## Monday, March 28, 2011

### Killing a var and Threading a State

In my earlier post on CQRS using functional domain models and Akka actors, I had implemented a data store that accumulates all events associated with individual trades. We call this EventSourcing that allows us to rollback our system in time and replay all events over the earlier snapshot to bring it up to date. This has many uses to detect any problems that might have occured in the past or to profile your system on a retroactive basis. This post is not about event sourcing or the virtues of CQRS.

In this post I start taking cue from the EventSourcing example and discuss some strategies of improving some aspects of the domain model. This is mostly related to raising the level of abstraction at which to program the solution domain. Consider this post to be a random rant to record some of my iterations in the evolution of the domain model.

The code snippets that I present below may sometimes look out of context since they're part of a bigger model. The entire prototype is there in my github repository ..

Story #1 : How to kill a var

Have a look at the event store that I implemented earlier ..

class EventStore extends Actor with Listeners {   private var events = Map.empty[Trade, List[TradeEvent]]   def receive = //..   //.. } 
With an actor based implementation the mutable var events is ok since the state is confined within the actor itself. In another implementation where I was using a different synchronous event store, I had to get around this mutable shared state. It was around this time Tony Morris published his Writer Monad in Scala. This looked like a perfect fit .. Here's the implementation of the abstraction that logs all events, shamelessly adopted from Tony's Logging Without Side-effects example ..

import TradeModel._ object EventLog {   type LOG = List[(Trade, TradeEvent)] } import EventLog._ case class EventLogger[A](log: LOG, a: A) {   def map[B](f: A => B): EventLogger[B] =     EventLogger(log, f(a))   def flatMap[B](f: A => EventLogger[B]): EventLogger[B] = {     val EventLogger(log2, b) = f(a)     EventLogger(log ::: log2 /* accumulate */, b)   } } object EventLogger {   implicit def LogUtilities[A](a: A) = new {     def nolog =       EventLogger(Nil /* empty */, a)     def withlog(log: (Trade, TradeEvent)) =       EventLogger(List(log), a)     def withvaluelog(log: A => (Trade, TradeEvent)) =       withlog(log(a))   } } 
and here's a snippet that exercises the logging process ..

import EventLogger._ val trd = makeTrade("a-123", "google", "r-123", HongKong, 12.25, 200).toOption.get val r = for {   t1 <- enrichTrade(trd) withlog (trd, enrichTrade)   t2 <- addValueDate(t1) withlog (trd, addValueDate) } yield t2 
Now I can check what events have been logged and do some processing on the event store ..

// get the log from the EventLogger grouped by trade val m = r.log.groupBy(_._1) // play the event on the trades to get the current snapshot val x =   m.keys.map {t =>     m(t).map(_._2).foldLeft(t)((a,e) => e(a))   } // check the results x.size should equal(1) x.head.taxFees.get.size should equal(2)  x.head.netAmount.get should equal(3307.5000) 
Whenever you're appending data to an abstraction consider using the Writer monad. With this I killed the var events from modeling my event store.

Story #2: Stateful a la carte

This is a story that gives a tip for handling changing state of a domain model in a functional way. When the state does not change and you're using an abstraction repeatedly for reading, you have the Reader monad.

// enrichment of trade // Reader monad val enrich = for {   taxFeeIds      <- forTrade // get the tax/fee ids for a trade   taxFeeValues   <- taxFeeCalculate // calculate tax fee values   netAmount      <- enrichTradeWith // enrich trade with net amount } yield((taxFeeIds map taxFeeValues) map netAmount) val trd = makeTrade("a-123", "google", "r-123", HongKong, 12.25, 200) (trd map enrich) should equal(Success(Some(3307.5000))) 
Note how we derive the enrichment information for the trade keeping the original abstraction immutable - Reader monad FTW.

