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https://issues.apache.org/jira/browse/HIVE-17287?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16122955#comment-16122955
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liyunzhang_intel commented on HIVE-17287:
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[~lirui]: thanks for comments
{{spark.shuffle.reduceLocality.enabled}} is
[disabled|https://issues.apache.org/jira/browse/SPARK-10087] in spark1.5 so I
guess this value is false in my cluster because i use spark2.0.
{quote}
11 map-join tasks will output data to be shuffled again. Only 6 tasks have data
to output, and the other 5 tasks don't output because no records are generated
by the map join.However, this doesn't mean the following shuffle is necessarily
skewed.
{quote}
Yes, this does not mean the group by key is skewed. 1 thing i need to confirm
with you is that the 5 tasks without any records will also be sent to next
stage(groupby stage) even there is no record. That's why in spark history
server some tasks spends nearly 0 seconds to finish while others spends several
minutes to finish in groupby stage.
> HoS can not deal with skewed data group by
> ------------------------------------------
>
> Key: HIVE-17287
> URL: https://issues.apache.org/jira/browse/HIVE-17287
> Project: Hive
> Issue Type: Bug
> Reporter: liyunzhang_intel
> Assignee: liyunzhang_intel
>
> In
> [tpcds/query67.sql|https://github.com/kellyzly/hive-testbench/blob/hive14/sample-queries-tpcds/query67.sql],
> fact table {{store_sales}} joins with small tables {{date_dim}},
> {{item}},{{store}}. After join, groupby the intermediate data.
> Here the data of {{store_sales}} on 3TB tpcds is skewed: there are 1824
> partitions. The biggest partition is 25.7G and others are 715M.
> {code}
> hadoop fs -du -h
> /user/hive/warehouse/tpcds_bin_partitioned_parquet_3000.db/store_sales
> ....
> 715.0 M
> /user/hive/warehouse/tpcds_bin_partitioned_parquet_3000.db/store_sales/ss_sold_date_sk=2452639
> 713.9 M
> /user/hive/warehouse/tpcds_bin_partitioned_parquet_3000.db/store_sales/ss_sold_date_sk=2452640
> 714.1 M
> /user/hive/warehouse/tpcds_bin_partitioned_parquet_3000.db/store_sales/ss_sold_date_sk=2452641
> 712.9 M
> /user/hive/warehouse/tpcds_bin_partitioned_parquet_3000.db/store_sales/ss_sold_date_sk=2452642
> 25.7 G
> /user/hive/warehouse/tpcds_bin_partitioned_parquet_3000.db/store_sales/ss_sold_date_sk=__HIVE_DEFAULT_PARTITION__
> {code}
> The skewed table {{store_sales}} caused the failed job. Is there any way to
> solve the groupby problem of skewed table? I tried to enable
> {{hive.groupby.skewindata}} to first divide the data more evenly then start
> do group by. But the job still hangs.
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