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 >>
