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Informational 18 Bitcoin

common: remove tracing exponential behaviour from large numbers of requests.

Public commit record

What the developer wrote

Authored by Rusty Russell

83/100 · Strong
common: remove tracing exponential behaviour from large numbers of requests.

If we have USDT compiled in, scanning the array of spans becomes
prohibitive if we have really large numbers of requests. In the
bookkeeper code, when catching up with 1.6M channel events, this
became clear in profiling.

Use a hash table instead.

Before:
tests/test_coinmoves.py::test_generate_coinmoves (100,000, sqlite3):
Time (from start to end of l2 node): 269 seconds (vs 14 with HAVE_USDT=0)
Worst latency: 4.0 seconds

After:
tests/test_coinmoves.py::test_generate_coinmoves (100,000, sqlite3):
Time (from start to end of l2 node): 14 seconds
Worst latency: 4.3 seconds

Signed-off-by: Rusty Russell <rusty@rustcorp.com.au>
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context✓ Mentions testing or verification
The short version

What changed, and why it matters

This is a performance fix, not a security fix. The developer replaced a slow list-scan with a fast lookup table in the optional USDT tracing code. It removes a severe slowdown when handling very large numbers of tracing events, but there is no indication it could be exploited to attack the node or steal funds.

Recommended action

No security action required. Treat as a normal performance improvement. If running with USDT tracing enabled and high event volume, consider upgrading to benefit from reduced CPU usage.

Security signals we found

01

No security-relevant signals in commit message or diff

02

Performance optimization only

03

No input validation, authentication, cryptography, or network changes

04

No memory safety bug being fixed (allocation strategy changed, but not due to overflow/corruption)

Risk score

Why this scored 18/100

Our methodology →
Potential impact 2/30
Exploitability 0/25
Stealth signal 1/15
Affected reach 2/15
Confidence 9/10
Evidence quality 4/5
Human-validated context

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