chenjunjiedada commented on a change in pull request #830:
URL: https://github.com/apache/incubator-iceberg/pull/830#discussion_r429031390
##########
File path:
spark/src/test/java/org/apache/iceberg/spark/source/TestSparkTableUtil.java
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@@ -199,4 +204,41 @@ public void testImportAsHiveTable() throws Exception {
long count2 = spark.read().format("iceberg").load(DB_NAME +
".test_partitioned_table").count();
Assert.assertEquals("three values ", 3, count2);
}
+
+ @Test
+ public void testImportWithIncompatibleSchema() throws Exception {
+ spark.table(qualifiedTableName).write().mode("overwrite").format("parquet")
+ .saveAsTable("original_table");
+
+ // The field is different so that it will project with name mapping
+ Schema filteredSchema = new Schema(
+ optional(1, "data", Types.StringType.get())
+ );
+
+ TableIdentifier source = new TableIdentifier("original_table");
+ Table table = catalog.createTable(
+ org.apache.iceberg.catalog.TableIdentifier.of(DB_NAME, "target_table"),
+ filteredSchema,
+ SparkSchemaUtil.specForTable(spark, "original_table"));
+ File stagingDir = temp.newFolder("staging-dir");
+ SparkTableUtil.importSparkTable(spark, source, table,
stagingDir.toString());
Review comment:
Parsing name mapping make sense to me, that could be used for avro and
orc as well. While this would need to parse the string from table property to
NameMapping, how about we make this as a follow-up and use a default name
mapping created from table schema as I describe above?
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