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* [SPARK-3722][Docs]minor improvement and fix in docsWangTao2014-11-141-1/+1
| | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-3722 Author: WangTao <barneystinson@aliyun.com> Closes #2579 from WangTaoTheTonic/docsWork and squashes the following commits: 6f91cec [WangTao] use more wording express 29d22fa [WangTao] delete the specified version link 34cb4ea [WangTao] Update running-on-yarn.md 4ee1a26 [WangTao] minor improvement and fix in docs
* SPARK-4230. Doc for spark.default.parallelism is incorrectSandy Ryza2014-11-101-2/+5
| | | | | | | | | Author: Sandy Ryza <sandy@cloudera.com> Closes #3107 from sryza/sandy-spark-4230 and squashes the following commits: 37a1d19 [Sandy Ryza] Clear up a couple things 34d53de [Sandy Ryza] SPARK-4230. Doc for spark.default.parallelism is incorrect
* SPARK-4040. Update documentation to exemplify use of local (n) value, fo...jay@apache.org2014-11-051-2/+8
| | | | | | | | | | This is a minor docs update which helps to clarify the way local[n] is used for streaming apps. Author: jay@apache.org <jayunit100> Closes #2964 from jayunit100/SPARK-4040 and squashes the following commits: 35b5a5e [jay@apache.org] SPARK-4040: Update documentation to exemplify use of local (n) value.
* [SPARK-4183] Enable NettyBlockTransferService by defaultAaron Davidson2014-11-021-0/+10
| | | | | | | | | | Note that we're turning this on for at least the first part of the QA period as a trial. We want to enable this (and deprecate the NioBlockTransferService) as soon as possible in the hopes that NettyBlockTransferService will be more stable and easier to maintain. We will turn it off if we run into major issues. Author: Aaron Davidson <aaron@databricks.com> Closes #3049 from aarondav/enable-netty and squashes the following commits: bb981cc [Aaron Davidson] [SPARK-4183] Enable NettyBlockTransferService by default
* [SPARK-3466] Limit size of results that a driver collects for each actionDavies Liu2014-11-021-0/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | Right now, operations like collect() and take() can crash the driver with an OOM if they bring back too many data. This PR will introduce spark.driver.maxResultSize, after setting it, the driver will abort a job if its result is bigger than it. By default, it's 1g (for backward compatibility for most the cases). In local mode, the driver and executor share the same JVM, the default setting can not protect JVM from OOM. cc mateiz Author: Davies Liu <davies@databricks.com> Closes #3003 from davies/collect and squashes the following commits: 248ed5e [Davies Liu] fix compile 272522e [Davies Liu] address comments 2c35773 [Davies Liu] add sizes in message of abort() 5d62303 [Davies Liu] address comments bc3c077 [Davies Liu] Merge branch 'master' of github.com:apache/spark into collect 11f97c5 [Davies Liu] address comments 47b144f [Davies Liu] check the size of result before send and fetch 3d81af2 [Davies Liu] address comments ca8267d [Davies Liu] limit the size of data by collect
* Revert "[SPARK-4183] Enable NettyBlockTransferService by default"Patrick Wendell2014-11-011-10/+0
| | | | This reverts commit 59e626c701227634336110e1bc23afd94c535ede.
* [SPARK-4183] Enable NettyBlockTransferService by defaultAaron Davidson2014-11-011-0/+10
| | | | | | | | | | Note that we're turning this on for at least the first part of the QA period as a trial. We want to enable this (and deprecate the NioBlockTransferService) as soon as possible in the hopes that NettyBlockTransferService will be more stable and easier to maintain. We will turn it off if we run into major issues. Author: Aaron Davidson <aaron@databricks.com> Closes #3049 from aarondav/enable-netty and squashes the following commits: bb981cc [Aaron Davidson] [SPARK-4183] Enable NettyBlockTransferService by default
* [SPARK-2321] Stable pull-based progress / status APIJosh Rosen2014-10-251-1/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This pull request is a first step towards the implementation of a stable, pull-based progress / status API for Spark (see [SPARK-2321](https://issues.apache.org/jira/browse/SPARK-2321)). For now, I'd like to discuss the basic implementation, API names, and overall interface design. Once we arrive at a good design, I'll go back and add additional methods to expose more information via these API. #### Design goals: - Pull-based API - Usable from Java / Scala / Python (eventually, likely with a wrapper) - Can be extended to expose more information without introducing binary incompatibilities. - Returns immutable objects. - Don't leak any implementation details, preserving our freedom to change the implementation. #### Implementation: - Add public methods (`getJobInfo`, `getStageInfo`) to SparkContext to allow status / progress information to be retrieved. - Add public interfaces (`SparkJobInfo`, `SparkStageInfo`) for our API return values. These interfaces consist entirely of Java-style getter methods. The interfaces are currently implemented in Java. I decided to explicitly separate the interface from its implementation (`SparkJobInfoImpl`, `SparkStageInfoImpl`) in order to prevent users from constructing these responses themselves. -Allow an existing JobProgressListener to be used when constructing a live SparkUI. This allows us to re-use this listeners in the implementation of this status API. There are a few reasons why this listener re-use makes sense: - The status API and web UI are guaranteed to show consistent information. - These listeners are already well-tested. - The same garbage-collection / information retention configurations can apply to both this API and the web UI. - Extend JobProgressListener to maintain `jobId -> Job` and `stageId -> Stage` mappings. The progress API methods are implemented in a separate trait that's mixed into SparkContext. This helps to avoid SparkContext.scala from becoming larger and more difficult to read. Author: Josh Rosen <joshrosen@databricks.com> Author: Josh Rosen <joshrosen@apache.org> Closes #2696 from JoshRosen/progress-reporting-api and squashes the following commits: e6aa78d [Josh Rosen] Add tests. b585c16 [Josh Rosen] Accept SparkListenerBus instead of more specific subclasses. c96402d [Josh Rosen] Address review comments. 2707f98 [Josh Rosen] Expose current stage attempt id c28ba76 [Josh Rosen] Update demo code: 646ff1d [Josh Rosen] Document spark.ui.retainedJobs. 7f47d6d [Josh Rosen] Clean up SparkUI constructors, per Andrew's feedback. b77b3d8 [Josh Rosen] Merge remote-tracking branch 'origin/master' into progress-reporting-api 787444c [Josh Rosen] Move status API methods into trait that can be mixed into SparkContext. f9a9a00 [Josh Rosen] More review comments: 3dc79af [Josh Rosen] Remove creation of unused listeners in SparkContext. 249ca16 [Josh Rosen] Address several review comments: da5648e [Josh Rosen] Add example of basic progress reporting in Java. 7319ffd [Josh Rosen] Add getJobIdsForGroup() and num*Tasks() methods. cc568e5 [Josh Rosen] Add note explaining that interfaces should not be implemented outside of Spark. 6e840d4 [Josh Rosen] Remove getter-style names and "consistent snapshot" semantics: 08cbec9 [Josh Rosen] Begin to sketch the interfaces for a stable, public status API. ac2d13a [Josh Rosen] Add jobId->stage, stageId->stage mappings in JobProgressListener 24de263 [Josh Rosen] Create UI listeners in SparkContext instead of in Tabs:
