Hey Jay, Yeah, I agree that enforcing the CPU time is a little tricky. I am thinking that maybe we can use the existing request statistics. They are already very detailed so we can probably see the approximate CPU time from it, e.g. something like (total_time - request/response_queue_time - remote_time).
I agree with Guozhang that when a user is throttled it is likely that we need to see if anything has went wrong first, and if the users are well behaving and just need more resources, we will have to bump up the quota for them. It is true that pre-allocating CPU time quota precisely for the users is difficult. So in practice it would probably be more like first set a relative high protective CPU time quota for everyone and increase that for some individual clients on demand. Thanks, Jiangjie (Becket) Qin On Mon, Feb 20, 2017 at 5:48 PM, Guozhang Wang <wangg...@gmail.com> wrote: > This is a great proposal, glad to see it happening. > > I am inclined to the CPU throttling, or more specifically processing time > ratio instead of the request rate throttling as well. Becket has very well > summed my rationales above, and one thing to add here is that the former > has a good support for both "protecting against rogue clients" as well as > "utilizing a cluster for multi-tenancy usage": when thinking about how to > explain this to the end users, I find it actually more natural than the > request rate since as mentioned above, different requests will have quite > different "cost", and Kafka today already have various request types > (produce, fetch, admin, metadata, etc), because of that the request rate > throttling may not be as effective unless it is set very conservatively. > > Regarding to user reactions when they are throttled, I think it may differ > case-by-case, and need to be discovered / guided by looking at relative > metrics. So in other words users would not expect to get additional > information by simply being told "hey, you are throttled", which is all > what throttling does; they need to take a follow-up step and see "hmm, I'm > throttled probably because of ..", which is by looking at other metric > values: e.g. whether I'm bombarding the brokers with metadata request, > which are usually cheap to handle but I'm sending thousands per second; or > is it because I'm catching up and hence sending very heavy fetching request > with large min.bytes, etc. > > Regarding to the implementation, as once discussed with Jun, this seems not > very difficult since today we are already collecting the "thread pool > utilization" metrics, which is a single percentage "aggregateIdleMeter" > value; but we are already effectively aggregating it for each requests in > KafkaRequestHandler, and we can just extend it by recording the source > client id when handling them and aggregating by clientId as well as the > total aggregate. > > > Guozhang > > > > > On Mon, Feb 20, 2017 at 4:27 PM, Jay Kreps <j...@confluent.io> wrote: > > > Hey Becket/Rajini, > > > > When I thought about it more deeply I came around to the "percent of > > processing time" metric too. It seems a lot closer to the thing we > actually > > care about and need to protect. I also think this would be a very useful > > metric even in the absence of throttling just to debug whose using > > capacity. > > > > Two problems to consider: > > > > 1. I agree that for the user it is understandable what lead to their > > being throttled, but it is a bit hard to figure out the safe range for > > them. i.e. if I have a new app that will send 200 messages/sec I can > > probably reason that I'll be under the throttling limit of 300 > req/sec. > > However if I need to be under a 10% CPU resources limit it may be a > bit > > harder for me to know a priori if i will or won't. > > 2. Calculating the available CPU time is a bit difficult since there > are > > actually two thread pools--the I/O threads and the network threads. I > > think > > it might be workable to count just the I/O thread time as in the > > proposal, > > but the network thread work is actually non-trivial (e.g. all the disk > > reads for fetches happen in that thread). If you count both the > network > > and > > I/O threads it can skew things a bit. E.g. say you have 50 network > > threads, > > 10 I/O threads, and 8 cores, what is the available cpu time available > > in a > > second? I suppose this is a problem whenever you have a bottleneck > > between > > I/O and network threads or if you end up significantly > over-provisioning > > one pool (both of which are hard to avoid). > > > > An alternative for CPU throttling would be to use this api: > > http://docs.oracle.com/javase/1.5.0/docs/api/java/lang/ > > management/ThreadMXBean.html#getThreadCpuTime(long) > > > > That would let you track actual CPU usage across the network, I/O > threads, > > and purgatory threads and look at it as a percentage of total cores. I > > think this fixes many problems in the reliability of the metric. It's > > meaning is slightly different as it is just CPU (you don't get charged > for > > time blocking on I/O) but that may be okay because we already have a > > throttle on I/O. The downside is I think it is possible this api can be > > disabled or isn't always available and it may also be expensive (also > I've > > never used it so not sure if it really works the way i think). > > > > -Jay > > > > On Mon, Feb 20, 2017 at 3:17 PM, Becket Qin <becket....