| Commit message (Collapse) | Author | Age | Files | Lines |
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default is "python2.7"
Author: Christopher C. Aycock <chris@chrisaycock.com>
Closes #11239 from chrisaycock/master.
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mechanism.
https://issues.apache.org/jira/browse/SPARK-11627
Spark Streaming backpressure mechanism has no initial input rate limit, it might cause OOM exception.
In the firest batch task ,receivers receive data at the maximum speed they can reach,it might exhaust executors memory resources. Add a initial input rate limit value can make sure the Streaming job execute success in the first batch,then the backpressure mechanism can adjust receiving rate adaptively.
Author: junhao <junhao@mogujie.com>
Closes #9593 from junhaoMg/junhao-dev.
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filtering in general
This documents the implementation of ALS in `spark.ml` with example code in scala, java and python.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes #10411 from BenFradet/SPARK-12247.
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include_example
Replace example code in mllib-pmml-model-export.md using include_example
https://issues.apache.org/jira/browse/SPARK-13018
The example code in the user guide is embedded in the markdown and hence it is not easy to test. It would be nice to automatically test them. This JIRA is to discuss options to automate example code testing and see what we can do in Spark 1.6.
Goal is to move actual example code to spark/examples and test compilation in Jenkins builds. Then in the markdown, we can reference part of the code to show in the user guide. This requires adding a Jekyll tag that is similar to https://github.com/jekyll/jekyll/blob/master/lib/jekyll/tags/include.rb, e.g., called include_example.
`{% include_example scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala %}`
Jekyll will find `examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala` and pick code blocks marked "example" and replace code block in
`{% highlight %}`
in the markdown.
See more sub-tasks in parent ticket: https://issues.apache.org/jira/browse/SPARK-11337
Author: Xin Ren <iamshrek@126.com>
Closes #11126 from keypointt/SPARK-13018.
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Response to JIRA https://issues.apache.org/jira/browse/SPARK-13312.
This contribution is my original work and I license the work to this project.
Author: JeremyNixon <jnixon2@gmail.com>
Closes #11199 from JeremyNixon/update_train_val_split_example.
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Looks like pygments.rb gem is also required for jekyll build to work. At least on Ubuntu/RHEL I could not do build without this dependency. So added this to steps.
Author: Amit Dev <amitdev@gmail.com>
Closes #11180 from amitdev/master.
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This JIRA is related to
https://github.com/apache/spark/pull/5852
Had to do some minor rework and test to make sure it
works with current version of spark.
Author: Sanket <schintap@untilservice-lm>
Closes #10838 from redsanket/limit-outbound-connections.
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When the HistoryServer is showing an incomplete app, it needs to check if there is a newer version of the app available. It does this by checking if a version of the app has been loaded with a larger *filesize*. If so, it detaches the current UI, attaches the new one, and redirects back to the same URL to show the new UI.
https://issues.apache.org/jira/browse/SPARK-7889
Author: Steve Loughran <stevel@hortonworks.com>
Author: Imran Rashid <irashid@cloudera.com>
Closes #11118 from squito/SPARK-7889-alternate.
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In spark-env.sh.template, there are multi-byte characters, this PR will remove it.
Author: Sasaki Toru <sasakitoa@nttdata.co.jp>
Closes #11149 from sasakitoa/remove_multibyte_in_sparkenv.
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Remove spark.closure.serializer option and use JavaSerializer always
CC andrewor14 rxin I see there's a discussion in the JIRA but just thought I'd offer this for a look at what the change would be.
Author: Sean Owen <sowen@cloudera.com>
Closes #11150 from srowen/SPARK-12414.
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grained mesos mode.
This is the next iteration of tnachen's previous PR: https://github.com/apache/spark/pull/4027
In that PR, we resolved with andrewor14 and pwendell to implement the Mesos scheduler's support of `spark.executor.cores` to be consistent with YARN and Standalone. This PR implements that resolution.
