## What changes were proposed in this pull request?
This patch fixes wrong HTML tag in configuration.md which breaks the table tag.
This is originally reported in dev mailing list: https://lists.apache.org/thread.html/744bdc83b3935776c8d91bf48fdf80d9a3fed3858391e60e343206f9%3Cdev.spark.apache.org%3E
## How was this patch tested?
This change is one-liner and pretty obvious so I guess we may be able to skip testing.
Closes#24304 from HeartSaVioR/MINOR-configuration-doc-html-tag-error.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add AL2 license to metadata of all .md files.
This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing.
## How was this patch tested?
Doc build
Closes#24243 from srowen/SPARK-26918.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Adding missing spaces after commas.
Closes#24205 from attilapiros/minor-doc-changes.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
[SPARK-23155](https://issues.apache.org/jira/browse/SPARK-23155) enables SHS to set up custom executor log URLs. This patch proposes to extend this feature to to Spark UI as well.
Unlike the approach we did for SHS (replace executor log URLs when executor information is requested so it's like a change of view), here this patch replaces executor log URLs while registering executor, which also affects event log as well. In point of SHS's view, it will be treated as original log url when custom log url is applied to Spark UI.
## How was this patch tested?
Added UT.
Closes#23790 from HeartSaVioR/SPARK-26792.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR adds a note that explicitly `spark.executor.pyspark.memory` is dependent on resource module's behaviours at Python memory usage.
For instance, I at least see some difference at https://github.com/apache/spark/pull/21977#discussion_r251220966
## How was this patch tested?
Manually built the doc.
Closes#23664 from HyukjinKwon/note-resource-dependent.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a followup of #16989
The fetch-big-block-to-disk feature is disabled by default, because it's not compatible with external shuffle service prior to Spark 2.2. The client sends stream request to fetch block chunks, and old shuffle service can't support it.
After 2 years, Spark 2.2 has EOL, and now it's safe to turn on this feature by default
## How was this patch tested?
existing tests
Closes#23625 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Added a new configuration 'spark.mesos.driver.memoryOverhead' for providing the driver memory overhead in mesos cluster mode.
## How was this patch tested?
Verified it manually, Resource Scheduler allocates (drivermemory+ driver memoryOverhead) for driver in mesos cluster mode.
Closes#17726 from devaraj-kavali/SPARK-17928.
Authored-by: Devaraj K <devaraj@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove spark.memory.useLegacyMode and StaticMemoryManager. Update tests that used the StaticMemoryManager to equivalent use of UnifiedMemoryManager.
## How was this patch tested?
Existing tests, with modifications to make them work with a different mem manager.
Closes#23457 from srowen/SPARK-26539.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Introducing shared polled ByteBuf allocators.
This feature can be enabled via the "spark.network.sharedByteBufAllocators.enabled" configuration.
When it is on then only two pooled ByteBuf allocators are created:
- one for transport servers where caching is allowed and
- one for transport clients where caching is disabled
This way the cache allowance remains as before.
Both shareable pools are created with numCores parameter set to 0 (which defaults to the available processors) as conf.serverThreads() and conf.clientThreads() are module dependant and the lazy creation of this allocators would lead to unpredicted behaviour.
When "spark.network.sharedByteBufAllocators.enabled" is false then a new allocator is created for every transport client and server separately as was before this PR.
## How was this patch tested?
Existing unit tests.
Closes#23278 from attilapiros/SPARK-24920.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
`resource` package is a Unix specific package. See https://docs.python.org/2/library/resource.html and https://docs.python.org/3/library/resource.html.
Note that we document Windows support:
> Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS).
This should be backported into branch-2.4 to restore Windows support in Spark 2.4.1.
## How was this patch tested?
Manually mocking the changed logics.
Closes#23055 from HyukjinKwon/SPARK-26080.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fix Typos.
This PR is the complete version of https://github.com/apache/spark/pull/23145.
## How was this patch tested?
NA
Closes#23185 from kjmrknsn/docUpdate.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Introducing spark.ui.requestHeaderSize for configuring Jetty's HTTP requestHeaderSize.
This way long authorization field does not lead to HTTP 413.
## How was this patch tested?
Manually with curl (which version must be at least 7.55).
