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https://issues.apache.org/jira/browse/IMPALA-2522?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Tim Armstrong updated IMPALA-2522:
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Priority: Major (was: Critical)
> Improve the reliability and effectiveness of ETL
> ------------------------------------------------
>
> Key: IMPALA-2522
> URL: https://issues.apache.org/jira/browse/IMPALA-2522
> Project: IMPALA
> Issue Type: Epic
> Components: Backend
> Affects Versions: Impala 2.2, Impala 2.3.0, Impala 2.5.0, Impala 2.4.0,
> Impala 2.6.0, Impala 2.7.0
> Reporter: Mostafa Mokhtar
> Assignee: Lars Volker
> Priority: Major
> Labels: ETL, performance
>
> h4. Reduce the memory requirements of INSERTs into partitioned tables.
> Impala inserts into partitioned Parquet tables suffer from high memory
> requirements because each Impala Daemon will keep ~256MB of buffer space per
> open partition in the table sink. This often leads to large insert jobs
> hitting "Memory limit exceeded" errors. The behavior can be improved by
> pre-clustering the data such that only one partition needs to be buffered at
> a time in the table sink.
> Add a new "clustered" plan hint for insert statements. Example:
> {code}
> CREATE TABLE dst (...) PARTITIONED BY (year INT, month INT);
> INSERT INTO dst PARTITION(year,month) /*+ clustered */ SELECT * FROM src;
> {code}
> The hint specifies that the data fed into the table sink should be clustered
> based on the partition columns. For now, we'll use a sort to achieve
> clustering, and the plan should look like this:
> SCAN -> SORT (year,month) -> TABLE SINK
> h4. Give users additional control over the insertion order.
> In order to improve compression and/or the effectiveness of min/max pruning,
> it is desirable to control the order in which rows are inserted into table
> (mostly for Parquet).
> Introduce a "sortby" plan hint for insert statements: Example
> {code}
> CREATE TABLE dst (...) PARTITIONED BY (year INT, month INT);
> INSERT INTO dst PARTITION(year,month) /*+ clustered sortby(day,hour) */
> SELECT * FROM src
> {code}
> This would produce the following plan:
> SCAN -> SORT(year,month,day,hour) -> TABLE SINK
> h4. Improve the sort efficiency
> The additional sorting step introduced by both solutions above should be as
> efficient as possible.
> Codegen TupleRowComparator and Tuple::MaterializeExprs.
> h4. Summary
> With more predictable and resource-efficient ETL users will extract more
> value out of Impala and will need to rely less on slow legacy ETL tools like
> Hive.
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