flink实时读取kafka数据到mysql flink 读取kafka 依赖 Flink 1.8.0

flink实时读取kafka数据到mysql flink 读取kafka

Flink提供了Kafka连接器，用于从或向Kafka读写数据。

本文总结Flink与Kafka集成中的问题,并对一些疑点进行总结和梳理。

问题一: 读Kafka的方式

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## 读取一个Topic
FlinkKafkaConsumer010#FlinkKafkaConsumer010(String topic, DeserializationSchema<T> valueDeserializer, Properties props)
FlinkKafkaConsumer010#FlinkKafkaConsumer010(String topic, KafkaDeserializationSchema<T> deserializer, Properties props)
举例: 
    FlinkKafkaConsumer010<String> kafkaConsumer = new FlinkKafkaConsumer010<>("userActionLog1", new SimpleStringSchema(), kafkaProperties);
    DataStream<String> source = env.addSource(kafkaConsumer).name("KafkaSource").uid("source-id");
## 读取多个Topic
FlinkKafkaConsumer010#FlinkKafkaConsumer010(List<String> topics, DeserializationSchema<T> deserializer, Properties props)
FlinkKafkaConsumer010#FlinkKafkaConsumer010(List<String> topics, KafkaDeserializationSchema<T> deserializer, Properties props)
举例:
    FlinkKafkaConsumer010<String> kafkaConsumer = new FlinkKafkaConsumer010<>(Arrays.asList("userActionLog1","userActionLog2","userActionLog3"), new SimpleStringSchema(), kafkaProperties);
    DataStream<String> source = env.addSource(kafkaConsumer).name("KafkaSource").uid("source-id");
# 读取多个Topic
FlinkKafkaConsumer010#FlinkKafkaConsumer010(Pattern subscriptionPattern, DeserializationSchema<T> valueDeserializer, Properties props)
FlinkKafkaConsumer010#FlinkKafkaConsumer010(Pattern subscriptionPattern, KafkaDeserializationSchema<T> deserializer, Properties props)
举例:
    FlinkKafkaConsumer010<String> kafkaConsumer = new FlinkKafkaConsumer010<>(Pattern.compile("userActionLog[1-9]{1}"), new SimpleStringSchema(), kafkaProperties);
    DataStream<String> source = env.addSource(kafkaConsumer).name("KafkaSource").uid("source-id");
# 从指定的时间戳开始消费
kafkaConsumer.setStartFromTimestamp(long startupOffsetsTimestamp)
# 从指定的偏移量开始消费,可为每个分区单独设置偏移量
kafkaConsumer.setStartFromSpecificOffsets(Map<KafkaTopicPartition, Long> specificStartupOffsets)
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问题二: 读Kafka与反序列化器

可通过org.apache.flink.api.common.serialization.DeserializationSchema或org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema,将从Kafka读取的二进制字节流反序列化成Flink内部支持的Java/Scala对象。

Flink内置支持以下2种常用反序列化器:

1. org.apache.flink.api.common.serialization.SimpleStringSchema:反序列化成String。
2. org.apache.flink.streaming.util.serialization.JSONKeyValueDeserializationSchema:反序列化成jackson ObjectNode。

如果想实现Kafka复杂JSON直接转换成想要的Object，可仿照org.apache.flink.api.common.serialization.SimpleStringSchema自定义即可。主要实现deserialize反序列化方法。

问题三: 读Kafka动态发现Topic、Partition

之前使用Spark Streaming，Spark 2.2.2不支持动态发现Kafka 0.10.1中新增的Topic(基于正则指定)和Partition。当新增了Topic或Partition，需要重启Spark Streaming任务。

在Flink中, 默认支持动态发现Kafka中新增的Topic或Partition，但需要手动开启。

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kafkaProperties.put("flink.partition-discovery.interval-millis","10000");
flink.partition-discovery.interval-millis: 检查间隔,单位毫秒。
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问题四: 读Kafka与Exactly Once语义

没有开启Checkpoint，默认自动提交Offset至外部存储(如Kafka Broker或Zookeeper),自动提交的间隔是5秒。Flink Kafka Consumer的容错依赖于自动提交的Offset。

开启Checkpoint,默认在Checkpoint完成后将存储在Checkpoint中的Offset再提交至外部存储(如Kafka Broker或0.8版本中的Zookeeper)，Flink Kafka Consumer在Flink作业运行过程中的容错依赖于Checkpoint中的Offset，Flink作业恢复，则可能是从Checkpoint中的Offset恢复，也可能是从外部存储如Kafka Broker中的Offset恢复，具体取决于恢复方式。注意: 在这种方式下，Kafka Broker(或0.8中Zookeeper)存储的Offset仅用于监控消费进度。

