Java tutorial
/* * 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 dbx.compute.spark.jobs.sql.hive; // $example on:spark_hive$ import java.io.Serializable; import java.util.ArrayList; import java.util.List; import org.apache.spark.api.java.function.MapFunction; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Encoders; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; // $example off:spark_hive$ public class JavaSparkHiveExample { // $example on:spark_hive$ public static class Record implements Serializable { private int key; private String value; public int getKey() { return key; } public void setKey(int key) { this.key = key; } public String getValue() { return value; } public void setValue(String value) { this.value = value; } } // $example off:spark_hive$ public static void main(String[] args) { // $example on:spark_hive$ // warehouseLocation points to the default location for managed databases and tables String warehouseLocation = "file:" + System.getProperty("user.dir") + "spark-warehouse"; SparkSession spark = SparkSession.builder().appName("Java Spark Hive Example") .config("spark.sql.warehouse.dir", warehouseLocation).enableHiveSupport().getOrCreate(); spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"); spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src"); // Queries are expressed in HiveQL spark.sql("SELECT * FROM src").show(); // +---+-------+ // |key| value| // +---+-------+ // |238|val_238| // | 86| val_86| // |311|val_311| // ... // Aggregation queries are also supported. spark.sql("SELECT COUNT(*) FROM src").show(); // +--------+ // |count(1)| // +--------+ // | 500 | // +--------+ // The results of SQL queries are themselves DataFrames and support all normal functions. Dataset<Row> sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key"); // The items in DaraFrames are of type Row, which lets you to access each column by ordinal. Dataset<String> stringsDS = sqlDF.map(new MapFunction<Row, String>() { public String call(Row row) throws Exception { return "Key: " + row.get(0) + ", Value: " + row.get(1); } }, Encoders.STRING()); stringsDS.show(); // +--------------------+ // | value| // +--------------------+ // |Key: 0, Value: val_0| // |Key: 0, Value: val_0| // |Key: 0, Value: val_0| // ... // You can also use DataFrames to create temporary views within a HiveContext. List<Record> records = new ArrayList<Record>(); for (int key = 1; key < 100; key++) { Record record = new Record(); record.setKey(key); record.setValue("val_" + key); records.add(record); } Dataset<Row> recordsDF = spark.createDataFrame(records, Record.class); recordsDF.createOrReplaceTempView("records"); // Queries can then join DataFrames data with data stored in Hive. spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show(); // +---+------+---+------+ // |key| value|key| value| // +---+------+---+------+ // | 2| val_2| 2| val_2| // | 2| val_2| 2| val_2| // | 4| val_4| 4| val_4| // ... // $example off:spark_hive$ spark.stop(); } }