Jan, Thanks for the example of reprocessing the messages. I think in any case, reconsuming all the messages will definitely work. What we want to do here is to see if we can avoid doing that by only reconsuming necessary messages.
In the scenario you mentioned, can you store an "offset-of-last-update" for each window? It is essentially the offset of the last message that goes into the window. During the reprocess, if the earlier messages has an offset less than or equals to the "offset-of-last-update" for the corresponding window, the processor knows that this message has been added to the aggregated window. This may need some work, but seems cheaper than reconsuming everything. Admittedly, if the messages are in create time and the timestamp is spread over a wide range, users may not be able to gain much from search by timestamp. Regarding OffstRequest. I think what Jun meant was that we are going to deprecate the current OffsetRequest v0 which returns a list of segment base offsets. I am working on a new KIP for OffsetReqeust v1 which returns the accurate message offset based on timestamp. I can hardly imagine we will let users deal with a physical position directly :) Thanks, Jiangjie (Becket) Qin On Fri, Aug 26, 2016 at 3:41 PM, Jan Filipiak <jan.filip...@trivago.com> wrote: > Hi Jun, > > thanks for taking the time to answer on such a detailed level. You are > right Log.fetchOffsetByTimestamp works, the comment is just confusing > "// Get all the segments whose largest timestamp is smaller than target > timestamp" wich is apparently is not what takeWhile does (I am more on the > Java end of things, so I relied on the comment). > > Regarding the frequent file rolling i didn't think of Logcompaction but > that indeed is a place where **** can hit the fan pretty easy. especially > if you don't have many updates in there and you pass the timestamp along in > a kafka-streams application. Bootstrapping a new application then indeed > could produce quite a few old messages kicking this logrolling of until a > recent message appears. I guess that makes it a practical issue again even > with the 7 days. Thanks for pointing out! Id like to see the appendTime as > default, I am very happy that I have it in the backpocket for purpose of > tighter sleep and not to worry to much about someone accidentally doing > something dodgy on a weekend with our clusters > > Regarding the usefulness, you will not be able to sell it for me. I don't > know how people build applications with this ¯\_(ツ)_/¯ but I don't want to > see them. > Look at the error recovery with timestamp seek: > For fixing a bug, a user needs to stop the SP, truncate all his downstream > data perfectly based on their time window.Then restart and do the first > fetch based > again on the perfect window timeout. From then on, he still has NO clue > whatsoever if messages that come later now with an earlier timestamp need > to go into the > previous window or not. (Note that there is >>>absolutly no<<< way to > determine this in aggregated downstream windowed stores). So the user is in > .... even though he can seek, he > can't rule out his error. IMO it helps them to build the wrong thing, that > will just be operational pain *somewhere* > > Look at the error recovery without timestamp seek: > start your application from beginning with a different output > (version,key,partition) wait for it to fully catch up. drop the timewindows > the error happend + confidence interval (if your data isnt there anymore, > no seek will help) in from the old version. Stop the stream processor, > merge the data it created, switch back to the original > (version,key,partition) and start the SP again. > Done. As bigger you choose the confidence interval, the more correct, the > less the index helps. usually you want maximum confidence => no index > usage, get everything that is still there. (Maybe even redump from hadoop > in extreme cases) ironically causing the log to roll all the time (as you > probably publish to a new topic and have the streams application use both) > :( > > As you can see, even though the users can seek, if they want to create > proper numbers, Billing information eg. They are in trouble, and giving > them this index will just make them implement the wrong solution! It boils > down to: this index is not the kafka way of doing things. The index can > help the second approach but usually one chooses the confidence interval = > as much as one can get. > > Then the last thing. "OffsetRequest is a legacy request. It's awkward to > use and we plan to deprecate it over time". You got