Revision 432ea6924142c9688d8b6c64b46a531810691a8c authored by Liupengcheng on 12 March 2019, 20:53:42 UTC, committed by Marcelo Vanzin on 12 March 2019, 21:13:20 UTC
There is a race condition in the `ExecutorAllocationManager` that the `SparkListenerExecutorRemoved` event is posted before the `SparkListenerTaskStart` event, which will cause the incorrect result of `executorIds`. Then, when some executor idles, the real executors will be removed even actual executor number is equal to `minNumExecutors` due to the incorrect computation of `newExecutorTotal`(may greater than the `minNumExecutors`), thus may finally causing zero available executors but a wrong positive number of executorIds was kept in memory. What's more, even the `SparkListenerTaskEnd` event can not make the fake `executorIds` released, because later idle event for the fake executors can not cause the real removal of these executors, as they are already removed and they are not exist in the `executorDataMap` of `CoaseGrainedSchedulerBackend`, so that the `onExecutorRemoved` method will never be called again. For details see https://issues.apache.org/jira/browse/SPARK-26927 This PR is to fix this problem. existUT and added UT Closes #23842 from liupc/Fix-race-condition-that-casues-dyanmic-allocation-not-working. Lead-authored-by: Liupengcheng <liupengcheng@xiaomi.com> Co-authored-by: liupengcheng <liupengcheng@xiaomi.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> (cherry picked from commit d5cfe08fdc7ad07e948f329c0bdeeca5c2574a18) Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
1 parent dba5bac
README.md
# Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides
high-level APIs in Scala, Java, Python, and R, 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 DataFrames,
MLlib for machine learning, GraphX for graph processing,
and Spark Streaming for stream processing.
<http://spark.apache.org/>
## Online Documentation
You can find the latest Spark documentation, including a programming
guide, on the [project web page](http://spark.apache.org/documentation.html).
This README file only contains basic setup instructions.
## Building Spark
Spark is built using [Apache Maven](http://maven.apache.org/).
To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see ["Parallel builds in Maven 3"](https://cwiki.apache.org/confluence/display/MAVEN/Parallel+builds+in+Maven+3).
More detailed documentation is available from the project site, at
["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html).
For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](http://spark.apache.org/developer-tools.html).
## 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" 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](#building-spark). Once Spark is built, tests
can be run using:
./dev/run-tests
Please see the guidance on how to
[run tests for a module, or individual tests](http://spark.apache.org/developer-tools.html#individual-tests).
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
## 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 and Enabling YARN"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn)
for detailed guidance on building for a particular distribution of Hadoop, including
building for particular Hive and Hive Thriftserver distributions.
## Configuration
Please refer to the [Configuration Guide](http://spark.apache.org/docs/latest/configuration.html)
in the online documentation for an overview on how to configure Spark.
## Contributing
Please review the [Contribution to Spark guide](http://spark.apache.org/contributing.html)
for information on how to get started contributing to the project.
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