* Avoiding Ruby language features
* Rewriting it in a different language
This is why I lean towards static languages like Go, Scala, and Java.
1. Build a site in ruby (insert langauge/framework the creators are most comfortable/familiar with and can iterate quickly). 2. Build a community of a certain size that causes it scaling problems. 3. Identify the hotspots and re-implement them in more performant way.
This seems entirely reasonable to me. In fact, I'm guessing this is the path that all applications that get to Github scale take. Of course, it's easy to be snarky and identify Ruby as the problem. (Building the community is the hard part!)
A pet peeve of mine is how people can do all the CS necessary to get nice data structures for their in-RAM data, but seem to forget everything they know and use very bad structures when they spill to flash or disk storage.
Interestingly enough you can consider hash tables and Tries to be O(log n) since in order to have N distinct keys, the maximum key length must be at least log(N).
This post is about Github moving some code from ruby to C for performance, MRI's GC, and judy trie-like data structure and how it compares wrt to both performance and memory consuption with hash implementations.
It's a reference to the Beatles' song, "Hey Jude" (which opens with "Hey, Jude, don't make it bad | Take a sad song and make it better ...").[1]
[1] http://en.wikipedia.org/wiki/Hey_Jude
Edited for clarity.
This just feels like "we have this huge and slow Ruby behemoth here, but using magic Judy arrays we made this little gear turn insanely fast!".
A web app involved in a site as complex as Github's should really contain only the parts required to service web pages. A service like language classification is clearly better designed as a standalone service.
Aside from working around GC overhead, compositing big app from many smaller apps have many advantages. You force yourself to encapsulate complexity with strictly focused, network-friendly APIs (we use REST ourselves), which changes the way you have to think about programs. Hiding the implementation behind an API also allows you to swap out the implementation without the client ever knowing. Since the client and server are separated, you have fine-grained control over how much resources you are willing to spend on which components. And so on.
Having done monolithic Rails apps for many years, it's a model I am not going to back. Today, our web apps contain only UI logic. No database, no models, no data logic, just UI all the way. Everything else is handled by specialized components.
1) Is there an easy tutorial somewhere on calling out to native code from Ruby?
2) Could the team at Github possible give a little more detail on what you did to get all of those pretty benchmarking graphs? How did you get all of the info on memory usage and CPU activity (I assume it wasn't just time {command} > file.txt)
The canonical documentation is here: https://github.com/ruby/ruby/blob/trunk/README.EXT
The graphs never show a 30K token mark, which is what the heap-allocation counter showed for their [programming language] classifier.
It's not clear to me that much RSS was saved. Maybe 20Mb? So, the next question is how many classifiers are running, where such a "hit" actually matters. Again, there is no 30K mark on the runtime graphs, but let's assume the generation to the graphs' left are linear. It looks like we're saving a half-second and removing most of the jitter, but it's not made clear how the removal of GC chunking has any effect on processing outside of the classifier. I just can't imagine how a couple of these classifiers can't keep up with the push rate of files to Github -- the classification improvements are neither low-hanging or part of the BigO in Github's stack. The process seems very able to run asynchronous at push time and un-deferred, if unrun, on page view. Unless they're seeing more than 1 million pageviews/sec (per classifier; just run more? one 20Mb service per httpd server?), because I can't really imagine hitting more than 10x their tokens (300K tokens), which is still only ~50Mb; the RSS graph, again, has a weird scale to suggest they might grow their tokens by 67x.
The software from HP is under the LGPLv2.1 which says "Finally, software patents pose a constant threat to the existence of any free program. We wish to make sure that a company cannot effectively restrict the users of a free program by obtaining a restrictive license from a patent holder. Therefore, we insist that any patent license obtained for a version of the library must be consistent with the full freedom of use specified in this license."
That can imply that there are no additional patent protections should you use the LGPL'ed version of Judy or its derivatives. YMMV. Consult your nearest patent attorney for details.
The patent expires in 7.5 years.
Replace the token matcher with a simple classifier (a maxent will work very well here) with n-gram of characters features through a hash-kernel and you get a very accurate, fast and low memory system.
I've build one two-years ago who accurately classified a bit over 100 different languages with only 64k features in the end. (So requiring only 8*64ko of memory) And this was without using file extensions as they weren't available in our case.
Before any hard optimizations, first check the methods used, and next the algorithm, anything else should go after.
the issue is with the patents - has anyone been contacted yet about the usage?
2. unconvinced that they can't do better w/ the right hash table (which will be thousands of lines fewer of code). but since they're using a library, i'll give them a pass. not even a benchmark vs. a quick hash table version makes all the claims about "judy arrays are better for our purpose" suspect.
I wouldn't have used the prefix approach, instead storing a token once in the judy array, and using the data stored to indicate which languages match the token.
