Using Spark SQL to query data

Spark SQL allows you to execute Spark queries using a variation of the SQL language. Spark SQL includes APIs for returning Spark Datasets in Scala and Java, and interactively using a SQL shell.

Spark SQL basics

In DSE, Spark SQL allows you to perform relational queries over data stored in DSE clusters, and executed using Spark. Spark SQL is a unified relational query language for traversing over distributed collections of data, and supports a variation of the SQL language used in relational databases. Spark SQL is intended as a replacement for Shark and Hive, including the ability to run SQL queries over Spark data sets. You can use traditional Spark applications in conjunction with Spark SQL queries to analyze large data sets.

The SparkSession class and its subclasses are the entry point for running relational queries in Spark.

DataFrames are Spark Datasets organized into named columns, and are similar to tables in a traditional relational database. You can create DataFrame instances from any Spark data source, like CSV files, Spark RDDs, or, for DSE, tables in the database. In DSE, when you access a Spark SQL table from the data in DSE transactional cluster, it registers that table to the Hive metastore so SQL queries can be run against it.

Any tables you create or destroy, and any table data you delete, in a Spark SQL session will not be reflected in the underlying DSE database, but only in that session’s metastore.

Starting the Spark SQL shell

The Spark SQL shell allows you to interactively perform Spark SQL queries. To start the shell, run dse spark-sql:

dse spark-sql

The Spark SQL shell in DSE automatically creates a Spark session and connects to the Spark SQL Thrift server to handle the underlying JDBC connections.

If the schema changes in the underlying database table during a Spark SQL session (for example, a column was added using CQL), drop the table and then refresh the metastore to continue querying the table with the correct schema.

DROP TABLE <tablename>;

Queries to a table whose schema has been modified cause a runtime exception.

Spark SQL limitations

  • You cannot load data from one file system to a table in a different file system.

    LOAD DATA INPATH 'hdfs2://localhost/colors.txt' OVERWRITE INTO TABLE test PARTITION (ds ='2008-08-15');

    The first line creates a table on the default file system. The second line attempts to load data into that table from a path on a different file system, and will fail.

Querying database data using Spark SQL in Scala

You can execute Spark SQL queries in Scala by starting the Spark shell. When you start Spark, DataStax Enterprise creates a Spark session instance to allow you to run Spark SQL queries against database tables.

Querying database data using Spark SQL in Java

You can execute Spark SQL queries in Java applications that traverse over tables. Java applications that query table data using Spark SQL require a Spark session instance.

Querying DSE Graph vertices and edges with Spark SQL

Spark SQL can query DSE Graph vertex and edge tables.

Using Spark predicate push down in Spark SQL queries

Spark predicate push down to database allows for better optimized Spark SQL queries.

Supported syntax of Spark SQL

Spark SQL supports a subset of the SQL-92 language.

Inserting data into tables with static columns using Spark SQL

Static columns are mapped to different columns in Spark SQL and require special handling.

Running HiveQL queries using Spark SQL

Spark SQL supports queries that are written using HiveQL, a SQL-like language that produces queries that are converted to Spark jobs.

Using the DataFrames API

The Spark DataFrame API encapsulates data sources, including DataStax Enterprise data, organized into named columns.

Using the Spark SQL Thriftserver

The Spark SQL Thriftserver uses a JDBC and an ODBC interface for client connections to DSE.

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