This PR implements two high-level features. These two features are co-dependent, so they're implemented both here:
- Mesos support for spark.executor.cores
- Multiple executors per slave
We at Mesosphere have been working with Typesafe on a Spark/Mesos integration test suite: https://github.com/typesafehub/mesos-spark-integration-tests, which passes for this PR.
The contribution is my original work and I license the work to the project under the project's open source license.
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes #10993 from mgummelt/executor_sizing.
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Author: Luciano Resende <lresende@apache.org>
Closes #11092 from lresende/SPARK-13189.
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Update JDBC documentation based on http://stackoverflow.com/a/30947090/219530 as SPARK_CLASSPATH is deprecated.
Also, that's how it worked, it didn't work with the SPARK_CLASSPATH or the --jars alone.
This would solve issue: https://issues.apache.org/jira/browse/SPARK-13040
Author: Sebastián Ramírez <tiangolo@gmail.com>
Closes #10948 from tiangolo/patch-docs-jdbc.
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Fix for [SPARK-13002](https://issues.apache.org/jira/browse/SPARK-13002) about the initial number of executors when running with dynamic allocation on Mesos.
Instead of fixing it just for the Mesos case, made the change in `ExecutorAllocationManager`. It is already driving the number of executors running on Mesos, only no the initial value.
The `None` and `Some(0)` are internal details on the computation of resources to reserved, in the Mesos backend scheduler. `executorLimitOption` has to be initialized correctly, otherwise the Mesos backend scheduler will, either, create to many executors at launch, or not create any executors and not be able to recover from this state.
Removed the 'special case' description in the doc. It was not totally accurate, and is not needed anymore.
This doesn't fix the same problem visible with Spark standalone. There is no straightforward way to send the initial value in standalone mode.
Somebody knowing this part of the yarn support should review this change.
Author: Luc Bourlier <luc.bourlier@typesafe.com>
Closes #11047 from skyluc/issue/initial-dyn-alloc-2.
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Author: Bill Chambers <bill@databricks.com>
Closes #11094 from anabranch/dynamic-docs.
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minor fix for api link in ml onevsrest
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #11068 from hhbyyh/onevsrestDoc.
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dir with mesos conf and add docs.
Fix zookeeper dir configuration used in cluster mode, and also add documentation around these settings.
Author: Timothy Chen <tnachen@gmail.com>
Closes #10057 from tnachen/fix_mesos_dir.
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In [ml-guide](https://spark.apache.org/docs/latest/ml-guide.html#example-model-selection-via-cross-validation), there is invalid reference to `MulticlassClassificationEvaluator` apidoc.
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.evaluation.MultiClassClassificationEvaluator
Author: Lewuathe <lewuathe@me.com>
Closes #10996 from Lewuathe/fix-typo-in-ml-guide.
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ISTM `lib` is better because `datanucleus` jars are located in `lib` for release builds.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes #10901 from maropu/DocFix.
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This patch changes Spark's build to make Scala 2.11 the default Scala version. To be clear, this does not mean that Spark will stop supporting Scala 2.10: users will still be able to compile Spark for Scala 2.10 by following the instructions on the "Building Spark" page; however, it does mean that Scala 2.11 will be the default Scala version used by our CI builds (including pull request builds).
The Scala 2.11 compiler is faster than 2.10, so I think we'll be able to look forward to a slight speedup in our CI builds (it looks like it's about 2X faster for the Maven compile-only builds, for instance).
After this patch is merged, I'll update Jenkins to add new compile-only jobs to ensure that Scala 2.10 compilation doesn't break.
Author: Josh Rosen <joshrosen@databricks.com>
Closes #10608 from JoshRosen/SPARK-6363.
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this is stated for --packages and --repositories. Without stating it for --jars, people expect a standard java classpath to work, with expansion and using a different delimiter than a comma. Currently this is only state in the --help for spark-submit "Comma-separated list of local jars to include on the driver and executor classpaths."
Author: James Lohse <jimlohse@users.noreply.github.com>
Closes #10890 from jimlohse/patch-1.
