You can follow the monster of a ticket https://issues.apache.org/jira/browse/CASSANDRA-8844 and see if it looks like the tradeoffs there are headed in the right direction for you.
even CDC I think would have the logically same issue of not deduping for you as triggers and dual write due to replication factor and consistently level issues. Otherwise you'd be stuck doing an all replica comparison when a late event came in and when a node was down what would you do then? what if one replica got it as well and then came on line much later? Even if you were using a single source of truth style database, you'll find failover has a way of losing late events anyway (due to async replication) not to mention once you go multiple dc it's all a matter of what DC you're in. Anyway for the cold storage I think a trailing amount that is just greater than your old events would do it. IE if you choose to only accept 30 days out then cold storage for 32 days. At some point there is no free lunch as you point out when replicating between two data sources. ie CDC, triggers really anything that marks a "new event" will have the same problem and you'll have to choose an acceptable level of lateness or check for lateness indefinitely. Alternatively you can just accept duplication and handle it cold storage read side (like event sourcing pattern, this would be ideal if the lateness is uncommon) or clean it up over time in cold storage as it's detected (similar to an event sourcing pattern, but snapshotting data down to a single record when you encounter it on a read). Best of luck, this is a corner case that requires hard tradeoffs in all technology I've encountered. Regards, Ryan Svihla On Aug 9, 2016, 12:21 PM -0500, Ben Vogan <b...@shopkick.com>, wrote: > Thanks Ryan. I was hoping there was a change data capture framework. We have > late arriving events, some of which can be very late. We would have to batch > collect data for a large time period every so often to go back and collect > those or accept that we are going to lose a small percentage of events. > Neither of which is ideal. > > On Tue, Aug 9, 2016 at 10:30 AM, Ryan Svihla <r...@foundev.pro > (mailto:r...@foundev.pro)> wrote: > > The typical pattern I've seen in the field is kafka + consumers for each > > destination (variant of dual write I know), this of course would not work > > for your goal of relying on C* for dedup. Triggers would also suffer the > > same problem unfortunately so you're really left with a batch job (most > > likely Spark) to move data from C* into HDFS on a given interval. If this > > is really a cold storage use case that can work quite well especially > > assuming you've modeled your data as a time series or with some sort of > > time based bucketing so you can quickly get full partitions data out of C* > > in a deterministic fashion and not have to scan your entire data set. > > > > I've also for similar needs have seen Spark streaming + querying cassandra > > for duplication checks to dedup then output to another source (form of dual > > write but with dedup), this was really silly and slow. I only bring it up > > to save you the trouble in case you end up in the same path chasing for > > something more 'real time'. > > > > Regards, > > Ryan Svihla > > > > > > On Aug 9, 2016, 11:09 AM -0500, Ben Vogan <b...@shopkick.com > > (mailto:b...@shopkick.com)>, wrote: > > > Hi all, > > > > > > We are investigating using Cassandra in our data platform. We would like > > > data to go into Cassandra first and to eventually be replicated into our > > > data lake in HDFS for long term cold storage. Does anyone know of a good > > > way of doing this? We would rather not have parallel writes to HDFS and > > > Cassandra because we were hoping that we could use Cassandra primary keys > > > to de-duplicate events. > > > > > > Thanks, -- > > > > > > BENJAMIN VOGAN | Data Platform Team Lead > > > shopkick (http://www.shopkick.com/) > > > The indispensable app that rewards you for shopping. > > > -- > > BENJAMIN VOGAN | Data Platform Team Lead > shopkick (http://www.shopkick.com/) > The indispensable app that rewards you for shopping.
