Some Abstract Talk

@sadache

[sadache]

prismic.io interviews

Introducing prismic.io

by Sadek Drobi & Guillaume Bort, founders October 02, 2013

Content is everywhere. When you are creating a website, a web application, a game or a native mobile app - big or small, you have to deal with content at some point. Which inevitably has you asking questions: where do I store content? How can I modify texts? Fix typos, evolve and expand my work?

Traditionally, the industry's answer to these questions has been to adopt a Content Management System. We've been particularly dissatisfied with this answer, not only because of the inherent limitations of available CMS's offerings, but because we think that there is a fundamental problem with the concept of a CMS: it has the wrong focus, and it does too many things. We've been especially uncomfortable with the implication of imposing a traditional CMS on developers and designers' work and tools.

This dissatisfaction led us to create prismic.io. We see prismic.io as the much-needed disruption of the cluttered CMS world. Following is a video that embodies our motivation behind prismic.io, explains the concept further and shares some technical details of what the prismic.io offering is.

We think that such a disruption will greatly enhance creativity and will free developers and designers from what we refer to as CMS hell.

In this release, we are introducing quite a few concepts, and we still have a lot to come. That's why we'll shortly be posting more in-depth details around these powerful concepts, our scalable architecture and great opportunities opened by prismic.io.

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At Zenexity, we build web applications all the time, at different sizes and scales. In recent years, we've seen dramatic growth in Software as a Service (SaaS) and Web Services architectures. This architecture offers many scalability advantages, but it also introduces many new challenges, including data distribution, managing various data formats, flexibility, adaptability, big data, and stream processing.

The Play Framework is an open source framework for web application development created at Zenexity in 2007 and opened to the community in 2009. It targets the Java Virtual Machine (JVM) and focuses on simplicity, developer productivity, and HTTP/Web friendliness. As we got together with Guillaume Bort (founder of the framework) in 2011, we decided to start from scratch and design the Play Framework with a toolset adapted to the new challenges faced by web programmers.

We started working on a brand new architecture and design of Play, with a focus on a programming paradigm that we believe is proven for handling these challenges: functional programming. In this blog post I'd like to share with you the background on what motivated - and continues to motivate - the architecture, design, and evolution of Play.

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Playframework: Async, Reactive, Threads, Futures, ExecutionContexts

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Applicatives are too restrictive, breaking Applicatives and introducing Functional Builders

