Mathias Brandewinder on .NET, F#, VSTO and Excel development, and quantitative analysis / machine learning.
by admin 21. September 2014 11:40

If you have ever come across my blog before, it will probably come as no surprise if I tell you that I enjoy coding with F# tremendously. However, there is another reason why I enjoy F#, and that is the Community aspect. One thing we have been trying to do in San Francisco is to build a group that is inclusive, and focused on learning together.

This is why we started the coding dojos a while back: one of our members mentioned that while he was convinced from talks that F# was a good language, presentations were not quite enough to help him get over the hump and feel comfortable coding, so we started sessions completely focused on writing code in groups to solve fun problems. This has been an amazingly fun experience.

During a discussion with my sister last year, we ended up talking about gender inequality, a topic that is also dear to my heart – and, in her great wisdom, she made the following remark: scheduling a meeting at 6:00 PM is possibly the worst time you could pick for a mom. In hindsight, this is totally obvious; it also goes to show that everyone has blind spots.  For that matter, it applies more broadly: choosing to go coding after work instead of going back home is not feasible for everyone. So I thought, why not try meetings in completely different time slots?

At the same time, I came across the Alt.NET Paris group (which is pretty awesome); one thing they do is run Coding Breakfasts, which they expanded into Coding Mojitos, and Coding Candies. I really liked the idea, and adapted it a bit for F# Coding Breakfast.

Here is the format we have been following so far:

  • If you want people to carve out a bit of time in a working day, respecting their time is crucial. So the format is strict: start on time, code in pairs for 45 minutes, show-and-tell for 15 minutes, and then, off you go!
  • In order to be able to code something from scratch in 45 minutes, the problem needs to be reasonably small, and accessible for beginners. We have been working initially on some of the 99 ocaml problems, and lately settled on Project Rosalind, which people seemed to find more interesting.
  • Some of the early feedback I got was that knowing the problems in advance would help, especially for beginners – so every time we pick and announce two problems. If people want to work on other stuff, that’s fine, too :). As an illustration, here is how the current prototypical event invite looks like.
  • One of the nice aspects is that the logistics requirements are virtually zero. Essentially all you need is a couple of tables, and ideally some wifi. In San Francisco, we have been meeting in a bakery. People show up around 8:15 in the morning, grab coffee and pastries, and start coding. No projector, no speaker – just open your laptop and go.
  • While the equipment of the venue is not that important, location matters. If you want to reach people before they go to work, it makes sense to find a place that is close to offices. In San Francisco, we are meeting downtown, close to public transportation.
  • I shamelessly borrowed another idea, this time from the NashFP group. They have a GitHub organization repository, which makes it possible for everyone to share their code, see what others have been doing, and potentially reuse bits of code.

So far, we have had 4 breakfasts in San Francisco, and the response has been very positive. It’s usually a smaller crowd than the evenings, but different people show up, and it has a different energy than evening sessions. Minds are still fresh (well, most minds – I have a hard time booting my brain before 9 AM), there is light outside...



The next step in San Francisco is to try out different time slots. After all, mornings are also not convenient for all, so this week, we will have our first F# Coding Lunch, hosted at Terrace Software (thanks Clayton!). Same general idea, but, you guessed it, 12:00 to 1:00. We’ll see how that goes!

So if you are considering starting or developing an F# community in your area, I encourage you to try that out! It is tremendously easier to setup than an evening presentation (you don’t really need a venue or a speaker), it has potential to be owned or replicated by multiple people (my dream is to see regular F# breakfasts everywhere in the Bay Area), and I suspect it would make a great way to introduce F# in a company as well…

If you have questions or comments, ping me on Twitter – I’d love to hear your thoughts or ideas!

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by admin 13. September 2014 17:18

Let’s face it, @fsibot in its initial release came with a couple flaws undocumented features. One aspect that was particularly annoying was the mild Tourette’s syndrom that affected the bot; on a fairly regular basis, it would pick up the same message, and send the same answer over and over again to the brave soul that tried to engage in a constructive discussion.

I wasn’t too happy about that (nobody likes spam), and, being all about the enterprise and stuff, I thought it was time to inject a couple more buzzwords. In this post, I’ll briefly discuss how I ended up using the Azure Service Bus to address the problem, with a sprinkle of Azure Storage for good measure, and ended up liking it quite a bit.

