Flink基于Kafka-Connector 数据流容错回放机制

淡淡的烟草味﹌ 2022-11-20 09:56 203阅读 0赞

# Flink基于Kafka-Connector 数据流容错回放机制及代码案例实战-Flink牛刀小试 #

**Flink牛刀小试系列目录**

*  [Flink牛刀小试-Flink 集群运行原理兼部署及Yarn运行模式深入剖析][Flink_-Flink _Yarn]
 *  [Flink牛刀小试-Flink Window类型及使用原理案例实战][Flink_-Flink Window]
 *  [Flink牛刀小试-Flink Broadcast 与 Accumulators 应用案例实战][Flink_-Flink Broadcast _ Accumulators]
 *  [Flink牛刀小试-Flink与SparkStreaming之Counters& Accumulators 累加器双向应用案例实战][Flink_-Flink_SparkStreaming_Counters_ Accumulators]
 *  [Flink牛刀小试-Flink分布式缓存Distributed Cache应用案例实战][Flink_-Flink_Distributed Cache]
 *  [Flink牛刀小试-Flink状态管理与checkPoint数据容错机制深入剖析][Flink_-Flink_checkPoint]
 *  [Flink牛刀小试-Flink Window分析及Watermark解决乱序数据机制深入剖析][Flink_-Flink Window_Watermark]
 *  [Flink牛刀小试-Flink Restart Strategies 重启策略机制深入剖析][Flink_-Flink Restart Strategies]
 *  [Flink牛刀小试-Flink CheckPoint状态点恢复与savePoint机制对比剖析][Flink_-Flink CheckPoint_savePoint]
 *  [Flink牛刀小试-Flink SQL Table 我们一起去看2018中超联赛][Flink_-Flink SQL Table _2018]
 *  [Flink牛刀小试-Flink基于Kafka-Connector 数据流容错回放机制及代码案例实战][Flink_-Flink_Kafka-Connector]
 *  \[Flink牛刀小试-Flink DataStreamAPI与DataSetAPI应用案例实战\]
 *  \[Flink牛刀小试-Flink并行度 Parallel及Slots关系原理深入剖析\]
 *  \[Flink牛刀小试-Flink集群HA配置及高可用机制深入剖析\]
 *  \[Flink牛刀小试-Flink批处理与流处理案例实战深入剖析\]
 *  \[Flink牛刀小试-Flink综合性应用案例实践及垂直业务深入剖析\]

## 1 Kafka-connector 再次亲密牵手Flink ##

*  Kafka中的partition机制和Flink的并行度机制深度结合，实现数据恢复。
 *  Kafka可以作为Flink的source和sink，牛在这里。
 *  任务失败，通过设置kafka的offset来恢复应用

## 2 回顾Spark Streaming针对kafka使用技术 ##

// 设置检查点目录
        ssc.checkpoint("./streaming_checkpoint")
    
        // 获取Kafka配置(通过配置文件读取，ConfigurationManager自定义方法)
        val broker_list = ConfigurationManager.config.getString("kafka.broker.list")  
        val topics = ConfigurationManager.config.getString("kafka.topics")
    
        // kafka消费者配置
        val kafkaParam = Map(
          "bootstrap.servers" -> broker_list,//用于初始化链接到集群的地址
          "key.deserializer" -> classOf[StringDeserializer],
          "value.deserializer" -> classOf[StringDeserializer],
          //用于标识这个消费者属于哪个消费团体
          "group.id" -> "commerce-consumer-group",
          //如果没有初始化偏移量或者当前的偏移量不存在任何服务器上，可以使用这个配置属性
          //可以使用这个配置，latest自动重置偏移量为最新的偏移量
          "auto.offset.reset" -> "latest",
          //如果是true，则这个消费者的偏移量会在后台自动提交
          "enable.auto.commit" -> (false: java.lang.Boolean)
        )
    
        // 创建DStream，返回接收到的输入数据
        // LocationStrategies：根据给定的主题和集群地址创建consumer
        // LocationStrategies.PreferConsistent：持续的在所有Executor之间分配分区
        // ConsumerStrategies：选择如何在Driver和Executor上创建和配置Kafka Consumer
        // ConsumerStrategies.Subscribe：订阅一系列主题
        val adRealTimeLogDStream=KafkaUtils.createDirectStream[String,String](ssc,
          LocationStrategies.PreferConsistent,ConsumerStrategies.Subscribe[String,String](Array(topics),kafkaParam))

## 3 再论 Flink Kafka Consumer ##

### 3.1 理论时间 ###

*  setStartFromGroupOffsets()【默认消费策略】
    
    默认读取上次保存的offset信息  
    如果是应用第一次启动，读取不到上次的offset信息，则会根据这个参数auto.offset.reset的值来进行消费数据
 *  setStartFromEarliest()  
    从最早的数据开始进行消费，忽略存储的offset信息
 *  setStartFromLatest()  
    从最新的数据进行消费，忽略存储的offset信息
 *  setStartFromSpecificOffsets(Map<KafkaTopicPartition, Long>)

