I am working on an audio software that uses The EchoNest web service to identify and retrieve metadata about audio songs and I would like to have some advice on implementing a background processing chain.
(to get the full picture, I’ll show you what I did already)
I’ve implemented the following chain that is mandatory before querying the service about a song’s details:
Not much to say except that before being able to query song data, we need its fingerprint and a song ID returned by the Identify sub-process. Of all these steps, they all happen locally but the last one (Identify).
Now about the part of the system I’m seeking for advices:
Above are the different types of queries that can be requested for a song, they are not inter-dependent as we already have the only thing needed, the song ID (diagram 1). Note that I’ve only put a few but there are nearly 30 of them (see here).
Requirements/architecture:
- user can drop files at any time, they are queued for identification
- queries can run in parallel with step 1 and optionally concurrently
Ideally, the system should be:
- easily extensible when for instance, I implement a new type of query.
- easy to use, I just enqueue a query to the chain and wait for a response
Environment:
I am using C# with Dataflow (Task Parallel Library), currently I’ve had some good success implementing diagram 1 using this approach.
My question:
Is there a particular/well-known pattern to tackle this problem ?
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Use a finite state machine for the “chain,” where each step in the chain is a sub-state. For example, the states may be:
- Start
- Initial processing
- Adding to collection
- Fingerprinting
- Querying
- Ready
Finite state machines are good for coordinating asynchronous operations. I would also add tracing or logging to indicate when a state transition occurs.
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