7aacb7bfad
If Spark lunched multiple executors in one host for one application, every executor would download it dependent files and jars (if not using local: url) independently. It maybe result in huge latency. In my case, it result in 20 seconds latency to download dependent jars(size about 17M) when I lunched 32 executors in every host(total 4 hosts). This patch will cache downloaded files and jars for executors to reduce network throughput and download latency. In my case, the latency was reduced from 20 seconds to less than 1 second. Author: Li Zhihui <zhihui.li@intel.com> Author: li-zhihui <zhihui.li@intel.com> Closes #1616 from li-zhihui/cachefiles and squashes the following commits: 36940df [Li Zhihui] Close cache for local mode 935fed6 [Li Zhihui] Clean code. f9330d4 [Li Zhihui] Clean code again 7050d46 [Li Zhihui] Clean code 074a422 [Li Zhihui] Fix: deal with spark.files.overwrite 03ed3a8 [li-zhihui] rename cache file name as XXXXXXXXX_cache 2766055 [li-zhihui] Use url.hashCode + timestamp as cachedFileName 76a7b66 [Li Zhihui] Clean code & use applcation work directory as cache directory 3510eb0 [Li Zhihui] Keep fetchFile private 2ffd742 [Li Zhihui] add comment for FileLock e0ebd48 [Li Zhihui] Try and finally lock.release 7fb7c0b [Li Zhihui] Release lock before copy files 6b997bf [Li Zhihui] Executors of same application in same host should only download files & jars once |
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| data/mllib | ||
| dev | ||
| docker | ||
| docs | ||
| ec2 | ||
| examples | ||
| external | ||
| extras | ||
| graphx | ||
| mllib | ||
| project | ||
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| repl | ||
| sbin | ||
| sbt | ||
| sql | ||
| streaming | ||
| tools | ||
| yarn | ||
| .gitignore | ||
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| CONTRIBUTING.md | ||
| LICENSE | ||
| make-distribution.sh | ||
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| pom.xml | ||
| README.md | ||
| scalastyle-config.xml | ||
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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. 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.