464a3c1e02
This patch updates our custom Hive 1.2.1 fork in order to shade Kryo in Hive. This is a blocker for upgrading Spark to use Kryo 3 (see #12076). The source for this new fork of Hive can be found at https://github.com/JoshRosen/hive/tree/release-1.2.1-spark2 Here's the complete diff from the official Hive 1.2.1 release: https://github.com/apache/hive/compare/release-1.2.1...JoshRosen:release-1.2.1-spark2 Here's the diff from the sources that pwendell used to publish the current `1.2.1.spark` release of Hive: https://github.com/pwendell/hive/compare/release-1.2.1-spark...JoshRosen:release-1.2.1-spark2. This diff looks large because his branch used a shell script to rewrite the groupId, whereas I had to commit the groupId changes in order to prevent the find-and-replace from affecting the package names in our relocated Kryo classes: https://github.com/pwendell/hive/compare/release-1.2.1-spark...JoshRosen:release-1.2.1-spark2#diff-6ada9aaec70e069df8f2c34c5519dd1e Using these changes, I was able to publish a local version of Hive and verify that this change fixes the test failures which are blocking #12076. Note that this PR will not compile until we complete the review of the Hive POM changes and stage and publish a release. /cc vanzin, steveloughran, and pwendell for review. Author: Josh Rosen <joshrosen@databricks.com> Closes #12215 from JoshRosen/shade-kryo-in-hive. |
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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.