1. flink通常整合kafka实现消费和生产。在很大原因上是由于kafka很适合流处理
在我们平常的业务场景中，仅读取，写入和存储数据流是不够的，更多目的是启用流的实时处理。在Kafka中，流处理器是指从输入topic获取连续数据流，对该输入执行一些处理，并生成连续数据流以输出topic的任何内容。例如，零售应用程序可能会接受销售和装运的输入流，并输出一系列重新排序和根据此数据计算出的价格调整。
可以直接使用生产者API和消费者API进行简单的处理。然而，对于更复杂的转换，Kafka提供完全集成的Streams API。这允许构建应用程序进行非平凡的处理，从而计算聚合关闭流或将流连接在一起。在这过程中，fiink整合kafka来实现对流数据的处理是一个非常好的选择。
2. 采用flink的Api，实现消费者。
往kafka的“flinktest"这个topic不断发布消息，然后经过flink的消费之后，输出处理的时间和处理的字符串。采用的是flink1.4.2 和kafka1.0.0
pom文件如下
<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.maskwang.flink</groupId>
<artifactId>flink_kafka</artifactId>
<version>0.0.1-SNAPSHOT</version>
<packaging>jar</packaging>
<name>flink_kafka</name>
<url>http://maven.apache.org</url>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<flink.version>1.4.2</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-clients_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-kafka-0.10 -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka-0.10_2.11</artifactId>
<version>1.4.2</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.0.0</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<artifactSet>
<excludes>
<exclude>com.google.code.findbugs:jsr305</exclude>
<exclude>org.slf4j:*</exclude>
<exclude>log4j:*</exclude>
</excludes>
</artifactSet>
<filters>
<filter>
<!-- Do not copy the signatures in the META-INF folder.
Otherwise, this might cause SecurityExceptions when using the JAR. -->
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>com.maskwang.flink.ReadFromKafka</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
业务逻辑如下
package com.maskwang.flink;
import java.util.Date;
import java.util.Properties;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.streaming.util.serialization.SimpleStringSchema;
public class ReadFromKafka {
public static void main(String[] args) throws Exception {
// 构建环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties properties = new Properties();
//这里是由一个kafka
properties.setProperty("bootstrap.servers", "localhost:9092");
properties.setProperty("group.id", "flink_consumer");
//第一个参数是topic的名称
DataStream<String> stream=env.addSource(new FlinkKafkaConsumer010("flinktest", new SimpleStringSchema(), properties));
stream.map(new MapFunction<String, String>() {
@Override
public String map(String value) throws Exception {
return new Date().toString()+": "+value;
}
}).print();
env.execute();
}
}
- 没有运用到集群，所以只需要添加一个ip地址就可以。如果是集群，则把其他的地址加入到这里。
- 我采用的是flink1.4.2。所以这里使用
FlinkKafkaConsumer010。如果是其他版本，这里可能不同。详细的可以参考官网。
注意这里有个坑，之前我不是采用这种打包方式，导致会产生如下异常
java.lang.NoClassDefFoundError: org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumer010
at com.maskwang.flink.ReadFromKafka.main(ReadFromKafka.java:22)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.flink.client.program.PackagedProgram.callMainMethod(PackagedProgram.java:525)
at org.apache.flink.client.program.PackagedProgram.invokeInteractiveModeForExecution(PackagedProgram.java:417)
at org.apache.flink.client.program.ClusterClient.run(ClusterClient.java:396)
at org.apache.flink.client.CliFrontend.executeProgram(CliFrontend.java:802)
at org.apache.flink.client.CliFrontend.run(CliFrontend.java:282)
at org.apache.flink.client.CliFrontend.parseParameters(CliFrontend.java:1054)
at org.apache.flink.client.CliFrontend$1.call(CliFrontend.java:1101)
at org.apache.flink.client.CliFrontend$1.call(CliFrontend.java:1098)
at org.apache.flink.runtime.security.HadoopSecurityContext$$Lambda$8/989447607.run(Unknown Source)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1807)
at org.apache.flink.runtime.security.HadoopSecurityContext.runSecured(HadoopSecurityContext.java:41)
at org.apache.flink.client.CliFrontend.main(CliFrontend.java:1098)
Caused by: java.lang.ClassNotFoundException: org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 19 more
运行状态如下：
-
向kafka的topic推送数据
image.png
-
flink作为客户对订阅topic实现消费并输出
image.png
3. flink作为生产者向topic推送数据
package com.maskwang.flink;
import java.util.Properties;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer010;
import org.apache.flink.streaming.util.serialization.SimpleStringSchema;
public class WriteToKafka {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
DataStream<String> stream = env.addSource(new SimpleStringGenerator());
stream.addSink(new FlinkKafkaProducer010("flinkwrite", new SimpleStringSchema(), properties)); //配置topic
env.execute();
}
public static class SimpleStringGenerator implements SourceFunction<String> {
long i = 0;
boolean swith = true;
@Override
public void run(SourceContext<String> ctx) throws Exception {
for(int k=0;k<5;k++) {
ctx.collect("flink:" + k++);
//Thread.sleep(5); //家里这个后会有问题
}
}
@Override
public void cancel() {
swith = false;
}
}
}
在kafka上的finkwrite(与消费的队列不同,可以自定义) topic上可以看到,数据确实被生产了
运行结果:
image.png
参考文章
https://www.zhihu.com/question/28925721
https://github.com/tgrall/kafka-flink-101
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