Good to know but if there is an event blocker and sniffer then they should be a concern of the Engine. Otherwise you are hiding Engine specifics from the Engine. The most irrefutable need for the “input” method is kappa requirements and Lambda need for realtime changes to the model.
I understand what you are aiming for—namely data independence from model and engine—but it is impossible and seems a very odd place to abstract when you put it in real terms. A Recommender will never need the same data as a neural net, a clusterer, or a classifier. This abstraction does not exist in the data because it is not there in the algorithm and should not be forced away from the Engine. BTW the way the prototype server handles this data independence is allowing the user to ignore the engine (which may be under tuning or development and not reliable for validation) and simply mirroring un-validated events (PIO has this built into some client SDKs but this suffers from getting only a single clients events). Then they can be replayed or modified as with exported PIO events. The server also imports these maintaining event level compatibility with PIO. This even works with Kappa. If you want to re-create a kappa model you simply replay the mirrored events. But mirroring is optional and likely to be turned off once the Engine is running correctly. IMO it is a more flexible model than forcing data independence away from the Engine and maintaining it into the storage layer. So far I’ve written 3 PIO Templates from scratch, the UR, The Contextual Bandit (MAB type online learner), and the db-cleaner. What I have found with these rather different algorithms is: 1) PIO works ok with the UR but could use realtime validation and a better way of dropping old events. 2) Kappa doesn’t work well at all with PIO but does with the prototype server 3) event/dataset compatibility can be maintained between PIO and prototype Engines. 4) there is no need for a db-cleaner in the prototype. The Engine persists mutable objects and makes realtime changes to their state, and event streams can be handled as the Engine needs (Kappa discards without storing, Lambda may store) but since they are separate from $set, $unset these streams can have db TTLs to age out old data for Lambda Engines. The system is always self-cleaning with no heavyweight operation required to keep just the right data (the db cleaner is heavyweight and slow), the data does not grow forever by design. This was never addressed as a design requirement for PIO and the add-on we did is not a very good solution. On Jul 9, 2017, at 7:09 PM, Kenneth Chan <kenn...@apache.org> wrote: i think there is a philosophical discussion: 1) as PIO user, should i collect my event data based on my application uniqueness and ML needs (of course, i can use the template format as reference), then create engine or modify engine template to use these data to train model or 2) as PIO user, because i'm using this specific engine template, i must import and transform my data into the exact format required by template, and send to event server in order to make it work. however, regardless of above, PIO event server currently supports "event blocker" and "event sniffer" to solve these issues you mentioned 1) "event blocker" can be used for "event validation in real time" - the engine template can provide a sample event blocker implementation and can be used to reject improper events. 2) "event sniffer" can be used for "forwarding specific event to other processing system in real time" - the engine template can also provide a sample sniffer (e.g. send to UR's elasticsearch to update meta data) for advanced user, they can modify these based on their application needs (say, if they have multiple engines). for starter, they may use out of the box along with template. see http://mail-archives.apache.org/mod_mbox/incubator-predictionio-dev/201706.mbox/%3CCAF_HxLtEonOVALSQgrCRGXctAbL7eypxwG0ErHpaBJJym15j5Q%40mail.gmail.com%3E <http://mail-archives.apache.org/mod_mbox/incubator-predictionio-dev/201706.mbox/%3CCAF_HxLtEonOVALSQgrCRGXctAbL7eypxwG0ErHpaBJJym15j5Q%40mail.gmail.com%3E> On Sun, Jul 9, 2017 at 5:28 PM, Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>> wrote: I must disagree here, The Engine should decide the disposition of data, which cannot be left to a generic EventServer. Data is the concern of the Engine, not the EventSever or PIO framework for these reasons: 1) input needs to be validated and since it is defined by the Engine it seems rather obvious that the Engine must provide an “input” method like the “predict” method. This input method parses and validates input responding with errors of format that only it knows about. It also decides… 2) a Kappa learner must get data in realtime, and do not save datasets, only buffers of data at most. 3) Kappa and even some Lambda algorithms need to modify/update the model in realtime. Realtime model updates define "Kappa online learners" but there are also Lambda learners like the UR that need to update parts of the model when, for instance, item attributes change (out of stock, ...) As PIO stands now this can only be done at train time which is a rather troublesome limitation. 