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https://issues.apache.org/jira/browse/SPARK-30162?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16991137#comment-16991137
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Aman Omer commented on SPARK-30162:
-----------------------------------
Kindly share the results of spark-shell.
Thanks
> Filter is not being pushed down for Parquet files
> -------------------------------------------------
>
> Key: SPARK-30162
> URL: https://issues.apache.org/jira/browse/SPARK-30162
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.0
> Environment: pyspark 3.0 preview
> Ubuntu/Centos
> pyarrow 0.14.1
> Reporter: Nasir Ali
> Priority: Major
>
> Filters are not pushed down in Spark 3.0 preview. Also the output of
> "explain" method is different. It is hard to debug in 3.0 whether filters
> were pushed down or not. Below code could reproduce the bug:
>
> {code:java}
> // code placeholder
> df = spark.createDataFrame([("usr1",17.00, "2018-03-10T15:27:18+00:00"),
> ("usr1",13.00, "2018-03-11T12:27:18+00:00"),
> ("usr1",25.00, "2018-03-12T11:27:18+00:00"),
> ("usr1",20.00, "2018-03-13T15:27:18+00:00"),
> ("usr1",17.00, "2018-03-14T12:27:18+00:00"),
> ("usr2",99.00, "2018-03-15T11:27:18+00:00"),
> ("usr2",156.00, "2018-03-22T11:27:18+00:00"),
> ("usr2",17.00, "2018-03-31T11:27:18+00:00"),
> ("usr2",25.00, "2018-03-15T11:27:18+00:00"),
> ("usr2",25.00, "2018-03-16T11:27:18+00:00")
> ],
> ["user","id", "ts"])
> df = df.withColumn('ts', df.ts.cast('timestamp'))
> df.write.partitionBy("user").parquet("/home/cnali/data/")df2 =
> spark.read.load("/home/cnali/data/")df2.filter("user=='usr2'").explain(True)
> {code}
> {code:java}
> // Spark 2.4 output
> == Parsed Logical Plan ==
> 'Filter ('user = usr2)
> +- Relation[id#38,ts#39,user#40] parquet== Analyzed Logical Plan ==
> id: double, ts: timestamp, user: string
> Filter (user#40 = usr2)
> +- Relation[id#38,ts#39,user#40] parquet== Optimized Logical Plan ==
> Filter (isnotnull(user#40) && (user#40 = usr2))
> +- Relation[id#38,ts#39,user#40] parquet== Physical Plan ==
> *(1) FileScan parquet [id#38,ts#39,user#40] Batched: true, Format: Parquet,
> Location: InMemoryFileIndex[file:/home/cnali/data], PartitionCount: 1,
> PartitionFilters: [isnotnull(user#40), (user#40 = usr2)], PushedFilters: [],
> ReadSchema: struct<id:double,ts:timestamp>{code}
> {code:java}
> // Spark 3.0.0-preview output
> == Parsed Logical Plan ==
> 'Filter ('user = usr2)
> +- RelationV2[id#0, ts#1, user#2] parquet file:/home/cnali/data== Analyzed
> Logical Plan ==
> id: double, ts: timestamp, user: string
> Filter (user#2 = usr2)
> +- RelationV2[id#0, ts#1, user#2] parquet file:/home/cnali/data== Optimized
> Logical Plan ==
> Filter (isnotnull(user#2) AND (user#2 = usr2))
> +- RelationV2[id#0, ts#1, user#2] parquet file:/home/cnali/data== Physical
> Plan ==
> *(1) Project [id#0, ts#1, user#2]
> +- *(1) Filter (isnotnull(user#2) AND (user#2 = usr2))
> +- *(1) ColumnarToRow
> +- BatchScan[id#0, ts#1, user#2] ParquetScan Location:
> InMemoryFileIndex[file:/home/cnali/data], ReadSchema:
> struct<id:double,ts:timestamp>
> {code}
> I have tested it on much larger dataset. Spark 3.0 tries to load whole data
> and then apply filter. Whereas Spark 2.4 push down the filter. Above output
> shows that Spark 2.4 applied partition filter but not the Spark 3.0 preview.
>
> Minor: in Spark 3.0 "explain()" output is truncated (maybe fixed length?) and
> it's hard to debug. spark.sql.orc.cache.stripe.details.size=10000 doesn't
> work.
>
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