andygrove commented on code in PR #892:
URL: https://github.com/apache/datafusion-comet/pull/892#discussion_r1739273391
##########
spark/src/test/scala/org/apache/comet/exec/CometExecSuite.scala:
##########
@@ -1547,6 +1547,55 @@ class CometExecSuite extends CometTestBase {
})
}
+ test("SparkToColumnar over BatchScan (Spark Parquet reader)") {
+ Seq("", "parquet").foreach { v1List =>
+ Seq("true", "false").foreach { parquetVectorized =>
+ Seq(
+ "cast(id as tinyint)",
+ "cast(id as smallint)",
+ "cast(id as integer)",
+ "cast(id as bigint)",
+ "cast(id as float)",
+ "cast(id as double)",
+ "cast(id as decimal)",
+ "cast(id as timestamp)",
+ "cast(id as string)",
+ "cast(id as binary)",
+ "struct(id)").foreach { valueType =>
+ {
+ withSQLConf(
+ SQLConf.USE_V1_SOURCE_LIST.key -> v1List,
+ CometConf.COMET_NATIVE_SCAN_ENABLED.key -> "false",
+ CometConf.COMET_CONVERT_FROM_PARQUET_ENABLED.key -> "true",
+ SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key ->
parquetVectorized) {
+ withTempPath { dir =>
+ var df = spark
+ .range(10000)
+ .selectExpr("id as key", s"$valueType as value")
+ .toDF("key", "value")
+
+ df.write.parquet(dir.toString())
+
+ df = spark.read.parquet(dir.toString())
+ checkSparkAnswerAndOperator(
+ df.select("*").groupBy("key", "value").count(),
+ includeClasses = Seq(classOf[CometSparkToColumnarExec]))
+
+ // Verify that the BatchScanExec nodes supported columnar
output when requested.
+ val leaves = df.queryExecution.executedPlan.collectLeaves()
+ if (parquetVectorized == "true") {
Review Comment:
I don't have a strong opinion either way. I suppose it could be a good first
issue for someone and it would be nice to clean up existing tests.
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