第67课：SparkSQL下案例综合实战学习笔记

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最新推荐文章于 2025-06-10 13:58:44 发布 · 2.2k 阅读

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#SparkSQL #DataFrame #Join

本文是关于SparkSQL的实战学习笔记，通过Java实现SparkSQL的Join操作，包括读取JSON数据、创建DataFrame、注册临时表、执行SQL查询、数据转换等步骤。示例展示了如何获取分数大于90的人员信息，并与另一数据集进行Join操作，最终展示结果并保存为JSON格式。

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第67课：SparkSQL下案例综合实战学习笔记

1 SparkSQL案例分析

2 通过Java和Scala实现案例

本课直接通过实战练习SparkSQL下的Join操作：

先用Java编写代码：

package SparkSQLByJava;

import java.util.ArrayList;

import java.util.List;

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaPairRDD;

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.api.java.function.PairFunction;

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.types.DataTypes;

import org.apache.spark.sql.types.StructField;

import org.apache.spark.sql.types.StructType;

import scala.Tuple2;

public class SparkSQLwithJoin {

public static void main(String[] args) {

SparkConf conf = new SparkConf().setMaster("local").setAppName("SparkSQLwithJoin");

JavaSparkContext sc = new JavaSparkContext(conf);

SQLContext sqlContext = new SQLContext(sc);

//针对json文件数据源来创建DataFrame

DataFrame peoplesDF = sqlContext.read().json("D:\\DT-IMF\\testdata\\peoples.json");

//基于Json构建的DataFrame来注册临时表

peoplesDF.registerTempTable("peopleScores");

//查询出分数大于90的人

DataFrame excellentScoresDF = sqlContext.sql("select name,score from peopleScores where score >90");

/**

* 在DataFrame的基础上转化成为RDD,通过Map操作计算出分数大于90的所有人的姓名

List<String> execellentScoresNameList = excellentScoresDF.javaRDD().map(new Function<Row, String>() {

@Override

public String call(Row row) throws Exception {

return row.getAs("name");

}

}).collect();

//动态组拼出JSON

List<String> peopleInformations = new ArrayList<String>();

peopleInformations.add("{\"name\":\"Michael\", \"age\":20}");

peopleInformations.add("{\"name\":\"Andy\", \"age\":17}");

peopleInformations.add("{\"name\":\"Justin\", \"age\":19}");

//通过内容为JSON的RDD来构造DataFrame

JavaRDD<String> peopleInformationsRDD = sc.parallelize(peopleInformations);

DataFrame peopleInformationsDF = sqlContext.read().json(peopleInformationsRDD);

//注册成为临时表

peopleInformationsDF.registerTempTable("peopleInformations");

String sqlText = "select name, age from peopleInformations where name in (";

for(int i =0; i < execellentScoresNameList.size(); i++){

sqlText += "'" + execellentScoresNameList.get(i) + "'";

if (i < execellentScoresNameList.size()-1){

sqlText += ",";

}

sqlText += ")";

DataFrame execellentNameAgeDF = sqlContext.sql(sqlText);

JavaPairRDD<String, Tuple2<Integer, Integer>> resultRDD = excellentScoresDF.javaRDD().mapToPair(new PairFunction<Row, String, Integer>() {

private static final long serialVersionUID = 1L;

@Override

public Tuple2<String, Integer> call(Row row) throws Exception {

return new Tuple2<String, Integer>(row.getAs("name"), (int) row.getLong(1));

}

}).join(execellentNameAgeDF.javaRDD().mapToPair(new PairFunction<Row, String, Integer>() {

private static final long serialVersionUID = 1L;

@Override

public Tuple2<String, Integer> call(Row row) throws Exception {

return new Tuple2<String, Integer>(row.getAs("name"), (int) row.getLong(1));

}

}));

JavaRDD<Row> reusltRowRDD = resultRDD.map(new Function<Tuple2<String,Tuple2<Integer,Integer>>, Row>() {

@Override

public Row call(Tuple2<String, Tuple2<Integer, Integer>> tuple) throws Exception {

