b8e6886fb8
Here's a simple fix for SchemaRDD to JSON. Author: Dan McClary <dan.mcclary@gmail.com> Closes #3213 from dwmclary/SPARK-4228 and squashes the following commits: d714e1d [Dan McClary] fixed PEP 8 error cac2879 [Dan McClary] move pyspark comment and doctest to correct location f9471d3 [Dan McClary] added pyspark doc and doctest 6598cee [Dan McClary] adding complex type queries 1a5fd30 [Dan McClary] removing SPARK-4228 from SQLQuerySuite 4a651f0 [Dan McClary] cleaned PEP and Scala style failures. Moved tests to JsonSuite 47ceff6 [Dan McClary] cleaned up scala style issues 2ee1e70 [Dan McClary] moved rowToJSON to JsonRDD 4387dd5 [Dan McClary] Added UserDefinedType, cleaned up case formatting 8f7bfb6 [Dan McClary] Map type added to SchemaRDD.toJSON 1b11980 [Dan McClary] Map and UserDefinedTypes partially done 11d2016 [Dan McClary] formatting and unicode deserialization default fixed 6af72d1 [Dan McClary] deleted extaneous comment 4d11c0c [Dan McClary] JsonFactory rewrite of toJSON for SchemaRDD 149dafd [Dan McClary] wrapped scala toJSON in sql.py 5e5eb1b [Dan McClary] switched to Jackson for JSON processing 6c94a54 [Dan McClary] added toJSON to pyspark SchemaRDD aaeba58 [Dan McClary] added toJSON to pyspark SchemaRDD 1d171aa [Dan McClary] upated missing brace on if statement 319e3ba [Dan McClary] updated to upstream master with merged SPARK-4228 424f130 [Dan McClary] tests pass, ready for pull and PR 626a5b1 [Dan McClary] added toJSON to SchemaRDD f7d166a [Dan McClary] added toJSON method 5d34e37 [Dan McClary] merge resolved d6d19e9 [Dan McClary] pr example |
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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 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.