BigRedEye

Joined 3 years ago

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May 20

Software Engineer at Yandex

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Comment · 3 months ago

I think this single fact is a major source of enshittification in large software products, especially in the era of ML/AI. If your quality is 99%, it sounds like "you have solved your task", but in reality there is a long tail that over time affects nearly every customer.

I've seen this so many times. 99% of search results are good (so within 100 queries you'll hit at least one bad result with p≈0.63), 99% of dashboard panes load normally (so a dashboard with 20 panes is broken in nearly 1 in 5 loads), and so on. If your LLM gets 99% of tool calls right, nearly every session will contain a malformed tool call.

Probabilities are hard for humans, probably.

Comment · 7 months ago

At Perforator, we also started from Google's beautiful pprof, but then eliminated all nested repeated fields, converging to https://github.com/yandex/perforator/blob/main/perforator/pr.... Repeated fields in protobufs are really memory & CPU hungry.

This layout allows us to quickly merge hundreds of millions of samples into a single profile. The only practical limit is protobuf's 2GB message size cap.

Comment · 7 months ago

I believe this is a case of convergent invention – the idea of pushing DWARF/.eh_frame unwinding into eBPF seems to have occurred to several people around the same time. For example, there's a working implementation discussed as early as March 2021: https://github.com/iovisor/bcc/issues/1234#issuecomment-7875...

Comment · 1 year ago

At Yandex we have a similar profiler that supports native languages seamlessly, with addition to Python/Java: https://github.com/yandex/perforator. It's exciting to see new profilers from big players!

Comment · 1 year ago

Great question! Perforator indeed looks similar to Pyroscope. However, we think that the closest existing solutions are https://parca.dev, closed-source Google Wide Profiling, and, speaking of the agent, the beautiful OpenTelemetry eBPF profiler. The main technical differences with Pyroscope we see are:

- Pyroscope's Java support is superior as of now because Pyroscope offloads it to the amazing async-profiler.

- Pyroscope expects native binaries to be compiled with frame pointers: https://grafana.com/docs/pyroscope/latest/configure-client/g.... This is often not the case, and that's the problem we've tried to solve with Perforator. Perforator uses .eh_frame, which is nearly universal and does not impose additional requirements on compiled binaries.

- Pyroscope symbolizes using symtab: https://grafana.com/docs/pyroscope/latest/configure-client/g.... We use DWARF/GSYM to get as correct and verbose stacks as possible (we benchmark our stacks against stacks from gdb).

- Pyroscope symbolizes profiles on an agent, while Perforator symbolizes profiles offline, greatly reducing symbolization costs and agent's overhead. It seems Pyroscope is heading toward the same architecture we use: https://github.com/grafana/pyroscope/pull/3799.

- Perforator can be (and should be!) run as a standalone replacement for perf record.

- Perforator supports sPGO profiles.

In summary, we try to implement native profiling almost perfectly. It's worth noting that Pyroscope is a mature, well-established product that integrates excellently with the Grafana ecosystem. We have just focused on different things: our focus has been on optimizing native code profiling and making it as accurate and low-overhead as possible.

Comment · 1 year ago

It depends on the event that was sampled to generate the profiles. For example, if you sample instructions by collecting a stack trace every N instructions, you won't actually see foo() burning the CPU. However, if you look at CPU cycles, foo() will be very noticeable. Internally, we use sPGO profiles from sampling CPU cycles, not instructions.