spark-instrumented-optimizer/sql
Wojtek Szymanski f6471dc0d5 [SPARK-19709][SQL] Read empty file with CSV data source
## What changes were proposed in this pull request?

Bugfix for reading empty file with CSV data source. Instead of throwing `NoSuchElementException`, an empty data frame is returned.

## How was this patch tested?

Added new unit test in `org.apache.spark.sql.execution.datasources.csv.CSVSuite`

Author: Wojtek Szymanski <wk.szymanski@gmail.com>

Closes #17068 from wojtek-szymanski/SPARK-19709.
2017-03-06 13:19:36 -08:00
..
catalyst [SPARK-19211][SQL] Explicitly prevent Insert into View or Create View As Insert 2017-03-06 12:35:03 -08:00
core [SPARK-19709][SQL] Read empty file with CSV data source 2017-03-06 13:19:36 -08:00
hive [SPARK-19257][SQL] location for table/partition/database should be java.net.URI 2017-03-06 10:44:26 -08:00
hive-thriftserver [SPARK-19673][SQL] "ThriftServer default app name is changed wrong" 2017-02-25 21:47:02 +00:00
README.md [SPARK-16557][SQL] Remove stale doc in sql/README.md 2016-07-14 19:24:42 -07:00

Spark SQL

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 allows 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.