603c4f8ebd
## What changes were proposed in this pull request? Currently, Java Linter is disabled in Jenkins tests. https://github.com/apache/spark/blob/master/dev/run-tests.py#L554 However, as of today, Spark has 721 java files with 97362 code (without blank/comments). It's about 1/3 of Scala. ``` -------------------------------------------------------------------------------- Language files blank comment code -------------------------------------------------------------------------------- Scala 2353 62819 124060 318747 Java 721 18617 23314 97362 ``` This PR aims to take advantage of Travis CI to handle the following static analysis by adding a single file, `.travis.yml` without any additional burden on the existing servers. - Java Linter - JDK7/JDK8 maven compile Note that this PR does not propose to remove some of the above work items from the Jenkins. It's possible, but we need to observe the Travis CI stability for a while. The main goal of this issue is to remove committer's overhead on linter-related PRs (the original PR and the fixation PR). ## How was this patch tested? Pass the Travis CI tests. Please see the following link. https://travis-ci.org/dongjoon-hyun/spark/builds/128595350 https://travis-ci.org/dongjoon-hyun/spark/builds/128708372 Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12980 from dongjoon-hyun/SPARK-15207. |
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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 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:
build/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". For developing Spark using an IDE, see Eclipse and IntelliJ.
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.
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.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.