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https://issues.apache.org/jira/browse/HIVE-6455?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Prasanth J updated HIVE-6455:
-----------------------------
Description:
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.
was:
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.
> Scalable dynamic partitioning and bucketing optimization
> --------------------------------------------------------
>
> Key: HIVE-6455
> URL: https://issues.apache.org/jira/browse/HIVE-6455
> Project: Hive
> Issue Type: New Feature
> Components: Query Processor
> Affects Versions: 0.13.0
> Reporter: Prasanth J
> Assignee: Prasanth J
> Labels: optimization
>
> 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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