Kafka Producer NetworkException和超时异常 [英] Kafka Producer NetworkException and Timeout Exceptions

问题描述

我们在生产环境中随机得到 NetworkExceptions 和 TimeoutExceptions :

 经纪人:3动物园管理员:3名服务器:3卡夫卡:0.10.0.1动物园管理员:3.4.3 

我们有时在我的生产者日志中遇到此异常:

TOPIC:XXXXXX的10条记录已过期:自批处理以来已过去5608 ms创造再加上时间.

此类错误消息中的毫秒数不断变化.有时其他时间是〜5秒，最长是〜13秒！

我们很少得到:

  NetworkException:服务器在收到响应之前已断开连接. 

集群由 3 个经纪人和 3 个饲养员组成.生产者服务器和Kafka群集位于相同网络中.

我正在进行同步调用.有一个Web服务，多个用户请求向其发送数据.Kafka Web服务有一个生产者对象，该对象负责所有发送.生产者的请求超时最初为 1000ms ，已更改为 15000ms (15秒).即使在延长超时时间之后， TimeoutExceptions 仍会显示在错误日志中.

可能是什么原因?

解决方案

要找到根本原因是有些棘手的事情，我将在此基础上做我的总结，希望有人会发现它有用.通常，与 ack = ALL 结合使用，可能是网络问题或过多的网络洪水.这是一个解释 TimeoutException 的图，来自

排除网络配置问题或错误，您可以根据自己的情况调整以下属性，以缓解或解决问题:

• buffer.memory 控制生产者可用于缓冲的总内存.如果记录的发送速度超过了它们可以传输到Kafka的速度，那么该缓冲区将被超出，然后其他发送调用会阻塞，直到 max.block.ms 之后，Producer会抛出 TimeoutException .

• max.block.ms 已经具有很高的值，我不建议进一步增加它. buffer.memory 的默认值为32MB，根据您的消息大小，您可能希望增加它；如有必要，请增加jvm堆空间.

• 重试定义在放弃之前发生错误的情况下重试记录的尝试次数.如果您使用零重试，则可以尝试通过增加该值来缓解此问题，请注意，除非将 max.in.flight.requests.per.connection 设置为1，否则不再保证记录顺序./p>

• 一旦达到批处理大小或经过了延迟时间(以先到者为准)，就会发送记录.如果 batch.size (默认16kb)小于最大请求大小，则可能应使用更高的值.另外，将 linger.ms 更改为较高的值，例如10、50或100，以优化批处理和压缩的使用.这将减少网络泛洪，并在使用时优化压缩.

对于此类问题，由于它们还取决于实现方式，因此尚无确切答案.在我的案例中，尝试使用上述值很有帮助.

We are getting random NetworkExceptions and TimeoutExceptions in our production environment:

Brokers: 3
Zookeepers: 3
Servers: 3
Kafka: 0.10.0.1
Zookeeeper: 3.4.3

We are occasionally getting this exception in my producer logs:

Expiring 10 record(s) for TOPIC:XXXXXX: 5608 ms has passed since batch creation plus linger time.

Number of milliseconds in such error messages keep changing. Sometimes its ~5 seconds other times it's up to ~13 seconds!

And very rarely we get:

NetworkException: Server disconnected before response received. 

Cluster consists of 3 brokers and 3 zookeepers. Producer server and Kafka cluster are in same network.

I am making synchronous calls. There's a web service to which multiple user requests call to send their data. Kafka web service has one Producer object which does all the sending. Producer's Request timeout was 1000ms initially that has been changed to 15000ms (15 seconds). Even after increasing timeout period TimeoutExceptions are still showing up in error logs.

What can be the reason?

解决方案

It is a bit tricky to find the root cause, I'll drop my experience on that, hopefully someone may find it useful. In general it can be a network issue or too much network flood in combination with ack=ALL. Here a diagram that explain the TimeoutException from Kafka KIP-91 at he time of writing (still applicable till 1.1.0):

Excluding network configuration issues or errors, this are the properties you can adjust depending on your scenario in order to mitigate or solve the problem:

• The buffer.memory controls the total memory available to a producer for buffering. If records get sent faster than they can be transmitted to Kafka then and this buffer will get exceeded then additional send calls block up to max.block.ms after then Producer throws a TimeoutException.

• The max.block.ms has already a high value and I do not suggest to further increment it. buffer.memory has the default value of 32MB and depending on you message size you may want to increase it; if necessary increase the jvm heap space.

• Retries define how many attempts to resend the record in case of error before giving up. If you are using zero retries you can try to mitigate the problem by increasing this value, beware record order is not guarantee anymore unless you set max.in.flight.requests.per.connection to 1.

• Records are sent as soon as the batch size is reached or the linger time is passed, whichever comes first. if batch.size (default 16kb) is smaller than the maximum request size perhaps you should use a higher value. In addition, change linger.ms to a higher value such as 10, 50 or 100 to optimize the use of the batch and the compression. This will cause less flood in the network and optimize compression if you are using it.

There is not an exact answer on this kind of issues since they depends also on the implementation, in my case experimenting with the values above helped.

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