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https://issues.apache.org/jira/browse/SPARK-20364?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15974214#comment-15974214
]
Hyukjin Kwon commented on SPARK-20364:
--------------------------------------
Probably, let me open a PR first at least to show/check if the latter
suggestion works.
> Parquet predicate pushdown on columns with dots return empty results
> --------------------------------------------------------------------
>
> Key: SPARK-20364
> URL: https://issues.apache.org/jira/browse/SPARK-20364
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Hyukjin Kwon
>
> Currently, if there are dots in the column name, predicate pushdown seems
> being failed in Parquet.
> **With dots**
> {code}
> val path = "/tmp/abcde"
> Seq(Some(1), None).toDF("col.dots").write.parquet(path)
> spark.read.parquet(path).where("`col.dots` IS NOT NULL").show()
> {code}
> {code}
> +--------+
> |col.dots|
> +--------+
> +--------+
> {code}
> **Without dots**
> {code}
> val path = "/tmp/abcde2"
> Seq(Some(1), None).toDF("coldots").write.parquet(path)
> spark.read.parquet(path).where("`coldots` IS NOT NULL").show()
> {code}
> {code}
> +-------+
> |coldots|
> +-------+
> | 1|
> +-------+
> {code}
> It seems dot in the column names via {{FilterApi}} tries to separate the
> field name with dot ({{ColumnPath}} with multiple column paths) whereas the
> actual column name is {{col.dots}}. (See [FilterApi.java#L71
> |https://github.com/apache/parquet-mr/blob/apache-parquet-1.8.2/parquet-column/src/main/java/org/apache/parquet/filter2/predicate/FilterApi.java#L71]
> and it calls
> [ColumnPath.java#L44|https://github.com/apache/parquet-mr/blob/apache-parquet-1.8.2/parquet-common/src/main/java/org/apache/parquet/hadoop/metadata/ColumnPath.java#L44].
> I just tried to come up with ways to resolve it and I came up with two as
> below:
> One is simply to don't push down filters when there are dots in column names
> so that it reads all and filters in Spark-side.
> The other way creates Spark's {{FilterApi}} for those columns (it seems
> final) to get always use single column path it in Spark-side (this seems
> hacky) as we are not pushing down nested columns currently. So, it looks we
> can get a field name via {{ColumnPath.get}} not {{ColumnPath.fromDotString}}
> in this way.
> I just made a rough version of the latter.
> {code}
> private[parquet] object ParquetColumns {
> def intColumn(columnPath: String): Column[Integer] with SupportsLtGt = {
> new Column[Integer] (ColumnPath.get(columnPath), classOf[Integer]) with
> SupportsLtGt
> }
> def longColumn(columnPath: String): Column[java.lang.Long] with
> SupportsLtGt = {
> new Column[java.lang.Long] (
> ColumnPath.get(columnPath), classOf[java.lang.Long]) with SupportsLtGt
> }
> def floatColumn(columnPath: String): Column[java.lang.Float] with
> SupportsLtGt = {
> new Column[java.lang.Float] (
> ColumnPath.get(columnPath), classOf[java.lang.Float]) with SupportsLtGt
> }
> def doubleColumn(columnPath: String): Column[java.lang.Double] with
> SupportsLtGt = {
> new Column[java.lang.Double] (
> ColumnPath.get(columnPath), classOf[java.lang.Double]) with SupportsLtGt
> }
> def booleanColumn(columnPath: String): Column[java.lang.Boolean] with
> SupportsEqNotEq = {
> new Column[java.lang.Boolean] (
> ColumnPath.get(columnPath), classOf[java.lang.Boolean]) with
> SupportsEqNotEq
> }
> def binaryColumn(columnPath: String): Column[Binary] with SupportsLtGt = {
> new Column[Binary] (ColumnPath.get(columnPath), classOf[Binary]) with
> SupportsLtGt
> }
> }
> {code}
> Both sound not the best. Please let me know if anyone has a better idea.
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