Update 2.0 documentation to reflect changed quota behaviors by
Updated the 2.0 document for changed quota behaviors.
Author: Jon Lee
Reviewers: Ismael Juma Dong Lin
Closes from jonlee2/KAFKA-7177
having a fixed cluster wide bandwidth per client because that would require a mechanism to share client quota usage among all the brokers. This can be harder to get right than the quota implementation itself!
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- How does a broker react when it detects a quota violation? In our solution, the broker does not return an error rather it attempts to slow down a client exceeding its quota.
- It computes the amount of delay needed to bring a guilty client under its quota and delays the response for that time. This approach keeps the quota violation transparent to clients
- (outside of client-side metrics). This also keeps them from having to implement any special backoff and retry behavior which can get tricky. In fact, bad client behavior (retry without backoff)
- can exacerbate the very problem quotas are trying to solve.
+ How does a broker react when it detects a quota violation? In our solution, the broker first computes the amount of delay needed to bring the violating client under its quota
+ and returns a response with the delay immediately. In case of a fetch request, the response will not contain any data. Then, the broker mutes the channel to the client,
+ not to process requests from the client anymore, until the delay is over. Upon receiving a response with a non-zero delay duration, the Kafka client will also refrain from
+ sending further requests to the broker during the delay. Therefore, requests from a throttled client are effectively blocked from both sides.
+ Even with older client implementations that do not respect the delay response from the broker, the back pressure applied by the broker via muting its socket channel
+ can still handle the throttling of badly behaving clients. Those clients who sent further requests to the throttled channel will receive responses only after the delay is over.
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Byte-rate and thread utilization are measured over multiple small windows (e.g. 30 windows of 1 second each) in order to detect and correct quota violations quickly. Typically, having large measurement windows
