This change exposes the application ID generated by the Spark Master, Mesos or Yarn
via the SparkListenerApplicationStart event. It then uses that information to expose the
application via its ID in the history server, instead of using the internal directory name
generated by the event logger as an application id. This allows someone who knows
the application ID to easily figure out the URL for the application's entry in the HS, aside
from looking better.
In Yarn mode, this is used to generate a direct link from the RM application list to the
Spark history server entry (thus providing a fix for SPARK-2150).
Note this sort of assumes that the different managers will generate app ids that are
sufficiently different from each other that clashes will not occur.
Author: Marcelo Vanzin <vanzin@cloudera.com>
This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrewor14@gmail.com>
Closes#1218 from vanzin/yarn-hs-link-2 and squashes the following commits:
2d19f3c [Marcelo Vanzin] Review feedback.
6706d3a [Marcelo Vanzin] Implement applicationId() in base classes.
56fe42e [Marcelo Vanzin] Fix cluster mode history address, plus a cleanup.
44112a8 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
8278316 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
a86bbcf [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
a0056e6 [Marcelo Vanzin] Unbreak test.
4b10cfd [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
cb0cab2 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
25f2826 [Marcelo Vanzin] Add MIMA excludes.
f0ba90f [Marcelo Vanzin] Use BufferedIterator.
c90a08d [Marcelo Vanzin] Remove unused code.
3f8ec66 [Marcelo Vanzin] Review feedback.
21aa71b [Marcelo Vanzin] Fix JSON test.
b022bae [Marcelo Vanzin] Undo SparkContext cleanup.
c6d7478 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
4e3483f [Marcelo Vanzin] Fix test.
57517b8 [Marcelo Vanzin] Review feedback. Mostly, more consistent use of Scala's Option.
311e49d [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
d35d86f [Marcelo Vanzin] Fix yarn backend after rebase.
36dc362 [Marcelo Vanzin] Don't use Iterator::takeWhile().
0afd696 [Marcelo Vanzin] Wait until master responds before returning from start().
abc4697 [Marcelo Vanzin] Make FsHistoryProvider keep a map of applications by id.
26b266e [Marcelo Vanzin] Use Mesos framework ID as Spark application ID.
b3f3664 [Marcelo Vanzin] [yarn] Make the RM link point to the app direcly in the HS.
2fb7de4 [Marcelo Vanzin] Expose the application ID in the ApplicationStart event.
ed10348 [Marcelo Vanzin] Expose application id to spark context.
These are not used by the UI but are useful when analysing the
logs from a spark job.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#654 from vanzin/bm-event-tstamp and squashes the following commits:
d5d6e66 [Marcelo Vanzin] Fix tests.
ec06218 [Marcelo Vanzin] Review feedback.
f134dbc [Marcelo Vanzin] Merge branch 'master' into bm-event-tstamp
b495b7c [Marcelo Vanzin] Merge branch 'master' into bm-event-tstamp
7d2fe9e [Marcelo Vanzin] Review feedback.
d6f381c [Marcelo Vanzin] Update tests added after patch was created.
45e3bf8 [Marcelo Vanzin] Fix unit test after merge.
b37a10f [Marcelo Vanzin] Use === in test assertions.
ef72824 [Marcelo Vanzin] Handle backwards compatibility with 1.0.0.
aca1151 [Marcelo Vanzin] Fix unit test to check new fields.
efdda8e [Marcelo Vanzin] Add timestamps to block manager events.
RDD.countApproxDistinct(relativeSD=0.05):
:: Experimental ::
Return approximate number of distinct elements in the RDD.
The algorithm used is based on streamlib's implementation of
"HyperLogLog in Practice: Algorithmic Engineering of a State
of The Art Cardinality Estimation Algorithm", available
<a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
This support all the types of objects, which is supported by
Pyrolite, nearly all builtin types.
param relativeSD Relative accuracy. Smaller values create
counters that require more space.
It must be greater than 0.000017.
>>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct()
>>> 950 < n < 1050
True
>>> n = sc.parallelize([i % 20 for i in range(1000)]).countApproxDistinct()
>>> 18 < n < 22
True
Author: Davies Liu <davies.liu@gmail.com>
Closes#2142 from davies/countApproxDistinct and squashes the following commits:
e20da47 [Davies Liu] remove the correction in Python
c38c4e4 [Davies Liu] fix doc tests
2ab157c [Davies Liu] fix doc tests
9d2565f [Davies Liu] add commments and link for hash collision correction
d306492 [Davies Liu] change range of hash of tuple to [0, maxint]
ded624f [Davies Liu] calculate hash in Python
4cba98f [Davies Liu] add more tests
a85a8c6 [Davies Liu] Merge branch 'master' into countApproxDistinct
e97e342 [Davies Liu] add countApproxDistinct()
...job fails while reading from Hadoop
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1956 from sryza/sandy-spark-3052 and squashes the following commits:
815813a [Sandy Ryza] SPARK-3052. Misleading and spurious FileSystem closed errors whenever a job fails while reading from Hadoop
The Maven build was failing on Windows because it tried to call the unix `unzip` utility to extract the Py4J files into core's build directory. I've fixed this issue by using the `maven-antrun-plugin` to perform the unzipping.
I also fixed an issue that prevented tests from running under Windows:
In the Maven ScalaTest plugin, the filename listed in <filereports> is placed under the <reportsDirectory>; the current code places it in a subdirectory of reportsDirectory, e.g.
```
${project.build.directory}/surefire-reports/${project.build.directory}/SparkTestSuite.txt
```
This caused problems under Windows because it would try to create a subdirectory named "c:\\".
Note that the tests still fail under Windows (for other reasons); this PR just allows them to run and fail rather than crash when trying to create the test reports directory.
Author: Josh Rosen <joshrosen@apache.org>
Author: Josh Rosen <rosenville@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#2165 from JoshRosen/windows-support and squashes the following commits:
651d210 [Josh Rosen] Unzip to python/build instead of core/build
fbf3e61 [Josh Rosen] 4 spaces -> 2 spaces
e347668 [Josh Rosen] Fix Maven scalatest filereports path:
4994af1 [Josh Rosen] [SPARK-3061] Use maven-antrun-plugin to unzip Py4J.
This PR adds the async actions to the Java API. User can call these async actions to get the FutureAction and use JobWaiter (for SimpleFutureAction) to retrieve job Id.
Author: lirui <rui.li@intel.com>
Closes#2176 from lirui-intel/SPARK-2636 and squashes the following commits:
ccaafb7 [lirui] SPARK-2636: fix java doc
5536d55 [lirui] SPARK-2636: mark the async API as experimental
e2e01d5 [lirui] SPARK-2636: add mima exclude
0ca320d [lirui] SPARK-2636: fix method name & javadoc
3fa39f7 [lirui] SPARK-2636: refine the patch
af4f5d9 [lirui] SPARK-2636: remove unused imports
843276c [lirui] SPARK-2636: only keep foreachAsync in the java API
fbf5744 [lirui] SPARK-2636: add more async actions for java api
1b25abc [lirui] SPARK-2636: expose some fields in JobWaiter
d09f732 [lirui] SPARK-2636: fix build
eb1ee79 [lirui] SPARK-2636: change some parameters in SimpleFutureAction to member field
6e2b87b [lirui] SPARK-2636: add java API for async actions
This also enables supporting broadcast variables larger than 2G.
Author: Reynold Xin <rxin@apache.org>
Closes#2054 from rxin/ByteArrayChunkOutputStream and squashes the following commits:
618d9c8 [Reynold Xin] Code review.
93f5a51 [Reynold Xin] Added comments.
ee88e73 [Reynold Xin] to -> until
bbd1cb1 [Reynold Xin] Renamed a variable.
36f4d01 [Reynold Xin] Sort imports.
8f1a8eb [Reynold Xin] [SPARK-3135] Created ByteArrayChunkOutputStream and used it to avoid memory copy in TorrentBroadcast.
Rather than specifying the path to SparkFiles we need to use the filename.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#2210 from holdenk/SPARK-3318-documentation-for-addfiles-should-say-to-use-file-not-path and squashes the following commits:
a25d27a [Holden Karau] Update the JavaSparkContext addFile method to be clear about using fileName with SparkFiles as well
0ebcb05 [Holden Karau] Documentation update in addFile on how to use SparkFiles.get to specify filename rather than path
Different places in the code were instantiating Configuration / YarnConfiguration objects in different ways. This could lead to confusion for people who actually expected "spark.hadoop.*" options to end up in the configs used by Spark code, since that would only happen for the SparkContext's config.
This change modifies most places to use SparkHadoopUtil to initialize configs, and make that method do the translation that previously was only done inside SparkContext.
The places that were not changed fall in one of the following categories:
- Test code where this doesn't really matter
- Places deep in the code where plumbing SparkConf would be too difficult for very little gain
- Default values for arguments - since the caller can provide their own config in that case
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#1843 from vanzin/SPARK-2889 and squashes the following commits:
52daf35 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
f179013 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
51e71cf [Marcelo Vanzin] Add test to ensure that overriding Yarn configs works.
53f9506 [Marcelo Vanzin] Add DeveloperApi annotation.
3d345cb [Marcelo Vanzin] Restore old method for backwards compat.
fc45067 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
0ac3fdf [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
3f26760 [Marcelo Vanzin] Compilation fix.
f16cadd [Marcelo Vanzin] Initialize config in SparkHadoopUtil.
b8ab173 [Marcelo Vanzin] Update Utils API to take a Configuration argument.
1e7003f [Marcelo Vanzin] Replace explicit Configuration instantiation with SparkHadoopUtil.
By Hiding the shuffleblockmanager behind Shufflemanager, we decouple the shuffle data's block mapping management work from Diskblockmananger. This give a more clear interface and more easy for other shuffle manager to implement their own block management logic. the jira ticket have more details.
Author: Raymond Liu <raymond.liu@intel.com>
Closes#1241 from colorant/shuffle and squashes the following commits:
0e01ae3 [Raymond Liu] Move ShuffleBlockmanager behind shuffleManager
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2200 from sarutak/SPARK-3305 and squashes the following commits:
3cbd6ee [Kousuke Saruta] Removed unused import from classes related to UI
This is a simplified fix for SPARK-1912.
Author: Reynold Xin <rxin@apache.org>
Closes#2179 from rxin/SPARK-1912 and squashes the following commits:
b2f0e9e [Reynold Xin] Fix unit tests.
a8eddfe [Reynold Xin] [SPARK-1912] Lazily initialize buffers for local shuffle blocks.
**Summary of the changes**
The bulk of this PR is comprised of tests and documentation; the actual fix is really just adding 1 line of code (see `BlockObjectWriter.scala`). We currently do not run the `External*` test suites with different compression codecs, and this would have caught the bug reported in [SPARK-3277](https://issues.apache.org/jira/browse/SPARK-3277). This PR extends the existing code to test spilling using all compression codecs known to Spark, including `LZ4`.
**The bug itself**
In `DiskBlockObjectWriter`, we only report the shuffle bytes written before we close the streams. With `LZ4`, all the bytes written reported by our metrics were 0 because `flush()` was not taking effect for some reason. In general, compression codecs may write additional bytes to the file after we call `close()`, and so we must also capture those bytes in our shuffle write metrics.
Thanks mridulm and pwendell for help with debugging.
Author: Andrew Or <andrewor14@gmail.com>
Author: Patrick Wendell <pwendell@gmail.com>
Closes#2187 from andrewor14/fix-lz4-spilling and squashes the following commits:
1b54bdc [Andrew Or] Speed up tests by not compressing everything
1c4624e [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-lz4-spilling
6b2e7d1 [Andrew Or] Fix compilation error
92e251b [Patrick Wendell] Better documentation for BlockObjectWriter.
a1ad536 [Andrew Or] Fix tests
089593f [Andrew Or] Actually fix SPARK-3277 (tests still fail)
4bbcf68 [Andrew Or] Update tests to actually test all compression codecs
b264a84 [Andrew Or] ExternalAppendOnlyMapSuite code style fixes (minor)
1bfa743 [Andrew Or] Add more information to assert for better debugging
Netty functionality will be added back in subsequent PRs by using the BlockTransferService interface.
Author: Reynold Xin <rxin@apache.org>
Closes#2181 from rxin/SPARK-3281 and squashes the following commits:
5494b0e [Reynold Xin] Fix extra port.
ff6d1e1 [Reynold Xin] [SPARK-3281] Remove Netty specific code in BlockManager.
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
This is another try after #2145 to fix [SPARK-2608](https://issues.apache.org/jira/browse/SPARK-2608).
The basic idea is to pass `extraJavaOpts` and `extraLibraryPath` together via environment variable `SPARK_EXECUTOR_OPTS`. This variable is recognized by `spark-class` and not used anywhere else. In this way, we still launch Mesos executors with `spark-class`/`spark-executor`, but avoids the executor side Spark home issue.
Quoted string with spaces is not allowed in either `extraJavaOpts` or `extraLibraryPath` when using Spark over Mesos. The reason is that Mesos passes the whole command line as a single string argument to `sh -c` to start the executor, and this makes shell string escaping non-trivial to handle. This should be fixed in a later release.
Classes in package `org.apache.spark.deploy` shouldn't be used as they assume Spark is deployed in standalone mode, and give wrong executor side Spark home directory. Please refer to comments in #2145 for more details.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2161 from liancheng/mesos-fix-with-env-var and squashes the following commits:
ba59190 [Cheng Lian] Added fine grained Mesos executor support
1174076 [Cheng Lian] Draft fix for CoarseMesosSchedulerBackend
(cherry picked from commit 935bffe3bf)
Signed-off-by: Reynold Xin <rxin@apache.org>
The issue happens when Spark is run standalone on a cluster.
When master and driver fall simultaneously on one node in a cluster, master tries to recover its state and restart spark driver.
While restarting driver, it falls with NPE exception (stacktrace is below).
After falling, it restarts and tries to recover its state and restart Spark driver again. It happens over and over in an infinite cycle.
Namely, Spark tries to read DriverInfo state from zookeeper, but after reading it happens to be null in DriverInfo.worker.
https://issues.apache.org/jira/browse/SPARK-3150
Author: Tatiana Borisova <tanyatik@yandex.ru>
Closes#2062 from tanyatik/spark-3150 and squashes the following commits:
9936043 [Tatiana Borisova] Add initializing default values in DriverInfo.init()
In `SparkSubmitDriverBootstrapper`, we wait for the parent process to send us an `EOF` before finishing the application. This is applicable for the PySpark shell because we terminate the application the same way. However if we run a python application, for instance, the JVM actually never exits unless it receives a manual EOF from the user. This is causing a few tests to timeout.
We only need to do this for the PySpark shell because Spark submit runs as a python subprocess only in this case. Thus, the normal Spark shell doesn't need to go through this case even though it is also a REPL.
Thanks davies for reporting this.
Author: Andrew Or <andrewor14@gmail.com>
Closes#2170 from andrewor14/bootstrap-hotfix and squashes the following commits:
42963f5 [Andrew Or] Do not wait for EOF unless this is the pyspark shell
delete no used method in Utils
Author: scwf <wangfei1@huawei.com>
Closes#2160 from scwf/delete-no-use-method and squashes the following commits:
d8f6b0d [scwf] delete no use method in Utils
If we set both `spark.driver.extraClassPath` and `--driver-class-path`, then the latter correctly overrides the former. However, the value of the system property `spark.driver.extraClassPath` still uses the former, which is actually not added to the class path. This may cause some confusion...
Of course, this also affects other options (i.e. java options, library path, memory...).
Author: Andrew Or <andrewor14@gmail.com>
Closes#2154 from andrewor14/driver-submit-configs-fix and squashes the following commits:
17ec6fc [Andrew Or] Fix tests
0140836 [Andrew Or] Don't forget spark.driver.memory
e39d20f [Andrew Or] Also set spark.driver.extra* configs in client mode
compeleted stage only need to remove its own partitions that are no longer cached. However, "StorageTab" may lost some rdds which are cached actually. Not only in "StorageTab", "ExectutorTab" may also lose some rdd info which have been overwritten by last rdd in a same task.
1. "StorageTab": when multiple stages run simultaneously, completed stage will remove rdd info which belong to other stages that are still running.
2. "ExectutorTab": taskcontext may lose some "updatedBlocks" info of rdds in a dependency chain. Like the following example:
val r1 = sc.paralize(..).cache()
val r2 = r1.map(...).cache()
val n = r2.count()
When count the r2, r1 and r2 will be cached finally. So in CacheManager.getOrCompute, the taskcontext should contain "updatedBlocks" of r1 and r2. Currently, the "updatedBlocks" only contain the info of r2.
Author: uncleGen <hustyugm@gmail.com>
Closes#2131 from uncleGen/master_ui_fix and squashes the following commits:
a6a8a0b [uncleGen] fix some coding style
3a1bc15 [uncleGen] fix some error in unit test
56ea488 [uncleGen] there's some line too long
c82ba82 [uncleGen] Bug Fix: RDD info loss in "StorageTab" and "ExecutorTab"
As a workaround for SPARK-3015, the ContextCleaner was made "blocking", that is, it cleaned items one-by-one. But shuffles can take a long time to be deleted. Given that the RC for 1.1 is imminent, this PR makes a narrow change in the context cleaner - not wait for shuffle cleanups to complete. Also it changes the error messages on failure to delete to be milder warnings, as exceptions in the delete code path for one item does not really stop the actual functioning of the system.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#2143 from tdas/cleaner-shuffle-fix and squashes the following commits:
9c84202 [Tathagata Das] Restoring default blocking behavior in ContextCleanerSuite, and added docs to identify that spark.cleaner.referenceTracking.blocking does not control shuffle.
