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* [SPARK-17638][STREAMING] Stop JVM StreamingContext when the Python process ↵Shixiong Zhu2016-09-223-2/+35
| | | | | | | | | | | | | | | | is dead ## What changes were proposed in this pull request? When the Python process is dead, the JVM StreamingContext is still running. Hence we will see a lot of Py4jException before the JVM process exits. It's better to stop the JVM StreamingContext to avoid those annoying logs. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #15201 from zsxwing/stop-jvm-ssc.
* [SPARK-17365][CORE] Remove/Kill multiple executors together to reduce RPC ↵Dhruve Ashar2016-09-222-2/+9
| | | | | | | | | | | | | | | call time. ## What changes were proposed in this pull request? We are killing multiple executors together instead of iterating over expensive RPC calls to kill single executor. ## How was this patch tested? Executed sample spark job to observe executors being killed/removed with dynamic allocation enabled. Author: Dhruve Ashar <dashar@yahoo-inc.com> Author: Dhruve Ashar <dhruveashar@gmail.com> Closes #15152 from dhruve/impr/SPARK-17365.
* [SPARK-17359][SQL][MLLIB] Use ArrayBuffer.+=(A) instead of ↵Liwei Lin2016-09-071-1/+1
| | | | | | | | | | | | | | | | ArrayBuffer.append(A) in performance critical paths ## What changes were proposed in this pull request? We should generally use `ArrayBuffer.+=(A)` rather than `ArrayBuffer.append(A)`, because `append(A)` would involve extra boxing / unboxing. ## How was this patch tested? N/A Author: Liwei Lin <lwlin7@gmail.com> Closes #14914 from lw-lin/append_to_plus_eq_v2.
* [SPARK-17110] Fix StreamCorruptionException in BlockManager.getRemoteValues()Josh Rosen2016-09-061-2/+3
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch fixes a `java.io.StreamCorruptedException` error affecting remote reads of cached values when certain data types are used. The problem stems from #11801 / SPARK-13990, a patch to have Spark automatically pick the "best" serializer when caching RDDs. If PySpark cached a PythonRDD, then this would be cached as an `RDD[Array[Byte]]` and the automatic serializer selection would pick KryoSerializer for replication and block transfer. However, the `getRemoteValues()` / `getRemoteBytes()` code path did not pass proper class tags in order to enable the same serializer to be used during deserialization, causing Java to be inappropriately used instead of Kryo, leading to the StreamCorruptedException. We already fixed a similar bug in #14311, which dealt with similar issues in block replication. Prior to that patch, it seems that we had no tests to ensure that block replication actually succeeded. Similarly, prior to this bug fix patch it looks like we had no tests to perform remote reads of cached data, which is why this bug was able to remain latent for so long. This patch addresses the bug by modifying `BlockManager`'s `get()` and `getRemoteValues()` methods to accept ClassTags, allowing the proper class tag to be threaded in the `getOrElseUpdate` code path (which is used by `rdd.iterator`) ## How was this patch tested? Extended the caching tests in `DistributedSuite` to exercise the `getRemoteValues` path, plus manual testing to verify that the PySpark bug reproduction in SPARK-17110 is fixed. Author: Josh Rosen <joshrosen@databricks.com> Closes #14952 from JoshRosen/SPARK-17110.
* [SPARK-17308] Improved the spark core code by replacing all pattern match on ↵Shivansh2016-09-041-3/+4
| | | | | | | | | | | | | | | boolean value by if/else block. ## What changes were proposed in this pull request? Improved the code quality of spark by replacing all pattern match on boolean value by if/else block. ## How was this patch tested? By running the tests Author: Shivansh <shiv4nsh@gmail.com> Closes #14873 from shiv4nsh/SPARK-17308.
* [SPARK-17038][STREAMING] fix metrics retrieval source of 'lastReceivedBatch'Xin Ren2016-08-171-3/+3
| | | | | | | | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-17038 ## What changes were proposed in this pull request? StreamingSource's lastReceivedBatch_submissionTime, lastReceivedBatch_processingTimeStart, and lastReceivedBatch_processingTimeEnd all use data from lastCompletedBatch instead of lastReceivedBatch. In particular, this makes it impossible to match lastReceivedBatch_records with a batchID/submission time. This is apparent when looking at StreamingSource.scala, lines 89-94. ## How was this patch tested? Manually running unit tests on local laptop Author: Xin Ren <iamshrek@126.com> Closes #14681 from keypointt/SPARK-17038.