With the original default value (8k limit):
```bash
# Starting history server with default requestHeaderSize
$ ./sbin/start-history-server.sh
starting org.apache.spark.deploy.history.HistoryServer, logging to /Users/attilapiros/github/spark/logs/spark-attilapiros-org.apache.spark.deploy.history.HistoryServer-1-apiros-MBP.lan.out
# Creating huge header
$ echo -n "X-Custom-Header: " > cookie
$ printf 'A%.0s' {1..9500} >> cookie
# HTTP GET with huge header fails with 431
$ curl -H cookie http://458apiros-MBP.lan:18080/
<h1>Bad Message 431</h1><pre>reason: Request Header Fields Too Large</pre>
# The log contains the error
$ tail -1 /Users/attilapiros/github/spark/logs/spark-attilapiros-org.apache.spark.deploy.history.HistoryServer-1-apiros-MBP.lan.out
18/11/19 21:24:28 WARN HttpParser: Header is too large 8193>8192
```
After:
```bash
# Creating the history properties file with the increased requestHeaderSize
$ echo spark.ui.requestHeaderSize=10000 > history.properties
# Starting Spark History Server with the settings
$ ./sbin/start-history-server.sh --properties-file history.properties
starting org.apache.spark.deploy.history.HistoryServer, logging to /Users/attilapiros/github/spark/logs/spark-attilapiros-org.apache.spark.deploy.history.HistoryServer-1-apiros-MBP.lan.out
# HTTP GET with huge header gives back HTML5 (I have added here only just a part of the response)
$ curl -H cookie http://458apiros-MBP.lan:18080/
<!DOCTYPE html><html>
<head>...
<link rel="shortcut icon" href="/static/spark-logo-77x50px-hd.png"></link>
<title>History Server</title>
</head>
<body>
...
```
Closes#23090 from attilapiros/JettyHeaderSize.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
Currently, we do not have a mechanism to collect driver logs if a user chooses
to run their application in client mode. This is a big issue as admin teams need
to create their own mechanisms to capture driver logs.
This commit adds a logger which, if enabled, adds a local log appender to the
root logger and asynchronously syncs it an application specific log file on hdfs
(Spark Driver Log Dir).
Additionally, this collects spark-shell driver logs at INFO level by default.
The change is that instead of setting root logger level to WARN, we will set the
consoleAppender threshold to WARN, in case of spark-shell. This ensures that
only WARN logs are printed on CONSOLE but other log appenders still capture INFO
(or the default log level logs).
1. Verified that logs are written to local and remote dir
2. Added a unit test case
3. Verified this for spark-shell, client mode and pyspark.
4. Verified in both non-kerberos and kerberos environment
5. Verified with following unexpected termination conditions: Ctrl + C, Driver
OOM, Large Log Files
6. Ran an application in spark-shell and ensured that driver logs were
captured at INFO level
7. Started the application at WARN level, programmatically changed the level to
INFO and ensured that logs on console were printed at INFO level
Closes#22504 from ankuriitg/ankurgupta/SPARK-25118.
Authored-by: ankurgupta <ankur.gupta@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Every time a task is unschedulable because of the condition where no. of task failures < no. of executors available, we currently abort the taskSet - failing the job. This change tries to acquire new executors so that we can complete the job successfully. We try to acquire a new executor only when we can kill an existing idle executor. We fallback to the older implementation where we abort the job if we cannot find an idle executor.
## How was this patch tested?
I performed some manual tests to check and validate the behavior.
```scala
val rdd = sc.parallelize(Seq(1 to 10), 3)
import org.apache.spark.TaskContext
val mapped = rdd.mapPartitionsWithIndex ( (index, iterator) => { if (index == 2) { Thread.sleep(30 * 1000); val attemptNum = TaskContext.get.attemptNumber; if (attemptNum < 3) throw new Exception("Fail for blacklisting")}; iterator.toList.map (x => x + " -> " + index).iterator } )
mapped.collect
```
Closes#22288 from dhruve/bug/SPARK-22148.
Lead-authored-by: Dhruve Ashar <dhruveashar@gmail.com>
Co-authored-by: Dhruve Ashar <dhruve@users.noreply.github.com>
Co-authored-by: Tom Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Change ptats.Stats() to pstats.Stats() for `spark.python.profile.dump` in configuration.md.