2181329 [Tathagata Das] Mark shuffle cleanup as non-blocking.
e337cc2 [Tathagata Das] Changed semantics based on PR comments.
387b578 [Tathagata Das] Made ContextCleaner to not block on shuffles
This is an effort to bring the Windows scripts up to speed after recent splashing changes in #1845.
Author: Andrew Or <andrewor14@gmail.com>
Closes#2129 from andrewor14/windows-config and squashes the following commits:
881a8f0 [Andrew Or] Add reference to Windows taskkill
92e6047 [Andrew Or] Update a few comments (minor)
22b1acd [Andrew Or] Fix style again (minor)
afcffea [Andrew Or] Fix style (minor)
72004c2 [Andrew Or] Actually respect --driver-java-options
803218b [Andrew Or] Actually respect SPARK_*_CLASSPATH
eeb34a0 [Andrew Or] Update outdated comment (minor)
35caecc [Andrew Or] In Windows, actually kill Java processes on exit
f97daa2 [Andrew Or] Fix Windows spark shell stdin issue
83ebe60 [Andrew Or] Parse special driver configs in Windows (broken)
This is a HOTFIX for 1.1.
Author: Reynold Xin <rxin@apache.org>
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#2127 from rxin/SPARK-3224 and squashes the following commits:
effb1ce [Reynold Xin] Move log message.
49282b3 [Reynold Xin] Kay's feedback.
3f01847 [Reynold Xin] Merge pull request #2 from kayousterhout/SPARK-3224
796d282 [Kay Ousterhout] Added unit test for SPARK-3224
3d3d356 [Reynold Xin] Remove map output loc even for repeated FetchFaileds.
1dd3eb5 [Reynold Xin] [SPARK-3224] FetchFailed reduce stages should only show up once in the failed stages UI.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#1886 from sarutak/SPARK-2964 and squashes the following commits:
8ef8751 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2964
26e7c95 [Kousuke Saruta] Revert "Shorten timeout to more reasonable value"
ffb68fa [Kousuke Saruta] Modified spark-sql and start-thriftserver.sh to use bin/utils.sh
8c6f658 [Kousuke Saruta] Merge branch 'spark-3026' of https://github.com/liancheng/spark into SPARK-2964
81b43a8 [Cheng Lian] Shorten timeout to more reasonable value
a89e66d [Cheng Lian] Fixed command line options quotation in scripts
9c894d3 [Cheng Lian] Fixed bin/spark-sql -S option typo
be4736b [Cheng Lian] Report better error message when running JDBC/CLI without hive-thriftserver profile enabled
As of #1777 we log the name of the actor system when it binds to a port. The current name "spark" is super general and does not convey any meaning. For instance, the following line is taken from my driver log after setting `spark.driver.port` to 5001.
```
14/08/13 19:33:29 INFO Remoting: Remoting started; listening on addresses:
[akka.tcp://sparkandrews-mbp:5001]
14/08/13 19:33:29 INFO Remoting: Remoting now listens on addresses:
[akka.tcp://sparkandrews-mbp:5001]
14/08/06 13:40:05 INFO Utils: Successfully started service 'spark' on port 5001.
```
This commit renames this to "sparkDriver" and "sparkExecutor". The goal of this unambitious PR is simply to make the logged information more explicit without introducing any change in functionality.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1810 from andrewor14/service-name and squashes the following commits:
8c459ed [Andrew Or] Use a common variable for driver/executor actor system names
3a92843 [Andrew Or] Change actor name to sparkDriver and sparkExecutor
921363e [Andrew Or] Merge branch 'master' of github.com:apache/spark into service-name
c8c6a62 [Andrew Or] Do not include hyphens in actor name
1c1b42e [Andrew Or] Avoid spaces in akka system name
f644b55 [Andrew Or] Use more specific service name
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#1895 from sarutak/SPARK-2976 and squashes the following commits:
1cf7e69 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2976
d1e0666 [Kousuke Saruta] Modified styles
c5e80a4 [Kousuke Saruta] Remove tab from JavaPageRank.java and JavaKinesisWordCountASL.java
c003b36 [Kousuke Saruta] Removed tab from sorttable.js
rxin
Author: Xiangrui Meng <meng@databricks.com>
Closes#2120 from mengxr/sendMessageReliably and squashes the following commits:
b14400c [Xiangrui Meng] fix error message in sendMessageReliably
Just clean unused code which have been moved into ExternalSorter.
Author: Raymond Liu <raymond.liu@intel.com>
Closes#1882 from colorant/sortShuffleWriter and squashes the following commits:
e6337be [Raymond Liu] Clean unused code in SortShuffleWriter
RDD.countApprox(self, timeout, confidence=0.95)
:: Experimental ::
Approximate version of count() that returns a potentially incomplete
result within a timeout, even if not all tasks have finished.
>>> rdd = sc.parallelize(range(1000), 10)
>>> rdd.countApprox(1000, 1.0)
1000
RDD.sumApprox(self, timeout, confidence=0.95)
Approximate operation to return the sum within a timeout
or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(xrange(1000))
>>> (rdd.sumApprox(1000) - r) / r < 0.05
RDD.meanApprox(self, timeout, confidence=0.95)
:: Experimental ::
Approximate operation to return the mean within a timeout
or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(xrange(1000)) / 1000.0
>>> (rdd.meanApprox(1000) - r) / r < 0.05
True
Author: Davies Liu <davies.liu@gmail.com>
Closes#2095 from davies/approx and squashes the following commits:
e8c252b [Davies Liu] add approx API for RDD
Fix a typo in comment.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#2105 from viirya/fix_typo and squashes the following commits:
6596a80 [Liang-Chi Hsieh] fix typo.
In JVM 1.8.0, MaxPermSize is no longer supported.
In spark `stderr` output, there would be a line of
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#2011 from adrian-wang/maxpermsize and squashes the following commits:
ef1d660 [Daoyuan Wang] direct get java version in runtime
37db9c1 [Daoyuan Wang] code refine
3c1d554 [Daoyuan Wang] remove MaxPermSize option for jvm 1.8
For further discussion, please check the JIRA entry.
This change moves Guava classes to a different package so that they don't conflict with the user-provided Guava (or the Hadoop-provided one). Since one class (Optional) was exposed through Spark's public API, that class was forked from Guava at the current dependency version (14.0.1) so that it can be kept going forward (until the API is cleaned).
Note this change has a few implications:
- *all* classes in the final jars will reference the relocated classes. If Hadoop classes are included (i.e. "-Phadoop-provided" is not activated), those will also reference the Guava 14 classes (instead of the Guava 11 classes from the Hadoop classpath).
- if the Guava version in Spark is ever changed, the new Guava will still reference the forked Optional class; this may or may not be a problem, but in the long term it's better to think about removing Optional from the public API.
For the end user, there are two visible implications:
- Guava is not provided as a transitive dependency anymore (since it's "provided" in Spark)
- At runtime, unless they provide their own, they'll either have no Guava or Hadoop's version of Guava (11), depending on how they set up their classpath.
Note that this patch does not change the sbt deliverables; those will still contain guava in its original package, and provide guava as a compile-time dependency. This assumes that maven is the canonical build, and sbt-built artifacts are not (officially) published.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#1813 from vanzin/SPARK-2848 and squashes the following commits:
9bdffb0 [Marcelo Vanzin] Undo sbt build changes.
819b445 [Marcelo Vanzin] Review feedback.
05e0a3d [Marcelo Vanzin] Merge branch 'master' into SPARK-2848
fef4370 [Marcelo Vanzin] Unfork Optional.java.
d3ea8e1 [Marcelo Vanzin] Exclude asm classes from final jar.
637189b [Marcelo Vanzin] Add hacky filter to prefer Spark's copy of Optional.
2fec990 [Marcelo Vanzin] Shade Guava in the sbt build.
616998e [Marcelo Vanzin] Shade Guava in the maven build, fork Guava's Optional.java.
Simple way to reproduce this in the UI:
```scala
val f = new java.io.File("/tmp/test")
f.delete()
sc.parallelize(1 to 2, 2).map(x => (x,x )).repartition(3).mapPartitionsWithContext { case (context, iter) =>
if (context.partitionId == 0) {
val f = new java.io.File("/tmp/test")
if (!f.exists) {
f.mkdir()
System.exit(0);
}
}
iter
}.count()
```
Author: Reynold Xin <rxin@apache.org>
Closes#1545 from rxin/stage-attempt and squashes the following commits:
3ee1d2a [Reynold Xin] - Rename attempt to retry in UI. - Properly report stage failure in FetchFailed.
40a6bd5 [Reynold Xin] Updated test suites.
c414c36 [Reynold Xin] Fixed the hanging in JobCancellationSuite.
b3e2eed [Reynold Xin] Oops previous code didn't compile.
0f36075 [Reynold Xin] Mark unknown stage attempt with id -1 and drop that in JobProgressListener.
6c08b07 [Reynold Xin] Addressed code review feedback.
4e5faa2 [Reynold Xin] [SPARK-2298] Encode stage attempt in SparkListener & UI.
In client deploy mode, the driver is launched from within `SparkSubmit`'s JVM. This means by the time we parse Spark configs from `spark-defaults.conf`, it is already too late to control certain properties of the driver's JVM. We currently ignore these configs in client mode altogether.
```
spark.driver.memory
spark.driver.extraJavaOptions
spark.driver.extraClassPath
spark.driver.extraLibraryPath
```
This PR handles these properties before launching the driver JVM. It achieves this by spawning a separate JVM that runs a new class called `SparkSubmitDriverBootstrapper`, which spawns `SparkSubmit` as a sub-process with the appropriate classpath, library paths, java opts and memory.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1845 from andrewor14/handle-configs-bash and squashes the following commits:
bed4bdf [Andrew Or] Change a few comments / messages (minor)
24dba60 [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
08fd788 [Andrew Or] Warn against external usages of SparkSubmitDriverBootstrapper
ff34728 [Andrew Or] Minor comments
51aeb01 [Andrew Or] Filter out JVM memory in Scala rather than Bash (minor)
9a778f6 [Andrew Or] Fix PySpark: actually kill driver on termination
d0f20db [Andrew Or] Don't pass empty library paths, classpath, java opts etc.
a78cb26 [Andrew Or] Revert a few changes in utils.sh (minor)
9ba37e2 [Andrew Or] Don't barf when the properties file does not exist
8867a09 [Andrew Or] A few more naming things (minor)
19464ad [Andrew Or] SPARK_SUBMIT_JAVA_OPTS -> SPARK_SUBMIT_OPTS
d6488f9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
1ea6bbe [Andrew Or] SparkClassLauncher -> SparkSubmitDriverBootstrapper
a91ea19 [Andrew Or] Fix precedence of library paths, classpath, java opts and memory
158f813 [Andrew Or] Remove "client mode" boolean argument
c84f5c8 [Andrew Or] Remove debug print statement (minor)
b71f52b [Andrew Or] Revert a few more changes (minor)
7d94a8d [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
3a8235d [Andrew Or] Only parse the properties file if special configs exist
c37e08d [Andrew Or] Revert a few more changes
a396eda [Andrew Or] Nullify my own hard work to simplify bash
0effa1e [Andrew Or] Add code in Scala that handles special configs
c886568 [Andrew Or] Fix lines too long + a few comments / style (minor)
7a4190a [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
7396be2 [Andrew Or] Explicitly comment that multi-line properties are not supported
fa11ef8 [Andrew Or] Parse the properties file only if the special configs exist
371cac4 [Andrew Or] Add function prefix (minor)
be99eb3 [Andrew Or] Fix tests to not include multi-line configs
bd0d468 [Andrew Or] Simplify parsing config file by ignoring multi-line arguments
56ac247 [Andrew Or] Use eval and set to simplify splitting
8d4614c [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
aeb79c7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
2732ac0 [Andrew Or] Integrate BASH tests into dev/run-tests + log error properly
8d26a5c [Andrew Or] Add tests for bash/utils.sh
4ae24c3 [Andrew Or] Fix bug: escape properly in quote_java_property
b3c4cd5 [Andrew Or] Fix bug: count the number of quotes instead of detecting presence
c2273fc [Andrew Or] Fix typo (minor)
e793e5f [Andrew Or] Handle multi-line arguments
5d8f8c4 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
c7b9926 [Andrew Or] Minor changes to spark-defaults.conf.template
a992ae2 [Andrew Or] Escape spark.*.extraJavaOptions correctly
aabfc7e [Andrew Or] escape -> split (minor)
45a1eb9 [Andrew Or] Fix bug: escape escaped backslashes and quotes properly...
1cdc6b1 [Andrew Or] Fix bug: escape escaped double quotes properly
c854859 [Andrew Or] Add small comment
c13a2cb [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
8e552b7 [Andrew Or] Include an example of spark.*.extraJavaOptions
de765c9 [Andrew Or] Print spark-class command properly
a4df3c4 [Andrew Or] Move parsing and escaping logic to utils.sh
dec2343 [Andrew Or] Only export variables if they exist
fa2136e [Andrew Or] Escape Java options + parse java properties files properly
ef12f74 [Andrew Or] Minor formatting
4ec22a1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
e5cfb46 [Andrew Or] Collapse duplicate code + fix potential whitespace issues
4edcaa8 [Andrew Or] Redirect stdout to stderr for python
130f295 [Andrew Or] Handle spark.driver.memory too
98dd8e3 [Andrew Or] Add warning if properties file does not exist
8843562 [Andrew Or] Fix compilation issues...
75ee6b4 [Andrew Or] Remove accidentally added file
63ed2e9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
0025474 [Andrew Or] Revert SparkSubmit handling of --driver-* options for only cluster mode
a2ab1b0 [Andrew Or] Parse spark.driver.extra* in bash
250cb95 [Andrew Or] Do not ignore spark.driver.extra* for client mode
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2060 from sarutak/SPARK-3149 and squashes the following commits:
1cc89af [Kousuke Saruta] Modified log message of accepting connection
#1891 was to avoid IOException when EventLogging is enabled.
The solution used ShutdownHookManager but it was defined only Hadoop 2.x. Hadoop 1.x don't have ShutdownHookManager so #1891 doesn't compile on Hadoop 1.x
Now, I had a compromised solution for both Hadoop 1.x and 2.x.
Only for FileLogger, an unique FileSystem object is created.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#1970 from sarutak/SPARK-2970 and squashes the following commits:
240c91e [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2970
0e7b45d [Kousuke Saruta] Revert "[SPARK-2970] [SQL] spark-sql script ends with IOException when EventLogging is enabled"
e1262ec [Kousuke Saruta] Modified Filelogger to use unique FileSystem instance
This PR fixes two bugs related to `spark.local.dirs` and `SPARK_LOCAL_DIRS`, one where `Utils.getLocalDir()` might return an invalid directory (SPARK-2974) and another where the `SPARK_LOCAL_DIRS` override didn't affect the driver, which could cause problems when running tasks in local mode (SPARK-2975).
This patch fixes both issues: the new `Utils.getOrCreateLocalRootDirs(conf: SparkConf)` utility method manages the creation of local directories and handles the precedence among the different configuration options, so we should see the same behavior whether we're running in local mode or on a worker.
It's kind of a pain to mock out environment variables in tests (no easy way to mock System.getenv), so I added a `private[spark]` method to SparkConf for accessing environment variables (by default, it just delegates to System.getenv). By subclassing SparkConf and overriding this method, we can mock out SPARK_LOCAL_DIRS in tests.
I also fixed a typo in PySpark where we used `SPARK_LOCAL_DIR` instead of `SPARK_LOCAL_DIRS` (I think this was technically innocuous, but it seemed worth fixing).
Author: Josh Rosen <joshrosen@apache.org>
Closes#2002 from JoshRosen/local-dirs and squashes the following commits:
efad8c6 [Josh Rosen] Address review comments:
1dec709 [Josh Rosen] Minor updates to Javadocs.
7f36999 [Josh Rosen] Use env vars to detect if running in YARN container.
399ac25 [Josh Rosen] Update getLocalDir() documentation.
bb3ad89 [Josh Rosen] Remove duplicated YARN getLocalDirs() code.
3e92d44 [Josh Rosen] Move local dirs override logic into Utils; fix bugs:
b2c4736 [Josh Rosen] Add failing tests for SPARK-2974 and SPARK-2975.
007298b [Josh Rosen] Allow environment variables to be mocked in tests.
6d9259b [Josh Rosen] Fix typo in PySpark: SPARK_LOCAL_DIR should be SPARK_LOCAL_DIRS
This is a re-implementation of TorrentBroadcast, with the following changes:
1. Removes most of the mutable, transient state from TorrentBroadcast (e.g. totalBytes, num of blocks fetched).
2. Removes TorrentInfo and TorrentBlock
3. Replaces the BlockManager.getSingle call in readObject with a getLocal, resuling in one less RPC call to the BlockManagerMasterActor to find the location of the block.
4. Removes the metadata block, resulting in one less block to fetch.
5. Removes an extra memory copy for deserialization (by using Java's SequenceInputStream).
Basically for a regular broadcasted object with only one block, the number of RPC calls goes from 5+1 to 2+1).
Old TorrentBroadcast for object of a single block:
1 RPC to ask for location of the broadcast variable
1 RPC to ask for location of the metadata block
1 RPC to fetch the metadata block
1 RPC to ask for location of the first data block
1 RPC to fetch the first data block
1 RPC to tell the driver we put the first data block in
i.e. 5 + 1
New TorrentBroadcast for object of a single block:
1 RPC to ask for location of the first data block
1 RPC to get the first data block
1 RPC to tell the driver we put the first data block in
i.e. 2 + 1
Author: Reynold Xin <rxin@apache.org>
Closes#2030 from rxin/torrentBroadcast and squashes the following commits:
5bacb9d [Reynold Xin] Always add the object to driver's block manager.
