Alex C. Viana's blog

Posted Sun 19 January 2014

Faster File Existence Testing with Sets

It's Time to Think about Performance

Lately at work I've been thinking a lot about the performance of my code. In the past most of my work fell into one of two performance categories: (roughly) overnight or (roughly) right now. In either case I didn't really care about performance. Either the task was going to take so long I had time to go do something else, in which case I didn't care if it took 1 hour or 10. Or it was going to be done fast enough I could immediately start iterating on the results, again in which case I didn't really care if was going to take 1 second or 10. I think this is indicative of the scientific computing mindset where you are both the programmer and the user: fast means fast enough for you.

But recently my datasets have been getting bigger (which is awesome) which has forced me to be more careful about my programming. I'm routinely finding my scripts out-growing both of my performance "categories" and either taking several minutes to run or several days. Both scenarios leave me waiting around, which is the real problem. While I always try, to the best of my abilities, to write high-quality code my time is more scarce and expensive than CPU time. This means that I optimize my time, not the CPU's. However, when I do find myself waiting around for some code to run, it's time to roll up my sleeves and find some speedups.

The work I do is very I/O intensive involving lots of databases and data files. I/O is extremely expensive in terms of latency so reducing trips to the disk can yield sizable speedups. Here's an example I found today that includes an introduction to a handy (and I would argue underutilized) Python type called sets.

The Slow Way

I was working on a project where I wanted to verify that all the files I had listed in a database actually existed in my file system 1. To do this I wrote a SQL query in SQLAlchemy to grab all the file names listed in the database. Then I looped over the the records returned by the query and used os.path.exists to test the existence of each file in the file system.

for record in database_query:
    if os.path.exists(record.fits_file) == False:
        print 'Missing {}'.format(record.fits_file)

There were 3,096 iterations (records) in this loop and the IPython %%timeit cell magic gave the following result:

1 loops, best of 3: 103 s per loop

This is a bit too long of a wait for me. It's long enough for me to get distracted by Facebook or maybe writing a blog post. I kid but task switching does have a real mental overhead. I'm not advocating optimizing every task that makes you sit around for a few minutes, but in this case the solution was trivial and applicable to lots of my projects.

The Fast Way

It occurred to me that I was making 3,096 separate trips to the disk. It's my understanding that there is some overhead for each disk read so I thought maybe it would be faster to read everything I needed at once and then work with the result in memory. To do this I used glob and create a list of all the files in my file system I wanted to check my query against. This gave me all the data I wanted in memory from one SQL query and one glob command. That reduced the problem to a membership testing problem and Python has a great built-in type for this, sets. Sets are unordered hash tables which means their average performance for a lookup operation is the holy grail of speed, O(1). Incorporating all this into my code looks like this:

file_set = set(glob.glob(file_search_string))
for record in database_query:
    if record.fits_file in file_set == False:
        print 'Missing {}'.format(record.fits_file)

It turns out I was right, this is almost a full order of magnitude faster than my original code.

1 loops, best of 3: 10.6 s per loop


So I think the principles behind this speed up are solid but, as always, your mileage my vary and there are some caveats I can think of.

First of all, the file system I am searching is a network file system that I'm connecting to over VPN, this makes each disk read exceptionally expensive. Secondly, the glob operation is very expensive, almost all the run time is spent in that step. So if you're only checking a few files it might be faster to just look them up one-by-one than to use wildcards to scan a file tree. I'm not sure where the tipping point is, but it's certainly worthwhile if you're checking every file like I am.

I've just starting thinking about these topics so if I missed something in my code or my explanation I would love to hear about it the comments.

  1. If you're wondering why I want to do this, yes, it's because I screwed up and put the wrong files in the database. 

Category: Work
Tags: python code