5030923ea8
**Problem.** In unified memory management, acquiring execution memory may lead to eviction of storage memory. However, the space freed from evicting cached blocks is distributed among all active tasks. Thus, an incorrect upper bound on the execution memory per task can cause the acquisition to fail, leading to OOM's and premature spills. **Example.** Suppose total memory is 1000B, cached blocks occupy 900B, `spark.memory.storageFraction` is 0.4, and there are two active tasks. In this case, the cap on task execution memory is 100B / 2 = 50B. If task A tries to acquire 200B, it will evict 100B of storage but can only acquire 50B because of the incorrect cap. For another example, see this [regression test](https://github.com/andrewor14/spark/blob/fix-oom/core/src/test/scala/org/apache/spark/memory/UnifiedMemoryManagerSuite.scala#L233) that I stole from JoshRosen. **Solution.** Fix the cap on task execution memory. It should take into account the space that could have been freed by storage in addition to the current amount of memory available to execution. In the example above, the correct cap should have been 600B / 2 = 300B. This patch also guards against the race condition (SPARK-12253): (1) Existing tasks collectively occupy all execution memory (2) New task comes in and blocks while existing tasks spill (3) After tasks finish spilling, another task jumps in and puts in a large block, stealing the freed memory (4) New task still cannot acquire memory and goes back to sleep Author: Andrew Or <andrew@databricks.com> Closes #10240 from andrewor14/fix-oom. |
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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 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:
build/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". For developing Spark using an IDE, see Eclipse and IntelliJ.
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.