This post aims to document a practical design implementation we came up with when designing some APIs in Scala. Of course the concept is not Scala specific and applies to other functional languages. _If you don't want to go into the full introduction, this post talks about how Applicatives are too restrictive and breaking them into two independent components can be interesting for Contravariant and Invariant Functors. Jump to implementation attached._ We are taking JSON serialization/deserialization as a motivating example. `JsValue` is the name of the type that represents the JSON AST. What we need is to be able to read a `JsValue` and produce an `A`: trait Reads[A]{ def reads(json:JsValue):JsResult[A] } Implementing `Reads` interface provides a way to deserialize a JSON value into an `A` (and here giving you an opportunity to fail with JsError which is a valid JsResult). Writing JSON we have its dual: trait Writes[A]{ def writes(a:A):JsValue } Implementing Writes provides a way to write JSON for a type `A`. We have also a type that represents both, being able to read and write a JSON for an `A` type: trait Format[A] extends Reads[A] with Writes[A] Reads happens to be a monad, which is interesting but we are not going to talk about this much more. What we are interested in is having a simple way of combining JSON serializers, meaning if we have val readAge: Reads[Int] val readName: Reads[String] it could be very interesting to have a `Reads[(Int,String)]` or even better, a `Reads[User]` having for instance `case class User(age:Int, name:String)` This is actually quite easy to achieve with Applicative Functors. To simplify, Applicative Functors are Functors (having the map or fmap function implemented) together with the Applicative part which adds the power we need to achieve the combination we want to do. Explaining Applicative Functors is out of scope and there is a lot of material on the web explaining these. val readUser: Reads[User] = readAge.and(readName).apply(User) // or using symbolic methods: val readUser: Reads[User] = (readAge ~ readName).apply(User) This composition simply means that if we have two readers of two different types, they could read from the same JSON and provide two values which I can pack into another type (User here). Now that we solved our problem of combining the Reads with Applicative Functors, wouldn't it be even more interesting if we could combine similarly the Writes? Having: val writeAge: Writes[Int] val writeName: Writes[String] then we could have a `Writes[User]`, or can we? Almost. If we have two writers (capable of writing two different JsValue from two different types, Int and String here) then we end up with two `JsValue`s, and there is no way of merging these into one, except if they are both `JsObject`s. Let's do a new type that only writes `JsObject` trait OWrites[A] extends Writes[A]{ def writes(a:A):JsObject } Now it makes sense to have one writer out of our two writes above ( writeAge, writeName) by simply merging the resulting objects. So logically it makes sense, except that we can't use our Applicative based builder since an OWrites is not a Functor at all! Actually our OWrite is, naturally, the dual of a Functor, a CoFunctor (or Contravariant). Put simply, this means that to change the `A` type to `B` in OWrites we should provide a function backwards from B => A. This makes a lot of sense since if we know how to write an A and we know how to transform a B to A then we can write a B. Instead of fmap we got a contramap def contramap[B](B => A):OWrite[B] So or status here, we know how to transform the type of OWrites, we know how to merge two OWrites but all of that doesn't help us to use the builder which based on an Applicative Functor. What if we split our problem into two components. Let's introduce a new type that we call FunctionalCanBuild: trait FunctionalCanBuild[M[_]]{ def apply[A,B](ma:M[A],mb:M[B]):M[(A,B)] } Meaning for a given container M, we can some how compose two instances into one with the tuple of the type parameters. So for Reads it will be: val fpReads = new FunctionalCanBuild[Reads]{ // read the same value using the two readers and return a tuple def apply[A,B](ra:Reads[A],rb:Reads[B]):Reads[(A,B)] = ... } and for the OWrites it will make: val fpOwrites = new FunctionalCanBuild[OWrites]{ /* write two objects using the two writers and merge the two resulting JsObjects */ def apply[A,B](oa:OWrites[A],ob:OWrites[B]):OWrites[(A,B)] = ... } But that gets us only half way through what we want to achieve. What we want is actually a Reads and OWrites of User and not tuple of Int, String. For the `Reads[(Int,String)]` we can solve this problem easily using the fmap function of Functor. But even for `OWrites[(Int,String)]` it is easy, OWrites is a CoFunctor (or ContraVariant) and we can use contramap to get back from `(Int,String)` to `User`. So it seems that separating the problem into two sub problems is working for us. But what about format? Format is not a Functor and a Contra, it is actually Invariant. This means that it need functions in both directions, `A => B` and `B => A` to transform the `A` type to `B`. Like the `OWrites` we will use `OFormat`: trait OFormat[A] extends OWrites[A] with Reads[A] Now we can implement FunctionalCanBuild[OFormat] : val fbf: FunctionalCanBuild[OFormat] = new FunctionalCanBuild[OFormat]{ // merge reads and writes def apply[A,B](fa: OFormat[A], fb: OFormat[B]): OFormat[(A,B)] = ... } and once we have the `OFormat[(Int,String)]` we can transform it to a User by using inmap and passing `(Int,String) => User` and `User => (Int,String)`. This led us to an API that is capable of building compositions for Functors (`Reads`), CoFunctors (`Writes`) and Invariant (`Format`). Using the `FunctionBuilder` type class and the correct `Variance` we can offer a nice unified API for composing different abstractions. Bottom Line: By breaking `Applicative`s into two independent components (`FunctionalCanBuild` and `Variance` we could use them for more than Functors, ie Covariant and Invariant Functors. *ps: FunctionalCanBuild[M[_]] may look like a monoid but it is not exactly one.* *ps2: This is a work I did together with [@mandubian](https://github.com/mandubian) and other [Zenexity](www.zenexity.com) guys* *ps3: The Format,Reads and Writes approach is copied and adapted from [@debasishg](https://github.com/debasishg) on [sjson](https://github.com/debasishg/sjson)* *ps4: This work is integrated into [Play's included JSON library](https://github.com/playframework/Play20/tree/master/framework/src/play/src/main/scala/play/api/libs/json)* *ps5: A potentially more readable version as a [gist](https://gist.github.com/3646092)*

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[sadache]

Is socket.push(bytes) all what you need to program Realtime Web apps?

