[big data bibiji 20210120] how to realize the high reliability of Kafka

Big data is fun 2021-01-21 19:57:01
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Data reliability

Kafka As a commercial message middleware , The importance of message reliability can be imagined . This paper starts from Producter Go to Broker Send a message 、Topic Partition copy and Leader Election several angles introduce the reliability of data .

Topic Partition copy

Producer Go to Broker Send a message

If we are going to Kafka The corresponding subject sends a message , We need to pass Producer complete . We talked about Kafka The theme corresponds to multiple partitions , There are multiple replicas under each partition ; In order to let users set data reliability , Kafka stay Producer It provides message confirmation mechanism . That is to say, we can decide to send a message to several copies of the corresponding partition through configuration . You can define Producer Through acks Parameter assignment ( stay 0.8.2.X The previous version was through request.required.acks Parameter setting ).

This parameter supports the following three values :

acks = 0: It means that if producers can send messages over the network , The message is considered to have been successfully written Kafka. In this case, it's still possible to make mistakes , For example, the sent object cannot be serialized or the network card fails , But if the partition is offline or the whole cluster is unavailable for a long time , Then you don't get any mistakes . stay acks=0 The running speed in mode is very fast ( That's why many benchmarks are based on this pattern ), You get amazing throughput and bandwidth utilization , But if you choose this model , There's bound to be some loss of information .

acks = 1: Meaning if Leader After receiving the message and writing it to the partition data file ( It doesn't have to be synchronized to disk ) Will return a confirmation or error response . In this mode , If normal Leader The election , The producer will receive a LeaderNotAvailableException abnormal , If the producer can handle this error properly , It will try again to send the message , Eventually the message will arrive safely in the new Leader Where? . However, it is still possible to lose data in this mode , For example, the message has been successfully written Leader, But before the message is copied to follower Before the copy Leader There's a breakdown .

acks = all( This and request.required.acks = -1 Same meaning ): signify Leader Before returning an acknowledgement or error response , Will wait for all synchronized copies to receive a silent message . If and min.insync.replicas Parameters together , You can decide at least how many copies can receive the message before you return the confirmation , The producer will try again until the message is successfully submitted . But it's also the slowest way , Because the producer needs to wait for all copies to receive the current message before continuing to send other messages .

According to the actual application scenario , We set up different acks, In order to ensure the reliability of data .

in addition ,Producer Sending messages can also choose to synchronize ( Default , adopt producer.type=sync To configure ) Or asynchronous (producer.type=async) Pattern . If set to asynchronous , Although it will greatly improve the performance of message sending , But this increases the risk of data loss . If you need to make sure the message is reliable , Must be producer.type Set to sync.

Leader The election

Introducing Leader Before the election , Let's take a look at ISR(in-sync replicas) list . For each division leader Will maintain a ISR list ,ISR It's in the list follower Replica Borker Number , Only to keep up with Leader Of follower Copy can be added to ISR Inside , This is through replica.lag.time.max.ms Parameter configuration . Only ISR Only the members in have been chosen as leader The possibility of .

2) Data consistency

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Original publication time : 2021-01-20

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