| Commit message (Collapse) | Author | Age | Files | Lines |
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Author: Raymond Liu <raymond.liu@intel.com>
Closes #611 from colorant/curator and squashes the following commits:
7556aa1 [Raymond Liu] Address review comments
af92e1f [Raymond Liu] Fix coding style
964f3c2 [Raymond Liu] Ignore NodeExists exception
6df2966 [Raymond Liu] Rewrite zookeeper client code with curator
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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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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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Upgrade junit-interface plugin from 0.9 to 0.10.
I noticed that the JavaAPISuite tests didn't
appear to display any output locally or under
Jenkins, making it difficult to know whether they
were running. This change increases the verbosity
to more closely match the ScalaTest tests.
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SPARK-998: Support Launching Driver Inside of Standalone Mode
[NOTE: I need to bring the tests up to date with new changes, so for now they will fail]
This patch provides support for launching driver programs inside of a standalone cluster manager. It also supports monitoring and re-launching of driver programs which is useful for long running, recoverable applications such as Spark Streaming jobs. For those jobs, this patch allows a deployment mode which is resilient to the failure of any worker node, failure of a master node (provided a multi-master setup), and even failures of the applicaiton itself, provided they are recoverable on a restart. Driver information, such as the status and logs from a driver, is displayed in the UI
There are a few small TODO's here, but the code is generally feature-complete. They are:
- Bring tests up to date and add test coverage
- Restarting on failure should be optional and maybe off by default.
- See if we can re-use akka connections to facilitate clients behind a firewall
A sensible place to start for review would be to look at the `DriverClient` class which presents users the ability to launch their driver program. I've also added an example program (`DriverSubmissionTest`) that allows you to test this locally and play around with killing workers, etc. Most of the code is devoted to persisting driver state in the cluster manger, exposing it in the UI, and dealing correctly with various types of failures.
Instructions to test locally:
- `sbt/sbt assembly/assembly examples/assembly`
- start a local version of the standalone cluster manager
```
./spark-class org.apache.spark.deploy.client.DriverClient \
-j -Dspark.test.property=something \
-e SPARK_TEST_KEY=SOMEVALUE \
launch spark://10.99.1.14:7077 \
../path-to-examples-assembly-jar \
org.apache.spark.examples.DriverSubmissionTest 1000 some extra options --some-option-here -X 13
```
- Go in the UI and make sure it started correctly, look at the output etc
- Kill workers, the driver program, masters, etc.
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Conflicts:
core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
pom.xml
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Conflicts:
README.md
core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala
core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala
pom.xml
project/SparkBuild.scala
repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
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Refactored the streaming project to separate external libraries like Twitter, Kafka, Flume, etc.
At a high level, these are the following changes.
1. All the external code was put in `SPARK_HOME/external/` as separate SBT projects and Maven modules. Their artifact names are `spark-streaming-twitter`, `spark-streaming-kafka`, etc. Both SparkBuild.scala and pom.xml files have been updated. References to external libraries and repositories have been removed from the settings of root and streaming projects/modules.
2. To avail the external functionality (say, creating a Twitter stream), the developer has to `import org.apache.spark.streaming.twitter._` . For Scala API, the developer has to call `TwitterUtils.createStream(streamingContext, ...)`. For the Java API, the developer has to call `TwitterUtils.createStream(javaStreamingContext, ...)`.
3. Each external project has its own scala and java unit tests. Note the unit tests of each external library use classes of the streaming unit tests (`TestSuiteBase`, `LocalJavaStreamingContext`, etc.). To enable this code sharing among test classes, `dependsOn(streaming % "compile->compile,test->test")` was used in the SparkBuild.scala . In the streaming/pom.xml, an additional `maven-jar-plugin` was necessary to capture this dependency (see comment inside the pom.xml for more information).
4. Jars of the external projects have been added to examples project but not to the assembly project.
5. In some files, imports have been rearrange to conform to the Spark coding guidelines.
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Conflicts:
examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
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repositoris from other poms and sbt.
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SPARK-1005 Ning upgrade
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Approximate distinct count
Added countApproxDistinct() to RDD and countApproxDistinctByKey() to PairRDDFunctions to approximately count distinct number of elements and distinct number of values per key, respectively. Both functions use HyperLogLog from stream-lib for counting. Both functions take a parameter that controls the trade-off between accuracy and memory consumption. Also added Scala docs and test suites for both methods.
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The correct format is to not have a trailing slash.
For me this caused non-deterministic failures due to issues fetching
certain artifacts. The issue was that some of the maven caches would
fail to fetch the artifact (due to the way that the artifact
path was concatenated with the repository) and this short-circuited
the download process in a silent way. Here is what the log output
looked like:
Downloading: http://repo.maven.apache.org/maven2/org/spark-project/akka/akka-remote_2.10/2.2.3-shaded-protobuf/akka-remote_2.10-2.2.3-shaded-protobuf.pom
[WARNING] The POM for org.spark-project.akka:akka-remote_2.10:jar:2.2.3-shaded-protobuf is missing, no dependency information available
This was pretty brutal to debug since there was no error message
anywhere and the path *looks* correct as reported by the Maven log.
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Conflicts:
core/pom.xml
core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
pom.xml
project/SparkBuild.scala
streaming/pom.xml
yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
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build/dep-resolution atleast a bit faster.
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Conflicts:
core/src/main/scala/org/apache/spark/rdd/RDD.scala
project/SparkBuild.scala
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Conflicts:
core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala
project/SparkBuild.scala
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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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using Scala 2.10.3,
resolved maven-scala-plugin warning
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Conflicts:
core/src/main/scala/org/apache/spark/SparkContext.scala
project/SparkBuild.scala
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