6a4bfcd62b
JIRA: https://issues.apache.org/jira/browse/SPARK-13658 ## What changes were proposed in this pull request? Quoted from JIRA description: When run TPCDS Q3 [1] with lots predicates to filter out the partitions, the optimizer rule BooleanSimplification take about 2 seconds (it use lots of sematicsEqual, which require copy the whole tree). It will great if we could speedup it. [1] https://github.com/cloudera/impala-tpcds-kit/blob/master/queries/q3.sql How to speed up it: When we ask the canonicalized expression in `Expression`, it calls `Canonicalize.execute` on itself. `Canonicalize.execute` basically transforms up all expressions included in this expression. However, we don't keep the canonicalized versions for these children expressions. So in next time we ask the canonicalized expressions for the children expressions (e.g., `BooleanSimplification`), we will rerun `Canonicalize.execute` on each of them. It wastes much time. By forcing the children expressions to get and keep their canonicalized versions first, we can avoid re-canonicalize these expressions. I simply benchmark it with an expression which is part of the where clause in TPCDS Q3: val testRelation = LocalRelation('ss_sold_date_sk.int, 'd_moy.int, 'i_manufact_id.int, 'ss_item_sk.string, 'i_item_sk.string, 'd_date_sk.int) val input = ('d_date_sk === 'ss_sold_date_sk) && ('ss_item_sk === 'i_item_sk) && ('i_manufact_id === 436) && ('d_moy === 12) && (('ss_sold_date_sk > 2415355 && 'ss_sold_date_sk < 2415385) || ('ss_sold_date_sk > 2415720 && 'ss_sold_date_sk < 2415750) || ('ss_sold_date_sk > 2416085 && 'ss_sold_date_sk < 2416115) || ('ss_sold_date_sk > 2416450 && 'ss_sold_date_sk < 2416480) || ('ss_sold_date_sk > 2416816 && 'ss_sold_date_sk < 2416846) || ('ss_sold_date_sk > 2417181 && 'ss_sold_date_sk < 2417211) || ('ss_sold_date_sk > 2417546 && 'ss_sold_date_sk < 2417576) || ('ss_sold_date_sk > 2417911 && 'ss_sold_date_sk < 2417941) || ('ss_sold_date_sk > 2418277 && 'ss_sold_date_sk < 2418307) || ('ss_sold_date_sk > 2418642 && 'ss_sold_date_sk < 2418672) || ('ss_sold_date_sk > 2419007 && 'ss_sold_date_sk < 2419037) || ('ss_sold_date_sk > 2419372 && 'ss_sold_date_sk < 2419402) || ('ss_sold_date_sk > 2419738 && 'ss_sold_date_sk < 2419768) || ('ss_sold_date_sk > 2420103 && 'ss_sold_date_sk < 2420133) || ('ss_sold_date_sk > 2420468 && 'ss_sold_date_sk < 2420498) || ('ss_sold_date_sk > 2420833 && 'ss_sold_date_sk < 2420863) || ('ss_sold_date_sk > 2421199 && 'ss_sold_date_sk < 2421229) || ('ss_sold_date_sk > 2421564 && 'ss_sold_date_sk < 2421594) || ('ss_sold_date_sk > 2421929 && 'ss_sold_date_sk < 2421959) || ('ss_sold_date_sk > 2422294 && 'ss_sold_date_sk < 2422324) || ('ss_sold_date_sk > 2422660 && 'ss_sold_date_sk < 2422690) || ('ss_sold_date_sk > 2423025 && 'ss_sold_date_sk < 2423055) || ('ss_sold_date_sk > 2423390 && 'ss_sold_date_sk < 2423420) || ('ss_sold_date_sk > 2423755 && 'ss_sold_date_sk < 2423785) || ('ss_sold_date_sk > 2424121 && 'ss_sold_date_sk < 2424151) || ('ss_sold_date_sk > 2424486 && 'ss_sold_date_sk < 2424516) || ('ss_sold_date_sk > 2424851 && 'ss_sold_date_sk < 2424881) || ('ss_sold_date_sk > 2425216 && 'ss_sold_date_sk < 2425246) || ('ss_sold_date_sk > 2425582 && 'ss_sold_date_sk < 2425612) || ('ss_sold_date_sk > 2425947 && 'ss_sold_date_sk < 2425977) || ('ss_sold_date_sk > 2426312 && 'ss_sold_date_sk < 2426342) || ('ss_sold_date_sk > 2426677 && 'ss_sold_date_sk < 2426707) || ('ss_sold_date_sk > 2427043 && 'ss_sold_date_sk < 2427073) || ('ss_sold_date_sk > 2427408 && 'ss_sold_date_sk < 2427438) || ('ss_sold_date_sk > 2427773 && 'ss_sold_date_sk < 2427803) || ('ss_sold_date_sk > 2428138 && 'ss_sold_date_sk < 2428168) || ('ss_sold_date_sk > 2428504 && 'ss_sold_date_sk < 2428534) || ('ss_sold_date_sk > 2428869 && 'ss_sold_date_sk < 2428899) || ('ss_sold_date_sk > 2429234 && 'ss_sold_date_sk < 2429264) || ('ss_sold_date_sk > 2429599 && 'ss_sold_date_sk < 2429629) || ('ss_sold_date_sk > 2429965 && 'ss_sold_date_sk < 2429995) || ('ss_sold_date_sk > 2430330 && 'ss_sold_date_sk < 2430360) || ('ss_sold_date_sk > 2430695 && 'ss_sold_date_sk < 2430725) || ('ss_sold_date_sk > 2431060 && 'ss_sold_date_sk < 2431090) || ('ss_sold_date_sk > 2431426 && 'ss_sold_date_sk < 2431456) || ('ss_sold_date_sk > 2431791 && 'ss_sold_date_sk < 2431821) || ('ss_sold_date_sk > 2432156 && 'ss_sold_date_sk < 2432186) || ('ss_sold_date_sk > 2432521 && 'ss_sold_date_sk < 2432551) || ('ss_sold_date_sk > 2432887 && 'ss_sold_date_sk < 2432917) || ('ss_sold_date_sk > 2433252 && 'ss_sold_date_sk < 2433282) || ('ss_sold_date_sk > 2433617 && 'ss_sold_date_sk < 2433647) || ('ss_sold_date_sk > 2433982 && 'ss_sold_date_sk < 2434012) || ('ss_sold_date_sk > 2434348 && 'ss_sold_date_sk < 2434378) || ('ss_sold_date_sk > 2434713 && 'ss_sold_date_sk < 2434743))) val plan = testRelation.where(input).analyze val actual = Optimize.execute(plan) With this patch: 352 milliseconds 346 milliseconds 340 milliseconds Without this patch: 585 milliseconds 880 milliseconds 677 milliseconds ## How was this patch tested? Existing tests should pass. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11647 from viirya/improve-expr-canonicalize. |
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scalastyle-config.xml |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark". For developing Spark using an IDE, see Eclipse and IntelliJ.
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.