ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/sql
History
Bogdan Raducanu cd6dff78be [SPARK-25209][SQL] Avoid deserializer check in Dataset.apply when Dataset is actually DataFrame 
## What changes were proposed in this pull request?
Dataset.apply calls dataset.deserializer (to provide an early error) which ends up calling the full Analyzer on the deserializer. This can take tens of milliseconds, depending on how big the plan is.
Since Dataset.apply is called for many Dataset operations such as Dataset.where it can be a significant overhead for short queries.
According to a comment in the PR that introduced this check, we can at least remove this check for DataFrames: https://github.com/apache/spark/pull/20402#discussion_r164338267

## How was this patch tested?
Existing tests + manual benchmark

Author: Bogdan Raducanu <bogdan@databricks.com>

Closes #22201 from bogdanrdc/deserializer-fix.
2018-08-24 04:13:07 +02:00
..
catalyst [SPARK-25208][SQL] Loosen Cast.forceNullable for DecimalType. 2018-08-23 22:48:26 +08:00
core [SPARK-25209][SQL] Avoid deserializer check in Dataset.apply when Dataset is actually DataFrame 2018-08-24 04:13:07 +02:00
hive [SPARK-23425][SQL] Support wildcard in HDFS path for load table command 2018-08-24 09:54:30 +08:00
hive-thriftserver [SPARK-25043] print master and appId from spark-sql on startup 2018-08-14 13:15:55 -05:00
create-docs.sh [MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script 2017-08-26 13:56:24 +09:00
gen-sql-markdown.py [SPARK-21485][FOLLOWUP][SQL][DOCS] Describes examples and arguments separately, and note/since in SQL built-in function documentation 2017-08-05 10:10:56 -07:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [MINOR][DOC] Fix some typos and grammar issues 2018-04-06 13:37:08 +08:00

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 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.

Running sql/create-docs.sh generates SQL documentation for built-in functions under sql/site.