fb20084313
This patch addresses a minor scheduler performance issue that was introduced in #13603. If you run ``` sc.parallelize(1 to 100000, 100000).map(identity).count() ``` then most of the time ends up being spent in `TaskSetManager.abortIfCompletelyBlacklisted()`:  When processing resource offers, the scheduler uses a nested loop which considers every task set at multiple locality levels: ```scala for (taskSet <- sortedTaskSets; maxLocality <- taskSet.myLocalityLevels) { do { launchedTask = resourceOfferSingleTaskSet( taskSet, maxLocality, shuffledOffers, availableCpus, tasks) } while (launchedTask) } ``` In order to prevent jobs with globally blacklisted tasks from hanging, #13603 added a `taskSet.abortIfCompletelyBlacklisted` call inside of `resourceOfferSingleTaskSet`; if a call to `resourceOfferSingleTaskSet` fails to schedule any tasks, then `abortIfCompletelyBlacklisted` checks whether the tasks are completely blacklisted in order to figure out whether they will ever be schedulable. The problem with this placement of the call is that the last call to `resourceOfferSingleTaskSet` in the `while` loop will return `false`, implying that `resourceOfferSingleTaskSet` will call `abortIfCompletelyBlacklisted`, so almost every call to `resourceOffers` will trigger the `abortIfCompletelyBlacklisted` check for every task set. Instead, I think that this call should be moved out of the innermost loop and should be called _at most_ once per task set in case none of the task set's tasks can be scheduled at any locality level. Before this patch's changes, the microbenchmark example that I posted above took 35 seconds to run, but it now only takes 15 seconds after this change. /cc squito and kayousterhout for review. Author: Josh Rosen <joshrosen@databricks.com> Closes #14871 from JoshRosen/bail-early-if-no-cpus. |
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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.)
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 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.