szehon-ho commented on code in PR #6980:
URL: https://github.com/apache/iceberg/pull/6980#discussion_r1122500658
##########
spark/v3.3/spark-extensions/src/test/java/org/apache/iceberg/spark/extensions/TestMetadataTables.java:
##########
@@ -488,6 +489,54 @@ public void testMetadataLogEntries() throws Exception {
metadataLogWithProjection);
}
+ @Test
+ public void testFilesVersionAsOf() throws Exception {
+ // Create table and insert data
+ sql(
+ "CREATE TABLE %s (id bigint, data string) "
+ + "USING iceberg "
+ + "PARTITIONED BY (data) "
+ + "TBLPROPERTIES"
+ + "('format-version'='2',
'write.delete.mode'='merge-on-read')",
+ tableName);
+
+ List<SimpleRecord> recordsA =
+ Lists.newArrayList(new SimpleRecord(1, "a"), new SimpleRecord(2,
"a"));
+ spark
+ .createDataset(recordsA, Encoders.bean(SimpleRecord.class))
+ .coalesce(1)
+ .writeTo(tableName)
+ .append();
+
+ Table table = Spark3Util.loadIcebergTable(spark, tableName);
+ Long olderSnapshotId = table.currentSnapshot().snapshotId();
+
+ sql(
+ "ALTER TABLE %s ADD COLUMNS (data2 string)",
+ tableName);
+
+ List<SimpleExtraColumnRecord> recordsB =
+ Lists.newArrayList(new SimpleExtraColumnRecord(1, "b", "c"), new
SimpleExtraColumnRecord (2, "b", "c"));
+ spark
+ .createDataset(recordsB,
Encoders.bean(SimpleExtraColumnRecord.class))
+ .coalesce(1)
+ .writeTo(tableName)
+ .append();
+
+
+ List<Object[]> res1 = sql("SELECT * from %s.files VERSION AS OF %s",
tableName, olderSnapshotId );
+
+ Dataset<Row> ds = spark.read().format("iceberg").option("snapshot-id",
olderSnapshotId ).load(tableName + ".files");
+ List<Row> res2 = ds.collectAsList();
+
+ Long currentSnapshotId = table.currentSnapshot().snapshotId();
+
+ List<Object[]> res3 = sql("SELECT * from %s.files VERSION AS OF %s",
tableName, currentSnapshotId);
+
+ Dataset<Row> ds2 = spark.read().format("iceberg").option("snapshot-id",
currentSnapshotId ).load(tableName + ".files");
Review Comment:
Or, on the topic of using's
TestIcebergSourceTablesBase::testSnapshotReadAfterAddAndDropColumn mechanism,
maybe an easier way is to even use the RowFactory to construct the expected
results?
For example, select a few columns you want to check like :
```
Dataset<Row> actual =
spark.sql("SELECT file_path, file_format, record_count FROM " +
tableName + ".files VERSION AS OF %s", tableName, currentSnasphotId);
```
and construct expected value RowFactory, with data you get from:
```
TestHelpers.dataFiles(table).stream().map(df -> RowFactory.create(df.path(),
df.format(), df.recordCount())).collect(Collectors.toList())
```
up to you, if that is easier.
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