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Gengliang Wang 95e55720d4 [SPARK-26990][SQL] FileIndex: use user specified field names if possible 
## What changes were proposed in this pull request?

WIth the following file structure:
```
/tmp/data
└── a=5
```

In the previous release:
```
scala> spark.read.schema("A int, ID long").parquet("/tmp/data/").printSchema
root
 |-- ID: long (nullable = true)
 |-- A: integer (nullable = true)
```

While in current code:
```
scala> spark.read.schema("A int, ID long").parquet("/tmp/data/").printSchema
root
 |-- ID: long (nullable = true)
 |-- a: integer (nullable = true)
```

We can see that the partition column name `a` is different from `A` as user specifed. This PR is to fix the case and make it more user-friendly.

## How was this patch tested?

Unit test

Closes #23894 from gengliangwang/fileIndexSchema.

Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2019-02-27 14:38:35 +08:00
..
catalyst [SPARK-22000][SQL] Address missing Upcast in JavaTypeInference.deserializerFor 2019-02-27 13:47:20 +08:00
core [SPARK-26990][SQL] FileIndex: use user specified field names if possible 2019-02-27 14:38:35 +08:00
hive [SPARK-26978][CORE][SQL] Avoid magic time constants 2019-02-26 09:08:12 -06:00
hive-thriftserver [SPARK-24570][SQL] Implement Spark own GetTablesOperation to fix SQL client tools cannot show tables 2019-02-17 23:35:45 -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.