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* [SPARK-17800] Introduce InterfaceStability annotationReynold Xin2016-10-071-0/+49
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch introduces three new annotations under InterfaceStability: - Stable - Evolving - Unstable This is inspired by Hadoop's InterfaceStability, and the first step towards switching over to a new API stability annotation framework. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #15374 from rxin/SPARK-17800.
* [SPARK-17495][SQL] Add Hash capability semantically equivalent to Hive'sTejas Patil2016-10-041-0/+49
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-17495 Spark internally uses Murmur3Hash for partitioning. This is different from the one used by Hive. For queries which use bucketing this leads to different results if one tries the same query on both engines. For us, we want users to have backward compatibility to that one can switch parts of applications across the engines without observing regressions. This PR includes `HiveHash`, `HiveHashFunction`, `HiveHasher` which mimics Hive's hashing at https://github.com/apache/hive/blob/master/serde/src/java/org/apache/hadoop/hive/serde2/objectinspector/ObjectInspectorUtils.java#L638 I am intentionally not introducing any usages of this hash function in rest of the code to keep this PR small. My eventual goal is to have Hive bucketing support in Spark. Once this PR gets in, I will make hash function pluggable in relevant areas (eg. `HashPartitioning`'s `partitionIdExpression` has Murmur3 hardcoded : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala#L265) ## How was this patch tested? Added `HiveHashSuite` Author: Tejas Patil <tejasp@fb.com> Closes #15047 from tejasapatil/SPARK-17495_hive_hash.
* [SPARK-16962][CORE][SQL] Fix misaligned record accesses for SPARC architecturessumansomasundar2016-10-042-7/+82
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Made changes to record length offsets to make them uniform throughout various areas of Spark core and unsafe ## How was this patch tested? This change affects only SPARC architectures and was tested on X86 architectures as well for regression. Author: sumansomasundar <suman.somasundar@oracle.com> Closes #14762 from sumansomasundar/master.
* [SPARK-15962][SQL] Introduce implementation with a dense format for ↵Kazuaki Ishizaki2016-09-271-0/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | UnsafeArrayData ## What changes were proposed in this pull request? This PR introduces more compact representation for ```UnsafeArrayData```. ```UnsafeArrayData``` needs to accept ```null``` value in each entry of an array. In the current version, it has three parts ``` [numElements] [offsets] [values] ``` `Offsets` has the number of `numElements`, and represents `null` if its value is negative. It may increase memory footprint, and introduces an indirection for accessing each of `values`. This PR uses bitvectors to represent nullability for each element like `UnsafeRow`, and eliminates an indirection for accessing each element. The new ```UnsafeArrayData``` has four parts. ``` [numElements][null bits][values or offset&length][variable length portion] ``` In the `null bits` region, we store 1 bit per element, represents whether an element is null. Its total size is ceil(numElements / 8) bytes, and it is aligned to 8-byte boundaries. In the `values or offset&length` region, we store the content of elements. For fields that hold fixed-length primitive types, such as long, double, or int, we store the value directly in the field. For fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the base address of the array) that points to the beginning of the variable-length field and length (they are combined into a long). Each is word-aligned. For `variable length portion`, each is aligned to 8-byte boundaries. The new format can reduce memory footprint and improve performance of accessing each element. An example of memory foot comparison: 1024x1024 elements integer array Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024 + 1024x1024 = 2M bytes Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024/8 + 1024x1024 = 1.25M bytes In summary, we got 1.0-2.6x performance improvements over the code before applying this PR. Here are performance results of [benchmark programs](https://github.com/kiszk/spark/blob/04d2e4b6dbdc4eff43ce18b3c9b776e0129257c7/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/UnsafeArrayDataBenchmark.scala): **Read UnsafeArrayData**: 1.7x and 1.6x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 430 / 436 390.0 2.6 1.0X Double 456 / 485 367.8 2.7 0.9X With SPARK-15962 Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 252 / 260 666.1 1.5 1.0X Double 281 / 292 597.7 1.7 0.9X ```` **Write UnsafeArrayData**: 1.0x and 1.1x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 203 / 273 103.4 9.7 1.0X Double 239 / 356 87.9 11.4 0.8X With SPARK-15962 Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 196 / 249 107.0 9.3 1.0X Double 227 / 367 92.3 10.8 0.9X ```` **Get primitive array from UnsafeArrayData**: 2.6x and 1.6x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 207 / 217 304.2 3.3 1.0X Double 257 / 363 245.2 4.1 0.8X With SPARK-15962 Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 151 / 198 415.8 2.4 1.0X Double 214 / 394 293.6 3.4 0.7X ```` **Create UnsafeArrayData from primitive array**: 1.7x and 2.1x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 340 / 385 185.1 5.4 1.0X Double 479 / 705 131.3 7.6 0.7X With SPARK-15962 Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 206 / 211 306.0 3.3 1.0X Double 232 / 406 271.6 3.7 0.9X ```` 1.7x and 1.4x performance improvements in [```UDTSerializationBenchmark```](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/mllib/linalg/UDTSerializationBenchmark.scala) over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ serialize 442 / 533 0.0 441927.1 1.0X deserialize 217 / 274 0.0 217087.6 2.0X With SPARK-15962 VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ serialize 265 / 318 0.0 265138.5 1.0X deserialize 155 / 197 0.0 154611.4 1.7X ```` ## How was this patch tested? Added unit tests into ```UnsafeArraySuite``` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #13680 from kiszk/SPARK-15962.
