Revision 14fda6f313c63c9d5a86595c12acfb1e36df43ad authored by jerryshao on 01 June 2017, 05:34:53 UTC, committed by Wenchen Fan on 01 June 2017, 05:35:02 UTC
## What changes were proposed in this pull request? Hadoop FileSystem's statistics in based on thread local variables, this is ok if the RDD computation chain is running in the same thread. But if child RDD creates another thread to consume the iterator got from Hadoop RDDs, the bytesRead computation will be error, because now the iterator's `next()` and `close()` may run in different threads. This could be happened when using PySpark with PythonRDD. So here building a map to track the `bytesRead` for different thread and add them together. This method will be used in three RDDs, `HadoopRDD`, `NewHadoopRDD` and `FileScanRDD`. I assume `FileScanRDD` cannot be called directly, so I only fixed `HadoopRDD` and `NewHadoopRDD`. ## How was this patch tested? Unit test and local cluster verification. Author: jerryshao <sshao@hortonworks.com> Closes #17617 from jerryshao/SPARK-20244. (cherry picked from commit 5854f77ce1d3b9491e2a6bd1f352459da294e369) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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README.md
# Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides
high-level APIs in Scala, Java, Python, and R, 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 DataFrames,
MLlib for machine learning, GraphX for graph processing,
and Spark Streaming for stream processing.
<http://spark.apache.org/>
## Online Documentation
You can find the latest Spark documentation, including a programming
guide, on the [project web page](http://spark.apache.org/documentation.html).
This README file only contains basic setup instructions.
## Building Spark
Spark is built using [Apache Maven](http://maven.apache.org/).
To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see ["Parallel builds in Maven 3"](https://cwiki.apache.org/confluence/display/MAVEN/Parallel+builds+in+Maven+3).
More detailed documentation is available from the project site, at
["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html).
For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](http://spark.apache.org/developer-tools.html).
## 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" 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](#building-spark). Once Spark is built, tests
can be run using:
./dev/run-tests
Please see the guidance on how to
[run tests for a module, or individual tests](http://spark.apache.org/developer-tools.html#individual-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"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version)
for detailed guidance on building for a particular distribution of Hadoop, including
building for particular Hive and Hive Thriftserver distributions.
## Configuration
Please refer to the [Configuration Guide](http://spark.apache.org/docs/latest/configuration.html)
in the online documentation for an overview on how to configure Spark.
## Contributing
Please review the [Contribution to Spark guide](http://spark.apache.org/contributing.html)
for information on how to get started contributing to the project.
![swh spinner](/static/img/swh-spinner.gif)
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