* [SPARK-16736][CORE][SQL] purge superfluous fs callsSteve Loughran2016-08-173-30/+38
| | | | | | | | | | | | | | | A review of the code, working back from Hadoop's `FileSystem.exists()` and `FileSystem.isDirectory()` code, then removing uses of the calls when superfluous. 1. delete is harmless if called on a nonexistent path, so don't do any checks before deletes 1. any `FileSystem.exists()` check before `getFileStatus()` or `open()` is superfluous as the operation itself does the check. Instead the `FileNotFoundException` is caught and triggers the downgraded path. When a `FileNotFoundException` was thrown before, the code still creates a new FNFE with the error messages. Though now the inner exceptions are nested, for easier diagnostics. Initially, relying on Jenkins test runs. One troublespot here is that some of the codepaths are clearly error situations; it's not clear that they have coverage anyway. Trying to create the failure conditions in tests would be ideal, but it will also be hard. Author: Steve Loughran <stevel@apache.org> Closes #14371 from steveloughran/cloud/SPARK-16736-superfluous-fs-calls.
* [SPARK-15869][STREAMING] Fix a potential NPE in ↵Shixiong Zhu2016-08-011-1/+1
| | | | | | | | | | | | | | | | StreamingJobProgressListener.getBatchUIData ## What changes were proposed in this pull request? Moved `asScala` to a `map` to avoid NPE. ## How was this patch tested? Existing unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #14443 from zsxwing/SPARK-15869.
* [SPARK-16416][CORE] force eager creation of loggers to avoid shutdown hook ↵Mikael Ståldal2016-07-241-0/+1
| | | | | | | | | | | | | | | | conflicts ## What changes were proposed in this pull request? Force eager creation of loggers to avoid shutdown hook conflicts. ## How was this patch tested? Manually tested with a project using Log4j 2, verified that the shutdown hook conflict issue was solved. Author: Mikael Ståldal <mikael.staldal@magine.com> Closes #14320 from mikaelstaldal/shutdown-hook-logging.
* [SPARK-16487][STREAMING] Fix some batches might not get marked as fully ↵Ahmed Mahran2016-07-221-1/+3
| | | | | | | | | | | | | | | | processed in JobGenerator ## What changes were proposed in this pull request? In `JobGenerator`, the code reads like that some batches might not get marked as fully processed. In the following flowchart, the batch should get marked fully processed before endpoint C however it is not. Currently, this does not actually cause an issue, as the condition `(time - zeroTime) is multiple of checkpoint duration?` always evaluates to `true` as the `checkpoint duration` is always set to be equal to the `batch duration`. ![Flowchart](https://s31.postimg.org/udy9lti2j/spark_streaming_job_generator.png) This PR fixes this issue so as to improve code readability and to avoid any potential issue in case there is any future change making checkpoint duration to be set different from batch duration. Author: Ahmed Mahran <ahmed.mahran@mashin.io> Closes #14145 from ahmed-mahran/b-mark-batch-fully-processed.
* [SPARK-16395][STREAMING] Fail if too many CheckpointWriteHandlers are queued ↵Sean Owen2016-07-191-3/+10
| | | | | | | | | | | | | | | | up in the fixed thread pool ## What changes were proposed in this pull request? Begin failing if checkpoint writes will likely keep up with storage's ability to write them, to fail fast instead of slowly filling memory ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #14152 from srowen/SPARK-16395.
* [SPARK-16129][CORE][SQL] Eliminate direct use of commons-lang classes in ↵Sean Owen2016-06-242-7/+4
| | | | | | | | | | | | | | | | favor of commons-lang3 ## What changes were proposed in this pull request? Replace use of `commons-lang` in favor of `commons-lang3` and forbid the former via scalastyle; remove `NotImplementedException` from `comons-lang` in favor of JDK `UnsupportedOperationException` ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #13843 from srowen/SPARK-16129.
* [SPARK-15875] Try to use Seq.isEmpty and Seq.nonEmpty instead of Seq.length ↵wangyang2016-06-101-1/+1
| | | | | | | | | | | | | | | | == 0 and Seq.length > 0 ## What changes were proposed in this pull request? In scala, immutable.List.length is an expensive operation so we should avoid using Seq.length == 0 or Seq.lenth > 0, and use Seq.isEmpty and Seq.nonEmpty instead. ## How was this patch tested? existing tests Author: wangyang <wangyang@haizhi.com> Closes #13601 from yangw1234/isEmpty.
* [MINOR][X][X] Replace all occurrences of None: Option with Option.emptySandeep Singh2016-06-101-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Replace all occurrences of `None: Option[X]` with `Option.empty[X]` ## How was this patch tested? Exisiting Tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #13591 from techaddict/minor-7.
* [MINOR] Fix Typos 'an -> a'Zheng RuiFeng2016-06-065-8/+8
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `an -> a` Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one. ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13515 from zhengruifeng/an_a.
