iemejia commented on code in PR #55923:
URL: https://github.com/apache/spark/pull/55923#discussion_r3403534666
##########
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetVectorUpdaterBenchmark.scala:
##########
@@ -264,6 +264,10 @@ object ParquetVectorUpdaterBenchmark extends BenchmarkBase
{
TimestampNTZType,
descriptor(PrimitiveTypeName.INT32, LogicalTypeAnnotation.dateType()),
longVec, intBytes)
+ addReadValuesCase(benchmark, "LongAsNanosUpdater (TimeType)",
+ TimeType(),
+ descriptor(PrimitiveTypeName.INT64,
LogicalTypeAnnotation.timeType(false, LogicalTypeAnnotation.TimeUnit.MICROS)),
Review Comment:
Fixed, thanks for catching that.
##########
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/ParquetVectorUpdaterFactory.java:
##########
@@ -470,8 +470,10 @@ public void readValues(
int offset,
WritableColumnVector values,
VectorizedValuesReader valuesReader) {
- for (int i = 0; i < total; ++i) {
- readValue(offset + i, values, valuesReader);
+ valuesReader.readIntegersAsLongs(total, values, offset);
Review Comment:
Good idea -- done in this PR. Migrated `DateToTimestampNTZUpdater` to the
same two-pass pattern (`readIntegersAsLongs` + in-place `daysToMicros`) and
removed `readIntegersAsTimestampMicros` from `VectorizedValuesReader` and
`VectorizedPlainValuesReader`. The two-pass form is indeed the stronger pattern
since it uses the mandatory `readIntegersAsLongs` that every encoding already
implements.
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