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join enumeration
## What changes were proposed in this pull request?
Implements star-join filter to reduce the search space for dynamic programming join enumeration. Consider the following join graph:
```
T1 D1 - T2 - T3
\ /
F1
|
D2
star-join: {F1, D1, D2}
non-star: {T1, T2, T3}
```
The following join combinations will be generated:
```
level 0: (F1), (D1), (D2), (T1), (T2), (T3)
level 1: {F1, D1}, {F1, D2}, {T2, T3}
level 2: {F1, D1, D2}
level 3: {F1, D1, D2, T1}, {F1, D1, D2, T2}
level 4: {F1, D1, D2, T1, T2}, {F1, D1, D2, T2, T3 }
level 6: {F1, D1, D2, T1, T2, T3}
```
## How was this patch tested?
New test suite ```StarJOinCostBasedReorderSuite.scala```.
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes #17546 from ioana-delaney/starSchemaCBOv3.
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## What changes were proposed in this pull request?
This PR allows to use `SerializationStream` and `DeserializationStream` in try-with-resources.
## How was this patch tested?
`core` unit tests.
Author: Sergei Lebedev <s.lebedev@criteo.com>
Closes #17598 from superbobry/compression-stream-closeable.
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## What changes were proposed in this pull request?
Improve PrefixSpan pre-processing efficency by preventing sequences of zero in the cleaned database.
The efficiency gain is reflected in the following graph : https://postimg.org/image/9x6ireuvn/
## How was this patch tested?
Using MLlib's PrefixSpan existing tests and tests of my own on the 8 datasets shown in the graph. All
result obtained were stricly the same as the original implementation (without this change).
dev/run-tests was also runned, no error were found.
Author : Cyril de Vogelaere <cyril.devogelaeregmail.com>
Author: Syrux <pokcyril@hotmail.com>
Closes #17575 from Syrux/SPARK-20265.
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createStream and use Builders
## What changes were proposed in this pull request?
The spark-kinesis testcases use the KinesisUtils.createStream which are deprecated now. Modify the testcases to use the recommended KinesisInputDStream.builder instead.
This change will also enable the testcases to automatically use the session tokens automatically.
## How was this patch tested?
All the existing testcases work fine as expected with the changes.
https://issues.apache.org/jira/browse/SPARK-20189
Author: Yash Sharma <ysharma@atlassian.com>
Closes #17506 from yssharma/ysharma/cleanup_kinesis_testcases.
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## What changes were proposed in this pull request?
`o.a.s.streaming.StreamingContextSuite.SPARK-18560 Receiver data should be deserialized properly` is flaky is because there is a potential dead-lock in StandaloneSchedulerBackend which causes `await` timeout. Here is the related stack trace:
```
"Thread-31" #211 daemon prio=5 os_prio=31 tid=0x00007fedd4808000 nid=0x16403 waiting on condition [0x00007000239b7000]
java.lang.Thread.State: TIMED_WAITING (parking)
at sun.misc.Unsafe.park(Native Method)
- parking to wait for <0x000000079b49ca10> (a scala.concurrent.impl.Promise$CompletionLatch)
at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedNanos(AbstractQueuedSynchronizer.java:1037)
at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1328)
at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:201)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:92)
at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:76)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stop(CoarseGrainedSchedulerBackend.scala:402)
at org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend.org$apache$spark$scheduler$cluster$StandaloneSchedulerBackend$$stop(StandaloneSchedulerBackend.scala:213)
- locked <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
at org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend.stop(StandaloneSchedulerBackend.scala:116)
- locked <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:517)
at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1657)
at org.apache.spark.SparkContext$$anonfun$stop$8.apply$mcV$sp(SparkContext.scala:1921)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1302)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1920)
at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:708)
at org.apache.spark.streaming.StreamingContextSuite$$anonfun$43$$anonfun$apply$mcV$sp$66$$anon$3.run(StreamingContextSuite.scala:827)
"dispatcher-event-loop-3" #18 daemon prio=5 os_prio=31 tid=0x00007fedd603a000 nid=0x6203 waiting for monitor entry [0x0000700003be4000]
java.lang.Thread.State: BLOCKED (on object monitor)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.org$apache$spark$scheduler$cluster$CoarseGrainedSchedulerBackend$DriverEndpoint$$makeOffers(CoarseGrainedSchedulerBackend.scala:253)
- waiting to lock <0x00000007066fca38> (a org.apache.spark.scheduler.cluster.StandaloneSchedulerBackend)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anonfun$receive$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:124)
at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:117)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:205)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:101)
at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
This PR removes `synchronized` and changes `stopping` to AtomicBoolean to ensure idempotent to fix the dead-lock.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #17610 from zsxwing/SPARK-20131.
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in 2 racks
## What changes were proposed in this pull request?
`TopologyAwareBlockReplicationPolicyBehavior.Peers in 2 racks` is failing occasionally: https://spark-tests.appspot.com/test-details?suite_name=org.apache.spark.storage.TopologyAwareBlockReplicationPolicyBehavior&test_name=Peers+in+2+racks.
This is because, when we generate 10 block manager id to test, they may all belong to the same rack, as the rack is randomly picked. This PR fixes this problem by forcing each rack to be picked at least once.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17624 from cloud-fan/test.
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Structured Streaming tests
## What changes were proposed in this pull request?
Some Structured Streaming tests show flakiness such as:
```
[info] - prune results by current_date, complete mode - 696 *** FAILED *** (10 seconds, 937 milliseconds)
[info] Timed out while stopping and waiting for microbatchthread to terminate.: The code passed to failAfter did not complete within 10 seconds.
```
This happens when we wait for the stream to stop, but it doesn't. The reason it doesn't stop is that we interrupt the microBatchThread, but Hadoop's `Shell.runCommand` swallows the interrupt exception, and the exception is not propagated upstream to the microBatchThread. Then this thread continues to run, only to start blocking on the `streamManualClock`.