* [MINOR][BUILD] Fix CheckStyle ErrorWeiqing Yang2016-09-204-10/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is to fix the code style errors before 2.0.1 release. ## How was this patch tested? Manual. Before: ``` ./dev/lint-java Using `mvn` from path: /usr/local/bin/mvn Checkstyle checks failed at following occurrences: [ERROR] src/main/java/org/apache/spark/network/client/TransportClient.java:[153] (sizes) LineLength: Line is longer than 100 characters (found 107). [ERROR] src/main/java/org/apache/spark/network/client/TransportClient.java:[196] (sizes) LineLength: Line is longer than 100 characters (found 108). [ERROR] src/main/java/org/apache/spark/network/client/TransportClient.java:[239] (sizes) LineLength: Line is longer than 100 characters (found 115). [ERROR] src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:[119] (sizes) LineLength: Line is longer than 100 characters (found 107). [ERROR] src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:[129] (sizes) LineLength: Line is longer than 100 characters (found 104). [ERROR] src/main/java/org/apache/spark/network/util/LevelDBProvider.java:[124,11] (modifier) ModifierOrder: 'static' modifier out of order with the JLS suggestions. [ERROR] src/main/java/org/apache/spark/network/util/TransportConf.java:[26] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[33] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[38] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[43] (sizes) LineLength: Line is longer than 100 characters (found 106). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[48] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java:[0] (misc) NewlineAtEndOfFile: File does not end with a newline. [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java:[67] (sizes) LineLength: Line is longer than 100 characters (found 106). [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[200] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[309] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[332] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[348] (regexp) RegexpSingleline: No trailing whitespace allowed. ``` After: ``` ./dev/lint-java Using `mvn` from path: /usr/local/bin/mvn Checkstyle checks passed. ``` Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15170 from Sherry302/fixjavastyle.
* [SPARK-17611][YARN][TEST] Make shuffle service test really test auth.Marcelo Vanzin2016-09-201-5/+6
| | | | | | | | | | | Currently, the code is just swallowing exceptions, and not really checking whether the auth information was being recorded properly. Fix both problems, and also avoid tests inadvertently affecting other tests by modifying the shared config variable (by making it not shared). Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #15161 from vanzin/SPARK-17611.
* [SPARK-17543] Missing log4j config file for tests in common/network-…Jagadeesan2016-09-161-0/+24
| | | | | | | | | | | | ## What changes were proposed in this pull request? The Maven module `common/network-shuffle` does not have a log4j configuration file for its test cases. So, added `log4j.properties` in the directory `src/test/resources`. …shuffle] Author: Jagadeesan <as2@us.ibm.com> Closes #15108 from jagadeesanas2/SPARK-17543.
* [SPARK-17379][BUILD] Upgrade netty-all to 4.0.41 final for bug fixesAdam Roberts2016-09-151-0/+5
| | | | | | | | | | | | ## What changes were proposed in this pull request? Upgrade netty-all to latest in the 4.0.x line which is 4.0.41, mentions several bug fixes and performance improvements we may find useful, see netty.io/news/2016/08/29/4-0-41-Final-4-1-5-Final.html. Initially tried to use 4.1.5 but noticed it's not backwards compatible. ## How was this patch tested? Existing unit tests against branch-1.6 and branch-2.0 using IBM Java 8 on Intel, Power and Z architectures Author: Adam Roberts <aroberts@uk.ibm.com> Closes #14961 from a-roberts/netty.
