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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