unical1988 commented on code in PR #728: URL: https://github.com/apache/incubator-xtable/pull/728#discussion_r2346959018
########## xtable-core/src/test/java/org/apache/xtable/parquet/ITParquetConversionSource.java: ########## @@ -0,0 +1,566 @@ +/* + * 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.xtable.parquet; + +import static org.apache.xtable.GenericTable.getTableName; +import static org.apache.xtable.model.storage.TableFormat.*; +import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; + +import java.net.URISyntaxException; +import java.nio.file.Path; +import java.nio.file.Paths; +import java.sql.Timestamp; +import java.time.Duration; +import java.time.Instant; +import java.time.ZoneId; +import java.time.format.DateTimeFormatter; +import java.time.temporal.ChronoUnit; +import java.util.*; +import java.util.Arrays; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Optional; +import java.util.Properties; +import java.util.function.Function; +import java.util.stream.Collectors; +import java.util.stream.Stream; + +import lombok.Builder; +import lombok.Value; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.*; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.MetadataBuilder; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.junit.jupiter.api.AfterAll; +import org.junit.jupiter.api.BeforeAll; +import org.junit.jupiter.api.io.TempDir; +import org.junit.jupiter.params.ParameterizedTest; +import org.junit.jupiter.params.provider.Arguments; +import org.junit.jupiter.params.provider.MethodSource; + +import org.apache.hudi.client.HoodieReadClient; +import org.apache.hudi.common.config.HoodieMetadataConfig; + +import org.apache.xtable.GenericTable; +import org.apache.xtable.conversion.*; +import org.apache.xtable.conversion.ConversionConfig; +import org.apache.xtable.conversion.ConversionController; +import org.apache.xtable.conversion.ConversionSourceProvider; +import org.apache.xtable.conversion.SourceTable; +import org.apache.xtable.conversion.TargetTable; +import org.apache.xtable.hudi.HudiTestUtil; +import org.apache.xtable.model.sync.SyncMode; + +public class ITParquetConversionSource { + public static final String PARTITION_FIELD_SPEC_CONFIG = + "xtable.parquet.source.partition_field_spec_config"; + private static final DateTimeFormatter DATE_FORMAT = + DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSS").withZone(ZoneId.of("UTC")); + @TempDir public static Path tempDir; + private static JavaSparkContext jsc; + private static SparkSession sparkSession; + private static StructType schema; + + @BeforeAll + public static void setupOnce() { + SparkConf sparkConf = HudiTestUtil.getSparkConf(tempDir); + + String extraJavaOptions = "--add-opens=java.base/sun.nio.ch=ALL-UNNAMED"; + sparkConf.set("spark.driver.extraJavaOptions", extraJavaOptions); + sparkConf = HoodieReadClient.addHoodieSupport(sparkConf); + sparkConf.set("parquet.avro.write-old-list-structure", "false"); + String javaOpts = + "--add-opens=java.base/java.nio=ALL-UNNAMED " + + "--add-opens=java.base/java.lang=ALL-UNNAMED " + + "--add-opens=java.base/java.util=ALL-UNNAMED " + + "--add-opens=java.base/java.util.concurrent=ALL-UNNAMED " + + "--add-opens=java.base/java.io=ALL-UNNAMED" + + "--add-opens=java.base/java.lang.invoke=ALL-UNNAMED" + + "--add-opens=java.base/java.lang.invoke=ALL-UNNAMED"; + + sparkConf.set("spark.driver.extraJavaOptions", javaOpts); + sparkConf.set("spark.executor.extraJavaOptions", javaOpts); + sparkConf.set("spark.sql.parquet.writeLegacyFormat", "false"); + sparkConf.set("spark.sql.parquet.outputTimestampType", "TIMESTAMP_MICROS"); + + sparkSession = SparkSession.builder().config(sparkConf).getOrCreate(); + jsc = JavaSparkContext.fromSparkContext(sparkSession.sparkContext()); + } + + @AfterAll + public static void teardown() { + if (jsc != null) { + jsc.stop(); + jsc = null; + } + if (sparkSession != null) { + sparkSession.stop(); + sparkSession = null; + } + } + + private static Stream<Arguments> provideArgsForFilePartitionTesting() { + boolean isPartitioned = true; + String timestampFilter = + String.format( + "timestamp_micros_nullable_field < timestamp_millis(%s)", + Instant.now().truncatedTo(ChronoUnit.DAYS).minus(2, ChronoUnit.DAYS).toEpochMilli()); + String levelFilter = "level = 'INFO'"; + String nestedLevelFilter = "nested_record.level = 'INFO'"; + String severityFilter = "severity = 1"; + String timestampAndLevelFilter = String.format("%s and %s", timestampFilter, levelFilter); + String partitionConfig = "timestamp:MONTH:year=YYYY/month=MM"; + return Stream.of( + Arguments.of( + buildArgsForPartition( + PARQUET, + Arrays.asList(ICEBERG, DELTA, HUDI), + // "timestamp:YEAR:year=YYYY", // ts:DAY:year=YYYY/month=MM/day=DD + partitionConfig, // ts:DAY:year=YYYY/month=MM/day=DD + // "year=YYYY/month=MM/day=DD" + // "timestamp:YEAR:year=YYYY", + partitionConfig, + levelFilter))); + } + + private static TableFormatPartitionDataHolder buildArgsForPartition( + String sourceFormat, + List<String> targetFormats, + String hudiPartitionConfig, + String xTablePartitionConfig, + String filter) { + return TableFormatPartitionDataHolder.builder() + .sourceTableFormat(sourceFormat) + .targetTableFormats(targetFormats) + .hudiSourceConfig(Optional.ofNullable(hudiPartitionConfig)) + .xTablePartitionConfig(xTablePartitionConfig) + .filter(filter) + .build(); + } + + private static ConversionConfig getTableSyncConfig( + String sourceTableFormat, + SyncMode syncMode, + String tableName, + GenericTable table, + List<String> targetTableFormats, + String partitionConfig, + Duration metadataRetention) { + Properties sourceProperties = new Properties(); + if (partitionConfig != null) { + sourceProperties.put(PARTITION_FIELD_SPEC_CONFIG, partitionConfig); + } + SourceTable sourceTable = + SourceTable.builder() + .name(tableName) + .formatName(sourceTableFormat) + .basePath(table.getBasePath()) + .dataPath(table.getDataPath()) + .additionalProperties(sourceProperties) + .build(); + + List<TargetTable> targetTables = + targetTableFormats.stream() + .map( + formatName -> + TargetTable.builder() + .name(tableName) + .formatName(formatName) + // set the metadata path to the data path as the default (required by Hudi) + .basePath(table.getDataPath()) + .metadataRetention(metadataRetention) + .build()) + .collect(Collectors.toList()); + + return ConversionConfig.builder() + .sourceTable(sourceTable) + .targetTables(targetTables) + .syncMode(syncMode) + .build(); + } + + private static Stream<Arguments> provideArgsForFileNonPartitionTesting() { + boolean isPartitioned = false; + String timestampFilter = + String.format( + "timestamp_micros_nullable_field < timestamp_millis(%s)", + Instant.now().truncatedTo(ChronoUnit.DAYS).minus(2, ChronoUnit.DAYS).toEpochMilli()); + String levelFilter = "level = 'INFO'"; + String nestedLevelFilter = "nested_record.level = 'INFO'"; + String severityFilter = "severity = 1"; + String timestampAndLevelFilter = String.format("%s and %s", timestampFilter, levelFilter); + String partitionConfig = null; + return Stream.of( + Arguments.of( + buildArgsForPartition( + PARQUET, + Arrays.asList(ICEBERG, DELTA, HUDI), + partitionConfig, + partitionConfig, + levelFilter))); + } + + private ConversionSourceProvider<?