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hyukjinkwon b964a172a8 [SPARK-17212][SQL] TypeCoercion supports widening conversion between DateType and TimestampType 
## What changes were proposed in this pull request?

Currently, type-widening does not work between `TimestampType` and `DateType`.

This applies to `SetOperation`, `Union`, `In`, `CaseWhen`, `Greatest`,  `Leatest`, `CreateArray`, `CreateMap`, `Coalesce`, `NullIf`, `IfNull`, `Nvl` and `Nvl2`, .

This PR adds the support for widening `DateType` to `TimestampType` for them.

For a simple example,

**Before**

```scala
Seq(Tuple2(new Timestamp(0), new Date(0))).toDF("a", "b").selectExpr("greatest(a, b)").show()
```

shows below:

```
cannot resolve 'greatest(`a`, `b`)' due to data type mismatch: The expressions should all have the same type, got GREATEST(timestamp, date)
```

or union as below:

```scala
val a = Seq(Tuple1(new Timestamp(0))).toDF()
val b = Seq(Tuple1(new Date(0))).toDF()
a.union(b).show()
```

shows below:

```
Union can only be performed on tables with the compatible column types. DateType <> TimestampType at the first column of the second table;
```

**After**

```scala
Seq(Tuple2(new Timestamp(0), new Date(0))).toDF("a", "b").selectExpr("greatest(a, b)").show()
```

shows below:

```
+----------------------------------------------------+
|greatest(CAST(a AS TIMESTAMP), CAST(b AS TIMESTAMP))|
+----------------------------------------------------+
|                                1969-12-31 16:00:...|
+----------------------------------------------------+
```

or union as below:

```scala
val a = Seq(Tuple1(new Timestamp(0))).toDF()
val b = Seq(Tuple1(new Date(0))).toDF()
a.union(b).show()
```

shows below:

```
+--------------------+
|                  _1|
+--------------------+
|1969-12-31 16:00:...|
|1969-12-31 00:00:...|
+--------------------+
```

## How was this patch tested?

Unit tests in `TypeCoercionSuite`.

Author: hyukjinkwon <gurwls223@gmail.com>
Author: HyukjinKwon <gurwls223@gmail.com>

Closes #14786 from HyukjinKwon/SPARK-17212.
2016-08-26 08:58:43 +08:00
..
catalyst [SPARK-17212][SQL] TypeCoercion supports widening conversion between DateType and TimestampType 2016-08-26 08:58:43 +08:00
core [SPARK-17187][SQL] Supports using arbitrary Java object as internal aggregation buffer object 2016-08-25 16:36:16 -07:00
hive [SPARK-17205] Literal.sql should handle Infinity and NaN 2016-08-26 00:15:01 +02:00
hive-thriftserver [SPARK-17190][SQL] Removal of HiveSharedState 2016-08-25 12:50:03 +08:00
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.