## How was this patch tested?
Thousand retries locally and [Jenkins](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75720/testReport) of the flaky tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes #17613 from brkyvz/flaky-stream-agg.
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## What changes were proposed in this pull request?
SPARK-15236 do this for scala shell, this ticket is for pyspark shell. This is not only for pyspark itself, but can also benefit downstream project like livy which use shell.py for its interactive session. For now, livy has no control of whether enable hive or not.
## How was this patch tested?
I didn't find a way to add test for it. Just manually test it.
Run `bin/pyspark --master local --conf spark.sql.catalogImplementation=in-memory` and verify hive is not enabled.
Author: Jeff Zhang <zjffdu@apache.org>
Closes #16906 from zjffdu/SPARK-19570.
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representation
## What changes were proposed in this pull request?
AssertNotNull's toString/simpleString dumps the entire walkedTypePath. walkedTypePath is used for error message reporting and shouldn't be part of the output.
## How was this patch tested?
Manually tested.
Author: Reynold Xin <rxin@databricks.com>
Closes #17616 from rxin/SPARK-20304.
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### What changes were proposed in this pull request?
Session catalog API `createTempFunction` is being used by Hive build-in functions, persistent functions, and temporary functions. Thus, the name is confusing. This PR is to rename it by `registerFunction`. Also we can move construction of `FunctionBuilder` and `ExpressionInfo` into the new `registerFunction`, instead of duplicating the logics everywhere.
In the next PRs, the remaining Function-related APIs also need cleanups.
### How was this patch tested?
Existing test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes #17615 from gatorsmile/cleanupCreateTempFunction.
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## What changes were proposed in this pull request?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #17477 from HyukjinKwon/SPARK-18692.
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## What changes were proposed in this pull request?
Update count distinct error message for streaming datasets/dataframes to match current behavior. These aggregations are not yet supported, regardless of whether the dataset/dataframe is aggregated.
Author: jtoka <jason.tokayer@gmail.com>
Closes #17609 from jtoka/master.
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structurally the same
## What changes were proposed in this pull request?
When we perform a cast expression and the from and to types are structurally the same (having the same structure but different field names), we should be able to skip the actual cast.
## How was this patch tested?
Added unit tests for the newly introduced functions.
Author: Reynold Xin <rxin@databricks.com>
Closes #17614 from rxin/SPARK-20302.
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## What changes were proposed in this pull request?
Fixed spelling of "charactor"
## How was this patch tested?
Spelling change only
Author: Brendan Dwyer <brendan.dwyer@ibm.com>
Closes #17611 from bdwyer2/SPARK-20298.
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## What changes were proposed in this pull request?
This PR proposes corrections related to JSON APIs as below:
- Rendering links in Python documentation
- Replacing `RDD` to `Dataset` in programing guide
- Adding missing description about JSON Lines consistently in `DataFrameReader.json` in Python API
- De-duplicating little bit of `DataFrameReader.json` in Scala/Java API
## How was this patch tested?
Manually build the documentation via `jekyll build`. Corresponding snapstops will be left on the codes.
Note that currently there are Javadoc8 breaks in several places. These are proposed to be handled in https://github.com/apache/spark/pull/17477. So, this PR does not fix those.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #17602 from HyukjinKwon/minor-json-documentation.
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## What changes were proposed in this pull request?
1. Omitted space between the sentences: `... on static data.The Spark SQL engine will ...` -> `... on static data. The Spark SQL engine will ...`
2. Omitted colon in Output Model section.
## How was this patch tested?
None.
Author: Lee Dongjin <dongjin@apache.org>
Closes #17564 from dongjinleekr/feature/fix-programming-guide.
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does not work.
## What changes were proposed in this pull request?
The sameResult() method does not work when the logical plan contains subquery expressions.
**Before the fix**
```SQL
scala> val ds = spark.sql("select * from s1 where s1.c1 in (select s2.c1 from s2 where s1.c1 = s2.c1)")
ds: org.apache.spark.sql.DataFrame = [c1: int]
scala> ds.cache
res13: ds.type = [c1: int]
scala> spark.sql("select * from s1 where s1.c1 in (select s2.c1 from s2 where s1.c1 = s2.c1)").explain(true)
== Analyzed Logical Plan ==
c1: int
Project [c1#86]
+- Filter c1#86 IN (list#78 [c1#86])
: +- Project [c1#87]
: +- Filter (outer(c1#86) = c1#87)
: +- SubqueryAlias s2
: +- Relation[c1#87] parquet
+- SubqueryAlias s1
+- Relation[c1#86] parquet
== Optimized Logical Plan ==
Join LeftSemi, ((c1#86 = c1#87) && (c1#86 = c1#87))
:- Relation[c1#86] parquet
+- Relation[c1#87] parquet
```
**Plan after fix**
```SQL
== Analyzed Logical Plan ==
c1: int
Project [c1#22]
+- Filter c1#22 IN (list#14 [c1#22])
: +- Project [c1#23]
: +- Filter (outer(c1#22) = c1#23)
: +- SubqueryAlias s2
: +- Relation[c1#23] parquet
+- SubqueryAlias s1
+- Relation[c1#22] parquet
== Optimized Logical Plan ==
InMemoryRelation [c1#22], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- *BroadcastHashJoin [c1#1, c1#1], [c1#2, c1#2], LeftSemi, BuildRight
:- *FileScan parquet default.s1[c1#1] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/Users/dbiswal/mygit/apache/spark/bin/spark-warehouse/s1], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<c1:int>
+- BroadcastExchange HashedRelationBroadcastMode(List((shiftleft(cast(input[0, int, true] as bigint), 32) | (cast(input[0, int, true] as bigint) & 4294967295))))
+- *FileScan parquet default.s2[c1#2] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/Users/dbiswal/mygit/apache/spark/bin/spark-warehouse/s2], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<c1:int>
```
## How was this patch tested?
New tests are added to CachedTableSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #17330 from dilipbiswal/subquery_cache_final.
