Jon, You know I've not actually spent the hour to read the ticket so I was just guessing it didn't handle dedup...all the same semantics apply though..you'd have to do a read before write and then allow some window of failure mode. Maybe if you were LWT everything but that sounds really slow...I'd be curious of your thoughts on how to do that well..maybe I'm missing something.
Regards, Ryan Svihla On Aug 9, 2016, 1:13 PM -0500, Jonathan Haddad <j...@jonhaddad.com>, wrote: > I'm having a hard time seeing how anyone would be able to work with CDC in > it's currently implementation of not doing any dedupe. Unless you really want > to write all your own logic for that including failure handling + a > distributed state machine I wouldn't count on it as a solution. > On Tue, Aug 9, 2016 at 10:49 AM Ryan Svihla <r...@foundev.pro > (mailto:r...@foundev.pro)> wrote: > > 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 > > (mailto: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.