## How was this patch tested?
Doc test
Closes#22933 from AlexHagerman/doc_fix.
Authored-by: Alex Hagerman <alex@unexpectedeof.net>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This turns off hdfs erasure coding by default for event logs, regardless of filesystem defaults. Because this requires apis only available in hadoop 3, this uses reflection. EC isn't a very good choice for event logs, as hflush() is a no-op, and so updates to the file are not visible for a long time. This can still be configured by setting "spark.eventLog.allowErasureCoding=true", which will use filesystem defaults.
## How was this patch tested?
deployed a cluster with the changes with HDFS EC on. By default, event logs didn't use EC, but configuration still would allow EC. Also tried writing to the local fs (which doesn't support EC at all) and things worked fine.
Closes#22881 from squito/SPARK-25855.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Remove Kafka 0.8 integration
## How was this patch tested?
Existing tests, build scripts
Closes#22703 from srowen/SPARK-25705.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Documentation is updated with proper classname org.apache.spark.io.ZStdCompressionCodec
## How was this patch tested?
we used the spark.io.compression.codec = org.apache.spark.io.ZStdCompressionCodec
and verified the logs.
Closes#22669 from shivusondur/CompressionIssue.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
This is a rework of #21433 to address some concerns there.
Closes#22398 from michaelmior/long-callsite2.
Authored-by: Michael Mior <mmior@uwaterloo.ca>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add spark.executor.pyspark.memory limit for K8S
## How was this patch tested?
Unit and Integration tests
Closes#22298 from ifilonenko/SPARK-25021.
Authored-by: Ilan Filonenko <if56@cornell.edu>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
As described in [SPARK-25261](https://issues.apache.org/jira/projects/SPARK/issues/SPARK-25261)，the unit of spark.executor.memory and spark.driver.memory is parsed as bytes in some cases if no unit specified, while in https://spark.apache.org/docs/latest/configuration.html#application-properties, they are descibed as MiB, which may lead to some misunderstandings.
## How was this patch tested?
N/A
Closes#22252 from ivoson/branch-correct-configuration.
Lead-authored-by: huangtengfei02 <huangtengfei02@baidu.com>
Co-authored-by: Huang Tengfei <tengfei.h@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
After SPARK-18371, it is guaranteed that there would be at least one message per partition per batch using direct kafka API when new messages exist in the topics. This change will give the user the option of setting the minimum instead of just a hard coded 1 limit
The related unit test is updated and some internal tests verified that the topic partitions with new messages will be progressed by the specified minimum.
Author: Reza Safi <rezasafi@cloudera.com>
Closes#22223 from rezasafi/streaminglag.
## What changes were proposed in this pull request?
This adds `spark.executor.pyspark.memory` to configure Python's address space limit, [`resource.RLIMIT_AS`](https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS). Limiting Python's address space allows Python to participate in memory management. In practice, we see fewer cases of Python taking too much memory because it doesn't know to run garbage collection. This results in YARN killing fewer containers. This also improves error messages so users know that Python is consuming too much memory:
```
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item, []), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
```
The new pyspark memory setting is used to increase requested YARN container memory, instead of sharing overhead memory between python and off-heap JVM activity.
## How was this patch tested?
Tested memory limits in our YARN cluster and verified that MemoryError is thrown.
Author: Ryan Blue <blue@apache.org>
Closes#21977 from rdblue/SPARK-25004-add-python-memory-limit.
changed $SPARK_HOME/conf/spark-default.conf to $SPARK_HOME/conf/spark-defaults.conf
no testing necessary as this was a change to documentation.
Closes#22116 from KraFusion/patch-1.
Authored-by: Joey Krabacher <jkrabacher@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
This pr add `spark.broadcast.checksum` to configuration.
## How was this patch tested?
manually tested
Author: liuxian <liu.xian3@zte.com.cn>
Closes#21825 from 10110346/checksum_config.
Fetch-to-mem is guaranteed to fail if the message is bigger than 2 GB,
so we might as well use fetch-to-disk in that case. The message includes
some metadata in addition to the block data itself (in particular
UploadBlock has a lot of metadata), so we leave a little room.
Author: Imran Rashid <irashid@cloudera.com>
Closes#21474 from squito/SPARK-24297.
