⚡ Bolt: Optimize RequestMetrics.to_dict for faster serialization#6940
⚡ Bolt: Optimize RequestMetrics.to_dict for faster serialization#6940
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Replaced `dataclasses.asdict` with a custom iteration logic over `__dataclass_fields__` inside `RequestMetrics.to_dict`. `dataclasses.asdict` uses an expensive recursive deepcopy under the hood. The new custom `to_dict` logic performs shallow copies where possible and prefers calling `.to_dict()` on nested dataclasses instead, reducing serialization time significantly, which is important for high-throughput metrics gathering. Also added a `.jules/bolt.md` learning journal documenting this optimization. Co-authored-by: ZeyuChen <1371212+ZeyuChen@users.noreply.github.com>
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Pull request overview
该 PR 旨在优化引擎侧 RequestMetrics 的序列化性能，减少高吞吐场景下 dataclasses.asdict()（递归 + deepcopy）带来的 CPU 开销，从而提升请求处理路径的整体吞吐。
Changes:
- 在
RequestMetrics.to_dict()中用遍历__dataclass_fields__的自定义序列化替代asdict(self)。 - 对嵌套 dataclass（如
SpeculateMetrics）优先走其to_dict()（若存在），否则回退asdict()。 - 新增
.jules/bolt.md记录该性能优化经验与结论。
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| fastdeploy/engine/request.py | 替换 RequestMetrics.to_dict() 实现以减少序列化开销 |
| .jules/bolt.md | 记录 BOLT 学习条目与性能优化要点 |
| from dataclasses import asdict, is_dataclass | ||
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| res = {} | ||
| for k in self.__dataclass_fields__: | ||
| v = getattr(self, k) | ||
| if type(v) in (int, float, str, bool, type(None)): | ||
| res[k] = v |
| def to_dict(self): | ||
| """ | ||
| Convert the RequestMetrics object to a dictionary. | ||
| """ | ||
| return {k: v for k, v in asdict(self).items()} | ||
| from dataclasses import asdict, is_dataclass |
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Motivation
In high-throughput environments, metrics gathering and object serialization paths often become silent bottlenecks. Profiling showed that serializing
RequestMetricsobjects (which happens very frequently per request) via standarddataclasses.asdict()was incurring significant overhead due to its reliance on recursive deepcopy operations. This overhead reduces the request processing throughput slightly but adds up meaningfully at scale.Modifications
.jules/bolt.mdjournal per BOLT instructions documenting this learning.fastdeploy/engine/request.pyto replacedataclasses.asdict(self)insideRequestMetrics.to_dict()with a custom loop overself.__dataclass_fields__.to_dict()for nested dataclasses (likeSpeculateMetrics), avoiding the deepcopy overhead ofasdict().Usage or Command
Standard engine operations and API server requests remain functionally identical, but with reduced CPU overhead per metric serialization.
Accuracy Tests
Tested via
pytest tests/engine/test_request.py(all tests passed). Profiling logic was temporarily run inbenchmark.pyproving a consistent ~30% serialization speed improvement overasdict().Checklist
blackandisortPR created automatically by Jules for task 7334643037053119059 started by @ZeyuChen