Spark - SQL Framework

About

The Spark SQL Framework is a library based around an sql in order to create dataset, data frame with bindings in Python, Scala, Java, and R

The Spark SQL Framework

• can execute SQL queries (Hive as syntax)
• returns the result as:
  • a Dataset or DataFrame
  • or a ResultSet via JDBC/ODBC

This library is part of the core distribution since Spark 1.0 (April 2014)

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Architecture

This module provides support for executing relational queries expressed in either :

• SQL
• or the DataFrame/Dataset API.

Spark SQL is broken up into four subprojects:

• Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
• Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
• Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
• HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.