## What changes were proposed in this pull request?
Address comments in #21370 and add more test.
## How was this patch tested?
Enhance test in pyspark/sql/test.py and DataFrameSuite
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#21553 from xuanyuanking/SPARK-24215-follow.
## What changes were proposed in this pull request?
spark.blacklist.killBlacklistedExecutors is defined as
(Experimental) If set to "true", allow Spark to automatically kill, and attempt to re-create, executors when they are blacklisted. Note that, when an entire node is added to the blacklist, all of the executors on that node will be killed.
I presume the killing of blacklisted executors only happens after the stage completes successfully and all tasks have completed or on fetch failures (updateBlacklistForFetchFailure/updateBlacklistForSuccessfulTaskSet). It is confusing because the definition states that the executor will be attempted to be recreated as soon as it is blacklisted. This is not true while the stage is in progress and an executor is blacklisted, it will not attempt to cleanup until the stage finishes.
Author: Sanket Chintapalli <schintap@yahoo-inc.com>
Closes#21475 from redsanket/SPARK-24416.
## What changes were proposed in this pull request?
Spark provides four codecs: `lz4`, `lzf`, `snappy`, and `zstd`. This pr add missing shortCompressionCodecNames to configuration.
## How was this patch tested?
manually tested
Author: Yuming Wang <yumwang@ebay.com>
Closes#21431 from wangyum/SPARK-19112.
## What changes were proposed in this pull request?
This change changes spark behavior to use the correct environment variable set by Mesos in the container on startup.
Author: Jake Charland <jakec@uber.com>
Closes#18894 from jakecharland/MesosSandbox.
## What changes were proposed in this pull request?
Added support to specify the 'spark.executor.extraJavaOptions' value in terms of the `{{APP_ID}}` and/or `{{EXECUTOR_ID}}`, `{{APP_ID}}` will be replaced by Application Id and `{{EXECUTOR_ID}}` will be replaced by Executor Id while starting the executor.
## How was this patch tested?
I have verified this by checking the executor process command and gc logs. I verified the same in different deployment modes(Standalone, YARN, Mesos) client and cluster modes.
Author: Devaraj K <devaraj@apache.org>
Closes#21088 from devaraj-kavali/SPARK-24003.
## What changes were proposed in this pull request?
By default, the dynamic allocation will request enough executors to maximize the
parallelism according to the number of tasks to process. While this minimizes the
latency of the job, with small tasks this setting can waste a lot of resources due to
executor allocation overhead, as some executor might not even do any work.
This setting allows to set a ratio that will be used to reduce the number of
target executors w.r.t. full parallelism.
The number of executors computed with this setting is still fenced by
`spark.dynamicAllocation.maxExecutors` and `spark.dynamicAllocation.minExecutors`
## How was this patch tested?
Units tests and runs on various actual workloads on a Yarn Cluster
Author: Julien Cuquemelle <j.cuquemelle@criteo.com>
Closes#19881 from jcuquemelle/AddTaskPerExecutorSlot.
## What changes were proposed in this pull request?
Easy fix in the documentation.
## How was this patch tested?
N/A
Closes#20948
Author: Daniel Sakuma <dsakuma@gmail.com>
Closes#20928 from dsakuma/fix_typo_configuration_docs.
This change basically rewrites the security documentation so that it's
up to date with new features, more correct, and more complete.
Because security is such an important feature, I chose to move all the
relevant configuration documentation to the security page, instead of
having them peppered all over the place in the configuration page. This
allows an almost one-stop shop for security configuration in Spark. The
only exceptions are some YARN-specific minor features which I left in
the YARN page.
I also re-organized the page's topics, since they didn't make a lot of
sense. You had kerberos features described inside paragraphs talking
about UI access control, and other oddities. It should be easier now
to find information about specific Spark security features. I also
enabled TOCs for both the Security and YARN pages, since that makes it
easier to see what is covered.
I removed most of the comments from the SecurityManager javadoc since
they just replicated information in the security doc, with different
levels of out-of-dateness.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20742 from vanzin/SPARK-23572.
## What changes were proposed in this pull request?
Fix spelling in quick-start doc.
## How was this patch tested?
Doc only.
