0eb4a7fb0f
**Summary.** Currently, we may spill many small files in `ExternalAppendOnlyMap` and `ExternalSorter`. The underlying root cause of this is summarized in [SPARK-4452](https://issues.apache.org/jira/browse/SPARK-4452). This PR does not address this root cause, but simply provides the guarantee that we never spill the in-memory data structure if its size is less than a configurable threshold of 5MB. This config is not documented because we don't want users to set it themselves, and it is not hard-coded because we need to change it in tests. **Symptom.** Each spill is orders of magnitude smaller than 1MB, and there are many spills. In environments where the ulimit is set, this frequently causes "too many open file" exceptions observed in [SPARK-3633](https://issues.apache.org/jira/browse/SPARK-3633). ``` 14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4792 B to disk (292769 spills so far) 14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4760 B to disk (292770 spills so far) 14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4520 B to disk (292771 spills so far) 14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4560 B to disk (292772 spills so far) 14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4792 B to disk (292773 spills so far) 14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4784 B to disk (292774 spills so far) ``` **Reproduction.** I ran the following on a small 4-node cluster with 512MB executors. Note that the back-to-back shuffle here is necessary for reasons described in [SPARK-4522](https://issues.apache.org/jira/browse/SPARK-4452). The second shuffle is a `reduceByKey` because it performs a map-side combine. ``` sc.parallelize(1 to 100000000, 100) .map { i => (i, i) } .groupByKey() .reduceByKey(_ ++ _) .count() ``` Before the change, I notice that each thread may spill up to 1000 times, and the size of each spill is on the order of 10KB. After the change, each thread spills only up to 20 times in the worst case, and the size of each spill is on the order of 1MB. Author: Andrew Or <andrew@databricks.com> Closes #3353 from andrewor14/avoid-small-spills and squashes the following commits: 49f380f [Andrew Or] Merge branch 'master' of https://git-wip-us.apache.org/repos/asf/spark into avoid-small-spills 27d6966 [Andrew Or] Merge branch 'master' of github.com:apache/spark into avoid-small-spills f4736e3 [Andrew Or] Fix tests a919776 [Andrew Or] Avoid many small spills |
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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 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:
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.