Spark Streaming+kafka订单实时统计实现
前几篇文章我们分别学习Spark RDD和PairRDD编程，本文小编将通过简单实例来加深对RDD的理解。
一.前期准备
开发环境：window7+eclipse+jdk1.7
部署环境：linux+zookeeper+kafka+hadoop+spark
本实例开发之前，默认已搭好了开发环境和部署环境，如果未搭建，可以参考本人相关大数据开发搭建博客。
二.概念理解
Spark Streaming 是Spark核心API的一个扩展，可以实现高吞吐量的、具备容错机制的实时流数据的处理。支持从多种数据源获取数据，包括Kafka、Flume、Kinesis 以及TCP sockets，从数据源获取数据之后，可以使用诸如map、reduce、join和window等高级函数进行复杂算法的处理。最后还可以将处理结果存储到HDFS、Databases和Dashboards等。实际上，你可以将流数据应用于Spark的机器学习和图形处理的算法上。
Spark Streaming处理的数据流图
Spark Streaming内部工作原理，其接收实时输入数据流，同时将数据划分成批次，然后通过Spark引擎处理生成按照批次的结果流。
Spark Streaming内部工作原理
Spark Streaming提供了表示连续数据流的、高度抽象的被称为离散流的DStream。DStream本质上表示RDD的序列。任何对DStream的操作都会转变为对底层RDD的操作。
三.实例需求
通过Spark Streaming+kafka,实时统计订单的订单总数，所有订单价格数。
四.实例实现
4.1 订单实体order
package com.lm.sparkLearning.orderexmaple;
import java.io.Serializable;
/**
* 简单订单
* @author liangming.deng
*
*/
public class Order implements Serializable {
/**
*
*/
private static final long serialVersionUID = 1L;
//订单商品名称
private String name;
//订单价格
private Float price;
public Order() {
super();
}
public Order(String name, Float price) {
super();
this.name = name;
this.price = price;
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public Float getPrice() {
return price;
}
public void setPrice(Float price) {
this.price = price;
}
@Override
public String toString() {
return "Order [name=" + name + ", price=" + price + "]";
}
}
4.2 kafka订单生产者orderProducer
kafka生产者定时发送随机数量订单
package com.lm.sparkLearning.orderexmaple;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.lm.sparkLearning.utils.ConstantUtils;
import com.lm.sparkLearning.utils.RandomUtils;
import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;
/**
* 订单 kafka消息生产者
*
* @author liangming.deng
*
*/
public class OrderProducer {
private static Logger logger = LoggerFactory.getLogger(OrderProducer.class);
public static void main(String[] args) throws IOException {
// set up the producer
Producer<String, String> producer = null;
ObjectMapper mapper = new ObjectMapper();
try {
Properties props = new Properties();
// kafka集群
props.put("metadata.broker.list", ConstantUtils.METADATA_BROKER_LIST_VALUE);
// 配置value的序列化类
props.put("serializer.class", ConstantUtils.SERIALIZER_CLASS_VALUE);
// 配置key的序列化类
props.put("key.serializer.class", ConstantUtils.SERIALIZER_CLASS_VALUE);
ProducerConfig config = new ProducerConfig(props);
producer = new Producer<String, String>(config);
// 定义发布消息体
List<KeyedMessage<String, String>> messages = new ArrayList<>();
// 每隔3秒生产随机个订单消息
while (true) {
int random = RandomUtils.getRandomNum(20);
if (random == 0) {
continue;
}
messages.clear();
for (int i = 0; i < random; i++) {
int orderRandom = RandomUtils.getRandomNum(random * 10);
Order order = new Order("name" + orderRandom, Float.valueOf("" + orderRandom));
// 订单消息体:topic和消息
KeyedMessage<String, String> message = new KeyedMessage<String, String>(
ConstantUtils.ORDER_TOPIC, mapper.writeValueAsString(order));
messages.add(message);
}
producer.send(messages);
logger.warn("orderNum:" + random + ",message:" + messages.toString());
Thread.sleep(10000);
}
} catch (Exception e) {
e.printStackTrace();
logger.error("-------------:" + e.getStackTrace());
} finally {
producer.close();
}
}
}
4.3 Spark Streaming+kafka订单实时统计OrderSparkStreaming
