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Hyukjin Kwon 9c5bcac61e [SPARK-36626][PYTHON] Support TimestampNTZ in createDataFrame/toPandas and Python UDFs 
### What changes were proposed in this pull request?

This PR proposes to implement `TimestampNTZType` support in PySpark's `SparkSession.createDataFrame`, `DataFrame.toPandas`, Python UDFs, and pandas UDFs with and without Arrow.

### Why are the changes needed?

To complete `TimestampNTZType` support.

### Does this PR introduce _any_ user-facing change?

Yes.

- Users now can use `TimestampNTZType` type in `SparkSession.createDataFrame`, `DataFrame.toPandas`, Python UDFs, and pandas UDFs with and without Arrow.

- If `spark.sql.timestampType` is configured to `TIMESTAMP_NTZ`, PySpark will infer the `datetime` without timezone as `TimestampNTZType`. If it has a timezone, it will be inferred as `TimestampType` in `SparkSession.createDataFrame`.

    - If `TimestampType` and `TimestampNTZType` conflict during merging inferred schema, `TimestampType` has a higher precedence.

- If the type is `TimestampNTZType`, treat this internally as an unknown timezone, and compute w/ UTC (same as JVM side), and avoid localization externally.

### How was this patch tested?

Manually tested and unittests were added.

Closes #33876 from HyukjinKwon/SPARK-36626.

Lead-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-09-02 14:00:27 +09:00
..
catalyst [SPARK-36607][SQL] Support BooleanType in UnwrapCastInBinaryComparison 2021-09-01 14:27:30 +08:00
core [SPARK-36626][PYTHON] Support TimestampNTZ in createDataFrame/toPandas and Python UDFs 2021-09-02 14:00:27 +09:00
hive [SPARK-36327][SQL] Spark sql creates staging dir inside database directory rather than creating inside table directory 2021-08-27 12:58:28 -07:00
hive-thriftserver [SPARK-36611] Remove unused listener in HiveThriftServer2AppStatusStore 2021-08-31 11:40:34 +08:00
create-docs.sh [SPARK-34010][SQL][DODCS] Use python3 instead of python in SQL documentation build 2021-01-05 19:48:10 +09:00
gen-sql-api-docs.py [SPARK-34747][SQL][DOCS] Add virtual operators to the built-in function document 2021-03-19 10:19:26 +09:00
gen-sql-config-docs.py [SPARK-32194][PYTHON] Use proper exception classes instead of plain Exception 2021-05-26 11:54:40 +09:00
gen-sql-functions-docs.py [SPARK-31562][SQL] Update ExpressionDescription for substring, current_date, and current_timestamp 2020-04-26 11:46:52 -07:00
mkdocs.yml [SPARK-30731] Update deprecated Mkdocs option 2020-02-19 17:28:58 +09:00
README.md [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00

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 extensions that allow 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, and SQL configuration documentation that gets included as part of configuration.md in the main docs directory.