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maryannxue b4fd75fb9b [SPARK-24972][SQL] PivotFirst could not handle pivot columns of complex types 
## What changes were proposed in this pull request?

When the pivot column is of a complex type, the eval() result will be an UnsafeRow, while the keys of the HashMap for column value matching is a GenericInternalRow. As a result, there will be no match and the result will always be empty.
So for a pivot column of complex-types, we should:
1) If the complex-type is not comparable (orderable), throw an Exception. It cannot be a pivot column.
2) Otherwise, if it goes through the `PivotFirst` code path, `PivotFirst` should use a TreeMap instead of HashMap for such columns.

This PR has also reverted the walk-around in Analyzer that had been introduced to avoid this `PivotFirst` issue.

## How was this patch tested?

Added UT.

Author: maryannxue <maryannxue@apache.org>

Closes #21926 from maryannxue/pivot_followup.
2018-07-30 23:43:53 -07:00
..
catalyst [SPARK-24972][SQL] PivotFirst could not handle pivot columns of complex types 2018-07-30 23:43:53 -07:00
core [SPARK-24972][SQL] PivotFirst could not handle pivot columns of complex types 2018-07-30 23:43:53 -07:00
hive [SPARK-24957][SQL] Average with decimal followed by aggregation returns wrong result 2018-07-30 20:53:45 +08:00
hive-thriftserver [SPARK-24829][STS] In Spark Thrift Server, CAST AS FLOAT inconsistent with spark-shell or spark-sql 2018-07-27 13:27:17 +08:00
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.