1. on write amplification: i dont see x6 the writes, at worst i see x2 the writes - once to the "tx log", then read and again to the destination partition. if you have some != 1 replication factor than both the 1st and the 2nd writes get replicated, but it is still a relative factor of x2. what am I missing?
2. the effect of write amplification on broker throughput really depends on the hardware youre running. just as an example - here ( http://www.samsung.com/semiconductor/minisite/ssd/product/consumer/ssd960.html) is a laptop ssd that can max out a 10gig ethernet NIC on writes. I would expect that on "high performance" hardware kafka would be CPU bound. 3. why do writes to a TX need the same guarantees as "plain" writes? in cases where the user can live with a TX rollback on change of leadership/broker crash the TX log can be unreplicated, and even live in the leader's memory. that would cut down on writes. this is also an acceptable default in SQL - if your socket connection to a DB dies mid-TX your TX is toast (mysql is even worse) 4. even if we replicate the TX log, why do we need to re-read it and re-write it to the underlying partition? if its already written to disk all I would need is to make that file the current segment of the "real" partition and i've avoided the double write (at the cost of complicating segment management). if the data is replicated fetchers could do the same. 5. on latency - youre right, what im suggesting would result in tx ordering of messages ,"read committed" semantics and therefore higher latency. its theoretically possible to implement "read uncommitted" on top of it but it would inevitably result in opt-in vs vanilla clients seeing a different order of msgs. 6. the added delay (vs your read uncommitted) would be roughly the time span of a TX. are you designing for long-running transactions? seems to me that if the common use case is start a TX, deliver some batch of msgs that I already have on hand, then commit the delay isnt very long. 7. the need to buffer (or re-read) messages on consumers who do not opt-in (which are expected to be the majority in your use case?), and do so again and again if clients reset to earlier offsets/reconsume might make the system as a whole less efficient. 8. what is the ratio of read-uncommitted vs read-committed clients you expect to see? 9. what is the ratio of TX writes vs vanilla writes you expect to see? On Mon, Dec 19, 2016 at 3:15 PM, Guozhang Wang <wangg...@gmail.com> wrote: > One more thing about the double journal proposal: when discussing about > this method back at LinkedIn, another raised issue besides double writing > was that it will void the offset ordering and enforce people to accept > "transaction ordering", that is, consumer will not see messages from the > same partition in the order where they were produced, but only in the order > of when the corresponding transaction was committed. For some scenarios, we > believe that offset ordering would still be preferred than transaction > ordering and that is why in KIP-98 proposal we default to the former while > leave the door open if users want to switch to the latter case. > > > Guozhang > > On Mon, Dec 19, 2016 at 10:56 AM, Jay Kreps <j...@confluent.io> wrote: > > > Hey Radai, > > > > I'm not sure if I fully understand what you are proposing, but I > > interpreted it to be similar to a proposal we worked through back at > > LinkedIn. The proposal was to commit to a central txlog topic, and then > > recopy to the destination topic upon transaction commit. The observation > on > > that approach at the time were the following: > > > > 1. It is cleaner since the output topics have only committed data! > > 2. You need full replication on the txlog topic to ensure atomicity. > We > > weren't able to come up with a solution where you buffer in memory or > > use > > renaming tricks the way you are describing. The reason is that once > you > > begin committing you must ensure that the commit eventually succeeds > to > > guarantee atomicity. If you use a transient store you might commit > some > > data and then have a server failure that causes you to lose the rest > of > > the > > transaction. > > 3. Having a single log allows the reader to choose a "read > uncommitted" > > mode that hands out messages immediately. This is important for cases > > where > > latency is important, especially for stream processing topologies > where > > these latencies stack up across multiple stages. > > > > For the stream processing use case, item (2) is a bit of a deal killer. > > This takes the cost of a transient message write (say the intermediate > > result of a stream processing topology) from 3x writes (assuming 3x > > replication) to 6x writes. This means you basically can't default it on. > If > > we can in fact get the cost down to a single buffered write (i.e. 1x the > > data is written to memory and buffered to disk if the transaction is > large) > > as in the KIP-98 proposal without too many other negative side effects I > > think that could be compelling. > > > > -Jay > > > > > > > > On Mon, Dec 19, 2016 at 9:36 AM, radai <radai.rosenbl...