Additionally to Jay's recommendation, you also need to have some special cares in error handling of the producer in order to preserve ordering since producer uses batching and async sending. That is, if you already sent messages 1,2,3,4,5 to producer but later on be notified that message 3 failed to send, you need to avoid continue sending messages 4,5 before 3 gets fixed or dropped.
Guozhang On Tue, Mar 3, 2015 at 3:45 PM, Xiao <lixiao1...@gmail.com> wrote: > Hey Josh, > > Transactions can be applied in parallel in the consumer side based on > transaction dependency checking. > > http://www.google.com.ar/patents/US20080163222 > > This patent documents how it work. It is easy to understand, however, you > also need to consider the hash collision issues. This has been implemented > in IBM Q Replication since 2001. > > Thanks, > > Xiao Li > > > On Mar 3, 2015, at 3:36 PM, Jay Kreps <jay.kr...@gmail.com> wrote: > > > Hey Josh, > > > > As you say, ordering is per partition. Technically it is generally > possible > > to publish all changes to a database to a single partition--generally the > > kafka partition should be high throughput enough to keep up. However > there > > are a couple of downsides to this: > > 1. Consumer parallelism is limited to one. If you want a total order to > the > > consumption of messages you need to have just 1 process, but often you > > would want to parallelize. > > 2. Often what people want is not a full stream of all changes in all > tables > > in a database but rather the changes to a particular table. > > > > To some extent the best way to do this depends on what you will do with > the > > data. However if you intend to have lots > > > > I have seen pretty much every variation on this in the wild, and here is > > what I would recommend: > > 1. Have a single publisher process that publishes events into Kafka > > 2. If possible use the database log to get these changes (e.g. mysql > > binlog, Oracle xstreams, golden gate, etc). This will be more complete > and > > more efficient than polling for changes, though that can work too. > > 3. Publish each table to its own topic. > > 4. Partition each topic by the primary key of the table. > > 5. Include in each message the database's transaction id, scn, or other > > identifier that gives the total order within the record stream. Since > there > > is a single publisher this id will be monotonic within each partition. > > > > This seems to be the best set of tradeoffs for most use cases: > > - You can have parallel consumers up to the number of partitions you > chose > > that still get messages in order per ID'd entity. > > - You can subscribe to just one table if you like, or to multiple tables. > > - Consumers who need a total order over all updates can do a "merge" > across > > the partitions to reassemble the fully ordered set of changes across all > > tables/partitions. > > > > One thing to note is that the requirement of having a single consumer > > process/thread to get the total order isn't really so much a Kafka > > restriction as it just is a restriction about the world, since if you had > > multiple threads even if you delivered messages to them in order their > > processing might happen out of order (just do to the random timing of the > > processing). > > > > -Jay > > > > > > > > On Tue, Mar 3, 2015 at 3:15 PM, Josh Rader <jrader...@gmail.com> wrote: > > > >> Hi Kafka Experts, > >> > >> > >> > >> We have a use case around RDBMS replication where we are investigating > >> Kafka. In this case ordering is very important. Our understanding is > >> ordering is only preserved within a single partition. This makes sense > as > >> a single thread will consume these messages, but our question is can we > >> somehow parallelize this for better performance? Is there maybe some > >> partition key strategy trick to have your cake and eat it too in terms > of > >> keeping ordering, but also able to parallelize the processing? > >> > >> > >> > >> I am sorry if this has already been asked, but we tried to search > through > >> the archives and couldn’t find this response. > >> > >> > >> > >> Thanks, > >> > >> Josh > >> > > -- -- Guozhang