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cluster mode
JIRA 1680 added a property called spark.yarn.appMasterEnv. This PR draws users' attention to this special case by adding an explanation in configuration.html#environment-variables
Author: Andrew <weiner.andrew.j@gmail.com>
Closes #10869 from weineran/branch-yarn-docs.
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for streaming-akka project
Since `actorStream` is an external project, we should add the linking and deploying instructions for it.
A follow up PR of #10744
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #10856 from zsxwing/akka-link-instruction.
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inconsistent with Scala's Iterator->Iterator
Fix Java function API methods for flatMap and mapPartitions to require producing only an Iterator, not Iterable. Also fix DStream.flatMap to require a function producing TraversableOnce only, not Traversable.
CC rxin pwendell for API change; tdas since it also touches streaming.
Author: Sean Owen <sowen@cloudera.com>
Closes #10413 from srowen/SPARK-3369.
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Update user guide for RFormula feature interactions. Meanwhile we also update other new features such as supporting string label in Spark 1.6.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #10222 from yanboliang/spark-11965.
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cluster mode in closure handling
Clarify that modifying a driver local variable won't have the desired effect in cluster modes, and may or may not work as intended in local mode
Author: Sean Owen <sowen@cloudera.com>
Closes #10866 from srowen/SPARK-12760.
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…local vs cluster
srowen thanks for the PR at https://github.com/apache/spark/pull/10866! sorry it took me a while.
This is related to https://github.com/apache/spark/pull/10866, basically the assignment in the lambda expression in the python example is actually invalid
```
In [1]: data = [1, 2, 3, 4, 5]
In [2]: counter = 0
In [3]: rdd = sc.parallelize(data)
In [4]: rdd.foreach(lambda x: counter += x)
File "<ipython-input-4-fcb86c182bad>", line 1
rdd.foreach(lambda x: counter += x)
^
SyntaxError: invalid syntax
```
Author: Mortada Mehyar <mortada.mehyar@gmail.com>
Closes #10867 from mortada/doc_python_fix.
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- Remove Akka dependency from core. Note: the streaming-akka project still uses Akka.
- Remove HttpFileServer
- Remove Akka configs from SparkConf and SSLOptions
- Rename `spark.akka.frameSize` to `spark.rpc.message.maxSize`. I think it's still worth to keep this config because using `DirectTaskResult` or `IndirectTaskResult` depends on it.
- Update comments and docs
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #10854 from zsxwing/remove-akka.
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properties
Several Spark properties equivalent to Spark submit command line options are missing.
Author: felixcheung <felixcheung_m@hotmail.com>
Closes #10491 from felixcheung/sparksubmitdoc.
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Author: Sun Rui <rui.sun@intel.com>
Closes #10201 from sun-rui/SPARK-12204.
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Include the following changes:
1. Add "streaming-akka" project and org.apache.spark.streaming.akka.AkkaUtils for creating an actorStream
2. Remove "StreamingContext.actorStream" and "JavaStreamingContext.actorStream"
3. Update the ActorWordCount example and add the JavaActorWordCount example
4. Make "streaming-zeromq" depend on "streaming-akka" and update the codes accordingly
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #10744 from zsxwing/streaming-akka-2.
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shivaram sorry it took longer to fix some conflicts, this is the change to add an alias for `table`
Author: felixcheung <felixcheung_m@hotmail.com>
Closes #10406 from felixcheung/readtable.
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Author: scwf <wangfei1@huawei.com>
Author: Marcelo Vanzin <vanzin@cloudera.com>
Author: WangTaoTheTonic <wangtao111@huawei.com>
Author: w00228970 <wangfei1@huawei.com>
Closes #10238 from vanzin/SPARK-2750.
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integration doc
This PR added instructions to get Kinesis assembly jar for Python users in the Kinesis integration page like Kafka doc.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #10822 from zsxwing/kinesis-doc.
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integration doc
This PR added instructions to get flume assembly jar for Python users in the flume integration page like Kafka doc.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #10746 from zsxwing/flume-doc.