to be kidding me. It > was wired to get the byteposition back then, but getting the offsets is > perfectly reasonable and one of the best things in the world. want to know > how your stream looked at a specific point in time? get start and end > offset, fetch whenever you like, you get an perfect snapshot in wall time. > this is usefull for compacted topis aswell as streaming topics. Offsets are > a well known thing in kafka and in no way awkward as its monotonically > increasing property is just great. > > For seeking the log based on a confidence interval (the only chance you > get in non-key logs reprocessing) one can also bisect the log from the > client. As the case is rare it is intensive and causes at least a few > hundreds seeks for bigger topics. but I guess the broker does these extra > for the new index file now. > > This index, I feel is just not following the whole "kafka-way". Can you > suggest on the proposed re-factoring? what are the chance to get it > upstream if I could pull it off? (unlikely) > > Thanks for all the effort you put in into listening to my concerns. highly > appreciated! > > Best Jan > > > > On 25.08.2016 23:36, Jun Rao wrote: > >> Jan, >> >> Thanks a lot for the feedback. Now I understood your concern better. The >> following are my comments. >> >> The first odd thing that you pointed out could be a real concern. >> Basically, if a producer publishes messages with really old timestamp, our >> default log.roll.hours (7 days) will indeed cause the broker to roll a log >> on ever message, which would be bad. Time-based rolling is actually used >> infrequently. The only use case that I am aware of is that for compacted >> topics, rolling logs based on time could allow the compaction to happen >> sooner (since the active segment is never cleaned). One option is to change >> the default log.roll.hours to infinite and also document the impact on >> changing log.roll.hours. Jiangjie, what do you think? >> >> For the second odd thing, the OffsetRequest is a legacy request. It's >> awkward to use and we plan to deprecate it over time. That's why we haven't >> change the logic in serving OffsetRequest after KIP-33. The plan is to >> introduce a new OffsetRequest that will be exploiting the time based index. >> It's possible to have log segments with non-increasing largest timestamp. >> As you can see in Log.fetchOffsetsByTimestamp(), we simply iterate the >> segments in offset order and stop when we see the target timestamp. >> >> For the third odd thing, one of the original reasons why the time-based >> index points to an offset instead of the file position is that it makes >> truncating the time index to an offset easier since the offset is in the >> index. Looking at the code, we could also store the file position in the >> time index and do truncation based on position, instead of offset. It >> probably has a slight advantage of consistency between the two indexes and >> avoiding another level of indirection when looking up the time index. >> Jiangjie, have we ever considered that? >> >> The idea of log.message.timestamp.difference.max.ms < >> http://log.message.timestamp.difference.max.ms/> is to prevent the >> timestamp in the published messages to drift too far away from the current >> timestamp. The default value is infinite though. >> >> Lastly, for the usefulness of time-based index, it's actually a feature >> that the community wanted and voted for, not just for Confluent customers. >> For example, being able to seek to an offset based on timestamp has been a >> frequently asked feature. This can be useful for at least the following >> scenarios: (1) If there is a bug in a consumer application, the user will >> want to rewind the consumption after fixing the logic. In this case, it's >> more convenient to rewind the consumption based on a timestamp. (2) In a >> multi data center setup, it's common for people to mirror the data from one >> Kafka cluster in one data center to another cluster in a different data >> center. If one data center fails, people want to be able to resume the >> consumption in the other data center. Since the offsets are not preserving >> between the two clusters through mirroring, being able to find a starting >> offset based on timestamp will allow the consumer to resume the consumption >> without missing any messages and also not replaying too many messages. >> >> Thanks, >> >> Jun >> >> >> On Wed, Aug 24, 2016 at 5:05 PM, Jan Filipiak <jan.filip...@trivago.com >> <mailto:jan.filip...