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Posted to issues@spark.apache.org by "Mikalai Surta (JIRA)" <ji...@apache.org> on 2018/11/19 09:38:00 UTC
[jira] [Issue Comment Deleted] (SPARK-20236) Overwrite a
partitioned data source table should only overwrite related partitions
[ https://issues.apache.org/jira/browse/SPARK-20236?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Mikalai Surta updated SPARK-20236:
----------------------------------
Comment: was deleted
(was: Could you please explain why it doesn't work as I expect in my case?
I create a table with dynamic partitions. Insert data into 2 partitions. Then insert overwrite data for 1 partition. Expect to see 2 partitions as a result, but got only 1 that overwrite the whole table. Doesn't seem to be the same case as [~deepanker] had as I create table with explicit PARTITIONED BY clause.
spark.sparkContext.getConf().getAll()
...
('spark.conf.set("spark.sql.sources.partitionOverwriteMode","dynamic")', ''),
...
sqlContext.sql("CREATE TABLE `debug2` (....) PARTITIONED BY (inputDate)")
sqlContext.sql("insert into debug2 select * from debug1")
sqlContext.sql("select * from debug2").select('ean', 'inputDate').show(10, False)
+-------------+---------+
|ean |inputDate|
+-------------+---------+
|4019238159363|*20181025* |
|3188642344151|*20181026* |
+-------------+---------+
sqlContext.sql("insert overwrite table debug2 select * from debug1 where inputDate=='*20181025*'")
+-------------+---------+
|ean |inputDate|
+-------------+---------+
|4019238159363|*20181025* |
+-------------+---------+
*20181026* record is lost.)
> 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.
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