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This removes some loose ends not caught by the other (incubating -> tlp) patches. @markhamstra this updates the version as you mentioned earlier.
Author: Patrick Wendell <pwendell@gmail.com>
Closes #51 from pwendell/tlp and squashes the following commits:
d553b1b [Patrick Wendell] Remove remaining references to incubation
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(Ported from https://github.com/apache/incubator-spark/pull/637 )
Author: Sean Owen <sowen@cloudera.com>
Closes #31 from srowen/SPARK-1084.1 and squashes the following commits:
6c4a32c [Sean Owen] Suppress warnings about legitimate unchecked array creations, or change code to avoid it
f35b833 [Sean Owen] Fix two misc javadoc problems
254e8ef [Sean Owen] Fix one new style error introduced in scaladoc warning commit
5b2fce2 [Sean Owen] Fix scaladoc invocation warning, and enable javac warnings properly, with plugin config updates
007762b [Sean Owen] Remove dead scaladoc links
b8ff8cb [Sean Owen] Replace deprecated Ant <tasks> with <target>
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https://spark-project.atlassian.net/browse/SPARK-1089
copied from JIRA, reported by @ash211
"Using the ADD_JARS environment variable with spark-shell used to add the jar to both the shell and the various workers. Now it only adds to the workers and importing a custom class in the shell is broken.
The workaround is to add custom jars to both ADD_JARS and SPARK_CLASSPATH.
We should fix ADD_JARS so it works properly again.
See various threads on the user list:
https://mail-archives.apache.org/mod_mbox/incubator-spark-user/201402.mbox/%3CCAJbo4neMLiTrnm1XbyqomWmp0m+EUcg4yE-txuRGSVKOb5KLeA@mail.gmail.com%3E
(another one that doesn't appear in the archives yet titled "ADD_JARS not working on 0.9")"
The reason of this bug is two-folds
in the current implementation of SparkILoop.scala, the settings.classpath is not set properly when the process() method is invoked
the weird behaviour of Scala 2.10, (I personally thought it is a bug)
if we simply set value of a PathSettings object (like settings.classpath), the isDefault is not set to true (this is a flag showing if the variable is modified), so it makes the PathResolver loads the default CLASSPATH environment variable value to calculated the path (see https://github.com/scala/scala/blob/2.10.x/src/compiler/scala/tools/util/PathResolver.scala#L215)
what we have to do is to manually make this flag set, (https://github.com/CodingCat/incubator-spark/blob/e3991d97ddc33e77645e4559b13bf78b9e68239a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala#L884)
Author: CodingCat <zhunansjtu@gmail.com>
Closes #13 from CodingCat/SPARK-1089 and squashes the following commits:
8af81e7 [CodingCat] impose non-null settings
9aa2125 [CodingCat] code cleaning
ce36676 [CodingCat] code cleaning
e045582 [CodingCat] fix the regression problem on ADD_JARS in 0.9
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Prompted by a recent thread on the mailing list, I tried and failed to see if Spark can be made independent of log4j. There are a few cases where control of the underlying logging is pretty useful, and to do that, you have to bind to a specific logger.
Instead I propose some tidying that leaves Spark's use of log4j, but gets rid of warnings and should still enable downstream users to switch. The idea is to pipe everything (except log4j) through SLF4J, and have Spark use SLF4J directly when logging, and where Spark needs to output info (REPL and tests), bind from SLF4J to log4j.
This leaves the same behavior in Spark. It means that downstream users who want to use something except log4j should:
- Exclude dependencies on log4j, slf4j-log4j12 from Spark
- Include dependency on log4j-over-slf4j
- Include dependency on another logger X, and another slf4j-X
- Recreate any log config that Spark does, that is needed, in the other logger's config
That sounds about right.
Here are the key changes:
- Include the jcl-over-slf4j shim everywhere by depending on it in core.
- Exclude dependencies on commons-logging from third-party libraries.
- Include the jul-to-slf4j shim everywhere by depending on it in core.
- Exclude slf4j-* dependencies from third-party libraries to prevent collision or warnings
- Added missing slf4j-log4j12 binding to GraphX, Bagel module tests
And minor/incidental changes:
- Update to SLF4J 1.7.5, which happily matches Hadoop 2’s version and is a recommended update over 1.7.2
- (Remove a duplicate HBase dependency declaration in SparkBuild.scala)
- (Remove a duplicate mockito dependency declaration that was causing warnings and bugging me)
Author: Sean Owen <sowen@cloudera.com>
Closes #570 from srowen/SPARK-1071 and squashes the following commits:
52eac9f [Sean Owen] Add slf4j-over-log4j12 dependency to core (non-test) and remove it from things that depend on core.
77a7fa9 [Sean Owen] SPARK-1071: Tidy logging strategy and use of log4j
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https://spark-project.atlassian.net/browse/SPARK-1090
spark-shell should print help information about parameters and should allow user to configure exe memory
there is no document about hot to set --cores/-c in spark-shell
and also
users should be able to set executor memory through command line options
In this PR I also check the format of the options passed by the user
Author: CodingCat <zhunansjtu@gmail.com>
Closes #599 from CodingCat/spark_shell_improve and squashes the following commits:
de5aa38 [CodingCat] add parameter to set driver memory
915cbf8 [CodingCat] improvement on spark_shell (help information, configure memory)
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SPARK-1058, Fix Style Errors and Add Scala Style to Spark Build.