@trivago.com>> wrote: >> >> Hey Jun, >> >> I go and try again :), wrote the first one in quite a stressful >> environment. The bottom line is that I, for our use cases, see a >> to small use/effort ratio in this time index. >> We do not bootstrap new consumers for key-less logs so frequently >> and when we do it, they usually want everything (prod deployment) >> or just start at the end ( during development). >> That caused quite some frustration. Would be better if I could >> just have turned it off and don't bother any more. Anyhow in the >> meantime I had to dig deeper into the inner workings >> and the impacts are not as dramatic as I initially assumed. But it >> still carries along some oddities I want to list here. >> >> first odd thing: >> Quote >> --- >> Enforce time based log rolling >> >> Currently time based log rolling is based on the creating time of >> the log segment. With this KIP, the time based rolling would be >> changed to based on the largest timestamp ever seen in a log >> segment. A new log segment will be rolled out if current time is >> greater than largest timestamp ever seen in the log segment + >> log.roll.ms <http://log.roll.ms>. When >> message.timestamp.type=CreateTime, user should set >> max.message.time.difference.ms >> <http://max.message.time.difference.ms> appropriately together >> with log.roll.ms <http://log.roll.ms> to avoid frequent log >> segment roll out. >> >> --- >> imagine a Mirrormaker falls behind and the Mirrormaker has a delay >> of some time > log.roll.ms <http://log.roll.ms>. >> From my understanding, when noone else is producing to this >> partition except the mirror maker, the broker will start rolling >> on every append? >> Just because you maybe under DOS-attack and your application only >> works in the remote location. (also a good occasion for MM to fall >> behind) >> But checking the default values indicates that it should indeed >> not become a problem as log.roll.ms <http://log.roll.ms> defaults >> to ~>7 days. >> >> >> second odd thing: >> Quote >> --- >> A time index entry (/T/, /offset/) means that in this segment any >> message whose timestamp is greater than /T/ come after /offset./ >> >> The OffsetRequest behaves almost the same as before. If timestamp >> *T* is set in the OffsetRequest, the first offset in the returned >> offset sequence means that if user want to consume from *T*, that >> is the offset to start with. The guarantee is that any message >> whose timestamp is greater than T has a bigger offset. i.e. Any >> message before this offset has a timestamp < *T*. >> --- >> >> Given how the index is maintained, with a little bit of bad luck >> (rolling upgrade/config change of mirrormakers for different >> colocations) one ends with segmentN.timeindex.maxtimestamp > >> segmentN+1.timeindex.maxtimestamp. If I do not overlook something >> here, then the fetch code does not seem to take that into account. >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/Log.scala#L604 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/Log.scala#L604> >> >> In this case the Goal listed number 1, not loose any messages, is >> not achieved. easy fix seems to be to sort the segsArray by >> maxtimestamp but can't wrap my head around it just now. >> >> >> third odd thing: >> Regarding the worry of increasing complexity. Looking at the code >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L193 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L193> >> -196 >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L227 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L227> >> & 230 >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L265 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L265> >> -266 >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L305 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L305> >> -307 >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L408 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L408> >> - 410 >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L432 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/LogSegment.scala#L432> >> - 435 >> https://github.com/apache/kafka/blob/05d00b5aca2e1e59ad685a3 >> f051d2ab022f75acc/core/src/main/scala/kafka/log/LogSegment.scala#L104 >> <https://github.com/apache/kafka/blob/05d00b5aca2e1e59ad685a >> 3f051d2ab022f75acc/core/src/main/scala/kafka/log/LogSegment.scala#L104> >> -108 >> and especially >> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >> 8c403882ae8a9852e/core/src/main/scala/kafka/log/Log.scala#L717 >> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >> 88c403882ae8a9852e/core/src/main/scala/kafka/log/Log.scala#L717> >> it feels like the Log & Log