On Fri, Jul 22, 2016 at 12:58 AM, Shikhar Bhushan <shik...@confluent.io> wrote:
> flatMap() / supporting 1->n feels nice and general since filtering is just > the case of going from 1->0 > > I'm not sure why we'd need to do any more granular offset tracking (like > sub-offsets) for source connectors: after transformation of a given record > to n records, all of those n should map to same offset of the source > partition. The only thing to take care of here would be that we don't > commit a source offset while there are still records with that offset that > haven't been flushed to Kafka, but this is in the control of the connect > runtime. > > I'd like to be forward thinking with this and make sure we can get exactly once delivery when the producer can support it. For that you need to be able to track offsets at the granularity you actually publish messages to Kafka (or at least I can't think of a way of making it work without tracking them at that granularity). -Ewen > I see your point for sink connectors, though. Implementors can currently > assume 1:1ness of a record to its Kafka coordinates (topic, partition, > offset). > > On Thu, Jul 21, 2016 at 10:57 PM Ewen Cheslack-Postava <e...@confluent.io> > wrote: > > > Jun, The problem with it not being 1-1 is that Connect relies heavily on > > offsets, so we'd need to be able to track offsets at this finer > > granularity. Filtering is ok, but flatMap isn't. If you convert one > message > > to many, what are the offsets for the new messages? One possibility would > > be to assume that transformations are deterministic and then "enhance" > the > > offsets with an extra integer field that indicates its position in the > > subset. For sources this seems attractive since you can then reset to > > whatever the connector-provided offset is and then filter out any of the > > "sub"-messages that are earlier than the recorded "sub"-offset. But this > > might not actually work for sources since a) the offsets will include > extra > > fields that the connector doesn't expect (might be ok since we handle > that > > data as schemaless anyway) and b) if we allow multiple transformations > > (which seems likely given that people might want to do things like > > rearrange fields + filter messages) then offsets start getting quite > > complex as we add sub-sub-offsets and sub-sub-sub-offsets. It's doable, > but > > seems messy. > > > > Things aren't as easy on the sink side. Since we track offsets using > Kafka > > offsets we either need to use the extra metadata space to store the > > sub-offsets or we need to ensure that we only ever need to commit offsets > > on Kafka message boundaries. We might be able to get away with just > > delivering the entire set of generated messages in a single put() call, > > which the connector is expected to either fully accept or fully reject > (via > > exception). However, this may end up interacting poorly with assumptions > > connectors might make if we expose things like max.poll.records, where > they > > might expect one record at a time. > > > > I'm not really convinced of the benefit of support this -- at some point > it > > seems better to use Streams to do transformations if you need flatMap. I > > can't think of many generic transformations that would use 1-to-many, and > > single message transforms really should be quite general -- that's the > > reason for providing a separate interface isolated from Connectors or > > Converters. > > > > Gwen, re: using null and sending to dead letter queue, it would be useful > > to think about how this might interact with other uses of a dead letter > > queue. Similar ideas have been raised for messages that either can't be > > parsed or which the connector chokes on repeatedly. If we use a dead > letter > > queue for those, do we want these messages (which are explicitly filtered > > by a transform setup by the user) to end up in the same location? > > > > -Ewen > > > > On Sun, Jul 17, 2016 at 9:53 PM, Jun Rao <j...@confluent.io> wrote: > > > > > Does the transformation need to be 1-to-1? For example, some users > model > > > each Kafka message as schema + a batch of binary records. When using a > > sink > > > connector to push the Kafka data to a sink, if would be useful if the > > > transformer can convert each Kafka message to multiple records. > > > > > > Thanks, > > > > > > Jun > > > > > > On Sat, Jul 16, 2016 at 1:25 PM, Nisarg Shah <snis...