So what was the problem?

The issue came from a combination of factors. Fundamentally, @fsibot is doing two things: pulling on a regular basis recent mentions from Twitter, and passing them to the F# Compiler Services to produce a message with the result of the evaluation.

Mentions are pulled via the Twitter API, which offers two options: grab the latest 20, or grab all mentions since a given message ID. If you have no persistent storage, this implies that when the service starts, you pull the 20 most recent ones, and once you have retrieved some messages, you can start pulling only from the last one seen.

This is a great strategy, if your service runs like a champ and never goes down (It’s also very easy to implement – a coincidence, surely). Things start to look much less appealing when the service goes down. In that scenario, the service reboots, and starts re-processing the 20 most recent mentions. In a scenario where, say, a couple of enthusiastic F# community members decide to thoroughly test the bots’ utter lack of security, and send messages that cause it to have fits and go down in flames multiple times in a short time span, this is becoming a real problem.

So what can we do to fix this?

A first obvious problem is that a failure in one part of the service should not bring down the entire house. Running unsafe code in the F# Compiler Service should not impact the retrieval of Twitter mentions. In order to decouple the problem, we can separate these into two separate services, and connect them via a queue. This is much better: if the compiler service fails, messages keep being read and pushed to the queue, and when it comes back on line, they can be processed as if nothing happened. At that point, the only reasons that will disrupt the retrieval of mentions is either a problem in that code specifically, or a reboot of the machine itself.

So how did I go about implementing that? The most lazy way possible, of course. In that case, I used the Azure Service Bus queue. I won’t go into all the details of using the Service Bus; this tutorial does a pretty good job at covering the basic scenario, from creating a queue to sending and receiving messages. I really liked how it ended up looking from F#, though. In the first service, which reads recent mentions from Twitter, the code simply looks like this:

let queueMention (status:Status) =
    let msg = new BrokeredMessage ()
    msg.MessageId <- status.StatusID |> string
    msg.Properties.["StatusID"] <- status.StatusID
    msg.Properties.["Text"] <- status.Text
    msg.Properties.["Author"] <- status.User.ScreenNameResponse
    mentionsQueue.Send msg

From the Status (a LinqToTwitter class) I retrieve, I extract the 3 fields I care about, create a BrokeredMessage (the class used to communicate via the Azure Service Bus), add key-value pairs to  Properties and send it to the Queue.

On the processing side, this is the code I got:

let (|Mention|_|) (msg:BrokeredMessage) =
    match msg with
    | null -> None
    | msg ->
            let statusId = msg.Properties.["StatusID"] |> Convert.ToUInt64
            let text = msg.Properties.["Text"] |> string
            let user = msg.Properties.["Author"] |> string
            Some { StatusId = statusId; Body = text; User = user; }
        | _ -> None

let rec pullMentions( ) =
    let mention = mentionsQueue.Receive ()
    match mention with
    | Mention tweet -> 
        |> processMention
        |> composeResponse tweet
        |> respond
        mention.Complete ()
    | _ -> ignore ()

    Thread.Sleep pingInterval
    pullMentions ()

I declare a partial Active Pattern (the (|Mention|_|) “banana clip” bit), which allows me to use pattern matching against a BrokeredMessage, a class which by itself knows nothing about F# and discriminated unions. That piece of code itself is not beautiful (just it’s a try-catch block, trying to extract data from the BrokeredMessage into my own Record type), but the part I really like is the pullMentions () method: I can now directly grab messages from the queue, match against a Mention, and here we go, a nice and clean pipeline all the way through.

So now that the two services are decoupled, one has a fighting chance to survive when the other goes down. However, it is still possible for the Twitter reads to fail, too, and in that case we will still get mentions that get processed multiple times.

