【极数系列】Flink集成KafkaSink & 实时输出数据（11）

纸飞机_暖阳

发布于 2024-03-04 17:41:27

1230

发布于 2024-03-04 17:41:27

01 引言

KafkaSink 可将数据流写入一个或多个 Kafka topic
实战源码地址,一键下载可用：https://gitee.com/shawsongyue/aurora.git
模块：aurora_flink_connector_kafka
主类：KafkaSinkStreamingJob

02 连接器依赖

2.1 kafka连接器依赖

        <!--kafka依赖 start-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka</artifactId>
            <version>3.0.2-1.18</version>
        </dependency>
        <!--kafka依赖 end-->

2.2 base基础依赖

     若是不引入该依赖，项目启动直接报错：Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/flink/connector/base/source/reader/RecordEmitter

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-base</artifactId>
            <version>1.18.0</version>
        </dependency>

03 使用方法

Kafka sink 提供了构建类来创建 KafkaSink 的实例

DataStream<String> stream = ...;
        
KafkaSink<String> sink = KafkaSink.<String>builder()
        .setBootstrapServers(brokers)
        .setRecordSerializer(KafkaRecordSerializationSchema.builder()
            .setTopic("topic-name")
            .setValueSerializationSchema(new SimpleStringSchema())
            .build()
        )
        .setDeliveryGuarantee(DeliveryGuarantee.AT_LEAST_ONCE)
        .build();
        
stream.sinkTo(sink);

以下属性在构建 KafkaSink 时是必须指定的：
Bootstrap servers, setBootstrapServers(String)
消息序列化器（Serializer）, setRecordSerializer(KafkaRecordSerializationSchema)
如果使用DeliveryGuarantee.EXACTLY_ONCE 的语义保证，则需要使用 setTransactionalIdPrefix(String)

04 序列化器

构建时需要提供 KafkaRecordSerializationSchema 来将输入数据转换为 Kafka 的 ProducerRecord。Flink 提供了 schema 构建器以提供一些通用的组件，例如消息键（key）/消息体（value）序列化、topic 选择、消息分区，同样也可以通过实现对应的接口来进行更丰富的控制。
其中消息体（value）序列化方法和 topic 的选择方法是必须指定的，此外也可以通过 setKafkaKeySerializer(Serializer) 或 setKafkaValueSerializer(Serializer) 来使用 Kafka 提供而非 Flink 提供的序列化器

KafkaRecordSerializationSchema.builder()
    .setTopicSelector((element) -> {<your-topic-selection-logic>})
    .setValueSerializationSchema(new SimpleStringSchema())
    .setKeySerializationSchema(new SimpleStringSchema())
    .setPartitioner(new FlinkFixedPartitioner())
    .build();

05 容错恢复

`KafkaSink` 总共支持三种不同的语义保证（`DeliveryGuarantee`）。对于 `DeliveryGuarantee.AT_LEAST_ONCE` 和 `DeliveryGuarantee.EXACTLY_ONCE`，Flink checkpoint 必须启用。默认情况下 `KafkaSink` 使用 `DeliveryGuarantee.NONE`。 以下是对不同语义保证的解释：

DeliveryGuarantee.NONE 不提供任何保证：消息有可能会因 Kafka broker 的原因发生丢失或因 Flink 的故障发生重复。
DeliveryGuarantee.AT_LEAST_ONCE: sink 在 checkpoint 时会等待 Kafka 缓冲区中的数据全部被 Kafka producer 确认。消息不会因 Kafka broker 端发生的事件而丢失，但可能会在 Flink 重启时重复，因为 Flink 会重新处理旧数据。
DeliveryGuarantee.EXACTLY_ONCE: 该模式下，Kafka sink 会将所有数据通过在 checkpoint 时提交的事务写入。因此，如果 consumer 只读取已提交的数据（参见 Kafka consumer 配置 isolation.level），在 Flink 发生重启时不会发生数据重复。然而这会使数据在 checkpoint 完成时才会可见，因此请按需调整 checkpoint 的间隔。请确认事务 ID 的前缀（transactionIdPrefix）对不同的应用是唯一的，以保证不同作业的事务不会互相影响！此外，强烈建议将 Kafka 的事务超时时间调整至远大于 checkpoint 最大间隔 + 最大重启时间，否则 Kafka 对未提交事务的过期处理会导致数据丢失。