* [SPARK-15748][SQL] Replace inefficient foldLeft() call with flatMap() in ↵Josh Rosen2016-06-051-2/+2
| | | | | | | | | | | | PartitionStatistics `PartitionStatistics` uses `foldLeft` and list concatenation (`++`) to flatten an iterator of lists, but this is extremely inefficient compared to simply doing `flatMap`/`flatten` because it performs many unnecessary object allocations. Simply replacing this `foldLeft` by a `flatMap` results in decent performance gains when constructing PartitionStatistics instances for tables with many columns. This patch fixes this and also makes two similar changes in MLlib and streaming to try to fix all known occurrences of this pattern. Author: Josh Rosen <joshrosen@databricks.com> Closes #13491 from JoshRosen/foldleft-to-flatmap.
* [SPARK-10530][CORE] Kill other task attempts when one taskattempt belonging ↵Devaraj K2016-05-302-0/+2
| | | | | | | | | | | | | | | | | | | | | | | | the same task is succeeded in speculation ## What changes were proposed in this pull request? With this patch, TaskSetManager kills other running attempts when any one of the attempt succeeds for the same task. Also killed tasks will not be considered as failed tasks and they get listed separately in the UI and also shows the task state as KILLED instead of FAILED. ## How was this patch tested? core\src\test\scala\org\apache\spark\ui\jobs\JobProgressListenerSuite.scala core\src\test\scala\org\apache\spark\util\JsonProtocolSuite.scala I have verified this patch manually by enabling spark.speculation as true, when any attempt gets succeeded then other running attempts are getting killed for the same task and other pending tasks are getting assigned in those. And also when any attempt gets killed then they are considered as KILLED tasks and not considered as FAILED tasks. Please find the attached screen shots for the reference. ![stage-tasks-table](https://cloud.githubusercontent.com/assets/3174804/14075132/394c6a12-f4f4-11e5-8638-20ff7b8cc9bc.png) ![stages-table](https://cloud.githubusercontent.com/assets/3174804/14075134/3b60f412-f4f4-11e5-9ea6-dd0dcc86eb03.png) Ref : https://github.com/apache/spark/pull/11916 Author: Devaraj K <devaraj@apache.org> Closes #11996 from devaraj-kavali/SPARK-10530.
* [SPARK-15645][STREAMING] Fix some typos of Streaming moduleXin Ren2016-05-305-10/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? No code change, just some typo fixing. ## How was this patch tested? Manually run project build with testing, and build is successful. Author: Xin Ren <iamshrek@126.com> Closes #13385 from keypointt/codeWalkThroughStreaming.
* [MINOR] Fix Typos 'a -> an'Zheng RuiFeng2016-05-267-11/+11
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `a` -> `an` I use regex to generate potential error lines: `grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala` and review them line by line. ## How was this patch tested? local build `lint-java` checking Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13317 from zhengruifeng/a_an.
* [SPARK-14976][STREAMING] make StreamingContext.textFileStream support wildcardmwws2016-05-111-2/+8
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? make StreamingContext.textFileStream support wildcard like /home/user/*/file ## How was this patch tested? I did manual test and added a new unit test case Author: mwws <wei.mao@intel.com> Author: unknown <maowei@maowei-MOBL.ccr.corp.intel.com> Closes #12752 from mwws/SPARK_FileStream.
* [SPARK-15152][DOC][MINOR] Scaladoc and Code style ImprovementsJacek Laskowski2016-05-051-24/+25
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Minor doc and code style fixes ## How was this patch tested? local build Author: Jacek Laskowski <jacek@japila.pl> Closes #12928 from jaceklaskowski/SPARK-15152.
* [SPARK-9819][STREAMING][DOCUMENTATION] Clarify doc for invReduceFunc in ↵François Garillot2016-05-034-4/+8
| | | | | | | | | | | | incremental versions of reduceByWindow - that reduceFunc and invReduceFunc should be associative - that the intermediate result in iterated applications of inverseReduceFunc is its first argument Author: François Garillot <francois@garillot.net> Closes #8103 from huitseeker/issue/invReduceFuncDoc.
