e2f32ee45a
## What changes were proposed in this pull request? `WindowExec` currently improperly stores complex objects (UnsafeRow, UnsafeArrayData, UnsafeMapData, UTF8String) during aggregation by keeping a reference in the buffer used by `GeneratedMutableProjections` to the actual input data. Things go wrong when the input object (or the backing bytes) are reused for other things. This could happen in window functions when it starts spilling to disk. When reading the back the spill files the `UnsafeSorterSpillReader` reuses the buffer to which the `UnsafeRow` points, leading to weird corruption scenario's. Note that this only happens for aggregate functions that preserve (parts of) their input, for example `FIRST`, `LAST`, `MIN` & `MAX`. This was not seen before, because the spilling logic was not doing actual spills as much and actually used an in-memory page. This page was not cleaned up during window processing and made sure unsafe objects point to their own dedicated memory location. This was changed by https://github.com/apache/spark/pull/16909, after this PR Spark spills more eagerly. This PR provides a surgical fix because we are close to releasing Spark 2.2. This change just makes sure that there cannot be any object reuse at the expensive of a little bit of performance. We will follow-up with a more subtle solution at a later point. ## How was this patch tested? Added a regression test to `DataFrameWindowFunctionsSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #18470 from hvanhovell/SPARK-21258. |
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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, 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.
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:
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". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
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. 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.
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.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.