HyukjinKwon commented on a change in pull request #26973: [SPARK-30323][SQL]
Support filters pushdown in CSV datasource
URL: https://github.com/apache/spark/pull/26973#discussion_r366676457
##########
File path:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/csv/UnivocityParserSuite.scala
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@@ -267,4 +269,63 @@ class UnivocityParserSuite extends SparkFunSuite with
SQLHelper {
assert(convertedValue.isInstanceOf[UTF8String])
assert(convertedValue == expected)
}
+
+ test("skipping rows using pushdown filters") {
+ def check(
+ input: String = "1,a",
+ dataSchema: String = "i INTEGER, s STRING",
+ requiredSchema: String = "i INTEGER",
+ filters: Seq[Filter],
+ expected: Seq[InternalRow]): Unit = {
+ def getSchema(str: String): StructType = str match {
Review comment:
@MaxGekk, it's a nit but I wouldn't add this nested function. Nested
function is discouraged in general as it makes difficult to read. How about
just simply doing `requiredSchema = StructType.fromDDL("i INTEGER, s STRING")`
or `requiredSchema = new StructType()` in the caller sides? I think such one or
two line duplications are fine.
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