* [SPARK-17433] YarnShuffleService doesn't handle moving credentials levelDbThomas Graves2016-09-091-17/+39
| | | | | | | | | | | The secrets leveldb isn't being moved if you run spark shuffle services without yarn nm recovery on and then turn it on. This fixes that. I unfortunately missed this when I ported the patch from our internal branch 2 to master branch due to the changes for the recovery path. Note this only applies to master since it is the only place the yarn nm recovery dir is used. Unit tests ran and tested on 8 node cluster. Fresh startup with NM recovery, fresh startup no nm recovery, switching between no nm recovery and recovery. Also tested running applications to make sure wasn't affected by rolling upgrade. Author: Thomas Graves <tgraves@prevailsail.corp.gq1.yahoo.com> Author: Tom Graves <tgraves@apache.org> Closes #14999 from tgravescs/SPARK-17433.
* [SPARK-17299] TRIM/LTRIM/RTRIM should not strips characters other than spacesSandeep Singh2016-09-063-8/+18
| | | | | | | | | | | | ## What changes were proposed in this pull request? TRIM/LTRIM/RTRIM should not strips characters other than spaces, we were trimming all chars small than ASCII 0x20(space) ## How was this patch tested? fixed existing tests. Author: Sandeep Singh <sandeep@techaddict.me> Closes #14924 from techaddict/SPARK-17299.
* [SPARK-16711] YarnShuffleService doesn't re-init properly on YARN rolling ↵Thomas Graves2016-09-025-138/+301
| | | | | | | | | | | | | | upgrade The Spark Yarn Shuffle Service doesn't re-initialize the application credentials early enough which causes any other spark executors trying to fetch from that node during a rolling upgrade to fail with "java.lang.NullPointerException: Password cannot be null if SASL is enabled". Right now the spark shuffle service relies on the Yarn nodemanager to re-register the applications, unfortunately this is after we open the port for other executors to connect. If other executors connected before the re-register they get a null pointer exception which isn't a re-tryable exception and cause them to fail pretty quickly. To solve this I added another leveldb file so that it can save and re-initialize all the applications before opening the port for other executors to connect to it. Adding another leveldb was simpler from the code structure point of view. Most of the code changes are moving things to common util class. Patch was tested manually on a Yarn cluster with rolling upgrade was happing while spark job was running. Without the patch I consistently get the NullPointerException, with the patch the job gets a few Connection refused exceptions but the retries kick in and the it succeeds. Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com> Closes #14718 from tgravescs/SPARK-16711.
* [SPARK-17331][CORE][MLLIB] Avoid allocating 0-length arraysSean Owen2016-09-011-4/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Avoid allocating some 0-length arrays, esp. in UTF8String, and by using Array.empty in Scala over Array[T]() ## How was this patch tested? Jenkins Author: Sean Owen <sowen@cloudera.com> Closes #14895 from srowen/SPARK-17331.
* [SPARK-17332][CORE] Make Java Loggers static membersSean Owen2016-08-3121-21/+23
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Make all Java Loggers static members ## How was this patch tested? Jenkins Author: Sean Owen <sowen@cloudera.com> Closes #14896 from srowen/SPARK-17332.
* [SPARK-17314][CORE] Use Netty's DefaultThreadFactory to enable its fast ↵Shixiong Zhu2016-08-301-5/+2
| | | | | | | | | | | | | | | | ThreadLocal impl ## What changes were proposed in this pull request? When a thread is a Netty's FastThreadLocalThread, Netty will use its fast ThreadLocal implementation. It has a better performance than JDK's (See the benchmark results in https://github.com/netty/netty/pull/4417, note: it's not a fix to Netty's FastThreadLocal. It just fixed an issue in Netty's benchmark codes) This PR just changed the ThreadFactory to Netty's DefaultThreadFactory which will use FastThreadLocalThread. There is also a minor change to the thread names. See https://github.com/netty/netty/blob/netty-4.0.22.Final/common/src/main/java/io/netty/util/concurrent/DefaultThreadFactory.java#L94 ## How was this patch tested? Author: Shixiong Zhu <shixiong@databricks.com> Closes #14879 from zsxwing/netty-thread.