* SPARK-1813. Add a utility to SparkConf that makes using Kryo really easySandy Ryza2014-10-211-2/+13
| | | | | | | | | | | | Author: Sandy Ryza <sandy@cloudera.com> Closes #789 from sryza/sandy-spark-1813 and squashes the following commits: 48b05e9 [Sandy Ryza] Simplify b824932 [Sandy Ryza] Allow both spark.kryo.classesToRegister and spark.kryo.registrator at the same time 6a15bb7 [Sandy Ryza] Small fix a2278c0 [Sandy Ryza] Respond to review comments 6ef592e [Sandy Ryza] SPARK-1813. Add a utility to SparkConf that makes using Kryo really easy
* [SPARK-2546] Clone JobConf for each task (branch-1.0 / 1.1 backport)Josh Rosen2014-10-191-0/+9
| | | | | | | | | | | | | | | | | | | | This patch attempts to fix SPARK-2546 in `branch-1.0` and `branch-1.1`. The underlying problem is that thread-safety issues in Hadoop Configuration objects may cause Spark tasks to get stuck in infinite loops. The approach taken here is to clone a new copy of the JobConf for each task rather than sharing a single copy between tasks. Note that there are still Configuration thread-safety issues that may affect the driver, but these seem much less likely to occur in practice and will be more complex to fix (see discussion on the SPARK-2546 ticket). This cloning is guarded by a new configuration option (`spark.hadoop.cloneConf`) and is disabled by default in order to avoid unexpected performance regressions for workloads that are unaffected by the Configuration thread-safety issues. Author: Josh Rosen <joshrosen@apache.org> Closes #2684 from JoshRosen/jobconf-fix-backport and squashes the following commits: f14f259 [Josh Rosen] Add configuration option to control cloning of Hadoop JobConf. b562451 [Josh Rosen] Remove unused jobConfCacheKey field. dd25697 [Josh Rosen] [SPARK-2546] [1.0 / 1.1 backport] Clone JobConf for each task. (cherry picked from commit 2cd40db2b3ab5ddcb323fd05c171dbd9025f9e71) Signed-off-by: Josh Rosen <joshrosen@databricks.com> Conflicts: core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
* [SPARK-3890][Docs]remove redundant spark.executor.memory in docWangTaoTheTonic2014-10-161-12/+4
| | | | | | | | | | | | | Introduced in https://github.com/pwendell/spark/commit/f7e79bc42c1635686c3af01eef147dae92de2529, I'm not sure why we need two spark.executor.memory here. Author: WangTaoTheTonic <barneystinson@aliyun.com> Author: WangTao <barneystinson@aliyun.com> Closes #2745 from WangTaoTheTonic/redundantconfig and squashes the following commits: e7564dc [WangTao] too long line fdbdb1f [WangTaoTheTonic] trivial workaround d06b6e5 [WangTaoTheTonic] remove redundant spark.executor.memory in doc
* [SPARK-3923] Increase Akka heartbeat pause above heartbeat intervalAaron Davidson2014-10-161-1/+1
| | | | | | | | | | | | | Something about the 2.3.4 upgrade seems to have made the issue manifest where all the services disconnect from each other after exactly 1000 seconds (which is the heartbeat interval). [This post](https://groups.google.com/forum/#!topic/akka-user/X3xzpTCbEFs) suggests that heartbeat pause should be greater than heartbeat interval, and increasing the pause from 600s to 6000s seems to have rectified the issue. My current cluster has now exceeded 1400s of uptime without failure! I do not know why this fixed it, because the threshold we have set for the failure detector is the exponent of a timeout, and 300 is extremely large. Perhaps the default failure detector changed in 2.3.4 and now ignores threshold. Author: Aaron Davidson <aaron@databricks.com> Closes #2784 from aarondav/fix-timeout and squashes the following commits: bd1151a [Aaron Davidson] Increase pause, don't decrease interval 9cb0372 [Aaron Davidson] [SPARK-3923] Decrease Akka heartbeat interval below heartbeat pause
* add spark.driver.memory to config docsnartz2014-10-091-0/+8
| | | | | | | | | | | | | | It took me a minute to track this down, so I thought it could be useful to have it in the docs. I'm unsure if 512mb is the default for spark.driver.memory? Also - there could be a better value for the 'description' to differentiate it from spark.executor.memory. Author: nartz <nartzpod@gmail.com> Author: Nathan Artz <nathanartz@Nathans-MacBook-Pro.local> Closes #2410 from nartz/docs/add-spark-driver-memory-to-config-docs and squashes the following commits: a2f6c62 [nartz] Update configuration.md 74521b8 [Nathan Artz] add spark.driver.memory to config docs
* [SPARK-3535][Mesos] Fix resource handling.Brenden Matthews2014-10-031-0/+11
| | | | | | | | Author: Brenden Matthews <brenden@diddyinc.com> Closes #2401 from brndnmtthws/master and squashes the following commits: 4abaa5d [Brenden Matthews] [SPARK-3535][Mesos] Fix resource handling.
* SPARK-2058: Overriding SPARK_HOME/conf with SPARK_CONF_DIREugenCepoi2014-10-031-0/+7
| | | | | | | | | | | | | Update of PR #997. With this PR, setting SPARK_CONF_DIR overrides SPARK_HOME/conf (not only spark-defaults.conf and spark-env). Author: EugenCepoi <cepoi.eugen@gmail.com> Closes #2481 from EugenCepoi/SPARK-2058 and squashes the following commits: 0bb32c2 [EugenCepoi] use orElse orNull and fixing trailing percent in compute-classpath.cmd 77f35d7 [EugenCepoi] SPARK-2058: Overriding SPARK_HOME/conf with SPARK_CONF_DIR
* [SPARK-3766][Doc]Snappy is also the default compress codec for broadcast ↵scwf2014-10-021-4/+5
| | | | | | | | | | variables Author: scwf <wangfei1@huawei.com> Closes #2632 from scwf/compress-doc and squashes the following commits: 7983a1a [scwf] snappy is the default compression codec for broadcast
* [SPARK-3478] [PySpark] Profile the Python tasksDavies Liu2014-09-301-0/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This patch add profiling support for PySpark, it will show the profiling results before the driver exits, here is one example: ``` ============================================================ Profile of RDD<id=3> ============================================================ 5146507 function calls (5146487 primitive calls) in 71.094 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 5144576 68.331 0.000 68.331 0.000 statcounter.py:44(merge) 20 2.735 0.137 71.071 3.554 statcounter.py:33(__init__) 20 0.017 0.001 0.017 0.001 {cPickle.dumps} 1024 0.003 0.000 0.003 0.000 t.py:16(<lambda>) 20 0.001 0.000 0.001 0.000 {reduce} 21 0.001 0.000 0.001 0.000 {cPickle.loads} 20 0.001 0.000 0.001 0.000 copy_reg.py:95(_slotnames) 41 0.001 0.000 0.001 0.000 serializers.py:461(read_int) 40 0.001 0.000 0.002 0.000 serializers.py:179(_batched) 62 0.000 0.000 0.000 0.000 {method 'read' of 'file' objects} 20 0.000 0.000 71.072 3.554 rdd.py:863(<lambda>) 20 0.000 0.000 0.001 0.000 serializers.py:198(load_stream) 40/20 0.000 0.000 71.072 3.554 rdd.py:2093(pipeline_func) 41 0.000 0.000 0.002 0.000 serializers.py:130(load_stream) 40 0.000 0.000 71.072 1.777 rdd.py:304(func) 20 0.000 0.000 71.094 3.555 worker.py:82(process) ``` Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk by `sc.dump_profiles(path)`, such as ```python >>> sc._conf.set("spark.python.profile", "true") >>> rdd = sc.parallelize(range(100)).map(str) >>> rdd.count() 100 >>> sc.show_profiles() ============================================================ Profile of RDD<id=1> ============================================================ 284 function calls (276 primitive calls) in 0.001 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 4 0.000 0.000 0.000 0.000 serializers.py:198(load_stream) 4 0.000 0.000 0.000 0.000 {reduce} 12/4 0.000 0.000 0.001 0.000 rdd.py:2092(pipeline_func) 4 0.000 0.000 0.000 0.000 {cPickle.loads} 4 0.000 0.000 0.000 0.000 {cPickle.dumps} 104 0.000 0.000 0.000 0.000 rdd.py:852(<genexpr>) 8 0.000 0.000 0.000 0.000 serializers.py:461(read_int) 12 0.000 0.000 0.000 0.000 rdd.py:303(func) ``` The profiling is disabled by default, can be enabled by "spark.python.profile=true". Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump" This is bugfix of #2351 cc JoshRosen Author: Davies Liu <davies.liu@gmail.com> Closes #2556 from davies/profiler and squashes the following commits: e68df5a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 858e74c [Davies Liu] compatitable with python 2.6 7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles() 2b0daf2 [Davies Liu] fix docs 7a56c24 [Davies Liu] bugfix cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 09d02c3 [Davies Liu] Merge branch 'master' into profiler c23865c [Davies Liu] Merge branch 'master' into profiler 15d6f18 [Davies Liu] add docs for two configs dadee1a [Davies Liu] add docs string and clear profiles after show or dump 4f8309d [Davies Liu] address comment, add tests 0a5b6eb [Davies Liu] fix Python UDF 4b20494 [Davies Liu] add profile for python
* Revert "[SPARK-3478] [PySpark] Profile the Python tasks"Josh Rosen2014-09-261-19/+0
| | | | This reverts commit 1aa549ba9839565274a12c52fa1075b424f138a6.
* [SPARK-3478] [PySpark] Profile the Python tasksDavies Liu2014-09-261-0/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This patch add profiling support for PySpark, it will show the profiling results before the driver exits, here is one example: ``` ============================================================ Profile of RDD<id=3> ============================================================ 5146507 function calls (5146487 primitive calls) in 71.094 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 5144576 68.331 0.000 68.331 0.000 statcounter.py:44(merge) 20 2.735 0.137 71.071 3.554 statcounter.py:33(__init__) 20 0.017 0.001 0.017 0.001 {cPickle.dumps} 1024 0.003 0.000 0.003 0.000 t.py:16(<lambda>) 20 0.001 0.000 0.001 0.000 {reduce} 21 0.001 0.000 0.001 0.000 {cPickle.loads} 20 0.001 0.000 0.001 0.000 copy_reg.py:95(_slotnames) 41 0.001 0.000 0.001 0.000 serializers.py:461(read_int) 40 0.001 0.000 0.002 0.000 serializers.py:179(_batched) 62 0.000 0.000 0.000 0.000 {method 'read' of 'file' objects} 20 0.000 0.000 71.072 3.554 rdd.py:863(<lambda>) 20 0.000 0.000 0.001 0.000 serializers.py:198(load_stream) 40/20 0.000 0.000 71.072 3.554 rdd.py:2093(pipeline_func) 41 0.000 0.000 0.002 0.000 serializers.py:130(load_stream) 40 0.000 0.000 71.072 1.777 rdd.py:304(func) 20 0.000 0.000 71.094 3.555 worker.py:82(process) ``` Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk by `sc.dump_profiles(path)`, such as ```python >>> sc._conf.set("spark.python.profile", "true") >>> rdd = sc.parallelize(range(100)).map(str) >>> rdd.count() 100 >>> sc.show_profiles() ============================================================ Profile of RDD<id=1> ============================================================ 284 function calls (276 primitive calls) in 0.001 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 4 0.000 0.000 0.000 0.000 serializers.py:198(load_stream) 4 0.000 0.000 0.000 0.000 {reduce} 12/4 0.000 0.000 0.001 0.000 rdd.py:2092(pipeline_func) 4 0.000 0.000 0.000 0.000 {cPickle.loads} 4 0.000 0.000 0.000 0.000 {cPickle.dumps} 104 0.000 0.000 0.000 0.000 rdd.py:852(<genexpr>) 8 0.000 0.000 0.000 0.000 serializers.py:461(read_int) 12 0.000 0.000 0.000 0.000 rdd.py:303(func) ``` The profiling is disabled by default, can be enabled by "spark.python.profile=true". Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump" Author: Davies Liu <davies.liu@gmail.com> Closes #2351 from davies/profiler and squashes the following commits: 7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles() 2b0daf2 [Davies Liu] fix docs 7a56c24 [Davies Liu] bugfix cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 09d02c3 [Davies Liu] Merge branch 'master' into profiler c23865c [Davies Liu] Merge branch 'master' into profiler 15d6f18 [Davies Liu] add docs for two configs dadee1a [Davies Liu] add docs string and clear profiles after show or dump 4f8309d [Davies Liu] address comment, add tests 0a5b6eb [Davies Liu] fix Python UDF 4b20494 [Davies Liu] add profile for python
* [SPARK-3565]Fix configuration item not consistent with documentWangTaoTheTonic2014-09-171-1/+1
| | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-3565 "spark.ports.maxRetries" should be "spark.port.maxRetries". Make the configuration keys in document and code consistent. Author: WangTaoTheTonic <barneystinson@aliyun.com> Closes #2427 from WangTaoTheTonic/fixPortRetries and squashes the following commits: c178813 [WangTaoTheTonic] Use blank lines trigger Jenkins 646f3fe [WangTaoTheTonic] also in SparkBuild.scala 3700dba [WangTaoTheTonic] Fix configuration item not consistent with document
* [Docs] Correct spark.files.fetchTimeout default valueviper-kun2014-09-171-2/+2
| | | | | | | | | | | change the value of spark.files.fetchTimeout Author: viper-kun <xukun.xu@huawei.com> Closes #2406 from viper-kun/master and squashes the following commits: ecb0d46 [viper-kun] [Docs] Correct spark.files.fetchTimeout default value 7cf4c7a [viper-kun] Update configuration.md
* [SPARK-3030] [PySpark] Reuse Python workerDavies Liu2014-09-131-0/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Reuse Python worker to avoid the overhead of fork() Python process for each tasks. It also tracks the broadcasts for each worker, avoid sending repeated broadcasts. This can reduce the time for dummy task from 22ms to 13ms (-40%). It can help to reduce the latency for Spark Streaming. For a job with broadcast (43M after compress): ``` b = sc.broadcast(set(range(30000000))) print sc.parallelize(range(24000), 100).filter(lambda x: x in b.value).count() ``` It will finish in 281s without reused worker, and it will finish in 65s with reused worker(4 CPUs). After reusing the worker, it can save about 9 seconds for transfer and deserialize the broadcast for each tasks. It's enabled by default, could be disabled by `spark.python.worker.reuse = false`. Author: Davies Liu <davies.liu@gmail.com> Closes #2259 from davies/reuse-worker and squashes the following commits: f11f617 [Davies Liu] Merge branch 'master' into reuse-worker 3939f20 [Davies Liu] fix bug in serializer in mllib cf1c55e [Davies Liu] address comments 3133a60 [Davies Liu] fix accumulator with reused worker 760ab1f [Davies Liu] do not reuse worker if there are any exceptions 7abb224 [Davies Liu] refactor: sychronized with itself ac3206e [Davies Liu] renaming 8911f44 [Davies Liu] synchronized getWorkerBroadcasts() 6325fc1 [Davies Liu] bugfix: bid >= 0 e0131a2 [Davies Liu] fix name of config 583716e [Davies Liu] only reuse completed and not interrupted worker ace2917 [Davies Liu] kill python worker after timeout 6123d0f [Davies Liu] track broadcasts for each worker 8d2f08c [Davies Liu] reuse python worker