But what happens when you need to handle state that changes in the lifecycle of the abstraction ? You can use the State monad itself. It allows you to thread a changing state across a sequence transparently at the monad definition level. Here's how I would enrich a trade using the State monad as implemented in scalaz ..

val trd = makeTrade("a-123", "google", "r-123", HongKong, 12.25, 200).toOption.get val x =   for {     _ <- init[Trade]     _ <- modify((t: Trade) => refNoLens.set(t, "XXX-123"))     u <- modify((t: Trade) => taxFeeLens.set(t, some(List((TradeTax, 102.25), (Commission, 25.65)))))   } yield(u) x ~> trd == trd.copy(refNo = "XXX-123") 
In case you're jsut wondering what's going around above, here's a bit of an explanation. init initializes the state for Trade. init is defined as ..

def init[S]: State[S, S] = state[S, S](s => (s, s)) 
modify does the state change with the function passed to it as the argument ..

def modify[S](f: S => S) = init[S] flatMap (s => state(_ => (f(s), ()))) 
We apply the series of modify to enrich the trade. The actual threading takes place transparently through the magic of comprehensions.

There is another way of securely encapsulating stateful computations that allow in-place updates - all in the context of functional programming. This is the ST monad which has very recently been inducted into scalaz. But that is the subject of another post .. sometime later ..

## Tuesday, March 01, 2011

### Pushing the envelope on OO and functional with Scala

There has been quite a few inflamatory discussions in the twitterverse and the blogosphere recently regarding some of the ultra-functional voices dominating the Scala ecosystem. It's true that Scala is an object functional language. And it's also true that you can stretch Scala in both ways to get enough mileage out of it. Talking about the functional programming folks picking up Scala more and more, even Martin Odersky said this in his comment on one of such inflamatory posts ..

This is a good thing, IMO, because it pushes the envelope of what you can do with the language
Being a multi-paradigm language, I find Scala a wonderful mix. It has a very clean object model powered by its type system that helps programmers design scalable component abstractions. Scala also has a rich support for functional programming, which, though not as clean as Haskell, goes along very well complementing its OO capabilities. And from this perspective Scala offers an equal opportunity to developers coming from OO or FP paradigms.

In this post I will discuss one issue that can be solved elegantly in Scala using both of its paradigms. The OO version of the solution uses the elegance of mixins, abstract vals and the Cake pattern. While the functional version uses currying and partial applications.

Interface & Implementation

One of the recommended practices that we as software developers follow while designing domain models is to separate out the interface from an implementation. I am calling them by the terms interface and implementation. Replace them with the terminologies from your favorite language - contract, protocol, class, type .. etc. etc. But you know what I mean to say - distinguish between the nature of coupling of the generic and the specific parts of your abstraction.

Many people call this the Dependency Injection, where the actual implementation dependency is injected into your abstraction during runtime, resulting in reduced coupling. BTW I am not talking about DI frameworks, which may seem bolted on both in languages with a sane type system and those which don't have a static type system.

The OO way in Scala

Let's first consider how we can do dependency injection in Scala using the power of its object system. It has been covered in gory details by Jonas Boner in one of his Real World Scala blog posts. The example that I give here follows the same design that he used ..

Consider two abstractions that we use in our domain model for getting data out to the external world ..

// a repository for interacting with the underlying data store trait TradeRepository {   def fetch(refNo: String): Trade   def write(t: Trade): Unit } // a domain service trait TradeService {   def fetchTrade(refNo: String): Trade   def writeTrade(trade: Trade): Unit } 
And now the component for each of the above abstractions that contain the default implementations ..

trait TradeRepositoryComponent {   val tradeRepo: TradeRepository   class TradeRepositoryImpl extends TradeRepository {     def fetch(refNo: String): Trade = //..     def write(t: Trade): Unit = //..   } } trait TradeServiceComponent{ this: TradeRepositoryComponent => // self type annotation that indicates the dependency   val tradeService: TradeService   class TradeServiceImpl extends TradeService {     def fetchTrade(refNo: String) = tradeRepo.fetch(refNo)     def writeTrade(trade: Trade) = tradeRepo.write(trade)   } } 
Note how the self-type annotation is used in TradeServiceComponent to indicate a dependency on TradeRepositoryComponent. But still we are talking in terms of traits, without committing our final assembly to any specific object implementations. The abstract vals tradeRepo and tradeService have still not been materialized in terms of any concrete implementations. Thus we delay coupling to any implementation till the time we absolutely need them. And that is when we make the final assembly ..

