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Wenchen Fan fc5bd6da77 [SPARK-27576][SQL] table capability to skip the output column resolution 
## What changes were proposed in this pull request?

Currently we have an analyzer rule, which resolves the output columns of data source v2 writing plans, to make sure the schema of input query is compatible with the table.

However, not all data sources need this check. For example, the `NoopDataSource` doesn't care about the schema of input query at all.

This PR introduces a new table capability: ACCEPT_ANY_SCHEMA. If a table reports this capability, we skip resolving output columns for it during write.

Note that, we already skip resolving output columns for `NoopDataSource` because it implements `SupportsSaveMode`. However, `SupportsSaveMode` is a hack and will be removed soon.

## How was this patch tested?

new test cases

Closes #24469 from cloud-fan/schema-check.

Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-05-16 16:24:53 -07:00
..
catalyst [SPARK-27576][SQL] table capability to skip the output column resolution 2019-05-16 16:24:53 -07:00
core [SPARK-27576][SQL] table capability to skip the output column resolution 2019-05-16 16:24:53 -07:00
hive [SPARK-24923][SQL] Implement v2 CreateTableAsSelect 2019-05-15 11:24:03 +08:00
hive-thriftserver [SPARK-27354][SQL] Move incompatible code from the hive-thriftserver module to sql/hive-thriftserver/v1.2.1 2019-05-15 14:52:08 -07:00
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-27328][SQL] Add 'deprecated' in ExpressionDescription for extended usage and SQL doc 2019-04-09 13:49:42 +08:00
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.