![44344487349a4076ba4f8edc0649cb5a.png][]

*  当checkpoint机制开启的时候，KafkaConsumer会定期把kafka的offset信息还有其他operator的状态信息一块保存起来。当job失败重启的时候，Flink会从最近一次的checkpoint中进行恢复数据，重新消费kafka中的数据。
 *  为了能够使用支持容错的kafka Consumer，需要开启checkpoint  
    env.enableCheckpointing(5000); // 每5s checkpoint一次
 *  Kafka Consumers Offset 自动提交有以下两种方法来设置，可以根据job是否开启checkpoint来区分:
    
    (1) Checkpoint关闭时： 可以通过下面两个参数配置
    
    enable.auto.commit
    
    [auto.commit.interval.ms][]
    
    (2) Checkpoint开启时：当执行checkpoint的时候才会保存offset，这样保证了kafka的offset和checkpoint的状态偏移量保持一致。  
    可以通过这个参数设置
    
    setCommitOffsetsOnCheckpoints(boolean)
    
    这个参数默认就是true。表示在checkpoint的时候提交offset, 此时，kafka中的自动提交机制就会被忽略

### 3.2 案例实战 ###

依赖引入：
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-statebackend-rocksdb_2.11</artifactId>
            <version>1.6.1</version>
        </dependency>
    
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka-0.11_2.11</artifactId>
            <version>1.6.1</version>
        </dependency>
    
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>0.11.0.3</version>
        </dependency>
        
     案例实战：
     public class StreamingKafkaSource {
        
        public static void main(String[] args) throws Exception {
            //获取Flink的运行环境
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
            //checkpoint配置
            env.enableCheckpointing(5000);
            env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
            env.getCheckpointConfig().setMinPauseBetweenCheckpoints(500);
            env.getCheckpointConfig().setCheckpointTimeout(60000);
            env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
            env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
    
            //设置statebackend
    
            //env.setStateBackend(new RocksDBStateBackend("hdfs://hadoop100:9000/flink/checkpoints",true));
    
    
            String topic = "kafkaConsumer";
            Properties prop = new Properties();
            prop.setProperty("bootstrap.servers","SparkMaster:9092");
            prop.setProperty("group.id","kafkaConsumerGroup");
    
            FlinkKafkaConsumer011<String> myConsumer = new FlinkKafkaConsumer011<>(topic, new SimpleStringSchema(), prop);
    
            myConsumer.setStartFromGroupOffsets();//默认消费策略
    
            DataStreamSource<String> text = env.addSource(myConsumer);
    
            text.print().setParallelism(1);
    
            env.execute("StreamingFromCollection");
        }
    }

## 4 再论 Flink Kafka Producer ##

### 4.1 理论时间 ###

*  Kafka Producer的容错-Kafka 0.9 and 0.10
 *  如果Flink开启了checkpoint，针对FlinkKafkaProducer09和FlinkKafkaProducer010 可以提供 at-least-once的语义，还需要配置下面两个参数:
    
    setLogFailuresOnly(false)
    
    setFlushOnCheckpoint(true)
 *  注意：建议修改kafka 生产者的重试次数retries【这个参数的值默认是0】
 *  Kafka Producer的容错-Kafka 0.11，如果Flink开启了checkpoint，针对FlinkKafkaProducer011 就可以提供 exactly-once的语义,但是需要选择具体的语义
    
    Semantic.NONE
    
    Semantic.AT\_LEAST\_ONCE【默认】
    
    Semantic.EXACTLY\_ONCE

### 4.2 KafkaSink案例实战 ###

public class StreamingKafkaSink {
        public static void main(String[] args) throws Exception {
        //获取Flink的运行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
    
        //checkpoint配置
        env.enableCheckpointing(5000);
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        env.getCheckpointConfig().setMinPauseBetweenCheckpoints(500);
        env.getCheckpointConfig().setCheckpointTimeout(60000);
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
    
        //设置statebackend
    
        //env.setStateBackend(new RocksDBStateBackend("hdfs://SparkMaster:9000/flink/checkpoints",true));
    
    
        DataStreamSource<String> text = env.socketTextStream("SparkMaster", 9001, "\n");
    
        String brokerList = "SparkMaster:9092";
        String topic = "kafkaProducer";
    
        Properties prop = new Properties();
        prop.setProperty("bootstrap.servers",brokerList);
    
        //第一种解决方案，设置FlinkKafkaProducer011里面的事务超时时间
        //设置事务超时时间
        //prop.setProperty("transaction.timeout.ms",60000*15+"");
    
        //第二种解决方案，设置kafka的最大事务超时时间,主要是kafka的配置文件设置。
    
        //FlinkKafkaProducer011<String> myProducer = new FlinkKafkaProducer011<>(brokerList, topic, new SimpleStringSchema());
    
        //使用仅一次语义的kafkaProducer
        FlinkKafkaProducer011<String> myProducer = new FlinkKafkaProducer011<>(topic, new KeyedSerializationSchemaWrapper<String>(new SimpleStringSchema()), prop, FlinkKafkaProducer011.Semantic.EXACTLY_ONCE);
        text.addSink(myProducer);
    
    
        env.execute("StreamingFromCollection");
    
