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[SPARK-13511] [SQL] Add wholestage codegen for limit

Liang-Chi Hsieh authored 9 years ago

JIRA: https://issues.apache.org/jira/browse/SPARK-13511

## What changes were proposed in this pull request?

Current limit operator doesn't support wholestage codegen. This is open to add support for it.

In the `doConsume` of `GlobalLimit` and `LocalLimit`, we use a count term to count the processed rows. Once the row numbers catches the limit number, we set the variable `stopEarly` of `BufferedRowIterator` newly added in this pr to `true` that indicates we want to stop processing remaining rows. Then when the wholestage codegen framework checks `shouldStop()`, it will stop the processing of the row iterator.

Before this, the executed plan for a query `sqlContext.range(N).limit(100).groupBy().sum()` is:

    TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Final,isDistinct=false)], output=[sum(id)#6L])
    +- TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Partial,isDistinct=false)], output=[sum#9L])
       +- GlobalLimit 100
          +- Exchange SinglePartition, None
             +- LocalLimit 100
                +- Range 0, 1, 1, 524288000, [id#5L]

After add wholestage codegen support:

    WholeStageCodegen
    :  +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Final,isDistinct=false)], output=[sum(id)#41L])
    :     +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Partial,isDistinct=false)], output=[sum#44L])
    :        +- GlobalLimit 100
    :           +- INPUT
    +- Exchange SinglePartition, None
       +- WholeStageCodegen
          :  +- LocalLimit 100
          :     +- Range 0, 1, 1, 524288000, [id#40L]

## How was this patch tested?

A test is added into BenchmarkWholeStageCodegen.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #11391 from viirya/wholestage-limit.

c43899a0

c43899a0 9 years ago

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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.

http://spark.apache.org/

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Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

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./bin/pyspark

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>>> sc.parallelize(range(1000)).count()

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./bin/run-example SparkPi

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