Henry Robinson created SPARK-23500:
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Summary: Filters on named_structs could be pushed into scans
Key: SPARK-23500
URL: https://issues.apache.org/jira/browse/SPARK-23500
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 2.3.0
Reporter: Henry Robinson
Simple filters on dataframes joined with {{joinWith()}} are missing an
opportunity to get pushed into the scan because they're written in terms of
{{named_struct}} that could be removed by the optimizer.
Given the following simple query over two dataframes:
{code:java}
scala> val df = spark.read.parquet("one_million")
df: org.apache.spark.sql.DataFrame = [id: bigint, id2: bigint]
scala> val df2 = spark.read.parquet("one_million")
df2: org.apache.spark.sql.DataFrame = [id: bigint, id2: bigint]
scala> df.joinWith(df2, df2.col("id") === df.col("id2")).filter("_2.id >
30").explain
== Physical Plan ==
*(2) BroadcastHashJoin [_1#94.id2], [_2#95.id], Inner, BuildRight
:- *(2) Project [named_struct(id, id#0L, id2, id2#1L) AS _1#94]
: +- *(2) FileScan parquet [id#0L,id2#1L] Batched: true, Format: Parquet,
Location: InMemoryFileIndex[file:/Users/henry/src/spark/one_million],
PartitionFilters: [], PushedFilters: [], ReadSchema:
struct<id:bigint,id2:bigint>
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0,
struct<id:bigint,id2:bigint>, false].id))
+- *(1) Project [named_struct(id, id#90L, id2, id2#91L) AS _2#95]
+- *(1) Filter (named_struct(id, id#90L, id2, id2#91L).id > 30)
+- *(1) FileScan parquet [id#90L,id2#91L] Batched: true, Format: Parquet,
Location: InMemoryFileIndex[file:/Users/henry/src/spark/one_million],
PartitionFilters: [], PushedFilters: [], ReadSchema:
struct<id:bigint,id2:bigint>
{code}
Using {{joinWith}} means that the filter is placed on a {{named_struct}}, and
is then pushed down. When the filter is just above the scan, the
wrapping-and-projection of {{named_struct(id...).id}} is a no-op and could be
removed. Then the filter can be pushed down to Parquet.
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