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。
Demo：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。
package cn.spark.study.streaming;
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.function.Function;
import org.apache.spark.api.java.function.Function2;
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.hive.HiveContext;
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;
/**
* 与Spark SQL整合使用，top3热门商品实时统计
*/
public class Top3HotProduct {
public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setMaster("local[2]")
.setAppName("Top3HotProduct");
JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1));
// 输入日志的格式
// leo iphone mobile_phone
// 首先，获取输入数据流
JavaReceiverInputDStream<String> productClickLogsDStream = jssc.socketTextStream("hadoop1", 9999);
// 然后，应该是做一个映射，将每个种类的每个商品，映射为(category_product, 1)的这种格式
// 从而在后面可以使用window操作，对窗口中的这种格式的数据，进行reduceByKey操作
// 从而统计出来，一个窗口中的每个种类的每个商品的，点击次数
JavaPairDStream<String, Integer> categoryProductPairsDStream = productClickLogsDStream
.mapToPair(new PairFunction<String, String, Integer>() {
private static final long serialVersionUID = 1L;
@Override
public Tuple2<String, Integer> call(String productClickLog)
throws Exception {
String[] productClickLogSplited = productClickLog.split(" ");
return new Tuple2<String, Integer>(productClickLogSplited[2] + "_" +
productClickLogSplited[1], 1);
}
});
// 然后执行window操作
// 到这里，就可以做到，每隔10秒钟，对最近60秒的数据，执行reduceByKey操作
// 计算出来这60秒内，每个种类的每个商品的点击次数
JavaPairDStream<String, Integer> categoryProductCountsDStream =
categoryProductPairsDStream.reduceByKeyAndWindow(
new Function2<Integer, Integer, Integer>() {
private static final long serialVersionUID = 1L;
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
}, Durations.seconds(60), Durations.seconds(10));
// 然后针对60秒内的每个种类的每个商品的点击次数
// foreachRDD，在内部，使用Spark SQL执行top3热门商品的统计
categoryProductCountsDStream.foreachRDD(new Function<JavaPairRDD<String,Integer>, Void>() {
private static final long serialVersionUID = 1L;
@Override
public Void call(JavaPairRDD<String, Integer> categoryProductCountsRDD) throws Exception {
// 将该RDD，转换为JavaRDD<Row>的格式
JavaRDD<Row> categoryProductCountRowRDD = categoryProductCountsRDD.map(
new Function<Tuple2<String,Integer>, Row>() {
private static final long serialVersionUID = 1L;
@Override
public Row call(Tuple2<String, Integer> categoryProductCount)
throws Exception {
String category = categoryProductCount._1.split("_")[0];
String product = categoryProductCount._1.split("_")[1];
Integer count = categoryProductCount._2;
return RowFactory.create(category, product, count);
}
});
// 然后，执行DataFrame转换
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("click_count", DataTypes.IntegerType, true));
StructType structType = DataTypes.createStructType(structFields);
HiveContext hiveContext = new HiveContext(categoryProductCountsRDD.context());
DataFrame categoryProductCountDF = hiveContext.createDataFrame(
categoryProductCountRowRDD, structType);
// 将60秒内的每个种类的每个商品的点击次数的数据，注册为一个临时表
categoryProductCountDF.registerTempTable("product_click_log");
// 执行SQL语句，针对临时表，统计出来每个种类下，点击次数排名前3的热门商品
DataFrame top3ProductDF = hiveContext.sql(
"SELECT category,product,click_count "
+ "FROM ("
+ "SELECT "
+ "category,"
+ "product,"
+ "click_count,"
+ "row_number() OVER (PARTITION BY category ORDER BY click_count DESC) rank "
+ "FROM product_click_log"
+ ") tmp "
+ "WHERE rank<=3");
// 接下来应该将数据保存到redis缓存、或者是mysql db中
// 然后，配合一个J2EE系统，进行数据的展示和查询、图形报表
top3ProductDF.show();
return null;
}
});
jssc.start();
jssc.awaitTermination();
jssc.close();
}
}
package cn.spark.study.streaming
import org.apache.spark.SparkConf
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.hive.HiveContext
object Top3HotProduct {
def main(args: Array[String]): Unit = {
val conf = new SparkConf()
.setMaster("local[2]")
.setAppName("Top3HotProduct")
val ssc = new StreamingContext(conf, Seconds(1))
val productClickLogsDStream = ssc.socketTextStream("spark1", 9999)
val categoryProductPairsDStream = productClickLogsDStream
.map { productClickLog => (productClickLog.split(" ")(2) + "_" + productClickLog.split(" ")(1), 1)}
val categoryProductCountsDStream = categoryProductPairsDStream.reduceByKeyAndWindow(
(v1: Int, v2: Int) => v1 + v2,
Seconds(60),
Seconds(10))
categoryProductCountsDStream.foreachRDD(categoryProductCountsRDD => {
val categoryProductCountRowRDD = categoryProductCountsRDD.map(tuple => {
val category = tuple._1.split("_")(0)
val product = tuple._1.split("_")(1)
val count = tuple._2
Row(category, product, count)
})
val structType = StructType(Array(
StructField("category", StringType, true),
StructField("product", StringType, true),
StructField("click_count", IntegerType, true)))
val hiveContext = new HiveContext(categoryProductCountsRDD.context)
val categoryProductCountDF = hiveContext.createDataFrame(categoryProductCountRowRDD, structType)
categoryProductCountDF.registerTempTable("product_click_log")
val top3ProductDF = hiveContext.sql(
"SELECT category,product,click_count "
+ "FROM ("
+ "SELECT "
+ "category,"
+ "product,"
+ "click_count,"
+ "row_number() OVER (PARTITION BY category ORDER BY click_count DESC) rank "
+ "FROM product_click_log"
+ ") tmp "
+ "WHERE rank<=3")
top3ProductDF.show()
})
ssc.start()
ssc.awaitTermination()
}
}
