69c67abaa9
This patch brings Python API for Streaming.
This patch is based on work from @giwa
Author: giwa <ugw.gi.world@gmail.com>
Author: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Author: Davies Liu <davies.liu@gmail.com>
Author: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Ken <ugw.gi.world@gmail.com>
Author: Ken Takagiwa <ugw.gi.world@gmail.com>
Author: Matthew Farrellee <matt@redhat.com>
Closes #2538 from davies/streaming and squashes the following commits:
64561e4 [Davies Liu] fix tests
331ecce [Davies Liu] fix example
3e2492b [Davies Liu] change updateStateByKey() to easy API
182be73 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
02d0575 [Davies Liu] add wrapper for foreachRDD()
bebeb4a [Davies Liu] address all comments
6db00da [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
8380064 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
52c535b [Davies Liu] remove fix for sum()
e108ec1 [Davies Liu] address comments
37fe06f [Davies Liu] use random port for callback server
d05871e [Davies Liu] remove reuse of PythonRDD
be5e5ff [Davies Liu] merge branch of env, make tests stable.
8071541 [Davies Liu] Merge branch 'env' into streaming
c7bbbce [Davies Liu] fix sphinx docs
6bb9d91 [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
4d0ea8b [Davies Liu] clear reference of SparkEnv after stop
54bd92b [Davies Liu] improve tests
c2b31cb [Davies Liu] Merge branch 'master' of github.com:apache/spark into streaming
7a88f9f [Davies Liu] rollback RDD.setContext(), use textFileStream() to test checkpointing
bd8a4c2 [Davies Liu] fix scala style
7797c70 [Davies Liu] refactor
ff88bec [Davies Liu] rename RDDFunction to TransformFunction
d328aca [Davies Liu] fix serializer in queueStream
6f0da2f [Davies Liu] recover from checkpoint
fa7261b [Davies Liu] refactor
a13ff34 [Davies Liu] address comments
8466916 [Davies Liu] support checkpoint
9a16bd1 [Davies Liu] change number of partitions during tests
b98d63f [Davies Liu] change private[spark] to private[python]
eed6e2a [Davies Liu] rollback not needed changes
e00136b [Davies Liu] address comments
069a94c [Davies Liu] fix the number of partitions during window()
338580a [Davies Liu] change _first(), _take(), _collect() as private API
19797f9 [Davies Liu] clean up
6ebceca [Davies Liu] add more tests
c40c52d [Davies Liu] change first(), take(n) to has the same behavior as RDD
98ac6c2 [Davies Liu] support ssc.transform()
b983f0f [Davies Liu] address comments
847f9b9 [Davies Liu] add more docs, add first(), take()
e059ca2 [Davies Liu] move check of window into Python
fce0ef5 [Davies Liu] rafactor of foreachRDD()
7001b51 [Davies Liu] refactor of queueStream()
26ea396 [Davies Liu] refactor
74df565 [Davies Liu] fix print and docs
b32774c [Davies Liu] move java_import into streaming
604323f [Davies Liu] enable streaming tests
c499ba0 [Davies Liu] remove Time and Duration
3f0fb4b [Davies Liu] refactor fix tests
c28f520 [Davies Liu] support updateStateByKey
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assembly | ||
bagel | ||
bin | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
mllib | ||
project | ||
python | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
yarn | ||
.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. 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.