[GitHub] incubator-hawq-docs pull request #45: Revise section on work_mem

[janebeckman <gi...@git.apache.org>]

GitHub user janebeckman opened a pull request:

    https://github.com/apache/incubator-hawq-docs/pull/45

    Revise section on work_mem

    changes per tracker item

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/janebeckman/incubator-hawq-docs feature/work_mem

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/incubator-hawq-docs/pull/45.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #45
    
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[GitHub] incubator-hawq-docs pull request #45: Revise section on work_mem

[dyozie <gi...@git.apache.org>]

Github user dyozie commented on a diff in the pull request:

    https://github.com/apache/incubator-hawq-docs/pull/45#discussion_r85779030
  
    --- Diff: bestpractices/querying_data_bestpractices.html.md.erb ---
    @@ -16,14 +16,14 @@ If a query performs poorly, examine its query plan and ask the following questio
         If the plan is not choosing the optimal join order, set `join_collapse_limit=1` and use explicit `JOIN` syntax in your SQL statement to force the legacy query optimizer (planner) to the specified join order. You can also collect more statistics on the relevant join columns.
     
     -   **Does the optimizer selectively scan partitioned tables?** If you use table partitioning, is the optimizer selectively scanning only the child tables required to satisfy the query predicates? Scans of the parent tables should return 0 rows since the parent tables do not contain any data. See [Verifying Your Partition Strategy](../ddl/ddl-partition.html#topic74) for an example of a query plan that shows a selective partition scan.
    --   **Does the optimizer choose hash aggregate and hash join operations where applicable?** Hash operations are typically much faster than other types of joins or aggregations. Row comparison and sorting is done in memory rather than reading/writing from disk. To enable the query optimizer to choose hash operations, there must be sufficient memory available to hold the estimated number of rows. Try increasing work memory to improve performance for a query. If possible, run an `EXPLAIN  ANALYZE` for the query to show which plan operations spilled to disk, how much work memory they used, and how much memory was required to avoid spilling to disk. For example:
    +-   **Does the optimizer choose hash aggregate and hash join operations where applicable?** Hash operations are typically much faster than other types of joins or aggregations. Row comparison and sorting is done in memory rather than reading/writing from disk. To enable the query optimizer to choose hash operations, there must be sufficient memory available to hold the estimated number of rows. You may wish to  run an `EXPLAIN  ANALYZE` for the query to show which plan operations spilled to disk, how much work memory they used, and how much memory was required to avoid spilling to disk. For example:
    --- End diff --
    
    Let's change "You may wish to  run" to just "Run" here.


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[GitHub] incubator-hawq-docs pull request #45: Revise section on work_mem

[asfgit <gi...@git.apache.org>]

Github user asfgit closed the pull request at:

    https://github.com/apache/incubator-hawq-docs/pull/45


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