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Reynold Xin 6f20a92ca3 [SPARK-17845] [SQL] More self-evident window function frame boundary API 
## What changes were proposed in this pull request?
This patch improves the window function frame boundary API to make it more obvious to read and to use. The two high level changes are:

1. Create Window.currentRow, Window.unboundedPreceding, Window.unboundedFollowing to indicate the special values in frame boundaries. These methods map to the special integral values so we are not breaking backward compatibility here. This change makes the frame boundaries more self-evident (instead of Long.MinValue, it becomes Window.unboundedPreceding).

2. In Python, for any value less than or equal to JVM's Long.MinValue, treat it as Window.unboundedPreceding. For any value larger than or equal to JVM's Long.MaxValue, treat it as Window.unboundedFollowing. Before this change, if the user specifies any value that is less than Long.MinValue but not -sys.maxsize (e.g. -sys.maxsize + 1), the number we pass over to the JVM would overflow, resulting in a frame that does not make sense.

Code example required to specify a frame before this patch:
```
Window.rowsBetween(-Long.MinValue, 0)
```

While the above code should still work, the new way is more obvious to read:
```
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
```

## How was this patch tested?
- Updated DataFrameWindowSuite (for Scala/Java)
- Updated test_window_functions_cumulative_sum (for Python)
- Renamed DataFrameWindowSuite DataFrameWindowFunctionsSuite to better reflect its purpose

Author: Reynold Xin <rxin@databricks.com>

Closes #15438 from rxin/SPARK-17845.
2016-10-12 16:45:10 -07:00
..
catalyst [SPARK-17884][SQL] To resolve Null pointer exception when casting from empty string to interval type. 2016-10-12 10:14:45 -07:00
core [SPARK-17845] [SQL] More self-evident window function frame boundary API 2016-10-12 16:45:10 -07:00
hive [SPARK-17720][SQL] introduce static SQL conf 2016-10-11 20:27:08 -07:00
hive-thriftserver [SPARK-17707][WEBUI] Web UI prevents spark-submit application to be finished 2016-10-07 10:31:41 -07:00
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.