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Simeon Simeonov b34b4c59b4 [SPARK-26696][SQL] Makes Dataset encoder public 
## What changes were proposed in this pull request?

Implements the solution proposed in [SPARK-26696](https://issues.apache.org/jira/browse/SPARK-26696), a minor refactoring that allows frameworks to perform advanced type-preserving dataset transformations without carrying `Encoder` implicits from user code.

The change allows

```scala
def foo[A](ds: Dataset[A]): Dataset[A] =
  ds.toDF().as[A](ds.encoder)
```

instead of

```scala
def foo[A: Encoder](ds: Dataset[A]): Dataset[A] =
  ds.toDF().as[A](implicitly[Encoder[A]])
```

## How was this patch tested?

This patch was tested with an automated test that was later removed as it was deemed unnecessary per the discussion in this PR.

Closes #23620 from ssimeonov/ss_SPARK-26696.

Authored-by: Simeon Simeonov <sim@fastignite.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2019-02-12 11:04:26 +08:00
..
catalyst [SPARK-26740][SPARK-26654][SQL] Make statistics of timestamp/date columns independent from system time zones 2019-02-12 10:58:00 +08:00
core [SPARK-26696][SQL] Makes Dataset encoder public 2019-02-12 11:04:26 +08:00
hive [SPARK-26766][CORE] Remove the list of filesystems from HadoopDelegationTokenProvider.obtainDelegationTokens 2019-02-08 13:41:52 -08:00
hive-thriftserver [SPARK-26766][CORE] Remove the list of filesystems from HadoopDelegationTokenProvider.obtainDelegationTokens 2019-02-08 13:41:52 -08:00
create-docs.sh [MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script 2017-08-26 13:56:24 +09:00
gen-sql-markdown.py [SPARK-21485][FOLLOWUP][SQL][DOCS] Describes examples and arguments separately, and note/since in SQL built-in function documentation 2017-08-05 10:10:56 -07:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [MINOR][DOC] Fix some typos and grammar issues 2018-04-06 13:37:08 +08:00

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 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.