db34690466
There are no break changes (against 1.2) in this PR. I hide the PythonMLLibAPI, which is only called by Py4J, and renamed `SparseMatrix.diag` to `SparseMatrix.spdiag`. All other changes are documentation and annotations. The `Experimental` tag is removed from `ALS.setAlpha` and `Rating`. One issue not addressed in this PR is the `setCheckpointDir` in `LDA` (https://issues.apache.org/jira/browse/SPARK-5604). CC: srowen jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #4377 from mengxr/SPARK-5599 and squashes the following commits: 17975dc [Xiangrui Meng] fix tests 4487f20 [Xiangrui Meng] remove experimental tag from each stat method because Statistics is experimental already 3cd969a [Xiangrui Meng] remove freeman (sorry~) from StreamLA public doc 55900f5 [Xiangrui Meng] make IR experimental and update its doc 9b8eed3 [Xiangrui Meng] graduate Rating and setAlpha in ALS b854d28 [Xiangrui Meng] correct iid doc in RandomRDDs 27f5bdd [Xiangrui Meng] update linalg docs and some new method signatures 371721b [Xiangrui Meng] mark fpg as experimental and update its doc 8aca7ee [Xiangrui Meng] change SLR to experimental and update the doc ebbb2e9 [Xiangrui Meng] mark PIC experimental and update the doc 7830d3b [Xiangrui Meng] mark GMM experimental a378496 [Xiangrui Meng] use the correct subscript syntax in PIC c65c424 [Xiangrui Meng] update LDAModel doc a213b0c [Xiangrui Meng] update GMM constructor 3993054 [Xiangrui Meng] hide algorithm in SLR ad6b9ce [Xiangrui Meng] Revert "make ClassificatinModel.predict(JavaRDD) return JavaDoubleRDD" 0054684 [Xiangrui Meng] add doc to LRModel's constructor a89763b [Xiangrui Meng] make ClassificatinModel.predict(JavaRDD) return JavaDoubleRDD 7c0946c [Xiangrui Meng] hide PythonMLLibAPI |
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bagel | ||
bin | ||
build | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
mllib | ||
network | ||
project | ||
python | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
yarn | ||
.gitattributes | ||
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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 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.