* [SPARK-3280] Made sort-based shuffle the default implementationReynold Xin2014-09-071-5/+4
| | | | | | | | | | | | Sort-based shuffle has lower memory usage and seems to outperform hash-based in almost all of our testing. Author: Reynold Xin <rxin@apache.org> Closes #2178 from rxin/sort-shuffle and squashes the following commits: 713d341 [Reynold Xin] Fixed test failures by setting spark.shuffle.compress to the same value as spark.shuffle.spill.compress. 85165e6 [Reynold Xin] Fixed a comment typo. aa0d372 [Reynold Xin] [SPARK-3280] Made sort-based shuffle the default implementation
* [SPARK-3264] Allow users to set executor Spark home in MesosAndrew Or2014-08-281-0/+10
| | | | | | | | | | | | | | The executors and the driver may not share the same Spark home. There is currently one way to set the executor side Spark home in Mesos, through setting `spark.home`. However, this is neither documented nor intuitive. This PR adds a more specific config `spark.mesos.executor.home` and exposes this to the user. liancheng tnachen Author: Andrew Or <andrewor14@gmail.com> Closes #2166 from andrewor14/mesos-spark-home and squashes the following commits: b87965e [Andrew Or] Merge branch 'master' of github.com:apache/spark into mesos-spark-home f6abb2e [Andrew Or] Document spark.mesos.executor.home ca7846d [Andrew Or] Add more specific configuration for executor Spark home in Mesos
* [SPARK-2677] BasicBlockFetchIterator#next can wait foreverKousuke Saruta2014-08-161-0/+9
| | | | | | | | | | | | | | | | | | | Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #1632 from sarutak/SPARK-2677 and squashes the following commits: cddbc7b [Kousuke Saruta] Removed Exception throwing when ConnectionManager#handleMessage receives ack for non-referenced message d3bd2a8 [Kousuke Saruta] Modified configuration.md for spark.core.connection.ack.timeout e85f88b [Kousuke Saruta] Removed useless synchronized blocks 7ed48be [Kousuke Saruta] Modified ConnectionManager to use ackTimeoutMonitor ConnectionManager-wide 9b620a6 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2677 0dd9ad3 [Kousuke Saruta] Modified typo in ConnectionManagerSuite.scala 7cbb8ca [Kousuke Saruta] Modified to match with scalastyle 8a73974 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2677 ade279a [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2677 0174d6a [Kousuke Saruta] Modified ConnectionManager.scala to handle the case remote Executor cannot ack a454239 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2677 9b7b7c1 [Kousuke Saruta] (WIP) Modifying ConnectionManager.scala
* [SPARK-3029] Disable local execution of Spark jobs by defaultAaron Davidson2014-08-141-0/+9
| | | | | | | | | | | | | | | Currently, local execution of Spark jobs is only used by take(), and it can be problematic as it can load a significant amount of data onto the driver. The worst case scenarios occur if the RDD is cached (guaranteed to load whole partition), has very large elements, or the partition is just large and we apply a filter with high selectivity or computational overhead. Additionally, jobs that run locally in this manner do not show up in the web UI, and are thus harder to track or understand what is occurring. This PR adds a flag to disable local execution, which is turned OFF by default, with the intention of perhaps eventually removing this functionality altogether. Removing it now is a tougher proposition since it is part of the public runJob API. An alternative solution would be to limit the flag to take()/first() to avoid impacting any external users of this API, but such usage (or, at least, reliance upon the feature) is hopefully minimal. Author: Aaron Davidson <aaron@databricks.com> Closes #1321 from aarondav/allowlocal and squashes the following commits: 136b253 [Aaron Davidson] Fix DAGSchedulerSuite 5599d55 [Aaron Davidson] [RFC] Disable local execution of Spark jobs by default
* [Docs] Add missing <code> tags (minor)Andrew Or2014-08-131-2/+2
| | | | | | | | | | These configs looked inconsistent from the rest. Author: Andrew Or <andrewor14@gmail.com> Closes #1936 from andrewor14/docs-code and squashes the following commits: 15f578a [Andrew Or] Add <code> tag
* [SPARK-2953] Allow using short names for io compression codecsReynold Xin2014-08-121-3/+5
| | | | | | | | | | | | Instead of requiring "org.apache.spark.io.LZ4CompressionCodec", it is easier for users if Spark just accepts "lz4", "lzf", "snappy". Author: Reynold Xin <rxin@apache.org> Closes #1873 from rxin/compressionCodecShortForm and squashes the following commits: 9f50962 [Reynold Xin] Specify short-form compression codec names first. 63f78ee [Reynold Xin] Updated configuration documentation. 47b3848 [Reynold Xin] [SPARK-2953] Allow using short names for io compression codecs
* [SPARK-2635] Fix race condition at SchedulerBackend.isReady in standalone modeli-zhihui2014-08-081-6/+7
| | | | | | | | | | | | | | | | | In SPARK-1946(PR #900), configuration <code>spark.scheduler.minRegisteredExecutorsRatio</code> was introduced. However, in standalone mode, there is a race condition where isReady() can return true because totalExpectedExecutors has not been correctly set. Because expected executors is uncertain in standalone mode, the PR try to use CPU cores(<code>--total-executor-cores</code>) as expected resources to judge whether SchedulerBackend is ready. Author: li-zhihui <zhihui.li@intel.com> Author: Li Zhihui <zhihui.li@intel.com> Closes #1525 from li-zhihui/fixre4s and squashes the following commits: e9a630b [Li Zhihui] Rename variable totalExecutors and clean codes abf4860 [Li Zhihui] Push down variable totalExpectedResources to children classes ca54bd9 [li-zhihui] Format log with String interpolation 88c7dc6 [li-zhihui] Few codes and docs refactor 41cf47e [li-zhihui] Fix race condition at SchedulerBackend.isReady in standalone mode
* SPARK-2787: Make sort-based shuffle write files directly when there's no ↵Matei Zaharia2014-08-071-0/+18
