Using Custom Libraries with Spark

Spark comes equipped with a selection of libraries, including Spark SQL, Spark Streaming, and MLlib.

If you want to use a custom library, such as a compression library or Magellan, you can use one of the following two spark-submit script options:

The --jars option, which transfers associated .jar files to the cluster. Specify a list of comma-separated .jar files.
The --packages option, which pulls files directly from Spark packages. This approach requires an internet connection.

For example, you can use the --jars option to add codec files. The following example adds the LZO compression library:

spark-submit --driver-memory 1G \
    --executor-memory 1G \
    --master yarn-client \
    --jars /usr/hdp/2.6.0.3-8/hadoop/lib/hadoop-lzo-0.6.0.2.6.0.3-8.jar \
    test_read_write.py

For more information about the two options, see Advanced Dependency Management on the Apache Spark "Submitting Applications" web page.

Note

	Note
If you launch a Spark job that references a codec library without specifying where the codec resides, Spark returns an error similar to the following: Caused by: java.lang.IllegalArgumentException: Compression codec com.hadoop.compression.lzo.LzoCodec not found. To address this issue, specify the codec file with the `--jars` option in your job submit command.

If you launch a Spark job that references a codec library without specifying where the codec resides, Spark returns an error similar to the following:

Caused by: java.lang.IllegalArgumentException: Compression codec com.hadoop.compression.lzo.LzoCodec not found.

To address this issue, specify the codec file with the --jars option in your job submit command.

​Using Custom Libraries with Spark

Using Custom Libraries with Spark