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From "Prasanth J (JIRA)" <>
Subject [jira] [Updated] (HIVE-6455) Scalable dynamic partitioning and bucketing optimization
Date Sat, 22 Feb 2014 02:57:21 GMT


Prasanth J updated HIVE-6455:

    Attachment: HIVE-6455.7.patch

Added a fix that solved an issue with stats aggregation when stats aggregation key exceeds
the max key prefix length. Fixed other failing tests.

> 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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