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hyukjinkwon authored
[SPARK-20639][SQL] Add single argument support for to_timestamp in SQL with documentation improvement

## What changes were proposed in this pull request?

This PR proposes three things as below:

- Use casting rules to a timestamp in `to_timestamp` by default (it was `yyyy-MM-dd HH:mm:ss`).

- Support single argument for `to_timestamp` similarly with APIs in other languages.

  For example, the one below works

  ```
  import org.apache.spark.sql.functions._
  Seq("2016-12-31 00:12:00.00").toDF("a").select(to_timestamp(col("a"))).show()
  ```

  prints

  ```
  +----------------------------------------+
  |to_timestamp(`a`, 'yyyy-MM-dd HH:mm:ss')|
  +----------------------------------------+
  |                     2016-12-31 00:12:00|
  +----------------------------------------+
  ```

  whereas this does not work in SQL.

  **Before**

  ```
  spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
  Error in query: Invalid number of arguments for function to_timestamp; line 1 pos 7
  ```

  **After**

  ```
  spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
  2016-12-31 00:12:00
  ```

- Related document improvement for SQL function descriptions and other API descriptions accordingly.

  **Before**

  ```
  spark-sql> DESCRIBE FUNCTION extended to_date;
  ...
  Usage: to_date(date_str, fmt) - Parses the `left` expression with the `fmt` expression. Returns null with invalid input.
  Extended Usage:
      Examples:
        > SELECT to_date('2016-12-31', 'yyyy-MM-dd');
         2016-12-31
  ```

  ```
  spark-sql> DESCRIBE FUNCTION extended to_timestamp;
  ...
  Usage: to_timestamp(timestamp, fmt) - Parses the `left` expression with the `format` expression to a timestamp. Returns null with invalid input.
  Extended Usage:
      Examples:
        > SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
         2016-12-31 00:00:00.0
  ```

  **After**

  ```
  spark-sql> DESCRIBE FUNCTION extended to_date;
  ...
  Usage:
      to_date(date_str[, fmt]) - Parses the `date_str` expression with the `fmt` expression to
        a date. Returns null with invalid input. By default, it follows casting rules to a date if
        the `fmt` is omitted.

  Extended Usage:
      Examples:
        > SELECT to_date('2009-07-30 04:17:52');
         2009-07-30
        > SELECT to_date('2016-12-31', 'yyyy-MM-dd');
         2016-12-31
  ```

  ```
  spark-sql> DESCRIBE FUNCTION extended to_timestamp;
  ...
   Usage:
      to_timestamp(timestamp[, fmt]) - Parses the `timestamp` expression with the `fmt` expression to
        a timestamp. Returns null with invalid input. By default, it follows casting rules to
        a timestamp if the `fmt` is omitted.

  Extended Usage:
      Examples:
        > SELECT to_timestamp('2016-12-31 00:12:00');
         2016-12-31 00:12:00
        > SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
         2016-12-31 00:00:00
  ```

## How was this patch tested?

Added tests in `datetime.sql`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17901 from HyukjinKwon/to_timestamp_arg.
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Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

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The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to setup your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.4), but additional sub-packages have their own requirements (including numpy and pandas).