* [SPARK-17231][CORE] Avoid building debug or trace log messages unless the ↵Michael Allman2016-08-259-45/+55
| | | | | | | | | | | | | | | | | | | | | | | | | | respective log level is enabled (This PR addresses https://issues.apache.org/jira/browse/SPARK-17231) ## What changes were proposed in this pull request? While debugging the performance of a large GraphX connected components computation, we found several places in the `network-common` and `network-shuffle` code bases where trace or debug log messages are constructed even if the respective log level is disabled. According to YourKit, these constructions were creating substantial churn in the eden region. Refactoring the respective code to avoid these unnecessary constructions except where necessary led to a modest but measurable reduction in our job's task time, GC time and the ratio thereof. ## How was this patch tested? We computed the connected components of a graph with about 2.6 billion vertices and 1.7 billion edges four times. We used four different EC2 clusters each with 8 r3.8xl worker nodes. Two test runs used Spark master. Two used Spark master + this PR. The results from the first test run, master and master+PR: ![master](https://cloud.githubusercontent.com/assets/833693/17951634/7471cbca-6a18-11e6-9c26-78afe9319685.jpg) ![logging_perf_improvements](https://cloud.githubusercontent.com/assets/833693/17951632/7467844e-6a18-11e6-9a0e-053dc7650413.jpg) The results from the second test run, master and master+PR: ![master 2](https://cloud.githubusercontent.com/assets/833693/17951633/746dd6aa-6a18-11e6-8e27-606680b3f105.jpg) ![logging_perf_improvements 2](https://cloud.githubusercontent.com/assets/833693/17951631/74488710-6a18-11e6-8a32-08692f373386.jpg) Though modest, I believe these results are significant. Author: Michael Allman <michael@videoamp.com> Closes #14798 from mallman/spark-17231-logging_perf_improvements.
* [SPARK-17127] Make unaligned access in unsafe available for AArch64Richael2016-08-221-1/+1
| | | | | | | | | | | | | | | | | | ## # What changes were proposed in this pull request? From the spark of version 2.0.0 , when MemoryMode.OFF_HEAP is set , whether the architecture supports unaligned access or not is checked. If the check doesn't pass, exception is raised. We know that AArch64 also supports unaligned access , but now only i386, x86, amd64, and X86_64 are included. I think we should include aarch64 when performing the check. ## How was this patch tested? Unit test suite Author: Richael <Richael.Zhuang@arm.com> Closes #14700 from yimuxi/zym_change_unsafe.
* [HOTFIX] Remove unnecessary imports from #12944 that broke buildJosh Rosen2016-08-041-5/+0
| | | | | | Author: Josh Rosen <joshrosen@databricks.com> Closes #14499 from JoshRosen/hotfix.
* [SPARK-15074][SHUFFLE] Cache shuffle index file to speedup shuffle fetchSital Kedia2016-08-044-12/+131
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Shuffle fetch on large intermediate dataset is slow because the shuffle service open/close the index file for each shuffle fetch. This change introduces a cache for the index information so that we can avoid accessing the index files for each block fetch ## How was this patch tested? Tested by running a job on the cluster and the shuffle read time was reduced by 50%. Author: Sital Kedia <skedia@fb.com> Closes #12944 from sitalkedia/shuffle_service.
* [SPARK-16535][BUILD] In pom.xml, remove groupId which is redundant ↵Xin Ren2016-07-196-6/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | definition and inherited from the parent https://issues.apache.org/jira/browse/SPARK-16535 ## What changes were proposed in this pull request? When I scan through the pom.xml of sub projects, I found this warning as below and attached screenshot ``` Definition of groupId is redundant, because it's inherited from the parent ``` ![screen shot 2016-07-13 at 3 13 11 pm](https://cloud.githubusercontent.com/assets/3925641/16823121/744f893e-4916-11e6-8a52-042f83b9db4e.png) I've tried to remove some of the lines with groupId definition, and the build on my local machine is still ok. ``` <groupId>org.apache.spark</groupId> ``` As I just find now `<maven.version>3.3.9</maven.version>` is being used in Spark 2.x, and Maven-3 supports versionless parent elements: Maven 3 will remove the need to specify the parent version in sub modules. THIS is great (in Maven 3.1). ref: http://stackoverflow.com/questions/3157240/maven-3-worth-it/3166762#3166762 ## How was this patch tested? I've tested by re-building the project, and build succeeded. Author: Xin Ren <iamshrek@126.com> Closes #14189 from keypointt/SPARK-16535.