@gmail.com> > wrote: > > > > > > > Gwen, > > > > > > > > Yup, that sounds great! Instead of keeping it up to the transformers > to > > > > handle null, we can instead have the topic as null. Sounds good. To > get > > > rid > > > > of a message, set the topic to a special one (could be as simple as > > > null). > > > > > > > > Like I said before, the more interesting part would be ‘adding’ a new > > > > message to the existing list, based on say the current message in the > > > > transformer. Does that feature warrant to be included? > > > > > > > > > On Jul 14, 2016, at 22:25, Gwen Shapira <g...@confluent.io> wrote: > > > > > > > > > > I used to work on Apache Flume, where we used to allow users to > > filter > > > > > messages completely in the transformation and then we got rid of > it, > > > > > because we spent too much time trying to help users who had > "message > > > > > loss", where the loss was actually a bug in the filter... > > > > > > > > > > What we couldn't do in Flume, but perhaps can do in the simple > > > > > transform for Connect is the ability to route messages to different > > > > > topics, with "null" as one of the possible targets. This will allow > > > > > you to implement a dead-letter-queue functionality and redirect > > > > > messages that don't pass filter to an "errors" topic without > getting > > > > > rid of them completely, while also allowing braver users to get rid > > of > > > > > messages by directing them to "null". > > > > > > > > > > Does that make sense? > > > > > > > > > > Gwen > > > > > > > > > > On Thu, Jul 14, 2016 at 8:33 PM, Nisarg Shah <snis...@gmail.com> > > > wrote: > > > > >> Thank you for your inputs Gwen and Michael. > > > > >> > > > > >> The original reason why I suggested a set based processing is > > because > > > > of the flexibility is provides. The JIRA had a comment by a user > > > requesting > > > > a feature that could be achieved with this. > > > > >> > > > > >> After reading Gwen and Michael's points, (I went through the > > > > documentation and the code in detail) and agree with what you have to > > > say. > > > > Also, fewer guarantees make what I had in mind less certain and thus > > > > simplifying it to a single message based transformation would ensure > > that > > > > users who do require more flexibility with the transformations will > > > > automatically “turn to" Kafka Streams. The transformation logic on a > > > > message by message basis makes more sense. > > > > >> > > > > >> One usecase that Kafka Connect could consider is adding or > removing > > a > > > > message completely. (This was trivially possible with the collection > > > > passing). Should users be pointed towards Kafka Streams even for this > > use > > > > case? I think this is a very useful feature for Connect too, and I’ll > > try > > > > to rethink on the API too. > > > > >> > > > > >> Removing a message is as easy as returning a null and having the > > next > > > > transformer skip it, but adding messages would involve say a queue > > > between > > > > transformers and a method which says “pass message” to the next, > which > > > can > > > > be called multiple times from one “transform” function; a variation > on > > > the > > > > chain of responsibility design pattern. > > > > >> > > > > >>> On Jul 12, 2016, at 12:54 AM, Michael Noll <mich...@confluent.io > > > > > > wrote: > > > > >>> > > > > >>> As Gwen said, my initial thought is that message transformations > > that > > > > are > > > > >>> "more than trivial" should rather be done by Kafka Streams, > rather > > > > than by > > > > >>> Kafka Connect (for the reasons that Gwen mentioned). > > > > >>> > > > > >>> Transforming one message at a time would be a good fit for Kafka > > > > Connect. > > > > >>> An important use case is to remove sensitive data (such as PII) > > from > > > an > > > > >>> incoming data stream before it hits Kafka's persistent storage -- > > > this > > > > use > > > > >>> case can't be implemented well with Kafka Streams because, by > > design, > > > > Kafka > > > > >>> Streams is meant to read its input data from Kafka (i.e. at the > > point > > > > when > > > > >>> Kafka Streams could be used to removed sensitive data fields the > > data > > > > is > > > > >>> already stored persistently in Kafka, and this might be a no-go > > > > depending > > > > >>> on the use case). > > > > >>> > > > > >>> I'm of course interested to hear what other people think. > > > > >>> > > > > >>> > > > > >>> On Tue, Jul 12, 2016 at 6:06 AM, Gwen Shapira <g...