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d1dbb40362Tools for profiling Datasette's request/response cycle in-process with Python 3.15's profiling.sampling, plus the patches proposed by the study (one per optimisation, and ALL-combined.diff). The patches are not applied to the code. A report with measurements follows. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01F8mEgRS8swZfYqwqrVMeo5
26 lines
817 B
Python
26 lines
817 B
Python
import collections
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import json
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import sys
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import analyze_collapsed as A
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eps = sys.argv[1].split(",")
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prefix = sys.argv[2] if len(sys.argv) > 2 else "profiles/"
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out = {}
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for ep in eps:
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stacks = A.load(
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f"{prefix}{ep}.collapsed",
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"_run_once",
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r"(Driver\.setup|invoke_startup|_startup_sequence|make_ds)",
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)
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total = sum(c for _, c in stacks)
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b = collections.Counter()
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for f, c in stacks:
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b[A.bucket_for(f)] += c
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idle = b.pop(A.IDLE, 0)
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busy = total - idle
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out[ep] = {"idle_pct": idle / total, "busy": {k: v / busy for k, v in b.items()}}
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top = ", ".join(f"{k} {v/busy:.0%}" for k, v in b.most_common(7))
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print(f"{ep:18s} idle {idle/total:5.1%} | {top}")
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with open(prefix.rstrip("/") + "_buckets.json", "w") as fp:
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json.dump(out, fp, indent=1)
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