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https://issues.apache.org/jira/browse/DRILL-4363?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15142241#comment-15142241
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ASF GitHub Bot commented on DRILL-4363:
---------------------------------------
Github user jinfengni commented on a diff in the pull request:
https://github.com/apache/drill/pull/371#discussion_r52563924
--- Diff:
exec/java-exec/src/main/java/org/apache/drill/exec/store/parquet/ParquetGroupScan.java
---
@@ -791,6 +799,43 @@ public FileGroupScan clone(FileSelection selection)
throws IOException {
}
@Override
+ public GroupScan applyLimit(long maxRecords) {
--- End diff --
I gave some thoughts about this optimization as well. Then, I realized that
until we have some performance measurement, it's not very clear which way we
want to have. For example, I'm not clear whether 1000 small parquet files is
better than 1 large parquet files. 1000 files might have big metadata overhead
than 1 large file (?). But 1000 small files might be better option, in case we
do want to parallelize the execution.
I'll add some comment saying further optimization could be done in terms of
how subset of files are chosen.
> Apply row count based pruning for parquet table in LIMIT n query
> ----------------------------------------------------------------
>
> Key: DRILL-4363
> URL: https://issues.apache.org/jira/browse/DRILL-4363
> Project: Apache Drill
> Issue Type: Improvement
> Reporter: Jinfeng Ni
> Assignee: Aman Sinha
> Fix For: 1.6.0
>
>
> In interactive data exploration use case, one common and probably first query
> that users would use is " SELECT * from table LIMIT n", where n is a small
> number. Such query will give user idea about the columns in the table.
> Normally, user would expect such query should be completed in very short
> time, since it's just asking for small amount of rows, without any
> sort/aggregation.
> When table is small, there is no big problem for Drill. However, when the
> table is extremely large, Drill's response time is not as fast as what user
> would expect.
> In case of parquet table, it seems that query planner could do a bit better
> job : by applying row count based pruning for such LIMIT n query. The
> pruning is kind of similar to what partition pruning will do, except that it
> uses row count, in stead of partition column values. Since row count is
> available in parquet table, it's possible to do such pruning.
> The benefit of doing such pruning is clear: 1) for small "n", such pruning
> would end up with a few parquet files, in stead of thousands, or millions of
> files to scan. 2) execution probably does not have to put scan into multiple
> minor fragments and start reading the files concurrently, which will cause
> big IO overhead. 3) the physical plan itself is much smaller, since it does
> not include the long list of parquet files, reduce rpc cost of sending the
> fragment plans to multiple drillbits, and the overhead to
> serialize/deserialize the fragment plans.
>
>
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