4) It is the Engine’s concern, whether input modifies mutable or immutable data. One engine may use a named event to do something but the name of the event is only know by the Engine. So if you agree that data come in 2 forms, only the Engine can define and enforce this. This is certainly not to denigrate the EventStore, which is most certainly required by every existing PIO Lambda Engine. But it should be the concern of the Engine how it is used and the only way to do this is make “input” the concern of the Engine. This can be done generically if there is truly no validation beyond the current an so does not needlessly complicate Engines. I am also not arguing for a different encoding of data. The PIO event JSON is quite flexible and I have not seen a need to alter it. However because of its flexibility the EventServer cannot really validate it. The PIO events are even quite sufficient for Lambda and Kappa data encoding in fact we have a Lambda Template in PIO that we made into a Kappa Template with the prototype server and used exactly the same event encoding. Since the prototype requires that the Engine validate it and respond to the input request, we immediately found event encoding errors that were very serious and had been in the client for a long time but since the events looked perfectly fine to the PIO EventServer, the errors were never detected and the data was in fact ignored. Within a day of replaying exported PIO events to the prototype server the issue was resolved and fixed in the client. On Jul 8, 2017, at 12:48 AM, Kenneth Chan <kenn...@apache.org <mailto:kenn...@apache.org>> wrote: re: "bundling event server as engine" depending on how we wanna separate the concern. the way i look at it is decouple 1, data collection service (PIO event server) and 2. modeling and prediction service (PIO engine) - that's the separation of concern. Ideally data is agnostic to engine, and should be tied to user application. The original vision is user collect data, then can create multiple PIO engines which use the collected data. if combine 1 and 2, how could user create engine A and engine B to train model on collected data for different ML use case? for your input data problem, maybe other way is that the template should also provide a "event validator" which can be loaded into event server and advanced user can also customize it. On Sat, Jul 8, 2017 at 12:31 AM, Kenneth Chan <kenn...@apache.org <mailto:kenn...@apache.org>> wrote: # re: " I see it as objects you see it as data stores" not really. I see things based on what functionality and purpose it provides. like you mentioned - The way Elasticseach is used in UR is part of the model and where the algorithm write the computation result into and then used as serving. In a way, it's the model. just a more complex model than a simple linear regression function. If we define "Model" as output of the train() function, then UR is storing the model into Elasticsearch - and it is required because UR relies on Elasticsearch computation - meaning it's part of UR's "model".predict() # re: "In reality the input comes in 2 types, persistent mutable objects and immutable streams of events (that may well be usable as a time window of data, dropping old events)" like you said, basically there are two types of data type 1. mutable object (e.g meta data of a product, user profile, etc) 2. immutable event (e.g. behavior data) However, 1 can be considered as 2 if we treat the "changes" of mutable object as "event" as well - basically this's the current event server design. But i agree some use case may not care about changes of mutable object - for this, we can provide some API/option for people to store mutable objects and always overwrite. or use better storage structure to capture the changes of mutable object. On Fri, Jun 30, 2017 at 5:29 AM, Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>> wrote: Actually I think it’s a great solution. The question about different storage config (https://issues.apache.org/jira/browse/PIO-96 <https://issues.apache.org/jira/browse/PIO-96>) is because Elasticsearch performs the last step of the algorithm, it is not just a store for models, so it’s an integral part of the compute engine, not the storage. If it looks that way I hardly think it matters in the way implied (see below where Templates should come with compassable containers). This is actually the primary difference in the way you and I look at the problem. I see it as objects you see it as data stores. Let’s add the question of compute backends and unfortunately users will have to pick the solution along with the engines they require (TensorFlow anyone?) If PIO is going to be a viable ML/AI server in the long term it has to be a lot more flexible, not less so. In the proto server I mention, the Engine decides on the compute backend and the example Template does not use Spark. The prototype server I mentioned actually only handles metadata, installs engines, and mirrors input. To handle Kappa as well as Lambda algorithms the Engine must decide what and if it needs to store. Therefore instead of assuming an EventServer we have mirroring of un-validated events. This has many benefits. For one thing we can require validation from the Engine with every event. This is because the single most frequent mistake by users I’ve dealt with is malformed input. PIO’s input scheme is great because it is so flexible but because of that validation is nil. I have seen users that have been using a Template for a year without understanding that most of their data was ignored by the Template code (not the UR in this case) . I have spent literally thousands of hours helping correct bad input over email even though the UR has orders of magnitude better docs than any other Template. Yes, it’s also a lot more complicated but anyway, I’m tired of this—we need validation of every input. Then maybe I will only spend 90% of those hours :-P Anyway I think the separation of