总结，基于Kafka可重复消费的能力并结合Flink Checkpoint机制，Flink Kafka Consumer能提供Exactly-Once语义。

问题五: 写Kafka与Exactly Once语义

• Kafka 0.8 Flink不提供Exactly-Once或At-Least-Once语义。
• Kafka 0.9、0.10 Flink启用Checkpoint,FlinkKafkaProducer09和FlinkKafkaProducer010提供At-Least-Once语义。除此之外，还需设置以下参数:
  setLogFailuresOnly(false): 若为true,Producer遇到异常时,仅记录失败时的日志,流处理程序继续。需要设置为false,当遇到异常,流处理程序失败,抛出异常恢复任务并重试发送。
  setFlushOnCheckpoint(true): Checkpoint中包含Kafka Producer Buffer中的数据，设置为true, 确保Checkpoint成功前，Buffer中的所有记录都已写入Kafka。
  retries: 重试次数,默认0,建议设置更大。
• Kafka 0.11、1.0.0+ Flink启用Checkpoint，基于Two Phase Commit,FlinkKafkaProducer011和FlinkKafkaProducer(Kafka >=1.0.0) 默认提供Exactly-Once语义。
  如需要其他语义Semantic.NONE(可能会丢或重)、Semantic.AT_LEAST_ONCE(可能会重)、Semantic.EXACTLY_ONCE(默认)，可手动选择。

从Kafka 0.10.1读数据并写入到Kafka 0.11.0.3并实现PV统计

部分依赖

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<!--Kafka连接器-->
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka-0.10_2.11</artifactId>
    <version>1.8.0</version>
</dependency>
<!--Kafka连接器-->
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka-0.11_2.11</artifactId>
    <version>1.8.0</version>
</dependency>
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代码实现

