一、JSON数据源综合案例实战
1、概述
Spark SQL可以自动推断JSON文件的元数据，并且加载其数据，创建一个DataFrame。可以使用SQLContext.read.json()方法，针对一个元素类型为String的RDD，或者是一个JSON文件。但是要注意的是，这里使用的JSON文件与传统意义上的JSON文件是不一样的。每行都必须，也只能包含一个，单独的，自包含的，有效的JSON对象。不能让一个JSON对象分散在多行。否则会报错。###综合性复杂案例：查询成绩为80分以上的学生的基本信息与成绩信息
students.json
{"name":"Leo", "score":85}{ "name":"Marry", "score":99}{ "name":"Jack", "score":74} 2、java案例实现
package cn.spark.study.sql;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;/** * JSON数据源 * @author Administrator * */public class JSONDataSource { public static void main(String[] args) { SparkConf conf = new SparkConf() .setAppName("JSONDataSource"); JavaSparkContext sc = new JavaSparkContext(conf); SQLContext sqlContext = new SQLContext(sc); // 针对json文件，创建DataFrame（针对json文件创建DataFrame） DataFrame studentScoresDF = sqlContext.read().json( "hdfs://spark1:9000/spark-study/students.json"); // 针对学生成绩信息的DataFrame，注册临时表，查询分数大于80分的学生的姓名 // （注册临时表，针对临时表执行sql语句） studentScoresDF.registerTempTable("student_scores"); DataFrame goodStudentScoresDF = sqlContext.sql( "select name,score from student_scores where score>=80"); // （将DataFrame转换为rdd，执行transformation操作） List goodStudentNames = goodStudentScoresDF.javaRDD().map( new Function () { private static final long serialVersionUID = 1L; @Override public String call(Row row) throws Exception { return row.getString(0); } }).collect(); // 然后针对JavaRDD ，创建DataFrame // （针对包含json串的JavaRDD，创建DataFrame） List studentInfoJSONs = new ArrayList (); studentInfoJSONs.add("{\"name\":\"Leo\", \"age\":18}"); studentInfoJSONs.add("{\"name\":\"Marry\", \"age\":17}"); studentInfoJSONs.add("{\"name\":\"Jack\", \"age\":19}"); JavaRDD studentInfoJSONsRDD = sc.parallelize(studentInfoJSONs); DataFrame studentInfosDF = sqlContext.read().json(studentInfoJSONsRDD); // 针对学生基本信息DataFrame，注册临时表，然后查询分数大于80分的学生的基本信息 studentInfosDF.registerTempTable("student_infos"); String sql = "select name,age from student_infos where name in ("; for(int i = 0; i < goodStudentNames.size(); i++) { sql += "'" + goodStudentNames.get(i) + "'"; if(i < goodStudentNames.size() - 1) { sql += ","; } } sql += ")"; DataFrame goodStudentInfosDF = sqlContext.sql(sql); // 然后将两份数据的DataFrame，转换为JavaPairRDD，执行join transformation // （将DataFrame转换为JavaRDD，再map为JavaPairRDD，然后进行join） JavaPairRDD > goodStudentsRDD = goodStudentScoresDF.javaRDD().mapToPair(new PairFunction () { private static final long serialVersionUID = 1L; @Override public Tuple2 call(Row row) throws Exception { return new Tuple2 (row.getString(0), Integer.valueOf(String.valueOf(row.getLong(1)))); } }).join(goodStudentInfosDF.javaRDD().mapToPair(new PairFunction () { private static final long serialVersionUID = 1L; @Override public Tuple2 call(Row row) throws Exception { return new Tuple2 (row.getString(0), Integer.valueOf(String.valueOf(row.getLong(1)))); } })); // 然后将封装在RDD中的好学生的全部信息，转换为一个JavaRDD 的格式 // （将JavaRDD，转换为DataFrame） JavaRDD goodStudentRowsRDD = goodStudentsRDD.map( new Function >, Row>() { private static final long serialVersionUID = 1L; @Override public Row call( Tuple2 > tuple) throws Exception { return RowFactory.create(tuple._1, tuple._2._1, tuple._2._2); } }); // 创建一份元数据，将JavaRDD 转换为DataFrame List structFields = new ArrayList (); structFields.add(DataTypes.createStructField("name", DataTypes.StringType, true)); structFields.add(DataTypes.createStructField("score", DataTypes.IntegerType, true)); structFields.add(DataTypes.createStructField("age", DataTypes.IntegerType, true)); StructType structType = DataTypes.createStructType(structFields); DataFrame goodStudentsDF = sqlContext.createDataFrame(goodStudentRowsRDD, structType); // 将好学生的全部信息保存到一个json文件中去 // （将DataFrame中的数据保存到外部的json文件中去） goodStudentsDF.write().format("json").save("hdfs://spark1:9000/spark-study/good-students"); } }####students.json { "name":"Leo", "score":85}{ "name":"Marry", "score":99}{ "name":"Jack", "score":74}
3、scala案例实现
package cn.spark.study.sqlimport org.apache.spark.SparkConfimport org.apache.spark.SparkContextimport org.apache.spark.sql.SQLContextimport org.apache.spark.sql.types.StructTypeimport org.apache.spark.sql.types.StructFieldimport org.apache.spark.sql.types.StringTypeimport org.apache.spark.sql.types.IntegerTypeimport org.apache.spark.sql.Rowimport org.apache.spark.sql.types.LongType/** * @author Administrator */object JSONDataSource { def main(args: Array[String]): Unit = { val conf = new SparkConf() .setAppName("JSONDataSource") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) // 创建学生成绩DataFrame val studentScoresDF = sqlContext.read.json("hdfs://spark1:9000/spark-study/students.json") // 查询出分数大于80分的学生成绩信息，以及学生姓名 studentScoresDF.registerTempTable("student_scores") val goodStudentScoresDF = sqlContext.sql("select name,score from student_scores where score>=80") val goodStudentNames = goodStudentScoresDF.rdd.map { row => row(0) }.collect() // 创建学生基本信息DataFrame val studentInfoJSONs = Array("{\"name\":\"Leo\", \"age\":18}", "{\"name\":\"Marry\", \"age\":17}", "{\"name\":\"Jack\", \"age\":19}") val studentInfoJSONsRDD = sc.parallelize(studentInfoJSONs, 3); val studentInfosDF = sqlContext.read.json(studentInfoJSONsRDD) // 查询分数大于80分的学生的基本信息 studentInfosDF.registerTempTable("student_infos") var sql = "select name,age from student_infos where name in (" for(i <- 0 until goodStudentNames.length) { sql += "'" + goodStudentNames(i) + "'" if(i < goodStudentNames.length - 1) { sql += "," } } sql += ")" val goodStudentInfosDF = sqlContext.sql(sql) // 将分数大于80分的学生的成绩信息与基本信息进行join val goodStudentsRDD = goodStudentScoresDF.rdd.map { row => (row.getAs[String]("name"), row.getAs[Long]("score")) } .join(goodStudentInfosDF.rdd.map { row => (row.getAs[String]("name"), row.getAs[Long]("age")) }) // 将rdd转换为dataframe val goodStudentRowsRDD = goodStudentsRDD.map( info => Row(info._1, info._2._1.toInt, info._2._2.toInt)) val structType = StructType(Array( StructField("name", StringType, true), StructField("score", IntegerType, true), StructField("age", IntegerType, true))) val goodStudentsDF = sqlContext.createDataFrame(goodStudentRowsRDD, structType) // 将dataframe中的数据保存到json中 goodStudentsDF.write.format("json").save("hdfs://spark1:9000/spark-study/good-students-scala") } }