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NaNvl(DoubleType, DoubleType)
## What changes were proposed in this pull request?
`NaNvl(float value, null)` will be converted into `NaNvl(float value, Cast(null, DoubleType))` and finally `NaNvl(Cast(float value, DoubleType), Cast(null, DoubleType))`.
This will cause mismatching in the output type when the input type is float.
By adding extra rule in TypeCoercion can resolve this issue.
## How was this patch tested?
unite tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: DB Tsai <dbt@netflix.com>
Closes #17606 from dbtsai/fixNaNvl.
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## What changes were proposed in this pull request?
Since SPARK-18112 and SPARK-13446, Apache Spark starts to support reading Hive metastore 2.0 ~ 2.1.1. This updates the docs.
## How was this patch tested?
N/A
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #17612 from dongjoon-hyun/metastore.
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## What changes were proposed in this pull request?
Added `util._message_exception` helper to use `str(e)` when `e.message` is unavailable (Python3). Grepped for all occurrences of `.message` in `pyspark/` and these were the only occurrences.
## How was this patch tested?
- Doctests for helper function
## Legal
This is my original work and I license the work to the project under the project’s open source license.
Author: David Gingrich <david@textio.com>
Closes #16845 from dgingrich/topic-spark-19505-py3-exceptions.
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## What changes were proposed in this pull request?
Dataset typed API currently uses NewInstance to box primitive types (i.e. calling the constructor). Instead, it'd be slightly more idiomatic in Java to use PrimitiveType.valueOf, which can be invoked using StaticInvoke expression.
## How was this patch tested?
The change should be covered by existing tests for Dataset encoders.
Author: Reynold Xin <rxin@databricks.com>
Closes #17604 from rxin/SPARK-20289.
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## What changes were proposed in this pull request?
Similar to `ListQuery`, `Exists` should not be evaluated in `Join` operator too.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #17491 from viirya/dont-push-exists-to-join.
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## What changes were proposed in this pull request?
This is a regression caused by SPARK-19716.
Before SPARK-19716, we will cast an array field to the expected array type. However, after SPARK-19716, the cast is removed, but we forgot to push the cast to the element level.
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17587 from cloud-fan/array.
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## What changes were proposed in this pull request?
Add documentation for adding master url in multi host, port format for standalone cluster with high availability with zookeeper.
Referring documentation [Standby Masters with ZooKeeper](http://spark.apache.org/docs/latest/spark-standalone.html#standby-masters-with-zookeeper)
## How was this patch tested?
Documenting the functionality already present.
Author: MirrorZ <chandrika3437@gmail.com>
Closes #17584 from MirrorZ/master.
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degreesOfFreedom in LR and GLR
## What changes were proposed in this pull request?
- made `numInstances` public in GLR
- made `degreesOfFreedom` public in LR
## How was this patch tested?
reran the concerned test suites
Author: Benjamin Fradet <benjamin.fradet@gmail.com>
Closes #17431 from BenFradet/SPARK-20097.
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RequestTimeoutIntegrationSuite.furtherRequestsDelay
## What changes were proposed in this pull request?
This PR fixs the following failure:
```
sbt.ForkMain$ForkError: java.lang.AssertionError: null
at org.junit.Assert.fail(Assert.java:86)
at org.junit.Assert.assertTrue(Assert.java:41)
at org.junit.Assert.assertTrue(Assert.java:52)
at org.apache.spark.network.RequestTimeoutIntegrationSuite.furtherRequestsDelay(RequestTimeoutIntegrationSuite.java:230)
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:497)
at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:50)
at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:47)
at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:26)
at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:27)
at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:325)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:78)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:57)
at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
at org.junit.runners.Suite.runChild(Suite.java:128)
at org.junit.runners.Suite.runChild(Suite.java:27)
at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
at org.junit.runner.JUnitCore.run(JUnitCore.java:115)
at com.novocode.junit.JUnitRunner$1.execute(JUnitRunner.java:132)
at sbt.ForkMain$Run$2.call(ForkMain.java:296)
at sbt.ForkMain$Run$2.call(ForkMain.java:286)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
It happens several times per month on [Jenkins](http://spark-tests.appspot.com/test-details?suite_name=org.apache.spark.network.RequestTimeoutIntegrationSuite&test_name=furtherRequestsDelay). The failure is because `callback1` may not be called before `assertTrue(callback1.failure instanceof IOException);`. It's pretty easy to reproduce this error by adding a sleep before this line: https://github.com/apache/spark/blob/379b0b0bbdbba2278ce3bcf471bd75f6ffd9cf0d/common/network-common/src/test/java/org/apache/spark/network/RequestTimeoutIntegrationSuite.java#L267
The fix is straightforward: just use the latch to wait until `callback1` is called.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #17599 from zsxwing/SPARK-17564.
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## What changes were proposed in this pull request?
We currently have postHocOptimizationBatches, but not preOptimizationBatches. This patch adds preOptimizationBatches so the optimizer debugging extensions are symmetric.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes #17595 from rxin/SPARK-20283.