@gmail.com> > wrote: > > > > > regarding efficiency: > > > > > > I'd like to distinguish between server efficiency (resource utilization > > of > > > the broker machine alone) and overall network efficiency (resource > > > utilization on brokers, producers and consumers, including network > > > traffic). > > > my proposal is not as resource-efficient on the broker (although it can > > be, > > > depends on a few trade offs and implementation details). HOWEVER, if i > > look > > > at the overall efficiency: > > > > > > 1.clients would need to either buffer or double-read uncommitted > msgs. > > > for N clients reading the stream M times (after re-starts and > reconsumes) > > > this would mean a M*N factor in either network BW or disk/memory space > > > (depends on if buffer vs re-read). potentially N*M more broker-side > reads > > > too. > > > 2 to reduce the broker side cost several things can be done (this is > > not > > > an either-or list, these are commulative): > > > 2.1 - keep TX logs in mem (+overflow to disk) - trades disk > writes > > > for TX resiliency > > > 2.2 - when "appending" TX logs to real partitions - instead of > > > reading from (disk-based) TX log and writing to partition log (x2 disk > > > writes) the TX log can be made a segment file (so file rename, with > > > associated protocol changes). this would avoid double writing by simply > > > making the TX file part of the partition (for large enough TXs. smaller > > > ones can be rewritten). > > > 2.3 - the approach above could be combined with a background > > "defrag" > > > - similar in concept to compaction - to further reduce the total of > > > resulting number of files. > > > > > > I think my main issue with the current proposal, more important than > > > performance, is lack of proper "encapsulation" of transactions - I dont > > > think downstream consumers should see uncommitted msgs. ever. > > > > > > > > > On Sun, Dec 18, 2016 at 10:19 PM, Becket Qin <becket....@gmail.com> > > wrote: > > > > > > > @Jason > > > > > > > > Yes, second thought on the number of messages included, the offset > > delta > > > > will probably be sufficient. The use case I encounter before for > number > > > of > > > > messages in a message set is an embedded mirror maker on the > > destination > > > > broker side which fetches message directly from the source cluster. > > > Ideally > > > > the destination cluster only needs to check CRC and assign the > offsets > > > > because all the message verification has been done by the source > > cluster, > > > > but due to the lack of the number of messages in the message set, we > > have > > > > to decompress the message set to increment offsets correctly. By > > knowing > > > > the number of the messages in the message set, we can avoid doing > that. > > > The > > > > offset delta will also help. It's just then the offsets may have > holes > > > for > > > > log compacted topics, but that may be fine. > > > > > > > > @Apurva > > > > > > > > I am not sure if it is true that the consumer will either deliver all > > the > > > > message for the entire transaction or none of them from one poll() > > call. > > > If > > > > we allow the transactions to be across partitions, unless the > consumer > > > > consumes from all the partitions involved in a transactions, it seems > > > > impossible for it to deliver *all* the messages in a transaction, > > right? > > > A > > > > weaker guarantee is we will deliver all or none of the messages that > > > belong > > > > to the same transaction in ONE partition, but this would be different > > > from > > > > the guarantee from the producer side. > > > > > > > > My two cents on Radai's sideways partition design: > > > > 1. If we consider the producer side behavior as doing a two phase > > commit > > > > which including the committing the consumer offsets, it is a little > > > awkward > > > > that we allow uncommitted message goes into the main log and rely on > > the > > > > consumer to filter out. So semantic wise I think it would be better > if > > we > > > > can avoid this. Radai's suggestion is actually intuitive because if > the > > > > brokers do not want to expose uncommitted transactions to the > consumer, > > > the > > > > brokers have to buffer it. > > > > > > > > 2. Regarding the efficiency. I think may be it worth looking at the > > > > efficiency cost v.s benefit. The efficiency includes both server side > > > > efficiency and consumer side efficiency. > > > > > > > > Regarding the server side efficiency, the current proposal would > > probably > > > > have better efficiency regardless of whether something goes wrong. > > > Radai's > > > > suggestion would put more burden on the server side. If nothing goes > > > wrong > > > > we always pay the cost of having double copy of the transactional > > > messages > > > > and do not get the semantic benefit. But if something goes wrong, the > > > > efficiency cost we pay we get us a better semantic. > > > > > > > > For the consumer side efficiency, because there is no need to buffer > > the > > > > uncommitted messages. The current proposal may have to potentially > > buffer > > > > uncommitted messages so it would be less efficient than Radai's > > > suggestion > > > > when a transaction aborts. When everything goes well, both design > seems > > > > having the similar performance. However, it depends on whether we are > > > > willing to loosen the consumer side transaction guarantee that I > > > mentioned > > > > earlier to Apurva. > > > > > > > > Currently the biggest pressure on the consumer side is that it has to > > > > buffer incomplete transactions. There are two reasons for it, > > > > A. A transaction may be aborted so we cannot expose the messages to > the > > > > users. > > > > B. We want to return all or none of the messages in a transaction in > > ONE > > > > partition. > > > > > > > > While reason A is mandatory, I think reason B may be discussable. > > Radai's > > > > design actually removes reason A because there is no uncommitted > > messages > > > > exposed to the consumers. This may potentially give us a chance to > > > > significantly improve consumer side efficiency in normal cases. It > > again > > > > depends on the use case, i.e. whether user can process a transaction > > > > progressively (message by message) or it has to be buffered and > > returned > > > > all together. If in most cases, users can process the transactions > > > message > > > > by message (most stream processing tasks probably can do so), then > with > > > > Radai's proposal we don't need to buffer the transactions for the > users > > > > anymore, which is a big difference. For the latter case, the consumer > > may > > > > have to buffer the incomplete transactions otherwise we are just > > throwing > > > > the burden onto the users. > > > > > > > > Thanks, > > > > > > > > Jiangjie (Becket) Qin > > > > > > > > On Fri, Dec 16, 2016 at 4:56 PM, Jay Kreps <j...@confluent.io> wrote: > > > > > > > > > Yeah good point. I relent! > > > > > > > > > > -jay > > > > > > > > > > On Fri, Dec 16, 2016 at 1:46 PM Jason Gustafson < > ja...@confluent.io> > > > > > wrote: > > > > > > > > > > > Jay/Ismael, > > > > > > > > > > > > > > > > > > > > > > > > I agree that lazy initialization of metadata seems unavoidable. > > > > Ideally, > > > > > we > > > > > > > > > > > > could follow the same pattern for transactions, but remember that > > in > > > > the > > > > > > > > > > > > consumer+producer use case, the initialization needs to be > > completed > > > > > prior > > > > > > > > > > > > to setting the consumer's position. Otherwise we risk reading > stale > > > > > > > > > > > > offsets. But it would be pretty awkward if you have to begin a > > > > > transaction > > > > > > > > > > > > first to ensure that your consumer can read the right offset from > > the > > > > > > > > > > > > consumer, right? It's a bit easier to explain that you should > > always > > > > call > > > > > > > > > > > > `producer.init()` prior to initializing the consumer. Users would > > > > > probably > > > > > > > > > > > > get this right without any special effort. > > > > > > > > > > > > > > > > > > > > > > > > -Jason > > > > > > > > > > > > > > > > > > > > > > > > On Wed, Dec 14, 2016 at 1:52 AM, Rajini Sivaram < > > rsiva...@pivotal.io > > > > > > > > > > wrote: > > > > > > > > > > > > > > > > > > > > > > > > > Hi Apurva, > > > > > > > > > > > > > > > > > > > > > > > > > > Thank you for the answers. Just one follow-on. > > > > > > > > > > > > > > > > > > > > > > > > > > 15. Let me rephrase my original question. If all control > messages > > > > > > (messages > > > > > > > > > > > > > to transaction logs and markers on user logs) were acknowledged > > > only > > > > > > after > > > > > > > > > > > > > flushing the log segment, will transactions become durable in > the > > > > > > > > > > > > > traditional sense (i.e. not restricted to min.insync.replicas > > > > > failures) ? > > > > > > > > > > > > > This is not a suggestion to update the KIP. It seems to me that > > the > > > > > > design > > > > > > > > > > > > > enables full durability if required in the future with a rather > > > > > > > > > > > > > non-intrusive change. I just wanted to make sure I haven't > missed > > > > > > anything > > > > > > > > > > > > > fundamental that prevents Kafka from doing this. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > On Wed, Dec 14, 2016 at 5:30 AM, Ben Kirwin <b...