> getConversionSourceProvider(String sourceTableFormat) { + if (sourceTableFormat.equalsIgnoreCase(PARQUET)) { + ConversionSourceProvider<Long> parquetConversionSourceProvider = + new ParquetConversionSourceProvider(); + parquetConversionSourceProvider.init(jsc.hadoopConfiguration()); + return parquetConversionSourceProvider; + } else { + throw new IllegalArgumentException("Unsupported source format: " + sourceTableFormat); + } + } + + @ParameterizedTest + @MethodSource("provideArgsForFileNonPartitionTesting") + public void testFileNonPartitionedData( + TableFormatPartitionDataHolder tableFormatPartitionDataHolder) throws URISyntaxException { + String tableName = getTableName(); + String sourceTableFormat = tableFormatPartitionDataHolder.getSourceTableFormat(); + List<String> targetTableFormats = tableFormatPartitionDataHolder.getTargetTableFormats(); + // Optional<String> hudiPartitionConfig = tableFormatPartitionDataHolder.getHudiSourceConfig(); + String xTablePartitionConfig = tableFormatPartitionDataHolder.getXTablePartitionConfig(); + String filter = tableFormatPartitionDataHolder.getFilter(); + ConversionSourceProvider<?> conversionSourceProvider = + getConversionSourceProvider(sourceTableFormat); + + List<Row> data = + Arrays.asList( + RowFactory.create(1, "Alice", true, 30.1, new Timestamp(System.currentTimeMillis())), + RowFactory.create( + 2, "Bob", false, 24.6, new Timestamp(System.currentTimeMillis() + 1000)), + RowFactory.create( + 3, "Charlie", true, 35.2, new Timestamp(System.currentTimeMillis() + 2000)), + RowFactory.create( + 4, "David", false, 29.5, new Timestamp(System.currentTimeMillis() + 3000)), + RowFactory.create( + 5, "Eve", true, 22.2, new Timestamp(System.currentTimeMillis() + 4000))); + + schema = + DataTypes.createStructType( + new StructField[] { + DataTypes.createStructField("id", DataTypes.IntegerType, false), + DataTypes.createStructField("name", DataTypes.StringType, false), + DataTypes.createStructField("hasSiblings", DataTypes.BooleanType, false), + DataTypes.createStructField("age", DataTypes.DoubleType, false), + DataTypes.createStructField( + "timestamp", + DataTypes.TimestampType, + false, + new MetadataBuilder().putString("precision", "millis").build()) + }); + Dataset<Row> df = sparkSession.createDataFrame(data, schema); + String dataPath = tempDir.toAbsolutePath().toString() + "/non_partitioned_data"; + df.write().mode(SaveMode.Overwrite).parquet(dataPath); + GenericTable table; + table = + GenericTable.getInstance( + tableName, Paths.get(dataPath), sparkSession, jsc, sourceTableFormat, false); + try (GenericTable tableToClose = table) { + ConversionConfig conversionConfig = + getTableSyncConfig( + sourceTableFormat, + SyncMode.FULL, + tableName, + table, + targetTableFormats, + xTablePartitionConfig, + null); + ConversionController conversionController = + new ConversionController(jsc.hadoopConfiguration()); + conversionController.sync(conversionConfig, conversionSourceProvider); + checkDatasetEquivalenceWithFilter( + sourceTableFormat, tableToClose, targetTableFormats, filter); + } catch (URISyntaxException e) { + throw e; + } + } + + @ParameterizedTest + @MethodSource("provideArgsForFilePartitionTesting") + public void testFilePartitionedData(TableFormatPartitionDataHolder tableFormatPartitionDataHolder) + throws URISyntaxException { + String tableName = getTableName(); + String sourceTableFormat = tableFormatPartitionDataHolder.getSourceTableFormat(); + List<String> targetTableFormats = tableFormatPartitionDataHolder.getTargetTableFormats(); + // Optional<String> hudiPartitionConfig = tableFormatPartitionDataHolder.getHudiSourceConfig(); + String xTablePartitionConfig = tableFormatPartitionDataHolder.getXTablePartitionConfig(); + String filter = tableFormatPartitionDataHolder.getFilter(); + ConversionSourceProvider<?