05 指标监控

Kafka sink 会在不同的范围（Scope）中汇报下列指标。

范围	指标	用户变量	描述	类型
算子	currentSendTime	n/a	发送最近一条数据的耗时。该指标反映最后一条数据的瞬时值。	Gauge

06 项目源码实战

6.1 包结构

6.2 pom.xml依赖

<?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.xsy</groupId>
    <artifactId>aurora_flink_connector_kafka</artifactId>
    <version>1.0-SNAPSHOT</version>

    <!--属性设置-->
    <properties>
        <!--java_JDK版本-->
        <java.version>11</java.version>
        <!--maven打包插件-->
        <maven.plugin.version>3.8.1</maven.plugin.version>
        <!--编译编码UTF-8-->
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <!--输出报告编码UTF-8-->
        <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
        <!--json数据格式处理工具-->
        <fastjson.version>1.2.75</fastjson.version>
        <!--log4j版本-->
        <log4j.version>2.17.1</log4j.version>
        <!--flink版本-->
        <flink.version>1.18.0</flink.version>
        <!--scala版本-->
        <scala.binary.version>2.11</scala.binary.version>
    </properties>

    <!--通用依赖-->
    <dependencies>

        <!-- json -->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>${fastjson.version}</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-java -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-clients -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients</artifactId>
            <version>${flink.version}</version>
        </dependency>


        <!--================================集成外部依赖==========================================-->
        <!--集成日志框架 start-->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-slf4j-impl</artifactId>
            <version>${log4j.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-api</artifactId>
            <version>${log4j.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>${log4j.version}</version>
        </dependency>

        <!--集成日志框架 end-->

        <!--kafka依赖 start-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka</artifactId>
            <version>3.0.2-1.18</version>
        </dependency>
        <!--kafka依赖 end-->

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-base</artifactId>
            <version>1.18.0</version>
        </dependency>
    </dependencies>

    <!--编译打包-->
    <build>
        <finalName>${project.name}</finalName>
        <!--资源文件打包-->
        <resources>
            <resource>
                <directory>src/main/resources</directory>
            </resource>
            <resource>
                <directory>src/main/java</directory>
                <includes>
                    <include>**/*.xml</include>
                </includes>
            </resource>
        </resources>

        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.1.1</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <artifactSet>
                                <excludes>
                                    <exclude>org.apache.flink:force-shading</exclude>
                                    <exclude>org.google.code.flindbugs:jar305</exclude>
                                    <exclude>org.slf4j:*</exclude>
                                    <excluder>org.apache.logging.log4j:*</excluder>
                                </excludes>
                            </artifactSet>
                            <filters>
                                <filter>
                                    <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>org.aurora.KafkaStreamingJob</mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>

        <!--插件统一管理-->
        <pluginManagement>
            <plugins>
                <!--maven打包插件-->
                <plugin>
                    <groupId>org.springframework.boot</groupId>
                    <artifactId>spring-boot-maven-plugin</artifactId>
                    <version>${spring.boot.version}</version>
                    <configuration>
                        <fork>true</fork>
                        <finalName>${project.build.finalName}</finalName>
                    </configuration>
                    <executions>
                        <execution>
                            <goals>
                                <goal>repackage</goal>
                            </goals>
                        </execution>
                    </executions>
                </plugin>

                <!--编译打包插件-->
                <plugin>
                    <artifactId>maven-compiler-plugin</artifactId>
                    <version>${maven.plugin.version}</version>
                    <configuration>
                        <source>${java.version}</source>
                        <target>${java.version}</target>
                        <encoding>UTF-8</encoding>
                        <compilerArgs>
                            <arg>-parameters</arg>
                        </compilerArgs>
                    </configuration>
                </plugin>
            </plugins>
        </pluginManagement>
    </build>

    <!--配置Maven项目中需要使用的远程仓库-->
    <repositories>
        <repository>
            <id>aliyun-repos</id>
            <url>https://maven.aliyun.com/nexus/content/groups/public/</url>
            <snapshots>
                <enabled>false</enabled>
            </snapshots>
        </repository>
    </repositories>

    <!--用来配置maven插件的远程仓库-->
    <pluginRepositories>
        <pluginRepository>
            <id>aliyun-plugin</id>
            <url>https://maven.aliyun.com/nexus/content/groups/public/</url>
            <snapshots>
                <enabled>false</enabled>
            </snapshots>
        </pluginRepository>
    </pluginRepositories>

