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https://issues.apache.org/jira/browse/SPARK-25206?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16592384#comment-16592384
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yucai commented on SPARK-25206:
-------------------------------
[~dongjoon], I still think this bug is related to pushdown, but unfortunately,
there are two issues actually, which make it quite confusing. Let me explain:
1. The wrong column name is pushdown into parquet. Spark pushdowns "ID > 0",
but parquet file has "id" in its schema instead of "ID", so 0 record is
returned in {color:#FF0000}*PARQUET SCAN stage.*{color}
{color:#FF0000}*Attention*{color}: not because of the filter, no record from
*{color:#FF0000}parquet scan{color}* in this case.
We can confirm this in Spark's chart, "number of output rows" in Scan is 0.
{code:java}
rm -rf /tmp/data /tmp/data_csv
./bin/spark-shell
spark.range(10).write.parquet("/tmp/data")
sql("DROP TABLE t")
sql("CREATE TABLE t (ID LONG) USING parquet LOCATION '/tmp/data'")
scala> sql("select * from t where id > 0").show
+---+
| ID|
+---+
+---+
{code}
!image-2018-08-25-10-04-21-901.png!
That's why we need backport [https://github.com/apache/spark/pull/21696].
With it, Spark will pushdown correct filter "id > 0" into parquet.
2. Unfortunately, with only [https://github.com/apache/spark/pull/21696], it is
not enough, because of https://issues.apache.org/jira/browse/SPARK-25132, we
still need backport [https://github.com/apache/spark/pull/22183].
Does it make sense to you?
> Wrong data may be returned for Parquet
> --------------------------------------
>
> Key: SPARK-25206
> URL: https://issues.apache.org/jira/browse/SPARK-25206
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.2, 2.3.1
> Reporter: yucai
> Priority: Blocker
> Labels: correctness
> Attachments: image-2018-08-24-18-05-23-485.png,
> image-2018-08-24-22-33-03-231.png, image-2018-08-24-22-34-11-539.png,
> image-2018-08-24-22-46-05-346.png, image-2018-08-25-09-54-53-219.png,
> image-2018-08-25-10-04-21-901.png, pr22183.png
>
>
> In current Spark 2.3.1, below query returns wrong data silently.
> {code:java}
> spark.range(10).write.parquet("/tmp/data")
> sql("DROP TABLE t")
> sql("CREATE TABLE t (ID LONG) USING parquet LOCATION '/tmp/data'")
> scala> sql("select * from t").show
> +----+
> | ID|
> +----+
> |null|
> |null|
> |null|
> |null|
> |null|
> |null|
> |null|
> |null|
> |null|
> |null|
> +----+
> scala> sql("select * from t where id > 0").show
> +---+
> | ID|
> +---+
> +---+
> scala> sql("set spark.sql.parquet.filterPushdown").show
> +--------------------+-----+
> | key|value|
> +--------------------+-----+
> |spark.sql.parquet...| true|
> +--------------------+-----+
> scala> sql("set spark.sql.parquet.filterPushdown=false").show
> +--------------------+-----+
> | key|value|
> +--------------------+-----+
> |spark.sql.parquet...|false|
> +--------------------+-----+
> scala> sql("select * from t where id > 0").show
> +---+
> | ID|
> +---+
> +---+
> {code}
>
> *Root Cause*
> Spark pushdowns FilterApi.gt(intColumn("{color:#ff0000}ID{color}"), 0:
> Integer) into parquet, but {color:#ff0000}ID{color} does not exist in
> /tmp/data (parquet is case sensitive, it has {color:#ff0000}id{color}
> actually).
> So no records are returned.
> In Spark 2.1, the user will get Exception:
> {code:java}
> Caused by: java.lang.IllegalArgumentException: Column [ID] was not found in
> schema!{code}
> But in Spark 2.3, they will get the wrong results sliently.
>
> Since SPARK-24716, Spark uses Parquet schema instead of Hive metastore schema
> to do the pushdown, perfect for this issue.
> [~yumwang], [~cloud_fan], [~smilegator], any thoughts? Should we backport it?
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