- Add meta description tags on some of the most important doc pages
- Shorten the titles of some pages to have more relevant keywords; for
example there's no reason to have "Spark SQL Programming Guide - Spark
1.2.0 documentation", we can just say "Spark SQL - Spark 1.2.0
documentation".
Author: Matei Zaharia <matei@databricks.com>
Closes#4381 from mateiz/docs-seo and squashes the following commits:
4940563 [Matei Zaharia] [SPARK-5608] Improve SEO of Spark documentation pages
This patch introduces a new configuration option, `spark.extraListeners`, that allows SparkListeners to be specified in SparkConf and registered before the SparkContext is initialized. From the configuration documentation:
> A comma-separated list of classes that implement SparkListener; when initializing SparkContext, instances of these classes will be created and registered with Spark's listener bus. If a class has a single-argument constructor that accepts a SparkConf, that constructor will be called; otherwise, a zero-argument constructor will be called. If no valid constructor can be found, the SparkContext creation will fail with an exception.
This motivation for this patch is to allow monitoring code to be easily injected into existing Spark programs without having to modify those programs' code.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#4111 from JoshRosen/SPARK-5190-register-sparklistener-in-sc-constructor and squashes the following commits:
8370839 [Josh Rosen] Two minor fixes after merging with master
6e0122c [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-5190-register-sparklistener-in-sc-constructor
1a5b9a0 [Josh Rosen] Remove SPARK_EXTRA_LISTENERS environment variable.
2daff9b [Josh Rosen] Add a couple of explanatory comments for SPARK_EXTRA_LISTENERS.
b9973da [Josh Rosen] Add test to ensure that conf and env var settings are merged, not overriden.
d6f3113 [Josh Rosen] Use getConstructors() instead of try-catch to find right constructor.
d0d276d [Josh Rosen] Move code into setupAndStartListenerBus() method
b22b379 [Josh Rosen] Instantiate SparkListeners from classes listed in configurations.
9c0d8f1 [Josh Rosen] Revert "[SPARK-5190] Allow SparkListeners to be registered before SparkContext starts."
217ecc0 [Josh Rosen] Revert "Add addSparkListener to JavaSparkContext"
25988f3 [Josh Rosen] Add addSparkListener to JavaSparkContext
163ba19 [Josh Rosen] [SPARK-5190] Allow SparkListeners to be registered before SparkContext starts.
SPARK-3883: SSL support for Akka connections and Jetty based file servers.
This story introduced the following changes:
- Introduced SSLOptions object which holds the SSL configuration and can build the appropriate configuration for Akka or Jetty. SSLOptions can be created by parsing SparkConf entries at a specified namespace.
- SSLOptions is created and kept by SecurityManager
- All Akka actor address creation snippets based on interpolated strings were replaced by a dedicated methods from AkkaUtils. Those methods select the proper Akka protocol - whether akka.tcp or akka.ssl.tcp
- Added tests cases for AkkaUtils, FileServer, SSLOptions and SecurityManager
- Added a way to use node local SSL configuration by executors and driver in standalone mode. It can be done by specifying spark.ssl.useNodeLocalConf in SparkConf.
- Made CoarseGrainedExecutorBackend not overwrite the settings which are executor startup configuration - they are passed anyway from Worker
Refer to https://github.com/apache/spark/pull/3571 for discussion and details
Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Author: Jacek Lewandowski <jacek.lewandowski@datastax.com>
Closes#3571 from jacek-lewandowski/SPARK-3883-master and squashes the following commits:
9ef4ed1 [Jacek Lewandowski] Merge pull request #2 from jacek-lewandowski/SPARK-3883-docs2
fb31b49 [Jacek Lewandowski] SPARK-3883: Added SSL setup documentation
2532668 [Jacek Lewandowski] SPARK-3883: Refactored AkkaUtils.protocol method to not use Try
90a8762 [Jacek Lewandowski] SPARK-3883: Refactored methods to resolve Akka address and made it possible to easily configure multiple communication layers for SSL
72b2541 [Jacek Lewandowski] SPARK-3883: A reference to the fallback SSLOptions can be provided when constructing SSLOptions
93050f4 [Jacek Lewandowski] SPARK-3883: SSL support for HttpServer and Akka
... initial number
Author: Sandy Ryza <sandy@cloudera.com>
Closes#4051 from sryza/sandy-spark-4585 and squashes the following commits:
d1dd039 [Sandy Ryza] Add spark.dynamicAllocation.initialNumExecutors and make min and max not required
b7c59dc [Sandy Ryza] SPARK-4585. Spark dynamic executor allocation should use minExecutors as initial number
This PR is based on #3255 , fix conflicts and code style.
