ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/sql
History
Maxim Gekk a1dbcd13a3 [SPARK-31296][SQL][TESTS] Benchmark date-time rebasing in Parquet datasource 
### What changes were proposed in this pull request?
In the PR, I propose to add new benchmark `DateTimeRebaseBenchmark` which should measure the performance of rebasing of dates/timestamps from/to to the hybrid calendar (Julian+Gregorian) to/from Proleptic Gregorian calendar:
1. In write, it saves separately dates and timestamps before and after 1582 year w/ and w/o rebasing.
2. In read, it loads previously saved parquet files by vectorized reader and by regular reader.

Here is the summary of benchmarking:
- Saving timestamps is **~6 times slower**
- Loading timestamps w/ vectorized **off** is **~4 times slower**
- Loading timestamps w/ vectorized **on** is **~10 times slower**

### Why are the changes needed?
To know the impact of date-time rebasing introduced by #27915, #27953, #27807.

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

### How was this patch tested?
Run the `DateTimeRebaseBenchmark` benchmark using Amazon EC2:

| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1 (ami-06f2f779464715dc5) |
| Java | OpenJDK8/11 |

Closes #28057 from MaxGekk/rebase-bechmark.

Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2020-03-30 16:46:31 +08:00
..
catalyst [SPARK-31280][SQL] Perform propagating empty relation after RewritePredicateSubquery 2020-03-29 11:32:22 -07:00
core [SPARK-31296][SQL][TESTS] Benchmark date-time rebasing in Parquet datasource 2020-03-30 16:46:31 +08:00
hive [SPARK-31088][SQL] Add back HiveContext and createExternalTable 2020-03-26 23:51:15 -07:00
hive-thriftserver [SPARK-31170][SQL] Spark SQL Cli should respect hive-site.xml and spark.sql.warehouse.dir 2020-03-27 12:05:45 +08:00
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-30840][CORE][SQL] Add version property for ConfigEntry and ConfigBuilder 2020-02-22 09:46:42 +09:00
mkdocs.yml [SPARK-30731] Update deprecated Mkdocs option 2020-02-19 17:28:58 +09:00
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.