    
      }
    }

## 5 结语 ##

kafka必不可少，关于kafka还有很多要说的内容，详情请参考我的kafka商业环境实战系列吧。

*  [kafka 商业环境实战-kafka生产环境规划][kafka _-kafka]
 *  [kafka 商业环境实战-kafka生产者和消费者吞吐量测试][kafka _-kafka 1]
 *  [kafka 商业环境实战-kafka生产者Producer参数设置及参数调优建议][kafka _-kafka_Producer]
 *  [kafka 商业环境实战-kafka集群管理重要操作指令运维兵书][kafka _-kafka 2]
 *  [kafka 商业环境实战-kafka集群Broker端参数设置及调优准则建议][kafka _-kafka_Broker]
 *  [kafka 商业环境实战-kafka之Producer同步与异步消息发送及事务幂等性案例应用实战][kafka _-kafka_Producer 1]
 *  [kafka 商业环境实战-kafka Poll轮询机制与消费者组的重平衡分区策略剖析][kafka _-kafka Poll]
 *  [kafka 商业环境实战-kafka Rebalance 机制与Consumer多种消费模式案例应用实战][kafka _-kafka Rebalance _Consumer]
 *  [kafka 商业环境实战-kafka集群消息格式之V1版本到V2版本的平滑过渡详解][kafka _-kafka_V1_V2]
 *  [kafka 商业环境实战-kafka ISR设计及水印与leader epoch副本同步机制深入剖析][kafka _-kafka ISR_leader epoch]
 *  [kafka 商业环境实战-kafka日志索引存储及Compact机制深入剖析][kafka _-kafka_Compact]
 *  \[kafka 商业环境实战-kafka精确一次语义EOS的原理深入剖析\]
 *  \[kafka 商业环境实战-kafka消息的幂等性与事务支持机制深入剖析\]
 *  \[kafka 商业环境实战-kafka集群Controller竞选与责任设计思路架构详解\]
 *  \[kafka 商业环境实战-kafka集群日志文件系统设计与留存机制及Compact深入研究\]
 *  \[kafka 商业环境实战-kafka集群Consumer group状态机及Coordinaor管理深入剖析\]
 *  \[kafka 商业环境实战-kafka调优过程在吞吐量，持久性，低延时，可用性等指标的折中选择研究\]

[Flink_-Flink _Yarn]: https://blog.csdn.net/shenshouniu/article/details/84439459
[Flink_-Flink Window]: https://blog.csdn.net/shenshouniu/article/details/84481149
[Flink_-Flink Broadcast _ Accumulators]: https://blog.csdn.net/shenshouniu/article/details/84494871
[Flink_-Flink_SparkStreaming_Counters_ Accumulators]: https://blog.csdn.net/shenshouniu/article/details/84497270
[Flink_-Flink_Distributed Cache]: https://blog.csdn.net/shenshouniu/article/details/84499655
[Flink_-Flink_checkPoint]: https://blog.csdn.net/shenshouniu/article/details/84453692
[Flink_-Flink Window_Watermark]: https://blog.csdn.net/shenshouniu/article/details/84455619
[Flink_-Flink Restart Strategies]: https://blog.csdn.net/shenshouniu/article/details/84501658
[Flink_-Flink CheckPoint_savePoint]: https://blog.csdn.net/shenshouniu/article/details/84502519
[Flink_-Flink SQL Table _2018]: https://blog.csdn.net/shenshouniu/article/details/84558132
[Flink_-Flink_Kafka-Connector]: https://blog.csdn.net/shenshouniu/article/details/84558874
[44344487349a4076ba4f8edc0649cb5a.png]: /images/20221120/907f18af6c544e9290d3a5d1175dc79a.png
[auto.commit.interval.ms]: http://auto.commit.interval.ms/
[kafka _-kafka]: https://juejin.im/post/5bd464ccf265da0ac3735124
[kafka _-kafka 1]: https://juejin.im/post/5bd50b9451882529642b2581
[kafka _-kafka_Producer]: https://juejin.im/post/5bd51b32e51d457947024771
[kafka _-kafka 2]: https://juejin.im/post/5bdf04c0e51d452c846ea051
[kafka _-kafka_Broker]: https://blog.csdn.net/shenshouniu/article/details/83957355
[kafka _-kafka_Producer 1]: https://blog.csdn.net/shenshouniu/article/details/83960012
[kafka _-kafka Poll]: https://blog.csdn.net/shenshouniu/article/details/84076930
[kafka _-kafka Rebalance _Consumer]: https://juejin.im/post/5be7e0556fb9a049a62c1792
[kafka _-kafka_V1_V2]: https://blog.csdn.net/shenshouniu/article/details/84332141
[kafka _-kafka ISR_leader epoch]: https://blog.csdn.net/shenshouniu/article/details/84348648
[kafka _-kafka_Compact]: https://blog.csdn.net/shenshouniu/article/details/84504378