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tests
## What changes were proposed in this pull request?
This PR fixes the following failure:
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException:
Assert on query failed:
== Progress ==
AssertOnQuery(<condition>, )
StopStream
AddData to MemoryStream[value#30891]: 1,2
StartStream(OneTimeTrigger,org.apache.spark.util.SystemClock35cdc93a,Map())
CheckAnswer: [6],[3]
StopStream
=> AssertOnQuery(<condition>, )
AssertOnQuery(<condition>, )
StartStream(OneTimeTrigger,org.apache.spark.util.SystemClockcdb247d,Map())
CheckAnswer: [6],[3]
StopStream
AddData to MemoryStream[value#30891]: 3
StartStream(OneTimeTrigger,org.apache.spark.util.SystemClock55394e4d,Map())
CheckLastBatch: [2]
StopStream
AddData to MemoryStream[value#30891]: 0
StartStream(OneTimeTrigger,org.apache.spark.util.SystemClock749aa997,Map())
ExpectFailure[org.apache.spark.SparkException, isFatalError: false]
AssertOnQuery(<condition>, )
AssertOnQuery(<condition>, incorrect start offset or end offset on exception)
== Stream ==
Output Mode: Append
Stream state: not started
Thread state: dead
== Sink ==
0: [6] [3]
== Plan ==
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
at org.scalatest.Assertions$class.fail(Assertions.scala:1328)
at org.scalatest.FunSuite.fail(FunSuite.scala:1555)
at org.apache.spark.sql.streaming.StreamTest$class.failTest$1(StreamTest.scala:347)
at org.apache.spark.sql.streaming.StreamTest$class.verify$1(StreamTest.scala:318)
at org.apache.spark.sql.streaming.StreamTest$$anonfun$liftedTree1$1$1.apply(StreamTest.scala:483)
at org.apache.spark.sql.streaming.StreamTest$$anonfun$liftedTree1$1$1.apply(StreamTest.scala:357)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.sql.streaming.StreamTest$class.liftedTree1$1(StreamTest.scala:357)
at org.apache.spark.sql.streaming.StreamTest$class.testStream(StreamTest.scala:356)
at org.apache.spark.sql.streaming.StreamingQuerySuite.testStream(StreamingQuerySuite.scala:41)
at org.apache.spark.sql.streaming.StreamingQuerySuite$$anonfun$6.apply$mcV$sp(StreamingQuerySuite.scala:166)
at org.apache.spark.sql.streaming.StreamingQuerySuite$$anonfun$6.apply(StreamingQuerySuite.scala:161)
at org.apache.spark.sql.streaming.StreamingQuerySuite$$anonfun$6.apply(StreamingQuerySuite.scala:161)
at org.apache.spark.sql.catalyst.util.package$.quietly(package.scala:42)
at org.apache.spark.sql.test.SQLTestUtils$$anonfun$testQuietly$1.apply$mcV$sp(SQLTestUtils.scala:268)
at org.apache.spark.sql.test.SQLTestUtils$$anonfun$testQuietly$1.apply(SQLTestUtils.scala:268)
at org.apache.spark.sql.test.SQLTestUtils$$anonfun$testQuietly$1.apply(SQLTestUtils.scala:268)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68)
at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
at org.apache.spark.sql.streaming.StreamingQuerySuite.org$scalatest$BeforeAndAfterEach$$super$runTest(StreamingQuerySuite.scala:41)
at org.scalatest.BeforeAndAfterEach$class.runTest(BeforeAndAfterEach.scala:255)
at org.apache.spark.sql.streaming.StreamingQuerySuite.org$scalatest$BeforeAndAfter$$super$runTest(StreamingQuerySuite.scala:41)
at org.scalatest.BeforeAndAfter$class.runTest(BeforeAndAfter.scala:200)
at org.apache.spark.sql.streaming.StreamingQuerySuite.runTest(StreamingQuerySuite.scala:41)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483)
at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208)
at org.scalatest.FunSuite.runTests(FunSuite.scala:1555)
at org.scalatest.Suite$class.run(Suite.scala:1424)
at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
at org.scalatest.SuperEngine.runImpl(Engine.scala:545)
at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:31)
at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257)
at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256)
at org.apache.spark.sql.streaming.StreamingQuerySuite.org$scalatest$BeforeAndAfter$$super$run(StreamingQuerySuite.scala:41)
at org.scalatest.BeforeAndAfter$class.run(BeforeAndAfter.scala:241)
at org.apache.spark.sql.streaming.StreamingQuerySuite.run(StreamingQuerySuite.scala:41)
at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:357)
at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:502)
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:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
The failure is because `CheckAnswer` will run once `committedOffsets` is updated. Then writing the commit log may be interrupted by the following `StopStream`.
This PR just change the order to write the commit log first.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #17594 from zsxwing/SPARK-20282.
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## What changes were proposed in this pull request?
Saw the following failure locally:
```
Traceback (most recent call last):
File "/home/jenkins/workspace/python/pyspark/streaming/tests.py", line 351, in test_cogroup
self._test_func(input, func, expected, sort=True, input2=input2)
File "/home/jenkins/workspace/python/pyspark/streaming/tests.py", line 162, in _test_func
self.assertEqual(expected, result)
AssertionError: Lists differ: [[(1, ([1], [2])), (2, ([1], [... != []
First list contains 3 additional elements.
First extra element 0:
[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))]
+ []
- [[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))],
- [(1, ([1, 1, 1], [])), (2, ([1], [])), (4, ([], [1]))],
- [('', ([1, 1], [1, 2])), ('a', ([1, 1], [1, 1])), ('b', ([1], [1]))]]
```
It also happened on Jenkins: http://spark-tests.appspot.com/builds/spark-branch-2.1-test-sbt-hadoop-2.7/120
It's because when the machine is overloaded, the timeout is not enough. This PR just increases the timeout to 30 seconds.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #17597 from zsxwing/SPARK-20285.
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## What changes were proposed in this pull request?
Weigher.weigh needs to return Int but it is possible for an Array[FileStatus] to have size > Int.maxValue. To avoid this, the size is scaled down by a factor of 32. The maximumWeight of the cache is also scaled down by the same factor.
## How was this patch tested?
New test in FileIndexSuite
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes #17591 from bogdanrdc/SPARK-20280.