segment having a detailed knowledge >> about the maintained indexes is not the ideal way to model the >> problem. >> Having the Server maintian a Set of Indexes could reduce the code >> complexity, while also allowing an easy switch to turn it off. I >> think both indexes could point to the physical position, a client >> would do fetch(timestamp), and the continue with the offsets as >> usual. Is there any specific reason the timestamp index points >> into the offset index? >> For reading one would need to branch earlier, maybe already in >> ApiHandler and decide what indexes to query, but this branching >> logic is there now anyhow. >> >> Further I also can't think of a situation where one wants to have >> this log.message.timestamp.difference.max.ms >> <http://log.message.timestamp.difference.max.ms> take effect. I >> >> think this defeats goal 1 again. >> >> ITE having this index in the brokers now feels wired to me. Gives >> me a feeling of complexity that I don't need and have a hard time >> figuring out how much other people can benefit from it. I hope >> that this feedback is useful and helps to understand my scepticism >> regarding this thing. There were some other oddities that I have a >> hard time recalling now. So i guess the index was build for a >> specific confluent customer, will there be any blogpost about >> their usecase? or can you share it? >> >> Best Jan >> >> >> On 24.08.2016 16:47, Jun Rao wrote: >> >>> Jan, >>> >>> Thanks for the reply. I actually wasn't sure what your main >>> concern on time-based rolling is. Just a couple of >>> clarifications. (1) Time-based rolling doesn't control how long a >>> segment will be retained for. For retention, if you use >>> time-based, it will now be based on the timestamp in the message. >>> If you use size-based, it works the same as before. Is your >>> concern on time-based retention? If so, you can always configure >>> the timestamp in all topics to be log append time, which will >>> give you the same behavior as before. (2) The creation time of >>> the segment is never exposed to the consumer and therefore is >>> never preserved in MirrorMaker. In contrast, the timestamp in the >>> message will be preserved in MirrorMaker. So, not sure what your >>> concern on MirrorMaker is. >>> >>> Jun >>> >>> On Wed, Aug 24, 2016 at 5:03 AM, Jan Filipiak >>> <jan.filip...@trivago.com <mailto:jan.filip...@trivago.com>> wrote: >>> >>> Hi Jun, >>> >>> I copy pasted this mail from the archive, as I somehow didn't >>> receive it per mail. I will sill make some comments in line, >>> hopefully you can find them quick enough, my apologies. >>> >>> To make things more clear, you should also know, that all >>> messages in our kafka setup have a common way to access their >>> timestamp already (its encoded in the value the same way always) >>> Sometimes this is a logical time (eg same timestamp accross >>> many different topics / partitions), say PHP request start >>> time or the like. So kafkas internal timestamps are not >>> really attractive >>> for us anyways currently. >>> >>> I hope I can make a point and not waste your time. >>> >>> Best Jan, >>> >>> hopefully everything makes sense >>> >>> -------- >>> >>> Jan, >>> >>> Currently, there is no switch to disable the time based index. >>> >>> There are quite a few use cases of time based index. >>> >>> 1. From KIP-33's wiki, it allows us to do time-based >>> retention accurately. >>> Before KIP-33, the time-based retention is based on the last >>> modified time >>> of each log segment. The main issue is that last modified >>> time can change >>> over time. For example, if a broker loses storage and has to >>> re-replicate >>> all data, those re-replicated segments will be retained much >>> longer since >>> their last modified time is more recent. Having a time-based >>> index allows >>> us to retain segments based on the message time, not the last >>> modified >>> time. This can also benefit KIP-71, where we want to combine >>> time-based >>> retention and compaction. >>> >>> /If your sparse on discspace, one could try to get by that >>> with retention.bytes/ >>> or, as we did, ssh into the box and rm it, which worked quite >>> good when no one reads it. >>> Chuckles a little when its read but readers usually do an >>> auto.offset.reset >>> (they are to slow any ways if they reading the last segments >>> hrhr). >>> >>> 2. In KIP-58, we want to delay log compaction based on a >>> configurable >>> amount of time. Time-based index allows us to do this more >>> accurately. >>> >>> /good point, seems reasonable/ >>> >>> 3. We plan to add