Hey Xiao,
1. Nothing prevents applying transactions transactionally on the
destination side, though that is obviously more work. But I think the key
point here is that much of the time the replication is not Oracle=>Oracle,
but Oracle=>{W, X, Y, Z} where W/X/Y/Z are totally heterogenous systems
that aren't necessarily RDBMSs.
2. I don't think fsync is really relevant. You can fsync on every message
if you like, but Kafka's durability guarantees don't depend on this as it
allows synchronous commit across replicas. This changes the guarantee from
"won't be lost unless the disk dies" to "won't be lost unless all replicas
die" but the later is generally a stronger guarantee in practice given the
empirical reliability of disks (#1 reason for server failure in my
experience was disk failure).
-Jay
On Tue, Mar 3, 2015 at 4:23 PM, Xiao <lixiao1...@gmail.com> wrote:
> Hey Josh,
>
> If you put different tables into different partitions or topics, it might
> break transaction ACID at the target side. This is risky for some use
> cases. Besides unit of work issues, you also need to think about the load
> balancing too.
>
> For failover, you have to find the timestamp for point-in-time
> consistency. This part is tricky. You have to ensure all the changes before
> a specific timestamp have been flushed to the disk. Normally, you can
> maintain a bookmark for different partition at the target side to know what
> is the oldest transactions have been flushed to the disk. Unfortunately,
> based on my understanding, Kafka is unable to do it because it does not do
> fsync regularly for achieving better throughput.
>
> Best wishes,
>
> Xiao Li
>
>
> On 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
> >>>
> >
>
>