@gmail.com> > wrote: > > > > > If the purpose of the KIP is only to protect the cluster from being > > > overwhelmed by crazy clients and is not intended to address resource > > > allocation problem among the clients, I am wondering if using request > > > handling time quota (CPU time quota) is a better option. Here are the > > > reasons: > > > > > > 1. request handling time quota has better protection. Say we have > request > > > rate quota and set that to some value like 100 requests/sec, it is > > possible > > > that some of the requests are very expensive actually take a lot of > time > > to > > > handle. In that case a few clients may still occupy a lot of CPU time > > even > > > the request rate is low. Arguably we can carefully set request rate > quota > > > for each request and client id combination, but it could still be > tricky > > to > > > get it right for everyone. > > > > > > If we use the request time handling quota, we can simply say no clients > > can > > > take up to more than 30% of the total request handling capacity > (measured > > > by time), regardless of the difference among different requests or what > > is > > > the client doing. In this case maybe we can quota all the requests if > we > > > want to. > > > > > > 2. The main benefit of using request rate limit is that it seems more > > > intuitive. It is true that it is probably easier to explain to the user > > > what does that mean. However, in practice it looks the impact of > request > > > rate quota is not more quantifiable than the request handling time > quota. > > > Unlike the byte rate quota, it is still difficult to give a number > about > > > impact of throughput or latency when a request rate quota is hit. So it > > is > > > not better than the request handling time quota. In fact I feel it is > > > clearer to tell user that "you are limited because you have taken 30% > of > > > the CPU time on the broker" than otherwise something like "your request > > > rate quota on metadata request has reached". > > > > > > Thanks, > > > > > > Jiangjie (Becket) Qin > > > > > > > > > On Mon, Feb 20, 2017 at 2:23 PM, Jay Kreps <j...@confluent.io> wrote: > > > > > > > I think this proposal makes a lot of sense (especially now that it is > > > > oriented around request rate) and fills the biggest remaining gap in > > the > > > > multi-tenancy story. > > > > > > > > I think for intra-cluster communication (StopReplica, etc) we could > > avoid > > > > throttling entirely. You can secure or otherwise lock-down the > cluster > > > > communication to avoid any unauthorized external party from trying to > > > > initiate these requests. As a result we are as likely to cause > problems > > > as > > > > solve them by throttling these, right? > > > > > > > > I'm not so sure that we should exempt the consumer requests such as > > > > heartbeat. It's true that if we throttle an app's heartbeat requests > it > > > may > > > > cause it to fall out of its consumer group. However if we don't > > throttle > > > it > > > > it may DDOS the cluster if the heartbeat interval is set incorrectly > or > > > if > > > > some client in some language has a bug. I think the policy with this > > kind > > > > of throttling is to protect the cluster above any individual app, > > right? > > > I > > > > think in general this should be okay since for most deployments this > > > > setting is meant as more of a safety valve---that is rather than set > > > > something very close to what you expect to need (say 2 req/sec or > > > whatever) > > > > you would have something quite high (like 100 req/sec) with this > meant > > to > > > > prevent a client gone crazy. I think when used this way allowing > those > > to > > > > be throttled would actually provide meaningful protection. > > > > > > > > -Jay > > > > > > > > > > > > > > > > On Fri, Feb 17, 2017 at 9:05 AM, Rajini Sivaram < > > rajinisiva...@gmail.com > > > > > > > > wrote: > > > > > > > > > Hi all, > > > > > > > > > > I have just created KIP-124 to introduce request rate quotas to > > Kafka: > > > > > > > > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP- > > > > > 124+-+Request+rate+quotas > > > > > > > > > > The proposal is for a simple percentage request handling time quota > > > that > > > > > can be allocated to *<client-id>*, *<user>* or *<user, client-id>*. > > > There > > > > > are a few other suggestions also under "Rejected alternatives". > > > Feedback > > > > > and suggestions are welcome. > > > > > > > > > > Thank you... > > > > > > > > > > Regards, > > > > > > > > > > Rajini > > > > > > > > > > > > > > > > > > -- > -- Guozhang >
