rocco408 commented on code in PR #11419:
URL: https://github.com/apache/iceberg/pull/11419#discussion_r1838575824
##########
spark/v3.5/spark/src/test/java/org/apache/iceberg/spark/source/TestDataFrameWriterV2.java:
##########
@@ -244,4 +247,132 @@ public void testMergeSchemaSparkConfiguration() throws
Exception {
row(4L, "d", 140000.56F)),
sql("select * from %s order by id", tableName));
}
+
+ @TestTemplate
+ public void testMergeSchemaIgnoreCastingLongToInt() throws Exception {
+ sql(
+ "ALTER TABLE %s SET TBLPROPERTIES ('%s'='true')",
+ tableName, TableProperties.SPARK_WRITE_ACCEPT_ANY_SCHEMA);
+
+ Dataset<Row> bigintDF =
+ jsonToDF(
+ "id bigint, data string",
+ "{ \"id\": 1, \"data\": \"a\" }",
+ "{ \"id\": 2, \"data\": \"b\" }");
+
+ bigintDF.writeTo(tableName).append();
+
+ assertEquals(
+ "Should have initial rows with long column",
+ ImmutableList.of(row(1L, "a"), row(2L, "b")),
+ sql("select * from %s order by id", tableName));
+
+ Dataset<Row> intDF =
+ jsonToDF(
+ "id int, data string",
+ "{ \"id\": 3, \"data\": \"c\" }",
+ "{ \"id\": 4, \"data\": \"d\" }");
+
+ assertThatCode(() -> intDF.writeTo(tableName).option("merge-schema",
"true").append())
+ .doesNotThrowAnyException();
+
+ assertEquals(
+ "Should include new rows with unchanged long column type",
+ ImmutableList.of(row(1L, "a"), row(2L, "b"), row(3L, "c"), row(4L,
"d")),
+ sql("select * from %s order by id", tableName));
+
+ // verify the column type did not change
+ Types.NestedField idField =
+ Spark3Util.loadIcebergTable(spark, tableName).schema().findField("id");
+ assertThat(idField.type().typeId().equals(Type.TypeID.LONG));
+ }
+
+ @TestTemplate
+ public void testMergeSchemaIgnoreCastingDoubleToFloat() throws Exception {
+ removeTables();
+ sql("CREATE TABLE %s (id double, data string) USING iceberg", tableName);
+ sql(
+ "ALTER TABLE %s SET TBLPROPERTIES ('%s'='true')",
+ tableName, TableProperties.SPARK_WRITE_ACCEPT_ANY_SCHEMA);
+
+ Dataset<Row> doubleDF =
+ jsonToDF(
+ "id double, data string",
+ "{ \"id\": 1.0, \"data\": \"a\" }",
+ "{ \"id\": 2.0, \"data\": \"b\" }");
+
+ doubleDF.writeTo(tableName).append();
+
+ assertEquals(
+ "Should have initial rows with double column",
+ ImmutableList.of(row(1.0, "a"), row(2.0, "b")),
+ sql("select * from %s order by id", tableName));
+
+ Dataset<Row> floatDF =
+ jsonToDF(
+ "id float, data string",
+ "{ \"id\": 3.0, \"data\": \"c\" }",
+ "{ \"id\": 4.0, \"data\": \"d\" }");
+
+ assertThatCode(() -> floatDF.writeTo(tableName).option("merge-schema",
"true").append())
+ .doesNotThrowAnyException();
+
+ assertEquals(
+ "Should include new rows with unchanged double column type",
+ ImmutableList.of(row(1.0, "a"), row(2.0, "b"), row(3.0, "c"), row(4.0,
"d")),
+ sql("select * from %s order by id", tableName));
+
+ // verify the column type did not change
+ Types.NestedField idField =
+ Spark3Util.loadIcebergTable(spark, tableName).schema().findField("id");
+ assertThat(idField.type().typeId().equals(Type.TypeID.DOUBLE));
+ }
+
+ @TestTemplate
+ public void
testMergeSchemaIgnoreCastingDecimalToDecimalWithNarrowerPrecision() throws
Exception {
Review Comment:
I'm not in love with the length of this test but it's clear what it does 😅
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