0d8ed5b [Reynold Xin] Added getBytes to BlockManager and uses that in TorrentBroadcast.
2d6a5fb [Reynold Xin] Use putBytes/getRemoteBytes throughout.
3670f00 [Reynold Xin] Code review feedback.
c1185cd [Reynold Xin] [SPARK-3119] Re-implementation of TorrentBroadcast.
Previous pull request (#1907) was reverted. This brings it back. Still looking into the hang.
Author: Reynold Xin <rxin@apache.org>
Closes#1971 from rxin/netty1 and squashes the following commits:
b0be96f [Reynold Xin] Added test to make sure outstandingRequests are cleaned after firing the events.
4c6d0ee [Reynold Xin] Pass callbacks cleanly.
603dce7 [Reynold Xin] Upgrade Netty to 4.0.23 to fix the DefaultFileRegion bug.
88be1d4 [Reynold Xin] Downgrade to 4.0.21 to work around a bug in writing DefaultFileRegion.
002626a [Reynold Xin] Remove netty-test-file.txt.
db6e6e0 [Reynold Xin] Revert "Revert "[SPARK-2468] Netty based block server / client module""
Move a bracket in validateSettings of SparkConf
Author: hzw19900416 <carlmartinmax@gmail.com>
Closes#2012 from hzw19900416/codereading and squashes the following commits:
e717fb6 [hzw19900416] Move a bracket in validateSettings of SparkConf
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2000 from sarutak/SPARK-3089 and squashes the following commits:
02dfdea [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3089
e759ce7 [Kousuke Saruta] Improved error message when closing SendingConnection
Author: Reynold Xin <rxin@apache.org>
Closes#2028 from rxin/torrentBroadcast and squashes the following commits:
92c62a5 [Reynold Xin] Revert the MEMORY_AND_DISK_SER changes.
03a5221 [Reynold Xin] [SPARK-3116] Remove the excessive lockings in TorrentBroadcast
Due to the way Yarn runs things through bash, normal quoting doesn't
work as expected. This change applies the necessary voodoo to the user
args to avoid issues with bash and special characters.
The change also uncovered an issue with the event logger app name
sanitizing code; it wasn't cleaning up all "bad" characters, so
sometimes it would fail to create the log dirs. I just added some
more bad character replacements.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#1724 from vanzin/SPARK-2718 and squashes the following commits:
cc84b89 [Marcelo Vanzin] Review feedback.
c1a257a [Marcelo Vanzin] Add test for backslashes.
55571d4 [Marcelo Vanzin] Unbreak yarn-client.
515613d [Marcelo Vanzin] [SPARK-2718] [yarn] Handle quotes and other characters in user args.
Instead of keeping copies in all pages, just reference the values
kept in the base SparkUI instance (by making them available via
getters).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#1252 from vanzin/SPARK-2169 and squashes the following commits:
4412fc6 [Marcelo Vanzin] Simplify UIUtils.headerSparkPage signature.
4e5d35a [Marcelo Vanzin] [SPARK-2169] Don't copy appName / basePath everywhere.
Author: Chandan Kumar <chandan.kumar@imaginea.com>
Closes#1787 from nrchandan/spark-2862 and squashes the following commits:
a76bbf6 [Chandan Kumar] [SPARK-2862] Fix for a broken test case and add new test cases
4211eea [Chandan Kumar] [SPARK-2862] Add Scala bug id
13854f1 [Chandan Kumar] [SPARK-2862] Use shorthand range notation to avoid Scala bug
there's no need to use masterLock in Worker now since all communications are within Akka actor
Author: CrazyJvm <crazyjvm@gmail.com>
Closes#2008 from CrazyJvm/no-need-master-lock and squashes the following commits:
dd39e20 [CrazyJvm] fix format
58e7fa5 [CrazyJvm] there's no need to use masterLock now since all communications are within Akka actor
cc JoshRosen sarutak
Author: GuoQiang Li <witgo@qq.com>
Closes#1989 from witgo/cancel_ackTimeoutMonitor and squashes the following commits:
4a700fa [GuoQiang Li] In the stop method of ConnectionManager to cancel the ackTimeoutMonitor
Passing large object by py4j is very slow (cost much memory), so pass broadcast objects via files (similar to parallelize()).
Add an option to keep object in driver (it's False by default) to save memory in driver.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1912 from davies/broadcast and squashes the following commits:
e06df4a [Davies Liu] load broadcast from disk in driver automatically
db3f232 [Davies Liu] fix serialization of accumulator
631a827 [Davies Liu] Merge branch 'master' into broadcast
c7baa8c [Davies Liu] compress serrialized broadcast and command
9a7161f [Davies Liu] fix doc tests
e93cf4b [Davies Liu] address comments: add test
6226189 [Davies Liu] improve large broadcast
This is intended to fix SPARK-2977. Before, there was an implicit ordering dependency where we needed to know the ShuffleManager implementation before creating the ShuffleBlockManager. This patch makes that dependency explicit by adding ShuffleManager to a bunch of constructors.
I think it's a little odd for BlockManager to take a ShuffleManager only to pass it to ShuffleBlockManager without using it itself; there's an opportunity to clean this up later if we sever the circular dependencies between BlockManager and other components and pass those components to BlockManager's constructor.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1976 from JoshRosen/SPARK-2977 and squashes the following commits:
a9cd1e1 [Josh Rosen] [SPARK-2977] Ensure ShuffleManager is created before ShuffleBlockManager.
Author: Reynold Xin <rxin@apache.org>
Closes#1948 from rxin/kryo and squashes the following commits:
a3a80d8 [Reynold Xin] [SPARK-3046] use executor's class loader as the default serializer classloader
3d13277 [Reynold Xin] Reverted that in TestJavaSerializerImpl too.
196f3dc [Reynold Xin] Ok one more commit to revert the classloader change.
c49b50c [Reynold Xin] Removed JavaSerializer change.
afbf37d [Reynold Xin] Moved the test case also.
a2e693e [Reynold Xin] Removed the Kryo bug fix from this pull request.
c81bd6c [Reynold Xin] Use defaultClassLoader when executing user specified custom registrator.
68f261e [Reynold Xin] Added license check excludes.
0c28179 [Reynold Xin] [SPARK-3045] Make Serializer interface Java friendly [SPARK-3046] Set executor's class loader as the default serializer class loader
More detail on the issue is described in [SPARK-3015](https://issues.apache.org/jira/browse/SPARK-3015), but the TLDR is if we send too many blocking Akka messages that are dependent on each other in quick successions, then we end up causing a few of these messages to time out and ultimately kill the executors. As of #1498, we broadcast each RDD whether or not it is persisted. This means if we create many RDDs (each of which becomes a broadcast) and the driver performs a GC that cleans up all of these broadcast blocks, then we end up sending many `RemoveBroadcast` messages in parallel and trigger the chain of blocking messages at high frequencies.
We do not know of the Akka-level root cause yet, so this is intended to be a temporary solution until we identify the real issue. I have done some preliminary testing of enabling blocking and observed that the queue length remains quite low (< 1000) even under very intensive workloads.
In the long run, we should do something more sophisticated to allow a limited degree of parallelism through batching clean up tasks or processing them in a sliding window. In the longer run, we should clean up the whole `BlockManager*` message passing interface to avoid unnecessarily awaiting on futures created from Akka asks.
tdas pwendell mengxr
Author: Andrew Or <andrewor14@gmail.com>
Closes#1931 from andrewor14/reference-blocking and squashes the following commits:
d0f7195 [Andrew Or] Merge branch 'master' of github.com:apache/spark into reference-blocking
ce9daf5 [Andrew Or] Remove logic for logging queue length
111192a [Andrew Or] Add missing space in log message (minor)
a183b83 [Andrew Or] Switch order of code blocks (minor)
9fd1fe6 [Andrew Or] Remove outdated log
104b366 [Andrew Or] Use the actual reference queue length
0b7e768 [Andrew Or] Block on cleaning tasks by default + log error on queue full
The serializer is not always used in an executor thread (e.g. connection manager, broadcast), in which case the classloader might not have the user jar set, leading to corruption in deserialization.
https://issues.apache.org/jira/browse/SPARK-3046https://issues.apache.org/jira/browse/SPARK-2878
Author: Reynold Xin <rxin@apache.org>
Closes#1972 from rxin/kryoBug and squashes the following commits:
c1c7bf0 [Reynold Xin] Made change to JavaSerializer.
7204c33 [Reynold Xin] Added imports back.
d879e67 [Reynold Xin] [SPARK-3046] use executor's class loader as the default serializer class loader.
...Update
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1961 from sryza/sandy-spark-3028 and squashes the following commits:
dccdff5 [Sandy Ryza] Fix compile error
f883ded [Sandy Ryza] SPARK-3028. sparkEventToJson should support SparkListenerExecutorMetricsUpdate
Not supported in Scala 2.11. Split them into separate methods instead.
Author: Anand Avati <avati@redhat.com>
Closes#1704 from avati/SPARK-1812-default-args and squashes the following commits:
3e3924a [Anand Avati] SPARK-1812: Add Mima excludes for the broken ABI
901dfc7 [Anand Avati] SPARK-1812: core - Fix overloaded methods with default arguments
07f00af [Anand Avati] SPARK-1812: streaming - Fix overloaded methods with default arguments
I find it useful to see where in an RDD's DAG data is cached, so I figured others might too.
I've added both the caching level, and the actual memory state of the RDD.
Some of this is redundant with the web UI (notably the actual memory state), but (a) that is temporary, and (b) putting it in the DAG tree shows some context that can help a lot.
For example:
```
(4) ShuffledRDD[3] at reduceByKey at <console>:14
+-(4) MappedRDD[2] at map at <console>:14
| MapPartitionsRDD[1] at mapPartitions at <console>:12
| ParallelCollectionRDD[0] at parallelize at <console>:12
```
should change to
```
(4) ShuffledRDD[3] at reduceByKey at <console>:14 [Memory Deserialized 1x Replicated]
| CachedPartitions: 4; MemorySize: 50.8 MB; TachyonSize: 0.0 B; DiskSize: 0.0 B
+-(4) MappedRDD[2] at map at <console>:14 [Memory Deserialized 1x Replicated]
| MapPartitionsRDD[1] at mapPartitions at <console>:12 [Memory Deserialized 1x Replicated]
| CachedPartitions: 4; MemorySize: 109.1 MB; TachyonSize: 0.0 B; DiskSize: 0.0 B
| ParallelCollectionRDD[0] at parallelize at <console>:12 [Memory Deserialized 1x Replicated]
```
Author: Nathan Kronenfeld <nkronenfeld@oculusinfo.com>
Closes#1535 from nkronenfeld/feature/debug-caching2 and squashes the following commits:
40490bc [Nathan Kronenfeld] Back out DeveloperAPI and arguments to RDD.toDebugString, reinstate memory output
794e6a3 [Nathan Kronenfeld] Attempt to merge mima changes from master
6fe9e80 [Nathan Kronenfeld] Add exclusions to allow for signature change in toDebugString (will back out if necessary)
31d6769 [Nathan Kronenfeld] Attempt to get rid of style errors. Add comments for the new memory usage parameter.
a0f6f76 [Nathan Kronenfeld] Add parameter to RDD.toDebugString to allow detailed memory info to be shown or not. Default is for it not to be shown.
f8f565a [Nathan Kronenfeld] Fix code style error
8f54287 [Nathan Kronenfeld] Changed string addition to string interpolation as per PR comments
2a0cd4d [Nathan Kronenfeld] Fixed a small formatting issue I forgot to copy over from the old branch
8fbecb6 [Nathan Kronenfeld] Add caching information to rdd.toDebugString
JIRA: https://issues.apache.org/jira/browse/SPARK-2736
This patch includes:
1. An Avro converter that converts Avro data types to Python. It handles all 3 Avro data mappings (Generic, Specific and Reflect).
2. An example Python script for reading Avro files using AvroKeyInputFormat and the converter.
3. Fixing a classloading issue.
cc @MLnick @JoshRosen @mateiz
Author: Kan Zhang <kzhang@apache.org>
Closes#1916 from kanzhang/SPARK-2736 and squashes the following commits:
02443f8 [Kan Zhang] [SPARK-2736] Adding .avsc files to .rat-excludes
f74e9a9 [Kan Zhang] [SPARK-2736] nit: clazz -> className
82cc505 [Kan Zhang] [SPARK-2736] Update data sample
0be7761 [Kan Zhang] [SPARK-2736] Example pyspark script and data files
c8e5881 [Kan Zhang] [SPARK-2736] Trying to work with all 3 Avro data models
2271a5b [Kan Zhang] [SPARK-2736] Using the right class loader to find Avro classes
536876b [Kan Zhang] [SPARK-2736] Adding Avro to Java converter
This is a rewrite of the original Netty module that was added about 1.5 years ago. The old code was turned off by default and didn't really work because it lacked a frame decoder (only worked with very very small blocks).
For this pull request, I tried to make the changes non-instrusive to the rest of Spark. I only added an init and shutdown to BlockManager/DiskBlockManager, and a bunch of comments to help me understand the existing code base.
Compared with the old Netty module, this one features:
- It appears to work :)
- SPARK-2941: option to specicy nio vs oio vs epoll for channel/transport. By default nio is used. (Not using Epoll yet because I have found some bugs with its implementation)
- SPARK-2943: options to specify send buf and receive buf for users who want to do hyper tuning
- SPARK-2942: io errors are reported from server to client (the protocol uses negative length to indicate error)
- SPARK-2940: fetching multiple blocks in a single request to reduce syscalls
- SPARK-2959: clients share a single thread pool
- SPARK-2990: use PooledByteBufAllocator to reduce GC (basically a Netty managed pool of buffers with jmalloc)
- SPARK-2625: added fetchWaitTime metric and fixed thread-safety issue in metrics update.
- SPARK-2367: bump Netty version to 4.0.21.Final to address an Epoll bug (https://groups.google.com/forum/#!topic/netty/O7m-HxCJpCA)
Compared with the existing communication manager, this one features:
- IMO it is substantially easier to understand
- zero-copy send for the server for on-disk blocks
- one-copy receive (due to a frame decoder)
- don't quote me on this, but I think a lot less sys calls
- SPARK-2990: use PooledByteBufAllocator to reduce GC (basically a Netty managed pool of buffers with jmalloc)
- SPARK-2941: option to specicy nio vs oio vs epoll for channel/transport. By default nio is used. (Not using Epoll yet because I have found some bugs with its implementation)
- SPARK-2943: options to specify send buf and receive buf for users who want to do hyper tuning
TODOs before it can fully replace the existing ConnectionManager, if that ever happens (most of them should probably be done in separate PRs since this needs to be turned on explicitly)
- [x] Basic test cases
- [ ] More unit/integration tests for failures
- [ ] Performance analysis
- [ ] Support client connection reuse so we don't need to keep opening new connections (not sure how useful this would be)
- [ ] Support putting blocks in addition to fetching blocks (i.e. two way transfer)
- [x] Support serving non-disk blocks
- [ ] Support SASL authentication
For a more comprehensive list, see https://issues.apache.org/jira/browse/SPARK-2468
Thanks to @coderplay for peer coding with me on a Sunday.
Author: Reynold Xin <rxin@apache.org>
Closes#1907 from rxin/netty and squashes the following commits:
f921421 [Reynold Xin] Upgrade Netty to 4.0.22.Final to fix another Epoll bug.
4b174ca [Reynold Xin] Shivaram's code review comment.
4a3dfe7 [Reynold Xin] Switched to nio for default (instead of epoll on Linux).
56bfb9d [Reynold Xin] Bump Netty version to 4.0.21.Final for some bug fixes.
b443a4b [Reynold Xin] Added debug message to help debug Jenkins failures.
57fc4d7 [Reynold Xin] Added test cases for BlockHeaderEncoder and BlockFetchingClientHandlerSuite.
22623e9 [Reynold Xin] Added exception handling and test case for BlockServerHandler and BlockFetchingClientHandler.
6550dd7 [Reynold Xin] Fixed block mgr init bug.
60c2edf [Reynold Xin] Beefed up server/client integration tests.
38d88d5 [Reynold Xin] Added missing test files.
6ce3f3c [Reynold Xin] Added some basic test cases.
47f7ce0 [Reynold Xin] Created server and client packages and moved files there.
b16f412 [Reynold Xin] Added commit count.
f13022d [Reynold Xin] Remove unused clone() in BlockFetcherIterator.
c57d68c [Reynold Xin] Added back missing files.
842dfa7 [Reynold Xin] Made everything work with proper reference counting.
3fae001 [Reynold Xin] Connected the new netty network module with rest of Spark.
1a8f6d4 [Reynold Xin] Completed protocol documentation.
2951478 [Reynold Xin] New Netty implementation.
cc7843d [Reynold Xin] Basic skeleton.
Note this also passes the TaskContext itself to the TaskCompletionListener. In the future we can mark TaskContext with the exception object if exception occurs during task execution.
Author: Reynold Xin <rxin@apache.org>
Closes#1938 from rxin/TaskContext and squashes the following commits:
145de43 [Reynold Xin] Added JavaTaskCompletionListenerImpl for Java API friendly guarantee.
f435ea5 [Reynold Xin] Added license header for TaskCompletionListener.
dc4ed27 [Reynold Xin] [SPARK-3027] TaskContext: tighten the visibility and provide Java friendly callback API
...ationInfo is initialized properly after deserialization
Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Closes#1947 from jacek-lewandowski/master and squashes the following commits:
713b2f1 [Jacek Lewandowski] SPARK-3009: Reverted readObject method in ApplicationInfo so that ApplicationInfo is initialized properly after deserialization
- Added override.