Author: Shashwat Anand <me@shashwat.me>
Closes#20336 from ashashwat/SPARK-23165.
## What changes were proposed in this pull request?
This PR completes the docs, specifying the default units assumed in configuration entries of type size.
This is crucial since unit-less values are accepted and the user might assume the base unit is bytes, which in most cases it is not, leading to hard-to-debug problems.
## How was this patch tested?
This patch updates only documentation only.
Author: Fernando Pereira <fernando.pereira@epfl.ch>
Closes#20269 from ferdonline/docs_units.
What changes were proposed in this pull request?
This PR contains documentation on the usage of Kubernetes scheduler in Spark 2.3, and a shell script to make it easier to build docker images required to use the integration. The changes detailed here are covered by https://github.com/apache/spark/pull/19717 and https://github.com/apache/spark/pull/19468 which have merged already.
How was this patch tested?
The script has been in use for releases on our fork. Rest is documentation.
cc rxin mateiz (shepherd)
k8s-big-data SIG members & contributors: foxish ash211 mccheah liyinan926 erikerlandson ssuchter varunkatta kimoonkim tnachen ifilonenko
reviewers: vanzin felixcheung jiangxb1987 mridulm
TODO:
- [x] Add dockerfiles directory to built distribution. (https://github.com/apache/spark/pull/20007)
- [x] Change references to docker to instead say "container" (https://github.com/apache/spark/pull/19995)
- [x] Update configuration table.
- [x] Modify spark.kubernetes.allocation.batch.delay to take time instead of int (#20032)
Author: foxish <ramanathana@google.com>
Closes#19946 from foxish/update-k8s-docs.
This PR contains implementation of the basic submission client for the cluster mode of Spark on Kubernetes. It's step 2 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) vote which passed on Aug 31.
This PR and #19468 together form a MVP of Spark on Kubernetes that allows users to run Spark applications that use resources locally within the driver and executor containers on Kubernetes 1.6 and up. Some changes on pom and build/test setup are copied over from #19468 to make this PR self contained and testable.
The submission client is mainly responsible for creating the Kubernetes pod that runs the Spark driver. It follows a step-based approach to construct the driver pod, as the code under the `submit.steps` package shows. The steps are orchestrated by `DriverConfigurationStepsOrchestrator`. `Client` creates the driver pod and waits for the application to complete if it's configured to do so, which is the case by default.
This PR also contains Dockerfiles of the driver and executor images. They are included because some of the environment variables set in the code would not make sense without referring to the Dockerfiles.
* The patch contains unit tests which are passing.
* Manual testing: ./build/mvn -Pkubernetes clean package succeeded.
* It is a subset of the entire changelist hosted at http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
* There is integration testing enabled in the fork currently hosted by PepperData which is being moved over to RiseLAB CI.
* Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah foxish ash211 ssuchter varunkatta kimoonkim erikerlandson tnachen ifilonenko liyinan926
Author: Yinan Li <liyinan926@gmail.com>
Closes#19717 from liyinan926/spark-kubernetes-4.
## What changes were proposed in this pull request?
The spark properties for configuring the ContextCleaner are not documented in the official documentation at https://spark.apache.org/docs/latest/configuration.html#available-properties.
This PR adds the doc.
## How was this patch tested?
Manual.
```
cd docs
jekyll build
open _site/configuration.html
```
Author: gaborgsomogyi <gabor.g.somogyi@gmail.com>
Closes#19826 from gaborgsomogyi/SPARK-22428.
## What changes were proposed in this pull request?
This is a stripped down version of the `KubernetesClusterSchedulerBackend` for Spark with the following components:
- Static Allocation of Executors
- Executor Pod Factory
- Executor Recovery Semantics
It's step 1 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP vote](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) which passed on Aug 31 .
## How was this patch tested?
- The patch contains unit tests which are passing.
- Manual testing: `./build/mvn -Pkubernetes clean package` succeeded.
- It is a **subset** of the entire changelist hosted in http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
- There is integration testing enabled in the fork currently [hosted by PepperData](spark-k8s-jenkins.pepperdata.org:8080) which is being moved over to RiseLAB CI.
- Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah ash211 ssuchter varunkatta kimoonkim erikerlandson liyinan926 tnachen ifilonenko
Author: Yinan Li <liyinan926@gmail.com>
Author: foxish <ramanathana@google.com>
Author: mcheah <mcheah@palantir.com>
Closes#19468 from foxish/spark-kubernetes-3.
## What changes were proposed in this pull request?
Using zstd compression for Spark jobs spilling 100s of TBs of data, we could reduce the amount of data written to disk by as much as 50%. This translates to significant latency gain because of reduced disk io operations. There is a degradation CPU time by 2 - 5% because of zstd compression overhead, but for jobs which are bottlenecked by disk IO, this hit can be taken.
## Benchmark
Please note that this benchmark is using real world compute heavy production workload spilling TBs of data to disk
| | zstd performance as compred to LZ4 |
| ------------- | -----:|
| spill/shuffle bytes | -48% |
| cpu time | + 3% |
| cpu reservation time | -40%|
| latency | -40% |
## How was this patch tested?
Tested by running few jobs spilling large amount of data on the cluster and amount of intermediate data written to disk reduced by as much as 50%.
Author: Sital Kedia <skedia@fb.com>
Closes#18805 from sitalkedia/skedia/upstream_zstd.
## What changes were proposed in this pull request?
Event Log Server has a total of five configuration parameters, and now the description of the other two configuration parameters on the doc, user-friendly access and use.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: guoxiaolong <guo.xiaolong1@zte.com.cn>
Closes#19242 from guoxiaolongzte/addEventLogConf.
## What changes were proposed in this pull request?
I see that block updates are not logged to the event log.
This makes sense as a default for performance reasons.
However, I find it helpful when trying to get a better understanding of caching for a job to be able to log these updates.
This PR adds a configuration setting `spark.eventLog.blockUpdates` (defaulting to false) which allows block updates to be recorded in the log.
This contribution is original work which is licensed to the Apache Spark project.
## How was this patch tested?
Current and additional unit tests.
Author: Michael Mior <mmior@uwaterloo.ca>
Closes#19263 from michaelmior/log-block-updates.
## What changes were proposed in this pull request?
In the current BlockManager's `getRemoteBytes`, it will call `BlockTransferService#fetchBlockSync` to get remote block. In the `fetchBlockSync`, Spark will allocate a temporary `ByteBuffer` to store the whole fetched block. This will potentially lead to OOM if block size is too big or several blocks are fetched simultaneously in this executor.
So here leveraging the idea of shuffle fetch, to spill the large block to local disk before consumed by upstream code. The behavior is controlled by newly added configuration, if block size is smaller than the threshold, then this block will be persisted in memory; otherwise it will first spill to disk, and then read from disk file.
To achieve this feature, what I did is:
1. Rename `TempShuffleFileManager` to `TempFileManager`, since now it is not only used by shuffle.
2. Add a new `TempFileManager` to manage the files of fetched remote blocks, the files are tracked by weak reference, will be deleted when no use at all.
## How was this patch tested?
This was tested by adding UT, also manual verification in local test to perform GC to clean the files.
Author: jerryshao <sshao@hortonworks.com>
Closes#19476 from jerryshao/SPARK-22062.
## What changes were proposed in this pull request?
The number of cores assigned to each executor is configurable. When this is not explicitly set, multiple executors from the same application may be launched on the same worker too.
## How was this patch tested?
N/A
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18711 from 10110346/executorcores.
Right now the spark shuffle service has a cache for index files. It is based on a # of files cached (spark.shuffle.service.index.cache.entries). This can cause issues if people have a lot of reducers because the size of each entry can fluctuate based on the # of reducers.
We saw an issues with a job that had 170000 reducers and it caused NM with spark shuffle service to use 700-800MB or memory in NM by itself.
We should change this cache to be memory based and only allow a certain memory size used. When I say memory based I mean the cache should have a limit of say 100MB.
https://issues.apache.org/jira/browse/SPARK-21501
Manual Testing with 170000 reducers has been performed with cache loaded up to max 100MB default limit, with each shuffle index file of size 1.3MB. Eviction takes place as soon as the total cache size reaches the 100MB limit and the objects will be ready for garbage collection there by avoiding NM to crash. No notable difference in runtime has been observed.
Author: Sanket Chintapalli <schintap@yahoo-inc.com>
Closes#18940 from redsanket/SPARK-21501.
