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dori waldman commented on SPARK-20236: -------------------------------------- Hi I would like to use this new functionality: overwrite specific partition without delete all data in s3 I used the new flag (spark.sql.sources.partitionOverwriteMode="dynamic") and test it locally from my IDEA and it worked (I was able to overwrite specific partition in s3) but when I deployed it to hdp 2.6.5 with spark 2.3.0 same code didn't create the s3 folders as expected , folder didn't create at all My code : df.write .mode(SaveMode.Overwtite) .partitionBy("day","hour") .option("compression", "gzip") .parquet(s3Path) As this is a new feature maybe the issue is with HDP (hortonworks) > Overwrite a partitioned data source table should only overwrite related > partitions > ---------------------------------------------------------------------------------- > > Key: SPARK-20236 > URL: https://issues.apache.org/jira/browse/SPARK-20236 > Project: Spark > Issue Type: Improvement > Components: SQL > Affects Versions: 2.2.0 > Reporter: Wenchen Fan > Assignee: Wenchen Fan > Priority: Major > Labels: releasenotes > Fix For: 2.3.0 > > > When we overwrite a partitioned data source table, currently Spark will > truncate the entire table to write new data, or truncate a bunch of > partitions according to the given static partitions. > For example, {{INSERT OVERWRITE tbl ...}} will truncate the entire table, > {{INSERT OVERWRITE tbl PARTITION (a=1, b)}} will truncate all the partitions > that starts with {{a=1}}. > This behavior is kind of reasonable as we can know which partitions will be > overwritten before runtime. However, hive has a different behavior that it > only overwrites related partitions, e.g. {{INSERT OVERWRITE tbl SELECT > 1,2,3}} will only overwrite partition {{a=2, b=3}}, assuming {{tbl}} has only > one data column and is partitioned by {{a}} and {{b}}. > It seems better if we can follow hive's behavior. -- 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