[jira] [Resolved] (SPARK-32989) Performance regression when selecting from str_to_map

["Takeshi Yamamuro (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-32989?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Takeshi Yamamuro resolved SPARK-32989.
--------------------------------------
    Fix Version/s: 3.1.0
         Assignee: L. C. Hsieh
       Resolution: Fixed

> Performance regression when selecting from str_to_map
> -----------------------------------------------------
>
>                 Key: SPARK-32989
>                 URL: https://issues.apache.org/jira/browse/SPARK-32989
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 3.0.1
>            Reporter: Ondrej Kokes
>            Assignee: L. C. Hsieh
>            Priority: Minor
>             Fix For: 3.1.0
>
>
> When I create a map using str_to_map and select more than a single value, I notice a notable performance regression in 3.0.1 compared to 2.4.7. When selecting a single value, the performance is the same. Plans are identical between versions.
> It seems like in 2.x the map from str_to_map is preserved for a given row, but in 3.x it's recalculated for each column. One hint that it might be the case is that when I tried forcing materialisation of said map in 3.x (by a coalesce, don't know if there's a better way), I got the performance roughly to 2.x levels.
> Here's a reproducer (the csv in question gets autogenerated by the python code):
> {code:java}
> $ head regression.csv 
> foo
> foo=bar&baz=bak&bar=foo
> foo=bar&baz=bak&bar=foo
> foo=bar&baz=bak&bar=foo
> foo=bar&baz=bak&bar=foo
> foo=bar&baz=bak&bar=foo
> ... (10M more rows)
> {code}
> {code:python}
> import time
> import os
> import pyspark  
> from pyspark.sql import SparkSession
> import pyspark.sql.functions as f
> if __name__ == '__main__':
>     print(pyspark.__version__)
>     spark = SparkSession.builder.getOrCreate()
>     filename = 'regression.csv'
>     if not os.path.isfile(filename):
>         with open(filename, 'wt') as fw:
>             fw.write('foo\n')
>             for _ in range(10_000_000):
>                 fw.write('foo=bar&baz=bak&bar=foo\n')
>     df = spark.read.option('header', True).csv(filename)
>     t = time.time()
>     dd = (df
>             .withColumn('my_map', f.expr('str_to_map(foo, "&", "=")'))
>             .select(
>                 f.col('my_map')['foo'],
>             )
>         )
>     dd.write.mode('overwrite').csv('tmp')
>     t2 = time.time()
>     print('selected one', t2 - t)
>     dd = (df
>             .withColumn('my_map', f.expr('str_to_map(foo, "&", "=")'))
>             # .coalesce(100) # forcing evaluation before selection speeds it up in 3.0.1
>             .select(
>                 f.col('my_map')['foo'],
>                 f.col('my_map')['bar'],
>                 f.col('my_map')['baz'],
>             )
>         )
>     dd.explain(True)
>     dd.write.mode('overwrite').csv('tmp')
>     t3 = time.time()
>     print('selected three', t3 - t2)
> {code}
> Results for 2.4.7 and 3.0.1, both installed from PyPI, Python 3.7, macOS (times are in seconds)
> {code:java}
> # 3.0.1
> # selected one 6.375471830368042                                                  
> # selected three 14.847578048706055
> # 2.4.7
> # selected one 6.679579019546509                                                  
> # selected three 6.5622029304504395  
> {code}



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