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[SPARK-5063] More helpful error messages for several invalid operations

Josh Rosen authored 10 years ago

This patch adds more helpful error messages for invalid programs that define nested RDDs, broadcast RDDs, perform actions inside of transformations (e.g. calling `count()` from inside of `map()`), and call certain methods on stopped SparkContexts. Currently, these invalid programs lead to confusing NullPointerExceptions at runtime and have been a major source of questions on the mailing list and StackOverflow.

In a few cases, I chose to log warnings instead of throwing exceptions in order to avoid any chance that this patch breaks programs that worked "by accident" in earlier Spark releases (e.g. programs that define nested RDDs but never run any jobs with them).

In SparkContext, the new `assertNotStopped()` method is used to check whether methods are being invoked on a stopped SparkContext. In some cases, user programs will not crash in spite of calling methods on stopped SparkContexts, so I've only added `assertNotStopped()` calls to methods that always throw exceptions when called on stopped contexts (e.g. by dereferencing a null `dagScheduler` pointer).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #3884 from JoshRosen/SPARK-5063 and squashes the following commits:

a38774b [Josh Rosen] Fix spelling typo
a943e00 [Josh Rosen] Convert two exceptions into warnings in order to avoid breaking user programs in some edge-cases.
2d0d7f7 [Josh Rosen] Fix test to reflect 1.2.1 compatibility
3f0ea0c [Josh Rosen] Revert two unintentional formatting changes
8e5da69 [Josh Rosen] Remove assertNotStopped() calls for methods that were sometimes safe to call on stopped SC's in Spark 1.2
8cff41a [Josh Rosen] IllegalStateException fix
6ef68d0 [Josh Rosen] Fix Python line length issues.
9f6a0b8 [Josh Rosen] Add improved error messages to PySpark.
13afd0f [Josh Rosen] SparkException -> IllegalStateException
8d404f3 [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-5063
b39e041 [Josh Rosen] Fix BroadcastSuite test which broadcasted an RDD
99cc09f [Josh Rosen] Guard against calling methods on stopped SparkContexts.
34833e8 [Josh Rosen] Add more descriptive error message.
57cc8a1 [Josh Rosen] Add error message when directly broadcasting RDD.
15b2e6b [Josh Rosen] [SPARK-5063] Useful error messages for nested RDDs and actions inside of transformations

cef1f092

cef1f092 10 years ago

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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.

http://spark.apache.org/

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.