One obvious way to resolve this is to actually persist the last ID seen somewhere, so that when the Service starts, it can read that ID and restart from there. This is what I ended up doing, storing that ID in a blob (probably the smallest blob in all of Azure); the code to write and read that ID to a blob is pretty simple, and probably doesn’t warrant much comment:

let updateLastID (ID:uint64) =
    let lastmention = container.GetBlockBlobReference blobName
    ID |> string |> lastmention.UploadText

let readLastID () =
    let lastmention = container.GetBlockBlobReference blobName
    if lastmention.Exists ()
        lastmention.DownloadText () 
        |> System.Convert.ToUInt64
        |> Some
    else None

However, even before doing this, I went an even lazier road. One of the niceties about using the Service Bus is that the queue behavior is configurable in multiple ways. One of the properties available (thanks @petarvucetin for pointing it out!) is Duplicate Detection. As the name cleverly suggests, it allows you to specify a time window during which the Queue will detect and discard duplicate BrokeredMessages, a duplicate being defined as “a message with the same MessageID”.

So I simply set a window of 24 hours for Duplicate Detection, and the BrokeredMessage.MessageID equal to the Tweet Status ID. If the Queue sees a message, and the same message shows up withing 24 hours, no repeat processing. Nice!

Why did I add the blob then, you might ask? Well, the Duplicate Detection only takes care of most problem cases, but not all of them. Imagine that a Mention comes in, then less than 20 mentions arrive for 24 hours, and then the service crashes – in that case, the message WILL get re-processed, because the Duplicate Detection window has expired. I could have increased that to more than a day, but it already smelled like a rather hacky way to solve the problem, so I just added the blob, and called it a day.

So what’s the point here? Nothing earth shattering, really – I just wanted to share my experience using some of the options Azure offers, in the context of solving simple but real problems on @fsibot. What I got out of it is two things. First, Azure Service Bus and Azure Storage were way easier to use than what I expected. Reading the tutorials took me about half an hour, implementing the code took another half an hour, and it just worked. Then (and I will readily acknowledge some F# bias here), my feel is that Azure and F# just play very nicely together. In that particular case, I find that Active Patterns provide a very clean way to parse out BrokeredMessages, and extract out code which can then simply be plugged in the code with a Pattern Match, and, when combined with classic pipelines, ends up creating very readable workflows.

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by admin 24. August 2014 15:35

My recollection of how this all started is somewhat fuzzy at that point. I remember talking to @tomaspetricek about the recent “A pleasant round of golf” with @relentlessdev event in London. The idea of Code Golf is to write code that fits in as few characters as possible – a terrible idea in most cases, but an interesting one if you want to force your brain into unknown territory. Also, a very fun idea, with lots of possibilities. If I recall correctly, the discussion soon drifted to the conclusion that if you do it right (so to speak), your code should fit in a tweet. Tweet, or GTFO, as the kids would say (or so I hear).

Of course, I began obsessing about the idea, that’s what I do. The discussion kept going at LambdaJam, with @rickasaurus, @pblasucci and @bbqfrito (beers, too). So I thought I had to try it out: what if you set up a twitter bot, which would respond to your F# inquiries, and send back an evaluation of whatever F# expression you sent it?

As it turns out, it’s not that difficult to do, thanks to the fsharp Compiler Services, which lets you, among many things, host an FSI session. So without further due, I give you @fsibot. Tweet a valid expression to @fsibot, and it will run it in an F# interactive session, and reply with the result:

Note that you need to send an expression, as opposed to an interaction. As an example, printfn “Hello, world” won’t do anything, but sprintf “Hello, world” (which evaluates to a string) will.

What else is there to say?

A couple of things. First, my initial plan was to run this on an Azure worker role, which seemed to make a lot of sense. Turns out, after spending countless hours trying to figure out why it was working just great on my machine, using the Azure emulator, but exploding left and right the moment I deployed it in production, I just gave up, and changed track, rewriting it as a Windows Service hosted in an Azure virtual machine (it’s still a cloud-based architecture!), using the awesome TopShelf to simplify my life (thank you @phatboyg for saving my weekend, and @ReedCopsey for pointing me in the right direction).

You can find the whole code here on GitHub. As you might notice, the whole TopShelf part is in C# – nothing wrong with it, but I plan on moving this over to F# as soon as I can, using existing work by @henrikfeldt, who discreetly produces a lot of awesome code made in Sweden.

Another lesson learnt, which came by way of @panesofglass, was that if your code doesn’t do anything asynchronous, using async everywhere is probably not such a hot idea. Duh – but I recently got enamored with mailbox processors and async workflows, and started initially building a gigantic pipe factory, until Ryan aptly pointed out that this was rather counter-productive. So I simplified everything. Thanks for the input, Ryan!

