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e81dc452f7
We use several buffer pools across `connlib` that are all backed by the same buffer-pool library. Within that library, we currently use another object-pool library to provide the actual pooling functionality. Benchmarking has shown that spend quite a bit of time (a few % of total CPU time), fighting for the lock to either add or remote a buffer from the pool. This is unnecessary. By using a queue, we can remove buffers from the front and add buffers at the back, both of which can be implemented in a lock-free way such that they don't contend. Using the well-known `crossbeam-queue` library, we have such a queue directly available. I wasn't able to directly measure a performance gain in terms of throughput. What we can measure though, is how much time we spend dealing with our buffer pool vs everything else. If we compare the `perf` outputs that were recorded during an `iperf` run each, we can see that we spend about 60% less time dealing with the buffer pool than we did before. |Before|After| |---|---| |<img width="1982" height="553" alt="Screenshot From 2025-07-24 20-27-50" src="https://github.com/user-attachments/assets/1698f28b-5821-456f-95fa-d6f85d901920" />|<img width="1982" height="553" alt="Screenshot From 2025-07-24 20-27-53" src="https://github.com/user-attachments/assets/4f26a2d1-03e3-4c0d-84da-82c53b9761dd" />| The number in the thousands on the left is how often the respective function was the currently executing function during the profiling run. Resolves: #9972
22 lines
489 B
TOML
22 lines
489 B
TOML
[package]
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name = "bufferpool"
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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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[lib]
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path = "lib.rs"
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[dependencies]
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bytes = { workspace = true }
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crossbeam-queue = { workspace = true }
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opentelemetry = { workspace = true, features = ["metrics"] }
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tracing = { workspace = true }
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[dev-dependencies]
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opentelemetry_sdk = { workspace = true, features = ["testing", "metrics"] }
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tokio = { workspace = true, features = ["macros", "rt"] }
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[lints]
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workspace = true
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