接上文:一文说清flink从编码到摆设上线
之前写了kafka source,如今增补kafka sink。完满kafka干系利用。
环境分析:MySQL:5.7;flink:1.14.0;hadoop:3.0.0;利用体系:CentOS 7.6;JDK:1.8.0_401;kafka_2.12-2.5.0。
1. kafka 创建 topic
topic:rv-test-sink。
2.添加依赖
- <!--flink cdc kafka 相关依赖-->
- <dependency>
- <groupId>org.apache.flink</groupId>
- <artifactId>flink-connector-kafka_2.11</artifactId>
- <version>${flink.version}</version>
- </dependency>
复制代码 3.创建运行环境
- package com.zl.utils;
- import org.apache.flink.configuration.Configuration;
- import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
- import org.apache.flink.streaming.api.CheckpointingMode;
- import org.apache.flink.streaming.api.environment.CheckpointConfig;
- import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
- import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
- import java.time.Duration;
- import java.time.ZoneOffset;
- import java.util.concurrent.TimeUnit;
- /**
- * EnvUtil
- * @description:
- */
- public class EnvUtil {
- /**
- * 设置flink执行环境
- * @param parallelism 并行度
- */
- public static StreamExecutionEnvironment setFlinkEnv(int parallelism) {
- // System.setProperty("HADOOP_USER_NAME", "用户名") 对应的是 hdfs文件系统目录下的路径:/user/用户名的文件夹名,本文为root
- System.setProperty("HADOOP_USER_NAME", "root");
- Configuration conf = new Configuration();
- conf.setInteger("rest.port", 1000);
- StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(conf);
- if (parallelism >0 ){
- //设置并行度
- env.setParallelism(parallelism);
- } else {
- env.setParallelism(1);// 默认1
- }
- // 添加重启机制
- // env.setRestartStrategy(RestartStrategies.fixedDelayRestart(50, Time.minutes(6)));
- // 没有这个配置,会导致“Flink 任务没报错,但是无法同步数据到doris”。
- // 启动checkpoint,设置模式为精确一次 (这是默认值),10*60*1000=60000
- env.enableCheckpointing(60000, CheckpointingMode.EXACTLY_ONCE);
- //rocksdb状态后端,启用增量checkpoint
- env.setStateBackend(new EmbeddedRocksDBStateBackend(true));
- //设置checkpoint路径
- CheckpointConfig checkpointConfig = env.getCheckpointConfig();
- // 同一时间只允许一个 checkpoint 进行(默认)
- checkpointConfig.setMaxConcurrentCheckpoints(1);
- //最小间隔,10*60*1000=60000
- checkpointConfig.setMinPauseBetweenCheckpoints(60000);
- // 取消任务后,checkpoint仍然保存
- checkpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
- //checkpoint容忍失败的次数
- checkpointConfig.setTolerableCheckpointFailureNumber(5);
- //checkpoint超时时间 默认10分钟
- checkpointConfig.setCheckpointTimeout(TimeUnit.MINUTES.toMillis(10));
- //禁用operator chain(方便排查反压)
- env.disableOperatorChaining();
- return env;
- }
- public static StreamTableEnvironment getFlinkTenv(StreamExecutionEnvironment env) {
- StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
- //设置时区 东八
- tenv.getConfig().setLocalTimeZone(ZoneOffset.ofHours(8));
- Configuration configuration = tenv.getConfig().getConfiguration();
- // 开启miniBatch
- configuration.setString("table.exec.mini-batch.enabled", "true");
- // 批量输出的间隔时间
- configuration.setString("table.exec.mini-batch.allow-latency", "5 s");
- // 防止OOM设置每个批次最多缓存数据的条数,可以设为2万条
- configuration.setString("table.exec.mini-batch.size", "20000");
- // 开启LocalGlobal
- configuration.setString("table.optimizer.agg-phase-strategy", "TWO_PHASE");
- //设置TTL API指定
- tenv.getConfig().setIdleStateRetention(Duration.ofHours(25));
- return tenv;
- }
- }
复制代码 4.核心代码
- package com.zl.kafka;
- import com.alibaba.fastjson.JSONObject;
- import com.zl.utils.EnvUtil;
- import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
- import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
- import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
- import org.apache.kafka.clients.producer.ProducerRecord;
- import javax.annotation.Nullable;
- import java.nio.charset.StandardCharsets;
- import java.util.Properties;
- public class KafkaExampleSink {
- public static void main(String[] args) throws Exception {
- // 配置运行环境,并行度1
- StreamExecutionEnvironment env = EnvUtil.setFlinkEnv(1);
- // 程序间隔离,每个程序单独设置
- env.getCheckpointConfig().setCheckpointStorage("hdfs://10.86.97.191:9000/flinktest/KafkaExampleSink");
- /// ===== 构造kafka sink =====
- // 相关参数配置可以参考下面这两个文档:①https://cloud.tencent.com/developer/article/2089393
- // ②https://www.bilibili.com/opus/819228616166473783
- // kafka配置
- Properties prop = new Properties();
- prop.setProperty("bootstrap.servers", "10.86.97.21:9092,10.86.97.21:9093,10.86.97.21:9094");
- // 当设置为“true”时,生产者将确保流中只写入每条消息的一个副本。
- prop.setProperty("enable.idempotence", "true");
- // 指定了生产者在接收到服务器相应之前可以发送多个消息,值越高,占用的内存越大,
- // 当然也可以提升吞吐量,发生错误时,可能会造成数据的发送顺序改变,其默认值是5.
- prop.setProperty("max.in.flight.requests.per.connection", "5");
- prop.setProperty("acks", "all");
- // 在kafka中消息发送失败时,指定生产者可以重发消息的次数,默认情况下,
- // 生产者在每次重试之间默认等待100ms,可以通过参数retey.backoff.ms参数来改变这个时间间隔。retries的缺省值:0.
- prop.setProperty("retries", "5");
- // 事务超时时间
- prop.setProperty("transaction.timeout.ms", 15 * 60 * 1000 + "");
- String topic = "rv-test-sink";
- FlinkKafkaProducer<String> flinkKafkaProducer = new FlinkKafkaProducer<String>(
- topic,// topic
- new KafkaSerializationSchema<String>() {
- @Override
- public ProducerRecord<byte[], byte[]> serialize(String s, @Nullable Long aLong) {
- return new ProducerRecord<>(topic, s.getBytes(StandardCharsets.UTF_8));
- }
- },
- prop,
- FlinkKafkaProducer.Semantic.EXACTLY_ONCE
- );
- /// ===== 构造模拟数据 =====
- JSONObject rvJsonObject = new JSONObject();
- rvJsonObject.put("dt","2024-12-20");// 日期取当天
- rvJsonObject.put("uuid","data-stream-1");
- rvJsonObject.put("report_time",1733881971621L);
- String mockJson = JSONObject.toJSONString(rvJsonObject);
- /// ===== sink kafka =====
- env.fromElements(mockJson).addSink(flinkKafkaProducer).setParallelism(3).name("kafka-sink").uid("kafka-sink");
- env.execute("kafka-sink-job");
- }// main
- }
复制代码 5.运行
由于不是一连输入流,运行完会竣事。
sink到kafka的数据如下:
6.完备代码
完备代码见:完备代码
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