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190e8cebac
## Problem We don't have a convenient way to gather CPU profiles from a running binary, e.g. during production incidents or end-to-end benchmarks, nor during microbenchmarks (particularly on macOS). We would also like to have continuous profiling in production, likely using [Grafana Cloud Profiles](https://grafana.com/products/cloud/profiles-for-continuous-profiling/). We may choose to use either eBPF profiles or pprof profiles for this (pending testing and discussion with SREs), but pprof profiles appear useful regardless for the reasons listed above. See https://github.com/neondatabase/cloud/issues/14888. This PR is intended as a proof of concept, to try it out in staging and drive further discussions about profiling more broadly. Touches #9534. Touches https://github.com/neondatabase/cloud/issues/14888. ## Summary of changes Adds a HTTP route `/profile/cpu` that takes a CPU profile and returns it. Defaults to a 5-second pprof Protobuf profile for use with e.g. `pprof` or Grafana Alloy, but can also emit an SVG flamegraph. Query parameters: * `format`: output format (`pprof` or `svg`) * `frequency`: sampling frequency in microseconds (default 100) * `seconds`: number of seconds to profile (default 5) Also integrates pprof profiles into Criterion benchmarks, such that flamegraph reports can be taken with `cargo bench ... --profile-duration <seconds>`. Output under `target/criterion/*/profile/flamegraph.svg`. Example profiles: * pprof profile (use [`pprof`](https://github.com/google/pprof)): [profile.pb.gz](https://github.com/user-attachments/files/17756788/profile.pb.gz) * Web interface: `pprof -http :6060 profile.pb.gz` * Interactive flamegraph: [profile.svg.gz](https://github.com/user-attachments/files/17756782/profile.svg.gz)
77 lines
2.0 KiB
TOML
77 lines
2.0 KiB
TOML
[package]
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name = "utils"
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version = "0.1.0"
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edition.workspace = true
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license.workspace = true
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[features]
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default = []
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# Enables test-only APIs, incuding failpoints. In particular, enables the `fail_point!` macro,
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# which adds some runtime cost to run tests on outage conditions
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testing = ["fail/failpoints"]
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[dependencies]
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arc-swap.workspace = true
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sentry.workspace = true
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async-compression.workspace = true
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anyhow.workspace = true
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bincode.workspace = true
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bytes.workspace = true
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camino.workspace = true
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chrono.workspace = true
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git-version.workspace = true
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hex = { workspace = true, features = ["serde"] }
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humantime.workspace = true
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hyper0 = { workspace = true, features = ["full"] }
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fail.workspace = true
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futures = { workspace = true}
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jsonwebtoken.workspace = true
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nix.workspace = true
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once_cell.workspace = true
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pin-project-lite.workspace = true
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pprof.workspace = true
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regex.workspace = true
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routerify.workspace = true
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serde.workspace = true
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serde_json.workspace = true
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signal-hook.workspace = true
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thiserror.workspace = true
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tokio.workspace = true
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tokio-tar.workspace = true
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tokio-util.workspace = true
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toml_edit = { workspace = true, features = ["serde"] }
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tracing.workspace = true
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tracing-error.workspace = true
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tracing-subscriber = { workspace = true, features = ["json", "registry"] }
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rand.workspace = true
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strum.workspace = true
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strum_macros.workspace = true
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url.workspace = true
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uuid.workspace = true
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walkdir.workspace = true
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pq_proto.workspace = true
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postgres_connection.workspace = true
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metrics.workspace = true
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const_format.workspace = true
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# to use tokio channels as streams, this is faster to compile than async_stream
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# why is it only here? no other crate should use it, streams are rarely needed.
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tokio-stream = { version = "0.1.14" }
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serde_path_to_error.workspace = true
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[dev-dependencies]
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byteorder.workspace = true
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bytes.workspace = true
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criterion.workspace = true
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hex-literal.workspace = true
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camino-tempfile.workspace = true
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serde_assert.workspace = true
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tokio = { workspace = true, features = ["test-util"] }
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[[bench]]
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name = "benchmarks"
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harness = false
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