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package com.bigdata.flink;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple4;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.configuration.ConfigConstants;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.RestOptions;
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.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
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.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011;
import org.apache.flink.streaming.connectors.kafka.internals.KeyedSerializationSchemaWrapper;
import org.apache.flink.util.Collector;
import java.text.Format;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Properties;
/**
 * Author: Wang Pei
 * Summary: 读写Kafka
 */
public class ReadWriteKafka {
    public static void main(String[] args) throws Exception{
        /**解析命令行参数*/
        ParameterTool fromArgs = ParameterTool.fromArgs(args);
        ParameterTool parameterTool = ParameterTool.fromPropertiesFile(fromArgs.getRequired("applicationProperties"));
        //checkpoint参数
        String checkpointDirectory = parameterTool.getRequired("checkpointDirectory");
        long checkpointSecondInterval = parameterTool.getLong("checkpointSecondInterval");
        //fromKafka参数
        String fromKafkaBootstrapServers = parameterTool.getRequired("fromKafka.bootstrap.servers");
        String fromKafkaGroupID = parameterTool.getRequired("fromKafka.group.id");
        String fromKafkaAutoOffsetReset= parameterTool.getRequired("fromKafka.auto.offset.reset");
        String fromKafkaTopic = parameterTool.getRequired("fromKafka.topic");
        //toKafka参数
        String toKafkaBootstrapServers = parameterTool.getRequired("toKafka.bootstrap.servers");
        String toKafkaTopic = parameterTool.getRequired("toKafka.topic");
        //窗口参数
        long tumblingWindowLength = parameterTool.getLong("tumblingWindowLength");
        long outOfOrdernessSeconds = parameterTool.getLong("outOfOrdernessSeconds");
        /**配置运行环境*/
        //设置Local Web Server
        Configuration config = new Configuration();
        config.setInteger(RestOptions.PORT,8081);
        config.setBoolean(ConfigConstants.LOCAL_START_WEBSERVER, true);
        StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(config);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        //设置StateBackend
        env.setStateBackend((StateBackend) new FsStateBackend(checkpointDirectory, true));
        //设置Checkpoint
        CheckpointConfig checkpointConfig = env.getCheckpointConfig();
        checkpointConfig.setCheckpointInterval(checkpointSecondInterval * 1000);
        checkpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        checkpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        /**配置数据源*/
        Properties kafkaProperties = new Properties();
        kafkaProperties.put("bootstrap.servers",fromKafkaBootstrapServers);
        kafkaProperties.put("group.id",fromKafkaGroupID);
        kafkaProperties.put("auto.offset.reset",fromKafkaAutoOffsetReset);
        FlinkKafkaConsumer010<String> kafkaConsumer = new FlinkKafkaConsumer010<>(fromKafkaTopic, new SimpleStringSchema(), kafkaProperties);
        kafkaConsumer.setCommitOffsetsOnCheckpoints(true);
        DataStream<String> source = env.addSource(kafkaConsumer).name("KafkaSource").uid("source-id");
        /**抽取转换*/
        SingleOutputStreamOperator<Tuple4<String, String, String, Integer>> sourceMap = source
                .map((MapFunction<String, Tuple4<String, String, String, Integer>>) value -> {
                    Tuple4<String, String, String, Integer> output = new Tuple4<>();
                    try {
                        JSONObject obj = JSON.parseObject(value);
                        output.f0 = obj.getString("userID");
                        output.f1 = obj.getString("eventTime");
                        output.f2 = obj.getString("eventType");
                        output.f3 = obj.getInteger("productID");
                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                    return output;
                    }).returns(new TypeHint<Tuple4<String, String, String, Integer>>(){})
                .name("Map: ExtractTransform").uid("map-id");
        /**过滤掉异常数据*/
        SingleOutputStreamOperator<Tuple4<String, String, String, Integer>> sourceFilter = sourceMap
                .filter((FilterFunction<Tuple4<String, String, String, Integer>>) value -> value != null)
                .name("Filter: FilterExceptionData").uid("filter-id");
        /**抽取时间戳并发射水印*/
        SingleOutputStreamOperator<Tuple4<String, String, String, Integer>> assignTimestampsAndWatermarks = sourceFilter.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<Tuple4<String, String, String, Integer>>(Time.seconds(outOfOrdernessSeconds)) {
            SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
            @Override
            public long extractTimestamp(Tuple4<String, String, String, Integer> element) {
                long timestamp = 0L;
                try {
                    Date date = format.parse(element.f1);
                    timestamp = date.getTime();
                } catch (ParseException e) {
                    e.printStackTrace();
                }
                return timestamp;
            }
        }).uid("watermark-id");
        /**窗口统计*/
        SingleOutputStreamOperator<String> aggregate = assignTimestampsAndWatermarks
                //默认用Hash方式
                .keyBy((KeySelector<Tuple4<String, String, String, Integer>, String>) value -> value.f2)
                .window(TumblingEventTimeWindows.of(Time.seconds(tumblingWindowLength)))
                //在每个窗口(Window)上应用WindowFunction(CustomWindowFunction)
                //CustomAggFunction用于增量聚合
                //在每个窗口上，先进行增量聚合(CustomAggFunction),然后将增量聚合的结果作为WindowFunction(CustomWindowFunction)的输入,计算后并输出
                //具体: 可参考底层AggregateApplyWindowFunction的实现
                .aggregate(new CustomAggFunction(), new CustomWindowFunction());
        //aggregate.print();
        /**结果输出*/
        Properties kafkaProducerProperties = new Properties();
        kafkaProducerProperties.setProperty("bootstrap.servers",toKafkaBootstrapServers);
        kafkaProducerProperties.setProperty("transaction.timeout.ms",60000+"");
        FlinkKafkaProducer011<String> kafkaProducer011 = new FlinkKafkaProducer011<>(
                toKafkaTopic,
                new KeyedSerializationSchemaWrapper<>(new SimpleStringSchema()),
                kafkaProducerProperties,
                FlinkKafkaProducer011.Semantic.EXACTLY_ONCE
        );
        aggregate.addSink(kafkaProducer011).name("outputToKafka");
        env.execute();
    }
    /**
     * 自定义AggregateFunction
     * 增量聚合，这里实现累加效果
     */
    static class CustomAggFunction implements AggregateFunction<Tuple4<String, String, String, Integer>,Long,Long> {
        @Override
        public Long createAccumulator() {
            return 0L;
        }
        @Override
        public Long add(Tuple4<String, String, String, Integer> value, Long accumulator) {
            return accumulator + 1;
        }
        @Override
        public Long getResult(Long accumulator) {
            return accumulator;
        }
        @Override
        public Long merge(Long accumulator1, Long accumulator2) {
            return accumulator1 + accumulator2;
        }
    }
    /**
     * 自定义WindowFunction
     * 对增量聚合的结果再做处理，并输出
     */
    static class CustomWindowFunction implements WindowFunction<Long, String,String, TimeWindow> {
        @Override
        public void apply(String key, TimeWindow window, Iterable<Long> input, Collector<String> out) throws Exception {
            Format format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
            long windowStart = window.getStart();
            long windowEnd = window.getEnd();
            Long windowPV = input.iterator().next();
            String output=format.format(new Date(windowStart))+","+format.format(new Date(windowEnd))+","+key+","+windowPV;
            out.collect(output);
        }
    }
}
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原文链接：https://blog.51cto.com/u_14987/6694326