an api in the consumer to allow seeking to >>> an offset >>> based on a timestamp. The time based index allows us to do >>> this more >>> accurately and fast. >>> >>> /Sure, I personally feel that you rarely want to do this. For >>> Camus, we used max.pull.historic.days (or simmilliar) >>> successfully quite often. we just gave it an extra day and >>> got what we wanted >>> and for debugging my bisect tool works well enough. So these >>> are the 2 usecases we expierenced already and found a decent >>> way around it./ >>> >>> Now for the impact. >>> >>> a. There is a slight change on how time-based rolling works. >>> Before KIP-33, >>> rolling was based on the time when a segment was loaded in >>> the broker. >>> After KIP-33, rolling is based on the time of the first >>> message of a >>> segment. Not sure if this is your concern. In the common >>> case, the two >>> behave more or less the same. The latter is actually more >>> deterministic >>> since it's not sensitive to broker restarts. >>> >>> /This is part of my main concern indeed. This is what scares >>> me and I preffered to just opt out, instead of reviewing all >>> our pipelines to check whats gonna happen when we put it live. >>> For Example the Mirrormakers, If they want to preserve create >>> time from the source cluster and publish the same create time >>> (wich they should do, if you don't encode your own timestamps >>> and want >>> to have proper kafka-streams windowing). Then I am quite >>> concerned when have problems if our cross ocian links and >>> fall behind, say a day or two. Then I can think of an very up >>> to date MirrorMaker from >>> one colocation and a very laggy Mirrormaker from another >>> colocation. For me its not 100% clear whats gonna happen. But >>> I can't think of sane defaults there. That i love kafka for. >>> Just tricky to be convinced that an upgrade is safe, wich was >>> usually easy. >>> / >>> b. Time-based index potentially adds overhead to producing >>> messages and >>> loading segments. Our experiments show that the impact to >>> producing is >>> insignificant. The time to load segments when restarting a >>> broker can be >>> doubled. However, the absolute time is still reasonable. For >>> example, >>> loading 10K log segments with time-based index takes about 5 >>> seconds. >>> / >>> //Loading should be fine/, totally agree >>> >>> c Because time-based index is useful in several cases and the >>> impact seems >>> small, we didn't consider making time based index optional. >>> Finally, >>> although it's possible to make the time based index optional, >>> it will add >>> more complexity to the code base. So, we probably should only >>> consider it >>> if it's truly needed. Thanks, >>> >>> /I think one can get away with an easier codebase here. The >>> trick is not to have the LOG to implement all the logic, >>> but just have the broker maintain a Set of Indexes, that gets >>> initialized in starup and passed to the LOG. One could ask >>> each individual >>> index, if that logsegment should be rolled, compacted, >>> truncated whatever. Once could also give that LogSegment to >>> each index and make it rebuild >>> the index for example. I didn't figure out the details. But this >>> https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a298 >>> 8c403882ae8a9852e/core/src/main/scala/kafka/log/Log.scala#L715 >>> <https://github.com/apache/kafka/blob/79d3fd2bf0e5c89ff74a29 >>> 88c403882ae8a9852e/core/src/main/scala/kafka/log/Log.scala#L715> >>> might end up with for(Index i : indexes) >>> [i.shouldRoll(segment)}? wich should already be easier. >>> If users don't want time based indexing, just don't put the >>> timebased index in the Set then and everything should work >>> like a charm. >>> RPC calls that work on the specific indexes would need to >>> throw an exception of some kind. >>> Just an idea. >>> / >>> Jun >>> >>> >>> >>> >>> >>> On 22.08.2016 09 <tel:22.08.2016%2009>:24, Jan Filipiak wrote: >>> >>> Hello everyone, >>> >>> I stumbled across KIP-33 and the time based index, while >>> briefly checking the wiki and commits, I fail to find a >>> way to opt out. >>> I saw it having quite some impact on when logs are rolled >>> and was hoping not to have to deal with all of that. Is >>> there a disable switch I overlooked? >>> >>> Does anybody have a good use case where the timebase >>> index comes in handy? I made a custom console consumer >>> for me, >>> that can bisect a log based on time. Its just a quick >>> probabilistic shot into the log but is sometimes quite >>> useful for some debugging. >>> >>> Best Jan >>> >>> >>> >>> >> >> >
