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From "Gunther Hagleitner (JIRA)" <>
Subject [jira] [Commented] (HIVE-6455) Scalable dynamic partitioning and bucketing optimization
Date Tue, 25 Feb 2014 23:19:19 GMT


Gunther Hagleitner commented on HIVE-6455:

This is cool. Still reviewing but some ideas:

- Instead of adding a column to the record to be used in the file sink, it'd be cleaner (and
faster) to use the key to determine new files. I believe that could be achieved through startGroup/endGroup
- Looks like we'd end up duplicating partition column, bucket column, sort columns in both
key and value on the reduce sink. It might be possible to avoid that, making the intermediate
output smaller. Although I'm not sure this would require additional changes to rebuild the
row in the reduce task.

> Scalable dynamic partitioning and bucketing optimization
> --------------------------------------------------------
>                 Key: HIVE-6455
>                 URL:
>             Project: Hive
>          Issue Type: New Feature
>          Components: Query Processor
>    Affects Versions: 0.13.0
>            Reporter: Prasanth J
>            Assignee: Prasanth J
>              Labels: optimization
>         Attachments: HIVE-6455.1.patch, HIVE-6455.1.patch, HIVE-6455.2.patch, HIVE-6455.3.patch,
HIVE-6455.4.patch, HIVE-6455.4.patch, HIVE-6455.5.patch, HIVE-6455.6.patch, HIVE-6455.7.patch,
> The current implementation of dynamic partition works by keeping at least one record
writer open per dynamic partition directory. In case of bucketing there can be multispray
file writers which further adds up to the number of open record writers. The record writers
of column oriented file format (like ORC, RCFile etc.) keeps some sort of in-memory buffers
(value buffer or compression buffers) open all the time to buffer up the rows and compress
them before flushing it to disk. Since these buffers are maintained per column basis the amount
of constant memory that will required at runtime increases as the number of partitions and
number of columns per partition increases. This often leads to OutOfMemory (OOM) exception
in mappers or reducers depending on the number of open record writers. Users often tune the
JVM heapsize (runtime memory) to get over such OOM issues. 
> With this optimization, the dynamic partition columns and bucketing columns (in case
of bucketed tables) are sorted before being fed to the reducers. Since the partitioning and
bucketing columns are sorted, each reducers can keep only one record writer open at any time
thereby reducing the memory pressure on the reducers. This optimization is highly scalable
as the number of partition and number of columns per partition increases at the cost of sorting
the columns.

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