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Chao Sun b8acbf6d88 [SPARK-35846][SQL] Introduce ParquetReadState to track various states while reading a Parquet column chunk 
### What changes were proposed in this pull request?

Move all the bookkeeping states while scanning a Parquet column chunk into a single class `ParquetReadState`.

### Why are the changes needed?

As suggested [here](https://github.com/apache/spark/pull/32753#discussion_r655580942). To support column index in the vectorized reader path, we'll going to introduce more states to track. These are spread across different classes which make the code harder to maintain. Therefore, this proposes to move them into a single class so they can be managed better.

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

No.

### How was this patch tested?

Existing UTs.

Closes #33006 from sunchao/SPARK-35846.

Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2021-06-23 02:56:00 -07:00
..
catalyst [SPARK-35857][SQL] The ANSI flag of Cast should be kept after being copied 2021-06-23 16:52:33 +08:00
core [SPARK-35846][SQL] Introduce ParquetReadState to track various states while reading a Parquet column chunk 2021-06-23 02:56:00 -07:00
hive [SPARK-35857][SQL] The ANSI flag of Cast should be kept after being copied 2021-06-23 16:52:33 +08:00
hive-thriftserver [SPARK-35838][BUILD][TESTS] Ensure all modules can be maven test independently in Scala 2.13 2021-06-22 06:31:24 -07:00
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
README.md

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.