Scrobble Fetcher & Mapper

Update May 15th 2016 : New v3.0 build on the GitHub project page with many fixes and additions.

Update August 26th 2010 : New v2.1 build on google code that fixes the “Invalid XML Characters” issue! Try that one if you had errors when fetching scrobbles.

Updated June 26th 2010 : This project is now hosted on Google Code! I’m new to this so bear with me, but I’ll try to make it nice so that people can contribute. (Google Code no longer exists and the project is now on GitHub)

Updated March 5th 2010 : You shouldn’t need to install both iTunes and WMP for it work, just the one you want to use! Finally.

Original March 15th 2009 post follows.


Get the freshest releases (source or binaries) on the GitHub project page.


The Scrobble Fetcher & Mapper does exactly that. It fetches all your scrobbles from your (or anyone’s, really) account, assembles them in terms of Play Count and Date Last Played for each music track, and then exports this data to either Windows Media Player 11 or iTunes.

I’ve been working on this application on and off for some time now and I think it’s ready for deployment. I built it because I have quite an extensive music library, I like my playlists smart and automatic, and I have poor luck with music players, hardware failure and vendor lock-in.

It seems that the “Play Count” and “Date Last Played” metadata fields are not part of a file’s ID3 tags, but rather is stored in the player’s local library. This is usually fine, and probably more efficient than writing to files all the time, but it means that if your player’s database gets corrupted (as happened to me with Windows Media Player) or that you decide/are forced to use another player such as iTunes because your iPhone doesn’t want to sync with anything else, then you’re screwed. And same thing of course if you reinstall your machine or get a new one.

I feel that this metadata is important because I like to have automatic “best-of” playlists that are based upon it, and sometimes it’s nice to listen to a comfortable, time-proven playlist.

Thankfully, if like me you’ve been using the services since they were called Audioscrobbler, you’ve gathered an impressive amount of playback information in your account over the years. And using their web services, and my application, now you can take it back home!


I wanted this application to be my cleanest, most error-tolerant and most efficient piece of work yet in application design. I also tried to exploit all C# 3.5 features, having accumulated some months of experience with LINQ, lambda expressions, etc.

Here’s a rundown of its tech features :

  • Multi-threaded

    • Uses the Parallel Extensions for .NET June 2008 CTP and a little home-made framework for progress-reporting asynchronous foreground tasks.
    • Most operations will use multiple cores thanks to the Task Parallel Library’s capabilities, and will seldom lock because of its lockless concurrent data structures.
  • WCF-enabled

    • Uses the Windows Communication Foundation classes in .NET 3.5 to communicate with’s RESTful API.
    • All the response data objects are deserialized automatically from XML using the built-in XmlSerializer.
  • Error tolerant

    • Since track data will not always match your library’s perfectly, I used the Levenshtein distance string metric and “neutralized” strings (removes all accentuated characters, symbols, whitespace, etc.) to get accurate matches.
    • Will (should!) recover gracefully from errors, both caused by user input or unexpected conditions. It also allows graceful cancelation of long-running operations.


scrobblemapper9 scrobblemapper8scrobblemapper5 scrobblemapper3

Closing notes

– This code uses a Community Technical Preview version of the Parallel Extensions for .NET, and one that is almost a year old… So it’s delivered as-is, and you cannot (and should not) use that library in commercial products. Although I have functionally tested the program pretty extensively, I cannot guarantee it’s not going to corrupt your library or overwrite some information. Use at your own risk!

– I noticed that iTunes checks if the file is writable before setting any metadata concerning it. So you should make your files writables unless you’ll get a ton of errors in the error log after the scrobble-mapping.

– You need to keep the iTunes instance running for the iTunes mapping to work. The COM interface requires it to be open, but you can minimize it to the tray.

– The code is released under the Creative Commons Attribution-Share Alike 2.5 license.

If you encounter anything unusual, if you have ideas for extensions to this program or have comments/questions about how it works, do ask! That said I will probably blog some more about parts of this program, like the WCF usage or the asynchronous task classes. Enjoy!

Loop Parallelism Revisited

Here’s a follow-up on a previous post I had made on “Loop Parallelism”.

A faster PersistentThread

In my last post, I wrote a class that keeps a single thread alive in order to re-use it without having to create threads over and over, an operation that sounded like a lot of overhead when you have over 30 updates per second (i.e. in a game loop). However, the benchmarks showed that it was actually slower to use synchronization primitives than to just recreate the threads every time!

It turns out that using a Monitor for the wait/signal operations wasn’t so good. There are lighter primitives in .NET that can be used for that simple operation, where all you need is a boolean semaphore. After researching a little bit, the best object for the job is the ManualResetEvent. This allows you to put a thread in sleep mode with the WaitOne() method, and wake it from another thread using the Set() method.

