Hi Roger, You bring up good points, and I think the short answer is that there are trade-offs to everything, of course (:
What I described could definitely be implemented as a Samza job, and I think that would make a lot of sense if the data serving system was also deployed via YARN. This way, the Samza tasks responsible for ingesting and populating the data serving system's nodes could be spawned wherever YARN knows these nodes are located. For data serving systems not well integrated with YARN however, I'm not sure that there would be that much win in using the Samza deployment model. And since the consumers themselves are pretty simple (no joining of streams, no local state, etc.), this seems to be a case where Samza is a bit overkill and a regular Kafka consumer is perfectly fine (except for the YARN-enabled auto-deployment aspect, like I mentioned). As for push versus pull, I think the trade-off is the following: push is mostly simpler and more decoupled, as you said, but I think pull would be more efficient. The reason for that is that Kafka consumption is very efficient (thanks to batching and compression), but most data serving systems don't provide a streaming ingest API for pushing data efficiently to them, instead they have single record put/insert APIs which require a round-trip to be acknowledged. This is perfectly fine in low-throughput scenarios, but does not support very high throughput of ingestion like Kafka can provide. By co-locating the pulling process (i.e.: Kafka consumer) with the data serving node, it makes it a bit more affordable to do single puts since the (local) round-trip acks would be near-instantaneous. Pulling also makes the tracking of offsets across different nodes a bit easier, since each node can consume at its own pace, and resume at whatever point in the past it needs (i.e.: rewind) without affecting the other replicas. Tracking offsets across many replicas in the push model is a bit more annoying, though still doable, of course. -- Felix GV Data Infrastructure Engineer Distributed Data Systems LinkedIn [email protected] linkedin.com/in/felixgv ________________________________________ From: Roger Hoover [[email protected]] Sent: Tuesday, March 31, 2015 8:57 PM To: [email protected] Subject: Re: How do you serve the data computed by Samza? Ah, thanks for the great explanation. Any particular reason that the job(s) you described should not be Samza jobs? We're started experimenting with such jobs for Druid and Elasticsearch. For Elasticsearch, the Samza job containers join the Elasticsearch cluster as transport nodes and use the Java API to push ES data nodes. Likewise for Druid, the Samza job uses the Tranquility API to schedule jobs ( https://github.com/metamx/tranquility/tree/master/src/main/scala/com/metamx/tranquility/samza ). The nice part about push versus pull is that the downstream system does not need plugins (like ES rivers) that may complicate it's configuration or destabilize the system. Cheers, Roger On Tue, Mar 31, 2015 at 10:56 AM, Felix GV <[email protected]> wrote: > Thanks for your reply Roger! Very insightful (: > > > 6. If there was a highly-optimized and reliable way of ingesting > > partitioned streams quickly into your online serving system, would that > > help you leverage Samza more effectively? > > >> 6. Can you elaborate please? > > Sure. The feature set I have in mind is the following: > > * Provide a thinly-wrapped Kafka producer which does appropriate > partitioning and includes useful metadata (such as production timestamp, > etc.) alongside the payload. This producer would be used in the last step > of processing of a Samza topology, in order to emit to Kafka some > processed/joined/enriched data which is destined for online serving. > * Provide a consumer process which can be co-located on the same hosts > as your data serving system. This process consumes from the appropriate > partitions and checkpoints its offsets on its own. It leverages Kafka > batching and compression to make consumption very efficient. > * For each records the consumer process issues a put/insert locally to > the co-located serving process. Since this is a local operation, it is also > very cheap and efficient. > * The consumer process can also optionally throttle its insertion rate > by monitoring some performance metrics of the co-located data serving > process. For example, if the data serving process exposes a p99 latency via > JMX or other means, this can be used in a tight feedback loop to back off > if read latency degrades beyond a certain threshold. > * This ingestion platform should be easy to integrate with any > consistently-routed data serving system, by implementing some simple > interfaces to let the ingestion system understand the key-to-partition > assignment strategy, as well as the partition-to-node assignment strategy. > Optionally, a hook to access performance metrics could also be implemented > if throttling is deemed important (as described in the previous point). > * Since the consumer process lives in a separate process, the system > benefits from good isolation guarantees. The consumer process can be capped > to a low amount of heap, and its GC is inconsequential for the serving > platform. It's also possible to bounce the consumer and data serving > processes independently of each other, if need be. > > There are some more nuances and additional features which could be nice to > have, but that's the general idea. > > > It seems to me like such system would be valuable, but I'm wondering what > other people in the open-source community think, hence why I was interested > in starting this thread... > > > Thanks for your feedback! > > -F >
