4d4b249274
In YarnClusterSuite, a test case uses `SaveExecutorInfo` to handle ExecutorAddedEvent as follows.
```
private class SaveExecutorInfo extends SparkListener {
val addedExecutorInfos = mutable.Map[String, ExecutorInfo]()
override def onExecutorAdded(executor: SparkListenerExecutorAdded) {
addedExecutorInfos(executor.executorId) = executor.executorInfo
}
}
...
listener = new SaveExecutorInfo
val sc = new SparkContext(new SparkConf()
.setAppName("yarn \"test app\" 'with quotes' and \\back\\slashes and $dollarSigns"))
sc.addSparkListener(listener)
val status = new File(args(0))
var result = "failure"
try {
val data = sc.parallelize(1 to 4, 4).collect().toSet
assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
data should be (Set(1, 2, 3, 4))
result = "success"
} finally {
sc.stop()
Files.write(result, status, UTF_8)
}
```
But, the usage is wrong because Executors will spawn during initializing SparkContext and SparkContext#addSparkListener should be invoked after the initialization, thus after Executors spawn, so SaveExecutorInfo cannot handle ExecutorAddedEvent.
Following code refers the result of the handling ExecutorAddedEvent. Because of the reason above, we cannot reach the assertion.
```
// verify log urls are present
listener.addedExecutorInfos.values.foreach { info =>
assert(info.logUrlMap.nonEmpty)
}
```
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes #5417 from sarutak/SPARK-6769 and squashes the following commits:
8adc8ba [Kousuke Saruta] Fixed compile error
e258530 [Kousuke Saruta] Fixed style
591cf3e [Kousuke Saruta] Fixed style
48ec89a [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-6769
860c965 [Kousuke Saruta] Simplified code
207d325 [Kousuke Saruta] Added findListenersByClass method to ListenerBus
2408c84 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-6769
2d7e409 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-6769
3874adf [Kousuke Saruta] Fixed the usage of listener bus in LogUrlsStandaloneSuite
153a91b [Kousuke Saruta] Fixed the usage of listener bus in YarnClusterSuite
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| bagel | ||
| bin | ||
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| core | ||
| data/mllib | ||
| dev | ||
| docker | ||
| docs | ||
| ec2 | ||
| examples | ||
| external | ||
| extras | ||
| graphx | ||
| launcher | ||
| mllib | ||
| network | ||
| project | ||
| python | ||
| R | ||
| repl | ||
| sbin | ||
| sbt | ||
| sql | ||
| streaming | ||
| tools | ||
| yarn | ||
| .gitattributes | ||
| .gitignore | ||
| .rat-excludes | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| make-distribution.sh | ||
| NOTICE | ||
| pom.xml | ||
| README.md | ||
| scalastyle-config.xml | ||
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run all automated tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.