</project>

6.3 配置文件

（1）application.properties

#kafka集群地址
kafka.bootstrapServers=localhost:9092
#kafka主题
kafka.topic=topic_a
#kafka消费者组
kafka.group=aurora_group

（2）log4j2.properties

rootLogger.level=INFO
rootLogger.appenderRef.console.ref=ConsoleAppender
appender.console.name=ConsoleAppender
appender.console.type=CONSOLE
appender.console.layout.type=PatternLayout
appender.console.layout.pattern=%d{HH:mm:ss,SSS} %-5p %-60c %x - %m%n
log.file=D:\\tmprootLogger.level=INFO
rootLogger.appenderRef.console.ref=ConsoleAppender
appender.console.name=ConsoleAppender
appender.console.type=CONSOLE
appender.console.layout.type=PatternLayout
appender.console.layout.pattern=%d{HH:mm:ss,SSS} %-5p %-60c %x - %m%n
log.file=D:\\tmp

6.4 创建sink作业

package com.aurora;

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.connector.base.DeliveryGuarantee;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.KafkaSourceBuilder;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.runtime.state.StateBackend;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.ArrayList;

/**
 * @author 浅夏的猫
 * @description kafka 连接器使用demo作业
 * @datetime 22:21 2024/2/1
 */
public class KafkaSinkStreamingJob {

    private static final Logger logger = LoggerFactory.getLogger(KafkaSinkStreamingJob.class);

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

        //===============1.获取参数==============================
        //定义文件路径
        String propertiesFilePath = "E:\\project\\aurora_dev\\aurora_flink_connector_kafka\\src\\main\\resources\\application.properties";
        //方式一:直接使用内置工具类
        ParameterTool paramsMap = ParameterTool.fromPropertiesFile(propertiesFilePath);

        //================2.初始化kafka参数==============================
        String bootstrapServers = paramsMap.get("kafka.bootstrapServers");
        String topic = paramsMap.get("kafka.topic");

        KafkaSink<String> sink = KafkaSink.<String>builder()
                //设置kafka地址
                .setBootstrapServers(bootstrapServers)
                //设置消息序列号方式
                .setRecordSerializer(KafkaRecordSerializationSchema.builder()
                        .setTopic(topic)
                        .setValueSerializationSchema(new SimpleStringSchema())
                        .build()
                )
                //至少一次
                .setDeliveryGuarantee(DeliveryGuarantee.AT_LEAST_ONCE)
                .build();


        //=================4.创建Flink运行环境=================
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        ArrayList<String> listData = new ArrayList<>();
        listData.add("test");
        listData.add("java");
        listData.add("c++");
        DataStreamSource<String> dataStreamSource = env.fromCollection(listData);

        //=================5.数据简单处理======================
        SingleOutputStreamOperator<String> flatMap = dataStreamSource.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String record, Collector<String> collector) throws Exception {
                logger.info("正在处理kafka数据:{}", record);
                collector.collect(record);
            }
        });

        //数据输出算子
        flatMap.sinkTo(sink);

        //=================6.启动服务=========================================
        //开启flink的checkpoint功能：每隔1000ms启动一个检查点（设置checkpoint的声明周期）
        env.enableCheckpointing(1000);
        //checkpoint高级选项设置
        //设置checkpoint的模式为exactly-once（这也是默认值）
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        //确保检查点之间至少有500ms间隔（即checkpoint的最小间隔）
        env.getCheckpointConfig().setMinPauseBetweenCheckpoints(500);
        //确保检查必须在1min之内完成，否则就会被丢弃掉（即checkpoint的超时时间）
        env.getCheckpointConfig().setCheckpointTimeout(60000);
        //同一时间只允许操作一个检查点
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
        //程序即使被cancel后，也会保留checkpoint数据，以便根据实际需要恢复到指定的checkpoint
        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        //设置statebackend,指定state和checkpoint的数据存储位置(checkpoint的数据必须得有一个可以持久化存储的地方）
        env.getCheckpointConfig().setCheckpointStorage("file:///E:/flink/checkPoint");
        env.execute();
    }

}

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原始发表：2024-02-06，如有侵权请联系 cloudcommunity@tencent.com 删除

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