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locale bug" causes Spark problems
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes #17527 from srowen/SPARK-20156.
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Conditions
## What changes were proposed in this pull request?
```
sql("SELECT t1.b, rand(0) as r FROM cachedData, cachedData t1 GROUP BY t1.b having r > 0.5").show()
```
We will get the following error:
```
Job aborted due to stage failure: Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 8, localhost, executor driver): java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
```
Filters could be pushed down to the join conditions by the optimizer rule `PushPredicateThroughJoin`. However, Analyzer [blocks users to add non-deterministics conditions](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala#L386-L395) (For details, see the PR https://github.com/apache/spark/pull/7535).
We should not push down non-deterministic conditions; otherwise, we need to explicitly initialize the non-deterministic expressions. This PR is to simply block it.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes #17585 from gatorsmile/joinRandCondition.
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## What changes were proposed in this pull request?
This PR proposes to add `IGNORE NULLS` keyword in `first`/`last` in Spark's parser likewise http://docs.oracle.com/cd/B19306_01/server.102/b14200/functions057.htm. This simply maps the keywords to existing `ignoreNullsExpr`.
**Before**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input 'NULLS' expecting {')', ','}(line 1, pos 24)
== SQL ==
select first('a' IGNORE NULLS)
------------------------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:210)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:112)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:46)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:66)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:622)
... 48 elided
```
**After**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
+--------------+
|first(a, true)|
+--------------+
| a|
+--------------+
```
## How was this patch tested?
Unit tests in `ExpressionParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #17566 from HyukjinKwon/SPARK-19518.
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## What changes were proposed in this pull request?
Synchronize access to openStreams map.
## How was this patch tested?
Existing tests.
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes #17592 from bogdanrdc/SPARK-20243.
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## What changes were proposed in this pull request?
Like `Expression`, `QueryPlan` should also have a `semanticHash` method, then we can put plans to a hash map and look it up fast. This PR refactors `QueryPlan` to follow `Expression` and put all the normalization logic in `QueryPlan.canonicalized`, so that it's very natural to implement `semanticHash`.
follow-up: improve `CacheManager` to leverage this `semanticHash` and speed up plan lookup, instead of iterating all cached plans.
## How was this patch tested?
existing tests. Note that we don't need to test the `semanticHash` method, once the existing tests prove `sameResult` is correct, we are good.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17541 from cloud-fan/plan-semantic.
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when the default value is in double
## What changes were proposed in this pull request?
This bug was partially addressed in SPARK-18555 https://github.com/apache/spark/pull/15994, but the root cause isn't completely solved. This bug is pretty critical since it changes the member id in Long in our application if the member id can not be represented by Double losslessly when the member id is very big.
Here is an example how this happens, with
```
Seq[(java.lang.Long, java.lang.Double)]((null, 3.14), (9123146099426677101L, null),
(9123146560113991650L, 1.6), (null, null)).toDF("a", "b").na.fill(0.2),
```
the logical plan will be
```
== Analyzed Logical Plan ==
a: bigint, b: double
Project [cast(coalesce(cast(a#232L as double), cast(0.2 as double)) as bigint) AS a#240L, cast(coalesce(nanvl(b#233, cast(null as double)), 0.2) as double) AS b#241]
+- Project [_1#229L AS a#232L, _2#230 AS b#233]
+- LocalRelation [_1#229L, _2#230]
```
Note that even the value is not null, Spark will cast the Long into Double first. Then if it's not null, Spark will cast it back to Long which results in losing precision.
The behavior should be that the original value should not be changed if it's not null, but Spark will change the value which is wrong.
With the PR, the logical plan will be
```
== Analyzed Logical Plan ==
a: bigint, b: double
Project [coalesce(a#232L, cast(0.2 as bigint)) AS a#240L, coalesce(nanvl(b#233, cast(null as double)), cast(0.2 as double)) AS b#241]
+- Project [_1#229L AS a#232L, _2#230 AS b#233]
+- LocalRelation [_1#229L, _2#230]
```
which behaves correctly without changing the original Long values and also avoids extra cost of unnecessary casting.
## How was this patch tested?
unit test added.
+cc srowen rxin cloud-fan gatorsmile
Thanks.
Author: DB Tsai <dbt@netflix.com>
Closes #17577 from dbtsai/fixnafill.
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## What changes were proposed in this pull request?
sq/core module currently declares asm as a test scope dependency. Transitively it should actually be a normal dependency since the actual core module defines it. This occasionally confuses IntelliJ.
## How was this patch tested?
N/A - This is a build change.
Author: Reynold Xin <rxin@databricks.com>
Closes #17574 from rxin/SPARK-20264.
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Spark runtime routines in generated Java code
## What changes were proposed in this pull request?
This PR elminates unnecessary nullchecks of a return value from known Spark runtime routines. We know whether a given Spark runtime routine returns ``null`` or not (e.g. ``ArrayData.toDoubleArray()`` never returns ``null``). Thus, we can eliminate a null check for the return value from the Spark runtime routine.
When we run the following example program, now we get the Java code "Without this PR". In this code, since we know ``ArrayData.toDoubleArray()`` never returns ``null```, we can eliminate null checks at lines 90-92, and 97.