- Marked some variables as private.
Author: Reynold Xin <rxin@apache.org>
Closes#1943 from rxin/metricsSource and squashes the following commits:
fbfa943 [Reynold Xin] Minor cleanup of metrics.Source. - Added override. - Marked some variables as private.
The previous behaviour of swallowing ClassNotFound exceptions when running a custom Kryo registrator could lead to difficult to debug problems later on at serialisation / deserialisation time, see SPARK-2878. Instead it is better to fail fast.
Added test case.
Author: Graham Dennis <graham.dennis@gmail.com>
Closes#1827 from GrahamDennis/feature/spark-2893 and squashes the following commits:
fbe4cb6 [Graham Dennis] [SPARK-2878]: Update the test case to match the updated exception message
65e53c5 [Graham Dennis] [SPARK-2893]: Improve message when a spark.kryo.registrator fails.
f480d85 [Graham Dennis] [SPARK-2893] Fix typo.
b59d2c2 [Graham Dennis] SPARK-2893: Do not swallow Exceptions when running a custom spark.kryo.registrator
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
Author: Patrick Wendell <pwendell@gmail.com>
Closes#1933 from pwendell/speculation and squashes the following commits:
33a3473 [Patrick Wendell] Use OpenHashSet
8ce2ff0 [Patrick Wendell] SPARK-3020: Print completed indices rather than tasks in web UI
Author: Zhang, Liye <liye.zhang@intel.com>
Closes#1892 from liyezhang556520/lazy_memory_request and squashes the following commits:
335ab61 [Zhang, Liye] [SPARK-1777 (partial)] bugfix: make size of requested memory correctly
Since this is a file to file copy, using transferTo should be faster.
Author: Raymond Liu <raymond.liu@intel.com>
Closes#1884 from colorant/externalSorter and squashes the following commits:
6e42f3c [Raymond Liu] More code into copyStream
bfb496b [Raymond Liu] Use transferTo when copy merge files in ExternalSorter
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
This addresses SPARK-2931, a bug where getAllowedLocalityLevel() could throw ArrayIndexOutOfBoundsException. The fix here is to reset currentLocalityIndex after recomputing the locality levels.
Thanks to kayousterhout, mridulm, and lirui-intel for helping me to debug this.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1896 from JoshRosen/SPARK-2931 and squashes the following commits:
48b60b5 [Josh Rosen] Move FakeRackUtil.cleanUp() info beforeEach().
6fec474 [Josh Rosen] Set currentLocalityIndex after recomputing locality levels.
9384897 [Josh Rosen] Update SPARK-2931 test to reflect changes in 63bdb1f41b.
9ecd455 [Josh Rosen] Apply @mridulm's patch for reproducing SPARK-2931.
The Netty network module was originally written when Scala 2.9.x had a bug that prevents a pure Scala implementation, and a subset of the files were done in Java. We have since upgraded to Scala 2.10, and can migrate all Java files now to Scala.
https://github.com/netty/netty/issues/781https://github.com/mesos/spark/pull/522
Author: Reynold Xin <rxin@apache.org>
Closes#1865 from rxin/netty and squashes the following commits:
332422f [Reynold Xin] Code review feedback
ca9eeee [Reynold Xin] Minor update.
7f1434b [Reynold Xin] [SPARK-2936] Migrate Netty network module from Java to Scala
To enable Python consistency and `Experimental` label of the `sampleByKeyExact` API.
Author: Doris Xin <doris.s.xin@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#1866 from dorx/stratified and squashes the following commits:
0ad97b2 [Doris Xin] reviewer comments.
2948aae [Doris Xin] remove unrelated changes
e990325 [Doris Xin] Merge branch 'master' into stratified
555a3f9 [Doris Xin] separate out sampleByKeyExact as its own API
616e55c [Doris Xin] merge master
245439e [Doris Xin] moved minSamplingRate to getUpperBound
eaf5771 [Doris Xin] bug fixes.
17a381b [Doris Xin] fixed a merge issue and a failed unit
ea7d27f [Doris Xin] merge master
b223529 [Xiangrui Meng] use approx bounds for poisson fix poisson mean for waitlisting add unit tests for Java
b3013a4 [Xiangrui Meng] move math3 back to test scope
eecee5f [Doris Xin] Merge branch 'master' into stratified
f4c21f3 [Doris Xin] Reviewer comments
a10e68d [Doris Xin] style fix
a2bf756 [Doris Xin] Merge branch 'master' into stratified
680b677 [Doris Xin] use mapPartitionWithIndex instead
9884a9f [Doris Xin] style fix
bbfb8c9 [Doris Xin] Merge branch 'master' into stratified
ee9d260 [Doris Xin] addressed reviewer comments
6b5b10b [Doris Xin] Merge branch 'master' into stratified
254e03c [Doris Xin] minor fixes and Java API.
4ad516b [Doris Xin] remove unused imports from PairRDDFunctions
bd9dc6e [Doris Xin] unit bug and style violation fixed
1fe1cff [Doris Xin] Changed fractionByKey to a map to enable arg check
944a10c [Doris Xin] [SPARK-2145] Add lower bound on sampling rate
0214a76 [Doris Xin] cleanUp
90d94c0 [Doris Xin] merge master
9e74ab5 [Doris Xin] Separated out most of the logic in sampleByKey
7327611 [Doris Xin] merge master
50581fc [Doris Xin] added a TODO for logging in python
46f6c8c [Doris Xin] fixed the NPE caused by closures being cleaned before being passed into the aggregate function
7e1a481 [Doris Xin] changed the permission on SamplingUtil
1d413ce [Doris Xin] fixed checkstyle issues
9ee94ee [Doris Xin] [SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact sample size
e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample
7cab53a [Doris Xin] fixed import bug in rdd.py
ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD
1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
1. do not use signal handler for SIGCHILD, it's easy to cause deadlock
2. handle EINTR during accept()
3. pass errno into JVM
4. handle EAGAIN during fork()
Now, it can pass 50k tasks tests in 180 seconds.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1842 from davies/qa and squashes the following commits:
f0ea451 [Davies Liu] fix lint
03a2e8c [Davies Liu] cleanup dead children every seconds
32cb829 [Davies Liu] fix lint
0cd0817 [Davies Liu] fix bugs in deamon.py
The JobLogger is very useful for performing offline performance profiling of Spark jobs. GC Time and Shuffle Write time are available in TaskMetrics but are currently missed from the JobLogger output. This patch adds these two fields.
~~Since this is a small change, I didn't create a JIRA. Let me know if I should do that.~~
cc kayousterhout
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#1869 from shivaram/job-logger and squashes the following commits:
1b709fc [Shivaram Venkataraman] Add a space before GC_TIME
c418105 [Shivaram Venkataraman] Add gc time and shuffle write time to JobLogger
Author: GuoQiang Li <witgo@qq.com>
Closes#1876 from witgo/remove_semicolon_in_Task_scala and squashes the following commits:
c6ea732 [GuoQiang Li] Remove extra semicolon in Task.scala
This helps us log UpdateBlockInfo properly once #1870 is merged.
Author: Reynold Xin <rxin@apache.org>
Closes#1872 from rxin/UpdateBlockInfo and squashes the following commits:
0cee1c2 [Reynold Xin] Turn UpdateBlockInfo into case class.
As sryza reported, spark-shell doesn't accept any flags.
The root cause is wrong usage of spark-submit in spark-shell and it come to the surface by #1801
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1715, Closes#1864, and Closes#1861Closes#1825 from sarutak/SPARK-2894 and squashes the following commits:
47f3510 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2894
2c899ed [Kousuke Saruta] Removed useless code from java_gateway.py
98287ed [Kousuke Saruta] Removed useless code from java_gateway.py
513ad2e [Kousuke Saruta] Modified util.sh to enable to use option including white spaces
28a374e [Kousuke Saruta] Modified java_gateway.py to recognize arguments
5afc584 [Cheng Lian] Filter out spark-submit options when starting Python gateway
e630d19 [Cheng Lian] Fixing pyspark and spark-shell CLI options
Pedantry is underrated
Author: Chris Cope <ccope@resilientscience.com>
Closes#1859 from copester/master and squashes the following commits:
0fb4499 [Chris Cope] [SPARK-1766] sorted functions to meet pedantic requirements
Tested and ready to merge.
Author: Chandan Kumar <chandan.kumar@imaginea.com>
Closes#1786 from nrchandan/spark-2861 and squashes the following commits:
cb0bc1e [Chandan Kumar] [SPARK-2861] Fix a typo in the histogram doc comment
6a2a71b [Chandan Kumar] SPARK-2861. Fix Doc comment of histogram method
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
References to dependencies(j) for actually obtaining RDD parents are less common than I originally estimated. It does clarify UnionRDD (also will clarify some of my other PRs)
Use of firstParent[T] is ubiquitous, but not as sure that benefits from being replaced with parent(0)[T].
Author: Erik Erlandson <eerlands@redhat.com>
Closes#1858 from erikerlandson/spark-2911-pr2 and squashes the following commits:
7ffea74 [Erik Erlandson] [SPARK-2911] apply parent[T](j) to clarify UnionRDD code
The decision order of Worker's WebUI port is "--webui-port", SPARK_WORKER_WEBUI_POR, 8081(default), spark.worker.ui.port. But in Master, the order is "--webui-port", spark.master.ui.port, SPARK_MASTER_WEBUI_PORT and 8080(default).
So we change the order in Worker's to keep it consistent with Master.
Author: WangTao <barneystinson@aliyun.com>
Closes#1838 from WangTaoTheTonic/reOrder and squashes the following commits:
460f4d4 [WangTao] Make decision order of Worker's WebUI consistent with Master's
Author: GuoQiang Li <witgo@qq.com>
Closes#1836 from witgo/SPARK-2897 and squashes the following commits:
23cdc5b [GuoQiang Li] review commit
ada4fba [GuoQiang Li] TorrentBroadcast does not support broadcast compression
fb91792 [GuoQiang Li] org.apache.spark.broadcast.TorrentBroadcast does use the serializer class specified in the spark option "spark.serializer"
Author: Erik Erlandson <eerlands@redhat.com>
Closes#1841 from erikerlandson/spark-2911-pr and squashes the following commits:
4699e2f [Erik Erlandson] [SPARK-2911]: provide rdd.parent[T](j) to obtain jth parent RDD
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#1834 from sarutak/SPARK-2904 and squashes the following commits:
38e7d45 [Kousuke Saruta] Removed non-used variable in SparkSubmitArguments
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1507 from sryza/sandy-spark-2565 and squashes the following commits:
74dad41 [Sandy Ryza] SPARK-2565. Update ShuffleReadMetrics as blocks are fetched
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
fix bug of countApproxDistinct() when have more than one partition
Author: Davies Liu <davies.liu@gmail.com>
Closes#1812 from davies/approx and squashes the following commits:
bf757ce [Davies Liu] fix bug of countApproxDistinct() when have more than one partition
This patch modifies the ConnectionManager so that error messages are sent in reply when uncaught exceptions occur during message processing. This prevents message senders from hanging while waiting for an acknowledgment if the remote message processing failed.
This is an updated version of sarutak's PR, #1490. The main change is to use Futures / Promises to signal errors.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Author: Josh Rosen <joshrosen@apache.org>
Closes#1758 from JoshRosen/connection-manager-fixes and squashes the following commits:
68620cb [Josh Rosen] Fix test in BlockFetcherIteratorSuite:
83673de [Josh Rosen] Error ACKs should trigger IOExceptions, so catch only those exceptions in the test.
b8bb4d4 [Josh Rosen] Fix manager.id vs managerServer.id typo that broke security tests.
659521f [Josh Rosen] Include previous exception when throwing new one
a2f745c [Josh Rosen] Remove sendMessageReliablySync; callers can wait themselves.
c01c450 [Josh Rosen] Return Try[Message] from sendMessageReliablySync.
f1cd1bb [Josh Rosen] Clean up @sarutak's PR #1490 for [SPARK-2583]: ConnectionManager error reporting
7399c6b [Josh Rosen] Merge remote-tracking branch 'origin/pr/1490' into connection-manager-fixes
ee91bb7 [Kousuke Saruta] Modified BufferMessage.scala to keep the spark code style
9dfd0d8 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2583
e7d9aa6 [Kousuke Saruta] rebase to master
326a17f [Kousuke Saruta] Add test cases to ConnectionManagerSuite.scala for SPARK-2583
2a18d6b [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2583
22d7ebd [Kousuke Saruta] Add test cases to BlockManagerSuite for SPARK-2583
e579302 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2583
281589c [Kousuke Saruta] Add a test case to BlockFetcherIteratorSuite.scala for fetching block from remote from successfully
0654128 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2583
ffaa83d [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2583
12d3de8 [Kousuke Saruta] Added BlockFetcherIteratorSuite.scala
4117b8f [Kousuke Saruta] Modified ConnectionManager to be alble to handle error during processing message
717c9c3 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2583
6635467 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2583
e2b8c4a [Kousuke Saruta] Modify to propagete error using ConnectionManager
I haven't tested this out on a cluster yet, but wanted to make sure the approach (passing ShuffleWriteMetrics down to DiskBlockObjectWriter) was ok
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1481 from sryza/sandy-spark-2566 and squashes the following commits:
8090d88 [Sandy Ryza] Fix ExternalSorter
b2a62ed [Sandy Ryza] Fix more test failures
8be6218 [Sandy Ryza] Fix test failures and mark a couple variables private
c5e68e5 [Sandy Ryza] SPARK-2566. Update ShuffleWriteMetrics incrementally
JIRA issues:
- Main: [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678)
- Related: [SPARK-2874](https://issues.apache.org/jira/browse/SPARK-2874)
Related PR:
- #1715
This PR is both a fix for SPARK-2874 and a workaround for SPARK-2678. Fixing SPARK-2678 completely requires some API level changes that need further discussion, and we decided not to include it in Spark 1.1 release. As currently SPARK-2678 only affects Spark SQL scripts, this workaround is enough for Spark 1.1. Command line option handling logic in bash scripts looks somewhat dirty and duplicated, but it helps to provide a cleaner user interface as well as retain full downward compatibility for now.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1801 from liancheng/spark-2874 and squashes the following commits:
8045d7a [Cheng Lian] Make sure test suites pass
8493a9e [Cheng Lian] Using eval to retain quoted arguments
aed523f [Cheng Lian] Fixed typo in bin/spark-sql
f12a0b1 [Cheng Lian] Worked arount SPARK-2678
daee105 [Cheng Lian] Fixed usage messages of all Spark SQL related scripts
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
copied from original JIRA (https://issues.apache.org/jira/browse/SPARK-2294):
If an executor E is free, a task may be speculatively assigned to E when there are other tasks in the job that have not been launched (at all) yet. Similarly, a task without any locality preferences may be assigned to E when there was another NODE_LOCAL task that could have been scheduled.
This happens because TaskSchedulerImpl calls TaskSetManager.resourceOffer (which in turn calls TaskSetManager.findTask) with increasing locality levels, beginning with PROCESS_LOCAL, followed by NODE_LOCAL, and so on until the highest currently allowed level. Now, supposed NODE_LOCAL is the highest currently allowed locality level. The first time findTask is called, it will be called with max level PROCESS_LOCAL; if it cannot find any PROCESS_LOCAL tasks, it will try to schedule tasks with no locality preferences or speculative tasks. As a result, speculative tasks or tasks with no preferences may be scheduled instead of NODE_LOCAL tasks.
----
I added an additional parameter in resourceOffer and findTask, maxLocality, indicating when we should consider the tasks without locality preference
Author: CodingCat <zhunansjtu@gmail.com>
Closes#1313 from CodingCat/SPARK-2294 and squashes the following commits:
bf3f13b [CodingCat] rollback some forgotten changes
89f9bc0 [CodingCat] address matei's comments
18cae02 [CodingCat] add test case for node-local tasks
2ba6195 [CodingCat] fix failed test cases
87dd09e [CodingCat] fix style
9b9432f [CodingCat] remove hasNodeLocalOnlyTasks
fdd1573 [CodingCat] fix failed test cases
941a4fd [CodingCat] see my shocked face..........
f600085 [CodingCat] remove hasNodeLocalOnlyTasks checking
0b8a46b [CodingCat] test whether hasNodeLocalOnlyTasks affect the results
73ceda8 [CodingCat] style fix
b3a430b [CodingCat] remove fine granularity tracking for node-local only tasks
f9a2ad8 [CodingCat] simplify the logic in TaskSchedulerImpl
c8c1de4 [CodingCat] simplify the patch
be652ed [CodingCat] avoid unnecessary delay when we only have nopref tasks
dee9e22 [CodingCat] fix locality inversion bug in TaskManager by moving nopref branch
Scala 2.11 packages not available for the current version (3.2.6)
Signed-off-by: Anand Avati <avatiredhat.com>
Author: Anand Avati <avati@redhat.com>
Closes#1702 from avati/SPARK-1812-json4s-jackson-3.2.10 and squashes the following commits:
7be8324 [Anand Avati] SPARK-1812: core - upgrade to json4s 3.2.10
I inquired on dev mailing list about the motivation for checking the jdbc statement instead of the connection in the close() logic of JdbcRDD. Ted Yu believes there essentially is none- it is a simple cut and paste issue. So here is the tiny fix to patch it.
