When load testing server-side software there are two straight-forward options:
Why? How well caching works depends on your hit rate, which depends on what the distribution of your requests looks like. Situation (1) is a caching worst-case, (2) is a caching best case, and your real situation is somewhere in the middle. For example, from my access logs over the past couple years, here's the distribution of requests I've seen:
|[snip ~1k entries]|
|[snip ~10k entries]|
|[snip ~100k entries]|
|[snip ~1M entries]|
This is kind of a mess, but the main idea is that we have a few "hot" endpoints, and then a long tail of less popular entries. I can use this distribution in load testing, to get something between uniform sampling (option 1) and singleton sampling (option 2) that better represents what real load on the site would look like.
I spend most of my day standing in front of my work desktop, doing some combination of programming, code review, documentation, and email. Over the years I've figured out a relatively productive setup:
This looks like: more...
When I got a phone and started caring about how things looked on mobile I only fixed some of the pages on my site: home page, blog post template, etc. I have various pages scattered around, however, that I never got around to fixing. Most of why I had been putting this off is that it seemed like a lot of finicky work, like updating the EA forum display was. Except all of these pages are just simple html with no css: more...
The EA Forum is based on the Reddit codebase, forked long before Reddit added a good mobile version. While it would still be good to have a proper mobile version, here's an idea for how to get it mostly mobile friendly with a relatively small amount of work:
Luke Donforth is collecting responses for a caller directory. You can submit your info here if you'd like to be included. I was interested at looking at the data to see patterns, but so far the only aspect with enough responses to be interesting was zip code:
Here's an interactive version: map.
|Code||Apartment Price Map|