Spark Streaming与Spark SQL结合操作详解

谁践踏了优雅 2022-12-29 02:25 309阅读 0赞

Spark Streaming最强大的地方在于，可以与Spark Core、Spark SQL整合使用，之前已经通 过transform、foreachRDD等算子看到，如何将DStream中的RDD使用Spark Core执行批处理操 作。现在就来看看，如何将DStream中的RDD与Spark SQL结合起来使用。

**案例：每隔10秒，统计最近60秒的，每个种类的每个商品的点击次数，然后统计出每个种类 top3热门的商品。**

数据输入模型：

--------------------------
    姓名		商品分类		商品名称
    alex 	bigdata		hadoop
    alex 	bigdata		spark
    jack 	bigdata		flink
    alex 	bigdata		hadoop
    alex 	language	java
    pony 	language	python
    jone 	language	java
    lili 	bigdata		hadoop
    --------------------------

第一步：mapToPair

第一步：mapToPair
    <bigdata_hadoop,1>
    <bigdata_spark,1>
    <bigdata_flink,1>
    ...

第二步：reduceByKeyAndWindow

<bigdata_hadoop,5>
    <bigdata_spark,2>
    <bigdata_flink,4>
    ...

第三步：foreachRDD

foreachRDD -> DataFrame -> registerTempView -> sql

Java语言实现：

package com.kfk.spark.common;
    
    import org.apache.spark.SparkConf;
    import org.apache.spark.streaming.Durations;
    import org.apache.spark.streaming.api.java.JavaStreamingContext;
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/14 * @time : 8:23 下午 */
    public class CommStreamingContext { 
    
        public static JavaStreamingContext getJssc(){ 
            SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("CommStreamingContext");
            return new JavaStreamingContext(conf, Durations.seconds(2));
        }
    }

package com.kfk.spark.top_hot_product_project;
    
    import com.kfk.spark.common.CommSparkSession;
    import com.kfk.spark.common.CommStreamingContext;
    import org.apache.spark.api.java.JavaPairRDD;
    import org.apache.spark.api.java.JavaRDD;
    import org.apache.spark.api.java.function.Function;
    import org.apache.spark.api.java.function.Function2;
    import org.apache.spark.api.java.function.PairFunction;
    import org.apache.spark.api.java.function.VoidFunction;
    import org.apache.spark.sql.Dataset;
    import org.apache.spark.sql.Row;
    import org.apache.spark.sql.RowFactory;
    import org.apache.spark.sql.SparkSession;
    import org.apache.spark.sql.types.DataTypes;
    import org.apache.spark.sql.types.StructField;
    import org.apache.spark.sql.types.StructType;
    import org.apache.spark.streaming.Durations;
    import org.apache.spark.streaming.api.java.JavaPairDStream;
    import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
    import org.apache.spark.streaming.api.java.JavaStreamingContext;
    import scala.Tuple2;
    
    import java.util.ArrayList;
    import java.util.List;
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/19 * @time : 1:36 下午 */
    public class TopHotProduct { 
        public static void main(String[] args) throws InterruptedException { 
            JavaStreamingContext jssc = CommStreamingContext.getJssc();
    
            /** * 输入数据源模型 * -------------------------- * 姓名 商品分类 商品名称 * alex bigdata hadoop * alex bigdata spark * jack bigdata flink * alex bigdata hadoop * alex language java * pony language python * jone language java * lili bigdata hadoop * -------------------------- */
            JavaReceiverInputDStream<String> inputDstream = jssc.socketTextStream("bigdata-pro-m04",9999);
    
            /** * mapToPair * <bigdata_hadoop,1> * <bigdata_spark,1> * <bigdata_flink,1> */
            JavaPairDStream<String, Integer> pairDstream = inputDstream.mapToPair(new PairFunction<String, String, Integer>() { 
                @Override
                public Tuple2<String, Integer> call(String lines) throws Exception { 
    
                    String[] line = lines.split(" ");
                    return new Tuple2<>(line[1] + "_" + line[2], 1);
                }
            });
    
            /** * reduceByKeyAndWindow * <bigdata_hadoop,5> * <bigdata_spark,2> * <bigdata_flink,4> */
            JavaPairDStream<String, Integer> windowPairStream = pairDstream.reduceByKeyAndWindow(
                    new Function2<Integer, Integer, Integer>() { 
                @Override
                public Integer call(Integer v1, Integer v2) throws Exception { 
                    return v1+v2;
                }
            }, Durations.seconds(60),Durations.seconds(10));
    
            /** * foreachRDD */
            windowPairStream.foreachRDD(new VoidFunction<JavaPairRDD<String, Integer>>() { 
                @Override
                public void call(JavaPairRDD<String, Integer> stringIntegerJavaPairRdd) throws Exception { 
    
