基于Filebeat+Kafka+Flink仿天猫双11实时交易额

1. 写在前面

在大数据实时计算方向，天猫双11的实时交易额是最具权威性的，当然技术架构也是相当复杂的，不是本篇博客的简单实现，因为天猫双11的数据是多维度多系统，实时粒度更微小的。当然在技术的总体架构上是相近的，主要的组件都是用到大数据实时计算组件Flink(当然阿里是用了基于Flink深度定制和优化改装的Blink)。下图是天猫双11实时交易额的大体架构模型及数据流向(参照https://baijiahao.baidu.com/s?id=1588506573420812062&wfr=spider&for=pc)

2. 仿天猫双11实时交易额技术架构

利用Linux shell自动化模拟每秒钟产生一条交易额数据，数据内容为用户id，购买商品的付款金额，用户所在城市及所购买的商品

技术架构上利用Filebeat去监控每生产的一条交易额记录，Filebeat将交易额输出到Kafka(关于Filebeat和kafka的安装或应用请参照之前的博客)，然后编写Flink客户端程序去实时消费Kafka数据，对数据进行两块计算，一块是统计实时总交易额，一块是统计不同城市的实时交易额

技术架构图

3.具体实现

3.1. 模拟交易额数据double11.sh脚本

#!/bin/bash

i=1

for i in $(seq 1 60)

	do

        customernum=`openssl rand -base64 8 | cksum | cut -c1-8`

        pricenum=`openssl rand -base64 8 | cksum | cut -c1-4`

        citynum=`openssl rand -base64 8 | cksum | cut -c1-2`

        itemnum=`openssl rand -base64 8 | cksum | cut -c1-6`

        echo "customer"$customernum","$pricenum",""city"$citynum",""item"$itemnum >> /home/hadoop/tools/double11/double11.log

        sleep 1

    done

将double11.sh放入Linux crontab

#每分钟执行一次

* * * * * sh /home/hadoop/tools/double11/double11.sh

3.2. 实时监控double11.log

Filebeat实时监控double11.log产生的每条交易额记录，将记录实时流向到Kafka的topic，这里只需要对Filebeat的beat-kafka.yml做简单配置，kafka只需要启动就好

3.3. 核心:编写Flink客户端程序

这里将统计实时总交易额和不同城市的实时交易额区分写成两个类(只提供Flink Java API)

需要导入的maven依赖

    <dependencies>

        <dependency>

            <groupId>junit</groupId>

            <artifactId>junit</artifactId>

            <version>4.11</version>

            <scope>test</scope>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients -->

        <dependency>

            <groupId>org.apache.flink</groupId>

            <artifactId>flink-clients_2.11</artifactId>

            <version>1.0.0</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-streaming-java -->

        <dependency>

            <groupId>org.apache.flink</groupId>

            <artifactId>flink-streaming-java_2.11</artifactId>

            <version>1.0.0</version>

            <scope>provided</scope>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-java -->

        <dependency>

            <groupId>org.apache.flink</groupId>

            <artifactId>flink-java</artifactId>

            <version>1.0.0</version>

        </dependency>

        <dependency>

            <groupId>org.apache.flink</groupId>

            <artifactId>flink-connector-kafka-0.8_2.11</artifactId>

            <version>1.0.0</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->

        <dependency>

            <groupId>com.alibaba</groupId>

            <artifactId>fastjson</artifactId>

            <version>1.2.47</version>

        </dependency>

    </dependencies>

统计实时总交易额代码

package com.fastweb;

import java.util.Properties;

import com.alibaba.fastjson.JSONObject;

import org.apache.flink.api.common.functions.FlatMapFunction;

import org.apache.flink.api.java.tuple.Tuple2;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer08;

import org.apache.flink.streaming.util.serialization.SimpleStringSchema;

import org.apache.flink.util.Collector;

public class Double11Sum {

    public static void main(String[] args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

//        env.enableCheckpointing(1000);

        Properties properties = new Properties();

        properties.setProperty("bootstrap.servers", "192.168.184.12:9092");

        properties.setProperty("zookeeper.connect", "192.168.184.12:2181");

        properties.setProperty("group.id", "test");

        FlinkKafkaConsumer08<String> myConsumer = new FlinkKafkaConsumer08<String>("test", new SimpleStringSchema(),

                properties);

        DataStream<String> stream = env.addSource(myConsumer);

        DataStream<Tuple2<String, Integer>> counts = stream.flatMap(new LineSplitter()).keyBy(0).sum(1);

        counts.print();

        env.execute("Double 11 Real Time Transaction Volume");

    }

    //统计总的实时交易额

    public static final class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {

        private static final long serialVersionUID = 1L;

        public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {

            JSONObject object = JSONObject.parseObject(value);

            String message = object.getString("message");

            Integer price = Integer.parseInt(message.split(",")[1]);

            out.collect(new Tuple2<String, Integer>("price", price));

        }

    }

}

统计不同城市的实时交易额

package com.fastweb;

import java.util.Properties;

import com.alibaba.fastjson.JSONObject;

import org.apache.flink.api.common.functions.FlatMapFunction;

import org.apache.flink.api.java.tuple.Tuple2;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer08;

import org.apache.flink.streaming.util.serialization.SimpleStringSchema;

import org.apache.flink.util.Collector;

public class Double11SumByCity {

    public static void main(String[] args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

//        env.enableCheckpointing(1000);

        Properties properties = new Properties();

        properties.setProperty("bootstrap.servers", "192.168.184.12:9092");

        properties.setProperty("zookeeper.connect", "192.168.184.12:2181");

        properties.setProperty("group.id", "test");

        FlinkKafkaConsumer08<String> myConsumer = new FlinkKafkaConsumer08<String>("test", new SimpleStringSchema(),

                properties);

        DataStream<String> stream = env.addSource(myConsumer);

        DataStream<Tuple2<String, Integer>> cityCounts = stream.flatMap(new CitySplitter()).keyBy(0).sum(1);

        cityCounts.print();

        env.execute("Double 11 Real Time Transaction Volume");

    }

    //按城市分类汇总实时交易额

    public static final class CitySplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {

        private static final long serialVersionUID = 1L;

        public void flatMap(String value, Collector<Tuple2<String, Integer>> out) {

            JSONObject object = JSONObject.parseObject(value);

            String message = object.getString("message");

            Integer price = Integer.parseInt(message.split(",")[1]);

            String city = message.split(",")[2];

            out.collect(new Tuple2<String, Integer>(city, price));

        }

    }

}

代码解释:这里可以方向两个类里面只有flatMap的对数据处理的内部类不同，但两个内部类的结构基本相同，在内部类里面利用fastjson解析了一层获取要得到的数据，这是因为经过Filebeat监控的数据是json格式的，Filebeat这样实现是为了在正式的系统上确保每条数据的来源IP，时间戳等信息

3.4. 验证

启动Double11Sum类的main方法就可以得到实时的总交易额，按城市分类的实时交易额也一样，这个结果是实时更新的，每条记录都是新的

巴特西