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| Author | SHA1 | Message | Date | |
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 DottaandPaperclip
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00fea0da52 |
refactor(perf): reuse database test setup for profile benchmark (#18683)
## Thinking Path > - Paperclip is the open source app people use to manage AI agents for work. > - Tool profiles govern the tools that agents can use. > - Profile listing and app attention shared nested selector scans that consumed the server CPU. > - #18681 and #18673 replaced those scans with indexes for each computation. > - Their performance benchmarks need consistent setup and reproducible per-service measurements. > - This pull request uses the shared helper entry point and retains the exact measurement harness and results. > - Reviewers can inspect both the CPU savings and the limits of the evidence. ## Linked Issues or Issue Description Refs #18681 and #18673. **What existing behavior does this improve?** The consistency of benchmark setup and the reproducibility of evidence for the two merged CPU fixes. **Current behavior** The profile-review benchmark imports the database starter directly. The matching benchmark uses the shared server test-helper entry point. On the current base, that helper re-exports the database starter, which already supports `PAPERCLIP_TEST_POSTGRES_ADMIN_URL`. The previous committed benchmark measures both services together; the per-service measurements and separate HTTP/Inspector runs lacked a committed harness. **Proposed behavior** Use the same test-helper entry point in both benchmarks. Retain the compile, per-service timing, Inspector, and HTTP-probe programs with their exact commands and raw samples. Preserve the existing embedded and admin-server database behavior. **Reason and benefit** A shared entry point keeps benchmark setup consistent. The committed harness lets reviewers reproduce and compare each real owning service before and after the merged fixes. The failure-path checks verify that result-write errors still release the database and probe resources. **Breaking changes** None. This change affects benchmark setup and documentation. ## What Changed - Import the database starter from the repository test helper in `benchmark-profile-review-projection.ts`. - Record separate CPU and elapsed measurements for profile listing and app attention. - Retain the actual compile, per-service measurement, Inspector capture, and separate-process HTTP probe harness. - Always tear down the test database, HTTP server, and probe even when result writing fails. - Retain all timing and responsiveness samples as sanitized JSON. - State the runtime, fixture, source revisions, and limits of the local comparison. ## Verification - 24 focused tests passed across selector matching, pending projection, review-service freshness, and effective assignments. - Four selected real database-backed service/route tests passed for summaries, review changes, auto-allow behavior, and company isolation. The other 494 tests in that file were not selected. - Injected an `EISDIR` result-write failure into both profiling programs on an owned local admin server. Each child exited with the expected error and removed its disposable database. The responsiveness child also terminated its probe/server handles. - The changed benchmark completed with a configured local PostgreSQL admin URL. Its full responses matched the baseline, and query/row counts matched. - The exact documented compile, benchmark, and responsiveness commands completed from the committed harness at `ca458baddc6461fb6c888984246e329e2bb4bdfe`. The JSON records that build revision and the four owner source hashes. - Five alternating samples per service and implementation used Node 24.22.2 with compiled ES modules. No runtime TypeScript loader ran in those measurements. - Median process CPU from the committed harness: profile listing 3,164 ms to 385 ms; app attention 2,466 ms to 221 ms. Both versions returned identical ordered results and made seven queries per service. - Separate Inspector profiles confirmed that the repeated scope and summary scans were removed. Three separate-process HTTP probe trials per service showed less event-loop blocking. These are local synthetic service results, not hosted page-load acceptance. - Initial local setup failed on missing embedded PostgreSQL library aliases and an unbuilt plugin SDK. Restoring local aliases and building the SDK resolved those setup failures. The original failed setup logs were retained; the 28 selected tests then passed. - The merged summary fix CI run [41535304968](https://github.com/paperclipai/paperclip/actions/runs/41535304968) hit two 15-second timeouts in the unchanged attention-retention history cases. The server shard passed unchanged on one failed-jobs-only retry. Original and retry logs are retained. The compared runs used different runners and synthetic merge trees, so the original timeout cause is not established. No timeout or assertion was changed. - Full repository typecheck, test matrix, and build passed in CI on final head `2f484ed4284f3fa632d8e73d9be084827d1c987e`: 54 successful check runs, three expected skips, one non-blocking Paperclip review, and a successful Snyk status. No failures or pending checks remain. Those broad commands were not rerun locally for this benchmark-tooling follow-up; the 28 selected local regressions and the actual profiling/failure-path programs ran locally. - Fresh Greptile is 5/5 on that exact head. Both review P2 threads are resolved. The Paperclip review's current-base documentation note is corrected in this description; its optional tooling suggestions remain outside this bounded change. ## Risks - The test helper creates a unique disposable database on the configured admin server and removes it afterward. The operator must use a database server where those test operations are appropriate. - Synthetic data and shared-host activity affect absolute timings. The measurements do not establish a production latency percentage or capacity limit. - Production behavior is covered by the two linked merged fixes. This follow-up changes only benchmark setup and documentation. ## Model Used OpenAI GPT-6 through Codex, with code execution and repository tools. The exact runtime model revision and context window were not exposed in this session. The original linked performance fixes record their own model details. ## Checklist - [x] I have included a thinking path that traces from project context to this change - [x] I have specified the model used (with version and capability details) - [x] I have checked ROADMAP.md and confirmed this PR does not duplicate planned core work - [x] I have searched GitHub for duplicate or related PRs and linked them above - [x] I have either (a) linked existing issues with `Fixes: #` / `Closes #` / `Refs #` OR (b) described the issue in-PR following the relevant issue template - [x] I have not referenced internal/instance-local Paperclip issues or links (only public GitHub `#NNN` / `github.com/paperclipai/paperclip` URLs) - [x] My branch name describes the change (e.g. `docs/...`, `fix/...`) and contains no internal Paperclip ticket id or instance-derived details - [x] I have run tests locally and they pass - [x] I have added or updated tests where applicable - [x] I have updated relevant documentation to reflect my changes - [x] I have considered and documented any risks above - [x] All Paperclip CI gates are green - [x] Greptile is 5/5 with no open P2s, recommendations, or follow-ups - [x] I will address all Greptile and reviewer comments before requesting merge --------- Co-authored-by: Paperclip <noreply@paperclip.ing> |
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cad26c6bfb |
fix(tool-gateway): bound MCP discovery memory and concurrency (#14864)
## Thinking Path > - Paperclip is the open source app people use to manage AI agents for work. > - Agents discover governed tools through the MCP gateway. > - A listing repeated policy and full run-row reads for each catalog tool. > - Parallel listings multiplied those allocations during run startup. > - One 900-tool baseline listing used 11,489 queries and about 1.7 GiB of extra heap in a fixture. > - This pull request shares reads within a listing and bounds whole listings across the process. > - The benefit is lower discovery memory use while execution still checks current policy. ## Linked Issues or Issue Description Refs #13115. Its on-demand target change affects the same listing loop. **What happened?** MCP discovery repeated roughly 13 reads per tool. Full run snapshots and repeated connection configurations caused large allocations. Per-listing bounds alone did not limit concurrent listings across gateways. **Expected behavior** Discovery reads shared inputs once per listing. The process bounds active and queued listings. Catalog payload size and policy evaluation still grow with the catalog. Tool execution checks current access rules. **Steps to reproduce** Create a company with a remote MCP connection, 900 catalog tools, large schemas, and a large run snapshot. Send concurrent tools/list requests using a run-bound gateway token. Run the committed benchmark for a deterministic reproduction. **Paperclip version or commit** Baseline: |