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http://spark.apache.org/docs/latest/ml-guide.html#example-pipeline
```
val sameModel = Pipeline.load("/tmp/spark-logistic-regression-model")
```
should be
```
val sameModel = PipelineModel.load("/tmp/spark-logistic-regression-model")
```
cc: jkbradley
Author: Jeff Lam <sha0lin@alumni.carnegiemellon.edu>
Closes #10769 from Agent007/SPARK-12722.
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This patch adds a Hadoop 2.7 build profile in order to let us automate tests against that version.
/cc rxin srowen
Author: Josh Rosen <joshrosen@databricks.com>
Closes #10775 from JoshRosen/add-hadoop-2.7-profile.
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…enodes
Author: Tom Graves <tgraves@yahoo-inc.com>
Closes #10699 from tgravescs/SPARK-2930.
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guide example
Fixed WSSSE computeCost in Python mllib KMeans user guide example by using new computeCost method API in Python.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #10707 from jkbradley/kmeans-doc-fix.
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The default run has changed, but the documentation didn't fully reflect the change.
Author: Luc Bourlier <luc.bourlier@typesafe.com>
Closes #10740 from skyluc/issue/mesos-modes-doc.
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Use a much smaller step size in LinearRegressionWithSGD MLlib examples to achieve a reasonable RMSE.
Our training folks hit this exact same issue when concocting an example and had the same solution.
Author: Sean Owen <sowen@cloudera.com>
Closes #10675 from srowen/SPARK-5273.
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casting
Warning users about casting changes.
Author: Brandon Bradley <bradleytastic@gmail.com>
Closes #10708 from blbradley/spark-12758.
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Author: Reynold Xin <rxin@databricks.com>
Closes #10673 from rxin/SPARK-12735.
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Replace Guava `Optional` with (an API clone of) Java 8 `java.util.Optional` (edit: and a clone of Guava `Optional`)
See also https://github.com/apache/spark/pull/10512
Author: Sean Owen <sowen@cloudera.com>
Closes #10513 from srowen/SPARK-4819.
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spark.shuffle.service.enabled is spark application related configuration, it is not necessary to set it in yarn-site.xml
Author: Jeff Zhang <zjffdu@apache.org>
Closes #10657 from zjffdu/doc-fix.
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allowBatching configurations for Streaming
/cc tdas brkyvz
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #10453 from zsxwing/streaming-conf.
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Author: Jacek Laskowski <jacek@japila.pl>
Closes #10603 from jaceklaskowski/streaming-actor-custom-receiver.
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modify 'spark.memory.offHeap.enabled' default value to false
Author: zzcclp <xm_zzc@sina.com>
Closes #10633 from zzcclp/fix_spark.memory.offHeap.enabled_default_value.
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This PR removes `spark.cleaner.ttl` and the associated TTL-based metadata cleaning code.
Now that we have the `ContextCleaner` and a timer to trigger periodic GCs, I don't think that `spark.cleaner.ttl` is necessary anymore. The TTL-based cleaning isn't enabled by default, isn't included in our end-to-end tests, and has been a source of user confusion when it is misconfigured. If the TTL is set too low, data which is still being used may be evicted / deleted, leading to hard to diagnose bugs.
For all of these reasons, I think that we should remove this functionality in Spark 2.0. Additional benefits of doing this include marginally reduced memory usage, since we no longer need to store timetsamps in hashmaps, and a handful fewer threads.
Author: Josh Rosen <joshrosen@databricks.com>
Closes #10534 from JoshRosen/remove-ttl-based-cleaning.
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metricName
For the BinaryClassificationEvaluator, the scaladoc doesn't mention that "areaUnderPR" is supported, only that the default is "areadUnderROC".
Also, in the documentation, it is said that:
"The default metric used to choose the best ParamMap can be overriden by the setMetric method in each of these evaluators."
However, the method is called setMetricName.
This PR aims to fix both issues.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes #10328 from BenFradet/SPARK-12368.
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