Category: General

(These posts are prettified versions of my notes that I made whilst stumbling around HAppS for the first time. I hope they’ll be of use to future HAppS explorers!)

Note 1 .. In which we figure out what ServerPartT and WebT really are.

When we start HAppS running with ..

   simpleHTTP config list_of_handlers :: IO ()

… what type do the handlers have? Let’s explore some possible universes!

In a simple world, a handler could have type

    Request -> Maybe Response

This allows handlers to be selective about which Requests they handle, which is useful. But it also means that handlers must be pure functions so there’d be no way of maintaining any ‘application state’ between requests. Not very useful!

Let’s try to remedy that. If the handlers had type:

   Request -> State S Response

.. then we’d be able to retain and modify some application state (of type S) between handler invocations. But that’s all we’d be able to do. We still couldn’t write logs to disk, read file contents or talk to databases.

To allow handlers to do that, handlers would need to have type ..

  Request -> IO Response

(As an aside, note that whilst simpleHTTP has type “IO ()” it’s up to simpleHTTP whether or not it exposes the full power of the IO monad to the handlers. However, as we’ve just seen, not doing so would pretty much cripple the handlers)

So is this really what HAppS gives you? Let’s see. In the real world HAppS handlers have type “ServerPartT IO a” which is defined to be …

newtype ServerPartT m a = ServerPartT { unServerPartT :: Request -> WebT m a }

In other words, if you have a value of type “Request -> WebT m a”, you can tag it with the ServerPartT constructor to say “this ain’t just any function of that type, it’s part of a web server, dogammit!”.

But, apart from the tag, our handlers really just have type “Request -> WebT m a”. So what’s a WebT?

newtype WebT m a = WebT { unWebT :: m (Result a) }

So, ignoring the tag, it looks kinda like our handlers can produce any monadic computation that, when run, yields a “Result a” (the “Result a” type lets us say “I handled this request, here’s my answer” or “I didn’t handle this request”)

Hmm, there’s something not right there. If that was the case, it would be possible to use, for example, both “Result -> IO (Result a)” and “Result -> State S (Result a)” as handlers. But how would simpleHTTP ‘run’ such monadic computations? To run a State computation you use “evalState”. To run an IO computation, you have to return it from main. There’s no single way to run all possible monads.

Did I miss something? Yes, sort of – I just blindly assumed ‘m’ was a monad because of the way it was used. But there’s no “Monad m =>” constraints anywhere. And, in fact, if we go back to the start ..

simpleHTTP :: ToMessage a => Conf -> [ServerPartT IO a] -> IO ()

.. we can see that, right from the top, simpleHTTP only deals with ServerPartT’s and WebT’s where the ‘m’ type parameter is IO.

So, after all that, the handlers in HAppS handlers can be anything of type: Request -> IO (Response a). The ‘a’ type needs to be something that HApps can figure out a content-type and be able to make an HTTP message from (the ToMessage type class). The “IO (Response a)” part gets dressed up as a “WebT” (think: computation that produces the response). And the whole thing things gets dressed up as a “ServerPartT” (think: request handler).

What does each bit do? The IO part gives you free reign to do side-effecting computations, which get sequentially executed by the runtime. The Response part allows you to say ‘I handled this’ or ‘didn’t handle it’ and the ‘a’ part can be your XML or HTML data type (so long as its an instance of ToMessage).

So that’s it.

Man, that was a lot of complexity for such a simple end result. I don’t understand why there’s so much generality in the types of ServerT and WebT – I’d love to know!

I’m always on the lookout for more fun ways of programming – I hate doing dull boilerplate work. So I’ve been trying out HAppS, a webserver framework for Haskell. It’s interestingly different from most web frameworks. I want to capture my early observations before I forget them. To give some context, my “toy app” I’m building is an “IMDB for Computer Scientists” type website which gives structured information about people (eg. Guy Steele), what papers they’ve published (eg. Growing a Language), what events they’ve participated in (eg. a talk about “Growing a Language” at OOPSLA, available via Google video).

