A couple comments: 1) Why does the config use a broker list instead of discovering the brokers in ZooKeeper? It doesn't match the HighLevelConsumer API.
2) It looks like broker connections are created on demand. I'm wondering if sometimes you might want to flush out config or network connectivity issues before pushing the first message through. Should there also be a KafkaProducer.connect() or .open() method or connectAll()? I guess it would try to connect to all brokers in the BROKER_LIST_CONFIG HTH, Roger On Fri, Jan 24, 2014 at 11:54 AM, Jay Kreps <jay.kr...@gmail.com> wrote: > As mentioned in a previous email we are working on a re-implementation of > the producer. I would like to use this email thread to discuss the details > of the public API and the configuration. I would love for us to be > incredibly picky about this public api now so it is as good as possible and > we don't need to break it in the future. > > The best way to get a feel for the API is actually to take a look at the > javadoc, my hope is to get the api docs good enough so that it is > self-explanatory: > > http://empathybox.com/kafka-javadoc/index.html?kafka/clients/producer/KafkaProducer.html > > Please take a look at this API and give me any thoughts you may have! > > It may also be reasonable to take a look at the configs: > > http://empathybox.com/kafka-javadoc/kafka/clients/producer/ProducerConfig.html > > The actual code is posted here: > https://issues.apache.org/jira/browse/KAFKA-1227 > > A few questions or comments to kick things off: > 1. We need to make a decision on whether serialization of the user's key > and value should be done by the user (with our api just taking byte[]) or > if we should take an object and allow the user to configure a Serializer > class which we instantiate via reflection. We take the later approach in > the current producer, and I have carried this through to this prototype. > The tradeoff I see is this: taking byte[] is actually simpler, the user can > directly do whatever serialization they like. The complication is actually > partitioning. Currently partitioning is done by a similar plug-in api > (Partitioner) which the user can implement and configure to override how > partitions are assigned. If we take byte[] as input then we have no access > to the original object and partitioning MUST be done on the byte[]. This is > fine for hash partitioning. However for various types of semantic > partitioning (range partitioning, or whatever) you would want access to the > original object. In the current approach a producer who wishes to send > byte[] they have serialized in their own code can configure the > BytesSerialization we supply which is just a "no op" serialization. > 2. We should obsess over naming and make sure each of the class names are > good. > 3. Jun has already pointed out that we need to include the topic and > partition in the response, which is absolutely right. I haven't done that > yet but that definitely needs to be there. > 4. Currently RecordSend.await will throw an exception if the request > failed. The intention here is that producer.send(message).await() exactly > simulates a synchronous call. Guozhang has noted that this is a little > annoying since the user must then catch exceptions. However if we remove > this then if the user doesn't check for errors they won't know one has > occurred, which I predict will be a common mistake. > 5. Perhaps there is more we could do to make the async callbacks and future > we give back intuitive and easy to program against? > > Some background info on implementation: > > At a high level the primary difference in this producer is that it removes > the distinction between the "sync" and "async" producer. Effectively all > requests are sent asynchronously but always return a future response object > that gives the offset as well as any error that may have occurred when the > request is complete. The batching that is done in the async producer only > today is done whenever possible now. This means that the sync producer, > under load, can get performance as good as the async producer (preliminary > results show the producer getting 1m messages/sec). This works similar to > group commit in databases but with respect to the actual network > transmission--any messages that arrive while a send is in progress are > batched together. It is also possible to encourage batching even under low > load to save server resources by introducing a delay on the send to allow > more messages to accumulate; this is done using the linger.ms config (this > is similar to Nagle's algorithm in TCP). > > This producer does all network communication asynchronously and in parallel > to all servers so the performance penalty for acks=-1 and waiting on > replication should be much reduced. I haven't done much benchmarking on > this yet, though. > > The high level design is described a little here, though this is now a > little out of date: > https://cwiki.apache.org/confluence/display/KAFKA/Client+Rewrite > > -Jay >
