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Flink的sink实战之二:kafka
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Flink的sink实战之二:kafka
推荐 原创欢迎访问我的GitHub
这里分类和汇总了欣宸的全部原创(含配套源码): https://github.com/zq2599/blog_demos
- 本文是《Flink的sink实战》系列的第二篇,前文 《Flink的sink实战之一:初探》对sink有了基本的了解,本章来体验将数据sink到kafka的操作;
版本和环境准备
- 本次实战的环境和版本如下:
- JDK:1.8.0_211
- Flink:1.9.2
- Maven:3.6.0
- 操作系统:macOS Catalina 10.15.3 (MacBook Pro 13-inch, 2018)
- IDEA:2018.3.5 (Ultimate Edition)
- Kafka:2.4.0
- Zookeeper:3.5.5
请确保上述环境和服务已经就绪;
- 如果您不想写代码,整个系列的源码可在GitHub下载到,地址和链接信息如下表所示( https://github.com/zq2599/blog_demos):
名称 链接 备注
项目主页 https://github.com/zq2599/blog_demos 该项目在GitHub上的主页
git仓库地址(https) https://github.com/zq2599/blog_demos.git 该项目源码的仓库地址,https协议
git仓库地址(ssh) [email protected]:zq2599/blog_demos.git 该项目源码的仓库地址,ssh协议
- 这个git项目中有多个文件夹,本章的应用在flinksinkdemo文件夹下,如下图红框所示:
- 准备完毕,开始开发;
- 正式编码前,先去官网查看相关资料了解基本情况:
- 地址: https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/connectors/kafka.html
- 我这里用的kafka是2.4.0版本,在官方文档查找对应的库和类,如下图红框所示:
kafka准备
- 创建名为test006的topic,有四个分区,参考命令:
./kafka-topics.sh \
--create \
--bootstrap-server 127.0.0.1:9092 \
--replication-factor 1 \
--partitions 4 \
--topic test006
- 在控制台消费test006的消息,参考命令:
./kafka-console-consumer.sh \
--bootstrap-server 127.0.0.1:9092 \
--topic test006
- 此时如果该topic有消息进来,就会在控制台输出;
- 接下来开始编码;
- 用maven命令创建flink工程:
mvn \
archetype:generate \
-DarchetypeGroupId=org.apache.flink \
-DarchetypeArtifactId=flink-quickstart-java \
-DarchetypeVersion=1.9.2
- 根据提示,groupid输入com.bolingcavalry,artifactid输入flinksinkdemo,即可创建一个maven工程;
- 在pom.xml中增加kafka依赖库:
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_2.11</artifactId>
<version>1.9.0</version>
</dependency>
- 工程创建完成,开始编写flink任务的代码;
发送字符串消息的sink
- 先尝试发送字符串类型的消息:
- 创建KafkaSerializationSchema接口的实现类,后面这个类要作为创建sink对象的参数使用:
package com.bolingcavalry.addsink;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.nio.charset.StandardCharsets;
public class ProducerStringSerializationSchema implements KafkaSerializationSchema<String> {
private String topic;
public ProducerStringSerializationSchema(String topic) {
super();
this.topic = topic;
}
@Override
public ProducerRecord<byte[], byte[]> serialize(String element, Long timestamp) {
return new ProducerRecord<byte[], byte[]>(topic, element.getBytes(StandardCharsets.UTF_8));
}
}
- 创建任务类KafkaStrSink,请注意FlinkKafkaProducer对象的参数,FlinkKafkaProducer.Semantic.EXACTLY_ONCE表示严格一次:
package com.bolingcavalry.addsink;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
public class KafkaStrSink {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//并行度为1
env.setParallelism(1);
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "192.168.50.43:9092");
String topic = "test006";
FlinkKafkaProducer<String> producer = new FlinkKafkaProducer<>(topic,
new ProducerStringSerializationSchema(topic),
properties,
FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
//创建一个List,里面有两个Tuple2元素
List<String> list = new ArrayList<>();
list.add("aaa");
list.add("bbb");
list.add("ccc");
list.add("ddd");
list.add("eee");
list.add("fff");
list.add("aaa");
//统计每个单词的数量
env.fromCollection(list)
.addSink(producer)
.setParallelism(4);
env.execute("sink demo : kafka str");
}
}
- 使用mvn命令编译构建,在target目录得到文件flinksinkdemo-1.0-SNAPSHOT.jar;
- 在flink的web页面提交flinksinkdemo-1.0-SNAPSHOT.jar,并制定执行类,如下图:
- 提交成功后,如果flink有四个可用slot,任务会立即执行,会在消费kafak消息的终端收到消息,如下图:
- 任务执行情况如下图:
发送对象消息的sink
- 再来尝试如何发送对象类型的消息,这里的对象选择常用的Tuple2对象:
- 创建KafkaSerializationSchema接口的实现类,该类后面要用作sink对象的入参,请注意代码中捕获异常的那段注释:生产环境慎用printStackTrace()!!!
package com.bolingcavalry.addsink;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.core.JsonProcessingException;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.kafka.clients.producer.ProducerRecord;
import javax.annotation.Nullable;
public class ObjSerializationSchema implements KafkaSerializationSchema<Tuple2<String, Integer>> {
private String topic;
private ObjectMapper mapper;
public ObjSerializationSchema(String topic) {
super();
this.topic = topic;
}
@Override
public ProducerRecord<byte[], byte[]> serialize(Tuple2<String, Integer> stringIntegerTuple2, @Nullable Long timestamp) {
byte[] b = null;
if (mapper == null) {
mapper = new ObjectMapper();
}
try {
b= mapper.writeValueAsBytes(stringIntegerTuple2);
} catch (JsonProcessingException e) {
// 注意,在生产环境这是个非常危险的操作,
// 过多的错误打印会严重影响系统性能,请根据生产环境情况做调整
e.printStackTrace();
}
return new ProducerRecord<byte[], byte[]>(topic, b);
}
}
- 创建flink任务类:
package com.bolingcavalry.addsink;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
public class KafkaObjSink {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//并行度为1
env.setParallelism(1);
Properties properties = new Properties();
//kafka的broker地址
properties.setProperty("bootstrap.servers", "192.168.50.43:9092");
String topic = "test006";
FlinkKafkaProducer<Tuple2<String, Integer>> producer = new FlinkKafkaProducer<>(topic,
new ObjSerializationSchema(topic),
properties,
FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
//创建一个List,里面有两个Tuple2元素
List<Tuple2<String, Integer>> list = new ArrayList<>();
list.add(new Tuple2("aaa", 1));
list.add(new Tuple2("bbb", 1));
list.add(new Tuple2("ccc", 1));
list.add(new Tuple2("ddd", 1));
list.add(new Tuple2("eee", 1));
list.add(new Tuple2("fff", 1));
list.add(new Tuple2("aaa", 1));
//统计每个单词的数量
env.fromCollection(list)
.keyBy(0)
.sum(1)
.addSink(producer)
.setParallelism(4);
env.execute("sink demo : kafka obj");
}
}
- 像前一个任务那样编译构建,把jar提交到flink,并指定执行类是com.bolingcavalry.addsink.KafkaObjSink;
- 消费kafka消息的控制台输出如下:
- 在web页面可见执行情况如下:
- 至此,flink将计算结果作为kafka消息发送出去的实战就完成了,希望能给您提供参考,接下来的章节,我们会继续体验官方提供的sink能力;
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