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Herman van Hovell 879ba71110 [SPARK-18622][SQL] Fix the datatype of the Sum aggregate function 
## What changes were proposed in this pull request?
The result of a `sum` aggregate function is typically a Decimal, Double or a Long. Currently the output dataType is based on input's dataType.

The `FunctionArgumentConversion` rule will make sure that the input is promoted to the largest type, and that also ensures that the output uses a (hopefully) sufficiently large output dataType. The issue is that sum is in a resolved state when we cast the input type, this means that rules assuming that the dataType of the expression does not change anymore could have been applied in the mean time. This is what happens if we apply `WidenSetOperationTypes` before applying the casts, and this breaks analysis.

The most straight forward and future proof solution is to make `sum` always output the widest dataType in its class (Long for IntegralTypes, Decimal for DecimalTypes & Double for FloatType and DoubleType). This PR implements that solution.

We should move expression specific type casting rules into the given Expression at some point.

## How was this patch tested?
Added (regression) tests to SQLQueryTestSuite's `union.sql`.

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #16063 from hvanhovell/SPARK-18622.
2016-11-30 15:25:33 +08:00
..
catalyst [SPARK-18622][SQL] Fix the datatype of the Sum aggregate function 2016-11-30 15:25:33 +08:00
core [SPARK-18622][SQL] Fix the datatype of the Sum aggregate function 2016-11-30 15:25:33 +08:00
hive [SPARK-17680][SQL][TEST] Added a Testcase for Verifying Unicode Character Support for Column Names and Comments 2016-11-30 15:17:29 +08:00
hive-thriftserver [SPARK-14914][CORE] Fix Resource not closed after using, for unit tests and example 2016-11-10 10:54:36 +00:00
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.