This chapter introduces Spark SQL, Spark’s interface for working with structured and semistructured data. Structured data is any data that has a schema—that is, a known set of fields for each record. When you have this type of data, Spark SQL makes it both easier and more efficient to load and query. In particular, Spark SQL provides three main capabilities (illustrated in Figure 9-1):
It can load data from a variety of structured sources (e.g., JSON, Hive, and Parquet).
It lets you query the data using SQL, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC), such as business intelligence tools like Tableau.
When used within a Spark program, Spark SQL provides rich integration between SQL and regular Python/Java/Scala code, including the ability to join RDDs and SQL tables, expose custom functions in SQL, and more. Many jobs are easier to write using this combination.
To implement these capabilities, Spark SQL provides a special type of RDD called SchemaRDD. A SchemaRDD is an RDD of
Row objects, each representing a record. A SchemaRDD also knows the schema (i.e., data fields) of its rows. While SchemaRDDs look like regular RDDs, internally they store data in a more efficient manner, taking advantage of their schema. In addition, they provide new operations not available on RDDs, such as the ability to run SQL queries. SchemaRDDs can be created from external data sources, from ...