Author: Stephen Boesch <javadba>
Author: Stephen Boesch <javadba@gmail.com>
Closes#1792 from javadba/closejdbc and squashes the following commits:
363be4f [Stephen Boesch] SPARK-2869 - Fix tiny bug in JdbcRdd for closing jdbc connection (reformat with braces)
6518d36 [Stephen Boesch] SPARK-2689 Fix tiny bug in JdbcRdd for closing jdbc connection
3fb23ed [Stephen Boesch] SPARK-2689 Fix potential leak of connection/PreparedStatement in case of error in JdbcRDD
095b2c9 [Stephen Boesch] Fix tiny bug (likely copy and paste error) in closing jdbc connection
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.
This patch adds support for giving accumulators user-visible names and displaying accumulator values in the web UI. This allows users to create custom counters that can display in the UI. The current approach displays both the accumulator deltas caused by each task and a "current" value of the accumulator totals for each stage, which gets update as tasks finish.
Currently in Spark developers have been extending the `TaskMetrics` functionality to provide custom instrumentation for RDD's. This provides a potentially nicer alternative of going through the existing accumulator framework (actually `TaskMetrics` and accumulators are on an awkward collision course as we add more features to the former). The current patch demo's how we can use the feature to provide instrumentation for RDD input sizes. The nice thing about going through accumulators is that users can read the current value of the data being tracked in their programs. This could be useful to e.g. decide to short-circuit a Spark stage depending on how things are going.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#1309 from pwendell/metrics and squashes the following commits:
8815308 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into HEAD
93fbe0f [Patrick Wendell] Other minor fixes
cc43f68 [Patrick Wendell] Updating unit tests
c991b1b [Patrick Wendell] Moving some code into the Accumulators class
9a9ba3c [Patrick Wendell] More merge fixes
c5ace9e [Patrick Wendell] More merge conflicts
1da15e3 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into metrics
9860c55 [Patrick Wendell] Potential solution to posting listener events
0bb0e33 [Patrick Wendell] Remove "display" variable and assume display = name.isDefined
0ec4ac7 [Patrick Wendell] Java API's
e95bf69 [Patrick Wendell] Stash
be97261 [Patrick Wendell] Style fix
8407308 [Patrick Wendell] Removing examples in Hadoop and RDD class
64d405f [Patrick Wendell] Adding missing file
5d8b156 [Patrick Wendell] Changes based on Kay's review.
9f18bad [Patrick Wendell] Minor style changes and tests
7a63abc [Patrick Wendell] Adding Json serialization and responding to Reynold's feedback
ad85076 [Patrick Wendell] Example of using named accumulators for custom RDD metrics.
0b72660 [Patrick Wendell] Initial WIP example of supporing globally named accumulators.
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
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.
`master.ui.port` and `worker.ui.port` were never picked up by SparkConf, simply because they are not prefixed with "spark." Unfortunately, this is also currently the documented way of setting these values.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1779 from andrewor14/master-worker-port and squashes the following commits:
8475e95 [Andrew Or] Update docs to reflect changes in configs
4db3d5d [Andrew Or] Stop using configs that don't actually work
This tracks memory properly if there are multiple spilling collections in the same task (which was a problem before), and also implements an algorithm that lets each thread grow up to 1 / 2N of the memory pool (where N is the number of threads) before spilling, which avoids an inefficiency with small spills we had before (some threads would spill many times at 0-1 MB because the pool was allocated elsewhere).
Author: Matei Zaharia <matei@databricks.com>
Closes#1707 from mateiz/spark-2711 and squashes the following commits:
debf75b [Matei Zaharia] Review comments
24f28f3 [Matei Zaharia] Small rename
c8f3a8b [Matei Zaharia] Update ShuffleMemoryManager to be able to partially grant requests
315e3a5 [Matei Zaharia] Some review comments
b810120 [Matei Zaharia] Create central manager to track memory for all spilling collections
Replaces this with an O(1) operation that does not have to shift over
the whole tail of the array into the gap produced by the element removed.
Author: Matei Zaharia <matei@databricks.com>
Closes#1773 from mateiz/SPARK-2685 and squashes the following commits:
1ea028a [Matei Zaharia] Update comments in StreamBuffer and EAOM, and reuse ArrayBuffers
eb1abfd [Matei Zaharia] Update ExternalAppendOnlyMap to avoid buffer.remove()
Author: Reynold Xin <rxin@apache.org>
Closes#1772 from rxin/accumulator-dagscheduler and squashes the following commits:
6a58520 [Reynold Xin] [SPARK-2323] Exception in accumulator update should not crash DAGScheduler & SparkContext.
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
Kill only the python worker related to cancelled tasks.
The daemon will start a background thread to monitor all the opened sockets for all workers. If the socket is closed by JVM, this thread will kill the worker.
When an task is cancelled, the socket to worker will be closed, then the worker will be killed by deamon.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1643 from davies/kill and squashes the following commits:
8ffe9f3 [Davies Liu] kill worker by deamon, because runtime.exec() is too heavy
46ca150 [Davies Liu] address comment
acd751c [Davies Liu] kill the worker when task is canceled
Minor fixes on top of #1679.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1736 from andrewor14/amend-#1679 and squashes the following commits:
3b46f5e [Andrew Or] Minor fixes
This also Closes#1701.
Author: GuoQiang Li <witgo@qq.com>
Closes#1208 from witgo/SPARK-1470 and squashes the following commits:
422646b [GuoQiang Li] Remove scalalogging-slf4j dependency
`spark.test.home` was introduced in #1734. This is fine for SBT but is failing maven tests. Either way it shouldn't throw an NPE.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1739 from andrewor14/fix-spark-test-home and squashes the following commits:
ce2624c [Andrew Or] Do not throw NPE if spark.test.home is not set
When standalone Workers launch executors, they inherit the Spark home set by the driver. This means if the worker machines do not share the same directory structure as the driver node, the Workers will attempt to run scripts (e.g. bin/compute-classpath.sh) that do not exist locally and fail. This is a common scenario if the driver is launched from outside of the cluster.
The solution is to simply not pass the driver's Spark home to the Workers. This PR further makes an attempt to avoid overloading the usages of `spark.home`, which is now only used for setting executor Spark home on Mesos and in python.
This is based on top of #1392 and originally reported by YanTangZhai. Tested on standalone cluster.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1734 from andrewor14/spark-home-reprise and squashes the following commits:
f71f391 [Andrew Or] Revert changes in python
1c2532c [Andrew Or] Merge branch 'master' of github.com:apache/spark into spark-home-reprise
188fc5d [Andrew Or] Avoid using spark.home where possible
09272b7 [Andrew Or] Always use Worker's working directory as spark home
The existing code in `StorageUtils` is not the most efficient. Every time we want to update an `RDDInfo` we end up iterating through all blocks on all block managers just to discard most of them. The symptoms manifest themselves in the bountiful UI bugs observed in the wild. Many of these bugs are caused by the slow consumption of events in `LiveListenerBus`, which frequently leads to the event queue overflowing and `SparkListenerEvent`s being dropped on the floor. The changes made in this PR avoid this by first filtering out only the blocks relevant to us before computing storage information from them.
It's worth a mention that this corner of the Spark code is also not very well-tested at all. The bulk of the changes in this PR (more than 60%) is actually test cases for the various logic in `StorageUtils.scala` as well as `StorageTab.scala`. These will eventually be extended to cover the various listeners that constitute the `SparkUI`.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1679 from andrewor14/fix-drop-events and squashes the following commits:
f80c1fa [Andrew Or] Rewrite fold and reduceOption as sum
e132d69 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
14fa1c3 [Andrew Or] Simplify some code + update a few comments
a91be46 [Andrew Or] Make ExecutorsPage blazingly fast
bf6f09b [Andrew Or] Minor changes
8981de1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
af19bc0 [Andrew Or] *UsedByRDD -> *UsedByRdd (minor)
6970bc8 [Andrew Or] Add extensive tests for StorageListener and the new code in StorageUtils
e080b9e [Andrew Or] Reduce run time of StorageUtils.updateRddInfo to near constant
2c3ef6a [Andrew Or] Actually filter out only the relevant RDDs
6fef86a [Andrew Or] Add extensive tests for new code in StorageStatus
b66b6b0 [Andrew Or] Use more efficient underlying data structures for blocks
6a7b7c0 [Andrew Or] Avoid chained operations on TraversableLike
a9ec384 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
b12fcd7 [Andrew Or] Fix tests + simplify sc.getRDDStorageInfo
da8e322 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
8e91921 [Andrew Or] Iterate through a filtered set of blocks when updating RDDInfo
7b2c4aa [Andrew Or] Rewrite blockLocationsFromStorageStatus + clean up method signatures
41fa50d [Andrew Or] Add a legacy constructor for StorageStatus
53af15d [Andrew Or] Refactor StorageStatus + add a bunch of tests
Author: GuoQiang Li <witgo@qq.com>
Closes#1369 from witgo/SPARK-1470_new and squashes the following commits:
66a1641 [GuoQiang Li] IncompatibleResultTypeProblem
73a89ba [GuoQiang Li] Use the scala-logging wrapper instead of the directly sfl4j api.
Curently, daemon.py forks a pool of numProcessors subprocesses, and those processes fork themselves again to create the actual Python worker processes that handle data.
I think that this extra layer of indirection is unnecessary and adds a lot of complexity. This commit attempts to remove this middle layer of subprocesses by launching the workers directly from daemon.py.
See https://github.com/mesos/spark/pull/563 for the original PR that added daemon.py, where I raise some issues with the current design.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1680 from JoshRosen/pyspark-daemon and squashes the following commits:
5abbcb9 [Josh Rosen] Replace magic number: 4 -> EINTR
5495dff [Josh Rosen] Throw IllegalStateException if worker launch fails.
b79254d [Josh Rosen] Detect failed fork() calls; improve error logging.
282c2c4 [Josh Rosen] Remove daemon.py exit logging, since it caused problems:
8554536 [Josh Rosen] Fix daemon’s shutdown(); log shutdown reason.
4e0fab8 [Josh Rosen] Remove shared-memory exit_flag; don't die on worker death.
e9892b4 [Josh Rosen] [WIP] [SPARK-2764] Simplify daemon.py process structure.
If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf.
Author: Albert Chu <chu11@llnl.gov>
Closes#1059 from chu11/SPARK-2116 and squashes the following commits:
9f3ac94 [Albert Chu] SPARK-2116: If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf.
Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes.
This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance.
root
|-- field1: integer (nullable = true)
|-- field2: string (nullable = true)
|-- field3: struct (nullable = true)
| |-- field4: integer (nullable = true)
| |-- field5: array (nullable = true)
| | |-- element: integer (containsNull = false)
|-- field6: array (nullable = true)
| |-- element: struct (containsNull = false)
| | |-- field7: string (nullable = true)
Then we can access them by row.field3.field5[0] or row.field6[5].field7
It also will infer the schema in Python, convert Row/dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType.
You can use pyspark.sql.Row to convert unnamed structure into Row object, make the RDD can be inferable. Such as:
ctx.inferSchema(rdd.map(lambda x: Row(a=x[0], b=x[1]))
Or you could use Row to create a class just like namedtuple, for example:
Person = Row("name", "age")
ctx.inferSchema(rdd.map(lambda x: Person(*x)))
Also, you can call applySchema to apply an schema to a RDD of tuple/list and turn it into a SchemaRDD. The `schema` should be StructType, see the API docs for details.
schema = StructType([StructField("name, StringType, True),
StructType("age", IntegerType, True)])
ctx.applySchema(rdd, schema)
PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1598 from davies/nested and squashes the following commits:
f1d15b6 [Davies Liu] verify schema with the first few rows
8852aaf [Davies Liu] check type of schema
abe9e6e [Davies Liu] address comments
61b2292 [Davies Liu] add @deprecated to pythonToJavaMap
1e5b801 [Davies Liu] improve cache of classes
51aa135 [Davies Liu] use Row to infer schema
e9c0d5c [Davies Liu] remove string typed schema
353a3f2 [Davies Liu] fix code style
63de8f8 [Davies Liu] fix typo
c79ca67 [Davies Liu] fix serialization of nested data
6b258b5 [Davies Liu] fix pep8
9d8447c [Davies Liu] apply schema provided by string of names
f5df97f [Davies Liu] refactor, address comments
9d9af55 [Davies Liu] use arrry to applySchema and infer schema in Python
84679b3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into nested
0eaaf56 [Davies Liu] fix doc tests
b3559b4 [Davies Liu] use generated Row instead of namedtuple
c4ddc30 [Davies Liu] fix conflict between name of fields and variables
7f6f251 [Davies Liu] address all comments
d69d397 [Davies Liu] refactor
2cc2d45 [Davies Liu] refactor
182fb46 [Davies Liu] refactor
bc6e9e1 [Davies Liu] switch to new Schema API
547bf3e [Davies Liu] Merge branch 'master' into nested
a435b5a [Davies Liu] add docs and code refactor
2c8debc [Davies Liu] Merge branch 'master' into nested
644665a [Davies Liu] use tuple and namedtuple for schemardd
All changes from this PR are by mridulm and are drawn from his work in #1609. This patch is intended to fix all major issues related to shuffle file consolidation that mridulm found, while minimizing changes to the code, with the hope that it may be more easily merged into 1.1.
This patch is **not** intended as a replacement for #1609, which provides many additional benefits, including fixes to ExternalAppendOnlyMap, improvements to DiskBlockObjectWriter's API, and several new unit tests.
If it is feasible to merge #1609 for the 1.1 deadline, that is a preferable option.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1678 from aarondav/consol and squashes the following commits:
53b3f6d [Aaron Davidson] Correct behavior when writing unopened file
701d045 [Aaron Davidson] Rebase with sort-based shuffle
9160149 [Aaron Davidson] SPARK-2532: Minimal shuffle consolidation fixes
JIRA: https://issues.apache.org/jira/browse/SPARK-1612
Move the "close" statements into a "finally" block.
Author: zsxwing <zsxwing@gmail.com>
Closes#535 from zsxwing/SPARK-1612 and squashes the following commits:
ae52f50 [zsxwing] Update to follow the code style
549ba13 [zsxwing] SPARK-1612: Fix potential resource leaks
When performing some transformations on RDDs after many iterations, the dependencies of RDDs could be very long. It can easily cause StackOverflowError when recursively visiting these dependencies in Spark core. For example:
var rdd = sc.makeRDD(Array(1))
for (i <- 1 to 1000) {
rdd = rdd.coalesce(1).cache()
rdd.collect()
}
This PR changes recursive visiting on rdd's dependencies to iterative approach to avoid StackOverflowError.
In addition to the recursive visiting, since the Java serializer has a known [bug](http://bugs.java.com/bugdatabase/view_bug.do?bug_id=4152790) that causes StackOverflowError too when serializing/deserializing a large graph of objects. So applying this PR only solves part of the problem. Using KryoSerializer to replace Java serializer might be helpful. However, since KryoSerializer is not supported for `spark.closure.serializer` now, I can not test if KryoSerializer can solve Java serializer's problem completely.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#1418 from viirya/remove_recursive_visit and squashes the following commits:
6b2c615 [Liang-Chi Hsieh] change function name; comply with code style.
5f072a7 [Liang-Chi Hsieh] add comments to explain Stack usage.
8742dbb [Liang-Chi Hsieh] comply with code style.
900538b [Liang-Chi Hsieh] change recursive visiting on rdd's dependencies to iterative approach to avoid stackoverflowerror.
getPreferredLocs traverses a dependency graph of partitions using depth first search. Given a complex dependency graph, the old implementation may explore a set of paths in the graph that is exponential in the number of nodes. By maintaining a set of visited nodes the new implementation avoids revisiting nodes, preventing exponential blowup.
Some comment and whitespace cleanups are also included.
Author: Aaron Staple <aaron.staple@gmail.com>
Closes#1362 from staple/SPARK-695 and squashes the following commits:
ecea0f3 [Aaron Staple] address review comments
751c661 [Aaron Staple] [SPARK-695] Add a unit test.
5adf326 [Aaron Staple] Replace getPreferredLocsInternal's HashMap argument with a simpler HashSet.
58e37d0 [Aaron Staple] Replace comment documenting NarrowDependency.
6751ced [Aaron Staple] Revert "Remove unused variable."
04c7097 [Aaron Staple] Fix indentation.
0030884 [Aaron Staple] Remove unused variable.
33f67c6 [Aaron Staple] Clarify comment.
4e42b46 [Aaron Staple] Remove apparently incorrect comment describing NarrowDependency.
65c2d3d [Aaron Staple] [SPARK-695] In DAGScheduler's getPreferredLocs, track set of visited partitions.
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-2557](https://issues.apache.org/jira/browse/SPARK-2557)
Author: Ye Xianjin <advancedxy@gmail.com>
Closes#1464 from advancedxy/SPARK-2557 and squashes the following commits:
d844d67 [Ye Xianjin] add local-*-n-failures, bad-local-n, bad-local-n-failures test case
3bbc668 [Ye Xianjin] fix LOCAL_N_REGEX regular expression and make local_n_failures accept * as all cores on the computer
Author: Rahul Singhal <rahul.singhal@guavus.com>
Closes#1076 from rahulsinghaliitd/SPARK-2134 and squashes the following commits:
15f18b6 [Rahul Singhal] SPARK-2134: Report metrics before application finishes
This patch simply uses the ExternalSorter class from sort-based shuffle.