Author: Patrick Wendell <pwendell@gmail.com>
Author: Prashant Sharma <scrapcodes@gmail.com>
== Merge branch commits ==
commit 1a8bd1c059b842cb95cc246aaea74a79fec684f4
Author: Prashant Sharma <scrapcodes@gmail.com>
Date: Sun Feb 9 17:39:07 2014 +0530
scala style fixes
commit f91709887a8e0b608c5c2b282db19b8a44d53a43
Author: Patrick Wendell <pwendell@gmail.com>
Date: Fri Jan 24 11:22:53 2014 -0800
Adding scalastyle snapshot
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Version number to 1.0.0-SNAPSHOT
Since 0.9.0-incubating is done and out the door, we shouldn't be building 0.9.0-incubating-SNAPSHOT anymore.
@pwendell
Author: Mark Hamstra <markhamstra@gmail.com>
== Merge branch commits ==
commit 1b00a8a7c1a7f251b4bb3774b84b9e64758eaa71
Author: Mark Hamstra <markhamstra@gmail.com>
Date: Wed Feb 5 09:30:32 2014 -0800
Version number to 1.0.0-SNAPSHOT
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Added ‘-i’ command line option to Spark REPL
We had to create a new implementation of both scala.tools.nsc.CompilerCommand and scala.tools.nsc.Settings, because using scala.tools.nsc.GenericRunnerSettings would bring in other options (-howtorun, -save and -execute) which don’t make sense in Spark.
Any new Spark specific command line option could now be added to org.apache.spark.repl.SparkRunnerSettings class.
Since the behavior of loading a script from the command line should be the same as loading it using the “:load” command inside the shell, the script should be loaded when the SparkContext is available, that’s why we had to move the call to ‘loadfiles(settings)’ _after_ the call to postInitialization(). This still doesn’t work if ‘isAsync = true’.
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We had to create a new implementation of both scala.tools.nsc.CompilerCommand and scala.tools.nsc.Settings, because using scala.tools.nsc.GenericRunnerSettings would bring in other options (-howtorun, -save and -execute) which don’t make sense in Spark.
Any new Spark specific command line option could now be added to org.apache.spark.repl.SparkRunnerSettings class.
Since the behavior of loading a script from the command line should be the same as loading it using the “:load” command inside the shell, the script should be loaded when the SparkContext is available, that’s why we had to move the call to ‘loadfiles(settings)’ _after_ the call to postInitialization(). This still doesn’t work if ‘isAsync = true’.
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Conflicts:
core/src/test/scala/org/apache/spark/DriverSuite.scala
docs/python-programming-guide.md
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spark-915-segregate-scripts
Conflicts:
bin/spark-shell
core/pom.xml
core/src/main/scala/org/apache/spark/SparkContext.scala
core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala
core/src/test/scala/org/apache/spark/DriverSuite.scala
python/run-tests
sbin/compute-classpath.sh
sbin/spark-class
sbin/stop-slaves.sh
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Also replaced SparkConf.getOrElse with just a "get" that takes a default
value, and added getInt, getLong, etc to make code that uses this
simpler later on.
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- Got rid of global SparkContext.globalConf
- Pass SparkConf to serializers and compression codecs
- Made SparkConf public instead of private[spark]
- Improved API of SparkContext and SparkConf
- Switched executor environment vars to be passed through SparkConf
- Fixed some places that were still using system properties
- Fixed some tests, though others are still failing
This still fails several tests in core, repl and streaming, likely due
to properties not being set or cleared correctly (some of the tests run
fine in isolation).
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build/dep-resolution atleast a bit faster.
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I've diff'd this patch against my own -- since they were both created
independently, this means that two sets of eyes have gone over all the
merge conflicts that were created, so I'm feeling significantly more
confident in the resulting PR.
@rxin has looked at the changes to the repl and is resoundingly
confident that they are correct.
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spark-repl to the actual execution thread.
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Mainly, this occurs if you provide a messed up MASTER url (one that doesn't match one
of our regexes). Previously, we would default to Mesos, fail, and then start the shell
anyway, except that any Spark command would fail.
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Conflicts:
bagel/pom.xml
core/pom.xml
core/src/test/scala/org/apache/spark/ui/UISuite.scala
examples/pom.xml
mllib/pom.xml
pom.xml
project/SparkBuild.scala
repl/pom.xml
streaming/pom.xml
tools/pom.xml
In scala 2.10, a shorter representation is used for naming artifacts
so changed to shorter scala version for artifacts and made it a property in pom.
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Conflicts:
core/src/main/scala/org/apache/spark/ui/jobs/JobProgressUI.scala
docs/_config.yml
project/SparkBuild.scala
repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
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Conflicts:
core/src/main/scala/org/apache/spark/SparkContext.scala
project/SparkBuild.scala
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* RDD, *RDDFunctions -> org.apache.spark.rdd
* Utils, ClosureCleaner, SizeEstimator -> org.apache.spark.util
* JavaSerializer, KryoSerializer -> org.apache.spark.serializer
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