**One of the goals of [Play2](www.playframework.org) [architecture](http://sadache.tumblr.com/post/26258782102/bitsbout-play2-architecture) is to provide a programming model for what is called Realtime Web Applications.** ### Realtime Web Applications Realtime Web Applications are applications making use of Websockets, Server Sent Events, Comet or other protocols offering/simulating an open socket between the browser and the server for continuous communication. Basically, these applications offer to users delivery of information as it is published without having the user periodically pinging the service. There are quite a few web frameworks that target the development of this type of applications. Mostly, however, the solution is by providing an API that allows developers to push/receive messages from/to an open channel, something like: channel.push(message) //and channel.onMessage { ... } Though this kind of API offers an opportunity to get started doing Realtime Web, it doesn't offer a _programming model_ for dealing with the challenges encountered when programming with streams of data, including creating, adapting, manipulating, filtering and merging streams of data and all of the synchronization involved. ### A Programming Model Since development of Realtime Web Apps is mostly built around manipulation of streams of data, it is crucial to have a programming model which identifies clearly what a stream of data is and defines composable components to deal with it. It is obvious that the above channel api falls short when manipulating a rich set of streams of data, but even the classic InputStream/OutputStreams interfaces are not sufficient. Forgetting about their inefficient blocking runtime properties, they don't carry enough signals/information to allow building rich stream manipulation api. [Play2](www.playframework.org) uses Iteratees together with Futures for dealing with streams of data, providing a very rich model for programming rich Realtime Web Applications. ### A sample Realtime Web App The goal of this text isn't to provide a detailed description of what Iteratees and pals are. Nevertheless I will go into a fast introduction and then move into an example illustrating few aspects of how powerful this approach is. - An `Iteratee[E,A]` is an immutable interface that represents a consumer, it consumes chunks of data each of type `E` and eventually produces a computed value of type `A`. `Iteratee[String,Int]` is an iteratee that consumes chunks of strings and eventually produces an `Int` (that could be for instance number of charecters in the passed chunks) An iteratee can choose to terminate before the `EOF` sent from the stream, or could wait for `EOF` before it terminates, returning the computed `A` value. You can compose different `Iteratee`s together and this can be an opportunity for partitioning consuming logic into different parts. - An `Enumerator[E]` represents a stream that is pushing chunks of data of type `E`. An `Enumerator[String]` is a stream of strings. `Enumerator`s can be composed one after the other, or interleaved concurrently providing means of streams management. - An `Enumeratee[From,To]` is an adapter from a stream of `From`s to a stream of `To`s. Note that an `Enumeratee` can rechunk differently, add or remove chunks or parts of them. `Enumeratee`s as we will see are instrumental for stream manipulation. - There are convenient methods for creating different kinds of `Enumerator`s `Iteratee`s and `Enumeratee`s for different scenarios. Our sample application features two streams, one is a stream of financial operations: val operations: Enumerator[Event] = Enumerator.generateM[Event] { Promise.timeout( Some(Operation( if(Random.nextBoolean) "public" else "private", Random.nextInt(1000))), Random.nextInt(500)) } Here we are generating random values at random distances (of maximum 500ms). In the real world this stream could be coming from a datastore or an open socket with another server. An `Operation` is _private_ or _public_ and curries an _amount_ of type `Int`: case class Operation(level: String, amout: Int) extends Event The other stream we have is a stream of system messages, messages that talk about the status of the system: case class SystemStatus(message: String) extends Event val noise: Enumerator[Event] = Enumerator.generateM[Event] { Promise.timeout( Some(SystemStatus("System message")), Random.nextInt(5000) ) } This stream can be coming from another server or datastore. With these two streams at hand, we can prooduce one single stream that contains messages of both by interleaving them: val events: Enumerator[Event] = operations >- noise Actually those not comfortable using symbolic operators can use `interleave` method: val events: Enumerator[Event] = operations.interleave(noise) Now the model part of our application looks like: ### Our sample Realtime Web App features: Our application will publish this stream of `Event` as [Server Sent Event](http://dev.w3.org/html5/eventsource/) or [Comet](http://en.wikipedia.org/wiki/Comet_(programming)) (both are protocols for uni-directional socket from the server to the browser) and will be providing two simple features: #### Authorization: You can only see `Event`s that are allowed for your role. _Managers_ can see _private_ operations and system status whereas normal _users_ can see only _public_ operations, for this purpose we create an `Enumeratee` which collects appropriate messages: def feed(role: String) = Action { val secure: Enumeratee[Event, Event] = Enumeratee.collect[Event] { case e@SystemStatus(_) if role == "MANAGER" => e case e@Operation("private", _) if role == "MANAGER" => e case e@Operation("public", _) => e } #### Filtering: You can filter the stream by range of interest in the amount of the operation. By providing an _upper_ and _lower_ bounds you get only corresponding operations, for this we create another `Enumeratee` collecting appropriate operations: def feed(role: String, lowerBound: Int, higherBound: Int) = Action { val secure: Enumeratee[Event, Event] = ... val inBounds: Enumeratee[Event, Event] = Enumeratee.collect[Event] { case e@Operation(_, amout) if amout > lowerBound && amout < higherBound => e case e@SystemStatus(_) => e } } #### JSON: Our App will be pushing JSON messages to the browser, that's why we need one more `Enumeratee` for transforming `Event`s to `JSON` values, ie, `Enumeratee[Event,JsValue]`: val asJson: Enumeratee[Event, JsValue] = Enumeratee.map[Event] { case Operation(visibility, amount) => toJson(Map("type" -> toJson("operation"), "amount" -> toJson(amount), "visibility" -> toJson(visibility))) case SystemStatus(msg) => toJson(Map("type" -> "status", "message" -> msg)) } For convenience, let's produce one single adapter out of the three we got, for that we can use the `compose` method or its symbolic equivalent `><>`: val finalAdpater = secure ><> inBounds ><> asJson We're almost done. Now all what we need is to respond to the browser with an `Ok` status wrapping each message into the Server Sent Event (`EventSource`) protocol. Actually there is already an `Enumeratee` which adapts a stream and wraps its chunks into the SSE protocol: Ok.feed(Stream.events &> finalAdpater ><> EventSource()) .as("text/event-stream") Here we pass our stream through the `finalAdapter` and then through the `EventSource` adapter, applying then the appropriate header to the response. Our application now looks like: All what we need now from the client side is to connect to the stream using the following javascript: feed = new EventSource('/feed?role=@role&lower=' + min + '&higher=' + max) Snapshots of our Realtime Web App: /manager  /  ### Bottom Line Realtime Web involves dealing with different streams of data from different sources. It is hard to do any non-trivial application without having a programming model that contains an appropriate representation of a stream and necessary API for creating, adapting, filtering and consuming streams of data. Play2 uses `Iteratee`s to offer a programming model and a rich API. *Note: [Full working application source](https://github.com/sadache/RealtimeWebAppSample) was created by me @sadache and @guillaumebort for one of our talks* *Note2: [Probably a more readable version of this post](https://gist.github.com/3072893)* *Note3: Examples of Realtime Web Apps using Play2 [Typesafe Console](http://console-demo.typesafe.com) [lichess.org](http://en.lichess.org/)*

