948c2390ab
Demote some 'noisy' log messages to debug level. I added a few more, to include everything that gets logged in stanzas like this: ``` 15/03/01 00:03:54 INFO BlockManager: Removing broadcast 0 15/03/01 00:03:54 INFO BlockManager: Removing block broadcast_0_piece0 15/03/01 00:03:54 INFO MemoryStore: Block broadcast_0_piece0 of size 839 dropped from memory (free 277976091) 15/03/01 00:03:54 INFO BlockManagerInfo: Removed broadcast_0_piece0 on localhost:49524 in memory (size: 839.0 B, free: 265.1 MB) 15/03/01 00:03:54 INFO BlockManagerMaster: Updated info of block broadcast_0_piece0 15/03/01 00:03:54 INFO BlockManager: Removing block broadcast_0 15/03/01 00:03:54 INFO MemoryStore: Block broadcast_0 of size 1088 dropped from memory (free 277977179) 15/03/01 00:03:54 INFO ContextCleaner: Cleaned broadcast 0 ``` as well as regular messages like ``` 15/03/01 00:02:33 INFO MemoryStore: ensureFreeSpace(2640) called with curMem=47322, maxMem=278019440 ``` WDYT? good or should some be left alone? CC mengxr who suggested some of this. Author: Sean Owen <sowen@cloudera.com> Closes #4838 from srowen/SPARK-3357 and squashes the following commits: dce75c1 [Sean Owen] Back out some debug level changes d9b784d [Sean Owen] Demote some 'noisy' log messages to debug level |
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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.