![]() ![]() ![]() Feel free to give it a shot and add it in the comments below. I suppose it would be more idiomatic to return a Maybe string, but that complicates the string concatenation and requires us to discuss more advanced topics like monads. Imagine there is a function fizzBuzzCombo that will return the appropriate Fizz, Buzz or FizzBuzz, or an empty string. The next two lines are the function itself, on the highest abstraction level. The type inferencing in Haskell will mostly figure it out on its own, and your code will be just as strongly typed, but it is good practice, because it ensures that you and the compiler have the same idea about the involved types (a little bit like a unit test, or perhaps more like a code contract). ![]() You don’t really need to add definitions like this. The first line is a function definition that tells us that fizzBuzz takes an Integer and returns a String. This is the function we would like to run:įizzBuzz :: Integer -> String fizzBuzz n | fizzBuzzCombo n = "" = show n | otherwise = show n ++ " becomes " ++ fizzBuzzCombo n where fizzBuzzCombo n = fizz n ++ buzz n fizz = anyzz "Fizz" 3 buzz = anyzz "Buzz" 5 anyzz word factor n | n ` mod ` factor = 0 = word | otherwise = "" A more realistic use case would be a service that pre-calculates some statistics that need to be immediately available on request, based on changes (events) from other services.ĭon’t freak out if you don’t read Haskell, I will talk you through it, and it’s really quite simple and readable. By the way, in the real world you would probably not want to enqueue stored messages, spin up a hosted environment, run a script, load an executable and enqueue another message just to calculate one Fizz Buzz number like I do here. That said, there are certainly more complex domains where you would value Haskell’s stronger type system and distinction between pure and impure functions. I’m sure you could solve the Fizz Buzz Test using F#, maybe even C#. Why Haskell? No particular reason, it just serves to demonstrate that you can run any executable compiled from any language as an Azure Function. You’re supposed to replace numbers that are divisible by 3 with Fizz, numbers divisible by 5 with Buzz, and numbers divisible by 3*5 with FizzBuzz. If you’ve been to any programming job interview, you know what I’m talking about. The business example I’ve chosen is a real bread-and-butter operation from the recruitment domain, the much loved Fizz Buzz Test. It will get triggered by incoming messages in an Azure Storage Queue, do some processing, and return an output message in another queue. We will take advantage of that and run a Haskell service. Further, the managed environment offers a number of triggers, such as messages on a topic, a queue or a blob, and gives you easy access to inputs and outputs on those topics, queues and blobs, plus HTTP, databases and even email and SMS.Īzure Functions provide first-class support to languages such as C#, F#, Javascript, Python and PHP, but it also supports scripts, which in turn enables us to run any executable. Another is Functions, which is actually built on WebJobs, but can run on a managed hosting environment where instantiation and scaling is taken care of, and you don’t need to manage your own App Service (although you can if you wish). So how do you run those background operations on Azure? Well, you can always spin up virtual machines or Docker containers, but there are other options. Rather than duplicating the exact same data structures, they will pull the data they need, possibly aided by an Anti Corruption Layer. A billing, a shipping and an accounting service probably all have their own idea of what a customer order is. Think of your services as separate DDD Bounded Contexts. Yes, this means duplication of data, but not necessarily blind duplication. Interestingly, he talks about a rule they introduced, where a service can delegate to one other level of services, but no more.īetter than calling other services just-in-time, is to outfit your services with background, asynchronous operations that make sure that all the data a service needs is available to it in its own data store. As Jimmy Bogard talks about here, if one external visit to a web page results in a hierarchy of synchronous calls indirectly invoking almost all of your services, you’re in trouble. ![]() They will need to make use of data coming from other sources. That is a useful technique, but in some way, it also represents the opposite of autonomous services. Earlier this month I wrote about service to service delegation using JWT tokens. Isn’t it ironic how, when we think of microservices, we also tend to think about REST services? A microservices architecture is about autonomous services, in other words, services that are able to do their job without relying on help from other services. ![]()
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