Closes#3255.
Author: Yandu Oppacher <yandu.oppacher@jadedpixel.com>
Author: Davies Liu <davies@databricks.com>
Closes#3901 from davies/refactor-python-profile-code and squashes the following commits:
b4a9306 [Davies Liu] fix tests
4b79ce8 [Davies Liu] add docstring for profiler_cls
2700e47 [Davies Liu] use BasicProfiler as default
349e341 [Davies Liu] more refactor
6a5d4df [Davies Liu] refactor and fix tests
31bf6b6 [Davies Liu] fix code style
0864b5d [Yandu Oppacher] Remove unused method
76a6c37 [Yandu Oppacher] Added a profile collector to accumulate the profilers per stage
9eefc36 [Yandu Oppacher] Fix doc
9ace076 [Yandu Oppacher] Refactor of profiler, and moved tests around
8739aff [Yandu Oppacher] Code review fixes
9bda3ec [Yandu Oppacher] Refactor profiler code
As per the JIRA. I copied the `spark.executor.extra*` text, but removed info that appears to be specific to the `executor` config and not `driver`.
Author: Sean Owen <sowen@cloudera.com>
Closes#4185 from srowen/SPARK-3852 and squashes the following commits:
f60a8a1 [Sean Owen] Document spark.driver.extra* configs
Based on top of changes in https://github.com/apache/spark/pull/3806.
https://issues.apache.org/jira/browse/SPARK-1507
`--driver-cores` and `spark.driver.cores` for all cluster modes and `spark.yarn.am.cores` for yarn client mode.
Author: WangTaoTheTonic <barneystinson@aliyun.com>
Author: WangTao <barneystinson@aliyun.com>
Closes#4018 from WangTaoTheTonic/SPARK-1507 and squashes the following commits:
01419d3 [WangTaoTheTonic] amend the args name
b255795 [WangTaoTheTonic] indet thing
d86557c [WangTaoTheTonic] some comments amend
43c9392 [WangTao] fix compile error
b39a100 [WangTao] specify # cores for ApplicationMaster
There is a discrepancy in WAL implementation and configuration doc.
Author: uncleGen <hustyugm@gmail.com>
Closes#3930 from uncleGen/master-clean-doc and squashes the following commits:
3a4245f [uncleGen] doc typo
8e407d3 [uncleGen] doc typo
Because major OS page sizes is about 4KB, the default value of spark.storage.memoryMapThreshold is integrated to 2 * 4096
Author: lewuathe <lewuathe@me.com>
Closes#3900 from Lewuathe/integrate-memoryMapThreshold and squashes the following commits:
e417acd [lewuathe] [SPARK-5073] Update docs/configuration
834aba4 [lewuathe] [SPARK-5073] Fix style
adcea33 [lewuathe] [SPARK-5073] Integrate memory map threshold to 2MB
fcce2e5 [lewuathe] [SPARK-5073] spark.storage.memoryMapThreshold have two default value
Author: Reynold Xin <rxin@databricks.com>
Closes#3903 from rxin/timeout-120 and squashes the following commits:
7c2138e [Reynold Xin] [SPARK-5093] Set spark.network.timeout to 120s consistently.
This patch disables output spec. validation for jobs launched through Spark Streaming, since this interferes with checkpoint recovery.