| | | | | | | | | | | | | | | | | | | | sorting/aggregation and # partitions is small As described in https://issues.apache.org/jira/browse/SPARK-2787, right now sort-based shuffle is more expensive than hash-based for map operations that do no partial aggregation or sorting, such as groupByKey. This is because it has to serialize each data item twice (once when spilling to intermediate files, and then again when merging these files object-by-object). This patch adds a code path to just write separate files directly if the # of output partitions is small, and concatenate them at the end to produce a sorted file. On the unit test side, I added some tests that force or don't force this bypass path to be used, and checked that our tests for other features (e.g. all the operations) cover both cases. Author: Matei Zaharia <matei@databricks.com> Closes #1799 from mateiz/SPARK-2787 and squashes the following commits: 88cf26a [Matei Zaharia] Fix rebase 10233af [Matei Zaharia] Review comments 398cb95 [Matei Zaharia] Fix looking up shuffle manager in conf ca3efd9 [Matei Zaharia] Add docs for shuffle manager properties, and allow short names for them d0ae3c5 [Matei Zaharia] Fix some comments 90d084f [Matei Zaharia] Add code path to bypass merge-sort in ExternalSorter, and tests 31e5d7c [Matei Zaharia] Move existing logic for writing partitioned files into ExternalSorter
* [SPARK-2157] Enable tight firewall rules for SparkAndrew Or2014-08-061-0/+46
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The goal of this PR is to allow users of Spark to write tight firewall rules for their clusters. This is currently not possible because Spark uses random ports in many places, notably the communication between executors and drivers. The changes in this PR are based on top of ash211's changes in #1107. The list covered here may or may not be the complete set of port needed for Spark to operate perfectly. However, as of the latest commit there are no known sources of random ports (except in tests). I have not documented a few of the more obscure configs. My spark-env.sh looks like this: ``` export SPARK_MASTER_PORT=6060 export SPARK_WORKER_PORT=7070 export SPARK_MASTER_WEBUI_PORT=9090 export SPARK_WORKER_WEBUI_PORT=9091 ``` and my spark-defaults.conf looks like this: ``` spark.master spark://andrews-mbp:6060 spark.driver.port 5001 spark.fileserver.port 5011 spark.broadcast.port 5021 spark.replClassServer.port 5031 spark.blockManager.port 5041 spark.executor.port 5051 ``` Author: Andrew Or <andrewor14@gmail.com> Author: Andrew Ash <andrew@andrewash.com> Closes #1777 from andrewor14/configure-ports and squashes the following commits: 621267b [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports 8a6b820 [Andrew Or] Use a random UI port during tests 7da0493 [Andrew Or] Fix tests 523c30e [Andrew Or] Add test for isBindCollision b97b02a [Andrew Or] Minor fixes c22ad00 [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports 93d359f [Andrew Or] Executors connect to wrong port when collision occurs d502e5f [Andrew Or] Handle port collisions when creating Akka systems a2dd05c [Andrew Or] Patrick's comment nit 86461e2 [Andrew Or] Remove spark.executor.env.port and spark.standalone.client.port 1d2d5c6 [Andrew Or] Fix ports for standalone cluster mode cb3be88 [Andrew Or] Various doc fixes (broken link, format etc.) e837cde [Andrew Or] Remove outdated TODOs bfbab28 [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports de1b207 [Andrew Or] Update docs to reflect new ports b565079 [Andrew Or] Add spark.ports.maxRetries 2551eb2 [Andrew Or] Remove spark.worker.watcher.port 151327a [Andrew Or] Merge branch 'master' of github.com:apache/spark into configure-ports 9868358 [Andrew Or] Add a few miscellaneous ports 6016e77 [Andrew Or] Add spark.executor.port 8d836e6 [Andrew Or] Also document SPARK_{MASTER/WORKER}_WEBUI_PORT 4d9e6f3 [Andrew Or] Fix super subtle bug 3f8e51b [Andrew Or] Correct erroneous docs... e111d08 [Andrew Or] Add names for UI services 470f38c [Andrew Or] Special case non-"Address already in use" exceptions 1d7e408 [Andrew Or] Treat 0 ports specially + return correct ConnectionManager port ba32280 [Andrew Or] Minor fixes 6b550b0 [Andrew Or] Assorted fixes 73fbe89 [Andrew Or] Move start service logic to Utils ec676f4 [Andrew Or] Merge branch 'SPARK-2157' of github.com:ash211/spark into configure-ports 038a579 [Andrew Ash] Trust the server start function to report the port the service started on 7c5bdc4 [Andrew Ash] Fix style issue 0347aef [Andrew Ash] Unify port fallback logic to a single place 24a4c32 [Andrew Ash] Remove type on val to match surrounding style 9e4ad96 [Andrew Ash] Reformat for style checker 5d84e0e [Andrew Ash] Document new port configuration options 066dc7a [Andrew Ash] Fix up HttpServer port increments cad16da [Andrew Ash] Add fallover increment logic for HttpServer c5a0568 [Andrew Ash] Fix ConnectionManager to retry with increment b80d2fd [Andrew Ash] Make Spark's block manager port configurable 17c79bb [Andrew Ash] Add a configuration option for spark-shell's class server f34115d [Andrew Ash] SPARK-1176 Add port configuration for HttpBroadcast 49ee29b [Andrew Ash] SPARK-1174 Add port configuration for HttpFileServer 1c0981a [Andrew Ash] Make port in HttpServer configurable
* [SPARK-2503] Lower shuffle output buffer (spark.shuffle.file.buffer.kb) to 32KB.Reynold Xin2014-08-051-1/+1
| | | | | | | | | | This can substantially reduce memory usage during shuffle. Author: Reynold Xin <rxin@apache.org> Closes #1781 from rxin/SPARK-2503-spark.shuffle.file.buffer.kb and squashes the following commits: 104b8d8 [Reynold Xin] [SPARK-2503] Lower shuffle output buffer (spark.shuffle.file.buffer.kb) to 32KB.
* SPARK-1680: use configs for specifying environment variables on YARNThomas Graves2014-08-051-0/+8
| | | | | | | | | | | | | | Note that this also documents spark.executorEnv.* which to me means its public. If we don't want that please speak up. Author: Thomas Graves <tgraves@apache.org> Closes #1512 from tgravescs/SPARK-1680 and squashes the following commits: 11525df [Thomas Graves] more doc changes 553bad0 [Thomas Graves] fix documentation 152bf7c [Thomas Graves] fix docs 5382326 [Thomas Graves] try fix docs 32f86a4 [Thomas Graves] use configs for specifying environment variables on YARN
* SPARK-1890 and SPARK-1891- add admin and modify aclsThomas Graves2014-08-051-5/+22
| | | | | | | | | | | | | | | | | | | It was easier to combine these 2 jira since they touch many of the same places. This pr adds the following: - adds modify acls - adds admin acls (list of admins/users that get added to both view and modify acls) - modify Kill button on UI to take modify acls into account - changes config name of spark.ui.acls.enable to spark.acls.enable since I choose poorly in original name. We keep backwards compatibility so people can still use spark.ui.acls.enable. The acls should apply to any web ui as well as any CLI interfaces. - send view and modify acls information on to YARN so that YARN interfaces can use (yarn cli for killing applications for example). Author: Thomas Graves <tgraves@apache.org> Closes #1196 from tgravescs/SPARK-1890 and squashes the following commits: 8292eb1 [Thomas Graves] review comments b92ec89 [Thomas Graves] remove unneeded variable from applistener 4c765f4 [Thomas Graves] Add in admin acls 72eb0ac [Thomas Graves] Add modify acls
* [SPARK-2856] Decrease initial buffer size for Kryo to 64KB.Reynold Xin2014-08-051-1/+1
| | | | | | | | Author: Reynold Xin <rxin@apache.org> Closes #1780 from rxin/kryo-init-size and squashes the following commits: 551b935 [Reynold Xin] [SPARK-2856] Decrease initial buffer size for Kryo to 64KB.
