The unreasonable effectiveness of profiling and digging deep strikes again.
hinkley 1 hours ago [-]
The biggest tool in the performance toolbox is stubbornness. Without it all the mechanical sympathy in the world will go unexploited.
There’s about a factor of 3 improvement that can be made to most code after the profiler has given up. That probably means there are better profilers than could be written, but in 20 years of having them I’ve only seen 2 that tried. Sadly I think flame graphs made profiling more accessible to the unmotivated but didn’t actually improve overall results.
Negitivefrags 13 minutes ago [-]
I think the biggest tool is higher expectations. Most programmers really haven't come to grips with the idea that computers are fast.
If you see a database query that takes 1 hour to run, and only touches a few gb of data, you should be thinking "Well nvme bandwidth is multiple gigabytes per second, why can't it run in 1 second or less?"
The idea that anyone would accept a request to a website taking longer than 30ms, (the time it takes for a game to render it's entire world including both the CPU and GPU parts at 60fps) is insane, and nobody should really accept it, but we commonly do.
zahlman 23 minutes ago [-]
> The biggest tool in the performance toolbox is stubbornness. Without it all the mechanical sympathy in the world will go unexploited.
The sympathy is also needed. Problems aren't found when people don't care, or consider the current performance acceptable.
> There’s about a factor of 3 improvement that can be made to most code after the profiler has given up. That probably means there are better profilers than could be written, but in 20 years of having them I’ve only seen 2 that tried.
It's hard for profilers to identify slowdowns that are due to the architecture. Making the function do less work to get its result feels different from determining that the function's result is unnecessary.
nitinreddy88 22 hours ago [-]
The other way to look is why adding NS label is causing so much memory footprint in Kubernetes. Shouldn't be fixing that (could be much bigger design change), will benefit whole Kube community?
bstack 6 hours ago [-]
Author here: yeah that's a good point. tbh I was mostly unfamiliar with Vector so I took the shortest path to the goal but that could be interesting followup. It does seem like there's a lot of bytes per namespace!
hinkley 54 minutes ago [-]
Keys require O(logn) space per key or nlogn for the entire data set, simply to avoid key collisions. But human friendly key spaces grow much, much faster and I don’t think many people have looked too hard at that.
There were recent changes to the NodeJS Prometheus client that eliminates tag names from the keys used for storing the tag cardinality for metrics. The memory savings wasn’t reported but the cpu savings for recording data points was over 1/3. And about twice that when applied to the aggregation logic.
Lookups are rarely O(1), even in hash tables.
I wonder if there’s a general solution for keeping names concise without triggering transposition or reading comprehension errors. And what the space complexity is of such an algorithm.
Rendered at 22:24:36 GMT+0000 (Coordinated Universal Time) with Vercel.
There’s about a factor of 3 improvement that can be made to most code after the profiler has given up. That probably means there are better profilers than could be written, but in 20 years of having them I’ve only seen 2 that tried. Sadly I think flame graphs made profiling more accessible to the unmotivated but didn’t actually improve overall results.
If you see a database query that takes 1 hour to run, and only touches a few gb of data, you should be thinking "Well nvme bandwidth is multiple gigabytes per second, why can't it run in 1 second or less?"
The idea that anyone would accept a request to a website taking longer than 30ms, (the time it takes for a game to render it's entire world including both the CPU and GPU parts at 60fps) is insane, and nobody should really accept it, but we commonly do.
The sympathy is also needed. Problems aren't found when people don't care, or consider the current performance acceptable.
> There’s about a factor of 3 improvement that can be made to most code after the profiler has given up. That probably means there are better profilers than could be written, but in 20 years of having them I’ve only seen 2 that tried.
It's hard for profilers to identify slowdowns that are due to the architecture. Making the function do less work to get its result feels different from determining that the function's result is unnecessary.
There were recent changes to the NodeJS Prometheus client that eliminates tag names from the keys used for storing the tag cardinality for metrics. The memory savings wasn’t reported but the cpu savings for recording data points was over 1/3. And about twice that when applied to the aggregation logic.
Lookups are rarely O(1), even in hash tables.
I wonder if there’s a general solution for keeping names concise without triggering transposition or reading comprehension errors. And what the space complexity is of such an algorithm.