Good performance is not just big O - Julio Merino (jmmv.dev)
jmmv.dev
Having a fast and responsive app is orthogonal to “knowing your big Os”. Unfortunately, most tech companies over-emphasize algorithms in interviews and downplay systems knowledge, and I believe that’s one reason behind sluggish apps and bloated systems. I’ve seen this play out repeatedly. Interviewers ask a LeetCode-style coding question, which is then followed by the ritual of discussing time and memory complexity. Candidates ace the answers. But then… their “real” code suffers from subtle yet impactful performance problems. Focusing on big O complexity rarely matters in most apps. Sure, it’s important to think about your algorithmic choices, but there are so many more details to worry about that have a direct impact on app performance and responsiveness. Let’s look at a bunch of them!

Having a fast and responsive app is orthogonal to “knowing your big Os”. Unfortunately, most tech companies over-emphasize algorithms in interviews and downplay systems knowledge, and I believe that’s one reason behind sluggish apps and bloated systems.

I’ve seen this play out repeatedly. Interviewers ask a LeetCode-style coding question, which is then followed by the ritual of discussing time and memory complexity. Candidates ace the answers. But then… their “real” code suffers from subtle yet impactful performance problems.

  • @[email protected]
    12•1 year ago

    Sure, you can make it 10x faster by optimizing the complexity, but I can improve throughput by 1000x by putting it on 1000 machines.

    In practice, it’s a bit of both. Paying attention to Big-O and understanding the gross relative cost of various operations goes a long way, but being able to run an arbitrarily large number of them can get you out of a lot of jams.

    • @[email protected]
      16•1 year ago

      I can improve throughput by 1000x by putting it on 1000 machines

      My app is installed on 10000 phones! See how much better it runs!

    • snooggums
      3•1 year ago

      And sometimes a co pletely different approach is better than focusing on the runtime of a single piece. We had a system that was sql code that ran many functions that scaled horribly as data was added to the system. Each function was as fast as could be measured, but the process of running them as separate repetitive functions ended up being over 24 hours in some cases.

      Redoing them as a single stored procedure and optimizing that reduced it to a few minutes. This approach was much more complex than the individual functions and took about a year to replace what was oroginally written in 3 months.

      Many times the effort to make something more efficient takes far longer than getting it to work in the first place, and many processes are slow because they did not allocate time to properly optimize or include staff that are able to optimize.