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### Play2 Simple HTTP API Essential blueprint of Play2 architecture is pretty simple and it should be easy to explain in a fairly short blog post. The framework can be understood progressively at different levels; each time having better exposure to some aspects of its design. The core of Play2 is really small, surrounded by a fair amount of useful APIs, services and structure to make Web Programming tasks easier. Basically, Play2 is an API that abstractly have the folllowing type ``` RequestHeader -> Array[Byte] -> Result ``` Which is a computation that takes the request header `RequestHeader`, then takes bytes of the body of the request `Array[Byte]` and produces a `Result`. Now this type presumes putting request body entirely into memory (or disk), even if you only want to compute a value out of it, or better forward it to a storage service like Amazon S3. We rather want to receive request body chunks as a stream and be able to process them progressively if necessary. What we need to change is the second arrow to make it receive its input in chunks and eventually produce a result. There is a type that does exactly this, it is called Iteratee and takes two type parameters. `Iteratee[E,R]` is a type of arrow that will take its input in chunks of type `E` and eventually return `R`. For our API we need an Iteratee that takes chunks of `Array[Byte]` and eventually return a `Result`. So we slightly modify the type to be: ``` RequestHeader -> Iteratee[Array[Byte],Result] ``` For the first arrow, we are simply using the Function[From,To] type aliased with `=>`. Actually, and unimportantly, if I define an infix type alias `type ==>[E,R] = Iteratee[E,R]` then I can write the type in a funnier way: ``` RequestHeader => Array[Byte] ==> Result ``` And this reads: Take the request headers, take chunks of `Array[Byte]` which represent the request body and eventually return a `Result`. The Result type, on the other hand, can be abstractly thought of as the response headers and the body of the response ``` case class Result(headers: ResponseHeader, body:Array[Byte]) ``` But same here, what if we want to send the response body progressively to the client without filling it entirely into memory. We need to improve our type. We need to replace the body type from an `Array[Byte]` to something that produces chunks of `Array[Byte]` in case we need to send the body progressively. Anyway if we don't care then we can still send the entire body as a single chunk. Such type exists and is called `Enumerator[E]` which means that it is capable of producing chunks of `E`, in our case `Enumerator[Array[Byte]]` ``` case class Result(headers:ResponseHeaders, body:Enumerator[Array[Byte]]) ``` It is a bit more practical to be able to stream and write to the socket anything that is convertible to an `Array[Byte]`, that is what `Writeable[E]` insures for a given type `E`: ``` case class Result[E](headers:ResponseHeaders, body:Enumerator[E])(implicit writeable:Writeable[E]) ``` ### Bottom Line The essential Play2 HTTP API is quite simple: ``` RequestHeader -> Iteratee[Array[Byte],Result] ``` or the funnier ``` RequestHeader => Array[Byte] ==> Result ``` Which reads as the following: Take the `RequestHeader` then take chunks of `Array[Byte]` and return a response. A response consists of `ResponseHeaders` and a body which is chunks of values convertible to `Array[Byte]` to be written to the socket represented in the `Enumerator[E]` type. From this point we can explore how does this simple API [behave in the runtime management of Play2 (Threads)](#TODO), what [opportunities it opens (NIO, Reactive, Streams, File Upload ...)](#TODO) and how can we [easily extend it to model different architectural patterns (Classical MVC, Resource Oriented, ...)](#TODO) [Probably a more readable version of this post in this gist](https://gist.github.com/3026886)

