Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/15332#discussion_r81932694
--- Diff:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java
---
@@ -362,7 +363,15 @@ private void readLongBatch(int rowId, int num,
ColumnVector column) throws IOExc
if (column.dataType() == DataTypes.LongType ||
DecimalType.is64BitDecimalType(column.dataType())) {
defColumn.readLongs(
- num, column, rowId, maxDefLevel, (VectorizedValuesReader)
dataColumn);
+ num, column, rowId, maxDefLevel, (VectorizedValuesReader)
dataColumn);
--- End diff --
I've tested the following test case:
test("SPARK-10634 timestamp written and read as INT64 -
TIMESTAMP_MILLIS") {
val data = (1 to 1000).map { i =>
if (i < 500) {
Row(new java.sql.Timestamp(10))
} else {
Row(new java.sql.Timestamp(i))
}
}
val schema = StructType(List(StructField("time", TimestampType,
false)).toArray)
withSQLConf(ParquetOutputFormat.DICTIONARY_PAGE_SIZE -> "64",
ParquetOutputFormat.PAGE_SIZE -> "128") {
withSQLConf(SQLConf.PARQUET_INT64_AS_TIMESTAMP_MILLIS.key ->
"true") {
withTempPath { file =>
val df = spark.createDataFrame(sparkContext.parallelize(data),
schema)
df.coalesce(1).write.parquet(file.getCanonicalPath)
("true" :: Nil).foreach { vectorized =>
withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key ->
vectorized) {
val df2 = spark.read.parquet(file.getCanonicalPath)
checkAnswer(df2, df.collect().toSeq)
}
}
}
}
}
}
It will cause an exception:
[info] org.apache.spark.SparkException: Job aborted due to stage
failure: Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0
in stage 3.0 (TID 4, localhost): java.lang.UnsupportedOperationException:
Unimplemented type: TimestampType
[info] at
org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.decodeDictionaryIds(VectorizedColumnReader.java:256)
[info] at
org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBatch(VectorizedColumnReader.java:177)
[info] at
org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:230)
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