```java
val ds = sparkContext.parallelize(Seq(Array(1.1, 2.2)), 1).toDS.cache
ds.count
ds.map(e => e).show
```
Without this PR
```java
/* 050 */ protected void processNext() throws java.io.IOException {
/* 051 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 052 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 053 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 054 */ ArrayData inputadapter_value = inputadapter_isNull ? null : (inputadapter_row.getArray(0));
/* 055 */
/* 056 */ ArrayData deserializetoobject_value1 = null;
/* 057 */
/* 058 */ if (!inputadapter_isNull) {
/* 059 */ int deserializetoobject_dataLength = inputadapter_value.numElements();
/* 060 */
/* 061 */ Double[] deserializetoobject_convertedArray = null;
/* 062 */ deserializetoobject_convertedArray = new Double[deserializetoobject_dataLength];
/* 063 */
/* 064 */ int deserializetoobject_loopIndex = 0;
/* 065 */ while (deserializetoobject_loopIndex < deserializetoobject_dataLength) {
/* 066 */ MapObjects_loopValue2 = (double) (inputadapter_value.getDouble(deserializetoobject_loopIndex));
/* 067 */ MapObjects_loopIsNull2 = inputadapter_value.isNullAt(deserializetoobject_loopIndex);
/* 068 */
/* 069 */ if (MapObjects_loopIsNull2) {
/* 070 */ throw new RuntimeException(((java.lang.String) references[0]));
/* 071 */ }
/* 072 */ if (false) {
/* 073 */ deserializetoobject_convertedArray[deserializetoobject_loopIndex] = null;
/* 074 */ } else {
/* 075 */ deserializetoobject_convertedArray[deserializetoobject_loopIndex] = MapObjects_loopValue2;
/* 076 */ }
/* 077 */
/* 078 */ deserializetoobject_loopIndex += 1;
/* 079 */ }
/* 080 */
/* 081 */ deserializetoobject_value1 = new org.apache.spark.sql.catalyst.util.GenericArrayData(deserializetoobject_convertedArray); /*###*/
/* 082 */ }
/* 083 */ boolean deserializetoobject_isNull = true;
/* 084 */ double[] deserializetoobject_value = null;
/* 085 */ if (!inputadapter_isNull) {
/* 086 */ deserializetoobject_isNull = false;
/* 087 */ if (!deserializetoobject_isNull) {
/* 088 */ Object deserializetoobject_funcResult = null;
/* 089 */ deserializetoobject_funcResult = deserializetoobject_value1.toDoubleArray();
/* 090 */ if (deserializetoobject_funcResult == null) {
/* 091 */ deserializetoobject_isNull = true;
/* 092 */ } else {
/* 093 */ deserializetoobject_value = (double[]) deserializetoobject_funcResult;
/* 094 */ }
/* 095 */
/* 096 */ }
/* 097 */ deserializetoobject_isNull = deserializetoobject_value == null;
/* 098 */ }
/* 099 */
/* 100 */ boolean mapelements_isNull = true;
/* 101 */ double[] mapelements_value = null;
/* 102 */ if (!false) {
/* 103 */ mapelements_resultIsNull = false;
/* 104 */
/* 105 */ if (!mapelements_resultIsNull) {
/* 106 */ mapelements_resultIsNull = deserializetoobject_isNull;
/* 107 */ mapelements_argValue = deserializetoobject_value;
/* 108 */ }
/* 109 */
/* 110 */ mapelements_isNull = mapelements_resultIsNull;
/* 111 */ if (!mapelements_isNull) {
/* 112 */ Object mapelements_funcResult = null;
/* 113 */ mapelements_funcResult = ((scala.Function1) references[1]).apply(mapelements_argValue);
/* 114 */ if (mapelements_funcResult == null) {
/* 115 */ mapelements_isNull = true;
/* 116 */ } else {
/* 117 */ mapelements_value = (double[]) mapelements_funcResult;
/* 118 */ }
/* 119 */
/* 120 */ }
/* 121 */ mapelements_isNull = mapelements_value == null;
/* 122 */ }
/* 123 */
/* 124 */ serializefromobject_resultIsNull = false;
/* 125 */
/* 126 */ if (!serializefromobject_resultIsNull) {
/* 127 */ serializefromobject_resultIsNull = mapelements_isNull;
/* 128 */ serializefromobject_argValue = mapelements_value;
/* 129 */ }
/* 130 */
/* 131 */ boolean serializefromobject_isNull = serializefromobject_resultIsNull;
/* 132 */ final ArrayData serializefromobject_value = serializefromobject_resultIsNull ? null : org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.fromPrimitiveArray(serializefromobject_argValue);
/* 133 */ serializefromobject_isNull = serializefromobject_value == null;
/* 134 */ serializefromobject_holder.reset();
/* 135 */
/* 136 */ serializefromobject_rowWriter.zeroOutNullBytes();
/* 137 */
/* 138 */ if (serializefromobject_isNull) {
/* 139 */ serializefromobject_rowWriter.setNullAt(0);
/* 140 */ } else {
/* 141 */ // Remember the current cursor so that we can calculate how many bytes are
/* 142 */ // written later.
/* 143 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor;
/* 144 */
/* 145 */ if (serializefromobject_value instanceof UnsafeArrayData) {
/* 146 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes();
/* 147 */ // grow the global buffer before writing data.
