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 com.hxr.bigdata.spark.example141; import java.io.Serializable; import java.util.ArrayList; import java.util.Arrays; import java.util.List; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.Function; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.SaveMode; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType; public class JavaSparkSQL { public static class Person implements Serializable { private String name; private int age; public String getName() { return name; } public void setName(final String name) { this.name = name; } public int getAge() { return age; } public void setAge(final int age) { this.age = age; } } public static void main(final String[] args) throws Exception { SparkConf sparkConf = new SparkConf().setAppName("JavaSparkSQL"); JavaSparkContext ctx = new JavaSparkContext(sparkConf); SQLContext sqlContext = new SQLContext(ctx); System.out.println("=== Data source: RDD ==="); // Load a text file and convert each line to a Java Bean. // ?javabean? // hdfs://127.0.0.1:9000/spark/people.txt JavaRDD<Person> people = ctx.textFile("/spark/people.txt").map(new Function<String, Person>() { public Person call(final String line) { String[] parts = line.split(","); Person person = new Person(); person.setName(parts[0]); person.setAge(Integer.parseInt(parts[1].trim())); return person; } }); // Apply a schema to an RDD of Java Beans and register it as a table. // schema?javabeanRDD DataFrame schemaPeople = sqlContext.createDataFrame(people, Person.class); schemaPeople.registerTempTable("people"); // SQL can be run over RDDs that have been registered as tables. // ??sql DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); // The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. // DataFrame?RDD?RDD? List<String> teenagerNames = teenagers.toJavaRDD().map(new Function<Row, String>() { public String call(final Row row) { return "Name: " + row.getString(0); } }).collect(); for (String name : teenagerNames) { System.out.println(name); } // ------------------------??javabean-------------------------- // The schema is encoded in a string String schemaString = "name age"; // Generate the schema based on the string of schema List<StructField> fields = new ArrayList<StructField>(); for (String fieldName : schemaString.split(" ")) { fields.add(DataTypes.createStructField(fieldName, DataTypes.StringType, true)); } StructType schema = DataTypes.createStructType(fields); // Load a text file and convert each line to a JavaBean. JavaRDD<String> peopleT = ctx.textFile("/spark/people.txt"); // Convert records of the RDD (people) to Rows. JavaRDD<Row> rowRDD = peopleT.map(new Function<String, Row>() { public Row call(final String record) throws Exception { String[] fields = record.split(","); return RowFactory.create(fields[0], fields[1].trim()); } }); // Apply the schema to the RDD. DataFrame peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema); // Register the DataFrame as a table. peopleDataFrame.registerTempTable("people"); // SQL can be run over RDDs that have been registered as tables. DataFrame results = sqlContext.sql("SELECT name FROM people"); // The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. List<String> names = results.javaRDD().map(new Function<Row, String>() { public String call(final Row row) { return "Name: " + row.getString(0); } }).collect(); System.out.println("=== Data source: Parquet File ==="); // DataFrames can be saved as parquet files, maintaining the schema information. // hdfs??hdfs://127.0.0.1:9000/user/hanxirui/people.parquet // SaveMode.ErrorIfExists (default) When saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown. // SaveMode.Append When saving a DataFrame to a data source, if data/table already exists, contents of the DataFrame are expected to be appended to existing data. // SaveMode.Overwrite Overwrite mode means that when saving a DataFrame to a data source, if data/table already exists, existing data is expected to be overwritten by the contents of the DataFrame. // SaveMode.Ignore Ignore mode means that when saving a DataFrame to a data source, if data already exists, the save operation is expected to not save the contents of the DataFrame and to not change the existing data. This is similar to a CREATE TABLE IF NOT EXISTS in SQL. schemaPeople.write().mode(SaveMode.Ignore).parquet("people.parquet"); // Read in the parquet file created above. // Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a DataFrame. DataFrame parquetFile = sqlContext.read().parquet("people.parquet"); // Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); DataFrame teenagers2 = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); teenagerNames = teenagers2.toJavaRDD().map(new Function<Row, String>() { public String call(final Row row) { return "Name: " + row.getString(0); } }).collect(); for (String name : teenagerNames) { System.out.println(name); } System.out.println("=== Data source: JSON Dataset ==="); // A JSON dataset is pointed by path. // The path can be either a single text file or a directory storing text files. String path = "/spark/people.json"; // Create a DataFrame from the file(s) pointed by path DataFrame peopleFromJsonFile = sqlContext.read().json(path); // Because the schema of a JSON dataset is automatically inferred, to write queries, // it is better to take a look at what is the schema. peopleFromJsonFile.printSchema(); // The schema of people is ... // root // |-- age: IntegerType // |-- name: StringType // Register this DataFrame as a table. peopleFromJsonFile.registerTempTable("people"); // SQL statements can be run by using the sql methods provided by sqlContext. DataFrame teenagers3 = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); // The results of SQL queries are DataFrame and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. teenagerNames = teenagers3.toJavaRDD().map(new Function<Row, String>() { public String call(final Row row) { return "Name: " + row.getString(0); } }).collect(); for (String name : teenagerNames) { System.out.println(name); } // Alternatively, a DataFrame can be created for a JSON dataset represented by // a RDD[String] storing one JSON object per string. List<String> jsonData = Arrays .asList("{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); JavaRDD<String> anotherPeopleRDD = ctx.parallelize(jsonData); DataFrame peopleFromJsonRDD = sqlContext.read().json(anotherPeopleRDD.rdd()); // Take a look at the schema of this new DataFrame. peopleFromJsonRDD.printSchema(); // The schema of anotherPeople is ... // root // |-- address: StructType // | |-- city: StringType // | |-- state: StringType // |-- name: StringType peopleFromJsonRDD.registerTempTable("people2"); DataFrame peopleWithCity = sqlContext.sql("SELECT name, address.city FROM people2"); List<String> nameAndCity = peopleWithCity.toJavaRDD().map(new Function<Row, String>() { public String call(final Row row) { return "Name: " + row.getString(0) + ", City: " + row.getString(1); } }).collect(); for (String name : nameAndCity) { System.out.println(name); } ctx.stop(); } }