That’s it! I am not entirely sure the bot will handle gracefully non-terminating expressions, but in traditional San Francisco fashion, I’ll call this a Minimum Viable Product, and just ship it – we can pivot later. Now have fun with it :) And if you have some comments, questions or suggestions, feel free to ping me on twitter as @brandewinder.

Source code on GitHub

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by Admin 16. June 2014 22:15

Like many a good man, I too got caught into the 2048 trap, which explains in part why I have been rather quiet on this blog lately (there are a couple other reasons, too).

In case you don't know what 2048 is yet, first, consider yourself lucky - and, fair warning, you might want to back away now, while you still have a chance. 2048 is a very simple and fun game, and one of the greatest time sinks since Tetris. You can play it here, and the source code is here on GitHub.

I managed to dodge the bullet for a while, until @PrestonGuillot, a good friend of mine, decided to write a 2048 bot as a fun weekend project to sharpen his F# skills, and dragged me down with him in the process. This has been a ton of fun, and this post is a moderately organized collection of notes from my diary as a recovering 2048 addict.

Let's begin with the end result. The video below shows a F# bot, written by my friend @Blaise_V, masterfully playing the game. I recorded it a couple of weeks ago, accelerating time "for dramatic purposes":


One of the problems Preston and I ran into early was how to handle interactions with the game. A recent post by @shanselman was praising Canopy as a great library for web UI testing, which gave me the idea to try it for that purpose. In spite of my deep incompetence of things web related, I found the Canopy F# DSL super easy to pick up, and got something crude working in a jiffy. With a bit of extra help from the awesome @lefthandedgoat, the creator of Canopy (thanks Chris!), it went from crude to pretty OK, and I was ready to focus on the interesting bits, the game AI.

I had so much fun in the process, I figured others might too, and turned this into another Community for F# Dojo, which you can find here.


by admin 12. April 2014 11:05

A lightweight post this week. One of my favorite F# type providers is the World Bank type provider, which enables ridiculously easy access to a boatload of socio-economic data for every country in the world. However, numbers are cold – wouldn’t it be nice to visualize them using a map? Turns out it’s pretty easy to do, using another of my favorites, the R type provider. The rworldmap R package, as its name suggests, is all about world maps, and is a perfect fit with the World Bank data.

The video below shows you the results in action; I also added the code below, for good measure. The only caveat relates to the integration between the Deedle data frame library and R. I had to manually copy the Deedle.dll and Deedle.RProvider.Plugin.dll into packages\RProvider.1.0.5\lib for the R Provider to properly convert Deedle data frames into R data frames. Enjoy!

Here is the script I used:

#I @"..\packages\"
#r @"R.NET.1.5.5\lib\net40\RDotNet.dll"
#r @"RProvider.1.0.5\lib\RProvider.dll"
#r @"FSharp.Data.2.0.5\lib\net40\FSharp.Data.dll"
#r @"Deedle.0.9.12\lib\net40\Deedle.dll"
#r @"Deedle.RPlugin.0.9.12\lib\net40\Deedle.RProvider.Plugin.dll"

open FSharp.Data
open RProvider
open RProvider.``base``
open Deedle
open Deedle.RPlugin
open RProviderConverters

let wb = WorldBankData.GetDataContext()
wb.Countries.France.Indicators.``Population (Total)``.[2000]

let countries = wb.Countries

let pop2000 = series [ for c in countries -> c.Code => c.Indicators.``Population (Total)``.[2000]]
let pop2010 = series [ for c in countries -> c.Code => c.Indicators.``Population (Total)``.[2010]]
let surface = series [ for c in countries -> c.Code => c.Indicators.``Surface area (sq. km)``.[2010]]

let df = frame [ "Pop2000" => pop2000; "Pop2010" => pop2010; "Surface" => surface ]
df?Codes <- df.RowKeys

open RProvider.rworldmap

let map = R.joinCountryData2Map(df,"ISO3","Codes")

df?Density <- df?Pop2010 / df?Surface
df?Growth <- (df?Pop2010 - df?Pop2000) / df?Pop2000

let map2 = R.joinCountryData2Map(df,"ISO3","Codes")

Have a great week-end, everybody! And big thanks to Tomas for helping me figure out a couple of things about Deedle.

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