Hadoop OutputFormats have a `checkOutputSpecs` method which performs certain checks prior to writing output, such as checking whether the output directory already exists. SPARK-1100 added checks for FileOutputFormat, SPARK-1677 (#947) added a SparkConf configuration to disable these checks, and SPARK-2309 (#1088) extended these checks to run for all OutputFormats, not just FileOutputFormat.
In Spark Streaming, we might have to re-process a batch during checkpoint recovery, so `save` actions may be called multiple times. In addition to `DStream`'s own save actions, users might use `transform` or `foreachRDD` and call the `RDD` and `PairRDD` save actions. When output spec. validation is enabled, the second calls to these actions will fail due to existing output.
This patch automatically disables output spec. validation for jobs submitted by the Spark Streaming scheduler. This is done by using Scala's `DynamicVariable` to propagate the bypass setting without having to mutate SparkConf or introduce a global variable.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#3832 from JoshRosen/SPARK-4835 and squashes the following commits:
36eaf35 [Josh Rosen] Add comment explaining use of transform() in test.
6485cf8 [Josh Rosen] Add test case in Streaming; fix bug for transform()
7b3e06a [Josh Rosen] Remove Streaming-specific setting to undo this change; update conf. guide
bf9094d [Josh Rosen] Revise disableOutputSpecValidation() comment to not refer to Spark Streaming.
e581d17 [Josh Rosen] Deduplicate isOutputSpecValidationEnabled logic.
762e473 [Josh Rosen] [SPARK-4835] Disable validateOutputSpecs for Spark Streaming jobs.
Author: wangxiaojing <u9jing@gmail.com>
Closes#3818 from wangxiaojing/SPARK-4982 and squashes the following commits:
fe2ad5f [wangxiaojing] change stages to jobs
Once the external shuffle service is also documented, the dynamic allocation section will link to it. Let me know if the whole dynamic allocation should be moved to its separate page; I personally think the organization might be cleaner that way.
This patch builds on top of oza's work in #3689.
aarondav pwendell
Author: Andrew Or <andrew@databricks.com>
Author: Tsuyoshi Ozawa <ozawa.tsuyoshi@gmail.com>
Closes#3731 from andrewor14/document-dynamic-allocation and squashes the following commits:
1281447 [Andrew Or] Address a few comments
b9843f2 [Andrew Or] Document the configs as well
246fb44 [Andrew Or] Merge branch 'SPARK-4839' of github.com:oza/spark into document-dynamic-allocation
8c64004 [Andrew Or] Add documentation for dynamic allocation (without configs)
6827b56 [Tsuyoshi Ozawa] Fixing a documentation of spark.dynamicAllocation.enabled.
53cff58 [Tsuyoshi Ozawa] Adding a documentation about dynamic resource allocation.
Important updates to the streaming programming guide
- Make the fault-tolerance properties easier to understand, with information about write ahead logs
- Update the information about deploying the spark streaming app with information about Driver HA
- Update Receiver guide to discuss reliable vs unreliable receivers.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>
Author: Josh Rosen <rosenville@gmail.com>
Closes#3653 from tdas/streaming-doc-update-1.2 and squashes the following commits:
f53154a [Tathagata Das] Addressed Josh's comments.
ce299e4 [Tathagata Das] Minor update.
ca19078 [Tathagata Das] Minor change
f746951 [Tathagata Das] Mentioned performance problem with WAL
7787209 [Tathagata Das] Merge branch 'streaming-doc-update-1.2' of github.com:tdas/spark into streaming-doc-update-1.2
2184729 [Tathagata Das] Updated Kafka and Flume guides with reliability information.
2f3178c [Tathagata Das] Added more information about writing reliable receivers in the custom receiver guide.
91aa5aa [Tathagata Das] Improved API Docs menu
5707581 [Tathagata Das] Added Pythn API badge
b9c8c24 [Tathagata Das] Merge pull request #26 from JoshRosen/streaming-programming-guide
b8c8382 [Josh Rosen] minor fixes
a4ef126 [Josh Rosen] Restructure parts of the fault-tolerance section to read a bit nicer when skipping over the headings
65f66cd [Josh Rosen] Fix broken link to fault-tolerance semantics section.
f015397 [Josh Rosen] Minor grammar / pluralization fixes.
