1. Sounds good, just wanted to clarify; and it may worth documenting it so that users would not be surprised when monitoring their footprint.
2. Hmm I see... I think the trade-off can be described as "how much imbalance would bother you to be willing to pay another rebalance, along with potentially more restoration lag", and the current definition of rebalance_factor can be considered as a rough measurement of that imbalance. Of course one can argue that a finer grained measurement could be "resource footprint" like CPU / storage of each instance like we have in Kafka broker auto balancing tools, but I'd prefer not doing that as part of the library but more as an operational tool in the future. On the other hand, I've seen stateful and stateless tasks having very different load, and sometimes the only bottleneck of a Streams app is just one stateful sub-topology and whoever gets tasks of that sub-topology become hotspot (and that's why our algorithm tries to balance per sub-topology as well), so maybe we can just consider stateful tasks when calculating this factor as a very brute force heuristic? 3.a. Thinking about this a bit more, maybe it's better not try to tackle an unseen enemy just yet, and observe if it really emerges later, and by then we may have other ways to not starving the standby tasks, for example, by using dedicate threads for standby tasks or even consider having higher priority for standby than active so that we always try to caught up standby first, then process active; and if active's lagging compared to log-end-offset is increasing then we should increase capacity, etc etc. 4. Actually with KIP-429 this may not be the case: we may not call onPartitionsRevoked prior to rebalance any more so would not transit state to PARTITIONS_REVOKED, and hence not cause the state of the instance to be REBALANCING. In other words, even if a instance is undergoing a rebalance it's state may still be RUNNING and it may still be processing records at the same time. On Wed, Aug 7, 2019 at 12:14 PM John Roesler <[email protected]> wrote: > Hey Guozhang, > > Thanks for the review! > > 1. Yes, even with `num.standby.replicas := 0`, we will still temporarily > allocate standby tasks to accomplish a no-downtime task migration. > Although, I'd argue that this doesn't really violate the config, as the > task isn't a true hot standby. As soon as it catches up, we'll rebalance > again, that task will become active, and the original instance that hosted > the active task will no longer have the task assigned at all. Once the > stateDirCleaner kicks in, we'll free the disk space from it, and return to > the steady-state of having just one copy of the task in the cluster. > > We can of course do without this, but I feel the current proposal is > operationally preferable, since it doesn't make configuring hot-standbys a > pre-requisite for fast rebalances. > > 2. Yes, I think your interpretation is what we intended. The default > balance_factor would be 1, as it is implicitly today. What this does is > allows operators to trade off less balanced assignments against fewer > rebalances. If you have lots of space capacity in your instances, this may > be a perfectly fine tradeoff, and you may prefer for Streams not to bother > streaming GBs of data from the broker in pursuit of perfect balance. Not > married to this configuration, though. It was inspired by the related work > research we did. > > 3. I'll take a look > > 3a. I think this is a good idea. I'd classify it as a type of grey failure > detection. It may make more sense to tackle grey failures as part of the > heartbeat protocol (as I POCed here: > https://github.com/apache/kafka/pull/7096/files). WDYT? > > 4. Good catch! I didn't think about that before. Looking at it now, though, > I wonder if we're actually protected already. The stateDirCleaner thread > only executes if the instance is in RUNNING state, and KIP-441 proposes to > use "probing rebalances" to report task lag. Hence, during the window > between when the instance reports a lag and the assignor makes a decision > about it, the instance should remain in REBALANCING state, right? If so, > then this should prevent the race condition. If not, then we do indeed need > to do something about it. > > 5. Good idea. I think that today, you can only see the consumer lag, which > is a poor substitute. I'll add some metrics to the proposal. > > Thanks again for the comments! > -John > > On Tue, Aug 6, 2019 at 4:27 PM Guozhang Wang <[email protected]> wrote: > > > Hello Sophie, > > > > Thanks for the proposed KIP. I left some comments on the wiki itself, > and I > > think I'm still not very clear on a couple or those: > > > > 1. With this proposal, does that mean with num.standby.replicas == 0, we > > may sometimes still have some standby tasks which may violate the config? > > > > 2. I think I understand the rationale to consider lags that is below the > > specified threshold to be equal, rather than still considering 5000 is > > better than 5001 -- we do not want to "over-optimize" and potentially > falls > > into endless rebalances back and forth. > > > > But I'm not clear about the rationale of the second parameter of > > constrainedBalancedAssignment(StatefulTasksToRankedCandidates, > > balance_factor): > > > > Does that mean, e.g. with balance_factor of 3, we'd consider two > > assignments one resulting balance_factor 0 and one resulting > balance_factor > > 3 to be equally optimized assignment and therefore may "stop early"? This > > was not very convincing to me :P > > > > 3. There are a couple of minor comments about the algorithm itself, left > on > > the wiki page since it needs to refer to the exact line and better > > displayed there. > > > > 3.a Another wild thought about the threshold itself: today the assignment > > itself is memoryless, so we would not know if the reported `TaskLag` > itself > > is increasing or decreasing even if the current value is under the > > threshold. I wonder if it worthy to make it a bit more complicated to > track > > task lag trend at the assignor? Practically it may not be very uncommon > > that stand-by tasks are not keeping up due to the fact that other active > > tasks hosted on the same thread is starving the standby tasks. > > > > 4. There's a potential race condition risk when reporting `TaskLags` in > the > > subscription: right after reporting it to the leader, the cleanup thread > > kicks in and deletes the state directory. If the task was assigned to the > > host it would cause it to restore from beginning and effectively make the > > seemingly optimized assignment very sub-optimal. > > > > To be on the safer side we should consider either prune out those tasks > > that are "close to be cleaned up" in the subscription, or we should delay > > the cleanup right after we've included them in the subscription in case > > they are been selected as assigned tasks by the assignor. > > > > 5. This is a meta comment: I think it would be helpful to add some user > > visibility on the standby tasks lagging as well, via metrics for example. > > Today it is hard for us to observe how far are our current standby tasks > > compared to the active tasks and whether that lag is being increasing or > > decreasing. As a follow-up task, for example, the rebalance should also > be > > triggered if we realize that some standby task's lag is increasing > > indefinitely means that it cannot keep up (which is another indicator > > either you need to add more resources with the num.standbys or your are > > still not balanced enough). > > > > > > On Tue, Aug 6, 2019 at 1:32 PM Sophie Blee-Goldman <[email protected]> > > wrote: > > > > > Hey all, > > > > > > I'd like to kick off discussion on KIP-441, aimed at the long restore > > times > > > in Streams during which further active processing and IQ are blocked. > > > Please give it a read and let us know your thoughts > > > > > > > > > > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-441:+Smooth+Scaling+Out+for+Kafka+Streams > > > > > > Cheers, > > > Sophie > > > > > > > > > -- > > -- Guozhang > > > -- -- Guozhang