* [MINOR][BUILD] Fix Java Linter `LineLength` errorsDongjoon Hyun2016-07-192-2/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes four java linter `LineLength` errors. Those are all `LineLength` errors, but we had better remove all java linter errors before release. ## How was this patch tested? After pass the Jenkins, `./dev/lint-java`. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14255 from dongjoon-hyun/minor_java_linter.
* [SPARK-16505][YARN] Optionally propagate error during shuffle service startup.Marcelo Vanzin2016-07-141-32/+43
| | | | | | | | | | | This prevents the NM from starting when something is wrong, which would lead to later errors which are confusing and harder to debug. Added a unit test to verify startup fails if something is wrong. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #14162 from vanzin/SPARK-16505.
* [SPARK-14963][MINOR][YARN] Fix typo in YarnShuffleService recovery file namejerryshao2016-07-141-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Due to the changes of [SPARK-14963](https://issues.apache.org/jira/browse/SPARK-14963), external shuffle recovery file name is changed mistakenly, so here change it back to the previous file name. This only affects the master branch, branch-2.0 is correct [here](https://github.com/apache/spark/blob/branch-2.0/common/network-yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java#L195). ## How was this patch tested? N/A Author: jerryshao <sshao@hortonworks.com> Closes #14197 from jerryshao/fix-typo-file-name.
* [MINOR] Fix Java style errors and remove unused importsXin Ren2016-07-132-4/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix Java style errors and remove unused imports, which are randomly found ## How was this patch tested? Tested on my local machine. Author: Xin Ren <iamshrek@126.com> Closes #14161 from keypointt/SPARK-16437.
* [SPARK-16405] Add metrics and source for external shuffle serviceYangyang Liu2016-07-124-17/+105
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since externalShuffleService is essential for spark, better monitoring for shuffle service is necessary. In order to do so, we added various metrics in shuffle service and imported into ExternalShuffleServiceSource for metric system. Metrics added in shuffle service: * registeredExecutorsSize * openBlockRequestLatencyMillis * registerExecutorRequestLatencyMillis * blockTransferRateBytes JIRA Issue: https://issues.apache.org/jira/browse/SPARK-16405 ## How was this patch tested? Some test cases are added to verify metrics as expected in metric system. Those unit test cases are shown in `ExternalShuffleBlockHandlerSuite ` Author: Yangyang Liu <yangyangliu@fb.com> Closes #14080 from lovexi/yangyang-metrics.
* [SPARK-16477] Bump master version to 2.1.0-SNAPSHOTReynold Xin2016-07-116-6/+6
| | | | | | | | | | | | ## What changes were proposed in this pull request? After SPARK-16476 (committed earlier today as #14128), we can finally bump the version number. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #14130 from rxin/SPARK-16477.
* [SPARK-16420] Ensure compression streams are closed.Ryan Blue2016-07-081-0/+23
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This uses the try/finally pattern to ensure streams are closed after use. `UnsafeShuffleWriter` wasn't closing compression streams, causing them to leak resources until garbage collected. This was causing a problem with codecs that use off-heap memory. ## How was this patch tested? Current tests are sufficient. This should not change behavior. Author: Ryan Blue <blue@apache.org> Closes #14093 from rdblue/SPARK-16420-unsafe-shuffle-writer-leak.
* [SPARK-16021] Fill freed memory in test to help catch correctness bugsEric Liang2016-07-066-3/+56
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patches `MemoryAllocator` to fill clean and freed memory with known byte values, similar to https://github.com/jemalloc/jemalloc/wiki/Use-Case:-Find-a-memory-corruption-bug . Memory filling is flag-enabled in test only by default. ## How was this patch tested? Unit test that it's on in test. cc sameeragarwal Author: Eric Liang <ekl@databricks.com> Closes #13983 from ericl/spark-16021.
* [SPARK-16018][SHUFFLE] Shade netty to load shuffle jar in NodemangerDhruve Ashar2016-06-171-0/+7
| | | | | | | | | | | | ## What changes were proposed in this pull request? Shade the netty.io namespace so that we can use it in shuffle independent of the dependencies being pulled by hadoop jars. ## How was this patch tested? Ran a decent job involving shuffle write/read and tested the new spark-x-yarn-shuffle jar. After shading netty.io namespace, the nodemanager loads and shuffle job completes successfully. Author: Dhruve Ashar <dhruveashar@gmail.com> Closes #13739 from dhruve/bug/SPARK-16018.