package com.lm.sparkLearning.orderexmaple;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.atomic.AtomicLong;
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.VoidFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.google.common.util.concurrent.AtomicDouble;
import com.lm.sparkLearning.utils.ConstantUtils;
import com.lm.sparkLearning.utils.SparkUtils;
import kafka.serializer.StringDecoder;
import scala.Tuple2;
/**
* spark streaming统计订单量和订单总值
*
* @author liangming.deng
*
*/
public class OrderSparkStreaming {
private static Logger logger = LoggerFactory.getLogger(OrderSparkStreaming.class);
private static AtomicLong orderCount = new AtomicLong(0);
private static AtomicDouble totalPrice = new AtomicDouble(0);
public static void main(String[] args) {
// Create context with a 2 seconds batch interval
JavaStreamingContext jssc = SparkUtils.getJavaStreamingContext("JavaDirectKafkaWordCount",
"local[2]", null, Durations.seconds(20));
Set<String> topicsSet = new HashSet<>(Arrays.asList(ConstantUtils.ORDER_TOPIC.split(",")));
Map<String, String> kafkaParams = new HashMap<>();
kafkaParams.put("metadata.broker.list", ConstantUtils.METADATA_BROKER_LIST_VALUE);
kafkaParams.put("auto.offset.reset", ConstantUtils.AUTO_OFFSET_RESET_VALUE);
// Create direct kafka stream with brokers and topics
JavaPairInputDStream<String, String> orderMsgStream = KafkaUtils.createDirectStream(jssc,
String.class, String.class, StringDecoder.class, StringDecoder.class, kafkaParams,
topicsSet);
// json与对象映射对象
final ObjectMapper mapper = new ObjectMapper();
JavaDStream<Order> orderDStream = orderMsgStream
.map(new Function<Tuple2<String, String>, Order>() {
/**
*
*/
private static final long serialVersionUID = 1L;
@Override
public Order call(Tuple2<String, String> t2) throws Exception {
Order order = mapper.readValue(t2._2, Order.class);
return order;
}
}).cache();
// 对DStream中的每一个RDD进行操作
orderDStream.foreachRDD(new VoidFunction<JavaRDD<Order>>() {
/**
*
*/
private static final long serialVersionUID = 1L;
@Override
public void call(JavaRDD<Order> orderJavaRDD) throws Exception {
long count = orderJavaRDD.count();
if (count > 0) {
// 累加订单总数
orderCount.addAndGet(count);
// 对RDD中的每一个订单，首先进行一次Map操作，产生一个包含了每笔订单的价格的新的RDD
// 然后对新的RDD进行一次Reduce操作，计算出这个RDD中所有订单的价格众合
Float sumPrice = orderJavaRDD.map(new Function<Order, Float>() {
/**
*
*/
private static final long serialVersionUID = 1L;
@Override
public Float call(Order order) throws Exception {
return order.getPrice();
}
}).reduce(new Function2<Float, Float, Float>() {
/**
*
*/
private static final long serialVersionUID = 1L;
@Override
public Float call(Float a, Float b) throws Exception {
return a + b;
}
});
// 然后把本次RDD中所有订单的价格总和累加到之前所有订单的价格总和中。
totalPrice.getAndAdd(sumPrice);
// 数据订单总数和价格总和，生产环境中可以写入数据库
logger.warn("-------Total order count : " + orderCount.get()
+ " with total price : " + totalPrice.get());
}
}
});
orderDStream.print();
jssc.start(); // Start the computation
jssc.awaitTermination(); // Wait for the computation to terminate
}
}
4.4 实例实时结果
OrderProducer消息生产者
OrderSparkStreaming实时计算
五 代码地址
http://git.oschina.net/a123demi/sparklearning
作者：a123demi 发表于2017/6/1 8:50:29 原文链接
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