3019f3a [Josh Rosen] Fix minor Markdown formatting issues
aa8bb87 [Tathagata Das] Small update.
195852c [Tathagata Das] Updated based on Josh's comments, updated receiver reliability and deploying section, and also updated configuration.
17b99fb [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into streaming-doc-update-1.2
a0217c0 [Tathagata Das] Changed Deploying menu layout
67fcffc [Tathagata Das] Added cluster mode + supervise example to submitting application guide.
e45453b [Tathagata Das] Update streaming guide, added deploying section.
192c7a7 [Tathagata Das] Added more info about Python API, and rewrote the checkpointing section.
...umented default is incorrect for YARN
Author: Sandy Ryza <sandy@cloudera.com>
Closes#3624 from sryza/sandy-spark-4770 and squashes the following commits:
bd81a3a [Sandy Ryza] SPARK-4770. [DOC] [YARN] spark.scheduler.minRegisteredResourcesRatio documented default is incorrect for YARN
The link points to the old scala programming guide; it should point to the submitting applications page.
This should be backported to 1.1.2 (it's been broken as of 1.0).
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#3542 from kayousterhout/SPARK-4686 and squashes the following commits:
a8fc43b [Kay Ousterhout] [SPARK-4686] Link to allowed master URLs is broken
The documentation for the two parameters is the same with a pointer from the standalone parameter to the yarn parameter
Author: arahuja <aahuja11@gmail.com>
Closes#3209 from arahuja/yarn-classpath-first-param and squashes the following commits:
51cb9b2 [arahuja] [SPARK-4344][DOCS] adding documentation for YARN on userClassPathFirst
https://issues.apache.org/jira/browse/SPARK-3722
Author: WangTao <barneystinson@aliyun.com>
Closes#2579 from WangTaoTheTonic/docsWork and squashes the following commits:
6f91cec [WangTao] use more wording express
29d22fa [WangTao] delete the specified version link
34cb4ea [WangTao] Update running-on-yarn.md
4ee1a26 [WangTao] minor improvement and fix in docs
Author: Sandy Ryza <sandy@cloudera.com>
Closes#3107 from sryza/sandy-spark-4230 and squashes the following commits:
37a1d19 [Sandy Ryza] Clear up a couple things
34d53de [Sandy Ryza] SPARK-4230. Doc for spark.default.parallelism is incorrect
This is a minor docs update which helps to clarify the way local[n] is used for streaming apps.
Author: jay@apache.org <jayunit100>
Closes#2964 from jayunit100/SPARK-4040 and squashes the following commits:
35b5a5e [jay@apache.org] SPARK-4040: Update documentation to exemplify use of local (n) value.
Note that we're turning this on for at least the first part of the QA period as a trial. We want to enable this (and deprecate the NioBlockTransferService) as soon as possible in the hopes that NettyBlockTransferService will be more stable and easier to maintain. We will turn it off if we run into major issues.
Author: Aaron Davidson <aaron@databricks.com>
Closes#3049 from aarondav/enable-netty and squashes the following commits:
bb981cc [Aaron Davidson] [SPARK-4183] Enable NettyBlockTransferService by default
Right now, operations like collect() and take() can crash the driver with an OOM if they bring back too many data.
This PR will introduce spark.driver.maxResultSize, after setting it, the driver will abort a job if its result is bigger than it.
By default, it's 1g (for backward compatibility for most the cases).
