[GitHub] [iceberg] rdblue commented on a change in pull request #1184: Read support for parquet int96 timestamps

[GitBox <gi...@apache.org>]

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
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@@ -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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