RussellSpitzer commented on a change in pull request #2282: URL: https://github.com/apache/iceberg/pull/2282#discussion_r584896259
########## File path: spark/src/test/java/org/apache/iceberg/spark/source/TestTimestampWithoutZone.java ########## @@ -0,0 +1,225 @@ +/* + * 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.iceberg.spark.source; + +import java.io.File; +import java.io.IOException; +import java.time.LocalDateTime; +import java.util.List; +import java.util.Locale; +import java.util.UUID; +import java.util.stream.Collectors; +import org.apache.hadoop.conf.Configuration; +import org.apache.iceberg.DataFile; +import org.apache.iceberg.DataFiles; +import org.apache.iceberg.FileFormat; +import org.apache.iceberg.PartitionSpec; +import org.apache.iceberg.Schema; +import org.apache.iceberg.Table; +import org.apache.iceberg.data.GenericAppenderFactory; +import org.apache.iceberg.data.GenericRecord; +import org.apache.iceberg.data.Record; +import org.apache.iceberg.hadoop.HadoopTables; +import org.apache.iceberg.io.FileAppender; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.spark.data.GenericsHelpers; +import org.apache.iceberg.types.Types; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SparkSession; +import org.junit.AfterClass; +import org.junit.Assert; +import org.junit.Before; +import org.junit.BeforeClass; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.ExpectedException; +import org.junit.rules.TemporaryFolder; +import org.junit.runner.RunWith; +import org.junit.runners.Parameterized; + +import static org.apache.iceberg.Files.localOutput; + +@RunWith(Parameterized.class) +public abstract class TestTimestampWithoutZone { + private static final Configuration CONF = new Configuration(); + private static final HadoopTables TABLES = new HadoopTables(CONF); + + private static final Schema SCHEMA = new Schema( + Types.NestedField.required(1, "id", Types.LongType.get()), + Types.NestedField.optional(2, "ts", Types.TimestampType.withoutZone()), + Types.NestedField.optional(3, "data", Types.StringType.get()) + ); + + private static SparkSession spark = null; + + @BeforeClass + public static void startSpark() { + TestTimestampWithoutZone.spark = SparkSession.builder().master("local[2]").getOrCreate(); + } + + @AfterClass + public static void stopSpark() { + SparkSession currentSpark = TestTimestampWithoutZone.spark; + TestTimestampWithoutZone.spark = null; + currentSpark.stop(); + } + + @Rule + public TemporaryFolder temp = new TemporaryFolder(); + + private final String format; + private final boolean vectorized; + + @Parameterized.Parameters(name = "format = {0}, vectorized = {1}") + public static Object[][] parameters() { + return new Object[][] { + { "parquet", false }, + { "parquet", true }, + { "avro", false } + }; + } + + public TestTimestampWithoutZone(String format, boolean vectorized) { + this.format = format; + this.vectorized = vectorized; + } + + private File parent = null; + private File unpartitioned = null; + private List<Record> records = null; + + @Before + public void writeUnpartitionedTable() throws IOException { + this.parent = temp.newFolder("TestTimestampWithoutZone"); + this.unpartitioned = new File(parent, "unpartitioned"); + File dataFolder = new File(unpartitioned, "data"); + Assert.assertTrue("Mkdir should succeed", dataFolder.mkdirs()); + + Table table = TABLES.create(SCHEMA, PartitionSpec.unpartitioned(), unpartitioned.toString()); + Schema tableSchema = table.schema(); // use the table schema because ids are reassigned + + FileFormat fileFormat = FileFormat.valueOf(format.toUpperCase(Locale.ENGLISH)); + + File testFile = new File(dataFolder, fileFormat.addExtension(UUID.randomUUID().toString())); + + // create records using the table's schema + this.records = testRecords(tableSchema); + + try (FileAppender<Record> writer = new GenericAppenderFactory(tableSchema).newAppender( + localOutput(testFile), fileFormat)) { + writer.addAll(records); + } + + DataFile file = DataFiles.builder(PartitionSpec.unpartitioned()) + .withRecordCount(records.size()) + .withFileSizeInBytes(testFile.length()) + .withPath(testFile.toString()) + .build(); + + table.newAppend().appendFile(file).commit(); + } + + @Test + public void testUnpartitionedTimestampWithoutZone() { + assertEqualsSafe(SCHEMA.asStruct(), records, read(unpartitioned.toString(), vectorized)); + } + + @Test + public void testUnpartitionedTimestampWithoutZoneProjection() { + Schema projection = SCHEMA.select("id", "ts"); + assertEqualsSafe(projection.asStruct(), + records.stream().map(r -> projectFlat(projection, r)).collect(Collectors.toList()), + read(unpartitioned.toString(), vectorized, "id", "ts")); + } + + @Rule + public ExpectedException exception = ExpectedException.none(); + + @Test + public void testUnpartitionedTimestampWithoutZoneError() { + exception.expect(IllegalArgumentException.class); + exception.expectMessage("Spark does not support timestamp without time zone fields"); + + spark.read().format("iceberg") + .option("vectorization-enabled", String.valueOf(vectorized)) + .option("read-timestamp-without-zone", "false") + .load(unpartitioned.toString()) + .collectAsList(); + } + + private static Record projectFlat(Schema projection, Record record) { + Record result = GenericRecord.create(projection); + List<Types.NestedField> fields = projection.asStruct().fields(); + for (int i = 0; i < fields.size(); i += 1) { + Types.NestedField field = fields.get(i); + result.set(i, record.getField(field.name())); + } + return result; + } + + public static void assertEqualsSafe(Types.StructType struct, + List<Record> expected, List<Row> actual) { + Assert.assertEquals("Number of results should match expected", expected.size(), actual.size()); + for (int i = 0; i < expected.size(); i += 1) { + GenericsHelpers.assertEqualsSafe(struct, expected.get(i), actual.get(i)); + } + } + + private List<Record> testRecords(Schema schema) { + return Lists.newArrayList( + record(schema, 0L, parseToLocal("2017-12-22T09:20:44.294658"), "junction"), + record(schema, 1L, parseToLocal("2017-12-22T07:15:34.582910"), "alligator"), + record(schema, 2L, parseToLocal("2017-12-22T06:02:09.243857"), "forrest"), + record(schema, 3L, parseToLocal("2017-12-22T03:10:11.134509"), "clapping"), + record(schema, 4L, parseToLocal("2017-12-22T00:34:00.184671"), "brush"), + record(schema, 5L, parseToLocal("2017-12-21T22:20:08.935889"), "trap"), + record(schema, 6L, parseToLocal("2017-12-21T21:55:30.589712"), "element"), + record(schema, 7L, parseToLocal("2017-12-21T17:31:14.532797"), "limited"), + record(schema, 8L, parseToLocal("2017-12-21T15:21:51.237521"), "global"), + record(schema, 9L, parseToLocal("2017-12-21T15:02:15.230570"), "goldfish") + ); + } + + private static List<Row> read(String table, boolean vectorized) { + return read(table, vectorized, "*"); + } + + private static List<Row> read(String table, boolean vectorized, String select0, String... selectN) { + Dataset<Row> dataset = spark.read().format("iceberg") + .option("vectorization-enabled", String.valueOf(vectorized)) + .option("read-timestamp-without-zone", "true") Review comment: ah nvm, I see the test above ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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