37a5e272f8
The current way of shading Guava is a little problematic. Code that depends on "spark-core" does not see the transitive dependency, yet classes in "spark-core" actually depend on Guava. So it's a little tricky to run unit tests that use spark-core classes, since you need a compatible version of Guava in your dependencies when running the tests. This can become a little tricky, and is kind of a bad user experience. This change modifies the way Guava is shaded so that it's applied uniformly across the Spark build. This means Guava is shaded inside spark-core itself, so that the dependency issues above are solved. Aside from that, all Spark sub-modules have their Guava references relocated, so that they refer to the relocated classes now packaged inside spark-core. Before, this was only done by the time the assembly was built, so projects that did not end up inside the assembly (such as streaming backends) could still reference the original location of Guava classes. The Guava classes are added to the "first" artifact Spark generates (network-common), so that all downstream modules have the needed classes available. Since "network-common" is a dependency of spark-core, all Spark apps should get the relocated classes automatically. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #3658 from vanzin/SPARK-4809 and squashes the following commits: 3c93e42 [Marcelo Vanzin] Shade Guava in the network-common artifact. 5d69ec9 [Marcelo Vanzin] Merge branch 'master' into SPARK-4809 b3104fc [Marcelo Vanzin] Add comment. 941848f [Marcelo Vanzin] Merge branch 'master' into SPARK-4809 f78c48a [Marcelo Vanzin] Merge branch 'master' into SPARK-4809 8053dd4 [Marcelo Vanzin] Merge branch 'master' into SPARK-4809 107d7da [Marcelo Vanzin] Add fix for SPARK-5052 (PR #3874). 40b8723 [Marcelo Vanzin] Merge branch 'master' into SPARK-4809 4a4ed42 [Marcelo Vanzin] [SPARK-4809] Rework Guava library shading. |
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| bagel | ||
| bin | ||
| build | ||
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| core | ||
| data/mllib | ||
| dev | ||
| docker | ||
| docs | ||
| ec2 | ||
| examples | ||
| external | ||
| extras | ||
| graphx | ||
| mllib | ||
| network | ||
| project | ||
| python | ||
| 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 | ||
| tox.ini | ||
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 with Maven".
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.