I was using timers, and I was getting insanely different times for the same code, going anywhere from 0ms to 20ms without any obvious changes to the environment or anything.
I was banging my head against it for hours, until I realized that async code is weird. Async code isn’t directly “run”, it’s “scheduled” and the calling thread can yield until we get the result. By trying to do microbenchmarks, I wasn’t really testing “my code”, I was testing the .NET scheduler.
It was my first glimpse into seeing why benchmarking is deceptively hard. I think about it all the time whenever I have to write performance tests.
Maybe you don't always want to include this, I can see how it might be challenging to isolate just the code itself. It might be possible to swap out the scheduler, synchronization context etc for implementations more suited to that kind of benchmarks?
For example, because I was trying to use fine-grained timers for everything async, I thought the JSON parsing library we were using was a bottleneck, because I saw some numbers like 30ms to parse a simple thing. I wasn’t measuring total throughput, I was measuring individual items for parts of the flow and incorrectly assumed that that applied to everything.
You just have to be a bit more careful than I was with using timers. Either make sure than your timer isn’t going across any kind of yield points, or only use timers in a more “macro” sense (e.g. measure total throughput). Otherwise you risk misleading numbers and bad conclusions.
That's the entire point.
Finding out you have tens of milliseconds of slop because of TPL should instantly send you down a warpath to use threads directly, not encourage you to find a way to cheat the benchmarking figures.
Async/await for mostly CPU-bound workloads can be measured in terms of 100-1000x latency overhead. Accepting the harsh reality at face value is the best way to proceed most of the time.
Async/await can work on the producer side of an MPSC queue, but it is pretty awful on the consumer side. There's really no point in yielding every time you finish a batch. Your whole job is to crank through things as fast as possible, usually at the expense of energy efficiency and other factors.
* mean/med/p99/p999/p9999/max over day, minute, second, 10ms
* software timestamps of rdtsc counter for interval measurements - am17 says why below
* all of that not just on a timer - but also for each event - order triggered for send, cancel sent, etc - for ease of correlation to markouts.
* hw timestamps off some sort of port replicator that has under 3ns jitter - and a way to correlate to above.
* network card timestamps for similar - solar flare card (amd now) support start of frame to start of Ethernet frame measurements.
So basically you’re taking 18 measurements and checking if they’re <10ms? Is that your time budget to make a trading decision, or is that also counting the trip to the exchange? How frequently are these measurements taken, and how do HFT folks handle breaches of this quota?
Not in finance, but I operate a high-load, low latency service and I’ve always been curious about how y’all think about latency.
The number one mistake I see people make is measuring one time and taking the results at face value. If you do nothing else, measure three times and you will at least have a feeling for the variability of your data. If you want to compare two versions of your code with confidence there is usually no way around proper statistical analysis.
Which brings me to the second mistake. When measuring runtime, taking the mean is not a good idea. Runtime measurements usually skew heavily towards a theoretical minimum which is a hard lower bound. The distribution is heavily lopsided with a long tail. If your objective is to compare two versions of some code, the minimum is a much better measure than the mean.
You'll see this in any properly active online system. Back in the previous job we had to drill it to teams that mean() was never an acceptable latency measurement. For that reason the telemetry agent we used provided out-of-the-box p50 (median), p90, p95, p99 and max values for every timer measurement window.
The difference between p99 and max was an incredibly useful indicator of poor tail latency cases. After all, every one of those max figures was an occurrence of someone or something experiencing the long wait.
These days, if I had the pleasure of dealing with systems where individual nodes handled thousands of messages per second, I'd add p999 to the mix.
There was dedicated team of folks that had built a random forest model to predict what the latency SHOULD be based on features like:
- trading team
- exchange
- order volume
- time of day
- etc
If that system detected change/unexpected spike etc, it would fire off an alert and then it was my job, as part of the trade support desk, to go investigate why e.g. was it a different trading pattern, did the exchange modify something etc etc
One day, we get an alert for IEX (of Flash Boys fame). I end up on the phone with one of our network engineers and, from IEX, one of their engineers and their sales rep for our company.
We are describing the change in latency and the sales rep drops his voice and says:
"Bro, I've worked at other firms and totally get why you care about latency. Other exchanges also track their internal latencies for just this type of scenario so we can compare and figure out the the issue with the client firm. That being said, given who we are and our 'founding story', we actually don't track out latencies so I have to just go with your numbers."
The other thing is that L1/L2 switches provide this functionality, of taking switch timestamps and marking them, which is the true test of e2e latency without any clock drift etc.
Also, fast code is actually really really hard, you just to create the right test harness once
And measuring is hard. This us why consistently fast code is hard.
In any case, adding some crude performance testing into your CI/CD suite, and signaling a problem if a test ran for much longer than it used to, is very helpful at quickly detecting bad performance regressions.
...unless youre faang and can amortize the costs across your gigafleet
The article states the opposite.
> Writing fast algorithmic trading system code is hard. Measuring it properly is even harder.
