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Posted to issues@spark.apache.org by "Ondrej Kokes (Jira)" <ji...@apache.org> on 2020/09/27 11:30:00 UTC
[jira] [Updated] (SPARK-32989) Performance regression when
selecting from str_to_map
[ https://issues.apache.org/jira/browse/SPARK-32989?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ondrej Kokes updated SPARK-32989:
---------------------------------
Description:
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 gets autogenerated by the python code):
{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 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}
# 3.0.1
# selected one 6.375471830368042
# selected three 14.847578048706055
# 2.4.7
# selected one 6.679579019546509
# selected three 6.5622029304504395
{code}
was:
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}
> 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
> Priority: Minor
>
> 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 gets autogenerated by the python code):
> {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 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}
> # 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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