Closes#931 and Closes#1090
Author: Matei Zaharia <matei@databricks.com>
Closes#1677 from mateiz/spark-983 and squashes the following commits:
96b3fda [Matei Zaharia] SPARK-983. Support external sorting in sortByKey()
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#1578 from sarutak/SPARK-2670 and squashes the following commits:
85c8938 [Kousuke Saruta] Removed useless results.put for fail fast
e8713cc [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
d353984 [Kousuke Saruta] Refined assertion messages in BlockFetcherIteratorSuite.scala
03bcb02 [Kousuke Saruta] Merge branch 'SPARK-2670' of github.com:sarutak/spark into SPARK-2670
5d05855 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
4fca130 [Kousuke Saruta] Added test cases for BasicBlockFetcherIterator
b7b8250 [Kousuke Saruta] Modified BasicBlockFetchIterator to fail fast when local fetch error has been occurred
a3a9be1 [Kousuke Saruta] Modified BlockFetcherIterator for SPARK-2670
460dc01 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
e310c0b [Kousuke Saruta] Modified BlockFetcherIterator to handle local fetch failure as fatch fail
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1642 from sryza/sandy-spark-2738 and squashes the following commits:
a923e4e [Sandy Ryza] SPARK-2738. Remove redundant imports in BlockManagerSuite
Author: Haoyuan Li <haoyuan@cs.berkeley.edu>
Closes#1651 from haoyuan/upgrade-tachyon and squashes the following commits:
6f3f98f [Haoyuan Li] upgrade tachyon to 0.5.0
Prior to this change, every PySpark task completion opened a new socket to the accumulator server, passed its updates through, and then quit. I'm not entirely sure why PySpark always sends accumulator updates, but regardless this causes a very rapid buildup of ephemeral TCP connections that remain in the TCP_WAIT state for around a minute before being cleaned up.
Rather than trying to allow these sockets to be cleaned up faster, this patch simply reuses the connection between tasks completions (since they're fed updates in a single-threaded manner by the DAGScheduler anyway).
The only tricky part here was making sure that the AccumulatorServer was able to shutdown in a timely manner (i.e., stop polling for new data), and this was accomplished via minor feats of magic.
I have confirmed that this patch eliminates the buildup of ephemeral sockets due to the accumulator updates. However, I did note that there were still significant sockets being created against the PySpark daemon port, but my machine was not able to create enough sockets fast enough to fail. This may not be the last time we've seen this issue, though.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1503 from aarondav/accum and squashes the following commits:
b3e12f7 [Aaron Davidson] SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark
It should be more convenient if user can specify ascending and numPartitions when calling sortByKey.
Author: Rui Li <rui.li@intel.com>
Closes#1645 from lirui-intel/spark-2740 and squashes the following commits:
fb5d52e [Rui Li] SPARK-2740: allow user to specify ascending and numPartitions for sortByKey
The logging code that handles log4j initialization leads to an stack overflow error when used with log4j 2.x, which has just been released. This occurs even a downstream project has correctly adjusted SLF4J bindings, and that is the right thing to do for log4j 2.x, since it is effectively a separate project from 1.x.
Here is the relevant bit of Logging.scala:
```
private def initializeLogging() {
// If Log4j is being used, but is not initialized, load a default properties file
val binder = StaticLoggerBinder.getSingleton
val usingLog4j = binder.getLoggerFactoryClassStr.endsWith("Log4jLoggerFactory")
val log4jInitialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements
if (!log4jInitialized && usingLog4j) {
val defaultLogProps = "org/apache/spark/log4j-defaults.properties"
Option(Utils.getSparkClassLoader.getResource(defaultLogProps)) match {
case Some(url) =>
PropertyConfigurator.configure(url)
log.info(s"Using Spark's default log4j profile: $defaultLogProps")
case None =>
System.err.println(s"Spark was unable to load $defaultLogProps")
}
}
Logging.initialized = true
// Force a call into slf4j to initialize it. Avoids this happening from mutliple threads
// and triggering this: http://mailman.qos.ch/pipermail/slf4j-dev/2010-April/002956.html
log
}
```
The first minor issue is that there is a call to a logger inside this method, which is initializing logging. In this situation, it ends up causing the initialization to be called recursively until the stack overflow. It would be slightly tidier to log this only after Logging.initialized = true. Or not at all. But it's not the root problem, or else, it would not work at all now.
The calls to log4j classes here always reference log4j 1.2 no matter what. For example, there is not getAllAppenders in log4j 2.x. That's fine. Really, "usingLog4j" means "using log4j 1.2" and "log4jInitialized" means "log4j 1.2 is initialized".
usingLog4j should be false for log4j 2.x, because the initialization only matters for log4j 1.2. But, it's true, and that's the real issue. And log4jInitialized is always false, since calls to the log4j 1.2 API are stubs and no-ops in this setup, where the caller has swapped in log4j 2.x. Hence the loop.
This is fixed, I believe, if "usingLog4j" can be false for log4j 2.x. The SLF4J static binding class has the same name for both versions, unfortunately, which causes the issue. However they're in different packages. For example, if the test included "... and begins with org.slf4j", it should work, as the SLF4J binding for log4j 2.x is provided by log4j 2.x at the moment, and is in package org.apache.logging.slf4j.
Of course, I assume that SLF4J will eventually offer its own binding. I hope to goodness they at least name the binding class differently, or else this will again not work. But then some other check can probably be made.
Author: Sean Owen <srowen@gmail.com>
Closes#1547 from srowen/SPARK-2646 and squashes the following commits:
92a9898 [Sean Owen] System.out -> System.err
94be4c7 [Sean Owen] Add back log message as System.out, with informational comment
a7f8876 [Sean Owen] Updates from review
6f3c1d3 [Sean Owen] Remove log statement in logging initialization, and distinguish log4j 1.2 from 2.0, to avoid stack overflow in initialization
...ags
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1665 from sryza/sandy-spark-2664 and squashes the following commits:
0518c63 [Sandy Ryza] SPARK-2664. Deal with `--conf` options in spark-submit that relate to flags
This allows users to gain access to the InputSplit which backs each partition.
An alternative solution would have been to have a .withInputSplit() method which returns a new RDD[(InputSplit, (K, V))], but this is confusing because you could not cache this RDD or shuffle it, as InputSplit is not inherently serializable.
Author: Aaron Davidson <aaron@databricks.com>
Closes#973 from aarondav/hadoop and squashes the following commits:
9c9112b [Aaron Davidson] Add JavaAPISuite test
9942cd7 [Aaron Davidson] Add Java API
1284a3a [Aaron Davidson] SPARK-2028: Expose mapPartitionsWithInputSplit in HadoopRDD
The Java API's use of fake ClassTags doesn't seem to cause any problems for Java users, but it can lead to issues when passing JavaRDDs' underlying RDDs to Scala code (e.g. in the MLlib Java API wrapper code). If we call collect() on a Scala RDD with an incorrect ClassTag, this causes ClassCastExceptions when we try to allocate an array of the wrong type (for example, see SPARK-2197).
There are a few possible fixes here. An API-breaking fix would be to completely remove the fake ClassTags and require Java API users to pass java.lang.Class instances to all parallelize() calls and add returnClass fields to all Function implementations. This would be extremely verbose.
Instead, this patch adds internal APIs to "repair" a Scala RDD with an incorrect ClassTag by wrapping it and overriding its ClassTag. This should be okay for cases where the Scala code that calls collect() knows what type of array should be allocated, which is the case in the MLlib wrappers.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1639 from JoshRosen/SPARK-2737 and squashes the following commits:
572b4c8 [Josh Rosen] Replace newRDD[T] with mapPartitions().
469d941 [Josh Rosen] Preserve partitioner in retag().
af78816 [Josh Rosen] Allow retag() to get classTag implicitly.
d1d54e6 [Josh Rosen] [SPARK-2737] Add retag() method for changing RDDs' ClassTags.
We resolve relative paths to the local `file:/` system for `--jars` and `--files` in spark submit (#853). We should do the same for the history server.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1280 from andrewor14/hist-serv-fix and squashes the following commits:
13ff406 [Andrew Or] Merge branch 'master' of github.com:apache/spark into hist-serv-fix
b393e17 [Andrew Or] Strip trailing "/" from logging directory
622a471 [Andrew Or] Fix test in EventLoggingListenerSuite
0e20f71 [Andrew Or] Shift responsibility of resolving paths up one level
b037c0c [Andrew Or] Use resolved paths for everything in history server
c7e36ee [Andrew Or] Resolve paths for event logging too
40e3933 [Andrew Or] Resolve history server file paths
Author: Reynold Xin <rxin@apache.org>
Closes#1675 from rxin/unionrdd and squashes the following commits:
941d316 [Reynold Xin] Clear RDDs for checkpointing.
c9f05f2 [Reynold Xin] [SPARK-2758] UnionRDD's UnionPartition should not reference parent RDDs
This adds a new ShuffleManager based on sorting, as described in https://issues.apache.org/jira/browse/SPARK-2045. The bulk of the code is in an ExternalSorter class that is similar to ExternalAppendOnlyMap, but sorts key-value pairs by partition ID and can be used to create a single sorted file with a map task's output. (Longer-term I think this can take on the remaining functionality in ExternalAppendOnlyMap and replace it so we don't have code duplication.)
The main TODOs still left are:
- [x] enabling ExternalSorter to merge across spilled files
- [x] with an Ordering
- [x] without an Ordering, using the keys' hash codes
- [x] adding more tests (e.g. a version of our shuffle suite that runs on this)
- [x] rebasing on top of the size-tracking refactoring in #1165 when that is merged
- [x] disabling spilling if spark.shuffle.spill is set to false
Despite this though, this seems to work pretty well (running successfully in cases where the hash shuffle would OOM, such as 1000 reduce tasks on executors with only 1G memory), and it seems to be comparable in speed or faster than hash-based shuffle (it will create much fewer files for the OS to keep track of). So I'm posting it to get some early feedback.
After these TODOs are done, I'd also like to enable ExternalSorter to sort data within each partition by a key as well, which will allow us to use it to implement external spilling in reduce tasks in `sortByKey`.
Author: Matei Zaharia <matei@databricks.com>
Closes#1499 from mateiz/sort-based-shuffle and squashes the following commits:
bd841f9 [Matei Zaharia] Various review comments
d1c137fd [Matei Zaharia] Various review comments
a611159 [Matei Zaharia] Compile fixes due to rebase
62c56c8 [Matei Zaharia] Fix ShuffledRDD sometimes not returning Tuple2s.
f617432 [Matei Zaharia] Fix a failing test (seems to be due to change in SizeTracker logic)
9464d5f [Matei Zaharia] Simplify code and fix conflicts after latest rebase
0174149 [Matei Zaharia] Add cleanup behavior and cleanup tests for sort-based shuffle
eb4ee0d [Matei Zaharia] Remove customizable element type in ShuffledRDD
fa2e8db [Matei Zaharia] Allow nextBatchStream to be called after we're done looking at all streams
a34b352 [Matei Zaharia] Fix tracking of indices within a partition in SpillReader, and add test
03e1006 [Matei Zaharia] Add a SortShuffleSuite that runs ShuffleSuite with sort-based shuffle
3c7ff1f [Matei Zaharia] Obey the spark.shuffle.spill setting in ExternalSorter
ad65fbd [Matei Zaharia] Rebase on top of Aaron's Sorter change, and use Sorter in our buffer
44d2a93 [Matei Zaharia] Use estimateSize instead of atGrowThreshold to test collection sizes
5686f71 [Matei Zaharia] Optimize merging phase for in-memory only data:
5461cbb [Matei Zaharia] Review comments and more tests (e.g. tests with 1 element per partition)
e9ad356 [Matei Zaharia] Update ContextCleanerSuite to make sure shuffle cleanup tests use hash shuffle (since they were written for it)
c72362a [Matei Zaharia] Added bug fix and test for when iterators are empty
de1fb40 [Matei Zaharia] Make trait SizeTrackingCollection private[spark]
4988d16 [Matei Zaharia] tweak
c1b7572 [Matei Zaharia] Small optimization
ba7db7f [Matei Zaharia] Handle null keys in hash-based comparator, and add tests for collisions
ef4e397 [Matei Zaharia] Support for partial aggregation even without an Ordering
4b7a5ce [Matei Zaharia] More tests, and ability to sort data if a total ordering is given
e1f84be [Matei Zaharia] Fix disk block manager test
5a40a1c [Matei Zaharia] More tests
614f1b4 [Matei Zaharia] Add spill metrics to map tasks
cc52caf [Matei Zaharia] Add more error handling and tests for error cases
bbf359d [Matei Zaharia] More work
3a56341 [Matei Zaharia] More partial work towards sort-based shuffle
7a0895d [Matei Zaharia] Some more partial work towards sort-based shuffle
b615476 [Matei Zaharia] Scaffolding for sort-based shuffle
The Maven-based builds in the build matrix have been failing for a few days:
https://amplab.cs.berkeley.edu/jenkins/view/Spark/
On inspection, it looks like the Spark SQL Java tests don't compile:
https://amplab.cs.berkeley.edu/jenkins/view/Spark/job/Spark-Master-Maven-pre-YARN/hadoop.version=1.0.4,label=centos/244/consoleFull
I confirmed it by repeating the command vs master:
`mvn -Dhadoop.version=1.0.4 -Dlabel=centos -DskipTests clean package`
The problem is that this module doesn't depend on JUnit. In fact, none of the modules do, but `com.novocode:junit-interface` (the SBT-JUnit bridge) pulls it in, in most places. However this module doesn't depend on `com.novocode:junit-interface`
Adding the `junit:junit` dependency fixes the compile problem. In fact, the other modules with Java tests should probably depend on it explicitly instead of happening to get it via `com.novocode:junit-interface`, since that is a bit SBT/Scala-specific (and I am not even sure it's needed).
Author: Sean Owen <srowen@gmail.com>
Closes#1660 from srowen/SPARK-2749 and squashes the following commits:
858ff7c [Sean Owen] Add explicit junit dep to other modules with Java tests for robustness
9636794 [Sean Owen] Add junit dep so that Spark SQL Java tests compile
JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024
This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats.
* Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs.
* Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types.
* No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples.
* Added HBase and Cassandra output examples to show how custom output formats and converters can be used.
cc MLnick mateiz ahirreddy pwendell
Author: Kan Zhang <kzhang@apache.org>
Closes#1338 from kanzhang/SPARK-2024 and squashes the following commits:
c01e3ef [Kan Zhang] [SPARK-2024] code formatting
6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD
d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10
57a7a5e [Kan Zhang] [SPARK-2024] correcting typo
75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD
0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests
9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests
0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases
7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark
This is a resubmission of #1452. It was reverted because it broke the build.
Currently (as of Spark 1.0.1), Spark sends RDD object (which contains closures) using Akka along with the task itself to the executors. This is inefficient because all tasks in the same stage use the same RDD object, but we have to send RDD object multiple times to the executors. This is especially bad when a closure references some variable that is very large. The current design led to users having to explicitly broadcast large variables.
The patch uses broadcast to send RDD objects and the closures to executors, and use Akka to only send a reference to the broadcast RDD/closure along with the partition specific information for the task. For those of you who know more about the internals, Spark already relies on broadcast to send the Hadoop JobConf every time it uses the Hadoop input, because the JobConf is large.
The user-facing impact of the change include:
1. Users won't need to decide what to broadcast anymore, unless they would want to use a large object multiple times in different operations
2. Task size will get smaller, resulting in faster scheduling and higher task dispatch throughput.
In addition, the change will simplify some internals of Spark, eliminating the need to maintain task caches and the complex logic to broadcast JobConf (which also led to a deadlock recently).
A simple way to test this:
```scala
val a = new Array[Byte](1000*1000); scala.util.Random.nextBytes(a);
sc.parallelize(1 to 1000, 1000).map { x => a; x }.groupBy { x => a; x }.count
```
Numbers on 3 r3.8xlarge instances on EC2
```
master branch: 5.648436068 s, 4.715361895 s, 5.360161877 s
with this change: 3.416348793 s, 1.477846558 s, 1.553432156 s
```
Author: Reynold Xin <rxin@apache.org>
Closes#1498 from rxin/broadcast-task and squashes the following commits:
f7364db [Reynold Xin] Code review feedback.
f8535dc [Reynold Xin] Fixed the style violation.
252238d [Reynold Xin] Serialize the final task closure as well as ShuffleDependency in taskBinary.
111007d [Reynold Xin] Fix broadcast tests.
797c247 [Reynold Xin] Properly send SparkListenerStageSubmitted and SparkListenerStageCompleted.
bab1d8b [Reynold Xin] Check for NotSerializableException in submitMissingTasks.
cf38450 [Reynold Xin] Use TorrentBroadcastFactory.
991c002 [Reynold Xin] Use HttpBroadcast.
de779f8 [Reynold Xin] Fix TaskContextSuite.
cc152fc [Reynold Xin] Don't cache the RDD broadcast variable.
d256b45 [Reynold Xin] Fixed unit test failures. One more to go.
cae0af3 [Reynold Xin] [SPARK-2521] Broadcast RDD object (instead of sending it along with every task).
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
The current PR contains the following changes:
* Expose `DataType`s in the sql package (internal details are private to sql).
* Users can create Rows.
* Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`.
* Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
* `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases).
* `JsonRDD` has been refactored to use changes introduced by this PR.
* Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`.
New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at
[sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext).
An example of using `applySchema` is shown below.
```scala
import org.apache.spark.sql._
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val schema =
StructType(
StructField("name", StringType, false) ::
StructField("age", IntegerType, true) :: Nil)
val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt))
val peopleSchemaRDD = sqlContext. applySchema(people, schema)
peopleSchemaRDD.printSchema
// root
// |-- name: string (nullable = false)
// |-- age: integer (nullable = true)
peopleSchemaRDD.registerAsTable("people")
sqlContext.sql("select name from people").collect.foreach(println)
```
I will add new contents to the SQL programming guide later.