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New York


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 >>Sadek Drobi discusses functional programming sand Scala's multi paradigm approach at QCon London 2012. He also shares insights into the new Play 2.0

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Software human resources (HR) recruitment process

What Software HR dream about:

What they get:

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Functional Programming vs Mosquito Programming

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Functional programming is often perceived as being good for either mathematics or multi-core programming. As for its huge benefits for modern web architecture and development, they are not really known. This video gives a few arguments about why functional programming matters for today’s and tomorrow’s web.

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[sadache]

Most programmers from an imperative/object-oriented background go through this roller coaster when learning Haskell:

Stage One: Confusion

I don't get it. Why doesn't the class keyword instantiate an object? How do I declare a variable to be constant?

Stage Two: Fascination

I've never written more elegant code. It's like... pure mathematics, so beautiful and concise. Java and Python are clumsy in comparison.

Stage Three: Frustration

What the hell is a Monad, and why should I care? How do I unwrap IO? Why is random number generation so damn obtuse?

Stage Four: Dismissal

Haskell? Yeah, it was an interesting research language. Great for quicksort and other CS103 algorithms, but for real world programming it's obnoxious and restrictive.

Stage Five: Reawakening

I tried to code in Haskell again last night... it all makes sense now. This type system is incredible.

And then they're part of the fold.

The problem is that most people seem to quit at stages one and four. Even after twenty years there aren't many Haskell programmers. Do you guys think Haskell will ever manage to escape its poor-adoption limbo? Who or what do you think is holding the language back?