* [SPARK-14930][SPARK-13693] Fix race condition in CheckpointWriter.stop()Josh Rosen2016-04-271-12/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | CheckpointWriter.stop() is prone to a race condition: if one thread calls `stop()` right as a checkpoint write task begins to execute, that write task may become blocked when trying to access `fs`, the shared Hadoop FileSystem, since both the `fs` getter and `stop` method synchronize on the same lock. Here's a thread-dump excerpt which illustrates the problem: ```java "pool-31-thread-1" #156 prio=5 os_prio=31 tid=0x00007fea02cd2000 nid=0x5c0b waiting for monitor entry [0x000000013bc4c000] java.lang.Thread.State: BLOCKED (on object monitor) at org.apache.spark.streaming.CheckpointWriter.org$apache$spark$streaming$CheckpointWriter$$fs(Checkpoint.scala:302) - waiting to lock <0x00000007bf53ee78> (a org.apache.spark.streaming.CheckpointWriter) at org.apache.spark.streaming.CheckpointWriter$CheckpointWriteHandler.run(Checkpoint.scala:224) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) "pool-1-thread-1-ScalaTest-running-MapWithStateSuite" #11 prio=5 os_prio=31 tid=0x00007fe9ff879800 nid=0x5703 waiting on condition [0x000000012e54c000] java.lang.Thread.State: TIMED_WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <0x00000007bf564568> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject) at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215) at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078) at java.util.concurrent.ThreadPoolExecutor.awaitTermination(ThreadPoolExecutor.java:1465) at org.apache.spark.streaming.CheckpointWriter.stop(Checkpoint.scala:291) - locked <0x00000007bf53ee78> (a org.apache.spark.streaming.CheckpointWriter) at org.apache.spark.streaming.scheduler.JobGenerator.stop(JobGenerator.scala:159) - locked <0x00000007bf53ea90> (a org.apache.spark.streaming.scheduler.JobGenerator) at org.apache.spark.streaming.scheduler.JobScheduler.stop(JobScheduler.scala:115) - locked <0x00000007bf53d3f0> (a org.apache.spark.streaming.scheduler.JobScheduler) at org.apache.spark.streaming.StreamingContext$$anonfun$stop$1.apply$mcV$sp(StreamingContext.scala:680) at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1219) at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:679) - locked <0x00000007bf516a70> (a org.apache.spark.streaming.StreamingContext) at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:644) - locked <0x00000007bf516a70> (a org.apache.spark.streaming.StreamingContext) [...] ``` We can fix this problem by having `stop` and `fs` be synchronized on different locks: the synchronization on `stop` only needs to guard against multiple threads calling `stop` at the same time, whereas the synchronization on `fs` is only necessary for cross-thread visibility. There's only ever a single active checkpoint writer thread at a time, so we don't need to guard against concurrent access to `fs`. Thus, `fs` can simply become a `volatile` var, similar to `lastCheckpointTime`. This change should fix [SPARK-13693](https://issues.apache.org/jira/browse/SPARK-13693), a flaky `MapWithStateSuite` test suite which has recently been failing several times per day. It also results in a huge test speedup: prior to this patch, `MapWithStateSuite` took about 80 seconds to run, whereas it now runs in less than 10 seconds. For the `streaming` project's tests as a whole, they now run in ~220 seconds vs. ~354 before. /cc zsxwing and tdas for review. Author: Josh Rosen <joshrosen@databricks.com> Closes #12712 from JoshRosen/fix-checkpoint-writer-race.
* [MINOR][DOCS] Minor typo fixesJacek Laskowski2016-04-261-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Minor typo fixes (too minor to deserve separate a JIRA) ## How was this patch tested? local build Author: Jacek Laskowski <jacek@japila.pl> Closes #12469 from jaceklaskowski/minor-typo-fixes.
* [SPARK-14868][BUILD] Enable NewLineAtEofChecker in checkstyle and fix ↵Dongjoon Hyun2016-04-242-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | lint-java errors ## What changes were proposed in this pull request? Spark uses `NewLineAtEofChecker` rule in Scala by ScalaStyle. And, most Java code also comply with the rule. This PR aims to enforce the same rule `NewlineAtEndOfFile` by CheckStyle explicitly. Also, this fixes lint-java errors since SPARK-14465. The followings are the items. - Adds a new line at the end of the files (19 files) - Fixes 25 lint-java errors (12 RedundantModifier, 6 **ArrayTypeStyle**, 2 LineLength, 2 UnusedImports, 2 RegexpSingleline, 1 ModifierOrder) ## How was this patch tested? After the Jenkins test succeeds, `dev/lint-java` should pass. (Currently, Jenkins dose not run lint-java.) ```bash $ dev/lint-java Using `mvn` from path: /usr/local/bin/mvn Checkstyle checks passed. ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12632 from dongjoon-hyun/SPARK-14868.