> conversionSourceProvider = + getConversionSourceProvider(sourceTableFormat); + // create the data + List<Row> data = + Arrays.asList( + RowFactory.create(1, "Alice", true, 30.1, new Timestamp(System.currentTimeMillis())), + RowFactory.create( + 2, "Bob", false, 24.6, new Timestamp(System.currentTimeMillis() + 1000)), + RowFactory.create( + 3, "Charlie", true, 35.2, new Timestamp(System.currentTimeMillis() + 2000)), + RowFactory.create( + 4, "David", false, 29.5, new Timestamp(System.currentTimeMillis() + 3000)), + RowFactory.create( + 5, "Eve", true, 22.2, new Timestamp(System.currentTimeMillis() + 4000))); + + schema = + DataTypes.createStructType( + new StructField[] { + DataTypes.createStructField("id", DataTypes.IntegerType, false), + DataTypes.createStructField("name", DataTypes.StringType, false), + DataTypes.createStructField("hasSiblings", DataTypes.BooleanType, false), + DataTypes.createStructField("age", DataTypes.DoubleType, false), + DataTypes.createStructField( + "timestamp", + DataTypes.TimestampType, + false, + new MetadataBuilder().putString("precision", "millis").build()) + }); + Dataset<Row> df = sparkSession.createDataFrame(data, schema); + String dataPathPart = tempDir.toAbsolutePath().toString() + "/partitioned_data"; + df.withColumn("year", functions.year(functions.col("timestamp").cast(DataTypes.TimestampType))) + .withColumn( + "month", + functions.date_format(functions.col("timestamp").cast(DataTypes.TimestampType), "MM")) + .write() + .mode(SaveMode.Overwrite) + .partitionBy("year", "month") + .parquet(dataPathPart); + GenericTable table; + table = + GenericTable.getInstance( + tableName, Paths.get(dataPathPart), sparkSession, jsc, sourceTableFormat, true); + try (GenericTable tableToClose = table) { + ConversionConfig conversionConfig = + getTableSyncConfig( + sourceTableFormat, + SyncMode.FULL, + tableName, + table, + targetTableFormats, + xTablePartitionConfig, + null); + ConversionController conversionController = + new ConversionController(jsc.hadoopConfiguration()); + conversionController.sync(conversionConfig, conversionSourceProvider); + checkDatasetEquivalenceWithFilter( + sourceTableFormat, tableToClose, targetTableFormats, filter); + // update the current parquet file data with another attribute the sync again + List<Row> dataToAppend = + Arrays.asList( + RowFactory.create( + 10, + "BobAppended", + false, + 70.3, + new Timestamp(System.currentTimeMillis() + 1500))); + + Dataset<Row> dfAppend = sparkSession.createDataFrame(dataToAppend, schema); + String dataPathFinal = tempDir.toAbsolutePath().toString(); + dfAppend + .withColumn( + "year", functions.year(functions.col("timestamp").cast(DataTypes.TimestampType))) + .withColumn( + "month", + functions.date_format(functions.col("timestamp").cast(DataTypes.TimestampType), "MM")) + .write() + .mode(SaveMode.Append) + .partitionBy("year", "month") + .parquet(dataPathPart); + GenericTable tableAppend; + tableAppend = + GenericTable.getInstance( + tableName, Paths.get(dataPathPart), sparkSession, jsc, sourceTableFormat, true); + try (GenericTable tableToCloseAppended = tableAppend) { + ConversionConfig conversionConfigAppended = + getTableSyncConfig( + sourceTableFormat, + SyncMode.FULL, + tableName, + tableAppend, + targetTableFormats, + xTablePartitionConfig, + null); + ConversionController conversionControllerAppended = + new ConversionController(jsc.hadoopConfiguration()); + conversionControllerAppended.sync(conversionConfigAppended, conversionSourceProvider); + } + + } catch (URISyntaxException e) { + throw e; + } + } + + private void checkDatasetEquivalenceWithFilter( + String sourceFormat, + GenericTable<?, ?