3_selection.md

Documentation

Server-side data selection and filtering

In this section, you'll learn how to reduce the amount of data transferred from Cassandra to Spark to speed up processing.

Selecting a subset of columns

For performance reasons, you should not fetch columns you don't need. You can achieve this with the select method.

sc.cassandraTable("test", "users").select("username").toArray.foreach(println)
// CassandraRow{username: noemail} 
// CassandraRow{username: someone}

The select method can be chained. Every next call can be used to select a subset of columns already selected. Selecting a non-existing column would result in throwing an exception.

Filtering rows

To filter rows, you can use the filter transformation provided by Spark. However, this approach causes all rows to be fetched from Cassandra and then filtered by Spark. Also, some CPU cycles are wasted serializing and deserializing objects that wouldn't be included in the result. To avoid this overhead, CassandraRDD offers the where method, which lets you pass arbitrary CQL condition(s) to filter the row set on the server.

sc.cassandraTable("test", "cars").select("id", "model").where("color = ?", "black").toArray.foreach(println)
// CassandraRow[id: KF-334L, model: Ford Mondeo]
// CassandraRow[id: MT-8787, model: Hyundai x35]

sc.cassandraTable("test", "cars").select("id", "model").where("color = ?", "silver").toArray.foreach(println)
// CassandraRow[id: WX-2234, model: Toyota Yaris]

Note: Although the ALLOW FILTERING clause is implicitly added to the generated CQL query, not all predicates are currently allowed by the Cassandra engine. This limitation is going to be addressed in the future Cassandra releases. Currently, ALLOW FILTERING works well with columns indexed by secondary indexes or clustering columns.

Next - Working with user-defined case classes and tuples