Keeping track of what has been consumed, is, surprisingly, one of the key performance points of a messaging system.
Most messaging systems keep metadata about what messages have been consumed on the broker. That is, as a message is handed out to a consumer, the broker either records that fact locally immediately or it may wait for acknowledgement from the consumer. This is a fairly intuitive choice, and indeed for a single machine server it is not clear where else this state could go. Since the data structure used for storage in many messaging systems scale poorly, this is also a pragmatic choice–since the broker knows what is consumed it can immediately delete it, keeping the data size small.
What is perhaps not obvious, is that getting the broker and consumer to come into agreement about what has been consumed is not a trivial problem. If the broker records a message as consumed immediately every time it is handed out over the network, then if the consumer fails to process the message (say because it crashes or the request times out or whatever) that message will be lost. To solve this problem, many messaging systems add an acknowledgement feature which means that messages are only marked as sent not consumed when they are sent; the broker waits for a specific acknowledgement from the consumer to record the message as consumed. This strategy fixes the problem of losing messages, but creates new problems. First of all, if the consumer processes the message but fails before it can send an acknowledgement then the message will be consumed twice. The second problem is around performance, now the broker must keep multiple states about every single message (first to lock it so it is not given out a second time, and then to mark it as permanently consumed so that it can be removed). Tricky problems must be dealt with, like what to do with messages that are sent but never acknowledged.
Kafka handles this differently. Our topic is divided into a set of totally ordered partitions, each of which is consumed by one consumer at any given time. This means that the position of consumer in each partition is just a single integer, the offset of the next message to consume. This makes the state about what has been consumed very small, just one number for each partition. This state can be periodically checkpointed. This makes the equivalent of message acknowledgements very cheap.
There is a side benefit of this decision. A consumer can deliberately rewind back to an old offset and re-consume data. This violates the common contract of a queue, but turns out to be an essential feature for many consumers. For example, if the consumer code has a bug and is discovered after some messages are consumed, the consumer can re-consume those messages once the bug is fixed.
消费者需要自己保留一个offset，从kafka 获取消息时，只拉去当前offset 以后的消息。Kafka 的scala/java 版的client 已经实现了这部分的逻辑，将offset 保存到zookeeper 上
What to do when there is no initial offset in ZooKeeper or if an offset is out of range:
smallest : automatically reset the offset to the smallest offset
largest : automatically reset the offset to the largest offset
anything else: throw exception to the consumer
If true, periodically commit to ZooKeeper the offset of messages already fetched by the consumer. This committed offset will be used when the process fails as the position from which the new consumer will begin
The frequency in ms that the consumer offsets are committed to zookeeper.
Select where offsets should be stored (zookeeper or kafka).默认是Zookeeper
The Kafka consumer works by issuing “fetch” requests to the brokers leading the partitions it wants to consume. The consumer specifies its offset in the log with each request and receives back a chunk of log beginning from that position. The consumer thus has significant control over this position and can rewind it to re-consume data if need be.
- Kafka的可靠性保证(消息消费和Offset提交的时机决定了At most once和At least once语义)
At Most Once:
At Least Once:
Kafka默认实现了At least once语义