[ https://issues.apache.org/jira/browse/SPARK-39931?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
ASF GitHub Bot updated SPARK-39931: ----------------------------------- Labels: pull-request-available (was: ) > Improve performance of applyInPandas for very small groups > ---------------------------------------------------------- > > Key: SPARK-39931 > URL: https://issues.apache.org/jira/browse/SPARK-39931 > Project: Spark > Issue Type: Improvement > Components: PySpark > Affects Versions: 3.4.0 > Reporter: Enrico Minack > Priority: Major > Labels: pull-request-available > > Calling {{DataFrame.groupby(...).applyInPandas(...)}} for very small groups > in PySpark is very slow. The reason is that for each group, PySpark creates a > Pandas DataFrame and calls into the Python code. For very small groups, the > overhead is huge, for large groups, it is smaller. > Here is a benchmarks (seconds to {{groupBy(...).applyInPandas(...)}} 10m > rows): > ||groupSize||Scala||pyspark.sql||pyspark.pandas|| > |1024|8.9|16.2|7.8| > |512|9.4|26.7|9.8| > |256|9.3|44.5|20.2| > |128|9.5|82.7|48.8| > |64|9.5|158.2|91.9| > |32|9.6|319.8|207.3| > |16|9.6|652.6|261.5| > |8|9.5|1,376|663.0| > |4|9.8|2,656|1,168| > |2|10.4|5,412|2,456| > |1|11.3|9,491|4,642| > *Idea to overcome this* is to call into Python side with a Pandas DataFrame > that contains potentially multiple groups, then perform a Pandas > {{DataFrame.groupBy(...).apply(...)}} or provide the {{DataFrameGroupBy}} to > the Python method. With large groups, that Panadas DataFrame has all rows of > a single group, with small groups it contains many groups. This should > improve efficiency. -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org