Revision f28eab2de72f7da883b970fc19edd4f569340bd7 authored by Norman Maurer on 22 November 2019, 23:20:54 UTC, committed by Marcelo Vanzin on 22 November 2019, 23:20:54 UTC
### What changes were proposed in this pull request? - Correctly release `ByteBuf` in `TransportCipher` in all cases - Move closing / releasing logic to `handlerRemoved(...)` so we are guaranteed that is always called. - Correctly release `frameBuf` it is not null when the handler is removed (and so also when the channel becomes inactive) ### Why are the changes needed? We need to carefully manage the ownership / lifecycle of `ByteBuf` instances so we don't leak any of these. We did not correctly do this in all cases: - when end up in invalid cipher state. - when partial data was received and the channel is closed before the full frame is decoded Fixes https://github.com/netty/netty/issues/9784. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Pass the newly added UTs. Closes #26609 from normanmaurer/fix_leaks. Authored-by: Norman Maurer <norman_maurer@apple.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
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README.md
# Apache Spark
Spark is a unified analytics engine for large-scale data processing. 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 Structured Streaming for stream processing.
<https://spark.apache.org/>
[![Jenkins Build](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/badge/icon)](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7)
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## Online Documentation
You can find the latest Spark documentation, including a programming
guide, on the [project web page](https://spark.apache.org/documentation.html).
This README file only contains basic setup instructions.
## Building Spark
Spark is built using [Apache Maven](https://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"](https://spark.apache.org/docs/latest/building-spark.html).
For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](https://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 1,000,000,000:
scala> spark.range(1000 * 1000 * 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 1,000,000,000:
>>> spark.range(1000 * 1000 * 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](https://spark.apache.org/developer-tools.html#individual-tests).
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
## 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 and Enabling YARN"](https://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn)
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](https://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](https://spark.apache.org/contributing.html)
for information on how to get started contributing to the project.
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