// we are wiring up the components object TradeServiceAssembly extends TradeRepositoryComponent with TradeServiceComponent {   val tradeRepo = new TradeRepositoryImpl // impl   val tradeService = new TradeServiceImpl // impl } 
Now we have the final object which encapsulates all implementation details and which we can use thus ..

// usage import TradeServiceAssembly._ val t = tradeService.fetchTrade("r-123") tradeService.writeTrade(t) 
If we want to use different implementations (e.g. mocks for testing), we can either create another assembly module like the above and supply different implementation clasess to the abstract vals, or we can also directly mix in traits while we instantiate our assembly object ..

val assembly = new TradeRepositoryComponent with TradeServiceComponent {   val tradeRepo = new TradeRepositoryMock   val tradeService = new TradeServiceMock } import assembly._ val t = tradeService.fetchTrade("r-123") tradeService.writeTrade(t) 
So that was the implementation of DI using *only* the typesystem of Scala. All dependencies are indicated through self-type annotations and realized through concrete implementations specified in abstract vals right down to the object that you create as the final assembly. Shows the power of Scala's object model implemented over its type system.

The FP way in Scala

Now let's try to look at the same idiom through the functional lens of Scala.

We will still have the repository abstractions, but we implement the service contracts directly as functions.

trait TradeRepository {   def fetch(refNo: String): Trade   def update(trade: Trade): Trade   def write(trade: Trade): Unit } // service functions trait TradeService {   val fetchTrade: TradeRepository => String => Trade = {repo => refNo => repo.fetch(refNo)}   val updateTrade: TradeRepository => Trade => Trade = {repo => trade => //..   val writeTrade: TradeRepository => Trade => Unit = {repo => trade => repo.write(trade)} } 
Now let's say we would like to work with a Redis based implementation of our TradeRepository. So somewhere we need to indicate the actual TradeRepository implementation class that the service functions need to use. We can define partial applications of each of the above functions for Redis based repository and put them in a separate module ..

object TradeServiceRedisContext extends TradeService {   val fetchTrade_c = fetchTrade(new TradeRepositoryRedis)   val updateTrade_c = updateTrade(new TradeRepositoryRedis)   val writeTrade_c = writeTrade(new TradeRepositoryRedis) } 
So fetchTrade_c is now a function that has the type (String) => Trade - we have successfully abstracted the TradeRepository implementation class knowledge through currying of the first parameter.

These modules are somewhat like Spring ApplicationContext that can be swapped in and out and replaced with alternate implementations for other kinds of underlying storage. As with the OO implementation, you can plug in a mock implementation for testing.

We can now continue to use the curried versions of the service functions completely oblivious of the fact that a Redis based TradeRepository implementation has been sucked into it ..

import TradeServiceRedisContext._ val t = fetchTrade_c("ref-123") writeTrade_c(t) 
One of the potential advantages that you get with functional abstractions is the power of composability, which is, much better than what you get with objects. FP defines many abstraction models that composes in the mathematical sense of the term. If you can design your domain abstractions in compliance with these structures, then you can also get your models to compose as beautifully.

Instead of currying individual functions as above, we can curry a composed function ..

val withTrade = for {   t <- fetchTrade   u <- updateTrade }  yield(t map u) 
withTrade is now a function of type (TradeRepository) => (String) => Trade. In order to make this work, you will need to have scalaz which defines higher order abstractions to make operations like bind (flatMap) available to a much larger class of abstractions than those provided by the Scala standard library. In our case we are using the function application as a monad. We can now inject the Redis based implementation directly into this composition ..

val withTradeRedis = withTrade(new TradeRepositoryRedis) 

I find both variants quite elegant to implement in Scala. I use the version that goes better with my overall application design. If it's primarily an OO model, I go with the Cake based serving. When I am doing FP and scalaz, I use the functional approach. One advantage that I find with the functional approach is increased composability since free standing functions only need to agree on types to compose. While with objects, you need to cross over another layer of indirection which may not be that easy in some cases.