IMO thrift >> rest. Another option is good old RPC/dRPC :)
On 7/7/16, 9:17 AM, "Casey Stella" <[email protected]> wrote: >Yeah, I am slowly getting convinced that REST may be too much overhead and >tending closer to using Thrift and communicating to the model handler >(possibly in non-java) via some IPC. > >On Thu, Jul 7, 2016 at 9:15 AM, Simon Ball <[email protected]> wrote: > >> Hi Casey, >> >> Just to clarify, my thought was web sockets, not raw sockets, language >> agnostic, though thrift or proton if would be much better. Even with a non >> JSON payload, rest is very heavy over http. You be looking at probably >> 1-2kb header overhead per packet scored just on transport headers. Web >> socket frames carry slightly less overhead per message. >> >> Simon >> >> >> > On 7 Jul 2016, at 16:51, Casey Stella <[email protected]> wrote: >> > >> > Regarding the performance of REST: >> > >> > Yep, so everyone seems to be worried about the performance implications >> for >> > REST. I made this comment on the JIRA, but I'll repeat it here for >> broader >> > discussion: >> > >> > My choice of REST was mostly due to the fact that I want to support >> >> multi-language (I think that's a very important requirement) and there >> are >> >> REST libraries for pretty much everything. I do agree, however, that >> JSON >> >> transport can get chunky. How about a compromise and use REST, but the >> >> input and output payloads for scoring are Maps encoded in msgpack rather >> >> than JSON. There is a msgpack library for pretty much every language out >> >> there (almost) and certainly all of the ones we'd like to target. >> > >> > >> >> The other option is to just create and expose protobuf bindings (thrift >> >> doesn't have a native client for R) for all of the languages that we >> want >> >> to support. I'm perfectly fine with that, but I had some worries about >> the >> >> maturity of the bindings. >> > >> > >> >> The final option, as you suggest, is to just use raw sockets. I think if >> >> we went that route, we might have to create a layer for each language >> >> rather than relying on model creators to create a TCP server. I thought >> >> that might be a bit onerous for a MVP. >> > >> > >> >> Given the discussion, though, what it has made me aware of is that we >> >> might not want to dictate a transport mechanism at all, but rather allow >> >> that to be pluggable and extensible (so each model would be associated >> with >> >> a transport mechanism handler that would know how to communicate to it. >> We >> >> would provide default mechanisms for msgpack over REST, JSON over REST >> and >> >> maybe msgpack over raw TCP.) Thoughts? >> > >> > >> > Regarding PMML: >> > >> > I tend to agree with James that PMML is too restrictive as to models it >> can >> > represent and I have not had great experiences with it in production. >> > Also, the open source libraries for PMML have licensing issues (jpmml >> > requires an older version to accommodate our licensing requirements). >> > >> > Regarding workflow: >> > >> > At the moment, I'd like to focus on getting a generalized infrastructure >> > for model scoring and updating put in place. This means, this >> > architecture takes up the baton from the point when a model is >> > trained/created. Also, I have attempted to be generic in terms of output >> > of the model (a map of results) so it can fit any type of model that I >> can >> > think of. If that's not the case, let me know, though. >> > >> > For instance, for clustering, you would probably emit the cluster id >> > associated with the input and that would be added to the message as it >> > passes through the storm topology. The model is responsible for >> processing >> > the input and constructing properly formed output. >> > >> > Casey >> > >> > >> > On Tue, Jul 5, 2016 at 3:45 PM, Debo Dutta (dedutta) <[email protected]> >> > wrote: >> > >> >> Following up on the thread a little late …. Awesome start Casey. Some >> >> comments: >> >> * Model execution >> >> ** I am guessing the model execution will be on YARN only for now. This >> is >> >> fine, but the REST call could have an overhead - depends on the speed. >> >> * PMML: won’t we have to choose some DSL for describing models? >> >> * Model: >> >> ** workflow vs a model - do we care about the “workflow" that leads to >> >> the models or just the “model"? For example, we might start with n >> features >> >> —> do feature selection to choose k (or apply a transform function) —> >> >> apply a model etc >> >> * Use cases - I can see this working for n-ary classification style >> models >> >> easily. Will the same mechanism be used for stuff like clustering (or >> >> intermediate steps like feature selection alone). >> >> >> >> Thx >> >> debo >> >> >> >> >> >> >> >> >> >>> On 7/5/16, 3:24 PM, "James Sirota" <[email protected]> wrote: >> >>> >> >>> Simon, >> >>> >> >>> There are several reasons to decouple model execution from Storm: >> >>> >> >>> - Reliability: It's much easier to handle a failed service than a >> failed >> >> bolt. You can also troubleshoot without having to bring down the >> topology >> >>> - Complexity: you de-couple the model logic from Storm logic and can >> >> manage it independently of Storm >> >>> - Portability: you can swap the model guts (switch from Spark to Flink, >> >> etc) and as long as you maintain the interface you are good to go >> >>> - Consistency: since we want to expose our models the same way we >> expose >> >> threat intel then it makes sense to expose them as a service >> >>> >> >>> In our vision for Metron we want to make it easy to uptake and share >> >> models. I think well-defined interfaces and programmatic ways of >> >> deployment, lifecycle management, and scoring via well-defined REST >> >> interfaces will make this task easier. We can do a few things to >> >>> >> >>> With respect to PMML I personally had not had much luck with it in >> >> production. I would prefer models as POJOs. >> >>> >> >>> Thanks, >> >>> James >> >>> >> >>> 04.07.2016, 16:07, "Simon Ball" <[email protected]>: >> >>>> Since the models' parameters and execution algorithm are likely to be >> >> small, why not have the model store push the model changes and scoring >> >> direct to the bolts and execute within storm. This negates the overhead >> of >> >> a rest call to the model server, and the need for discovery of the model >> >> server in zookeeper. >> >>>> >> >>>> Something like the way ranger policies are updated / cached in plugins >> >> would seem to make sense, so that we're distributing the model execution >> >> directly into the enrichment pipeline rather than collecting in a >> central >> >> service. >> >>>> >> >>>> This would work with simple models on single events, but may struggle >> >> with correlation based models. However, those could be handled in storm >> by >> >> pushing into a windowing trident topology or something of the sort, or >> even >> >> with a parallel spark streaming job using the same method of >> distributing >> >> models. >> >>>> >> >>>> The real challenge here would be stateful online models, which seem >> >> like a minority case which could be handled by a shared state store >> such as >> >> HBase. >> >>>> >> >>>> You still keep the ability to run different languages, and platforms, >> >> but wrap managing the parallelism in storm bolts rather than yarn >> >> containers. >> >>>> >> >>>> We could also consider basing the model protocol on a a common model >> >> language like pmml, thong that is likely to be highly limiting. >> >>>> >> >>>> Simon >> >>>> >> >>>>> On 4 Jul 2016, at 22:35, Casey Stella <[email protected]> wrote: >> >>>>> >> >>>>> This is great! I'll capture any requirements that anyone wants to >> >>>>> contribute and ensure that the proposed architecture accommodates >> >> them. I >> >>>>> think we should focus on a minimal set of requirements and an >> >> architecture >> >>>>> that does not preclude a larger set. I have found that the best >> >> driver of >> >>>>> requirements are installed users. :) >> >>>>> >> >>>>> For instance, I think a lot of questions about how often to update a >> >> model >> >>>>> and such should be represented in the architecture by the ability to >> >>>>> manually update a model, so as long as we have the ability to update, >> >>>>> people can choose when and where to do it (i.e. time based or some >> >> other >> >>>>> trigger). That being said, we don't want to cause too much effort for >> >> the >> >>>>> user if we can avoid it with features. >> >>>>> >> >>>>> In terms of the questions laid out, here are the constraints from the >> >>>>> proposed architecture as I see them. It'd be great to get a sense of >> >>>>> whether these