* [SPARK-14701][STREAMING] First stop the event loop, then stop the checkpoint ↵Liwei Lin2016-04-221-2/+2
| | | | | | | | | | | | | | | | | | | | | writer in JobGenerator Currently if we call `streamingContext.stop` (e.g. in a `StreamingListener.onBatchCompleted` callback) when a batch is about to complete, a `rejectedException` may get thrown from `checkPointWriter.executor`, since the `eventLoop` will try to process `DoCheckpoint` events even after the `checkPointWriter.executor` was stopped. Please see [SPARK-14701](https://issues.apache.org/jira/browse/SPARK-14701) for details and stack traces. ## What changes were proposed in this pull request? Reversed the stopping order of `event loop` and `checkpoint writer`. ## How was this patch tested? Existing test suits. (no dedicated test suits were added because the change is simple to reason about) Author: Liwei Lin <lwlin7@gmail.com> Closes #12489 from lw-lin/spark-14701.
* [SPARK-6429] Implement hashCode and equals togetherJoan2016-04-221-3/+7
| | | | | | | | | | | ## What changes were proposed in this pull request? Implement some `hashCode` and `equals` together in order to enable the scalastyle. This is a first batch, I will continue to implement them but I wanted to know your thoughts. Author: Joan <joan@goyeau.com> Closes #12157 from joan38/SPARK-6429-HashCode-Equals.
* [SPARK-8393][STREAMING] JavaStreamingContext#awaitTermination() throws ↵Sean Owen2016-04-211-0/+2
| | | | | | | | | | | | | | | | non-declared InterruptedException ## What changes were proposed in this pull request? `JavaStreamingContext.awaitTermination` methods should be declared as `throws[InterruptedException]` so that this exception can be handled in Java code. Note this is not just a doc change, but an API change, since now (in Java) the method has a checked exception to handle. All await-like methods in Java APIs behave this way, so seems worthwhile for 2.0. ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #12418 from srowen/SPARK-8393.
* Revert "[SPARK-14719] WriteAheadLogBasedBlockHandler should ignore ↵Josh Rosen2016-04-191-14/+8
| | | | | | BlockManager put errors" This reverts commit ed2de0299a5a54b566b91ae9f47b6626c484c1d3.
* [SPARK-14676] Wrap and re-throw Await.result exceptions in order to capture ↵Josh Rosen2016-04-192-5/+6
| | | | | | | | | | | | | | | | full stacktrace When `Await.result` throws an exception which originated from a different thread, the resulting stacktrace doesn't include the path leading to the `Await.result` call itself, making it difficult to identify the impact of these exceptions. For example, I've seen cases where broadcast cleaning errors propagate to the main thread and crash it but the resulting stacktrace doesn't include any of the main thread's code, making it difficult to pinpoint which exception crashed that thread. This patch addresses this issue by explicitly catching, wrapping, and re-throwing exceptions that are thrown by `Await.result`. I tested this manually using https://github.com/JoshRosen/spark/commit/16b31c825197ee31a50214c6ba3c1df08148f403, a patch which reproduces an issue where an RPC exception which occurs while unpersisting RDDs manages to crash the main thread without any useful stacktrace, and verified that informative, full stacktraces were generated after applying the fix in this PR. /cc rxin nongli yhuai anabranch Author: Josh Rosen <joshrosen@databricks.com> Closes #12433 from JoshRosen/wrap-and-rethrow-await-exceptions.
* [SPARK-14719] WriteAheadLogBasedBlockHandler should ignore BlockManager put ↵Josh Rosen2016-04-181-8/+14
| | | | | | | | | | | | | | | | | | | errors WriteAheadLogBasedBlockHandler will currently throw exceptions if its BlockManager `put()` calls fail, even though those calls are only performed as a performance optimization. Instead, it should log and ignore exceptions during that `put()`. This is a longstanding issue that was masked by an incorrect test case. I think that we haven't noticed this in production because 1. most people probably use a `MEMORY_AND_DISK` storage level, and 2. typically, individual blocks may be small enough relative to the total storage memory such that they're able to evict blocks from previous batches, so `put()` failures here may be rare in practice. This patch fixes the faulty test and fixes the bug. /cc tdas Author: Josh Rosen <joshrosen@databricks.com> Closes #12484 from JoshRosen/received-block-hadndler-fix.
* [MINOR] Remove inappropriate type notation and extra anonymous closure ↵hyukjinkwon2016-04-161-8/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | within functional transformations ## What changes were proposed in this pull request? This PR removes - Inappropriate type notations For example, from ```scala words.foreachRDD { (rdd: RDD[String], time: Time) => ... ``` to ```scala words.foreachRDD { (rdd, time) => ... ``` - Extra anonymous closure within functional transformations. For example, ```scala .map(item => { ... }) ``` which can be just simply as below: ```scala .map { item => ... } ``` and corrects some obvious style nits. ## How was this patch tested? This was tested after adding rules in `scalastyle-config.xml`, which ended up with not finding all perfectly. The rules applied were below: - For the first correction, ```xml <check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true"> <parameters><parameter name="regex">(?m)\.[a-zA-Z_][a-zA-Z0-9]*\(\s*[^,]+s*=>\s*\{[^\}]+\}\s*\)</parameter></parameters> </check> ``` ```xml <check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true"> <parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]([^\n>,]+=>)?\s*\{([^()]|(?R))*\}^[,]</parameter></parameters> </check> ``` - For the second correction ```xml <check customId="TypeNotation" level="error" class="org.scalastyle.file.RegexChecker" enabled="true"> <parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]\s*\([^):]*:R))*\}^[,]</parameter></parameters> </check> ``` **Those rules were not added** Author: hyukjinkwon <gurwls223@gmail.com> Closes #12413 from HyukjinKwon/SPARK-style.
