/* * Copyright (c) 2017 Uber Technologies, Inc. (hoodie-dev-group@uber.com) * * Licensed 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. * * */ import com.beust.jcommander.JCommander; import com.beust.jcommander.Parameter; import com.uber.hoodie.DataSourceReadOptions; import com.uber.hoodie.DataSourceWriteOptions; import com.uber.hoodie.HoodieDataSourceHelpers; import com.uber.hoodie.common.HoodieTestDataGenerator; import com.uber.hoodie.common.model.HoodieTableType; import com.uber.hoodie.config.HoodieWriteConfig; import java.util.List; import org.apache.hadoop.fs.FileSystem; import org.apache.log4j.LogManager; import org.apache.log4j.Logger; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.DataFrameWriter; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SaveMode; import org.apache.spark.sql.SparkSession; /** * Sample program that writes & reads hoodie datasets via the Spark datasource */ public class HoodieJavaApp { @Parameter(names = {"--table-path", "-p"}, description = "path for Hoodie sample table") private String tablePath = "file:///tmp/hoodie/sample-table"; @Parameter(names = {"--table-name", "-n"}, description = "table name for Hoodie sample table") private String tableName = "hoodie_test"; @Parameter(names = {"--table-type", "-t"}, description = "One of COPY_ON_WRITE or MERGE_ON_READ") private String tableType = HoodieTableType.COPY_ON_WRITE.name(); @Parameter(names = {"--hive-sync", "-hv"}, description = "Enable syncing to hive") private Boolean enableHiveSync = false; @Parameter(names = {"--hive-db", "-hd"}, description = "hive database") private String hiveDB = "default"; @Parameter(names = {"--hive-table", "-ht"}, description = "hive table") private String hiveTable = "hoodie_sample_test"; @Parameter(names = {"--hive-user", "-hu"}, description = "hive username") private String hiveUser = "hive"; @Parameter(names = {"--hive-password", "-hp"}, description = "hive password") private String hivePass = "hive"; @Parameter(names = {"--hive-url", "-hl"}, description = "hive JDBC URL") private String hiveJdbcUrl = "jdbc:hive://localhost:10000"; @Parameter(names = {"--help", "-h"}, help = true) public Boolean help = false; private static Logger logger = LogManager.getLogger(HoodieJavaApp.class); public static void main(String[] args) throws Exception { HoodieJavaApp cli = new HoodieJavaApp(); JCommander cmd = new JCommander(cli, args); if (cli.help) { cmd.usage(); System.exit(1); } cli.run(); } public void run() throws Exception { // Spark session setup.. SparkSession spark = SparkSession.builder().appName("Hoodie Spark APP") .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer").master("local[1]") .getOrCreate(); JavaSparkContext jssc = new JavaSparkContext(spark.sparkContext()); FileSystem fs = FileSystem.get(jssc.hadoopConfiguration()); // Generator of some records to be loaded in. HoodieTestDataGenerator dataGen = new HoodieTestDataGenerator(); /** * Commit with only inserts */ // Generate some input.. List records1 = DataSourceTestUtils.convertToStringList( dataGen.generateInserts("001"/* ignore */, 100)); Dataset inputDF1 = spark.read().json(jssc.parallelize(records1, 2)); // Save as hoodie dataset (copy on write) DataFrameWriter writer = inputDF1.write().format("com.uber.hoodie") // specify the hoodie source .option("hoodie.insert.shuffle.parallelism", "2") // any hoodie client config can be passed like this .option("hoodie.upsert.shuffle.parallelism", "2") // full list in HoodieWriteConfig & its package .option(DataSourceWriteOptions.STORAGE_TYPE_OPT_KEY(), tableType) // Hoodie Table Type .option(DataSourceWriteOptions.OPERATION_OPT_KEY(), DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL()) // insert .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY(), "_row_key") // This is the record key .option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY(), "partition") // this is the partition to place it into .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY(), "timestamp") // use to combine duplicate records in input/with disk val .option(HoodieWriteConfig.TABLE_NAME, tableName) // Used by hive sync and queries .mode( SaveMode.Overwrite); // This will remove any existing data at path below, and create a updateHiveSyncConfig(writer); // new dataset if needed writer.save(tablePath); // ultimately where the dataset will be placed String commitInstantTime1 = HoodieDataSourceHelpers.latestCommit(fs, tablePath); logger.info("First commit at instant time :" + commitInstantTime1); /** * Commit that updates records */ List records2 = DataSourceTestUtils.convertToStringList( dataGen.generateUpdates("002"/* ignore */, 100)); Dataset inputDF2 = spark.read().json(jssc.parallelize(records2, 2)); writer = inputDF2.write().format("com.uber.hoodie").option("hoodie.insert.shuffle.parallelism", "2") .option("hoodie.upsert.shuffle.parallelism", "2") .option(DataSourceWriteOptions.STORAGE_TYPE_OPT_KEY(), tableType) // Hoodie Table Type .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY(), "_row_key") .option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY(), "partition") .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY(), "timestamp") .option(HoodieWriteConfig.TABLE_NAME, tableName).mode(SaveMode.Append); updateHiveSyncConfig(writer); writer.save(tablePath); String commitInstantTime2 = HoodieDataSourceHelpers.latestCommit(fs, tablePath); logger.info("Second commit at instant time :" + commitInstantTime1); /** * Read & do some queries */ Dataset hoodieROViewDF = spark.read().format("com.uber.hoodie") // pass any path glob, can include hoodie & non-hoodie // datasets .load(tablePath + "/*/*/*/*"); hoodieROViewDF.registerTempTable("hoodie_ro"); spark.sql("describe hoodie_ro").show(); // all trips whose fare was greater than 2. spark.sql("select fare, begin_lon, begin_lat, timestamp from hoodie_ro where fare > 2.0") .show(); if (tableType.equals(HoodieTableType.COPY_ON_WRITE.name())) { /** * Consume incrementally, only changes in commit 2 above. Currently only supported for COPY_ON_WRITE TABLE */ Dataset hoodieIncViewDF = spark.read().format("com.uber.hoodie") .option(DataSourceReadOptions.VIEW_TYPE_OPT_KEY(), DataSourceReadOptions.VIEW_TYPE_INCREMENTAL_OPT_VAL()) .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(), commitInstantTime1) // Only changes in write 2 above .load( tablePath); // For incremental view, pass in the root/base path of dataset logger.info("You will only see records from : " + commitInstantTime2); hoodieIncViewDF.groupBy(hoodieIncViewDF.col("_hoodie_commit_time")).count().show(); } } /** * Setup configs for syncing to hive * @param writer * @return */ private DataFrameWriter updateHiveSyncConfig(DataFrameWriter writer) { if (enableHiveSync) { logger.info("Enabling Hive sync to " + hiveJdbcUrl); writer = writer.option(DataSourceWriteOptions.HIVE_TABLE_OPT_KEY(), hiveTable) .option(DataSourceWriteOptions.HIVE_DATABASE_OPT_KEY(), hiveDB) .option(DataSourceWriteOptions.HIVE_URL_OPT_KEY(), hiveJdbcUrl) .option(DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY(), "dateStr") .option(DataSourceWriteOptions.HIVE_USER_OPT_KEY(), hiveUser) .option(DataSourceWriteOptions.HIVE_PASS_OPT_KEY(), hivePass) .option(DataSourceWriteOptions.HIVE_SYNC_ENABLED_OPT_KEY(), "true"); } return writer; } }