Fighting the profiler memory obesity

time to read 3 min | 597 words

When I started looking into persisting profiler objects to disk, I had several factors that I had to take into account:

  • Speed in serializing / deserializing.
  • Ability to intervene in the serialization process at a deep level.
  • Size (also effect speed).

The first two are pretty obvious, but the third requires some explanation. The issue is, quite simply, that I can apply some strategies to significantly reduce both speed & size of serialization by making sure that the serialization pipeline knows exactly what is going on (string tables & flyweight objects).

I started looking into the standard .NET serialization pipeline, but that was quickly ruled out. There are several reasons for that, first, you literally cannot hook deep enough into the serialization pipeline to do the sort of things that I wanted to do (you cannot override how System.String get persisted), and it is far too slow for my usages.

My test data started as a ~900Mb of messages, which I loaded into the profiler (resulting in a 4 GB footprint during processing and a 1.5GB footprint when processing is done). Persisting the in memory objects using BinaryFormatter resulted in a file whose size is 454Mb and whose deserialization I started before I started writing this post and at this point in time has not completed yet. Currently the application (simple cmd line test app that only does deserialization, takes 1.4 GB).

So that was utterly out. So I set out to write my own serialization format. Since I wanted it to be fast, I couldn’t use reflection, (BF app currently takes 1.6 GB) but by the same token, writing serialization by hand is labor intensive, error prone method. That lives aside the question of handling changes in the objects down the road, that is not something that I would like to do.

Having come to that conclusion, I decided to make use of CodeDOM to generate a serialization assembly on the fly. That would give me the benefits of no reflection, handle addition of new members to the serialized objects and would allow me to incrementally improve how (BF app now takes 2.2 GB, and I am getting ready to kill it). My first attempt in doing so, applying absolutely not optimization techniques, result in a 381 Mb file and an 8 seconds parsing time.

That is pretty good, but I wanted to do a bit more.

Now, note that this is an implementation specific for a single use. After applying a simple string table optimization, the results of the serialization are two files, the string table is 10Mb in length and the actual saved data is 215Mb and de-serialization takes ~10 seconds. Taking a look at what actually happened, it looked like the cost of maintaining string table is quite high. Since I care more about responsiveness than file size, and since the code for maintaining the string table is complex, I dropped that in favor of in memory only MRU string interning.

Initial testing shows that this should be quite efficient in reducing memory usage. In fact, in my test scenario, memory consumption during processing dropped down 4 GB to just 1.8 – 1.9 GB and 1.2 GB when processing is completed. And just using the application shows that the user level performance is pretty good, even if I say so myself.

There are additional options that I intend to take, but I’ll talk about them in a later post.