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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/11/01 22:15:27 UTC
[jira] [Assigned] (SPARK-10978) Allow PrunedFilterScan to eliminate
predicates from further evaluation
[ https://issues.apache.org/jira/browse/SPARK-10978?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-10978:
------------------------------------
Assignee: Apache Spark
> Allow PrunedFilterScan to eliminate predicates from further evaluation
> ----------------------------------------------------------------------
>
> Key: SPARK-10978
> URL: https://issues.apache.org/jira/browse/SPARK-10978
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 1.3.0, 1.4.0, 1.5.0
> Reporter: Russell Alexander Spitzer
> Assignee: Apache Spark
> Priority: Critical
>
> Currently PrunedFilterScan allows implementors to push down predicates to an underlying datasource. This is done solely as an optimization as the predicate will be reapplied on the Spark side as well. This allows for bloom-filter like operations but ends up doing a redundant scan for those sources which can do accurate pushdowns.
> In addition it makes it difficult for underlying sources to accept queries which reference non-existent to provide ancillary function. In our case we allow a solr query to be passed in via a non-existent solr_query column. Since this column is not returned when Spark does a filter on "solr_query" nothing passes.
> Suggestion on the ML from [~marmbrus]
> {quote}
> We have to try and maintain binary compatibility here, so probably the easiest thing to do here would be to add a method to the class. Perhaps something like:
> def unhandledFilters(filters: Array[Filter]): Array[Filter] = filters
> By default, this could return all filters so behavior would remain the same, but specific implementations could override it. There is still a chance that this would conflict with existing methods, but hopefully that would not be a problem in practice.
> {quote}
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