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Steven Aerts 109247f02e [SPARK-35985][SQL] push partitionFilters for empty readDataSchema 
this commit makes sure that for File Source V2 partition filters are
also taken into account when the readDataSchema is empty.
This is the case for queries like:

    SELECT count(*) FROM tbl WHERE partition=foo
    SELECT input_file_name() FROM tbl WHERE partition=foo

### What changes were proposed in this pull request?

As described in SPARK-35985 there is bug in the File Datasource V2 which prevents it to push down to the FileScanner for queries like the ones listed above.

### Why are the changes needed?

If partitions filters are not pushed down, the whole dataset will be scanned while only one partition is interesting.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?

An extra test was added which relies on the output of explain, as is done in other places.

Closes #33191 from steven-aerts/SPARK-35985.

Authored-by: Steven Aerts <steven.aerts@airties.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit f06aa4a3f3)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2021-07-16 04:53:23 +00:00
..
catalyst [SPARK-36135][SQL] Support TimestampNTZ type in file partitioning 2021-07-16 01:13:49 +08:00
core [SPARK-35985][SQL] push partitionFilters for empty readDataSchema 2021-07-16 04:53:23 +00:00
hive [SPARK-35985][SQL] push partitionFilters for empty readDataSchema 2021-07-16 04:53:23 +00:00
hive-thriftserver [SPARK-35780][SQL] Support DATE/TIMESTAMP literals across the full range 2021-07-14 18:11:53 +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
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.