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https://issues.apache.org/jira/browse/SPARK-24288?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16476372#comment-16476372
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Maryann Xue commented on SPARK-24288:
-------------------------------------
I agree with you, [~TomaszGaweda], that making this a special operator would be
a better way than adding a option. It would be finer-grained both in terms of
different DataFrames and in terms of different operators (for example, in
future, Spark is able to push down other operators to the data source).
> Enable preventing predicate pushdown
> ------------------------------------
>
> Key: SPARK-24288
> URL: https://issues.apache.org/jira/browse/SPARK-24288
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Tomasz Gawęda
> Priority: Major
>
> Issue discussed on Mailing List:
> [http://apache-spark-developers-list.1001551.n3.nabble.com/Preventing-predicate-pushdown-td23976.html]
> While working with JDBC datasource I saw that many "or" clauses with
> non-equality operators causes huge performance degradation of SQL query
> to database (DB2). For example:
> val df = spark.read.format("jdbc").(other options to parallelize
> load).load()
> df.where(s"(date1 > $param1 and (date1 < $param2 or date1 is null) or x
> > 100)").show() // in real application whose predicates were pushed
> many many lines below, many ANDs and ORs
> If I use cache() before where, there is no predicate pushdown of this
> "where" clause. However, in production system caching many sources is a
> waste of memory (especially is pipeline is long and I must do cache many
> times).There are also few more workarounds, but it would be great if Spark
> will support preventing predicate pushdown by user.
>
> For example: df.withAnalysisBarrier().where(...) ?
>
> Note, that this should not be a global configuration option. If I read 2
> DataFrames, df1 and df2, I would like to specify that df1 should not have
> some predicates pushed down, but some may be, but df2 should have all
> predicates pushed down, even if target query joins df1 and df2. As far as I
> understand Spark optimizer, if we use functions like `withAnalysisBarrier`
> and put AnalysisBarrier explicitly in logical plan, then predicates won't be
> pushed down on this particular DataFrames and PP will be still possible on
> the second one.
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