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[SPARK-12440][CORE] Avoid setCheckpoint warning when directory is not local

pierre-borckmans authored 9 years ago

In SparkContext method `setCheckpointDir`, a warning is issued when spark master is not local and the passed directory for the checkpoint dir appears to be local.

In practice, when relying on HDFS configuration file and using a relative path for the checkpoint directory (using an incomplete URI without HDFS scheme, ...), this warning should not be issued and might be confusing.
In fact, in this case, the checkpoint directory is successfully created, and the checkpointing mechanism works as expected.

This PR uses the `FileSystem` instance created with the given directory, and checks whether it is local or not.
(The rationale is that since this same `FileSystem` instance is used to create the checkpoint dir anyway and can therefore be reliably used to determine if it is local or not).

The warning is only issued if the directory is not local, on top of the existing conditions.

Author: pierre-borckmans <pierre.borckmans@realimpactanalytics.com>

Closes #10392 from pierre-borckmans/SPARK-12440_CheckpointDir_Warning_NonLocal.

ea4aab7e

ea4aab7e 9 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, 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.

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:

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.