So the biggest thing about HAppS it that it doesn’t have to use a database to persist state. It takes the Prevayler approach, which means all your data lives in RAM as normal in-memory data structures. When the data is about to be modified, a delta is first persisted to a fast log on disk (giving durability) and then the in-memory version is updated. Periodically, checkpoints are taken.

This is a huge win because it means you avoid the whole tedious ‘impedance mismatch’ involved with persisting to a database – O/R mappings, mismatches between DB types and language types. It means that you can create new datatypes easily, and get on with the real business of adding functionality rather than futzing with databases.

Of course, there are downsides to this approach which I will discuss next. But first, the big upside: I can develop features quicker. Life being what it is, I’m almost certainly making lots of mistakes whilst building my app. Without a DB layer to burn time on, I can find out that I’m (inevitably) building the Wrong Thing sooner and make a course correction.

Downside #1: you normally rely on a database to get fast indexed lookups. With HAppS, you do this by constructing an in-memory hash table to act as an index. Actually, that’d be quite boring to do by hand so HApps uses metaprogramming (template haskell) to build the index data structures for you. They are moderately smart, with lazy update strategies.

Downside #2: scalability and availability. A big benefit of the usual “stateless web frontend, stateful database” split is that you can trivially scale the web fleet (no state) and fairly easily scale the database (replication/partitioning). With the HAppS approach it sounds like you can only have a single webserver/state-store machine – which means no horizontal scalability and bad availability. Actually, that’s not the whole story – the HAppS team are working on a system whereby the state gets shared across multiple machines (permitting scalable reads) and sharded (each machine is the master for some subset of the state).

That should be a killer downside, right? The argument is looks like this: “all successful webapps are popular, and popular apps must scale”. However, I think that to become a successful webapp, you have to firstly be a webapp. My “projects” directory is littered with half-finished projects where I got bored before I finished it. Quite frankly, if using HAppS means that I can avoid a lot of tedious work and actually finish a project then I’ll happily embrace the ghost of future scaling pain. Also, not every successful website gets as much traffic as Flickr/Twitter – my toy app is an “IMDB for Computer Scientists” and there just aren’t that many computer scientists in the world. Not every website needs to be super-scalable from day one. I’m not being naive here – my dayjob is scalability central – but it’s a question of finding the most appropriate hammer for the particular nail you’re trying to hit.

Having said all that, let me qualify it a bit: I would ideally like to create a perfectly scalable webapp from day one if there was no overhead in doing so. Most of the industry is focused on scalability-via-database, and attempt to minimize tedious work in various ways (eg. metaprogramming and reflection in ActiveRecord). However, these invariably ends up being painfully ‘leaky’ abstractions. I’ve seen this many times, and it’s given me the motivation to look for other approaches which can avoid the persistence pain. So perhaps HAppS is the answer, especially if my question is “can I write a reasonable webapp quickly” rather than “can I write a superscalable website slowly?”.

Disadvantage #3: Data migrations. This is really a red herring. I don’t really believe that data migrations are an advantage of having your data in a database. If you are using an O/R mapping, you surely want to migrate old objects to new object, not old relations to new relations. The approach which HAppS takes is to version your datatypes (mostly done via metaprogramming) and you write haskell code to migrate from old versions.

Disadvantage #4: Language dependence. If you use a database, you can access your data from any language. You can produce reports, do adhoc SQL queries and such like. These are indeed useful; I do this a lot. However, language-independence comes at a cost (impedance mismatch between database/language data types, business rules usually not applied at the database layer). Often, language independent access can be better provided by service layers which wrap the database layer, providing insulation from the schema changes and allowing fine grained access control and throttling. I’ve never been able to query Flickr’s database directly, but I’ve accessed their data via their API many times. HAppS provides more metaprogramming support for exposing data types via XML.

So that’s why I’m investing the time into learning about HAppS. It’s good to challenge your assumptions about how to do things. In the next post, I’ll write a bit about how HAppS works under the hood.