* [SPARK-15391] [SQL] manage the temporary memory of timsortDavies Liu2016-06-031-1/+1
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, the memory for temporary buffer used by TimSort is always allocated as on-heap without bookkeeping, it could cause OOM both in on-heap and off-heap mode. This PR will try to manage that by preallocate it together with the pointer array, same with RadixSort. It both works for on-heap and off-heap mode. This PR also change the loadFactor of BytesToBytesMap to 0.5 (it was 0.70), it enables use to radix sort also makes sure that we have enough memory for timsort. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #13318 from davies/fix_timsort.
* [MINOR] Resolve a number of miscellaneous build warningsSean Owen2016-05-291-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This change resolves a number of build warnings that have accumulated, before 2.x. It does not address a large number of deprecation warnings, especially related to the Accumulator API. That will happen separately. ## How was this patch tested? Jenkins Author: Sean Owen <sowen@cloudera.com> Closes #13377 from srowen/BuildWarnings.
* [SPARK-15386][CORE] Master doesn't compile against Java 1.7 / Process.isAliveSean Owen2016-05-181-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Remove call to Process.isAlive -- Java 8 only. Introduced in https://github.com/apache/spark/pull/13042 / SPARK-15263 ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #13174 from srowen/SPARK-15386.
* [SPARK-15263][CORE] Make shuffle service dir cleanup faster by using `rm -rf`Tejas Patil2016-05-183-16/+61
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Jira: https://issues.apache.org/jira/browse/SPARK-15263 The current logic for directory cleanup is slow because it does directory listing, recurses over child directories, checks for symbolic links, deletes leaf files and finally deletes the dirs when they are empty. There is back-and-forth switching from kernel space to user space while doing this. Since most of the deployment backends would be Unix systems, we could essentially just do `rm -rf` so that entire deletion logic runs in kernel space. The current Java based impl in Spark seems to be similar to what standard libraries like guava and commons IO do (eg. http://svn.apache.org/viewvc/commons/proper/io/trunk/src/main/java/org/apache/commons/io/FileUtils.java?view=markup#l1540). However, guava removed this method in favour of shelling out to an operating system command (like in this PR). See the `Deprecated` note in older javadocs for guava for details : http://google.github.io/guava/releases/10.0.1/api/docs/com/google/common/io/Files.html#deleteRecursively(java.io.File) Ideally, Java should be providing such APIs so that users won't have to do such things to get platform specific code. Also, its not just about speed, but also handling race conditions while doing at FS deletions is tricky. I could find this bug for Java in similar context : http://bugs.java.com/bugdatabase/view_bug.do?bug_id=7148952 ## How was this patch tested? I am relying on existing test cases to test the method. If there are suggestions about testing it, welcome to hear about it. ## Performance gains *Input setup* : Created a nested directory structure of depth 3 and each entry having 50 sub-dirs. The input being cleaned up had total ~125k dirs. Ran both approaches (in isolation) for 6 times to get average numbers: Native Java cleanup | `rm -rf` as a separate process ------------ | ------------- 10.04 sec | 4.11 sec This change made deletion 2.4 times faster for the given test input. Author: Tejas Patil <tejasp@fb.com> Closes #13042 from tejasapatil/delete_recursive.
* [SPARK-15290][BUILD] Move annotations, like @Since / @DeveloperApi, into ↵Sean Owen2016-05-1713-11/+234
| | | | | | | | | | | | | | | | | | spark-tags ## What changes were proposed in this pull request? (See https://github.com/apache/spark/pull/12416 where most of this was already reviewed and committed; this is just the module structure and move part. This change does not move the annotations into test scope, which was the apparently problem last time.) Rename `spark-test-tags` -> `spark-tags`; move common annotations like `Since` to `spark-tags` ## How was this patch tested? Jenkins tests. Author: Sean Owen <sowen@cloudera.com> Closes #13074 from srowen/SPARK-15290.
* [SPARK-14963][YARN] Using recoveryPath if NM recovery is enabledjerryshao2016-05-101-11/+53
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? From Hadoop 2.5+, Yarn NM supports NM recovery which using recovery path for auxiliary services such as spark_shuffle, mapreduce_shuffle. So here change to use this path install of NM local dir if NM recovery is enabled. ## How was this patch tested? Unit test + local test. Author: jerryshao <sshao@hortonworks.com> Closes #12994 from jerryshao/SPARK-14963.
