Thanks Guozhang! Best, ShunKang
Guozhang Wang <wangg...@gmail.com> 于2022年9月23日周五 00:27写道： > Could you start a separate VOTE email thread calling for votes? > > On Thu, Sep 22, 2022 at 9:19 AM ShunKang Lin <linshunkang....@gmail.com> > wrote: > > > Hi Guozhang, > > > > Thanks for your help! By the way, what should I do next? > > > > Best, > > ShunKang > > > > Guozhang Wang <wangg...@gmail.com> 于2022年9月22日周四 23:21写道： > > > > > Thanks ShunKang, > > > > > > I made a few nit edits on the Motivation section as well. LGTM for me > > now. > > > > > > On Thu, Sep 22, 2022 at 7:33 AM ShunKang Lin < > linshunkang....@gmail.com> > > > wrote: > > > > > > > Hi Guozhang, > > > > > > > > I've updated the "Motivation" section of the KIP, please take a look. > > > > > > > > Thanks. > > > > ShunKang > > > > > > > > Guozhang Wang <wangg...@gmail.com> 于2022年9月21日周三 01:26写道： > > > > > > > > > In this case, could you update the KIP to clarify the allocation > > > savings > > > > > more clearly in the "Motivation" section? Also you could mention > that > > > for > > > > > user customizable serdes, if they could provide overwrites on the > > > > > overloaded function that's also possible for optimize memory > > > allocations. > > > > > > > > > > Guozhang > > > > > > > > > > On Tue, Sep 20, 2022 at 10:24 AM Guozhang Wang <wangg...@gmail.com > > > > > > wrote: > > > > > > > > > > > 1. Ack, thanks. > > > > > > 2. Sounds good, thanks for clarifying. > > > > > > > > > > > > On Tue, Sep 20, 2022 at 9:50 AM ShunKang Lin < > > > > linshunkang....@gmail.com> > > > > > > wrote: > > > > > > > > > > > >> Hi Guozhang, > > > > > >> > > > > > >> Thanks for your comments! > > > > > >> > > > > > >> 1. We can reduce memory allocation if the key/value types happen > > to > > > be > > > > > >> ByteBuffer or String. > > > > > >> 2. I would like to add `default ByteBuffer > > > > serializeToByteBuffer(String > > > > > >> topic, Headers headers, T data)` in Serializer to reduce memory > > copy > > > > in > > > > > >> `KafkaProducer#doSend(ProducerRecord, Callback)`, but this > change > > > is a > > > > > bit > > > > > >> big, I prefer to submit another one KIP to do the job. > > > > > >> > > > > > >> Thanks. > > > > > >> ShunKang > > > > > >> > > > > > >> Guozhang Wang <wangg...@gmail.com> 于2022年9月20日周二 06:32写道： > > > > > >> > > > > > >> > Hello ShunKang, > > > > > >> > > > > > > >> > Thanks for filing the proposal, and sorry for the late reply! > > > > > >> > > > > > > >> > I looked over your KIP proposal and the PR, in general I > think I > > > > agree > > > > > >> that > > > > > >> > adding an overloaded function with `ByteBuffer` param is > > > beneficial, > > > > > >> but I > > > > > >> > have a meta question regarding it's impact on Kafka consumer: > my > > > > > >> > understanding from your PR is that, we can only save memory > > > > > allocations > > > > > >> if > > > > > >> > the key/value types happen to be ByteBuffer as well, otherwise > > we > > > > > would > > > > > >> > still do the `return deserialize(topic, headers, > > > > > Utils.toArray(data));` > > > > > >> > from default impls unless the user customized deserializers is > > > > > >> augmented to > > > > > >> > handle ByteBuffer directly, right? > > > > > >> > > > > > > >> > > > > > > >> > Guozhang > > > > > >> > > > > > > >> > > > > > > >> > > > > > > >> > On Sun, Aug 21, 2022 at 9:56 AM ShunKang Lin < > > > > > linshunkang....@gmail.com > > > > > >> > > > > > > >> > wrote: > > > > > >> > > > > > > >> > > Hi all， > > > > > >> > > > > > > > >> > > I'd like to start a discussion on KIP-863 which is Reduce > > > > > >> > > Fetcher#parseRecord() memory copy. This KIP can reduce Kafka > > > > > Consumer > > > > > >> > > memory allocation by nearly 50% during fetch records. > > > > > >> > > > > > > > >> > > Please check > > > > > >> > > > > > > > >> > > > > > > >> > > > > > > > > > > > > > > > https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=225152035 > > > > > >> > > and https://github.com/apache/kafka/pull/12545 for more > > > details. > > > > > >> > > > > > > > >> > > Any feedbacks and comments are welcomed. > > > > > >> > > > > > > > >> > > Thanks. > > > > > >> > > > > > > > >> > > > > > > >> > > > > > > >> > -- > > > > > >> > -- Guozhang > > > > > >> > > > > > > >> > > > > > > > > > > > > > > > > > > -- > > > > > > -- Guozhang > > > > > > > > > > > > > > > > > > > > > -- > > > > > -- Guozhang > > > > > > > > > > > > > > > > > > -- > > > -- Guozhang > > > > > > > > -- > -- Guozhang >
