【Flink入门】Flink读取Kafka数据Demo

超、凢脫俗 2021-09-25 04:40 561阅读 0赞

flink算子操作主要分为三大部分：source(数据读取)、transform(数据处理)、sink(数据输出)，这篇博客简单聊聊flink读取kafka数据在控制台打印的Demo。废话不多说，直接上代码演示。

**pom.xml文件内容**

<?xml version="1.0" encoding="UTF-8"?>
    <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
        <modelVersion>4.0.0</modelVersion>
    
        <groupId>com.fuyun</groupId>
        <artifactId>flinkLearning</artifactId>
        <version>1.0-SNAPSHOT</version>
    
        <properties>
            <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
            <flink.version>1.12.0</flink.version>
        </properties>
    
        <dependencies>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-java</artifactId>
                <version>${flink.version}</version>
                
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-streaming-java_2.11</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-scala_2.11</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-streaming-scala_2.11</artifactId>
                <version>${flink.version}</version>
                
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-connector-kafka_2.11</artifactId>
                <version>${flink.version}</version>
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-clients_2.11</artifactId>
                <version>${flink.version}</version>
            </dependency>
            
            <dependency>
                <groupId>com.alibaba</groupId>
                <artifactId>fastjson</artifactId>
                <version>1.2.51</version>
            </dependency>
        </dependencies>
        </dependencies>
    </project>

**SourceTest代码**

package com.fuyun.flink
    
    import java.util.Properties
    
    import org.apache.flink.api.common.serialization.SimpleStringSchema
    import org.apache.flink.streaming.api.scala._
    import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
    
    object SourceTest { 
      def main(args: Array[String]): Unit = { 
        // 创建流处理环境
        val senv = StreamExecutionEnvironment.getExecutionEnvironment
    
        // kafka配置信息
        val properties = new Properties()
        properties.setProperty("bootstrap.servers", "bigdata-training.fuyun.com:9092")
        properties.setProperty("group.id", "test")
        // 读取kafka数据
        val kafkaStream = senv
          .addSource(new FlinkKafkaConsumer[String]("flinkSource", new SimpleStringSchema(), properties))
    
        // 读取的数据简单处理
        val resultDataStream = kafkaStream//.flatMap(_.split("\\s+"))
            .filter(_.nonEmpty)
    
        // 打印控制台
        resultDataStream.print()
        // 驱动执行程序，传入程序名称
        senv.execute("source test")
      }
    }

这个FlinkKafkaConsumer 构造函数有3 个参数，第一个参数定义的是读入的目标topic名。

第二个参数是一个DeserializationSchema 或KeyedDeserializationSchema。Kafka中的消息是以纯字节消息存储，所以需要被反序列化为Java或Scala 对象。在上例中用到的SimpleStringSchema 是一个内置的DeserializationSchema，可以将字节数字反序列化为一个String。Flink也提供了对Apache Avro以及基于text的JSON编码的实现。我们也可以通过实现DeserializationSchema 与KeyedDeserializationSchema 这两个公开的接口，用于实现自定义的反序列化逻辑。

第三个参数是一个Properties对象，用于配置Kafka的客户端。此对象至少要包含两个条目，“bootstrap.servers” 与"group.id"。

**程序执行流程：**

首先启动Zookeeper  
`${ZOOKEEPER_HOME}/bin/zkServer start`

启动kafka  
`${KAFKA_HOME}/bin/kafka-server-start.sh -daemon config/server.properties`

创建topic，副本数目小于等于Brokers数目  
`${KAFKA_HOME}/bin/kafka-topics.sh --create --zookeeper bigdata-training.fuyun.com:2181/kafka --replication-factor 1 --partitions 1 --topic flinkSource`

参数说明：  
`--create`：创建topic  
`--zookeeper`:zookeeper地址  
`--replication-factor`:副本数  
`--partition`:分区数  
`topic`:topic名称

查看topic是否创建成功  
`${KAFKA_HOME}/bin/kafka-topics.sh --list --zookeeper bigdata-training.fuyun.com:2181/kafka`

1.  通过Console向Topic发送数据  
    `${KAFKA_HOME}/bin/kafka-console-producer.sh --broker-list bigdata-training.fuyun.com:9092 --topic flinkSource`

