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https://issues.apache.org/jira/browse/HIVE-17114?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16092647#comment-16092647
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Rui Li commented on HIVE-17114:
-------------------------------
Hi [~kellyzly], I have an example in the description. Basically, Spark decides
the reducer task for each record by computing {{hash(key)%numReducers}}.
Currently, for a single int key, hash(key)==key. Therefore in my example, all
records go to the same reducer although they have different keys. I think it's
a rare case, but I did hit it in a benchmark.
By using MurmurHash, we can distribute the records more evenly, see HIVE-7121.
> HoS: Possible skew in shuffling when data is not really skewed
> --------------------------------------------------------------
>
> Key: HIVE-17114
> URL: https://issues.apache.org/jira/browse/HIVE-17114
> Project: Hive
> Issue Type: Bug
> Reporter: Rui Li
> Assignee: Rui Li
> Priority: Minor
> Attachments: HIVE-17114.1.patch
>
>
> Observed in HoS and may apply to other engines as well.
> When we join 2 tables on a single int key, we use the key itself as hash code
> in {{ObjectInspectorUtils.hashCode}}:
> {code}
> case INT:
> return ((IntObjectInspector) poi).get(o);
> {code}
> Suppose the keys are different but are all some multiples of 10. And if we
> choose 10 as #reducers, the shuffle will be skewed.
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