Spark SQL is Spark's module for working with structured data, either within Spark programs or through standard JDBC and ODBC connectors.

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure …

Jul 30, 2009 · There is a SQL config 'spark.sql.parser.escapedStringLiterals' that can be used to fallback to the Spark 1.6 behavior regarding string literal parsing. For example, if the config is …

Spark SQL is Apache Spark’s module for working with structured data. This guide is a reference for Structured Query Language (SQL) and includes syntax, semantics, keywords, and …

The SQL Syntax section describes the SQL syntax in detail along with usage examples when applicable. This document provides a list of Data Definition and Data Manipulation Statements, …

The sql function on a SparkSession enables applications to run SQL queries programmatically and returns the result as a DataFrame.

pyspark.sql.tvf.TableValuedFunction.posexplode_outer pyspark.sql.tvf.TableValuedFunction.variant_explode …

The below table describes the data type conversions from Spark SQL Data Types to Microsoft SQL Server data types, when creating, altering, or writing data to a Microsoft SQL Server table …

It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX …

All data types of Spark SQL are located in the package of pyspark.sql.types. You can access them by doing from pyspark.sql.types import *