In local mode, the driver and executor share the same JVM, the default setting can not protect JVM from OOM.
cc mateiz
Author: Davies Liu <davies@databricks.com>
Closes#3003 from davies/collect and squashes the following commits:
248ed5e [Davies Liu] fix compile
272522e [Davies Liu] address comments
2c35773 [Davies Liu] add sizes in message of abort()
5d62303 [Davies Liu] address comments
bc3c077 [Davies Liu] Merge branch 'master' of github.com:apache/spark into collect
11f97c5 [Davies Liu] address comments
47b144f [Davies Liu] check the size of result before send and fetch
3d81af2 [Davies Liu] address comments
ca8267d [Davies Liu] limit the size of data by collect
Note that we're turning this on for at least the first part of the QA period as a trial. We want to enable this (and deprecate the NioBlockTransferService) as soon as possible in the hopes that NettyBlockTransferService will be more stable and easier to maintain. We will turn it off if we run into major issues.
Author: Aaron Davidson <aaron@databricks.com>
Closes#3049 from aarondav/enable-netty and squashes the following commits:
bb981cc [Aaron Davidson] [SPARK-4183] Enable NettyBlockTransferService by default
This pull request is a first step towards the implementation of a stable, pull-based progress / status API for Spark (see [SPARK-2321](https://issues.apache.org/jira/browse/SPARK-2321)). For now, I'd like to discuss the basic implementation, API names, and overall interface design. Once we arrive at a good design, I'll go back and add additional methods to expose more information via these API.
#### Design goals:
- Pull-based API
- Usable from Java / Scala / Python (eventually, likely with a wrapper)
- Can be extended to expose more information without introducing binary incompatibilities.
- Returns immutable objects.
- Don't leak any implementation details, preserving our freedom to change the implementation.
#### Implementation:
- Add public methods (`getJobInfo`, `getStageInfo`) to SparkContext to allow status / progress information to be retrieved.
- Add public interfaces (`SparkJobInfo`, `SparkStageInfo`) for our API return values. These interfaces consist entirely of Java-style getter methods. The interfaces are currently implemented in Java. I decided to explicitly separate the interface from its implementation (`SparkJobInfoImpl`, `SparkStageInfoImpl`) in order to prevent users from constructing these responses themselves.
-Allow an existing JobProgressListener to be used when constructing a live SparkUI. This allows us to re-use this listeners in the implementation of this status API. There are a few reasons why this listener re-use makes sense:
- The status API and web UI are guaranteed to show consistent information.
- These listeners are already well-tested.
- The same garbage-collection / information retention configurations can apply to both this API and the web UI.
- Extend JobProgressListener to maintain `jobId -> Job` and `stageId -> Stage` mappings.
The progress API methods are implemented in a separate trait that's mixed into SparkContext. This helps to avoid SparkContext.scala from becoming larger and more difficult to read.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Josh Rosen <joshrosen@apache.org>
Closes#2696 from JoshRosen/progress-reporting-api and squashes the following commits:
e6aa78d [Josh Rosen] Add tests.
b585c16 [Josh Rosen] Accept SparkListenerBus instead of more specific subclasses.
c96402d [Josh Rosen] Address review comments.
2707f98 [Josh Rosen] Expose current stage attempt id
c28ba76 [Josh Rosen] Update demo code:
646ff1d [Josh Rosen] Document spark.ui.retainedJobs.
7f47d6d [Josh Rosen] Clean up SparkUI constructors, per Andrew's feedback.
b77b3d8 [Josh Rosen] Merge remote-tracking branch 'origin/master' into progress-reporting-api
787444c [Josh Rosen] Move status API methods into trait that can be mixed into SparkContext.
f9a9a00 [Josh Rosen] More review comments:
3dc79af [Josh Rosen] Remove creation of unused listeners in SparkContext.
249ca16 [Josh Rosen] Address several review comments:
da5648e [Josh Rosen] Add example of basic progress reporting in Java.
7319ffd [Josh Rosen] Add getJobIdsForGroup() and num*Tasks() methods.
cc568e5 [Josh Rosen] Add note explaining that interfaces should not be implemented outside of Spark.