@kirw.in> > wrote: > > > > > > > > > > > > > > > > > > > > > > > > > > > Hi Apurva, > > > > > > > > > > > > > > > > > > > > > > > > > > > > Thanks for the detailed answers... and sorry for the late > > reply! > > > > > > > > > > > > > > > > > > > > > > > > > > > > It does sound like, if the input-partitions-to-app-id mapping > > > never > > > > > > > > > > > > > > changes, the existing fencing mechanisms should prevent > > > duplicates. > > > > > > > > > > > > > Great! > > > > > > > > > > > > > > I'm a bit concerned the proposed API will be delicate to > > program > > > > > > against > > > > > > > > > > > > > > successfully -- even in the simple case, we need to create a > > new > > > > > > producer > > > > > > > > > > > > > > instance per input partition, and anything fancier is going > to > > > need > > > > > its > > > > > > > > > > > > > own > > > > > > > > > > > > > > implementation of the Streams/Samza-style 'task' idea -- but > > that > > > > may > > > > > > be > > > > > > > > > > > > > > fine for this sort of advanced feature. > > > > > > > > > > > > > > > > > > > > > > > > > > > > For the second question, I notice that Jason also elaborated > on > > > > this > > > > > > > > > > > > > > downthread: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > We also looked at removing the producer ID. > > > > > > > > > > > > > > > This was discussed somewhere above, but basically the idea > is > > > to > > > > > > store > > > > > > > > > > > > > > the > > > > > > > > > > > > > > > AppID in the message set header directly and avoid the > > mapping > > > to > > > > > > > > > > > > > > producer > > > > > > > > > > > > > > > ID altogether. As long as batching isn't too bad, the > impact > > on > > > > > total > > > > > > > > > > > > > > size > > > > > > > > > > > > > > > may not be too bad, but we were ultimately more comfortable > > > with > > > > a > > > > > > > > > > > > > fixed > > > > > > > > > > > > > > > size ID. > > > > > > > > > > > > > > > > > > > > > > > > > > > > ...which suggests that the distinction is useful for > > performance, > > > > but > > > > > > not > > > > > > > > > > > > > > necessary for correctness, which makes good sense to me. > > (Would a > > > > > > 128-bid > > > > > > > > > > > > > > ID be a reasonable compromise? That's enough room for a UUID, > > or > > > a > > > > > > > > > > > > > > reasonable hash of an arbitrary string, and has only a > marginal > > > > > > increase > > > > > > > > > > > > > on > > > > > > > > > > > > > > the message size.) > > > > > > > > > > > > > > > > > > > > > > > > > > > > On Tue, Dec 6, 2016 at 11:44 PM, Apurva Mehta < > > > apu...@confluent.io > > > > > > > > > > > > > > > > > > wrote: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Hi Ben, > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Now, on to your first question of how deal with consumer > > > > > rebalances. > > > > > > > > > > > > > The > > > > > > > > > > > > > > > short answer is that the application needs to ensure that > the > > > the > > > > > > > > > > > > > > > assignment of input partitions to appId is consistent > across > > > > > > > > > > > > > rebalances. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > For Kafka streams, they already ensure that the mapping of > > > input > > > > > > > > > > > > > > partitions > > > > > > > > > > > > > > > to task Id is invariant across rebalances by implementing a > > > > custom > > > > > > > > > > > > > sticky > > > > > > > > > > > > > > > assignor. Other non-streams apps can trivially have one > > > producer > > > > > per > > > > > > > > > > > > > > input > > > > > > > > > > > > > > > partition and have the appId be the same as the partition > > > number > > > > to > > > > > > > > > > > > > > achieve > > > > > > > > > > > > > > > the same effect. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > With this precondition in place, we can maintain > transactions > > > > > across > > > > > > > > > > > > > > > rebalances. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Hope this answers your question. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Thanks, > > > > > > > > > > > > > > > Apurva > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > On Tue, Dec 6, 2016 at 3:22 PM, Ben Kirwin <b...