> sourceTable, + List<String> targetFormats, + String filter) + throws URISyntaxException { + checkDatasetEquivalence( + sourceFormat, + sourceTable, + Collections.emptyMap(), + targetFormats, + Collections.emptyMap(), + null, + filter); + } + + private void checkDatasetEquivalence( + String sourceFormat, + GenericTable<?, ?> sourceTable, + List<String> targetFormats, + Integer expectedCount) + throws URISyntaxException { + checkDatasetEquivalence( + sourceFormat, + sourceTable, + Collections.emptyMap(), + targetFormats, + Collections.emptyMap(), + expectedCount, + "1 = 1"); + } + + private void checkDatasetEquivalence( + String sourceFormat, + GenericTable<?, ?> sourceTable, + Map<String, String> sourceOptions, + List<String> targetFormats, + Map<String, Map<String, String>> targetOptions, + Integer expectedCount) + throws URISyntaxException { + checkDatasetEquivalence( + sourceFormat, + sourceTable, + sourceOptions, + targetFormats, + targetOptions, + expectedCount, + "1 = 1"); + } + + private void checkDatasetEquivalence( + String sourceFormat, + GenericTable<?, ?> sourceTable, + Map<String, String> sourceOptions, + List<String> targetFormats, + Map<String, Map<String, String>> targetOptions, + Integer expectedCount, + String filterCondition) + throws URISyntaxException { + Dataset<Row> sourceRows = + sparkSession + .read() + .schema(schema) + .options(sourceOptions) + .option("recursiveFileLookup", "true") + .option("pathGlobFilter", "*.parquet") + .parquet(sourceTable.getDataPath()); // + "/**/*.parquet"); + // .orderBy(sourceTable.getOrderByColumn()) + // .filter(filterCondition); + Map<String, Dataset<Row>> targetRowsByFormat = + targetFormats.stream() + .collect( + Collectors.toMap( + Function.identity(), + targetFormat -> { + Map<String, String> finalTargetOptions = + targetOptions.getOrDefault(targetFormat, Collections.emptyMap()); + if (targetFormat.equals(HUDI)) { + finalTargetOptions = new HashMap<>(finalTargetOptions); + finalTargetOptions.put(HoodieMetadataConfig.ENABLE.key(), "true"); + finalTargetOptions.put( + "hoodie.datasource.read.extract.partition.values.from.path", "true"); + } + return sparkSession + .read() + .options(finalTargetOptions) + .format(targetFormat.toLowerCase()) + .load(sourceTable.getDataPath()); + // .orderBy(sourceTable.getOrderByColumn()) + // .filter(filterCondition); + })); + + String[] selectColumnsArr = schema.fieldNames(); + List<String> dataset1Rows = sourceRows.selectExpr(selectColumnsArr).toJSON().collectAsList(); + + Set<Map.Entry<String, Dataset<Row>>> entrySet = targetRowsByFormat.entrySet(); + + for (Map.Entry<String, Dataset<Row>> entry : entrySet) { + + String format = entry.getKey(); + + Dataset<Row> targetRows = entry.getValue(); + targetRows.show(); + + List<String> dataset2Rows = targetRows.selectExpr(selectColumnsArr).toJSON().collectAsList(); + + assertEquals( + dataset1Rows.size(), + dataset2Rows.size(), + String.format( + "Datasets have different row counts when reading from Spark. Source: %s, Target: %s", + sourceFormat, format)); + + if (expectedCount != null) { + assertEquals(expectedCount, dataset1Rows.size()); + } else { + assertFalse(dataset1Rows.isEmpty()); + } + if (!format.equals("HUDI")) { Review Comment: it is supported, but dataset equivalence are not the same for all formats given the parition approach of Hudi -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@xtable.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
