卡夫卡消費者收到一條消息之前

Question

我在XD中有一個消費者工作，一旦收到其他生產者工作產生的消息，該工作便會完成。 我每天都會觸發這些工作。 我發現有時該消費者收到一條消息，該消息以前曾被使用過。

記錄如下：

####OK
2019-06-28 02:06:13+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========consumed poll data ConsumerRecord(topic = my_consumer_topic, partition = 0, leaderEpoch = 0, offset = 4, CreateTime = 1561658772877, serialized key size = -1, serialized value size = 30, headers = RecordHeaders(headers = [], isReadOnly = false), key = null, value = message_from_producer) ==================
2019-06-28 02:06:13+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========message is  message_from_producer, task startTime is 1561658700108, timestamp is 1561658772877 ==================

####NG
2019-06-29 17:07:14+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========consumed poll data ConsumerRecord(topic = my_consumer_topic, partition = 0, leaderEpoch = 0, offset = 0, CreateTime = 1561399136840, serialized key size = -1, serialized value size = 30, headers = RecordHeaders(headers = [], isReadOnly = false), key = null, value = message_from_producer) ==================
2019-06-29 17:07:14+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========message is  message_from_producer, task startTime is 1561799100282, timestamp is 1561399136840 ==================

####OK
2019-06-29 22:16:58+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========consumed poll data ConsumerRecord(topic = my_consumer_topic, partition = 0, leaderEpoch = 2, offset = 5, CreateTime = 1561817817702, serialized key size = -1, serialized value size = 30, headers = RecordHeaders(headers = [], isReadOnly = false), key = null, value = message_from_producer) ==================
2019-06-29 22:16:58+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========message is  message_from_producer, task startTime is 1561817528447, timestamp is 1561817817702 ==================

####NG
2019-07-02 02:05:09+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========consumed poll data ConsumerRecord(topic = my_consumer_topic, partition = 0, leaderEpoch = 0, offset = 0, CreateTime = 1561399136840, serialized key size = -1, serialized value size = 30, headers = RecordHeaders(headers = [], isReadOnly = false), key = null, value = message_from_producer) ==================
2019-07-02 02:05:09+0800 INFO inbound.job:Consumer_Job_In_XD-redis:queue-inbound-channel-adapter1 myConsumer.ConsumeTasklet - ==========message is  message_from_producer, task startTime is 1562004300372, timestamp is 1561399136840 ==================

看起來它多次獲得了offset = 0消息。

Kakfa版本（1.0.0）

使用者手動提交偏移量。（consumer.commitSync（）;）
僅設置以下屬性：

bootstrap.servers  
auto.offset.reset=earliest  
group.id  
client.id  

    Properties config = new Properties();
    config.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
    config.put("auto.offset.reset", "earliest");
    config.put("group.id", group);
    config.put("client.id", config.getProperty("group.id") + "_" + System.currentTimeMillis());
    config.put("enable.auto.commit", false);
    try {
        consumer = new KafkaConsumer<>(config);
        consumer.subscribe(tList);
        while (true) {
            ConsumerRecords<?, ?> records = consumer.poll(10000);
            for (ConsumerRecord<?, ?> record : records) {
                //.........
                consumer.commitSync();
            }
            if (matched)
                break;
        }
    } finally {
        consumer.close();
    }

Answer 1

在Kafka 1.1中，默認情況下，偏移量僅保留24小時，因為offsets.retention.minutes設置為1440。

因此，如果您停止使用消費者超過24小時，則在重新啟動時，可能會刪除已提交的偏移量，從而迫使消費者使用auto.offset.reset來查找新位置。

由於對於許多人來說太短了，從Kafka 2.0開始， offsets.retention.minutes現在設置為10080（7天）。

您應該更改代理配置，以允許將偏移量保留更長的時間，或者更新到更新的Kafka版本。

Answer 2

嘗試設置auto.offset.reset = latest，以這種方式，在重新啟動后，使用者將在最近的提交偏移量之后開始消耗。

更多信息在這里https://kafka.apache.org/documentation/#consumerconfigs