* [SPARK-14630][BUILD][CORE][SQL][STREAMING] Code style: public abstract ↵Liwei Lin2016-04-145-14/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | methods should have explicit return types ## What changes were proposed in this pull request? Currently many public abstract methods (in abstract classes as well as traits) don't declare return types explicitly, such as in [o.a.s.streaming.dstream.InputDStream](https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala#L110): ```scala def start() // should be: def start(): Unit def stop() // should be: def stop(): Unit ``` These methods exist in core, sql, streaming; this PR fixes them. ## How was this patch tested? N/A ## Which piece of scala style rule led to the changes? the rule was added separately in https://github.com/apache/spark/pull/12396 Author: Liwei Lin <lwlin7@gmail.com> Closes #12389 from lw-lin/public-abstract-methods.
* [MINOR][SQL] Remove extra anonymous closure within functional transformationshyukjinkwon2016-04-143-8/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes extra anonymous closure within functional transformations. For example, ```scala .map(item => { ... }) ``` which can be just simply as below: ```scala .map { item => ... } ``` ## How was this patch tested? Related unit tests and `sbt scalastyle`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #12382 from HyukjinKwon/minor-extra-closers.
* [SPARK-14508][BUILD] Add a new ScalaStyle Rule `OmitBracesInCase`Dongjoon Hyun2016-04-124-24/+11
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? According to the [Spark Code Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) and [Scala Style Guide](http://docs.scala-lang.org/style/control-structures.html#curlybraces), we had better enforce the following rule. ``` case: Always omit braces in case clauses. ``` This PR makes a new ScalaStyle rule, 'OmitBracesInCase', and enforces it to the code. ## How was this patch tested? Pass the Jenkins tests (including Scala style checking) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12280 from dongjoon-hyun/SPARK-14508.
* [SPARK-14475] Propagate user-defined context from driver to executorsEric Liang2016-04-113-7/+15
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This adds a new API call `TaskContext.getLocalProperty` for getting properties set in the driver from executors. These local properties are automatically propagated from the driver to executors. For streaming, the context for streaming tasks will be the initial driver context when ssc.start() is called. ## How was this patch tested? Unit tests. cc JoshRosen Author: Eric Liang <ekl@databricks.com> Closes #12248 from ericl/sc-2813.
* [SPARK-14455][STREAMING] Fix NPE in allocatedExecutors when calling in ↵jerryshao2016-04-091-3/+9
| | | | | | | | | | | | | | | | | | receiver-less scenario ## What changes were proposed in this pull request? When calling `ReceiverTracker#allocatedExecutors` in receiver-less scenario, NPE will be thrown, since this `ReceiverTracker` actually is not started and `endpoint` is not created. This will be happened when playing streaming dynamic allocation with direct Kafka. ## How was this patch tested? Local integrated test is done. Author: jerryshao <sshao@hortonworks.com> Closes #12236 from jerryshao/SPARK-14455.
* [SPARK-12133][STREAMING] Streaming dynamic allocationTathagata Das2016-04-064-3/+270
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Added a new Executor Allocation Manager for the Streaming scheduler for doing Streaming Dynamic Allocation. ## How was this patch tested Unit tests, and cluster tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #12154 from tdas/streaming-dynamic-allocation.
* [SPARK-13211][STREAMING] StreamingContext throws NoSuchElementException when ↵Sean Owen2016-04-052-9/+5
| | | | | | | | | | | | | | | | created from non-existent checkpoint directory ## What changes were proposed in this pull request? Take 2: avoid None.get NoSuchElementException in favor of more descriptive IllegalArgumentException if a non-existent checkpoint dir is used without a SparkContext ## How was this patch tested? Jenkins test plus new test for this particular case Author: Sean Owen <sowen@cloudera.com> Closes #12174 from srowen/SPARK-13211.
