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Posted to dev@pig.apache.org by "Rohini Palaniswamy (JIRA)" <ji...@apache.org> on 2016/10/13 17:58:20 UTC

[jira] [Updated] (PIG-5041) RoundRobinPartitioner is not deterministic when order of input records change

     [ https://issues.apache.org/jira/browse/PIG-5041?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Rohini Palaniswamy updated PIG-5041:
------------------------------------
    Status: Patch Available  (was: Open)

> RoundRobinPartitioner is not deterministic when order of input records change
> -----------------------------------------------------------------------------
>
>                 Key: PIG-5041
>                 URL: https://issues.apache.org/jira/browse/PIG-5041
>             Project: Pig
>          Issue Type: Bug
>            Reporter: Rohini Palaniswamy
>            Assignee: Rohini Palaniswamy
>            Priority: Critical
>             Fix For: 0.16.1
>
>         Attachments: PIG-5041-1.patch
>
>
> Maps can be rerun due to shuffle fetch failures. Half of the reducers can end up successfully pulling partitions from first run of the map while other half could pull from the rerun after shuffle fetch failures. If the data is not partitioned by the Partitioner exactly the same way every time then it could lead to incorrect results (loss of records and duplicated records). 
> There is a good probability of order of input records changing
>     - With OrderedGroupedMergedKVInput (shuffle input), they keys are sorted but values can be in any order as the shuffle and merge depends on the order in which inputs are fetched. Anything involving FLATTEN can produce different order of output records.
>     - With UnorderedKVInput, the records could be in any order depending on order of shuffle fetch. 
> RoundRobinPartitioner can partition records differently everytime as order of input records change which is very bad. We need to get rid of RoundRobinPartitioner. Since the key is empty whenever we use  RoundRobinPartitioner we need to partitioning based on hashcode of values to produce consistent partitioning. It adds a lot of performance overhead, but required for correctness.



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