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https://issues.apache.org/jira/browse/DRILL-7313?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16878863#comment-16878863
]
ASF GitHub Bot commented on DRILL-7313:
---------------------------------------
arina-ielchiieva commented on pull request #1819: DRILL-7313: Use Hive schema
for MaprDB native reader when field was empty
URL: https://github.com/apache/drill/pull/1819#discussion_r300486007
##########
File path:
contrib/storage-hive/core/src/test/java/org/apache/drill/exec/store/hive/schema/TestSchemaConversion.java
##########
@@ -0,0 +1,184 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.drill.exec.store.hive.schema;
+
+import org.apache.calcite.sql.type.SqlTypeFactoryImpl;
+import org.apache.drill.categories.SlowTest;
+import org.apache.drill.common.types.TypeProtos;
+import org.apache.drill.common.types.Types;
+import org.apache.drill.exec.planner.types.DrillRelDataTypeSystem;
+import org.apache.drill.exec.planner.types.HiveToRelDataTypeConverter;
+import org.apache.drill.exec.record.metadata.ColumnMetadata;
+import org.apache.drill.exec.record.metadata.PrimitiveColumnMetadata;
+import org.apache.drill.exec.record.metadata.SchemaBuilder;
+import org.apache.drill.exec.store.hive.HiveUtilities;
+import org.apache.hadoop.hive.metastore.api.FieldSchema;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.experimental.categories.Category;
+import org.junit.rules.ExpectedException;
+
+import static org.hamcrest.CoreMatchers.containsString;
+import static org.junit.Assert.assertEquals;
+
+@Category({SlowTest.class})
+public class TestSchemaConversion {
+ private static final HiveToRelDataTypeConverter dataTypeConverter
+ = new HiveToRelDataTypeConverter(new SqlTypeFactoryImpl(new
DrillRelDataTypeSystem()));
+
+ @Rule
+ public ExpectedException thrown = ExpectedException.none();
+
+ @Test
+ public void testPrimitiveSchema() {
+ verifyConversion("int", Types.optional(TypeProtos.MinorType.INT));
+ verifyConversion("varchar(123)",
TypeProtos.MajorType.newBuilder().setMinorType(TypeProtos.MinorType.VARCHAR).setPrecision(123).build());
+ verifyConversion("timestamp",
Types.optional(TypeProtos.MinorType.TIMESTAMP));
+ }
+
+ @Test
+ public void testStructSchema() {
+ ColumnMetadata expectedSchema = new SchemaBuilder()
+ .addMap("a")
+ .addNullable("t1", TypeProtos.MinorType.BIT)
+ .addNullable("t2", TypeProtos.MinorType.INT)
+ .resumeSchema()
+ .buildSchema()
+ .metadata(0);
+ verifyConversion("struct<t1:boolean,t2:int>", expectedSchema);
+
+ expectedSchema = new SchemaBuilder()
+ .addMap("a")
+ .addNullable("t1", TypeProtos.MinorType.BIT)
+ .addMap("t2")
+ .addNullable("t3", TypeProtos.MinorType.VARDECIMAL, 38, 8)
+ .resumeMap()
+ .resumeSchema()
+ .buildSchema()
+ .metadata(0);
+ verifyConversion("struct<t1:boolean,t2:struct<t3:decimal(38,8)>>",
expectedSchema);
+ }
+
+ @Test
+ public void testRepeatedSchema() {
+ verifyConversion("array<boolean>",
Types.repeated(TypeProtos.MinorType.BIT));
+
+ ColumnMetadata expectedSchema = new SchemaBuilder()
+ .addRepeatedList("a")
+ .addArray(TypeProtos.MinorType.BIT)
+ .resumeSchema()
+ .buildSchema()
+ .metadata(0);
+ verifyConversion("array<array<boolean>>", expectedSchema);
+
+ expectedSchema = new SchemaBuilder()
+ .addRepeatedList("a")
+ .addDimension()
+ .addArray(TypeProtos.MinorType.BIT)
+ .resumeList()
+ .resumeSchema()
+ .buildSchema()
+ .metadata(0);
+ verifyConversion("array<array<array<boolean>>>", expectedSchema);
+ }
+
+ @Test
+ public void testRepeatedStructSchema() {
+ ColumnMetadata expectedSchema = new SchemaBuilder()
+ .addMapArray("a")
+ .addNullable("t1", TypeProtos.MinorType.VARCHAR, 999)
+ .addNullable("t2", TypeProtos.MinorType.INT)
+ .resumeSchema()
+ .buildSchema()
+ .metadata(0);
+ verifyConversion("array<struct<t1:varchar(999),t2:int>>", expectedSchema);
+
+ expectedSchema = new SchemaBuilder()
+ .addRepeatedList("a")
+ .addMapArray()
+ .addNullable("t1", TypeProtos.MinorType.VARCHAR, 999)
+ .addNullable("t2", TypeProtos.MinorType.INT)
+ .resumeList()
+ .resumeSchema()
+ .buildSchema()
+ .metadata(0);
+ verifyConversion("array<array<struct<t1:varchar(999),t2:int>>>",
expectedSchema);
+
+ expectedSchema = new SchemaBuilder()
+ .addRepeatedList("a")
+ .addDimension()
+ .addMapArray()
+ .addRepeatedList("t1")
+ .addArray(TypeProtos.MinorType.VARCHAR, 999)
+ .resumeMap()
+ .addArray("t2", TypeProtos.MinorType.VARDECIMAL, 28, 14)
+ .resumeList()
+ .resumeList()
+ .resumeSchema()
+ .buildSchema()
+ .metadata(0);
+
verifyConversion("array<array<array<struct<t1:array<array<varchar(999)>>,t2:array<decimal(28,14)>>>>>",
expectedSchema);
+ }
+
+ @Test
+ public void testUnionSchema() {
+ thrown.expect(UnsupportedOperationException.class);
+ thrown.expectMessage(containsString("Unsupported data type"));
Review comment:
Nit: checking exception message is brittle. I would say checking that we
receive `UnsupportedOperationException` is enough.
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> Use Hive schema for MaprDB native reader when field was empty
> -------------------------------------------------------------
>
> Key: DRILL-7313
> URL: https://issues.apache.org/jira/browse/DRILL-7313
> Project: Apache Drill
> Issue Type: Task
> Affects Versions: 1.16.0
> Reporter: Volodymyr Vysotskyi
> Assignee: Volodymyr Vysotskyi
> Priority: Major
> Fix For: 1.17.0
>
>
> Currently, when an external Hive MaprDB table is queried using hive plugin
> with enabled {{store.hive.maprdb_json.optimize_scan_with_native_reader}},
> some queries may fail due to soft schema change, though Hive knows actual
> data types.
> For example, when we have a table with several fields, and one of them has
> only several non-null values, queries with grouping by such field will fail
> due to schema change.
> The goal of this Jira is to allow using types from Hive when a non-existing
> field is created, so it will allow avoiding such issues.
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