/* 148 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes);
/* 149 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor);
/* 150 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes;
/* 151 */
/* 152 */ } else {
/* 153 */ final int serializefromobject_numElements = serializefromobject_value.numElements();
/* 154 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 8);
/* 155 */
/* 156 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) {
/* 157 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) {
/* 158 */ serializefromobject_arrayWriter.setNullDouble(serializefromobject_index);
/* 159 */ } else {
/* 160 */ final double serializefromobject_element = serializefromobject_value.getDouble(serializefromobject_index);
/* 161 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element);
/* 162 */ }
/* 163 */ }
/* 164 */ }
/* 165 */
/* 166 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor);
/* 167 */ }
/* 168 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize());
/* 169 */ append(serializefromobject_result);
/* 170 */ if (shouldStop()) return;
/* 171 */ }
/* 172 */ }
```
With this PR (removed most of lines 90-97 in the above code)
```java
/* 050 */ protected void processNext() throws java.io.IOException {
/* 051 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 052 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 053 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 054 */ ArrayData inputadapter_value = inputadapter_isNull ? null : (inputadapter_row.getArray(0));
/* 055 */
/* 056 */ ArrayData deserializetoobject_value1 = null;
/* 057 */
/* 058 */ if (!inputadapter_isNull) {
/* 059 */ int deserializetoobject_dataLength = inputadapter_value.numElements();
/* 060 */
/* 061 */ Double[] deserializetoobject_convertedArray = null;
/* 062 */ deserializetoobject_convertedArray = new Double[deserializetoobject_dataLength];
/* 063 */
/* 064 */ int deserializetoobject_loopIndex = 0;
/* 065 */ while (deserializetoobject_loopIndex < deserializetoobject_dataLength) {
/* 066 */ MapObjects_loopValue2 = (double) (inputadapter_value.getDouble(deserializetoobject_loopIndex));
/* 067 */ MapObjects_loopIsNull2 = inputadapter_value.isNullAt(deserializetoobject_loopIndex);
/* 068 */
/* 069 */ if (MapObjects_loopIsNull2) {
/* 070 */ throw new RuntimeException(((java.lang.String) references[0]));
/* 071 */ }
/* 072 */ if (false) {
/* 073 */ deserializetoobject_convertedArray[deserializetoobject_loopIndex] = null;
/* 074 */ } else {
/* 075 */ deserializetoobject_convertedArray[deserializetoobject_loopIndex] = MapObjects_loopValue2;
/* 076 */ }
/* 077 */
/* 078 */ deserializetoobject_loopIndex += 1;
/* 079 */ }
/* 080 */
/* 081 */ deserializetoobject_value1 = new org.apache.spark.sql.catalyst.util.GenericArrayData(deserializetoobject_convertedArray); /*###*/
/* 082 */ }
/* 083 */ boolean deserializetoobject_isNull = true;
/* 084 */ double[] deserializetoobject_value = null;
/* 085 */ if (!inputadapter_isNull) {
/* 086 */ deserializetoobject_isNull = false;
/* 087 */ if (!deserializetoobject_isNull) {
/* 088 */ Object deserializetoobject_funcResult = null;
/* 089 */ deserializetoobject_funcResult = deserializetoobject_value1.toDoubleArray();
/* 090 */ deserializetoobject_value = (double[]) deserializetoobject_funcResult;
/* 091 */
/* 092 */ }
/* 093 */
/* 094 */ }
/* 095 */
/* 096 */ boolean mapelements_isNull = true;
/* 097 */ double[] mapelements_value = null;
/* 098 */ if (!false) {
/* 099 */ mapelements_resultIsNull = false;
/* 100 */
/* 101 */ if (!mapelements_resultIsNull) {
/* 102 */ mapelements_resultIsNull = deserializetoobject_isNull;
/* 103 */ mapelements_argValue = deserializetoobject_value;
/* 104 */ }
/* 105 */
/* 106 */ mapelements_isNull = mapelements_resultIsNull;
/* 107 */ if (!mapelements_isNull) {
/* 108 */ Object mapelements_funcResult = null;
/* 109 */ mapelements_funcResult = ((scala.Function1) references[1]).apply(mapelements_argValue);
/* 110 */ if (mapelements_funcResult == null) {
/* 111 */ mapelements_isNull = true;
/* 112 */ } else {
/* 113 */ mapelements_value = (double[]) mapelements_funcResult;
/* 114 */ }
/* 115 */
/* 116 */ }
/* 117 */ mapelements_isNull = mapelements_value == null;
/* 118 */ }
/* 119 */
/* 120 */ serializefromobject_resultIsNull = false;
/* 121 */
/* 122 */ if (!serializefromobject_resultIsNull) {
/* 123 */ serializefromobject_resultIsNull = mapelements_isNull;
/* 124 */ serializefromobject_argValue = mapelements_value;
/* 125 */ }
/* 126 */
/* 127 */ boolean serializefromobject_isNull = serializefromobject_resultIsNull;
/* 128 */ final ArrayData serializefromobject_value = serializefromobject_resultIsNull ? null : org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.fromPrimitiveArray(serializefromobject_argValue);
/* 129 */ serializefromobject_isNull = serializefromobject_value == null;
/* 130 */ serializefromobject_holder.reset();
/* 131 */
/* 132 */ serializefromobject_rowWriter.zeroOutNullBytes();
/* 133 */
/* 134 */ if (serializefromobject_isNull) {
/* 135 */ serializefromobject_rowWriter.setNullAt(0);
/* 136 */ } else {
/* 137 */ // Remember the current cursor so that we can calculate how many bytes are
/* 138 */ // written later.
/* 139 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor;
/* 140 */
/* 141 */ if (serializefromobject_value instanceof UnsafeArrayData) {
/* 142 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes();
/* 143 */ // grow the global buffer before writing data.
/* 144 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes);
/* 145 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor);
/* 146 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes;
/* 147 */
/* 148 */ } else {
/* 149 */ final int serializefromobject_numElements = serializefromobject_value.numElements();
/* 150 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 8);
/* 151 */
/* 152 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) {
/* 153 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) {
/* 154 */ serializefromobject_arrayWriter.setNullDouble(serializefromobject_index);
/* 155 */ } else {
/* 156 */ final double serializefromobject_element = serializefromobject_value.getDouble(serializefromobject_index);
/* 157 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element);
/* 158 */ }
/* 159 */ }
/* 160 */ }
/* 161 */
/* 162 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor);
/* 163 */ }
/* 164 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize());
/* 165 */ append(serializefromobject_result);
/* 166 */ if (shouldStop()) return;
/* 167 */ }
/* 168 */ }
```
## How was this patch tested?
Add test suites to ``DatasetPrimitiveSuite``
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes #17569 from kiszk/SPARK-20253.
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## What changes were proposed in this pull request?