concerns should be Server handles metadata, installs engines, and mirrors input. The Template framework provides required APIs for Engines that must be implemented and a set of Tools they can use or ignore to use what ever they need. If the Engines provides an input method they can validate and if they are Kappa, learn immediately (update models in real time), if they are Lambda, store the valid data using something like an Event Store. The train method is then optional and, of course, query. BTW the reason I call it a PredictionServer (in PIO) is because it is not an Engine Server, all it does is provide a query endpoint. This corresponds to only one method of an Engine and there is no reason to look at a query endpoint any differently than the other public APIs of the Engine. I guess I look at this in an object oriented way, not a data oriented way. This leads to Template code/Engines making more decisions. The Kappa template we have for this proto server never uses Spark. Why would it to implement Kappa online learning? It also does not need an Event Store because it only stores models. This is also fine for Lambda where an Event Store is required because the Engine provides the input method too, where it can make the store/no-store decision. This has other benefits. Treating input as an immutable stream has some major flaws. Some of the data has to be dropped, we cannot store forever—no one can afford that much disk. And some data can never be dropped because only the aggregate of all object changes makes any sense. In reality the input comes in 2 types, persistent mutable objects and immutable streams of events (that may well be usable as a time window of data, dropping old events). With the above split, the mirror always has all input in case it’s needed, the Engine can decide what events operate on mutable objects and store the rest as a stream in the Event Store (with TTL for time windows). Once this is trusted to work correctly mirroring can be stopped. In fact the mutable objects can affect the model in real time now, even with Lambda Templates like the UR. When an object property changes in today’s PIO we have to wait till train before the model changes because the Engine does not have an input method. If it did, then input that should affect the model can. This solves all my pet peeves, internal API-wise, and allows one implementation of an SaaS capable multi-tenant, secure Server. And here multi-tenancy is super lightweight. Since most users have only one Template, they may have to install supporting compute engines or stores. This is a one time issue for them and Templates should come with containers and scripts to compose them. We’re already doing this with PIO. A fully clustered install takes an hour. Admin of such a monster is another issue that is not necessarily better or even good in this model but a subject for another day. On Jun 30, 2017, at 1:40 AM, Kenneth Chan <kenn...@apache.org <mailto:kenn...@apache.org>> wrote: I agree that there is confusion regarding event server VS event storage and the unclear usage definition of types of data storage (e.g. meta-data vs model) but i'm not sure if bundling Event Server with Engine Server (or Pat calls it PredictionServer) is a good solution. currently PIO has 3 "types" of storage - METADATA : store PIO's administrative data ("Apps", etc) - EVENTDATA: store the pure events - MODELDATA : store the model 1. one confusion is when universal recommendation is used, Elastichsearch is required in order to serve the Predicted Results. Is this type of storage considered as "MODELDATA" or "METADATA" or should introduce a new type of storage for "Serving" purpose (which can be tied to engine specific) ? 2. question regarding the problem described in ticket https://issues.apache.org/jira/browse/PIO-96 <https://issues.apache.org/jira/browse/PIO-96> ``` Problems emerge when a developer tries running multiple engines with different storage configs on the same underlying database, such as: a Classifier with Postgres meta, event, & model storage, and the Universal Recommender with Elasticsearch meta plus Postgres event & model storage. ``` why user want to use different storage config for different engine? can the classifier match the same configuration as universal recommender? because i thought the storage configuration is more tied to PIO as a whole rather than per engine. Kenneth On Thu, Jun 29, 2017 at 10:22 AM, Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>> wrote: Are you asking about the EventServer or PredictionServer? The EventServer is multi-tenant with access keys, not really pure REST. We (ActionML) did a hack for a client to The PredictionServer to allow Actors to respond on the same port for several engine queries. We used REST addressing for this, which adds yet another id. This makes for one process for the EventServe and one for the PredictionServer. Each responding engine was behind an Actor not a new process. So it’s possible but IMO makes the API as a total rather messy. We also had to change the workflow so metadata was read on `pio deploy` so one build could then deploy many times with different engine.jsons and different PredictionServer endpoints for queries only. This comes pretty close to clean multi-tenantcy but is not SaaS capable without solving SSL and Auth for both services. The hack was pretty ugly in the code and after doing that I concluded that a big chunk needed a rewrite and hence the prototype. It depends on what you want but if you want SaaS I think that mean SSL + Auth + multi-tenancy, and you also mention minimizing process boundaries. There are rather many implications to this. On Jun 29, 2017, at 9:57 AM, Mars Hall <m...