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⚡ [Performance Improvement] Parallelize check-updates script HTTP requests #2

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⚡ [Performance Improvement] Parallelize check-updates script HTTP requests #2

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33 changes: 18 additions & 15 deletions scripts/check-updates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
import sys
import tomllib
import urllib.request
import concurrent.futures
import urllib.error
from pathlib import Path

Expand Down Expand Up @@ -222,21 +223,23 @@ def main() -> None:
sys.exit(1)

updates = []
for formula_path in formula_files:
if formula_path.name == "simple.toml":
continue
result = check_formula(formula_path)
if result:
updates.append(result)

if apply_updates:
print(f" Applying update to {formula_path}...", file=sys.stderr)
update_formula_file(
formula_path,
result["new_version"],
result["new_url"],
result["new_sha256"],
)
formulas_to_check = [f for f in formula_files if f.name != "simple.toml"]

with concurrent.futures.ThreadPoolExecutor() as executor:
results = executor.map(check_formula, formulas_to_check)

Comment on lines +228 to +230

Copilot AI Mar 21, 2026

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ThreadPoolExecutor() is created without an explicit max_workers, so the concurrency level will vary by machine (defaults to min(32, os.cpu_count()+4)). As the number of formulas grows this can create a large burst of concurrent GitHub API requests + tarball downloads/hashing, increasing the chance of rate-limiting or flaky timeouts in the scheduled workflow. Consider setting a reasonable upper bound (and optionally making it configurable via CLI flag/env var) based on I/O-bound workload expectations.

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for formula_path, result in zip(formulas_to_check, results):
if result:
updates.append(result)

if apply_updates:
print(f" Applying update to {formula_path}...", file=sys.stderr)
update_formula_file(
formula_path,
result["new_version"],
result["new_url"],
result["new_sha256"],
)

print(json.dumps(updates, indent=2))

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