c5ed510135
I've added a static getOrCreate method to the static SparkContext object that allows one to either retrieve a previously created SparkContext or to instantiate a new one with the provided config. The method accepts an optional SparkConf to make usage intuitive. Still working on a test for this, basically want to create a new context from scratch, then ensure that subsequent calls don't overwrite that. Author: Ilya Ganelin <ilya.ganelin@capitalone.com> Closes #5501 from ilganeli/SPARK-6703 and squashes the following commits: db9a963 [Ilya Ganelin] Closing second spark context 1dc0444 [Ilya Ganelin] Added ref equality check 8c884fa [Ilya Ganelin] Made getOrCreate synchronized cb0c6b7 [Ilya Ganelin] Doc updates and code cleanup 270cfe3 [Ilya Ganelin] [SPARK-6703] Documentation fixes 15e8dea [Ilya Ganelin] Updated comments and added MiMa Exclude 0e1567c [Ilya Ganelin] Got rid of unecessary option for AtomicReference dfec4da [Ilya Ganelin] Changed activeContext to AtomicReference 733ec9f [Ilya Ganelin] Fixed some bugs in test code 8be2f83 [Ilya Ganelin] Replaced match with if e92caf7 [Ilya Ganelin] [SPARK-6703] Added test to ensure that getOrCreate both allows creation, retrieval, and a second context if desired a99032f [Ilya Ganelin] Spacing fix d7a06b8 [Ilya Ganelin] Updated SparkConf class to add getOrCreate method. Started test suite implementation |
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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".
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.