@confluent.io > > > > > > wrote: > > > > >>> > > > > >>>> I think we need to restrict the functionality to > > > > one-message-at-a-time. > > > > >>>> > > > > >>>> Basically, connect gives very little guarantees about the size > of > > > the > > > > set > > > > >>>> of the composition (you may get same messages over and over, mix > > of > > > > old and > > > > >>>> new, etc) > > > > >>>> > > > > >>>> In order to do useful things over a collection, you need better > > > > defined > > > > >>>> semantics of what's included. Kafka Streams is putting tons of > > > effort > > > > into > > > > >>>> having good windowing semantics, and I think apps that require > > > > modification > > > > >>>> of collections are a better fit there. > > > > >>>> > > > > >>>> I'm willing to change my mind though (have been known to > happen) - > > > > what are > > > > >>>> the comments about usage that point toward the collections > > approach? > > > > >>>> > > > > >>>> Gwen > > > > >>>> > > > > >>>> On Mon, Jul 11, 2016 at 3:32 PM, Nisarg Shah <snis...@gmail.com > > > > > > wrote: > > > > >>>> > > > > >>>>> Thanks Jay, added that to the KIP. > > > > >>>>> > > > > >>>>> Besides reviewing the KIP as a whole, I wanted to know about > what > > > > >>>> everyone > > > > >>>>> thinks about what data should be dealt at the Transformer > level. > > > > >>>> Transform > > > > >>>>> the whole Collection of Records (giving the flexibility of > > > modifying > > > > >>>>> messages across the set) OR > > > > >>>>> Transform messages one at a time, iteratively. This will > restrict > > > > >>>>> modifications across messages. > > > > >>>>> > > > > >>>>> I’ll get a working sample ready soon, to have a look. There > were > > > some > > > > >>>>> comments about Transformer usage that pointed to the first > > > approach, > > > > >>>> which > > > > >>>>> I prefer too given the flexibility. > > > > >>>>> > > > > >>>>>> On Jul 11, 2016, at 2:49 PM, Jay Kreps <j...@confluent.io> > > wrote: > > > > >>>>>> > > > > >>>>>> One minor thing, the Transformer interface probably needs a > > > close() > > > > >>>>> method > > > > >>>>>> (i.e. the opposite of initialize). This would be used for any > > > > >>>> transformer > > > > >>>>>> that uses a resource like a file/socket/db connection/etc that > > > > needs to > > > > >>>>> be > > > > >>>>>> closed. You usually don't need this but when you do need it > you > > > > really > > > > >>>>> need > > > > >>>>>> it. > > > > >>>>>> > > > > >>>>>> -Jay > > > > >>>>>> > > > > >>>>>> On Mon, Jul 11, 2016 at 1:47 PM, Nisarg Shah < > snis...@gmail.com > > > > > > > >>>> wrote: > > > > >>>>>> > > > > >>>>>>> Hello, > > > > >>>>>>> > > > > >>>>>>> This KIP < > > > > >>>>>>> > > > > >>>>> > > > > >>>> > > > > > > > > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-66:+Add+Kafka+Connect+Transformers+to+allow+transformations+to+messages > > > > >>>>>> > > > > >>>>>>> is for KAFKA-3209 < > > > > https://issues.apache.org/jira/browse/KAFKA-3209>. > > > > >>>>>>> It’s about capabilities to transform messages in Kafka > Connect. > > > > >>>>>>> > > > > >>>>>>> Some design decisions need to be taken, so please advise me > on > > > the > > > > >>>> same. > > > > >>>>>>> Feel free to express any thoughts or concerns as well. > > > > >>>>>>> > > > > >>>>>>> Many many thanks to Ewen Cheslack-Postava. > > > > >>>>>>> > > > > >>>>>>> -Nisarg > > > > >>>>> > > > > >>>>> > > > > >>>> > > > > >>> > > > > >>> > > > > >>> > > > > >>> -- > > > > >>> Best regards, > > > > >>> Michael Noll > > > > >>> > > > > >>> > > > > >>> > > > > >>> *Michael G. Noll | Product Manager | Confluent | +1 > > > > 650.453.5860Download > > > > >>> Apache Kafka and Confluent Platform: www.confluent.io/download > > > > >>> <http://www.confluent.io/download>* > > > > >> > > > > > > > > > > > > > > > > > > > -- > > Thanks, > > Ewen > > > -- Thanks, Ewen
