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Content ： All original articles classified summary and supporting source code , involve Java、Docker、Kubernetes、DevOPS etc. ;

An overview of this article

This article is about 《Flink Of sink actual combat 》 The second part of the series , above 《Flink Of sink One of the real battles ： On 》 Yes sink With a basic understanding of , In this chapter, we will experience the data sink To kafka The operation of ;

Full series Links

1. 《Flink Of sink One of the real battles ： On 》
2. 《Flink Of sink The second part of the actual battle ：kafka》
3. 《Flink Of sink The third part of the actual battle ：cassandra3》
4. 《Flink Of sink The fourth part of the actual battle ： Customize 》

Version and environment preparation

The actual combat environment and version are as follows ：

1. JDK：1.8.0_211
2. Flink：1.9.2
3. Maven：3.6.0
4. operating system ：macOS Catalina 10.15.3 （MacBook Pro 13-inch, 2018）
5. IDEA：2018.3.5 (Ultimate Edition)
6. Kafka：2.4.0
7. Zookeeper：3.5.5

<font color="red"> Please make sure that the above environment and services are in place ;</font>

Source download

If you don't want to write code , The source code of the whole series can be found in GitHub Download to , The address and link information is shown in the following table (https://github.com/zq2599/blog_demos)：

This git Multiple folders in project , The application of this chapter in <font color="blue">flinksinkdemo</font> Under the folder , As shown in the red box below ： Ready , Start developing ;

preparation

Before official coding , First go to the official website to check the relevant information for basic information ：

1. Address ：https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/connectors/kafka.html
2. I used it here kafka yes 2.4.0 edition , Find the corresponding libraries and classes in the official documents , As shown in the red box below ：

kafka Get ready

1. Create a test006 Of topic, There are four divisions , Refer to the order ：

./kafka-topics.sh \
--create \
--bootstrap-server 127.0.0.1:9092 \
--replication-factor 1 \
--partitions 4 \
--topic test006

2. Consume at the console test006 The news of , Refer to the order ：

./kafka-console-consumer.sh \
--bootstrap-server 127.0.0.1:9092 \
--topic test006

3. At this time, if the topic There's news coming in , It will output... On the console ;
4. Next, start coding ;

Create a project

1. use maven Command to create flink engineering ：

mvn \
archetype:generate \
-DarchetypeGroupId=org.apache.flink \
-DarchetypeArtifactId=flink-quickstart-java \
-DarchetypeVersion=1.9.2

2. According to the prompt ,groupid Input <font color="blue">com.bolingcavalry</font>,artifactid Input <font color="blue">flinksinkdemo</font>, You can create a maven engineering ;
3. stay pom.xml add kafka Dependency Library ：

<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_2.11</artifactId>
<version>1.9.0</version>
</dependency>

4. Project creation complete , Start writing flink The code for the task ;

Sending string messages sink

Try sending string type messages first ：

1. establish KafkaSerializationSchema Implementation class of interface , The latter class is to be created as sink Object's parameters use ：

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));
}
}

2. Create a task class KafkaStrSink, Please note that FlinkKafkaProducer Object's parameters ,FlinkKafkaProducer.Semantic.EXACTLY_ONCE Strictly once ：

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();
// The degree of parallelism is 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);
// Create a List, There are two in it Tuple2 Elements 
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");
// Count the number of words 
env.fromCollection(list)
.addSink(producer)
.setParallelism(4);
env.execute("sink demo : kafka str");
}
}

3. Use mvn Command compile build , stay target Directory to get files <font color="blue">flinksinkdemo-1.0-SNAPSHOT.jar</font>;
4. stay flink Of web Page submission flinksinkdemo-1.0-SNAPSHOT.jar, And make executive class , Here's the picture ：
5. After successful submission , If flink There are four available slot, The mission will be executed immediately , Will be spending kafak The message terminal receives the message , Here's the picture ：
6. The execution of the task is shown in the figure below ：

Sending the object message sink

Then try how to send the message of object type , The object selection here is commonly used Tuple2 object ：

1. establish KafkaSerializationSchema Implementation class of interface , This class is followed by sink Object input parameters , Notice the comment in the code that caught the exception ：<font color="red"> Careful use of production environment printStackTrace()!!!</font>

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) {
// Be careful , This is a very dangerous operation in the production environment ,
// Too much wrong printing will seriously affect system performance , Please adjust according to the production environment 
e.printStackTrace();
}
return new ProducerRecord<byte[], byte[]>(topic, b);
}
}

2. establish flink The task class ：

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();
// The degree of parallelism is 1
env.setParallelism(1);
Properties properties = new Properties();
//kafka Of broker Address 
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);
// Create a List, There are two in it Tuple2 Elements 
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));
// Count the number of words 
env.fromCollection(list)
.keyBy(0)
.sum(1)
.addSink(producer)
.setParallelism(4);
env.execute("sink demo : kafka obj");
}
}

3. Compile the build like the previous task , hold jar Submitted to the flink, And specify that the execution class is <font color="blue">com.bolingcavalry.addsink.KafkaObjSink</font>;
4. consumption kafka The console output of the message is as follows ：
5. stay web The execution of the page is as follows ： thus ,flink Take the calculation result as kafka The actual battle of sending the message is finished , I hope to provide you with reference , The next chapter , We will continue to experience the official sink Ability ;

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