* [SPARK-15178][CORE] Remove LazyFileRegion instead use netty's DefaultFileRegionSandeep Singh2016-05-072-112/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Remove LazyFileRegion instead use netty's DefaultFileRegion, since It was created so that we didn't create a file descriptor before having to send the file. ## How was this patch tested? Existing tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #12977 from techaddict/SPARK-15178.
* [SPARK-15134][EXAMPLE] Indent SparkSession builder patterns and update ↵Dongjoon Hyun2016-05-051-2/+5
| | | | | | | | | | | | | | | | | | | binary_classification_metrics_example.py ## What changes were proposed in this pull request? This issue addresses the comments in SPARK-15031 and also fix java-linter errors. - Use multiline format in SparkSession builder patterns. - Update `binary_classification_metrics_example.py` to use `SparkSession`. - Fix Java Linter errors (in SPARK-13745, SPARK-15031, and so far) ## How was this patch tested? After passing the Jenkins tests and run `dev/lint-java` manually. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12911 from dongjoon-hyun/SPARK-15134.
* [SPARK-15121] Improve logging of external shuffle handlerThomas Graves2016-05-041-1/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add more informative logging in the external shuffle service to aid in debugging who is connecting to the YARN Nodemanager when the external shuffle service runs under it. ## How was this patch tested? Ran and saw logs coming out in log file. Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com> Closes #12900 from tgravescs/SPARK-15121.
* Revert "[SPARK-14613][ML] Add @Since into the matrix and vector classes in ↵Yin Huai2016-04-2812-86/+8
| | | | | | spark-mllib-local" This reverts commit dae538a4d7c36191c1feb02ba87ffc624ab960dc.
* [SPARK-14613][ML] Add @Since into the matrix and vector classes in ↵Pravin Gadakh2016-04-2812-8/+86
| | | | | | | | | | | | | | | | spark-mllib-local ## What changes were proposed in this pull request? This PR adds `since` tag into the matrix and vector classes in spark-mllib-local. ## How was this patch tested? Scala-style checks passed. Author: Pravin Gadakh <prgadakh@in.ibm.com> Closes #12416 from pravingadakh/SPARK-14613.
* [SPARK-14756][CORE] Use parseLong instead of valueOfAzeem Jiva2016-04-261-4/+4
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Use Long.parseLong which returns a primative. Use a series of appends() reduces the creation of an extra StringBuilder type ## How was this patch tested? Unit tests Author: Azeem Jiva <azeemj@gmail.com> Closes #12520 from javawithjiva/minor.
* [SPARK-14731][shuffle]Revert SPARK-12130 to make 2.0 shuffle service ↵Lianhui Wang2016-04-256-27/+31
| | | | | | | | | | | | | | | compatible with 1.x ## What changes were proposed in this pull request? SPARK-12130 make 2.0 shuffle service incompatible with 1.x. So from discussion: [http://apache-spark-developers-list.1001551.n3.nabble.com/YARN-Shuffle-service-and-its-compatibility-td17222.html](url) we should maintain compatibility between Spark 1.x and Spark 2.x's shuffle service. I put string comparison into executor's register at first avoid string comparison in getBlockData every time. ## How was this patch tested? N/A Author: Lianhui Wang <lianhuiwang09@gmail.com> Closes #12568 from lianhuiwang/SPARK-14731.
* [SPARK-14667] Remove HashShuffleManagerReynold Xin2016-04-186-90/+10
| | | | | | | | | | | | ## What changes were proposed in this pull request? The sort shuffle manager has been the default since Spark 1.2. It is time to remove the old hash shuffle manager. ## How was this patch tested? Removed some tests related to the old manager. Author: Reynold Xin <rxin@databricks.com> Closes #12423 from rxin/SPARK-14667.
* [SPARK-14547] Avoid DNS resolution for reusing connectionsReynold Xin2016-04-121-11/+20
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch changes the connection creation logic in the network client module to avoid DNS resolution when reusing connections. ## How was this patch tested? Testing in production. This is too difficult to test in isolation (for high fidelity unit tests, we'd need to change the DNS resolution behavior in the JVM). Author: Reynold Xin <rxin@databricks.com> Closes #12315 from rxin/SPARK-14547.
