rdblue commented on a change in pull request #1184:
URL: https://github.com/apache/iceberg/pull/1184#discussion_r454035805
##########
File path:
spark/src/test/java/org/apache/iceberg/spark/data/TestSparkParquetReader.java
##########
@@ -67,4 +78,49 @@ protected void writeAndValidate(Schema schema) throws
IOException {
Assert.assertFalse("Should not have extra rows", rows.hasNext());
}
}
+
+ protected List<InternalRow> rowsFromFile(InputFile inputFile, Schema schema)
throws IOException {
+ try (CloseableIterable<InternalRow> reader =
+ Parquet.read(inputFile)
+ .project(schema)
+ .createReaderFunc(type -> SparkParquetReaders.buildReader(schema,
type))
+ .build()) {
+ return Lists.newArrayList(reader);
+ }
+ }
+
+ @Test
+ public void testInt96TimestampProducedBySparkIsReadCorrectly() throws
IOException {
+ final SparkSession spark =
+ SparkSession.builder()
+ .master("local[2]")
+ .config("spark.sql.parquet.int96AsTimestamp", "false")
+ .getOrCreate();
Review comment:
Is it possible to avoid creating a Spark session just to write a
timestamp? What about calling Spark's `FileFormat` to write directly instead?
We wrap Spark's `FileFormat` in our DSv2 table implementation:
https://github.com/Netflix/iceberg/blob/netflix-spark-2.4/metacat/src/main/java/com/netflix/iceberg/batch/BatchPatternWrite.java#L90
This test would run much faster by using that to create a file instead of
creating a Spark context.
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