3404a73f4c
Right now there are 3 different classes dealing with building the driver command to run inside the pod, one for each "binding" supported by Spark. This has two main shortcomings: - the code in the 3 classes is very similar; changing things in one place would probably mean making a similar change in the others. - it gives the false impression that the step implementation is the only place where binding-specific logic is needed. That is not true; there was code in KubernetesConf that was binding-specific, and there's also code in the executor-specific config step. So the 3 classes weren't really working as a language-specific abstraction. On top of that, the current code was propagating command line parameters in a different way depending on the binding. That doesn't seem necessary, and in fact using environment variables for command line parameters is in general a really bad idea, since you can't handle special characters (e.g. spaces) that way. This change merges the 3 different code paths for Java, Python and R into a single step, and also merges the 3 code paths to start the Spark driver in the k8s entry point script. This increases the amount of shared code, and also moves more feature logic into the step itself, so it doesn't live in KubernetesConf. Note that not all logic related to setting up the driver lives in that step. For example, the memory overhead calculation still lives separately, except it now happens in the driver config step instead of outside the step hierarchy altogether. Some of the noise in the diff is because of changes to KubernetesConf, which will be addressed in a separate change. Tested with new and updated unit tests + integration tests. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #22897 from vanzin/SPARK-25875. |
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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, 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.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. 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". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
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. 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.
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" 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 in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.