* SPARK-2792. Fix reading too much or too little data from each stream in ↵Matei Zaharia2014-08-041-1/+1
| | | | | | | | | | | | | | | | | | | | ExternalMap / Sorter All these changes are from mridulm's work in #1609, but extracted here to fix this specific issue and make it easier to merge not 1.1. This particular set of changes is to make sure that we read exactly the right range of bytes from each spill file in EAOM: some serializers can write bytes after the last object (e.g. the TC_RESET flag in Java serialization) and that would confuse the previous code into reading it as part of the next batch. There are also improvements to cleanup to make sure files are closed. In addition to bringing in the changes to ExternalAppendOnlyMap, I also copied them to the corresponding code in ExternalSorter and updated its test suite to test for the same issues. Author: Matei Zaharia <matei@databricks.com> Closes #1722 from mateiz/spark-2792 and squashes the following commits: 5d4bfb5 [Matei Zaharia] Make objectStreamReset counter count the last object written too 18fe865 [Matei Zaharia] Update docs on objectStreamReset 576ee83 [Matei Zaharia] Allow objectStreamReset to be 0 0374217 [Matei Zaharia] Remove super paranoid code to close file handles bda37bb [Matei Zaharia] Implement Mridul's ExternalAppendOnlyMap fixes in ExternalSorter too 0d6dad7 [Matei Zaharia] Added Mridul's test changes for ExternalAppendOnlyMap 9a78e4b [Matei Zaharia] Add @mridulm's fixes to ExternalAppendOnlyMap for batch sizes
* SPARK-2099. Report progress while task is running.Sandy Ryza2014-08-011-0/+7
| | | | | | | | | | | | | | | | | | This is a sketch of a patch that allows the UI to show metrics for tasks that have not yet completed. It adds a heartbeat every 2 seconds from the executors to the driver, reporting metrics for all of the executor's tasks. It still needs unit tests, polish, and cluster testing, but I wanted to put it up to get feedback on the approach. Author: Sandy Ryza <sandy@cloudera.com> Closes #1056 from sryza/sandy-spark-2099 and squashes the following commits: 93b9fdb [Sandy Ryza] Up heartbeat interval to 10 seconds and other tidying 132aec7 [Sandy Ryza] Heartbeat and HeartbeatResponse are already Serializable as case classes 38dffde [Sandy Ryza] Additional review feedback and restore test that was removed in BlockManagerSuite 51fa396 [Sandy Ryza] Remove hostname race, add better comments about threading, and some stylistic improvements 3084f10 [Sandy Ryza] Make TaskUIData a case class again 3bda974 [Sandy Ryza] Stylistic fixes 0dae734 [Sandy Ryza] SPARK-2099. Report progress while task is running.
* SPARK-2543: Allow user to set maximum Kryo buffer sizeKoert Kuipers2014-07-301-4/+12
| | | | | | | | | | | | Author: Koert Kuipers <koert@tresata.com> Closes #735 from koertkuipers/feat-kryo-max-buffersize and squashes the following commits: 15f6d81 [Koert Kuipers] change default for spark.kryoserializer.buffer.max.mb to 64mb and add some documentation 1bcc22c [Koert Kuipers] Merge branch 'master' into feat-kryo-max-buffersize 0c9f8eb [Koert Kuipers] make default for kryo max buffer size 16MB 143ec4d [Koert Kuipers] test resizable buffer in kryo Output 0732445 [Koert Kuipers] support setting maxCapacity to something different than capacity in kryo Output
* [SPARK-1777] Prevent OOMs from single partitionsAndrew Or2014-07-271-0/+9
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | **Problem.** When caching, we currently unroll the entire RDD partition before making sure we have enough free memory. This is a common cause for OOMs especially when (1) the BlockManager has little free space left in memory, and (2) the partition is large. **Solution.** We maintain a global memory pool of `M` bytes shared across all threads, similar to the way we currently manage memory for shuffle aggregation. Then, while we unroll each partition, periodically check if there is enough space to continue. If not, drop enough RDD blocks to ensure we have at least `M` bytes to work with, then try again. If we still don't have enough space to unroll the partition, give up and drop the block to disk directly if applicable. **New configurations.** - `spark.storage.bufferFraction` - the value of `M` as a fraction of the storage memory. (default: 0.2) - `spark.storage.safetyFraction` - a margin of safety in case size estimation is slightly off. This is the equivalent of the existing `spark.shuffle.safetyFraction`. (default 0.9) For more detail, see the [design document](https://issues.apache.org/jira/secure/attachment/12651793/spark-1777-design-doc.pdf). Tests pending for performance and memory usage patterns. Author: Andrew Or <andrewor14@gmail.com> Closes #1165 from andrewor14/them-rdd-memories and squashes the following commits: e77f451 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories c7c8832 [Andrew Or] Simplify logic + update a few comments 269d07b [Andrew Or] Very minor changes to tests 6645a8a [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories b7e165c [Andrew Or] Add new tests for unrolling blocks f12916d [Andrew Or] Slightly clean up tests 71672a7 [Andrew Or] Update unrollSafely tests 369ad07 [Andrew Or] Correct ensureFreeSpace and requestMemory behavior f4d035c [Andrew Or] Allow one thread to unroll multiple blocks a66fbd2 [Andrew Or] Rename a few things + update comments 68730b3 [Andrew Or] Fix weird scalatest behavior e40c60d [Andrew Or] Fix MIMA excludes ff77aa1 [Andrew Or] Fix tests 1a43c06 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories b9a6eee [Andrew Or] Simplify locking behavior on unrollMemoryMap ed6cda4 [Andrew Or] Formatting fix (super minor) f9ff82e [Andrew Or] putValues -> putIterator + putArray beb368f [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 8448c9b [Andrew Or] Fix tests a49ba4d [Andrew Or] Do not expose unroll memory check period 69bc0a5 [Andrew Or] Always synchronize on putLock before unrollMemoryMap 3f5a083 [Andrew Or] Simplify signature of ensureFreeSpace dce55c8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 8288228 [Andrew Or] Synchronize put and unroll properly 4f18a3d [Andrew Or] bufferFraction -> unrollFraction 28edfa3 [Andrew Or] Update a few comments / log messages 728323b [Andrew Or] Do not synchronize every 1000 elements 5ab2329 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 129c441 [Andrew Or] Fix bug: Use toArray rather than array 9a65245 [Andrew Or] Update a few comments + minor control flow changes 57f8d85 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories abeae4f [Andrew Or] Add comment clarifying the MEMORY_AND_DISK case 3dd96aa [Andrew Or] AppendOnlyBuffer -> Vector (+ a few small changes) f920531 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 0871835 [Andrew Or] Add an effective storage level interface to BlockManager 64e7d4c [Andrew Or] Add/modify a few comments (minor) 8af2f35 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 4f4834e [Andrew Or] Use original storage level for blocks dropped