560c658a74
Pull Request for: https://issues.apache.org/jira/browse/SPARK-8230 Primary issue resolved is to implement array/map size for Spark SQL. Code is ready for review by a committer. Chen Hao is on the JIRA ticket, but I don't know his username on github, rxin is also on JIRA ticket. Things to review: 1. Where to put added functions namespace wise, they seem to be part of a few operations on collections which includes `sort_array` and `array_contains`. Hence the name given `collectionOperations.scala` and `_collection_functions` in python. 2. In Python code, should it be in a `1.5.0` function array or in a collections array? 3. Are there any missing methods on the `Size` case class? Looks like many of these functions have generated Java code, is that also needed in this case? 4. Something else? Author: Pedro Rodriguez <ski.rodriguez@gmail.com> Author: Pedro Rodriguez <prodriguez@trulia.com> Closes #7462 from EntilZha/SPARK-8230 and squashes the following commits: 9a442ae [Pedro Rodriguez] fixed functions and sorted __all__ 9aea3bb [Pedro Rodriguez] removed imports from python docs 15d4bf1 [Pedro Rodriguez] Added null test case and changed to nullSafeCodeGen d88247c [Pedro Rodriguez] removed python code bd5f0e4 [Pedro Rodriguez] removed duplicate function from rebase/merge 59931b4 [Pedro Rodriguez] fixed compile bug instroduced when merging c187175 [Pedro Rodriguez] updated code to add size to __all__ directly and removed redundent pretty print 130839f [Pedro Rodriguez] fixed failing test aa9bade [Pedro Rodriguez] fix style e093473 [Pedro Rodriguez] updated python code with docs, switched classes/traits implemented, added (failing) expression tests 0449377 [Pedro Rodriguez] refactored code to use better abstract classes/traits and implementations 9a1a2ff [Pedro Rodriguez] added unit tests for map size 2bfbcb6 [Pedro Rodriguez] added unit test for size 20df2b4 [Pedro Rodriguez] Finished working version of size function and added it to python b503e75 [Pedro Rodriguez] First attempt at implementing size for maps and arrays 99a6a5c [Pedro Rodriguez] fixed failing test cac75ac [Pedro Rodriguez] fix style 933d843 [Pedro Rodriguez] updated python code with docs, switched classes/traits implemented, added (failing) expression tests 42bb7d4 [Pedro Rodriguez] refactored code to use better abstract classes/traits and implementations f9c3b8a [Pedro Rodriguez] added unit tests for map size 2515d9f [Pedro Rodriguez] added documentation 0e60541 [Pedro Rodriguez] added unit test for size acf9853 [Pedro Rodriguez] Finished working version of size function and added it to python 84a5d38 [Pedro Rodriguez] First attempt at implementing size for maps and arrays |
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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, 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.