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https://issues.apache.org/jira/browse/SPARK-21218?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16064608#comment-16064608
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Michael Styles edited comment on SPARK-21218 at 6/27/17 10:25 AM:
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By not pushing the filter to Parquet, are we not preventing Parquet from
skipping blocks during read operations? I have tests that show big improvements
when applying this transformation.
For instance, I have a Parquet file with 162,456,394 rows which is sorted on
column C1.
*IN Predicate*
{noformat}
df.filter[df['C1'].isin([42, 139])).collect()
{noformat}
!IN Predicate.png|thumbnail!
*OR Predicate*
{noformat}
df.filter((df['C1'] == 42) | (df['C1'] == 139)).collect()
{noformat}
!OR Predicate.png|thumbnail!
Notice the difference in the number of output rows for the scan.
was (Author: ptkool):
By not pushing the filter to Parquet, are we not preventing Parquet from
skipping blocks during read operations? I have tests that show big improvements
when applying this transformation.
For instance, I have a Parquet file with 162,456,394 rows which is sorted on
column C1.
*IN Predicate*
{noformat}
df.filter[df['C1'].isin([42, 139])).collect()
{noformat}
!IN Predicate.png|thumbnail!
*OR Predicate*
{noformat}
df.filter((df['C1'] == 42) | (df['C1'] == 139)).collect()
{noformat}
!OR Pedicate.png|thumbnail!
Notice the difference in the number of output rows for the scan.
> Convert IN predicate to equivalent Parquet filter
> -------------------------------------------------
>
> Key: SPARK-21218
> URL: https://issues.apache.org/jira/browse/SPARK-21218
> Project: Spark
> Issue Type: Improvement
> Components: Optimizer
> Affects Versions: 2.1.1
> Reporter: Michael Styles
> Attachments: IN Predicate.png, OR Predicate.png
>
>
> Convert IN predicate to equivalent expression involving equality conditions
> to allow the filter to be pushed down to Parquet.
> For instance,
> C1 IN (10, 20) is rewritten as (C1 = 10) OR (C1 = 20)
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