I ended up using two ManualResetEvents : one for a thread waiting to be started with a new work unit, another to simulate a thread “Join” operation (without actually killing the thread). And the cost of using them is MUCH smaller than Monitors! Here are the new performance numbers :

Test ‘Multi-Threaded, PersistentThread (single kept-alive thread w/ generic context & delegate)’ Started… Completed.
Time Elapsed : 00:00:11.3376303 s

Test ‘Multi-Threaded, ParameterizedThreadStart’ Started… Completed.
Time Elapsed : 00:00:11.4678206 s

Test ‘Single-Threaded’ Started… Completed.
Time Elapsed : 00:00:22.3374592 s

Seeing as the iterative version takes ­~22.33 seconds to execute, the theoretical minimum time it could take on my Core 2 Duo is ~11.17s. And the new PersistentThread takes only ~0.16s less than that!
Also notice that the former speed champion, the base-line ParameterizedThreadStart method, is now slightly slower than the new PersistentThread. Honestly, I don’t think one can do any better with just two threads.

Task Parallel Library

The fancy lads at Microsoft have been working hard lately on bringing new ways of working with concurrent code. It was one of the big subjects on Channel9 this autumn.

So they released a CTP back in June of the Parallel Extensions for .NET, which features the Task Parallel Library and a front-end for using it with the Parallel static class and Parallel LINQ. I love it, and I can’t wait to use the official, final release in .NET 4.0. But for now, the CTP is an excellent reference point to see what kind of performance and ease of integration the TPL brings to your project.

The simplicity of my test codepath for the TPL boggles the mind, compared to all the other methods I’ve tested.

for (int i = 0; i < OuterLoops; i++)
    Parallel.ForEach(testData, sample => { sample.Number = SlowFunctions.Bessel(sample.Number / rnd.Next()); });

The number of threads used, how the data is partitioned, and the actual thread/context handling is all abstracted to its simplest form. It really makes C# 3.0 lambda expressions shine.

So, it’s cute alright. But how does it perform? Actually, the TPL will allocate as many threads as it thinks would be beneficial, like an automatic thread pool, based on how many processors you have and the workload you give it. I’m not sure of the details, but that’s how I understood it. So I suspect that it uses more than two threads if doing so will give more processor time to your code; this is relevant when there are many other threads waiting for the processor, and more of these threads being your code = more chance of it being executed.

So here are the numbers :

Test ‘Task Parallel Library’ Started… Completed.
Time Elapsed : 00:00:11.4088743 s

It’s definitely fast, but not as fast as my new PersistentThread! But it’s so much more flexible, easy to use and scalable that it’s the obvious choice for concurrency in .NET,… when it’ll be officially released. :)

A final note on the Parallel Extensions, there’s a new class called ManualResetEventSlim in this CTP which suggests that it’s more optimized or leaner. I didn’t use it because it depends on libraries that cannot be shipped, but quick test showed that the performance was more or less the same as ManualResetEvent.

Vista > XP

Since the last post, I upgraded (though some might say downgraded…) to Windows Vista. The experience isn’t totally smooth since my laptop is being dumb, but one of the upsides of upgrading is having access to new kernel code that appears to be more efficient.

My last benchmarks had quite a margin between the different tests (generic context & delegate, class-local context, etc.). In Vista, it’s pretty much the same :

Test ‘Multi-Threaded, ParameterizedThreadStart’ Started… Completed.
Time Elapsed : 00:00:11.5178019 s

Test ‘Multi-Threaded, Class-Local Context’ Started… Completed.
Time Elapsed : 00:00:11.4912111 s

Test ‘Multi-Threaded, Generic Context & Delegate’ Started… Completed.
Time Elapsed : 00:00:11.4255807 s

Those are really insignificant differences. I suspect that either calling delegates, creating objects, or just casting objects has become faster in Vista. It’s really hard to tell, but the good news is that you can really choose whatever approach you like best : they’re all equally as fast.

Two threads per core?

A final test I wanted to share is to double the workload on an Idle processor and see what happens. So instead of creating a single PersistentThread, I created 3, which means 4 active threads including the current one. The data is split in four equal parts, and…

Test ‘Multi-Threaded, 3x PersistentThread’ Started… Completed.
Time Elapsed : 00:00:12.3893090
Time Elapsed : 00:00:11.3681673
Time Elapsed : 00:00:15.0850932
Time Elapsed : 00:00:10.2940759
Time Elapsed : 00:00:16.0883727

… and I don’t know what to say. The results are ridiculously unstable, and the code is just confusing and ugly. I wouldn’t recommend it.

Also, I did consider using the .NET 2.0 ThreadPool static class, but it seemed a little confusing to use, especially if I want to join the threads after everyone’s done his work. I think I can wait for the TPL for a proper solution. ;)

Updated test project

Here’s the updated project, now you’ll need the Parallel Extensions CTP for it to compile! (150kb), Visual Studio 2008 Solution for C# 3.5.