flink-connect-kafka

以下参数都必须/建议设置上
1.订阅的主题
2.反序列化规则
3.消费者属性-集群地址
4.消费者属性-消费者组id(如果不设置,会有默认的,但是默认的不方便管理)
5.消费者属性-offset重置规则,如earliest/latest…
6.动态分区检测(当kafka的分区数变化/增加时,Flink能够检测到!)
7.Checkpoint会把offset随着做Checkpoint的时候提交到Checkpoint和默认主题中
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//正则匹配动态发现主题
    Properties properties = new Properties();
    properties.setProperty("bootstrap.servers",kafka_server_dev);
    properties.setProperty("group.id","testtttt");
    Pattern topicPattern = Pattern.compile("topic[0-9]]"); // topic设置成正则匹配
    FlinkKafkaConsumerbase kafkaDataPattern = new FlinkKafkaConsumer<>(
        topicPattern,
        new SimpleStringSchema(),
        properties
    ).setStartFromEarliest();

//动态发现分区
properties.setProperty("FlinkKafkaConsumerbase.KEY_PARTITION_DISCOVERY_INTERVAL_MILLIS",30*1000+"");

String topic = "odsEventDetail";
String groupId = "console-con-new-offline-final";
        //指定分区的偏移量开始消费
Map specificStartOffsets = new HashMap<>();
specificStartOffsets.put(new KafkaTopicPartition("myTopic", 0), 23L);
specificStartOffsets.put(new KafkaTopicPartition("myTopic", 1), 31L);
specificStartOffsets.put(new KafkaTopicPartition("myTopic", 2), 43L);

//        myConsumer.setStartFromSpecificOffsets(specificStartOffsets);
FlinkKafkaConsumerbase kafkaSource = MyKafkaUtil.getKafkaSource_ObjectNode(topic, groupId)
                .setStartFromSpecificOffsets(specificStartOffsets);
//                .setStartFromEarliest();
        DataStreamSource kafkaDS = env.addSource(kafkaSource);

//kafka单分区有序，多分区无序。
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers","");
        properties.setProperty("group.id","");

        FlinkKafkaConsumer kafkaData = new FlinkKafkaConsumer<>(
                "flinktest",
                new SimpleStringSchema(),
                properties
        );
//当数据许久没来时，是否需要设置watermark，此处可以设置一个空闲等待时间
        kafkaData.assignTimestampsAndWatermarks(
                (AssignerWithPeriodicWatermarks) WatermarkStrategy
                        .forBoundedOutOfOrderness(Duration.ofMinutes(2)) //从数据源生成watermark
                        .withIdleness(Duration.ofMinutes(5)) //设置空闲等待
        );