constraints are too onerous or where they're not >> >> opinionated >> >>>>> enough : >> >>>>> >> >>>>> - Model versioning and retention >> >>>>> - We do have the ability to update models, but the training and >> >> decision >> >>>>> of when to update the model is left up to the user. We may want >> >> to think >> >>>>> deeply about when and where automated model updates can fit >> >>>>> - Also, retention is currently manual. It might be an easier win >> >> to >> >>>>> set up policies around when to sunset models (after newer >> >> versions are >> >>>>> added, for instance). >> >>>>> - Model access controls management >> >>>>> - The architecture proposes no constraints around this. As it >> stands >> >>>>> now, models are held in HDFS, so it would inherit the same >> >> security >> >>>>> capabilities from that (user/group permissions + Ranger, etc) >> >>>>> - Requirements around concept drift >> >>>>> - I'd love to hear user requirements around how we could >> >> automatically >> >>>>> address concept drift. The architecture as it's proposed let's >> >> the user >> >>>>> decide when to update models. >> >>>>> - Requirements around model output >> >>>>> - The architecture as it stands just mandates a JSON map input and >> >> JSON >> >>>>> map output, so it's up to the model what they want to pass back. >> >>>>> - It's also up to the model to document its own output. >> >>>>> - Any model audit and logging requirements >> >>>>> - The architecture proposes no constraints around this. I'd love to >> >> see >> >>>>> community guidance around this. As it stands, we just log using >> >> the same >> >>>>> mechanism as any YARN application. >> >>>>> - What model metrics need to be exposed >> >>>>> - The architecture proposes no constraints around this. I'd love to >> >> see >> >>>>> community guidance around this. >> >>>>> - Requirements around failure modes >> >>>>> - We briefly touch on this in the document, but it is probably not >> >>>>> complete. Service endpoint failure will result in blacklisting >> >> from a >> >>>>> storm bolt perspective and node failure should result in a new >> >> container >> >>>>> being started by the Yarn application master. Beyond that, the >> >>>>> architecture isn't explicit. >> >>>>> >> >>>>>> On Mon, Jul 4, 2016 at 1:49 PM, James Sirota <[email protected]> >> >> wrote: >> >>>>>> >> >>>>>> I left a comment on the JIRA. I think your design is promising. One >> >>>>>> other thing I would suggest is for us to crowd source requirements >> >> around >> >>>>>> model management. Specifically: >> >>>>>> >> >>>>>> Model versioning and retention >> >>>>>> Model access controls management >> >>>>>> Requirements around concept drift >> >>>>>> Requirements around model output >> >>>>>> Any model audit and logging requirements >> >>>>>> What model metrics need to be exposed >> >>>>>> Requirements around failure modes >> >>>>>> >> >>>>>> 03.07.2016, 14:00, "Casey Stella" <[email protected]>: >> >>>>>>> Hi all, >> >>>>>>> >> >>>>>>> I think we are at the point where we should try to tackle Model as >> a >> >>>>>>> service for Metron. As such, I created a JIRA and proposed an >> >>>>>> architecture >> >>>>>>> for accomplishing this within Metron. >> >>>>>>> >> >>>>>>> My inclination is to be data science language/library agnostic and >> >> to >> >>>>>>> provide a general purpose REST infrastructure for managing and >> >> serving >> >>>>>>> models trained on historical data captured from Metron. The >> >> assumption is >> >>>>>>> that we are within the hadoop ecosystem, so: >> >>>>>>> >> >>>>>>> - Models stored on HDFS >> >>>>>>> - REST Model Services resource-managed via Yarn >> >>>>>>> - REST Model Services discovered via Zookeeper. >> >>>>>>> >> >>>>>>> I would really appreciate community comment on the JIRA ( >> >>>>>>> https://issues.apache.org/jira/browse/METRON-265). The proposed >> >>>>>>> architecture is attached as a document to that JIRA. >> >>>>>>> >> >>>>>>> I look forward to feedback! >> >>>>>>> >> >>>>>>> Best, >> >>>>>>> >> >>>>>>> Casey >> >>>>>> >> >>>>>> ------------------- >> >>>>>> Thank you, >> >>>>>> >> >>>>>> James Sirota >> >>>>>> PPMC- Apache Metron (Incubating) >> >>>>>> jsirota AT apache DOT org >> >>> >> >>> ------------------- >> >>> Thank you, >> >>> >> >>> James Sirota >> >>> PPMC- Apache Metron (Incubating) >> >>> jsirota AT apache DOT org >> >> >>