6e840d4 [Josh Rosen] Remove getter-style names and "consistent snapshot" semantics:
08cbec9 [Josh Rosen] Begin to sketch the interfaces for a stable, public status API.
ac2d13a [Josh Rosen] Add jobId->stage, stageId->stage mappings in JobProgressListener
24de263 [Josh Rosen] Create UI listeners in SparkContext instead of in Tabs:
Author: Sandy Ryza <sandy@cloudera.com>
Closes#789 from sryza/sandy-spark-1813 and squashes the following commits:
48b05e9 [Sandy Ryza] Simplify
b824932 [Sandy Ryza] Allow both spark.kryo.classesToRegister and spark.kryo.registrator at the same time
6a15bb7 [Sandy Ryza] Small fix
a2278c0 [Sandy Ryza] Respond to review comments
6ef592e [Sandy Ryza] SPARK-1813. Add a utility to SparkConf that makes using Kryo really easy
This patch attempts to fix SPARK-2546 in `branch-1.0` and `branch-1.1`. The underlying problem is that thread-safety issues in Hadoop Configuration objects may cause Spark tasks to get stuck in infinite loops. The approach taken here is to clone a new copy of the JobConf for each task rather than sharing a single copy between tasks. Note that there are still Configuration thread-safety issues that may affect the driver, but these seem much less likely to occur in practice and will be more complex to fix (see discussion on the SPARK-2546 ticket).
This cloning is guarded by a new configuration option (`spark.hadoop.cloneConf`) and is disabled by default in order to avoid unexpected performance regressions for workloads that are unaffected by the Configuration thread-safety issues.
Author: Josh Rosen <joshrosen@apache.org>
Closes#2684 from JoshRosen/jobconf-fix-backport and squashes the following commits:
f14f259 [Josh Rosen] Add configuration option to control cloning of Hadoop JobConf.
b562451 [Josh Rosen] Remove unused jobConfCacheKey field.
dd25697 [Josh Rosen] [SPARK-2546] [1.0 / 1.1 backport] Clone JobConf for each task.
(cherry picked from commit 2cd40db2b3)
Signed-off-by: Josh Rosen <joshrosen@databricks.com>
Conflicts:
core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
Introduced in f7e79bc42c, I'm not sure why we need two spark.executor.memory here.
Author: WangTaoTheTonic <barneystinson@aliyun.com>
Author: WangTao <barneystinson@aliyun.com>
Closes#2745 from WangTaoTheTonic/redundantconfig and squashes the following commits:
e7564dc [WangTao] too long line
fdbdb1f [WangTaoTheTonic] trivial workaround
d06b6e5 [WangTaoTheTonic] remove redundant spark.executor.memory in doc
Something about the 2.3.4 upgrade seems to have made the issue manifest where all the services disconnect from each other after exactly 1000 seconds (which is the heartbeat interval). [This post](https://groups.google.com/forum/#!topic/akka-user/X3xzpTCbEFs) suggests that heartbeat pause should be greater than heartbeat interval, and increasing the pause from 600s to 6000s seems to have rectified the issue. My current cluster has now exceeded 1400s of uptime without failure!
I do not know why this fixed it, because the threshold we have set for the failure detector is the exponent of a timeout, and 300 is extremely large. Perhaps the default failure detector changed in 2.3.4 and now ignores threshold.
Author: Aaron Davidson <aaron@databricks.com>
Closes#2784 from aarondav/fix-timeout and squashes the following commits:
bd1151a [Aaron Davidson] Increase pause, don't decrease interval
9cb0372 [Aaron Davidson] [SPARK-3923] Decrease Akka heartbeat interval below heartbeat pause
It took me a minute to track this down, so I thought it could be useful to have it in the docs.
I'm unsure if 512mb is the default for spark.driver.memory? Also - there could be a better value for the 'description' to differentiate it from spark.executor.memory.
Author: nartz <nartzpod@gmail.com>
Author: Nathan Artz <nathanartz@Nathans-MacBook-Pro.local>
Closes#2410 from nartz/docs/add-spark-driver-memory-to-config-docs and squashes the following commits:
a2f6c62 [nartz] Update configuration.md
74521b8 [Nathan Artz] add spark.driver.memory to config docs
Author: Brenden Matthews <brenden@diddyinc.com>
Closes#2401 from brndnmtthws/master and squashes the following commits:
4abaa5d [Brenden Matthews] [SPARK-3535][Mesos] Fix resource handling.
