Hey Josh, Sorry, after reading codes, Kafka did fsync the data using a separate thread. The recovery point (oldest transaction timestamp) can be got from the file recovery-point-offset-checkpoint.
You can adjust the value config.logFlushOffsetCheckpointIntervalMs, if you think the speed is not quick enough. When the workloads is huge, the bottleneck could be in your target side or source side. That means, your apply could have enough jobs to do. Basically, you need to keep reading this file for determining the oldest timestamps of all relevant partitions. Then, apply the transactions until that timestamp. Note, this does not protect the transaction consistency. This is just for ensuring the data at the target side is consistent at one timestamp when you have multiple channel to send data changes. The implementation should be simple if you can understand the concepts. I am unable to find the filed patent application about it. This is one related paper. It covers the main concepts about the issues you are facing. "Inter-Data-Center Large-Scale Database Replication Optimization – A Workload Driven Partitioning Approach" Hopefully, you understood what I explained above. Best wishes, Xiao Li Best wishes, Xiao Li On 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 >>>> >> >
