Alright, so I wanted to chat a bit about something I was working on recently. I kind of informally named this little endeavor ‘speed and amy’. It’s not some big, official project, just me tinkering in my own time, you know how it is.
The Starting Point – What Was ‘Amy’?
So, ‘Amy’ was this little script I put together. Nothing super complicated, really. I needed something to go through a whole bunch of text files I had. Lots of them. The idea was for Amy to scan these files, pick out specific phrases, and then sort of categorize the files based on what it found. Seemed straightforward enough when I first sketched it out.
I got the basic logic working pretty quickly. Amy was reading files, finding some stuff. But man, oh man, was it slow. I mean, painfully slow. If I pointed it at a directory with even a hundred files, I’d go make a cup of tea, drink it, and it would still be chugging away. That was the ‘speed’ problem, or rather, the lack of it.
First Attempts at Getting More Speed
My first thought was, okay, where’s the bottleneck? I just dove in.
- I looked at how I was reading the files. Was I doing it line by line when I could read the whole thing in one go? Made a few tweaks there. That helped a tiny bit.
- Then, the searching part. My initial way of looking for phrases was pretty brute-force. Just looping and comparing. I figured I could be smarter about that. Maybe use some more efficient string searching methods. Spent an evening looking that up and changing the code.
After those first passes, Amy was a bit faster, sure. Maybe shaved off 20% of the time. But it was still not what I’d call speedy. It felt like I was just polishing the edges of something fundamentally sluggish.
Digging Deeper – The Real Work
This is where I really started to pull things apart. I realized the issue wasn’t just one single thing, but a bunch of small inefficiencies adding up.
I started logging times for each part of Amy’s process. How long to open a file? How long to read it? How long for the actual phrase searching per file? This was eye-opening.
It turned out that a lot of time was wasted in repeatedly processing some common patterns. My categorization logic was also a bit convoluted. I was re-evaluating things I already knew. So, I introduced a bit of caching for results I’d already figured out. If Amy saw a pattern it recognized from a previous file or a previous part of the same file, it wouldn’t have to do all the work again.
Another thing I did was to change how I handled the data once it was read. Instead of passing huge chunks of text around, I tried to be more selective, only working with the bits I absolutely needed for each step. This reduced the amount of data being shuffled in memory.
This part was frustrating, I won’t lie. There were moments I just wanted to scrap the whole thing and do it manually. But I’d already named her Amy, felt like I had to see it through, you know? It became a bit of a personal challenge.
The Breakthrough and Final Result
The biggest jump in speed came when I refactored the main loop that went through the phrases. I changed the order of operations and used a slightly more advanced data structure to hold the phrases I was looking for. This made the matching process much, much quicker.
So, after several evenings of tweaking, testing, getting annoyed, and then trying again, Amy was finally flying! Well, relatively speaking. What used to take ages, like processing a few hundred files, now just took a couple of minutes. For me, that was a huge win. The ‘speed’ was achieved, and ‘Amy’ was now a useful little tool instead of a slow pain.
It wasn’t about finding some magic bullet. It was just persistent, step-by-step refinement. Looking at the problem, trying something, seeing if it worked, and then doing it again. And that’s my little story of ‘speed and amy’. Just a practical example of how sometimes you gotta roll up your sleeves and really get into the nuts and bolts to make things work the way you want them to.
