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Burak Yavuz 7cbe216449 [SPARK-17569] Make StructuredStreaming FileStreamSource batch generation faster 
## What changes were proposed in this pull request?

While getting the batch for a `FileStreamSource` in StructuredStreaming, we know which files we must take specifically. We already have verified that they exist, and have committed them to a metadata log. When creating the FileSourceRelation however for an incremental execution, the code checks the existence of every single file once again!

When you have 100,000s of files in a folder, creating the first batch takes 2 hours+ when working with S3! This PR disables that check

## How was this patch tested?

Added a unit test to `FileStreamSource`.

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #15122 from brkyvz/SPARK-17569.
2016-09-21 17:12:52 -07:00
..
catalyst [SPARK-17590][SQL] Analyze CTE definitions at once and allow CTE subquery to define CTE 2016-09-21 06:53:42 -07:00
core [SPARK-17569] Make StructuredStreaming FileStreamSource batch generation faster 2016-09-21 17:12:52 -07:00
hive [SPARK-17051][SQL] we should use hadoopConf in InsertIntoHiveTable 2016-09-20 09:53:28 -07:00
hive-thriftserver [SPARK-17190][SQL] Removal of HiveSharedState 2016-08-25 12:50:03 +08:00
README.md [SPARK-16557][SQL] Remove stale doc in sql/README.md 2016-07-14 19:24:42 -07:00

README.md

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.