to disk ecc8c2d [Andrew Or] Fix binary incompatibility 24185ea [Andrew Or] Avoid dropping a block back to disk if reading from disk 2b7ee66 [Andrew Or] Fix bug in SizeTracking* 9b9a273 [Andrew Or] Fix tests 20eb3e5 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 649bdb3 [Andrew Or] Document spark.storage.bufferFraction a10b0e7 [Andrew Or] Add initial memory request threshold + rename a few things e9c3cb0 [Andrew Or] cacheMemoryMap -> unrollMemoryMap 198e374 [Andrew Or] Unfold -> unroll 0d50155 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories d9d02a8 [Andrew Or] Remove unused param in unfoldSafely ec728d8 [Andrew Or] Add tests for safe unfolding of blocks 22b2209 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 078eb83 [Andrew Or] Add check for hasNext in PrimitiveVector.iterator 0871535 [Andrew Or] Fix tests in BlockManagerSuite d68f31e [Andrew Or] Safely unfold blocks for all memory puts 5961f50 [Andrew Or] Fix tests 195abd7 [Andrew Or] Refactor: move unfold logic to MemoryStore 1e82d00 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 3ce413e [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories d5dd3b4 [Andrew Or] Free buffer memory in finally ea02eec [Andrew Or] Fix tests b8e1d9c [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories a8704c1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories e1b8b25 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 87aa75c [Andrew Or] Fix mima excludes again (typo) 11eb921 [Andrew Or] Clarify comment (minor) 50cae44 [Andrew Or] Remove now duplicate mima exclude 7de5ef9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories df47265 [Andrew Or] Fix binary incompatibility 6d05a81 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories f94f5af [Andrew Or] Update a few comments (minor) 776aec9 [Andrew Or] Prevent OOM if a single RDD partition is too large bbd3eea [Andrew Or] Fix CacheManagerSuite to use Array 97ea499 [Andrew Or] Change BlockManager interface to use Arrays c12f093 [Andrew Or] Add SizeTrackingAppendOnlyBuffer and tests
* SPARK-2680: Lower spark.shuffle.memoryFraction to 0.2 by defaultMatei Zaharia2014-07-261-1/+1
| | | | | | | | Author: Matei Zaharia <matei@databricks.com> Closes #1593 from mateiz/spark-2680 and squashes the following commits: 3c949c4 [Matei Zaharia] Lower spark.shuffle.memoryFraction to 0.2 by default
* [SPARK-2696] Reduce default value of spark.serializer.objectStreamResetHossein2014-07-261-2/+2
| | | | | | | | | | | | | | The current default value of spark.serializer.objectStreamReset is 10,000. When trying to re-partition (e.g., to 64 partitions) a large file (e.g., 500MB), containing 1MB records, the serializer will cache 10000 x 1MB x 64 ~= 640 GB which will cause out of memory errors. This patch sets the default value to a more reasonable default value (100). Author: Hossein <hossein@databricks.com> Closes #1595 from falaki/objectStreamReset and squashes the following commits: 650a935 [Hossein] Updated documentation 1aa0df8 [Hossein] Reduce default value of spark.serializer.objectStreamReset
* [SPARK-2538] [PySpark] Hash based disk spilling aggregationDavies Liu2014-07-241-0/+9
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | During aggregation in Python worker, if the memory usage is above spark.executor.memory, it will do disk spilling aggregation. It will split the aggregation into multiple stage, in each stage, it will partition the aggregated data by hash and dump them into disks. After all the data are aggregated, it will merge all the stages together (partition by partition). Author: Davies Liu <davies.liu@gmail.com> Closes #1460 from davies/spill and squashes the following commits: cad91bf [Davies Liu] call gc.collect() after data.clear() to release memory as much as possible. 37d71f7 [Davies Liu] balance the partitions 902f036 [Davies Liu] add shuffle.py into run-tests dcf03a9 [Davies Liu] fix memory_info() of psutil 67e6eba [Davies Liu] comment for MAX_TOTAL_PARTITIONS f6bd5d6 [Davies Liu] rollback next_limit() again, the performance difference is huge: e74b785 [Davies Liu] fix code style and change next_limit to memory_limit 400be01 [Davies Liu] address all the comments 6178844 [Davies Liu] refactor and improve docs fdd0a49 [Davies Liu] add long doc string for ExternalMerger 1a97ce4 [Davies Liu] limit used memory and size of objects in partitionBy() e6cc7f9 [Davies Liu] Merge branch 'master' into spill 3652583 [Davies Liu] address comments e78a0a0 [Davies Liu] fix style 24cec6a [Davies Liu] get local directory by SPARK_LOCAL_DIR 57ee7ef [Davies Liu] update docs 286aaff [Davies Liu] let spilled aggregation in Python configurable e9a40f6 [Davies Liu] recursive merger 6edbd1f [Davies Liu] Hash based disk spilling aggregation
* SPARK-2310. Support arbitrary Spark properties on the command line with ...Sandy Ryza2014-07-231-3/+5
| | | | | | | | | | | | | | | | ...spark-submit The PR allows invocations like spark-submit --class org.MyClass --spark.shuffle.spill false myjar.jar Author: Sandy Ryza <sandy@cloudera.com> Closes #1253 from sryza/sandy-spark-2310 and squashes the following commits: 1dc9855 [Sandy Ryza] More doc and cleanup 00edfb9 [Sandy Ryza] Review comments 91b244a [Sandy Ryza] Change format to --conf PROP=VALUE 8fabe77 [Sandy Ryza] SPARK-2310. Support arbitrary Spark properties on the command line with spark-submit
* [SPARK-2102][SQL][CORE] Add option for kryo registration required and use a ↵Ian O Connell2014-07-231-4/+15
| | | | | | | | | | | | | | | resource pool in Spark SQL for Kryo instances. Author: Ian O Connell <ioconnell@twitter.com> Closes #1377 from ianoc/feature/SPARK-2102 and squashes the following commits: 5498566 [Ian O Connell] Docs update suggested by Patrick 20e8555 [Ian O Connell] Slight style change f92c294 [Ian O Connell] Add docs for new KryoSerializer option f3735c8 [Ian O Connell] Add using a kryo resource pool for the SqlSerializer 4e5c342 [Ian O Connell] Register the SparkConf for kryo, it gets swept into serialization 665805a [Ian O Connell] Add a spark.kryo.registrationRequired option for configuring the Kryo Serializer
* [SPARK-2522] set default broadcast factory to torrentXiangrui Meng2014-07-161-1/+1
| | | | | | | | | | HttpBroadcastFactory is the current default broadcast factory. It sends the broadcast data to each worker one by one, which is slow when the cluster is big. TorrentBroadcastFactory scales much better than http. Maybe we should make torrent the default broadcast method. Author: Xiangrui Meng <meng@databricks.com> Closes #1437 from mengxr/bt-broadcast and squashes the following commits: ed492fe [Xiangrui Meng] set default broadcast factory to torrent
* Added LZ4 to compression codec in configuration page.Reynold Xin2014-07-151-5/+4
| | | | | | | | | Author: Reynold Xin <rxin@apache.org> Closes #1417 from rxin/lz4 and squashes the following commits: 472f6a1 [Reynold Xin] Set the proper default. 9cf0b2f [Reynold Xin] Added LZ4 to compression codec in configuration page.