* [SPARK-14134][CORE] Change the package name used for shading classes.Marcelo Vanzin2016-04-061-2/+2
| | | | | | | | | | | | | | | The current package name uses a dash, which is a little weird but seemed to work. That is, until a new test tried to mock a class that references one of those shaded types, and then things started failing. Most changes are just noise to fix the logging configs. For reference, SPARK-8815 also raised this issue, although at the time it did not cause any issues in Spark, so it was not addressed. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #11941 from vanzin/SPARK-14134.
* [SPARK-14290][CORE][NETWORK] avoid significant memory copy in netty's transferToZhang, Liye2016-04-061-1/+29
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When netty transfer data that is not `FileRegion`, data will be in format of `ByteBuf`, If the data is large, there will occur significant performance issue because there is memory copy underlying in `sun.nio.ch.IOUtil.write`, the CPU is 100% used, and network is very low. In this PR, if data size is large, we will split it into small chunks to call `WritableByteChannel.write()`, so that avoid wasting of memory copy. Because the data can't be written within a single write, and it will call `transferTo` multiple times. ## How was this patch tested? Spark unit test and manual test. Manual test: `sc.parallelize(Array(1,2,3),3).mapPartitions(a=>Array(new Array[Double](1024 * 1024 * 50)).iterator).reduce((a,b)=> a).length` For more details, please refer to [SPARK-14290](https://issues.apache.org/jira/browse/SPARK-14290) Author: Zhang, Liye <liye.zhang@intel.com> Closes #12083 from liyezhang556520/spark-14290.
* [SPARK-14355][BUILD] Fix typos in Exception/Testcase/Comments and static ↵Dongjoon Hyun2016-04-034-6/+6
| | | | | | | | | | | | | | | | | | | | | analysis results ## What changes were proposed in this pull request? This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines). - Fix typos(exception/log strings, testcase name, comments) in 44 lines. - Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011) - Use diamond operators in 40 lines. (New codes after SPARK-13702) - Fix redundant semicolon in 5 lines. - Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala. ## How was this patch tested? Manual and pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12139 from dongjoon-hyun/SPARK-14355.
* [SPARK-13992] Add support for off-heap cachingJosh Rosen2016-04-011-0/+32
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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-14242][CORE][NETWORK] avoid copy in compositeBuffer for frame decoderZhang, Liye2016-03-311-1/+1
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In this patch, we set the initial `maxNumComponents` to `Integer.MAX_VALUE` instead of the default size ( which is 16) when allocating `compositeBuffer` in `TransportFrameDecoder` because `compositeBuffer` will introduce too many memory copies underlying if `compositeBuffer` is with default `maxNumComponents` when the frame size is large (which result in many transport messages). For details, please refer to [SPARK-14242](https://issues.apache.org/jira/browse/SPARK-14242). ## How was this patch tested? spark unit tests and manual tests. For manual tests, we can reproduce the performance issue with following code: `sc.parallelize(Array(1,2,3),3).mapPartitions(a=>Array(new Array[Double](1024 * 1024 * 50)).iterator).reduce((a,b)=> a).length` It's easy to see the performance gain, both from the running time and CPU usage. Author: Zhang, Liye <liye.zhang@intel.com> Closes #12038 from liyezhang556520/spark-14242.
* [SPARK-14254][CORE] Add logs to help investigate the network performanceShixiong Zhu2016-03-291-1/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? It would be very helpful for network performance investigation if we log the time spent on connecting and resolving host. ## How was this patch tested? Jenkins unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #12046 from zsxwing/connection-time.
* [SPARK-12181] Check Cached unaligned-access capability before using Unsafetedyu2016-03-291-0/+28
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? For MemoryMode.OFF_HEAP, Unsafe.getInt etc. are used with no restriction. However, the Oracle implementation uses these methods only if the class variable unaligned (commented as "Cached unaligned-access capability") is true, which seems to be calculated whether the architecture is i386, x86, amd64, or x86_64. I think we should perform similar check for the use of Unsafe. Reference: https://github.com/netty/netty/blob/4.1/common/src/main/java/io/netty/util/internal/PlatformDependent0.java#L112 ## How was this patch tested? Unit test suite Author: tedyu <yuzhihong@gmail.com> Closes #11943 from tedyu/master.
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