IDEA启动程序，在虚拟机kafka的Console中输入单词，可以在IDEA上输出。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6NjM2Mw_size_16_color_FFFFFF_t_70]

1.  通过本地程序造一些测试数据

创建一个PlayStart类

package com.fuyun.flink.model;
    
    import java.util.Map;
    
    public class PlayStart { 
        public String userID;
        public long timestamp;
        public Map<String, Object> fields;
        public Map<String, String> tags;
    
        public PlayStart() { 
        }
    
        public PlayStart(String userID, long timestamp, Map<String, Object> fields, Map<String, String> tags) { 
            this.userID = userID;
            this.timestamp = timestamp;
            this.fields = fields;
            this.tags = tags;
        }
    
        @Override
        public String toString() { 
            return "PlayStart{" +
                    "userID='" + userID + '\'' +
                    ", timestamp='" + timestamp + '\'' +
                    ", fields=" + fields +
                    ", tags=" + tags +
                    '}';
        }
    
        public String getUserID() { 
            return userID;
        }
    
        public long getTimestamp() { 
            return timestamp;
        }
    
        public Map<String, Object> getFields() { 
            return fields;
        }
    
        public Map<String, String> getTags() { 
            return tags;
        }
    
        public void setUserID(String userID) { 
            this.userID = userID;
        }
    
        public void setTimestamp(long timestamp) { 
            this.timestamp = timestamp;
        }
    
        public void setFields(Map<String, Object> fields) { 
            this.fields = fields;
        }
    
        public void setTags(Map<String, String> tags) { 
            this.tags = tags;
        }
    }

创建KafkaUtils类向kafka对应的topic中发送数据

package com.fuyun.flink.utils;
    
    import com.alibaba.fastjson.JSON;
    import com.fuyun.flink.model.PlayStart;
    import org.apache.kafka.clients.producer.KafkaProducer;
    import org.apache.kafka.clients.producer.ProducerRecord;
    
    import java.util.HashMap;
    import java.util.Map;
    import java.util.Properties;
    import java.util.Random;
    
    public class KafkaUtils { 
        public static final String broker_list = "bigdata-training.fuyun.com:9092";
        public static final String topic = "flinkSource";  // kafka topic，Flink 程序中需要和这个统一
        public static void writeToKafka() throws InterruptedException { 
            Properties props = new Properties();
            props.put("bootstrap.servers", broker_list);
            props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); //key 序列化
            props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer"); //value 序列化
            KafkaProducer producer = new KafkaProducer<String, String>(props);
    
            PlayStart playStart = new PlayStart();
            playStart.setTimestamp(System.currentTimeMillis());
            int user = new Random().nextInt(10000000);
            playStart.setUserID("user"+user);
            Map<String, String> tags = new HashMap<>();
            Map<String, Object> fields = new HashMap<>();
    
            int ip = new Random().nextInt(100000)%255;
            tags.put("user_agent", "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36");
            tags.put("ip", "192.168.99."+ip);
    
            int program_id = new Random().nextInt(100000);
            int content_id = new Random().nextInt(100);
            int duration = new Random().nextInt(1000);
            fields.put("program_id", program_id);
            fields.put("content_id", program_id+""+content_id);
            fields.put("play_duration", duration);
    
            playStart.setTags(tags);
            playStart.setFields(fields);
    
            ProducerRecord record = new ProducerRecord<String, String>(topic, null, null, JSON.toJSONString(playStart));
            producer.send(record);
            System.out.println("发送数据: " + JSON.toJSONString(playStart));
    
            producer.flush();
        }
    
        public static void main(String[] args) throws InterruptedException { 
            while (true) { 
                Thread.sleep(3000);
                writeToKafka();
            }
        }
    }

在IDEA本地启动KafkaUtils程序和SourceTest  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6NjM2Mw_size_16_color_FFFFFF_t_70 1]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6NjM2Mw_size_16_color_FFFFFF_t_70 2]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6NjM2Mw_size_16_color_FFFFFF_t_70]: /images/20210923/09bcb016a0de43998de0b98b69cc35d4.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6NjM2Mw_size_16_color_FFFFFF_t_70 1]: /images/20210923/f618c06f5eec4b25bdf2e2a1ae220878.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x6NjM2Mw_size_16_color_FFFFFF_t_70 2]: /images/20210923/d4612bd92b71401a9d9d422ae76365ca.png