JIRA: https://issues.apache.org/jira/browse/SPARK-2179
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1346 from yhuai/dataTypeAndSchema and squashes the following commits:
1d45977 [Yin Huai] Clean up.
a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c712fbf [Yin Huai] Converts types of values based on defined schema.
4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e5f8df5 [Yin Huai] Scaladoc.
122d1e7 [Yin Huai] Address comments.
03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2476ed0 [Yin Huai] Minor updates.
ab71f21 [Yin Huai] Format.
fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
bd40a33 [Yin Huai] Address comments.
991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala.
1cb35fe [Yin Huai] Add "valueContainsNull" to MapType.
3edb3ae [Yin Huai] Python doc.
692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
1d93395 [Yin Huai] Python APIs.
246da96 [Yin Huai] Add java data type APIs to javadoc index.
1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
d48fc7b [Yin Huai] Minor updates.
33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b9f3071 [Yin Huai] Java API for applySchema.
1c9f33c [Yin Huai] Java APIs for DataTypes and Row.
624765c [Yin Huai] Tests for applySchema.
aa92e84 [Yin Huai] Update data type tests.
8da1a17 [Yin Huai] Add Row.fromSeq.
9c99bc0 [Yin Huai] Several minor updates.
1d9c13a [Yin Huai] Update applySchema API.
85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e495e4e [Yin Huai] More comments.
42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc.
68525a2 [Yin Huai] Update JSON unit test.
3209108 [Yin Huai] Add unit tests.
dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false.
9168b83 [Yin Huai] Update comments.
fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType.
949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema.
7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema.
43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit.
0266761 [Yin Huai] Format
03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type.
3fa0df5 [Yin Huai] Provide easier ways to construct a StructType.
16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
The main thing was that spark configs were not propagated to the driver, and so applications that do not specify `master` or `appName` automatically failed. This PR fixes that and a couple of miscellaneous things that are related.
One thing that may or may not be an issue is that the jars must be available on the driver node. In `standalone-cluster` mode, this effectively means these jars must be available on all the worker machines, since the driver is launched on one of them. The semantics here are not the same as `yarn-cluster` mode, where all the relevant jars are uploaded to a distributed cache automatically and shipped to the containers. This is probably not a concern, but still worth a mention.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1538 from andrewor14/standalone-cluster and squashes the following commits:
8c11a0d [Andrew Or] Clean up imports / comments (minor)
2678d13 [Andrew Or] Handle extraJavaOpts properly
7660547 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster
6f64a9b [Andrew Or] Revert changes in YARN
2f2908b [Andrew Or] Fix tests
ed01491 [Andrew Or] Don't go overboard with escaping
8e105e1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster
b890949 [Andrew Or] Abstract usages of converting spark opts to java opts
79f63a3 [Andrew Or] Move sparkProps into javaOpts
78752f8 [Andrew Or] Fix tests
5a9c6c7 [Andrew Or] Fix line too long
c141a00 [Andrew Or] Don't display "unknown app" on driver log pages
d7e2728 [Andrew Or] Avoid deprecation warning in standalone Client
6ceb14f [Andrew Or] Allow relevant configs to propagate to standalone Driver
7f854bc [Andrew Or] Fix test
855256e [Andrew Or] Fix standalone-cluster mode
fd9da51 [Andrew Or] Formatting changes (minor)
As of Spark 1.0, RangePartitioner goes through data twice: once to compute the count and once to do sampling. As a result, to do sortByKey, Spark goes through data 3 times (once to count, once to sample, and once to sort).
`RangePartitioner` should go through data only once, collecting samples from input partitions as well as counting. If the data is balanced, this should give us a good sketch. If we see big partitions, we re-sample from them in order to collect enough items.
The downside is that we need to collect more from each partition in the first pass. An alternative solution is caching the intermediate result and decide whether to fetch the data after.
Author: Xiangrui Meng <meng@databricks.com>
Author: Reynold Xin <rxin@apache.org>
Closes#1562 from mengxr/range-partitioner and squashes the following commits:
6cc2551 [Xiangrui Meng] change foreach to for
eb39b08 [Xiangrui Meng] Merge branch 'master' into range-partitioner
eb95dd8 [Xiangrui Meng] separate sketching and determining bounds impl
c436d30 [Xiangrui Meng] fix binary metrics unit tests
db58a55 [Xiangrui Meng] add unit tests
a6e35d6 [Xiangrui Meng] minor update
60be09e [Xiangrui Meng] remove importance sampler
9ee9992 [Xiangrui Meng] update range partitioner to run only one job on roughly balanced data
cc12f47 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part
06ac2ec [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part
17bcbf3 [Reynold Xin] Added seed.
badf20d [Reynold Xin] Renamed the method.
6940010 [Reynold Xin] Reservoir sampling implementation.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1626 from JoshRosen/SPARK-2305 and squashes the following commits:
03fb283 [Josh Rosen] Update Py4J to version 0.8.2.1.
Implemented stratified sampling that guarantees exact sample size using ScaRSR with two passes over the RDD for sampling without replacement and three passes for sampling with replacement.
Author: Doris Xin <doris.s.xin@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#1025 from dorx/stratified and squashes the following commits:
245439e [Doris Xin] moved minSamplingRate to getUpperBound
eaf5771 [Doris Xin] bug fixes.
17a381b [Doris Xin] fixed a merge issue and a failed unit
ea7d27f [Doris Xin] merge master
b223529 [Xiangrui Meng] use approx bounds for poisson fix poisson mean for waitlisting add unit tests for Java
b3013a4 [Xiangrui Meng] move math3 back to test scope
eecee5f [Doris Xin] Merge branch 'master' into stratified
f4c21f3 [Doris Xin] Reviewer comments
a10e68d [Doris Xin] style fix
a2bf756 [Doris Xin] Merge branch 'master' into stratified
680b677 [Doris Xin] use mapPartitionWithIndex instead
9884a9f [Doris Xin] style fix
bbfb8c9 [Doris Xin] Merge branch 'master' into stratified
ee9d260 [Doris Xin] addressed reviewer comments
6b5b10b [Doris Xin] Merge branch 'master' into stratified
254e03c [Doris Xin] minor fixes and Java API.
4ad516b [Doris Xin] remove unused imports from PairRDDFunctions
bd9dc6e [Doris Xin] unit bug and style violation fixed
1fe1cff [Doris Xin] Changed fractionByKey to a map to enable arg check
944a10c [Doris Xin] [SPARK-2145] Add lower bound on sampling rate
0214a76 [Doris Xin] cleanUp
90d94c0 [Doris Xin] merge master
9e74ab5 [Doris Xin] Separated out most of the logic in sampleByKey
7327611 [Doris Xin] merge master
50581fc [Doris Xin] added a TODO for logging in python
46f6c8c [Doris Xin] fixed the NPE caused by closures being cleaned before being passed into the aggregate function
7e1a481 [Doris Xin] changed the permission on SamplingUtil
1d413ce [Doris Xin] fixed checkstyle issues
9ee94ee [Doris Xin] [SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact sample size
e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample
7cab53a [Doris Xin] fixed import bug in rdd.py
ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD
1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema().
In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1601 from davies/date and squashes the following commits:
f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list
c9d607a [Davies Liu] convert datetype for runtime
709d40d [Davies Liu] remove brackets
96db384 [Davies Liu] support datetime type for SchemaRDD
The pull request includes two changes:
1. Removes SortOrder introduced by SPARK-2125. The key ordering already includes the SortOrder information since an Ordering can be reverse. This is similar to Java's Comparator interface. Rarely does an API accept both a Comparator as well as a SortOrder.
2. Replaces the sortWith call in HashShuffleReader with an in-place quick sort.
Author: Reynold Xin <rxin@apache.org>
Closes#1631 from rxin/sortOrder and squashes the following commits:
c9d37e1 [Reynold Xin] [SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.
Spark only transitively depends on the latter, based on the Hadoop version.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1621 from aarondav/lang3 and squashes the following commits:
93c93bf [Aaron Davidson] Use commons-lang3 in SignalLogger rather than commons-lang
JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
Another try for #1399 & #1600. Those two PR breaks Jenkins builds because we made a separate profile `hive-thriftserver` in sub-project `assembly`, but the `hive-thriftserver` module is defined outside the `hive-thriftserver` profile. Thus every time a pull request that doesn't touch SQL code will also execute test suites defined in `hive-thriftserver`, but tests fail because related .class files are not included in the assembly jar.
In the most recent commit, module `hive-thriftserver` is moved into its own profile to fix this problem. All previous commits are squashed for clarity.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1620 from liancheng/jdbc-with-maven-fix and squashes the following commits:
629988e [Cheng Lian] Moved hive-thriftserver module definition into its own profile
ec3c7a7 [Cheng Lian] Cherry picked the Hive Thrift server
**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
(This is a replacement of #1399, trying to fix potential `HiveThriftServer2` port collision between parallel builds. Please refer to [these comments](https://github.com/apache/spark/pull/1399#issuecomment-50212572) for details.)
JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
Merging the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).
Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1600 from liancheng/jdbc and squashes the following commits:
ac4618b [Cheng Lian] Uses random port for HiveThriftServer2 to avoid collision with parallel builds
090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
This will decrease object allocation from the "update" closure used in map.changeValue.
Author: Matei Zaharia <matei@databricks.com>
Closes#1607 from mateiz/spark-2684 and squashes the following commits:
b7d89e6 [Matei Zaharia] Add insertAll for Iterables too, and fix some code style
561fc97 [Matei Zaharia] Update ExternalAppendOnlyMap to take an iterator as input
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
Similar to SPARK-1034, the problem was that Py4J didn’t cope well with the fake ClassTags used in the Java API. It doesn’t look like there’s any reason why PythonRDD needs to take a ClassTag, since it just ignores the type of the previous RDD, so I removed the type parameter and we no longer pass ClassTags from Python.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1605 from JoshRosen/spark-2601 and squashes the following commits:
b68e118 [Josh Rosen] Fix Py4J error when transforming pickleFiles [SPARK-2601]
handleMessageExecutor, handleReadWriteExecutor, and handleConnectExecutor are not marked as daemon and not named. I think there exists some condition in which Spark programs won't terminate because of this.
Stack dump attached in https://issues.apache.org/jira/browse/SPARK-2704
Author: Reynold Xin <rxin@apache.org>
Closes#1604 from rxin/daemon and squashes the following commits:
98d6a6c [Reynold Xin] [SPARK-2704] Name threads in ConnectionManager and mark them as daemon.
Added emptyRDD method to Java API with tests.
Author: bpaulin <bob@bobpaulin.com>
Closes#1597 from bobpaulin/SPARK-2279 and squashes the following commits:
5ad57c2 [bpaulin] [SPARK-2279] Added emptyRDD method to Java API
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
Author: Josh Rosen <joshrosen@apache.org>
Closes#1596 from JoshRosen/spark-1458 and squashes the following commits:
fdbb0bf [Josh Rosen] Add SparkContext.version to Python & Java [SPARK-1458]
This is part of the scheduler cleanup/refactoring effort to make the scheduler code easier to maintain.
@kayousterhout @markhamstra please take a look ...
Author: Reynold Xin <rxin@apache.org>
Closes#1561 from rxin/dagSchedulerHashMaps and squashes the following commits:
1c44e15 [Reynold Xin] Clear pending tasks in submitMissingTasks.
620a0d1 [Reynold Xin] Use filterKeys.
5b54404 [Reynold Xin] Code review feedback.
c1e9a1c [Reynold Xin] Removed some HashMaps from DAGScheduler by storing information in Stage.
This reverts commit 06dc0d2c6b.
#1399 is making Jenkins fail. We should investigate and put this back after its passing tests.
Author: Michael Armbrust <michael@databricks.com>
Closes#1594 from marmbrus/revertJDBC and squashes the following commits:
59748da [Michael Armbrust] Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC server"
Due to problems with when we update runningStages (in DAGScheduler.scala)
and how we decide to send a SparkListenerStageCompleted message to
SparkListeners, sometimes stages can be shown as "running" in the UI forever
(even after they have failed). This issue can manifest when stages are
resubmitted with 0 tasks, or when the DAGScheduler catches non-serializable
tasks. The problem also resulted in a (small) memory leak in the DAGScheduler,
where stages can stay in runningStages forever. This commit fixes
that problem and adds a unit test.
Thanks tsudukim for helping to look into this issue!
cc markhamstra rxin
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#1566 from kayousterhout/dag_fix and squashes the following commits:
217d74b [Kay Ousterhout] [SPARK-1726] [SPARK-2567] Eliminate zombie stages in UI.
This patch adds a sort flag into ShuffleDependecy and moves sort into hash shuffle implementation.
Moving sort into shuffle implementation can give space for other shuffle implementations (like sort-based shuffle) to better optimize sort through shuffle.
Author: jerryshao <saisai.shao@intel.com>
Closes#1210 from jerryshao/SPARK-2125 and squashes the following commits:
2feaf7b [jerryshao] revert MimaExcludes
ceddf75 [jerryshao] add MimaExeclude
f674ff4 [jerryshao] Add missing Scope restriction
b9fe0dd [jerryshao] Fix some style issues according to comments
ef6b729 [jerryshao] Change sort flag into Option
3f6eeed [jerryshao] Fix issues related to unit test
2f552a5 [jerryshao] Minor changes about naming and order
c92a281 [jerryshao] Move sort into shuffle implementations
JIRA issue:
- Main: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410)
- Related: [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678)
Cherry picked the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc).
(Thanks chenghao-intel for his initial contribution of the Spark SQL CLI.)
TODO
- [x] Use `spark-submit` to launch the server, the CLI and beeline
- [x] Migration guideline draft for Shark users
----
Hit by a bug in `SparkSubmitArguments` while working on this PR: all application options that are recognized by `SparkSubmitArguments` are stolen as `SparkSubmit` options. For example:
```bash
$ spark-submit --class org.apache.hive.beeline.BeeLine spark-internal --help
```
This actually shows usage information of `SparkSubmit` rather than `BeeLine`.
~~Fixed this bug here since the `spark-internal` related stuff also touches `SparkSubmitArguments` and I'd like to avoid conflict.~~
**UPDATE** The bug mentioned above is now tracked by [SPARK-2678](https://issues.apache.org/jira/browse/SPARK-2678). Decided to revert changes to this bug since it involves more subtle considerations and worth a separate PR.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1399 from liancheng/thriftserver and squashes the following commits:
090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR
21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs
fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd]
199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver
1083e9d [Cheng Lian] Fixed failed test suites
7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic
9cc0f06 [Cheng Lian] Starts beeline with spark-submit
cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile
061880f [Cheng Lian] Addressed all comments by @pwendell
7755062 [Cheng Lian] Adapts test suites to spark-submit settings
40bafef [Cheng Lian] Fixed more license header issues
e214aab [Cheng Lian] Added missing license headers
b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh
f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft
3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit
a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit
61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit
2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
Renaming `short` to `shortForm` and `long` to `longForm`.
JIRA: https://issues.apache.org/jira/browse/SPARK-2683
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1585 from yhuai/SPARK-2683 and squashes the following commits:
5ddb843 [Yin Huai] "short" and "long" are Java keyworks. In order to generate javadoc, renaming "short" to "shortForm" and "long" to "longForm".
Author: Reynold Xin <rxin@apache.org>
Closes#1583 from rxin/closureClean and squashes the following commits:
8982fe6 [Reynold Xin] [SPARK-2529] Clean closures in foreach and foreachPartition.
JIRA: https://issues.apache.org/jira/browse/SPARK-2657
Our current code uses ArrayBuffers for each group of values in groupBy, as well as for the key's elements in CoGroupedRDD. ArrayBuffers have a lot of overhead if there are few values in them, which is likely to happen in cases such as join. In particular, they have a pointer to an Object[] of size 16 by default, which is 24 bytes for the array header + 128 for the pointers in there, plus at least 32 for the ArrayBuffer data structure. This patch replaces the per-group buffers with a CompactBuffer class that can store up to 2 elements more efficiently (in fields of itself) and acts like an ArrayBuffer beyond that. For a key's elements in CoGroupedRDD, we use an Array of CompactBuffers instead of an ArrayBuffer of ArrayBuffers.
There are some changes throughout the code to deal with CoGroupedRDD returning Array instead. We can also decide not to do that but CoGroupedRDD is a `DeveloperAPI` so I think it's okay to change it here.
Author: Matei Zaharia <matei@databricks.com>
Closes#1555 from mateiz/compact-groupby and squashes the following commits:
845a356 [Matei Zaharia] Lower initial size of CompactBuffer's vector to 8
07621a7 [Matei Zaharia] Review comments
0c1cd12 [Matei Zaharia] Don't use varargs in CompactBuffer.apply
bdc8a39 [Matei Zaharia] Small tweak to +=, and typos
f61f040 [Matei Zaharia] Fix line lengths
59da88b0 [Matei Zaharia] Fix line lengths
197cde8 [Matei Zaharia] Make CompactBuffer extend Seq to make its toSeq more efficient
775110f [Matei Zaharia] Change CoGroupedRDD to give (K, Array[Iterable[_]]) to avoid wrappers
9b4c6e8 [Matei Zaharia] Use CompactBuffer in CoGroupedRDD
ed577ab [Matei Zaharia] Use CompactBuffer in groupByKey
10f0de1 [Matei Zaharia] A CompactBuffer that's more memory-efficient than ArrayBuffer for small buffers
exact sample size not supported for now.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1554 from dorx/pystratified and squashes the following commits:
4ba927a [Doris Xin] use rel diff (+- 50%) instead of abs diff (+- 50)
bdc3f8b [Doris Xin] updated unit to check sample holistically
7713c7b [Doris Xin] Python version of stratified sampling
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
Author: Neville Li <neville@spotify.com>
Closes#1188 from nevillelyh/neville/ui and squashes the following commits:
d3ac425 [Neville Li] SPARK-2250: show persisted RDD in stage UI
f075db9 [Neville Li] SPARK-2035: show call stack even when description is available
...rce manager UI
Use the event logger directory to provide a direct link to finished
application UI in yarn resourcemanager UI.
