852f4de2d3
This is a somewhat obscure bug, but I think that it will seriously impact KryoSerializer users who use custom registrators which disabled auto-reset. When auto-reset is disabled, then this breaks things in some of our shuffle paths which actually end up creating multiple OutputStreams from the same shared SerializerInstance (which is unsafe). This was introduced by a patch (SPARK-3386) which enables serializer re-use in some of the shuffle paths, since constructing new serializer instances is actually pretty costly for KryoSerializer. We had already fixed another corner-case (SPARK-7766) bug related to this, but missed this one. I think that the root problem here is that KryoSerializerInstance can be used in a way which is unsafe even within a single thread, e.g. by creating multiple open OutputStreams from the same instance or by interleaving deserialize and deserializeStream calls. I considered a smaller patch which adds assertions to guard against this type of "misuse" but abandoned that approach after I realized how convoluted the Scaladoc became. This patch fixes this bug by making it legal to create multiple streams from the same KryoSerializerInstance. Internally, KryoSerializerInstance now implements a `borrowKryo()` / `releaseKryo()` API that's backed by a "pool" of capacity 1. Each call to a KryoSerializerInstance method will borrow the Kryo, do its work, then release the serializer instance back to the pool. If the pool is empty and we need an instance, it will allocate a new Kryo on-demand. This makes it safe for multiple OutputStreams to be opened from the same serializer. If we try to release a Kryo back to the pool but the pool already contains a Kryo, then we'll just discard the new Kryo. I don't think there's a clear benefit to having a larger pool since our usages tend to fall into two cases, a) where we only create a single OutputStream and b) where we create a huge number of OutputStreams with the same lifecycle, then destroy the KryoSerializerInstance (this is what's happening in the bypassMergeSort code path that my regression test hits). Author: Josh Rosen <joshrosen@databricks.com> Closes #6415 from JoshRosen/SPARK-7873 and squashes the following commits: 00b402e [Josh Rosen] Initialize eagerly to fix a failing test ba55d20 [Josh Rosen] Add explanatory comments 3f1da96 [Josh Rosen] Guard against duplicate close() ab457ca [Josh Rosen] Sketch a loan/release based solution. 9816e8f [Josh Rosen] Add a failing test showing how deserialize() and deserializeStream() can interfere. 7350886 [Josh Rosen] Add failing regression test for SPARK-7873 |
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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".
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 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. 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.