// TODO Auto-generated method stub

return RowFactory.create(tuple._1, tuple._2._2,tuple._2._1 );

}

});

List<StructField> structFields = new ArrayList<StructField>();

structFields.add(DataTypes.createStructField("name", DataTypes.StringType, true));

structFields.add(DataTypes.createStructField("age", DataTypes.IntegerType, true));

structFields.add(DataTypes.createStructField("score", DataTypes.IntegerType, true));

//构建StructType，用于最后DataFrame元数据的描述

StructType structType =DataTypes.createStructType(structFields);

DataFrame personsDF = sqlContext.createDataFrame(reusltRowRDD, structType);

personsDF.show();

personsDF.write().format("json").save("D:\\DT-IMF\\testdata\\peopleresult");

}

在eclipse中运行时的console:

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties

16/04/08 00:01:27 INFO SparkContext: Running Spark version 1.6.0

16/04/08 00:01:29 INFO SecurityManager: Changing view acls to: think

16/04/08 00:01:29 INFO SecurityManager: Changing modify acls to: think

16/04/08 00:01:29 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(think); users with modify permissions: Set(think)

16/04/08 00:01:32 INFO Utils: Successfully started service 'sparkDriver' on port 52189.

16/04/08 00:01:33 INFO Slf4jLogger: Slf4jLogger started

16/04/08 00:01:33 INFO Remoting: Starting remoting

16/04/08 00:01:34 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.56.1:52202]

16/04/08 00:01:34 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 52202.

16/04/08 00:01:34 INFO SparkEnv: Registering MapOutputTracker

16/04/08 00:01:34 INFO SparkEnv: Registering BlockManagerMaster

16/04/08 00:01:34 INFO DiskBlockManager: Created local directory at C:\Users\think\AppData\Local\Temp\blockmgr-0efb49ea-2c12-4819-8543-efcad6cbe9ee

16/04/08 00:01:34 INFO MemoryStore: MemoryStore started with capacity 1773.8 MB

16/04/08 00:01:35 INFO SparkEnv: Registering OutputCommitCoordinator

16/04/08 00:01:36 INFO Utils: Successfully started service 'SparkUI' on port 4040.

16/04/08 00:01:36 INFO SparkUI: Started SparkUI at http://192.168.56.1:4040

16/04/08 00:01:36 INFO Executor: Starting executor ID driver on host localhost

16/04/08 00:01:36 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 52209.

16/04/08 00:01:36 INFO NettyBlockTransferService: Server created on 52209

16/04/08 00:01:36 INFO BlockManagerMaster: Trying to register BlockManager

16/04/08 00:01:36 INFO BlockManagerMasterEndpoint: Registering block manager localhost:52209 with 1773.8 MB RAM, BlockManagerId(driver, localhost, 52209)

16/04/08 00:01:36 INFO BlockManagerMaster: Registered BlockManager

16/04/08 00:01:40 WARN : Your hostname, think-PC resolves to a loopback/non-reachable address: fe80:0:0:0:d401:a5b5:2103:6d13%eth8, but we couldn't find any external IP address!

16/04/08 00:01:41 INFO JSONRelation: Listing file:/D:/DT-IMF/testdata/peoples.json on driver

16/04/08 00:01:42 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 208.9 KB, free 208.9 KB)

16/04/08 00:01:43 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 19.4 KB, free 228.3 KB)

16/04/08 00:01:43 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:52209 (size: 19.4 KB, free: 1773.7 MB)

16/04/08 00:01:43 INFO SparkContext: Created broadcast 0 from json at SparkSQLwithJoin.java:28

16/04/08 00:01:43 INFO FileInputFormat: Total input paths to process : 1

16/04/08 00:01:43 INFO SparkContext: Starting job: json at SparkSQLwithJoin.java:28

16/04/08 00:01:43 INFO DAGScheduler: Got job 0 (json at SparkSQLwithJoin.java:28) with 1 output partitions

16/04/08 00:01:43 INFO DAGScheduler: Final stage: ResultStage 0 (json at SparkSQLwithJoin.java:28)