Ondrej Kokes created SPARK-32989:
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             Summary: 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


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:

{code}
$ 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 pyspark  
from pyspark.sql import SparkSession

import pyspark.sql.functions as f

if __name__ == '__main__':
    print(pyspark.__version__)
    spark = SparkSession.builder.getOrCreate()
    df = spark.read.option('header', True).csv('regression.csv')
    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}
# 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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