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Documentation

PySpark with Data Frames

With the inclusion of the Cassandra Data Source, PySpark can now be used with the Connector to access Cassandra data. This does not require DataStax Enterprise but you are limited to DataFrame only operations.

Setup

To enable Cassandra access the Spark Cassandra Connector assembly jar must be included on both the driver and executor classpath for the PySpark Java Gateway. This can be done by starting the PySpark shell similarlly to how the spark shell is started. The preferred method is now to use the Spark Packages website. http://spark-packages.org/package/datastax/spark-cassandra-connector

./bin/pyspark \
  --packages com.datastax.spark:spark-cassandra-connector_2.10:1.4.0

Loading a DataFrame in Python

A DataFrame can be created which links to cassandra by using the the org.apache.spark.sql.cassandra source and by specifying keyword arguements for keyspace and table.

 sqlContext.read\
    .format("org.apache.spark.sql.cassandra")\
    .options(table="kv", keyspace="test")\
    .load().show()
+-+-+
|k|v|
+-+-+
|5|5|
|1|1|
|2|2|
|4|4|
|3|3|
+-+-+

Saving a DataFrame in Python to Cassandra

A DataFrame can be saved to an existing Cassandra table by using the the org.apache.spark.sql.cassandra source and by specifying keyword arguements for keyspace and table and saving mode (append, overwrite, error or ignore, see Data Sources API doc).

 sqlContext.write\
    .format("org.apache.spark.sql.cassandra")\
    .mode('append')\
    .options(table="kv", keyspace="test")\
    .save()

The options and parameters are identical to the Scala Data Frames Api so please see Data Frames for more information.

Next - Spark Partitioners