                    // 转换成RDD[Row]
                    JavaRDD<Row> countRowRdd = stringIntegerJavaPairRdd.map(new Function<Tuple2<String, Integer>, Row>() { 
                        @Override
                        public Row call(Tuple2<String, Integer> tuple) throws Exception { 
    
                            String category = tuple._1.split("_")[0];
                            String product = tuple._1.split("_")[1];
                            Integer productCount = tuple._2;
                            return RowFactory.create(category,product,productCount);
                        }
                    });
    
                    List<StructField> structFields = new ArrayList<StructField>();
                    structFields.add(DataTypes.createStructField("category",DataTypes.StringType,true));
                    structFields.add(DataTypes.createStructField("product",DataTypes.StringType,true));
                    structFields.add(DataTypes.createStructField("productCount",DataTypes.IntegerType,true));
    
                    // 构造Schema
                    StructType structType = DataTypes.createStructType(structFields);
    
                    // 创建SparkSession
                    SparkSession spark = CommSparkSession.getSparkSession();
    
                    // 通过rdd和scheme创建DataFrame
                    Dataset<Row> df = spark.createDataFrame(countRowRdd,structType);
    
                    // 创建临时表
                    df.createOrReplaceTempView("product_click");
    
                    /** * 统计每个种类下点击次数排名前3名的商品 */
                    spark.sql("select category,product,productCount from product_click " +
                            "order by productCount desc limit 3").show();
    
                    /** * +--------+-------+------------+ * |category|product|productCount| * +--------+-------+------------+ * | bigdata| hadoop| 4| * | bigdata| kafka| 2| * | bigdata| flink| 2| * +--------+-------+------------+ */
                }
            });
    
            jssc.start();
            jssc.awaitTermination();
        }
    }

Scala语言实现：

package com.kfk.spark.top_hot_product_project
    
    import com.kfk.spark.common.{ CommSparkSessionScala, CommStreamingContextScala}
    import org.apache.spark.sql.Row
    import org.apache.spark.sql.types.{ IntegerType, StringType, StructField, StructType}
    import org.apache.spark.streaming.Seconds
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/19 * @time : 3:59 下午 */
    object TopHotProductScala { 
        def main(args: Array[String]): Unit = { 
            val jssc = CommStreamingContextScala.getJssc
    
            /** * 输入数据源模型 * -------------------------- * 姓名 商品分类 商品名称 * alex bigdata hadoop * alex bigdata spark * jack bigdata flink * alex bigdata hadoop * alex language java * pony language python * jone language java * lili bigdata hadoop * -------------------------- */
            val inputDstream = jssc.socketTextStream("bigdata-pro-m04", 9999)
    
            /** * mapToPair * <bigdata_hadoop,1> * <bigdata_spark,1> * <bigdata_flink,1> */
            val pairDStream = inputDstream.map(x => (x.split(" ")(1) + "_" + x.split(" ")(2),1))
    
            /** * reduceByKeyAndWindow * <bigdata_hadoop,5> * <bigdata_spark,2> * <bigdata_flink,4> */
            val windowDStream = pairDStream.reduceByKeyAndWindow((x:Int,y:Int) => x+y,Seconds(60),Seconds(10))
    
            windowDStream.foreachRDD(x => { 
    
                // 转换成RDD[Row]
                val rowRDD = x.map(tuple => { 
                    val category = tuple._1.split("_")(0)
                    val product = tuple._1.split("_")(1)
                    val productCount = tuple._2
                    Row(category,product,productCount)
                })
    
                // 构造Schema
                val structType = StructType(Array(
                    StructField("category",StringType,true),
                    StructField("product",StringType,true),
                    StructField("productCount",IntegerType,true)
                ))
    
                // 创建SparkSession
                val spark = CommSparkSessionScala.getSparkSession()
    
                // 通过rdd和scheme创建DataFrame
                val df = spark.createDataFrame(rowRDD,structType)
    
                // 创建临时表
                df.createOrReplaceTempView("product_click")
    
                /** * 统计每个种类下点击次数排名前3名的商品 */
                spark.sql("select category,product,productCount from product_click "
                        + "order by productCount desc limit 3").show()
    
                /** * +--------+-------+------------+ * |category|product|productCount| * +--------+-------+------------+ * | bigdata| hadoop| 4| * | bigdata| kafka| 2| * | bigdata| flink| 2| * +--------+-------+------------+ */
            })
    
            jssc.start()
            jssc.awaitTermination()
        }
    
    }

测试数据源：

alex bigdata hadoop
    alex bigdata spark
    jack bigdata flink
    alex bigdata hadoop
    alex language java
    pony language python
    jone language java
    lili bigdata hadoop
    lili bigdata hive
    lili bigdata hbase
    lili bigdata hadoop
    lili bigdata spark
    lili bigdata flink
    lili bigdata kafka
    lili bigdata kafka

运行结果：

--------------------

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