flink从kafka-topic固定offset消费的5种方式

package flinkdemo.sinkDemo;

import flinkdemo.sourceDemo.deserialization.ConsumerRecordKafkaDeserializationSchema;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;
import java.nio.charset.StandardCharsets;
import java.util.*;

import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartition;
import org.apache.kafka.clients.consumer.ConsumerRecord;

/**
 * @author zhangkai
 * @create 2019/12/11
 */
public class SinkToKafka {
    public static void main(String[] args) throws Exception {
        String topicList = "otter_sms_0";
        System.out.println(topicList);

        StreamExecutionEnvironment see = StreamExecutionEnvironment.getExecutionEnvironment();

//        设置checkpoint
//        see.setStateBackend((StateBackend)new RocksDBStateBackend("hdfs://localhost:9000/user/zhangkai/flink-checkpoints"));

//        see.setStateBackend((StateBackend)new RocksDBStateBackend("hdfs:///user/zhangkai/flink-checkpoints"));//
//        CheckpointConfig checkpointConfig = see.getCheckpointConfig();
//        checkpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
//        checkpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
//        checkpointConfig.setCheckpointInterval(30000);
//        checkpointConfig.setMaxConcurrentCheckpoints(3);
//        checkpointConfig.setCheckpointTimeout(60000);

        Properties props = new Properties();
        props.put("bootstrap.servers", "10.2.40.10:9092");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");  //key 反序列化
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");//value 反序列化
//        props.put("auto.offset.reset", "latest");
        props.put("group.id","GROUP_ID");


        FlinkKafkaConsumer011 topicData = new FlinkKafkaConsumer011<>(Arrays.asList(topicList.split(",")), new ConsumerRecordKafkaDeserializationSchema(), props);
        //  从指定时间戳消费
        topicData.setStartFromTimestamp(1577433868000l);

        // 从指定offset位置开始消费
        /**
         * Map 
         * KafkaTopicPartition构造函数有两个参数，第一个为topic名字，第二个为分区数
         * Long参数指定的offset位置
         * 获取offset信息，可以用过Kafka自带的kafka-consumer-groups.sh脚本获取
         */
        Map specificStartupOffsets = new HashMap<>();
        specificStartupOffsets.put(new KafkaTopicPartition("otter_sms_0",0),3387036l);
        specificStartupOffsets.put(new KafkaTopicPartition("otter_sms_0",1),3182960l);
        specificStartupOffsets.put(new KafkaTopicPartition("otter_sms_0",2),2815761l);
        specificStartupOffsets.put(new KafkaTopicPartition("otter_sms_0",3),3591033l);
        specificStartupOffsets.put(new KafkaTopicPartition("otter_sms_0",4),3346657l);
        topicData.setStartFromSpecificOffsets(specificStartupOffsets);

        // 从topic的earliest开始消费，也就是从这个topic的最早消息开始消费
        topicData.setStartFromEarliest();

        // 从topic的latest开始消费，也就是从这个topic的最新消息开始消费
        topicData.setStartFromLatest();

        // 从topic中指定的group上次消费的位置开始消费，所以必须配置group.id参数
        topicData.setStartFromGroupOffsets();


        DataStreamSource consumerRecordDataStreamSource = see.addSource(topicData);
        SingleOutputStreamOperator