Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/13701#discussion_r73728732
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
---
@@ -368,73 +378,75 @@ class ParquetFilterSuite extends QueryTest with
ParquetTest with SharedSQLContex
test("SPARK-11103: Filter applied on merged Parquet schema with new
column fails") {
import testImplicits._
-
- withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true",
- SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "true") {
- withTempPath { dir =>
- val pathOne = s"${dir.getCanonicalPath}/table1"
- (1 to 3).map(i => (i, i.toString)).toDF("a",
"b").write.parquet(pathOne)
- val pathTwo = s"${dir.getCanonicalPath}/table2"
- (1 to 3).map(i => (i, i.toString)).toDF("c",
"b").write.parquet(pathTwo)
-
- // If the "c = 1" filter gets pushed down, this query will throw
an exception which
- // Parquet emits. This is a Parquet issue (PARQUET-389).
- val df = spark.read.parquet(pathOne, pathTwo).filter("c =
1").selectExpr("c", "b", "a")
- checkAnswer(
- df,
- Row(1, "1", null))
-
- // The fields "a" and "c" only exist in one Parquet file.
-
assert(df.schema("a").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-
assert(df.schema("c").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-
- val pathThree = s"${dir.getCanonicalPath}/table3"
- df.write.parquet(pathThree)
-
- // We will remove the temporary metadata when writing Parquet file.
- val schema = spark.read.parquet(pathThree).schema
-
assert(schema.forall(!_.metadata.contains(StructType.metadataKeyForOptionalField)))
-
- val pathFour = s"${dir.getCanonicalPath}/table4"
- val dfStruct = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
- dfStruct.select(struct("a").as("s")).write.parquet(pathFour)
-
- val pathFive = s"${dir.getCanonicalPath}/table5"
- val dfStruct2 = sparkContext.parallelize(Seq((1, 1))).toDF("c",
"b")
- dfStruct2.select(struct("c").as("s")).write.parquet(pathFive)
-
- // If the "s.c = 1" filter gets pushed down, this query will throw
an exception which
- // Parquet emits.
- val dfStruct3 = spark.read.parquet(pathFour, pathFive).filter("s.c
= 1")
- .selectExpr("s")
- checkAnswer(dfStruct3, Row(Row(null, 1)))
-
- // The fields "s.a" and "s.c" only exist in one Parquet file.
- val field = dfStruct3.schema("s").dataType.asInstanceOf[StructType]
-
assert(field("a").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-
assert(field("c").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-
- val pathSix = s"${dir.getCanonicalPath}/table6"
- dfStruct3.write.parquet(pathSix)
-
- // We will remove the temporary metadata when writing Parquet file.
- val forPathSix = spark.read.parquet(pathSix).schema
-
assert(forPathSix.forall(!_.metadata.contains(StructType.metadataKeyForOptionalField)))
-
- // sanity test: make sure optional metadata field is not wrongly
set.
- val pathSeven = s"${dir.getCanonicalPath}/table7"
- (1 to 3).map(i => (i, i.toString)).toDF("a",
"b").write.parquet(pathSeven)
- val pathEight = s"${dir.getCanonicalPath}/table8"
- (4 to 6).map(i => (i, i.toString)).toDF("a",
"b").write.parquet(pathEight)
-
- val df2 = spark.read.parquet(pathSeven, pathEight).filter("a =
1").selectExpr("a", "b")
- checkAnswer(
- df2,
- Row(1, "1"))
-
- // The fields "a" and "b" exist in both two Parquet files. No
metadata is set.
-
assert(!df2.schema("a").metadata.contains(StructType.metadataKeyForOptionalField))
-
assert(!df2.schema("b").metadata.contains(StructType.metadataKeyForOptionalField))
+ Seq("true", "false").map { vectorized =>
--- End diff --
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