Author: Rahul Singhal <rahul.singhal@guavus.com>
Closes#1094 from rahulsinghaliitd/SPARK-2150 and squashes the following commits:
95f230c [Rahul Singhal] SPARK-2150: Provide direct link to finished application UI in yarn resource manager UI
...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
Author: GuoQiang Li <witgo@qq.com>
Closes#1511 from witgo/JsonProtocol and squashes the following commits:
2b6227f [GuoQiang Li] Fix NPE for JsonProtocol
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
Hi mridulm, I just think of this issue of [#1212](https://github.com/apache/spark/pull/1212): I added FakeRackUtil to hold the host -> rack mapping. It should be cleaned up after use so that it won't mess up with test cases others may add later.
Really sorry about this.
Author: Rui Li <rui.li@intel.com>
Closes#1454 from lirui-intel/SPARK-2277-fix-UT and squashes the following commits:
f8ea25c [Rui Li] SPARK-2277: clear host->rack info properly
Make spark's "local[N]" better.
In our company, we use "local[N]" in production. It works exellentlly. It's our best choice.
Author: woshilaiceshide <woshilaiceshide@qq.com>
Closes#1544 from woshilaiceshide/localX and squashes the following commits:
6c85154 [woshilaiceshide] [CORE] SPARK-2640: In "local[N]", free cores of the only executor should be touched by "spark.task.cpus" for every finish/start-up of tasks.
It's useful to know whether one thread is constantly spilling or multiple threads are spilling relatively infrequently. Right now everything looks a little jumbled and we can't tell which lines belong to the same thread. For instance:
```
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (194 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (198 times so far)
06:14:37 ExternalAppendOnlyMap: Spilling in-memory map of 10 MB to disk (197 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 9 MB to disk (45 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 23 MB to disk (198 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 38 MB to disk (25 times so far)
06:14:38 ExternalAppendOnlyMap: Spilling in-memory map of 161 MB to disk (25 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 0 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (166 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (199 times so far)
06:14:39 ExternalAppendOnlyMap: Spilling in-memory map of 4 MB to disk (200 times so far)
```
Author: Andrew Or <andrewor14@gmail.com>
Closes#1517 from andrewor14/external-log and squashes the following commits:
90e48bb [Andrew Or] Log thread ID when spilling
The name `preservesPartitioning` is ambiguous: 1) preserves the indices of partitions, 2) preserves the partitioner. The latter is correct and `preservesPartitioning` should really be called `preservesPartitioner` to avoid confusion. Unfortunately, this is already part of the API and we cannot change. We should be clear in the doc and fix wrong usages.
This PR
1. adds notes in `maPartitions*`,
2. makes `RDD.sample` preserve partitioner,
3. changes `preservesPartitioning` to false in `RDD.zip` because the keys of the first RDD are no longer the keys of the zipped RDD,
4. fixes some wrong usages in MLlib.
Author: Xiangrui Meng <meng@databricks.com>
Closes#1526 from mengxr/preserve-partitioner and squashes the following commits:
b361e65 [Xiangrui Meng] update doc based on pwendell's comments
3b1ba19 [Xiangrui Meng] update doc
357575c [Xiangrui Meng] fix unit test
20b4816 [Xiangrui Meng] Merge branch 'master' into preserve-partitioner
d1caa65 [Xiangrui Meng] add doc to explain preservesPartitioning fix wrong usage of preservesPartitioning make sample preserse partitioning
The issue is caused by #1112 .
Author: GuoQiang Li <witgo@qq.com>
Closes#1501 from witgo/webui_style and squashes the following commits:
4b34998 [GuoQiang Li] In some cases, pages display incorrect in WebUI
### Why and what?
Currently, the AppendOnlyMap performs an "in-place" sort by converting its array of [key, value, key, value] pairs into a an array of [(key, value), (key, value)] pairs. However, this causes us to allocate many Tuple2 objects, which come at a nontrivial overhead.
This patch adds a Sorter API, intended for in memory sorts, which simply ports the Android Timsort implementation (available under Apache v2) and abstracts the interface in a way which introduces no more than 1 virtual function invocation of overhead at each abstraction point.
Please compare our port of the Android Timsort sort with the original implementation: http://www.diffchecker.com/wiwrykcl
### Memory implications
An AppendOnlyMap contains N kv pairs, which results in roughly 2N elements within its underlying array. Each of these elements is 4 bytes wide in a [compressed OOPS](https://wikis.oracle.com/display/HotSpotInternals/CompressedOops) system, which is the default.
Today's approach immediately allocates N Tuple2 objects, which take up 24N bytes in total (exposed via YourKit), and undergoes a Java sort. The Java 6 version immediately copies the entire array (4N bytes here), while the Java 7 version has a worst-case allocation of half the array (2N bytes).
This results in a worst-case sorting overhead of 24N + 2N = 26N bytes (for Java 7).
The Sorter does not require allocating any tuples, but since it uses Timsort, it may copy up to half the entire array in the worst case.
This results in a worst-case sorting overhead of 4N bytes.
Thus, we have reduced the worst-case overhead of the sort by roughly 22 bytes times the number of elements.
### Performance implications
As the destructiveSortedIterator is used for spilling in an ExternalAppendOnlyMap, the purpose of this patch is to provide stability by reducing memory usage rather than improve performance. However, because it implements Timsort, it also brings a substantial performance boost over our prior implementation.
Here are the results of a microbenchmark that sorted 25 million, randomly distributed (Float, Int) pairs. The Java Arrays.sort() tests were run **only on the keys**, and thus moved less data. Our current implementation is called "Tuple-sort using Arrays.sort()" while the new implementation is "KV-array using Sorter".
<table>
<tr><th>Test</th><th>First run (JDK6)</th><th>Average of 10 (JDK6)</th><th>First run (JDK7)</th><th>Average of 10 (JDK7)</th></tr>
<tr><td>primitive Arrays.sort()</td><td>3216 ms</td><td>1190 ms</td><td>2724 ms</td><td>131 ms (!!)</td></tr>
<tr><td>Arrays.sort()</td><td>18564 ms</td><td>2006 ms</td><td>13201 ms</td><td>878 ms</td></tr>
<tr><td>Tuple-sort using Arrays.sort()</td><td>31813 ms</td><td>3550 ms</td><td>20990 ms</td><td>1919 ms</td></tr>
<tr><td><b>KV-array using Sorter</b></td><td></td><td></td><td><b>15020 ms</b></td><td><b>834 ms</b></td></tr>
</table>
The results show that this Sorter performs exactly as expected (after the first run) -- it is as fast as the Java 7 Arrays.sort() (which shares the same algorithm), but is significantly faster than the Tuple-sort on Java 6 or 7.
In short, this patch should significantly improve performance for users running either Java 6 or 7.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1502 from aarondav/sort and squashes the following commits:
652d936 [Aaron Davidson] Update license, move Sorter to java src
a7b5b1c [Aaron Davidson] fix licenses
5c0efaf [Aaron Davidson] Update tmpLength
ec395c8 [Aaron Davidson] Ignore benchmark (again) and fix docs
034bf10 [Aaron Davidson] Change to Apache v2 Timsort
b97296c [Aaron Davidson] Don't try to run benchmark on Jenkins + private[spark]
6307338 [Aaron Davidson] SPARK-2047: Introduce an in-mem Sorter, and use it to reduce mem usage
Changes RDD.toDebugString() to show hierarchy and shuffle transformations more clearly
New output:
```
(3) FlatMappedValuesRDD[325] at apply at Transformer.scala:22
| MappedValuesRDD[324] at apply at Transformer.scala:22
| CoGroupedRDD[323] at apply at Transformer.scala:22
+-(5) MappedRDD[320] at apply at Transformer.scala:22
| | MappedRDD[319] at apply at Transformer.scala:22
| | MappedValuesRDD[318] at apply at Transformer.scala:22
| | MapPartitionsRDD[317] at apply at Transformer.scala:22
| | ShuffledRDD[316] at apply at Transformer.scala:22
| +-(10) MappedRDD[315] at apply at Transformer.scala:22
| | ParallelCollectionRDD[314] at apply at Transformer.scala:22
+-(100) MappedRDD[322] at apply at Transformer.scala:22
| ParallelCollectionRDD[321] at apply at Transformer.scala:22
```
Author: Gregory Owen <greowen@gmail.com>
Closes#1364 from GregOwen/to-debug-string and squashes the following commits:
08f5c78 [Gregory Owen] toDebugString: prettier debug printing to show shuffles and joins more clearly
1603f7b [Gregory Owen] toDebugString: prettier debug printing to show shuffles and joins more clearly
As a result of shivaram's experience debugging long scheduler delay, I think we should improve the tooltip to point people in the right direction if scheduler delay is large.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#1488 from kayousterhout/better_tooltips and squashes the following commits:
22176fd [Kay Ousterhout] Improve scheduler delay tooltip.
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1474 from sryza/sandy-spark-2564 and squashes the following commits:
35b8388 [Sandy Ryza] Fix compile error on upmerge
7b985fb [Sandy Ryza] Fix test compile error
43f79e6 [Sandy Ryza] SPARK-2564. ShuffleReadMetrics.totalBlocksRead is redundant
We should fix this in branch-1.0 as well.
Author: Reynold Xin <rxin@apache.org>
Closes#1500 from rxin/rangePartitioner and squashes the following commits:
c0a94f5 [Reynold Xin] [SPARK-2598] RangePartitioner's binary search does not use the given Ordering.
...s of CoGroupedRDD and PairRDDFunctions
This also removes an unnecessary tuple creation in cogroup.
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1447 from sryza/sandy-spark-2519-2 and squashes the following commits:
b6d9699 [Sandy Ryza] Remove missed Tuple2 match in CoGroupedRDD
a109828 [Sandy Ryza] Remove another pattern matching in MappedValuesRDD and revert some changes in PairRDDFunctions
be10f8a [Sandy Ryza] SPARK-2519 part 2. Remove pattern matching on Tuple2 in critical sections of CoGroupedRDD and PairRDDFunctions
This is a minor change. We should first logDebug($curRequestSize) and then set it to 0.
Author: Lijie Xu <csxulijie@gmail.com>
Closes#1477 from JerryLead/patch-1 and squashes the following commits:
aed722d [Lijie Xu] put 'curRequestSize = 0' after 'logDebug' it
Currently (as of Spark 1.0.1), Spark sends RDD object (which contains closures) using Akka along with the task itself to the executors. This is inefficient because all tasks in the same stage use the same RDD object, but we have to send RDD object multiple times to the executors. This is especially bad when a closure references some variable that is very large. The current design led to users having to explicitly broadcast large variables.
The patch uses broadcast to send RDD objects and the closures to executors, and use Akka to only send a reference to the broadcast RDD/closure along with the partition specific information for the task. For those of you who know more about the internals, Spark already relies on broadcast to send the Hadoop JobConf every time it uses the Hadoop input, because the JobConf is large.
The user-facing impact of the change include:
1. Users won't need to decide what to broadcast anymore, unless they would want to use a large object multiple times in different operations
2. Task size will get smaller, resulting in faster scheduling and higher task dispatch throughput.
In addition, the change will simplify some internals of Spark, eliminating the need to maintain task caches and the complex logic to broadcast JobConf (which also led to a deadlock recently).
A simple way to test this:
```scala
val a = new Array[Byte](1000*1000); scala.util.Random.nextBytes(a);
sc.parallelize(1 to 1000, 1000).map { x => a; x }.groupBy { x => a; x }.count
```
Numbers on 3 r3.8xlarge instances on EC2
```
master branch: 5.648436068 s, 4.715361895 s, 5.360161877 s
with this change: 3.416348793 s, 1.477846558 s, 1.553432156 s
```
Author: Reynold Xin <rxin@apache.org>
Closes#1452 from rxin/broadcast-task and squashes the following commits:
762e0be [Reynold Xin] Warn large broadcasts.
ade6eac [Reynold Xin] Log broadcast size.
c3b6f11 [Reynold Xin] Added a unit test for clean up.
754085f [Reynold Xin] Explain why broadcasting serialized copy of the task.
04b17f0 [Reynold Xin] [SPARK-2521] Broadcast RDD object once per TaskSet (instead of sending it for every task).
Currently, shuffle read metrics are incorrectly reported when stages have multiple shuffle dependencies (they are set to be the metrics from just one of the shuffle dependencies, rather than the accumulated metrics from all of the shuffle dependencies). This fixes that problem, and should probably be back-ported to the 0.9 branch.
Thanks ryanra for discovering this problem!
cc rxin andrewor14
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#1476 from kayousterhout/join_bug and squashes the following commits:
0203a16 [Kay Ousterhout] Fix broken unit tests.
f463c2e [Kay Ousterhout] [SPARK-2571] Correctly report shuffle read metrics.
This is going to be used in https://issues.apache.org/jira/browse/SPARK-2568
Author: Reynold Xin <rxin@apache.org>
Closes#1478 from rxin/reservoirSample and squashes the following commits:
17bcbf3 [Reynold Xin] Added seed.
badf20d [Reynold Xin] Renamed the method.
6940010 [Reynold Xin] Reservoir sampling implementation.
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1479 from sryza/sandy-spark-2553 and squashes the following commits:
2cb5ed8 [Sandy Ryza] SPARK-2553. Fix compile error
... per key
My humble opinion is that avoiding allocations in this performance-critical section is worth the extra code.
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1461 from sryza/sandy-spark-2553 and squashes the following commits:
7eaf7f2 [Sandy Ryza] SPARK-2553. CoGroupedRDD unnecessarily allocates a Tuple2 per dependency per key
**Problem.** Right now, if you click on an application after it has finished, it simply refreshes the page if there are no event logs for the application. This is not super intuitive especially because event logging is not enabled by default. We should direct the user to enable this if they attempt to view a SparkUI after the fact without event logs.
**Fix.** The new page conveys different messages in each of the following scenarios:
(1) Application did not enable event logging,
(2) Event logs are not found in the specified directory, and
(3) Exception is thrown while replaying the logs
Here are screenshots of what the page looks like in each of the above scenarios:
(1)
<img src="https://issues.apache.org/jira/secure/attachment/12656204/Event%20logging%20not%20enabled.png" width="75%">
(2)
<img src="https://issues.apache.org/jira/secure/attachment/12656203/Application%20history%20not%20found.png">
(3)
<img src="https://issues.apache.org/jira/secure/attachment/12656202/Application%20history%20load%20error.png" width="95%">
Author: Andrew Or <andrewor14@gmail.com>
Closes#1336 from andrewor14/master-link and squashes the following commits:
2f06206 [Andrew Or] Merge branch 'master' of github.com:apache/spark into master-link
97cddc0 [Andrew Or] Add different severity levels
832b687 [Andrew Or] Mention spark.eventLog.dir in error message
51980c3 [Andrew Or] Merge branch 'master' of github.com:apache/spark into master-link
ded208c [Andrew Or] Merge branch 'master' of github.com:apache/spark into master-link
89d6405 [Andrew Or] Reword message
e7df7ed [Andrew Or] Add a history not found page to standalone Master
This should reduce memory usage for the web ui as well as slightly increase its speed in draining the UI event queue.
@andrewor14
Author: Reynold Xin <rxin@apache.org>
Closes#1262 from rxin/ui-consolidate-hashtables and squashes the following commits:
1ac3f97 [Reynold Xin] Oops. Properly handle description.
f5736ad [Reynold Xin] Code review comments.
b8828dc [Reynold Xin] Merge branch 'master' into ui-consolidate-hashtables
7a7b6c4 [Reynold Xin] Revert css change.
f959bb8 [Reynold Xin] [SPARK-2299] Consolidate various stageIdTo* hash maps in JobProgressListener to speed it up.
63256f5 [Reynold Xin] [SPARK-2320] Reduce <pre> block font size.
This should go into both master and branch-1.0.
Author: Reynold Xin <rxin@apache.org>
Closes#1450 from rxin/agg-closure and squashes the following commits:
e40f363 [Reynold Xin] Mima check excludes.
9186364 [Reynold Xin] Define the return type more explicitly.
38e348b [Reynold Xin] Fixed the cases in RDD.scala.
ea6b34d [Reynold Xin] Blah
89b9c43 [Reynold Xin] Fix other instances of accidentally pulling in extra stuff in closures.
73b2783 [Reynold Xin] [SPARK-2534] Avoid pulling in the entire RDD in groupByKey.
It is currently non-trivial to trace through how different combinations of cluster managers (e.g. yarn) and deploy modes (e.g. cluster) are processed in SparkSubmit. Moving forward, it will be easier to extend SparkSubmit if we first re-organize the code by grouping related logic together.
This is a precursor to fixing standalone-cluster mode, which is currently broken (SPARK-2260).
Author: Andrew Or <andrewor14@gmail.com>
Closes#1349 from andrewor14/submit-cleanup and squashes the following commits:
8f99200 [Andrew Or] script -> program (minor)
30f2e65 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-cleanup
fe484a1 [Andrew Or] Move deploy mode checks after yarn code
7167824 [Andrew Or] Re-order config options and update comments
0b01ff8 [Andrew Or] Clean up SparkSubmit for readability
