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Arwin Tio 25e9156bc0 [SPARK-29089][SQL] Parallelize blocking FileSystem calls in DataSource#checkAndGlobPathIfNecessary 
### What changes were proposed in this pull request?
See JIRA: https://issues.apache.org/jira/browse/SPARK-29089
Mailing List: http://apache-spark-developers-list.1001551.n3.nabble.com/DataFrameReader-bottleneck-in-DataSource-checkAndGlobPathIfNecessary-when-reading-S3-files-td27828.html

When using DataFrameReader#csv to read many files on S3, globbing and fs.exists on DataSource#checkAndGlobPathIfNecessary becomes a bottleneck.

From the mailing list discussions, an improvement that can be made is to parallelize the blocking FS calls:

> - have SparkHadoopUtils differentiate between files returned by globStatus(), and which therefore exist, and those which it didn't glob for -it will only need to check those.
> - add parallel execution to the glob and existence checks

### Why are the changes needed?

Verifying/globbing files happens on the driver, and if this operations take a long time (for example against S3), then the entire cluster has to wait, potentially sitting idle. This change hopes to make this process faster.

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

No

### How was this patch tested?

I added a test suite `DataSourceSuite` - open to suggestions for better naming.

See [here](https://github.com/apache/spark/pull/25899#issuecomment-534380034) and [here](https://github.com/apache/spark/pull/25899#issuecomment-534069194) for some measurements

Closes #25899 from cozos/master.

Lead-authored-by: Arwin Tio <Arwin.tio@adroll.com>
Co-authored-by: Arwin Tio <arwin.tio@hotmail.com>
Co-authored-by: Arwin Tio <arwin.tio@adroll.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
2020-02-17 09:30:35 -06:00
..
catalyst [SPARK-30793][SQL] Fix truncations of timestamps before the epoch to minutes and seconds 2020-02-17 22:51:56 +08:00
core [SPARK-29089][SQL] Parallelize blocking FileSystem calls in DataSource#checkAndGlobPathIfNecessary 2020-02-17 09:30:35 -06:00
hive [SPARK-30799][SQL] "spark_catalog.t" should not be resolved to temp view 2020-02-17 12:07:46 +08:00
hive-thriftserver [SPARK-30798][SQL] Scope Session.active in QueryExecution 2020-02-13 23:58:55 +01:00
create-docs.sh [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00
gen-sql-api-docs.py [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00
gen-sql-config-docs.py [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [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.