Update of PR #997.
With this PR, setting SPARK_CONF_DIR overrides SPARK_HOME/conf (not only spark-defaults.conf and spark-env).
Author: EugenCepoi <cepoi.eugen@gmail.com>
Closes#2481 from EugenCepoi/SPARK-2058 and squashes the following commits:
0bb32c2 [EugenCepoi] use orElse orNull and fixing trailing percent in compute-classpath.cmd
77f35d7 [EugenCepoi] SPARK-2058: Overriding SPARK_HOME/conf with SPARK_CONF_DIR
Author: scwf <wangfei1@huawei.com>
Closes#2632 from scwf/compress-doc and squashes the following commits:
7983a1a [scwf] snappy is the default compression codec for broadcast
https://issues.apache.org/jira/browse/SPARK-3565
"spark.ports.maxRetries" should be "spark.port.maxRetries". Make the configuration keys in document and code consistent.
Author: WangTaoTheTonic <barneystinson@aliyun.com>
Closes#2427 from WangTaoTheTonic/fixPortRetries and squashes the following commits:
c178813 [WangTaoTheTonic] Use blank lines trigger Jenkins
646f3fe [WangTaoTheTonic] also in SparkBuild.scala
3700dba [WangTaoTheTonic] Fix configuration item not consistent with document
change the value of spark.files.fetchTimeout
Author: viper-kun <xukun.xu@huawei.com>
Closes#2406 from viper-kun/master and squashes the following commits:
ecb0d46 [viper-kun] [Docs] Correct spark.files.fetchTimeout default value
7cf4c7a [viper-kun] Update configuration.md
Reuse Python worker to avoid the overhead of fork() Python process for each tasks. It also tracks the broadcasts for each worker, avoid sending repeated broadcasts.
This can reduce the time for dummy task from 22ms to 13ms (-40%). It can help to reduce the latency for Spark Streaming.
For a job with broadcast (43M after compress):
```
b = sc.broadcast(set(range(30000000)))
print sc.parallelize(range(24000), 100).filter(lambda x: x in b.value).count()
```
It will finish in 281s without reused worker, and it will finish in 65s with reused worker(4 CPUs). After reusing the worker, it can save about 9 seconds for transfer and deserialize the broadcast for each tasks.
It's enabled by default, could be disabled by `spark.python.worker.reuse = false`.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2259 from davies/reuse-worker and squashes the following commits:
f11f617 [Davies Liu] Merge branch 'master' into reuse-worker
3939f20 [Davies Liu] fix bug in serializer in mllib
cf1c55e [Davies Liu] address comments
3133a60 [Davies Liu] fix accumulator with reused worker
760ab1f [Davies Liu] do not reuse worker if there are any exceptions
7abb224 [Davies Liu] refactor: sychronized with itself
ac3206e [Davies Liu] renaming
8911f44 [Davies Liu] synchronized getWorkerBroadcasts()
6325fc1 [Davies Liu] bugfix: bid >= 0
e0131a2 [Davies Liu] fix name of config
583716e [Davies Liu] only reuse completed and not interrupted worker
ace2917 [Davies Liu] kill python worker after timeout
6123d0f [Davies Liu] track broadcasts for each worker
8d2f08c [Davies Liu] reuse python worker
Sort-based shuffle has lower memory usage and seems to outperform hash-based in almost all of our testing.
Author: Reynold Xin <rxin@apache.org>
Closes#2178 from rxin/sort-shuffle and squashes the following commits:
713d341 [Reynold Xin] Fixed test failures by setting spark.shuffle.compress to the same value as spark.shuffle.spill.compress.
85165e6 [Reynold Xin] Fixed a comment typo.
aa0d372 [Reynold Xin] [SPARK-3280] Made sort-based shuffle the default implementation
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
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
These configs looked inconsistent from the rest.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1936 from andrewor14/docs-code and squashes the following commits:
15f578a [Andrew Or] Add <code> tag
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
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
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
