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Marco Gaido 8012f55a9b [SPARK-26812][SQL] Report correct nullability for complex datatypes in Union 
## What changes were proposed in this pull request?

When there is a `Union`, the reported output datatypes are the ones of the first plan and the nullability is updated according to all the plans. For complex types, though, the nullability of their elements is not updated using the types from the other plans. This means that the nullability of the inner elements is the one of the first plan. If this is not compatible with the one of other plans, errors can happen (as reported in the JIRA).

The PR proposes to update the nullability of the inner elements of complex datatypes according to most permissive value of all the plans.

## How was this patch tested?

added UT

Closes #23726 from mgaido91/SPARK-26812.

Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2019-04-01 22:22:10 +08:00
..
catalyst [SPARK-26812][SQL] Report correct nullability for complex datatypes in Union 2019-04-01 22:22:10 +08:00
core [SPARK-26812][SQL] Report correct nullability for complex datatypes in Union 2019-04-01 22:22:10 +08:00
hive [SPARK-27326][SQL] Fall back all v2 file sources in InsertIntoTable to V1 FileFormat 2019-03-30 14:38:26 -07:00
hive-thriftserver [SPARK-26914][SQL] Fix scheduler pool may be unpredictable when we only want to use default pool and do not set spark.scheduler.pool for the session 2019-03-28 09:24:16 -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.