src="/static/timeline-view.js"></script><script src="/static/log-view.js"></script><script src="/static/webui.js"></script><script>setUIRoot('')</script>
<link rel="shortcut icon" href="/static/spark-logo-77x50px-hd.png"></link>
<title>Not Found</title>
</head>
<body>
<div class="container-fluid">
<div class="row">
<div class="col-12">
<h3 style="vertical-align: middle; display: inline-block;">
<a style="text-decoration: none" href="/">
<img src="/static/spark-logo-77x50px-hd.png"/>
<span class="version" style="margin-right: 15px;">3.1.0-SNAPSHOT</span>
</a>
Not Found
</h3>
</div>
</div>
<div class="row">
<div class="col-12">
<div class="row">Application local-1589239 not found.</div>
</div>
</div>
</div>
</body>
</html>
```
The reason is Content-Type not set. I confirmed it with `curl -I http://localhost:18080/history/<wrong-appId>`
```
HTTP/1.1 404 Not Found
Date: Wed, 13 May 2020 06:59:29 GMT
Cache-Control: no-cache, no-store, must-revalidate
X-Frame-Options: SAMEORIGIN
X-XSS-Protection: 1; mode=block
X-Content-Type-Options: nosniff
Content-Length: 1778
Server: Jetty(9.4.18.v20190429)
```
### Why are the changes needed?
This is a bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I added a test case for this issue.
Closes#28519 from sarutak/fix-content-type.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR removes `bootstrap-tooltip.js` which is no longer used.
That script is replaced with `bootstrap.bundle.min.js` in SPARK-30654 ( #27370 ).
### Why are the changes needed?
For cleaning up repository..
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually checked whether tooltips are shown in the UI and no error message shown in the debug console.
Closes#28515 from sarutak/remove-tooltipjs.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR adds `i` option to ignore additional `build/mvn` output which is irrelevant to version string.
### Why are the changes needed?
SPARK-28963 added additional output message, `Falling back to archive.apache.org to download Maven` in build/mvn. This breaks `dev/create-release/release-build.sh` and currently Spark Packaging Jenkins job is hitting this issue consistently and broken.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20Packaging/job/spark-master-maven-snapshots/2912/console
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This happens only when the mirror fails. So, this is verified manually hiject the script. It works like the following.
```
$ echo 'Falling back to archive.apache.org to download Maven' > out
$ build/mvn help:evaluate -Dexpression=project.version >> out
Using `mvn` from path: /Users/dongjoon/PRS/SPARK_RELEASE_2/build/apache-maven-3.6.3/bin/mvn
$ cat out | grep -v INFO | grep -v WARNING | grep -v Download
Falling back to archive.apache.org to download Maven
3.1.0-SNAPSHOT
$ cat out | grep -v INFO | grep -v WARNING | grep -vi Download
3.1.0-SNAPSHOT
```
Closes#28514 from dongjoon-hyun/SPARK_RELEASE_2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to update Prometheus-related output format to be consistent with DropWizard 4.1 result.
- Add `Number` metrics for gauges metrics.
- Add `type` labels.
### Why are the changes needed?
SPARK-29032 added Prometheus support. After that, SPARK-29674 upgraded DropWizard for JDK9+ support and this caused difference in output labels and number of keys for Guage metrics. The current status is different from Apache Spark 2.4.5. Since we cannot change DropWizard, this PR aims to be consistent in Apache Spark 3.0.0 only.
**DropWizard 3.x**
```
metrics_master_aliveWorkers_Value 1.0
```
**DropWizard 4.1**
```
metrics_master_aliveWorkers_Value{type="gauges",} 1.0
metrics_master_aliveWorkers_Number{type="gauges",} 1.0
```
### Does this PR introduce _any_ user-facing change?
Yes, but this is a new feature in 3.0.0.
### How was this patch tested?
Manually check the output like the following.
**JMXExporter Result**
```
$ curl -s http://localhost:8088/ | grep "^metrics_master" | sort
metrics_master_aliveWorkers_Number{type="gauges",} 1.0
metrics_master_aliveWorkers_Value{type="gauges",} 1.0
metrics_master_apps_Number{type="gauges",} 0.0
metrics_master_apps_Value{type="gauges",} 0.0
metrics_master_waitingApps_Number{type="gauges",} 0.0
metrics_master_waitingApps_Value{type="gauges",} 0.0
metrics_master_workers_Number{type="gauges",} 1.0
metrics_master_workers_Value{type="gauges",} 1.0
```
**This PR**
```
$ curl -s http://localhost:8080/metrics/master/prometheus/ | grep master
metrics_master_aliveWorkers_Number{type="gauges"} 1
metrics_master_aliveWorkers_Value{type="gauges"} 1
metrics_master_apps_Number{type="gauges"} 0
metrics_master_apps_Value{type="gauges"} 0
metrics_master_waitingApps_Number{type="gauges"} 0
metrics_master_waitingApps_Value{type="gauges"} 0
metrics_master_workers_Number{type="gauges"} 1
metrics_master_workers_Value{type="gauges"} 1
```
Closes#28510 from dongjoon-hyun/SPARK-31683.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: DB Tsai <d_tsai@apple.com>
Kent Yao
Izek Greenfield
Dongjoon Hyun
HyukjinKwon
Wenchen Fan
yi.wu
Ali Afroozeh
Eren Avsarogullari
Prakhar Jain
Max Gekk
David Toneian
Kris Mok
William Hyun
Sungpeo Kook
Jungtaek Lim (HeartSaVioR)
Yuanjian Li
Huaxin Gao
Weichen Xu
sunke.03
Karuppayya Rajendran
Xingcan Cui
Kousuke Saruta