* [SPARK-12425][STREAMING] DStream union optimisationGuillaume Poulin2016-04-052-11/+4
| | | | | | | | | Use PartitionerAwareUnionRDD when possbile for optimizing shuffling and preserving the partitioner. Author: Guillaume Poulin <poulin.guillaume@gmail.com> Closes #10382 from gpoulin/dstream_union_optimisation.
* [SPARK-14358] Change SparkListener from a trait to an abstract classReynold Xin2016-04-041-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Scala traits are difficult to maintain binary compatibility on, and as a result we had to introduce JavaSparkListener. In Spark 2.0 we can change SparkListener from a trait to an abstract class and then remove JavaSparkListener. ## How was this patch tested? Updated related unit tests. Author: Reynold Xin <rxin@databricks.com> Closes #12142 from rxin/SPARK-14358.
* [MINOR][DOCS] Use multi-line JavaDoc comments in Scala code.Dongjoon Hyun2016-04-024-20/+24
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR aims to fix all Scala-Style multiline comments into Java-Style multiline comments in Scala codes. (All comment-only changes over 77 files: +786 lines, −747 lines) ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12130 from dongjoon-hyun/use_multiine_javadoc_comments.
* [MINOR] Typo fixesJacek Laskowski2016-04-029-36/+39
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Typo fixes. No functional changes. ## How was this patch tested? Built the sources and ran with samples. Author: Jacek Laskowski <jacek@japila.pl> Closes #11802 from jaceklaskowski/typo-fixes.
* [SPARK-12857][STREAMING] Standardize "records" and "events" on "records"Liwei Lin2016-04-014-40/+41
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently the Streaming tab in web UI uses records and events interchangeably; this PR tries to standardize them on "records". "records" is chosen over "events" because: - "records" is used extensively throughout the streaming documents, codes, and comments - "events" is used only in Streaming UI related codes and comments ## How was this patch tested? - existing test suites - manually checking on the Streaming UI tab Author: Liwei Lin <lwlin7@gmail.com> Closes #12032 from lw-lin/streaming-events-to-records.
* [SPARK-13992] Add support for off-heap cachingJosh Rosen2016-04-011-1/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This patch adds support for caching blocks in the executor processes using direct / off-heap memory. ## User-facing changes **Updated semantics of `OFF_HEAP` storage level**: In Spark 1.x, the `OFF_HEAP` storage level indicated that an RDD should be cached in Tachyon. Spark 2.x removed the external block store API that Tachyon caching was based on (see #10752 / SPARK-12667), so `OFF_HEAP` became an alias for `MEMORY_ONLY_SER`. As of this patch, `OFF_HEAP` means "serialized and cached in off-heap memory or on disk". Via the `StorageLevel` constructor, `useOffHeap` can be set if `serialized == true` and can be used to construct custom storage levels which support replication. **Storage UI reporting**: the storage UI will now report whether in-memory blocks are stored on- or off-heap. **Only supported by UnifiedMemoryManager**: for simplicity, this feature is only supported when the default UnifiedMemoryManager is used; applications which use the legacy memory manager (`spark.memory.useLegacyMode=true`) are not currently able to allocate off-heap storage memory, so using off-heap caching will fail with an error when legacy memory management is enabled. Given that we plan to eventually remove the legacy memory manager, this is not a significant restriction. **Memory management policies:** the policies for dividing available memory between execution and storage are the same for both on- and off-heap memory. For off-heap memory, the total amount of memory available for use by Spark is controlled by `spark.memory.offHeap.size`, which is an absolute size. Off-heap storage memory obeys `spark.memory.storageFraction` in order to control the amount of unevictable storage memory. For example, if `spark.memory.offHeap.size` is 1 gigabyte and Spark uses the default `storageFraction` of 0.5, then up to 500 megabytes of off-heap cached blocks will be protected from eviction due to execution memory pressure. If necessary, we can split `spark.memory.storageFraction` into separate on- and off-heap configurations, but this doesn't seem necessary now and can be done later without any breaking changes. **Use of off-heap memory does not imply use of off-heap execution (or vice-versa)**: for now, the settings controlling the use of off-heap execution memory (`spark.memory.offHeap.enabled`) and off-heap caching are completely independent, so Spark SQL can be configured to use off-heap memory for execution while continuing to cache blocks on-heap. If desired, we can change this in a followup patch so that `spark.memory.offHeap.enabled` affect the default storage level for cached SQL tables. ## Internal changes - Rename `ByteArrayChunkOutputStream` to `ChunkedByteBufferOutputStream` - It now returns a `ChunkedByteBuffer` instead of an array of byte arrays. - Its constructor now accept an `allocator` function which is called to allocate `ByteBuffer`s. This allows us to control whether it allocates regular ByteBuffers or off-heap DirectByteBuffers. - Because block serialization is now performed during the unroll process, a `ChunkedByteBufferOutputStream` which is configured with a `DirectByteBuffer` allocator will use off-heap memory for both unroll and storage memory. - The `MemoryStore`'s MemoryEntries now tracks whether blocks are stored on- or off-heap. - `evictBlocksToFreeSpace()` now accepts a `MemoryMode` parameter so that we don't try to evict off-heap blocks in response to on-heap memory pressure (or vice-versa). - Make sure that off-heap buffers are properly de-allocated during MemoryStore eviction. - The JVM limits the total size of allocated direct byte buffers using the `-XX:MaxDirectMemorySize` flag and the default tends to be fairly low (< 512 megabytes in some JVMs). To work around this limitation, this patch adds a custom DirectByteBuffer allocator which ignores this memory limit. Author: Josh Rosen <joshrosen@databricks.com> Closes #11805 from JoshRosen/off-heap-caching.
