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qc-pr

Testing & Quality
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QC a cBioPortal optimization PR by replaying real captured traffic (cbioportal_qc) against OLD vs NEW builds to verify response parity and measure memory/latency. Use when asked to QC, verify, benchmark, or "confirm the same result / faster / lighter" for a performance or optimization PR.

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QC a cBioPortal optimization PR

Replays real production requests against two builds (OLD = baseline, NEW = the PR) and reports parity (identical responses?), memory, and latency. The tools live in dev/qc-harness/ (see its README.md for full detail and the gotcha list); this skill is the playbook for driving them.

Requires: the ClickHouse MCP

Corpus building and every bit of data exploration — discovering which endpoints have traffic, finding the pathological request shape for a memory test, checking study/value coverage, investigating a suspected regression — read the cbioportal_qc request-log database and the data clone. The read-only replay credential (apitests) cannot read cbioportal_qc, so do those queries through the ClickHouse MCP (mcp-clickhouse), whose user has the needed grants.

Configure it as a project MCP server in .mcp.json at the repo root (an example is shipped at dev/qc-harness/.mcp.json.example):

{
  "mcpServers": {
    "mcp-clickhouse": {
      "command": "uv",
      "args": ["run", "--with", "mcp-clickhouse", "--python", "3.13", "mcp-clickhouse"],
      "env": {
        "CLICKHOUSE_HOST": "dl96orhu96.us-east-1.aws.clickhouse.cloud",
        "CLICKHOUSE_PORT": "8443",
        "CLICKHOUSE_SECURE": "true",
        "CLICKHOUSE_VERIFY": "true",
        "CLICKHOUSE_USER": "<user-with-SELECT-on-cbioportal_qc-and-the-clone>",
        "CLICKHOUSE_PASSWORD": "<password>",
        "CLICKHOUSE_DATABASE": "cbioportal_qc"
      }
    }
  }
}

The MCP user needs SELECT on cbioportal_qc and the clone DB (e.g. cbioportal_public_clone_*). Never commit real credentials — keep .mcp.json out of git. It exposes list_databases, list_tables, and run_query.

Choose the approach first

  • Pure SQL / mapper change (e.g. a *Mapper.xml rewrite): skip the app. Render both query versions with real params and measure straight from the ClickHouse HTTP X-ClickHouse-Summary header (read_rows / read_bytes / elapsed). Pattern: dev/qc-harness/examples/coexp_sql_ab.py. read_rows/read_bytes are cache-independent and are exactly any "N× fewer rows read" claim; parity = identical sorted result rows.
  • App behavior change (controllers/services/serialization): full end-to-end via qc.sh.
  • Any memory claim: a broad corpus often shows zero benefit — see traps below.

Steps (end-to-end)

  1. cp dev/qc-harness/config.env.example dev/qc-harness/config.env and fill in creds.
  2. Find the PR's changed endpoint(s) and confirm there's traffic (MCP): SELECT endpoint, count(), uniqExact(body) FROM cbioportal_qc.logged_requests WHERE endpoint LIKE '%...%' GROUP BY endpoint.
  3. Run it: dev/qc-harness/qc.sh <old-ref> <new-ref> '<endpoint-LIKE>' <label> [count] [random|size] (random = representative mix; size = heaviest requests).
  4. Read the output: parity is the gate; latency as the OLD→NEW delta; peak heap / alloc.

Interpreting + traps (the parts that bite)

  • Parity is the gate. A single mismatch is a blocker — dump bodies with --save-bodies and investigate before trusting any speed/memory number.
  • Coverage trap (most important). A passing parity run only covers what you sampled. For changes that narrow behavior (filters, projections, IN-lists, case-sensitive matches), the regression hides in the rows you didn't replay. Before declaring parity: query the clone for the values the change could drop and confirm they still appear. (A case-sensitive property-name IN filter regression sat behind a green broad run exactly this way — caught only by querying the clone for lower/mixed-case values.)
  • Memory is request-shape-dependent. "Heavy by request-body size" ≠ "heap-stressing." The win usually needs a specific shape — a few rows of a huge entity, or one large response multiplied by concurrency (replay.py --workers N). Construct that shape (often from the DB via MCP) rather than assuming a big corpus exercises it.
  • Boot the A/B sequentially — one -Xmx2g JVM fits this box; two do not.
  • Other gotchas (the runnable jar is cbioportal-exec.jar; -Dmaven.gitcommitid.skip=true in worktrees; gzip request bodies are byte-corrupted in the log and unreplayable; preserve query strings): documented in dev/qc-harness/README.md.

Reporting

Lead with the parity verdict, then memory and latency as OLD→NEW deltas. State the corpus (endpoint, count, random/size) and call out coverage limits explicitly — what shapes were and were not exercised.