114ff926fc
This PR adds `PartitioningCollection`, which is used to represent the `outputPartitioning` for SparkPlans with multiple children (e.g. `ShuffledHashJoin`). So, a `SparkPlan` can have multiple descriptions of its partitioning schemes. Taking `ShuffledHashJoin` as an example, it has two descriptions of its partitioning schemes, i.e. `left.outputPartitioning` and `right.outputPartitioning`. So when we have a query like `select * from t1 join t2 on (t1.x = t2.x) join t3 on (t2.x = t3.x)` will only have three Exchange operators (when shuffled joins are needed) instead of four. The code in this PR was authored by yhuai; I'm opening this PR to factor out this change from #7685, a larger pull request which contains two other optimizations. <!-- Reviewable:start --> [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7773) <!-- Reviewable:end --> Author: Yin Huai <yhuai@databricks.com> Author: Josh Rosen <joshrosen@databricks.com> Closes #7773 from JoshRosen/multi-way-join-planning-improvements and squashes the following commits: 5c45924 [Josh Rosen] Merge remote-tracking branch 'origin/master' into multi-way-join-planning-improvements cd8269b [Josh Rosen] Refactor test to use SQLTestUtils 2963857 [Yin Huai] Revert unnecessary SqlConf change. 73913f7 [Yin Huai] Add comments and test. Also, revert the change in ShuffledHashOuterJoin for now. 4a99204 [Josh Rosen] Delete unrelated expression change 884ab95 [Josh Rosen] Carve out only SPARK-2205 changes. 247e5fa [Josh Rosen] Merge remote-tracking branch 'origin/master' into multi-way-join-planning-improvements c57a954 [Yin Huai] Bug fix. d3d2e64 [Yin Huai] First round of cleanup. f9516b0 [Yin Huai] Style c6667e7 [Yin Huai] Add PartitioningCollection. e616d3b [Yin Huai] wip 7c2d2d8 [Yin Huai] Bug fix and refactoring. 69bb072 [Yin Huai] Introduce NullSafeHashPartitioning and NullUnsafePartitioning. d5b84c3 [Yin Huai] Do not add unnessary filters. 2201129 [Yin Huai] Filter out rows that will not be joined in equal joins early. |
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docs | ||
ec2 | ||
examples | ||
external | ||
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graphx | ||
launcher | ||
mllib | ||
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README.md | ||
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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 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".
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 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. 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.