* [SPARK-2399] Add support for LZ4 compression.Reynold Xin2014-07-151-1/+9
| | | | | | | | | | | Based on Greg Bowyer's patch from JIRA https://issues.apache.org/jira/browse/SPARK-2399 Author: Reynold Xin <rxin@apache.org> Closes #1416 from rxin/lz4 and squashes the following commits: 6c8fefe [Reynold Xin] Fixed typo. 8a14d38 [Reynold Xin] [SPARK-2399] Add support for LZ4 compression.
* [SPARK-1946] Submit tasks after (configured ratio) executors have been ↵li-zhihui2014-07-141-0/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | registered Because submitting tasks and registering executors are asynchronous, in most situation, early stages' tasks run without preferred locality. A simple solution is sleeping few seconds in application, so that executors have enough time to register. The PR add 2 configuration properties to make TaskScheduler submit tasks after a few of executors have been registered. \# Submit tasks only after (registered executors / total executors) arrived the ratio, default value is 0 spark.scheduler.minRegisteredExecutorsRatio = 0.8 \# Whatever minRegisteredExecutorsRatio is arrived, submit tasks after the maxRegisteredWaitingTime(millisecond), default value is 30000 spark.scheduler.maxRegisteredExecutorsWaitingTime = 5000 Author: li-zhihui <zhihui.li@intel.com> Closes #900 from li-zhihui/master and squashes the following commits: b9f8326 [li-zhihui] Add logs & edit docs 1ac08b1 [li-zhihui] Add new configs to user docs 22ead12 [li-zhihui] Move waitBackendReady to postStartHook c6f0522 [li-zhihui] Bug fix: numExecutors wasn't set & use constant DEFAULT_NUMBER_EXECUTORS 4d6d847 [li-zhihui] Move waitBackendReady to TaskSchedulerImpl.start & some code refactor 0ecee9a [li-zhihui] Move waitBackendReady from DAGScheduler.submitStage to TaskSchedulerImpl.submitTasks 4261454 [li-zhihui] Add docs for new configs & code style ce0868a [li-zhihui] Code style, rename configuration property name of minRegisteredRatio & maxRegisteredWaitingTime 6cfb9ec [li-zhihui] Code style, revert default minRegisteredRatio of yarn to 0, driver get --num-executors in yarn/alpha 812c33c [li-zhihui] Fix driver lost --num-executors option in yarn-cluster mode e7b6272 [li-zhihui] support yarn-cluster 37f7dc2 [li-zhihui] support yarn mode(percentage style) 3f8c941 [li-zhihui] submit stage after (configured ratio of) executors have been registered
* [SPARK-1341] [Streaming] Throttle BlockGenerator to limit rate of data ↵Issac Buenrostro2014-07-101-0/+9
| | | | | | | | | | | | | consumption. Author: Issac Buenrostro <buenrostro@ooyala.com> Closes #945 from ibuenros/SPARK-1341-throttle and squashes the following commits: 5514916 [Issac Buenrostro] Formatting changes, added documentation for streaming throttling, stricter unit tests for throttling. 62f395f [Issac Buenrostro] Add comments and license to streaming RateLimiter.scala 7066438 [Issac Buenrostro] Moved throttle code to RateLimiter class, smoother pushing when throttling active ccafe09 [Issac Buenrostro] Throttle BlockGenerator to limit rate of data consumption.
* [SPARK-1940] Enabling rolling of executor logs, and automatic cleanup of old ↵Tathagata Das2014-06-101-0/+39
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | executor logs Currently, in the default log4j configuration, all the executor logs get sent to the file <code>[executor-working-dir]/stderr</code>. This does not all log files to be rolled, so old logs cannot be removed. Using log4j RollingFileAppender allows log4j logs to be rolled, but all the logs get sent to a different set of files, other than the files <code>stdout</code> and <code>stderr</code> . So the logs are not visible in the Spark web UI any more as Spark web UI only reads the files <code>stdout</code> and <code>stderr</code>. Furthermore, it still does not allow the stdout and stderr to be cleared periodically in case a large amount of stuff gets written to them (e.g. by explicit `println` inside map function). This PR solves this by implementing a simple `RollingFileAppender` within Spark (disabled by default). When enabled (using configuration parameter `spark.executor.rollingLogs.enabled`), the logs can get rolled over either by time interval (set with `spark.executor.rollingLogs.interval`, set to daily by default), or by size of logs (set with `spark.executor.rollingLogs.size`). Finally, old logs can be automatically deleted by specifying how many of the latest log files to keep (set with `spark.executor.rollingLogs.keepLastN`). The web UI has also been modified to show the logs across the rolled-over files. You can test this locally (without waiting a whole day) by setting configuration `spark.executor.rollingLogs.enabled=true` and `spark.executor.rollingLogs.interval=minutely`. Continuously generate logs by running spark jobs and the generated logs files would look like this (`stderr` and `stdout` are the most current log file that are being written to). ``` stderr stderr--2014-05-27--14-37 stderr--2014-05-27--14-47 stderr--2014-05-27--15-05 stdout stdout--2014-05-27--14-47 ``` The web ui should show logs across these files. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #895 from tdas/rolling-logs and squashes the following commits: fd8f87f [Tathagata Das] Minor change. d326aee [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs ad956c1 [Tathagata Das] Scala style fix. 1f0a6ec [Tathagata Das] Some more changes based on Patrick's PR comments. c8bfe4e [Tathagata Das] Refactore FileAppender to a package spark.util.logging and broke up the file into multiple files. Changed configuration parameter names. 4224409 [Tathagata Das] Style fix. 108a9f8 [Tathagata Das] Added better constraint handling for rolling policies. f7da977 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs 9134495 [Tathagata Das] Simplified rolling logs by removing Daily/Hourly/MinutelyRollingFileAppender, and removing the setting rollingLogs.enabled 312d874 [Tathagata Das] Minor fixes based on PR comments. 8a67d83 [Tathagata Das] Fixed comments. b36cfd6 [Tathagata Das] Implemented RollingPolicy, TimeBasedRollingPolicy and SizeBasedRollingPolicy, and changed RollingFileAppender accordingly. b7e8272 [Tathagata Das] Style fix, 374c9a9 [Tathagata Das] Added missing license. 24354ea [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs 6cc09c7 [Tathagata Das] Fixed bugs in rolling logs, and added more debug statements. adf4910 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into rolling-logs 931f8fb [Tathagata Das] Changed log viewer in Spark web UI to handle rolling log files. cb4fb6d [Tathagata Das] Added FileAppender and RollingFileAppender to generate rolling executor logs.
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