This error message doesn't get properly formatted because of a missing `s`. Currently the error looks like:
```
Caused by: java.lang.IllegalArgumentException: requirement failed: indices should be one-based and in ascending order; found current=$current, previous=$previous; line="$line"
```
(note the literal `$current` instead of the interpolated value)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Vijay Ramesh <vramesh@demandbase.com>
Closes #17572 from vijaykramesh/master.
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## What changes were proposed in this pull request?
Avoid `NoSuchElementException` every time `ConfigProvider.get(val, default)` falls back to default. This apparently causes non-trivial overhead in at least one path, and can easily be avoided.
See https://github.com/apache/spark/pull/17329
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes #17567 from srowen/SPARK-19991.
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## What changes were proposed in this pull request?
Came across the term "slice" when running some spark scala code. Consequently, a Google search indicated that "slices" and "partitions" refer to the same things; indeed see:
- [This issue](https://issues.apache.org/jira/browse/SPARK-1701)
- [This pull request](https://github.com/apache/spark/pull/2305)
- [This StackOverflow answer](http://stackoverflow.com/questions/23436640/what-is-the-difference-between-an-rdd-partition-and-a-slice) and [this one](http://stackoverflow.com/questions/24269495/what-are-the-differences-between-slices-and-partitions-of-rdds)
Thus this pull request fixes the occurrence of slice I came accross. Nonetheless, [it would appear](https://github.com/apache/spark/search?utf8=%E2%9C%93&q=slice&type=) there are still many references to "slice/slices" - thus I thought I'd raise this Pull Request to address the issue (sorry if this is the wrong place, I'm not too familar with raising apache issues).
## How was this patch tested?
(Not tested locally - only a minor exception message change.)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: asmith26 <asmith26@users.noreply.github.com>
Closes #17565 from asmith26/master.
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## What changes were proposed in this pull request?
AssertNotNull currently throws RuntimeException. It should throw NullPointerException, which is more specific.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes #17573 from rxin/SPARK-20262.
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non-deterministic expressions
## What changes were proposed in this pull request?
Similar to `Project`, when `Aggregate` has non-deterministic expressions, we should not push predicate down through it, as it will change the number of input rows and thus change the evaluation result of non-deterministic expressions in `Aggregate`.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17562 from cloud-fan/filter.
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## What changes were proposed in this pull request
Trying to get a grip on the `FileIndex` hierarchy, I was confused by the following inconsistency:
On the one hand, `PartitioningAwareFileIndex` defines `leafFiles` and `leafDirToChildrenFiles` as abstract, but on the other it fully implements `listLeafFiles` which does all the listing of files. However, the latter is only used by `InMemoryFileIndex`.
I'm hereby proposing to move this method (and all its dependencies) to the implementation class that actually uses it, and thus unclutter the `PartitioningAwareFileIndex` interface.
## How was this patch tested?
`./build/sbt sql/test`
Author: Adrian Ionescu <adrian@databricks.com>
Closes #17570 from adrian-ionescu/list-leaf-files.
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programming guide (did not converge)
## What changes were proposed in this pull request?
SparkR logistic regression example did not converge in programming guide (for IRWLS). All estimates are essentially zero:
```
training2 <- read.df("data/mllib/sample_binary_classification_data.txt", source = "libsvm")
df_list2 <- randomSplit(training2, c(7,3), 2)
binomialDF <- df_list2[[1]]
binomialTestDF <- df_list2[[2]]
binomialGLM <- spark.glm(binomialDF, label ~ features, family = "binomial")
17/04/07 11:42:03 WARN WeightedLeastSquares: Cholesky solver failed due to singular covariance matrix. Retrying with Quasi-Newton solver.
> summary(binomialGLM)
Coefficients:
Estimate
(Intercept) 9.0255e+00
features_0 0.0000e+00
features_1 0.0000e+00
features_2 0.0000e+00
features_3 0.0000e+00
features_4 0.0000e+00
features_5 0.0000e+00
features_6 0.0000e+00
features_7 0.0000e+00
```
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes #17571 from actuaryzhang/programGuide2.
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## What changes were proposed in this pull request?
Test failed because SPARK_HOME is not set before Spark is installed.
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes #17516 from felixcheung/rdircheckincran.
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## What changes were proposed in this pull request?
Add Tweedie example for SparkR in programming guide.
The doc was already updated in #17103.
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes #17553 from actuaryzhang/programGuide.
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## What changes were proposed in this pull request?
1. '/applications/[app-id]/stages' in rest api.status should add description '?status=[active|complete|pending|failed] list only stages in the state.'
Now the lack of this description, resulting in the use of this api do not know the use of the status through the brush stage list.
2.'/applications/[app-id]/stages/[stage-id]' in REST API,remove redundant description ‘?status=[active|complete|pending|failed] list only stages in the state.’.
Because only one stage is determined based on stage-id.
code:
GET
def stageList(QueryParam("status") statuses: JList[StageStatus]): Seq[StageData] = {
val listener = ui.jobProgressListener
val stageAndStatus = AllStagesResource.stagesAndStatus(ui)
val adjStatuses = {
if (statuses.isEmpty()) {
Arrays.asList(StageStatus.values(): _*)
} else {
statuses
}
};
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: 郭小龙 10207633 <guo.xiaolong1@zte.com.cn>
Closes #17534 from guoxiaolongzte/SPARK-20218.
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## What changes were proposed in this pull request?
The Dataframes-based support for the correlation statistics is added in #17108. This patch adds the Python interface for it.
## How was this patch tested?
Python unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #17494 from viirya/correlation-python-api.
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## What changes were proposed in this pull request?
Currently `LogicalRelation` has a `expectedOutputAttributes` parameter, which makes it hard to reason about what the actual output is. Like other leaf nodes, `LogicalRelation` should also take `output` as a parameter, to simplify the logic
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17552 from cloud-fan/minor.
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