@kirw.in> > > > wrote: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Thanks for this! I'm looking forward to going through the > > > full > > > > > > > > > > > > > proposal > > > > > > > > > > > > > > > in > > > > > > > > > > > > > > > > detail soon; a few early questions: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > First: what happens when a consumer rebalances in the > > middle > > > > of a > > > > > > > > > > > > > > > > transaction? The full documentation suggests that such a > > > > > > transaction > > > > > > > > > > > > > > > ought > > > > > > > > > > > > > > > > to be rejected: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > [...] if a rebalance has happened and this consumer > > > > > > > > > > > > > > > > > instance becomes a zombie, even if this offset message > is > > > > > > appended > > > > > > > > > > > > > in > > > > > > > > > > > > > > > the > > > > > > > > > > > > > > > > > offset topic, the transaction will be rejected later on > > > when > > > > it > > > > > > > > > > > > > tries > > > > > > > > > > > > > > > to > > > > > > > > > > > > > > > > > commit the transaction via the EndTxnRequest. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > ...but it's unclear to me how we ensure that a > transaction > > > > can't > > > > > > > > > > > > > > complete > > > > > > > > > > > > > > > > if a rebalance has happened. (It's quite possible I'm > > missing > > > > > > > > > > > > > something > > > > > > > > > > > > > > > > obvious!) > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > As a concrete example: suppose a process with PID 1 adds > > > > offsets > > > > > > for > > > > > > > > > > > > > > some > > > > > > > > > > > > > > > > partition to a transaction; a consumer rebalance happens > > that > > > > > > assigns > > > > > > > > > > > > > > the > > > > > > > > > > > > > > > > partition to a process with PID 2, which adds some > offsets > > to > > > > its > > > > > > > > > > > > > > current > > > > > > > > > > > > > > > > transaction; both processes try and commit. Allowing both > > > > commits > > > > > > > > > > > > > would > > > > > > > > > > > > > > > > cause the messages to be processed twice -- how is that > > > > avoided? > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Second: App IDs normally map to a single PID. It seems > like > > > one > > > > > > could > > > > > > > > > > > > > > do > > > > > > > > > > > > > > > > away with the PID concept entirely, and just use App IDs > in > > > > most > > > > > > > > > > > > > places > > > > > > > > > > > > > > > > that require a PID. This feels like it would be > > significantly > > > > > > > > > > > > > simpler, > > > > > > > > > > > > > > > > though it does increase the message size. Are there other > > > > reasons > > > > > > why > > > > > > > > > > > > > > the > > > > > > > > > > > > > > > > App ID / PID split is necessary? > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > On Wed, Nov 30, 2016 at 2:19 PM, Guozhang Wang < > > > > > wangg...@gmail.com > > > > > > > > > > > > > > > > > > > > > > wrote: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Hi all, > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > I have just created KIP-98 to enhance Kafka with > exactly > > > once > > > > > > > > > > > > > > delivery > > > > > > > > > > > > > > > > > semantics: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > *https://cwiki.apache.org/ > confluence/display/KAFKA/KIP- > > > > > > > > > > > > > > > > > 98+-+Exactly+Once+Delivery+and+Transactional+Messaging > > > > > > > > > > > > > > > > > <https://cwiki.apache.org/ > confluence/display/KAFKA/KIP- > > > > > > > > > > > > > > > > > 98+-+Exactly+Once+Delivery+ > and+Transactional+Messaging>* > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > This KIP adds a transactional messaging mechanism along > > > with > > > > an > > > > > > > > > > > > > > > > idempotent > > > > > > > > > > > > > > > > > producer implementation to make sure that 1) duplicated > > > > > messages > > > > > > > > > > > > > sent > > > > > > > > > > > > > > > > from > > > > > > > > > > > > > > > > > the same identified producer can be detected on the > > broker > > > > > side, > > > > > > > > > > > > > and > > > > > > > > > > > > > > > 2) a > > > > > > > > > > > > > > > > > group of messages sent within a transaction will > > atomically > > > > be > > > > > > > > > > > > > either > > > > > > > > > > > > > > > > > reflected and fetchable to consumers or not as a whole. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > The above wiki page provides a high-level view of the > > > > proposed > > > > > > > > > > > > > > changes > > > > > > > > > > > > > > > as > > > > > > > > > > > > > > > > > well as summarized guarantees. Initial draft of the > > > detailed > > > > > > > > > > > > > > > > implementation > > > > > > > > > > > > > > > > > design is described in this Google doc: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > https://docs.google.com/document/d/11Jqy_ > > > > > > > > > > > > > > > GjUGtdXJK94XGsEIK7CP1SnQGdp2eF > > > > > > > > > > > > > > > > > 0wSw9ra8 > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > We would love to hear your comments and suggestions. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Thanks, > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > -- Guozhang > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > -- > -- Guozhang >