* [SPARK-14075] Refactor MemoryStore to be testable independent of BlockManagerJosh Rosen2016-03-233-4/+7
| | | | | | | | | | | | | This patch refactors the `MemoryStore` so that it can be tested without needing to construct / mock an entire `BlockManager`. - The block manager's serialization- and compression-related methods have been moved from `BlockManager` to `SerializerManager`. - `BlockInfoManager `is now passed directly to classes that need it, rather than being passed via the `BlockManager`. - The `MemoryStore` now calls `dropFromMemory` via a new `BlockEvictionHandler` interface rather than directly calling the `BlockManager`. This change helps to enforce a narrow interface between the `MemoryStore` and `BlockManager` functionality and makes this interface easier to mock in tests. - Several of the block unrolling tests have been moved from `BlockManagerSuite` into a new `MemoryStoreSuite`. Author: Josh Rosen <joshrosen@databricks.com> Closes #11899 from JoshRosen/reduce-memorystore-blockmanager-coupling.
* [SPARK-13990] Automatically pick serializer when caching RDDsJosh Rosen2016-03-211-1/+2
| | | | | | | | | | | | Building on the `SerializerManager` introduced in SPARK-13926/ #11755, this patch Spark modifies Spark's BlockManager to use RDD's ClassTags in order to select the best serializer to use when caching RDD blocks. When storing a local block, the BlockManager `put()` methods use implicits to record ClassTags and stores those tags in the blocks' BlockInfo records. When reading a local block, the stored ClassTag is used to pick the appropriate serializer. When a block is stored with replication, the class tag is written into the block transfer metadata and will also be stored in the remote BlockManager. There are two or three places where we don't properly pass ClassTags, including TorrentBroadcast and BlockRDD. I think this happens to work because the missing ClassTag always happens to be `ClassTag.Any`, but it might be worth looking more carefully at those places to see whether we should be more explicit. Author: Josh Rosen <joshrosen@databricks.com> Closes #11801 from JoshRosen/pick-best-serializer-for-caching.
* [SPARK-13921] Store serialized blocks as multiple chunks in MemoryStoreJosh Rosen2016-03-172-7/+13
| | | | | | | | | | | | This patch modifies the BlockManager, MemoryStore, and several other storage components so that serialized cached blocks are stored as multiple small chunks rather than as a single contiguous ByteBuffer. This change will help to improve the efficiency of memory allocation and the accuracy of memory accounting when serializing blocks. Our current serialization code uses a ByteBufferOutputStream, which doubles and re-allocates its backing byte array; this increases the peak memory requirements during serialization (since we need to hold extra memory while expanding the array). In addition, we currently don't account for the extra wasted space at the end of the ByteBuffer's backing array, so a 129 megabyte serialized block may actually consume 256 megabytes of memory. After switching to storing blocks in multiple chunks, we'll be able to efficiently trim the backing buffers so that no space is wasted. This change is also a prerequisite to being able to cache blocks which are larger than 2GB (although full support for that depends on several other changes which have not bee implemented yet). Author: Josh Rosen <joshrosen@databricks.com> Closes #11748 from JoshRosen/chunked-block-serialization.
* [SPARK-13928] Move org.apache.spark.Logging into ↵Wenchen Fan2016-03-1727-25/+41
| | | | | | | | | | | | | | | | org.apache.spark.internal.Logging ## What changes were proposed in this pull request? Logging was made private in Spark 2.0. If we move it, then users would be able to create a Logging trait themselves to avoid changing their own code. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11764 from cloud-fan/logger.
* [MINOR][DOCS] Fix more typos in comments/strings.Dongjoon Hyun2016-03-148-9/+9
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes 135 typos over 107 files: * 121 typos in comments * 11 typos in testcase name * 3 typos in log messages ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11689 from dongjoon-hyun/fix_more_typos.
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