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Documentation

Connecting to Cassandra

This section describes how Spark connects to Cassandra and how to execute CQL statements from Spark applications.

See the reference section for Cassandra Connection Parameters.

Configuring Catalogs to Cassandra

DatasourceV2 introduced in Spark 3.0 makes connecting to Cassandra now easier than ever. Parameters for configuring your connection can be done in the SparkConf, SparkSession, spark-defaults file or individually for the Catalog. Once a catalog is configured it can be accessed through both SparkSql and DataFrames to read from, write to, create, and drop Cassandra tables.

While setting up a Catalog can be done before starting your application it can also be done at runtime by setting spark.sql.catalog.anyname to com.datastax.spark.connector.datasource.CassandraCatalog In your SparkSession. The "anyname" portion can be any name you would like to specify for this particular catalog.

Then any parameter for that catalog can be set by just appending the parameter name to the catalog name as in spark.sql.catalog.anyname.propertykey to propertyvalue

Example: Manually configuring a Catalog named Cass100

spark.conf.set(s"spark.sql.catalog.cass100", "com.datastax.spark.connector.datasource.CassandraCatalog")
spark.conf.set(s"spark.sql.catalog.cass100.spark.cassandra.connection.host", "127.0.0.100")

Once this is set, we can access the catalog by using the triple part identifier of catalog.keyspace.table which in this case would be cass100.ks.tab

Example of reading from one cluster and writing to another

Using multiple clusters can be done by setting up one catalog per underlying cluster.

//Catalog Cass100 for Cluster at 127.0.0.100
spark.conf.set(s"spark.sql.catalog.cass100", "com.datastax.spark.connector.datasource.CassandraCatalog")
spark.conf.set(s"spark.sql.catalog.cass100.spark.cassandra.connection.host", "127.0.0.100")

//Catalog Cass200 for Cluster at 127.0.0.200
spark.conf.set(s"spark.sql.catalog.cass200", "com.datastax.spark.connector.datasource.CassandraCatalog")
spark.conf.set(s"spark.sql.catalog.cass200.spark.cassandra.connection.host", "127.0.0.200")

spark.sql("INSERT INTO cass200.ks.tab SELECT * from cass100.ks.tab")
//Or
spark.read.table("cass100.ks.tab").writeTo("cass200.ks.tab").append

Connecting using an Astra Cloud Bundle or Driver Profile File (Since SCC 2.5)

Using a separate DSE Java Driver configuration file can also be used for your Catalog as long as

  • The file is either already accessible on a distributed file system (hdfs:// or s3a:// for example).

or

  • The file is distributed by Spark or is already on the Spark Classpath on Driver and Executor Nodes

If your file needs to be distributed by Spark use the --files option in Spark Submit or SparkContext.addFile. For files added in this way just pass the file name to either of the following parameters without any other path info.

Files are then referenced through one of the following parameters

  1. spark.cassandra.connection.config.cloud.path for use with a Cloud Secure Connect bundle from Datastax Astra. Please note that you must provide user name and password as well using corresponding configuration properties;
  2. spark.cassandra.connection.config.profile.path for use with a Java Driver Profile

When using a profile file all other configuration will be ignored. We are working on improving this behavior but at the moment, using a profile supersedes all other config.

Connection management

Whenever you call a method requiring access to Cassandra, the options in the SparkConf object will be used to create a new connection or to borrow one already open from the global connection cache.

Initial Contact

The initial contact node given inspark.cassandra.connection.host can be any node of the cluster. The driver will fetch the cluster topology from the contact node and will always try to connect to the closest node in the same data center. If possible, connections are established to the same node the task is running on. Consequently, good locality of data can be achieved and the amount of data sent across the network is minimized.

Inter-DataCenter Communication is forbidden by default

Connections are never made to data centers other than the data center of spark.cassandra.connection.host. If some nodes in the local data center are down and a read or write operation fails, the operation won't be retried on nodes in a different data center. This technique guarantees proper workload isolation so that a huge analytics job won't disturb the realtime part of the system.

Connection Pooling

Connections are cached internally. If you call two methods needing access to the same Cassandra cluster quickly, one after another, or in parallel from different threads, they will share the same logical connection represented by the underlying Java Driver Cluster object.

This means code like

  val connector = CassandraConnector(sc.getConf)
  connector.withSessionDo(session => ...)
  connector.withSessionDo(session => ...)

or

val connector = CassandraConnector(sc.getConf)
sc.parallelize(1 to 100).mapPartitions( it => connector.withSessionDo( session => ...))

Will not use more than one Cluster object or Session object per JVM

Eventually, when all the tasks needing Cassandra connectivity terminate, the connection to the Cassandra cluster will be closed shortly thereafter. The period of time for keeping unused connections open is controlled by the global spark.cassandra.connection.keep_alive_ms system property, see Cassandra Connection Parameters.

Connecting manually to Cassandra

If you ever need to manually connect to Cassandra in order to issue some CQL statements, this driver offers a handy CassandraConnector class which can be initialized from the SparkConf object and provides access to the Session objects. CassandraConnector instances are serializable and therefore can be safely used in lambdas passed to Spark transformations as seen in the examples above.

Assuming an appropriately configured SparkConf object is stored in the conf variable, the following code creates a keyspace and a table:

import com.datastax.spark.connector.cql.CassandraConnector

CassandraConnector(conf).withSessionDo { session =>
  session.execute("CREATE KEYSPACE test2 WITH REPLICATION = {'class': 'SimpleStrategy', 'replication_factor': 1 }")
  session.execute("CREATE TABLE test2.words (word text PRIMARY KEY, count int)")
}

(Recommended) Accessing data with DataFrames (Legacy) Accessing data with RDDs