@heroku.com <mailto:m...@heroku.com>> wrote: Donald, Pat, great to hear that this is a well-pondered design challenge of PIO 😄 The prototype, composable, all-in-one server sounds promising. I'm wondering if there's a more immediate possibility to address adding the `/events` REST API to Engine? Would it make sense to try invoking an `EventServiceActor` in the tools.commands.Engine#deploy method? If that would be a distasteful hack, just say so. I'm trying to understand possibility of solving this in the current codebase vs a visionary new version of PIO. *Mars ( <> .. <> ) > On Jun 28, 2017, at 18:01, Pat Ferrel <p...@occamsmachete.com > <mailto:p...@occamsmachete.com>> wrote: > > Ah, one of my favorite subjects. > > I’m working on a prototype server that handles online learning as well as > Lambda style. There is only one server with everything going through REST. > There are 2 resource types, Engines and Commands. Engines have REST APIs with > endpoints for Events and Queries. So something like POST > /engines/resouce-id/events would send an event to what is like a PIO app and > POST /engine/resource-id/queries does the PIO query equivalent. Note that > this is fully multi-tenant and has only one important id. It’s based on > akka-http in a fully microservice type architecture. While the Server is > running you can add completely new Templates for any algorithm, thereby > adding new endpoints for Events and Queries. Each “tenant” is super > lightweight since it’s just an Actor not a new JVM. The CLI is actually > Python that hits the REST API with a Python SDK, and there is a Java SDK too. > We support SSL and OAuth2 so having those baked into an SDK is really > important. Though a prototype it can support multi-tenant SaaS. > > We have a prototype online learner Template which does not save events at all > though it ingests events exactly like PIO in the same format in fact we have > the same template for both servers taking identical input. Instead of an > EventServer it mirrors received events events before validation (yes we have > full event validation that is template specific.) This allows some events to > affect mutable data in a database and some to just be an immutable stream or > even be thrown away for Kappa learners. For an online learner, each event > updates the model, which is stored periodically as a watermark. If you want > to change algo params you destroy the engine instance and replay the mirrored > events. For a Lambda learner the Events may be stored like PIO. > > This is very much along the lines of the proposal I put up for future PIO but > the philosophy internally is so different that I’m now not sure how it would > fit. I’d love to talk about it sometime and once we do a Lambda Template > we’ll at least have some nice comparisons to make. We migrated the Kappa > style Template to it so we have a good idea that it’s not that hard. I’d love > to donate it to PIO but only if it makes sense. > > > On Jun 28, 2017, at 4:27 PM, Donald Szeto <don...@apache.org > <mailto:don...@apache.org>> wrote: > > Hey Mars, > > Thanks for the suggestion and I agree with your point on the metadata part. > Essentially I think the app and channel concept should be instead logically > grouped together with event, not metadata. > > I think in some advanced use cases, event storage should not even be a hard > requirement as engine templates can source data differently. In the long run, > it might be cleaner to have event server (and all relevant concepts such as > its API, access keys, apps, etc) as a separable package, that is by default > turned on, embedded to engine server. Advanced users can either make it > standalone or even turn it off completely. > > I imagine this kind of refactoring would echo Pat's proposal on making a > clean and separate engine and metadata management system down the road. > > Regards, > Donald > > On Wed, Jun 28, 2017 at 3:29 PM Mars Hall <m...@heroku.com > <mailto:m...@heroku.com>> wrote: > One of the ongoing challenges we face with PredictionIO is the separation of > Engine & Eventserver APIs. This separation leads to several problems: > > 1. Deploying a complete PredictionIO app requires multiple processes, each > with its own network listener > 2. Eventserver & Engine must be configured to share exactly the same storage > backends (same `pio-env.sh`) > 3. Confusion between "Eventserver" (an optional REST API) & "event storage" > (a required database) > > These challenges are exacerbated by the fact that PredictionIO's docs & `pio > app` CLI make it appear that sharing an Eventserver between Engines is a good > idea. I recently filed a JIRA issue about this topic. TL;DR sharing an > eventserver between engines with different Meta Storage config will cause > data corruption: > https://issues.apache.org/jira/browse/PIO-96 > <https://issues.apache.org/jira/browse/PIO-96> > > > I believe a lot of these issues could be alleviated with one change to > PredictionIO core: > > By default, expose the Eventserver API from the `pio deploy` Engine process, > so that it is not necessary to deploy a second Eventserver-only process. > Separate `pio eventserver` could still be optional if you need the separation > of concerns for scalability. > > > I'd love to hear what you folks think. I will file a JIRA enhancement issue > if this seems like an acceptable approach. > > *Mars Hall > Customer Facing Architect > Salesforce Platform / Heroku > San Francisco, California > >
