[ https://issues.apache.org/jira/browse/SPARK-28304?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Eyal Farago updated SPARK-28304: -------------------------------- Summary: FileFormatWriter introduces an uncoditional sort, even when all attributes are constants (was: FileFormatWriter introduces an uncoditional join, even when all attributes are constants) > FileFormatWriter introduces an uncoditional sort, even when all attributes > are constants > ---------------------------------------------------------------------------------------- > > Key: SPARK-28304 > URL: https://issues.apache.org/jira/browse/SPARK-28304 > Project: Spark > Issue Type: Improvement > Components: SQL > Affects Versions: 2.3.2 > Reporter: Eyal Farago > Priority: Major > Labels: performance > > FileFormatWriter derives a required sort order based on the partition > columns, bucketing columns and explicitly required ordering. However in some > use cases Some (or even all) of these fields are constant, in these cases the > sort can be skipped. > i.e. in my use-case, we add a GUUID column identifying a specific > (incremental) load, this can be thought of as a batch id. Since we run one > batch at a time, this column is always a constant which means there's no need > to sort based on this column, since we don't use bucketing or require an > explicit ordering the entire sort can be skipped for our case. > > I suggest: > # filter away constant columns from the required ordering calculated by > FileFormatWriter > # generalizing this to any Sort operator in a spark plan. > # introduce optimizer rules to remove constants from sort ordering, > potentially eliminating the sort operator altogether. > # modify EnsureRequirements to be aware of constant field when deciding > whether to introduce a sort or not. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org