Page 3 of 8~112 min topic

Batch API lab

Implement the happy path

One clean transaction through **POST /v1/batches** must match the oracle: fixture of 20 lines yields 18 succeeded + 2 failed with error objects; cost ≤ $0.40.

~14 min this pageHappy path

1Learn the idea

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Order the successful transaction

Code the narrow path that serves QA lead who needs cost-capped offline eval by 07:00 UTC: accept → authorize/normalize → call dependency → validate → record. Keep stages named so a trace can show which boundary passed. Success must emit evidence useful to batch_line_success_ratio ≥ 0.90 and dollars_per_1k_lines ≤ 8, not only a 200 with prose. Predict the observable for POST /v1/batches before running: fixture of 20 lines yields 18 succeeded + 2 failed with error objects; cost ≤ $0.40.

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Run with fakes first

Drive the path with recording fakes or local stubs. Assert call order and arguments. Idempotency keys or stable ids should keep retries from duplicating costly work where the product requires it. Product under test remains overnight batch job that grades 50k support transcripts via provider Batch API — resist adding unrelated features mid-path.

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Implementation artifact

def write_jsonl(path, rows):
    seen = set()
    with open(path, "w") as f:
        for row in rows:
            assert row["custom_id"] not in seen, row["custom_id"]
            seen.add(row["custom_id"])
            f.write(json.dumps(row) + "\n")

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Compare prediction to result

For Batch API lab, paste the CLI/HTTP transcript beside your prediction for POST /v1/batches. If the oracle is unmet (fixture of 20 lines yields 18 succeeded + 2 failed with error objects; cost ≤ $0.40), stop and debug this page; do not compensate with prompt folktales. Re-run once after a clean process start to catch hidden global state that would invalidate BATCH-DUP-8841.

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Stage depth

Performance sketch: measure local p95 for the fake-backed path so later regressions are obvious. Keep concurrency modest until failure-handling proves limits. Log a single structured event per success with request id, revision, and the evidence field behind batch_line_success_ratio ≥ 0.90 and dollars_per_1k_lines ≤ 8. Avoid hidden global caches in the happy path unless the lab is about caching — and even then key by tenant. If the path calls a model, pin model id in config and echo it in the response for auditability. Remember QA lead who needs cost-capped offline eval by 07:00 UTC experiences wall-clock time, not your debugger’s single-step comfort.

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Field notes for `batch-api-lab` / `happy-path`

Prefer explicit function names over a single god-object handleRequest. Thread a correlation id from ingress to the last log line. When streaming, define what partial failure means before coding. Snapshot one successful response body in fixtures after redaction. If the path writes to a queue, assert message attributes in the fake. Stop adding retries on this page; that is the next concern. In this chapter the product is overnight batch job that grades 50k support transcripts via provider Batch API, the human stakeholder is QA lead who needs cost-capped offline eval by 07:00 UTC, and the incident id you design against is BATCH-DUP-8841. Re-state the oracle in your notes — fixture of 20 lines yields 18 succeeded + 2 failed with error objects; cost ≤ $0.40 — and keep the invariant visible: each input line has a stable custom_id; failed lines never poison the whole file. Track batch_line_success_ratio ≥ 0.90 and dollars_per_1k_lines ≤ 8 as the scoreboard. Surface under change control: POST /v1/batches. If you only have forty minutes, finish the fixture for duplicate custom_id causes silent overwrite of the better grade before polishing UI. Promotion language stays ternary: promote, hold, or roll back based on evidence, not hope.

Go deeper

Before you start

Why this matters

Without calling production, order the steps a single success takes for QA lead who needs cost-capped offline eval by 07:00 UTC. Circle the first irreversible side effect. Your prediction should mention POST /v1/batches and the evidence field that proves fixture of 20 lines yields 18 succeeded + 2 failed with error objects; cost ≤ $0.40.

In the wild

See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.

Check your understanding

Page assessment

Answer from memory. Completion is saved from this evidence, not from opening the next page.

1. Is call order asserted, not assumed?
2. Does success evidence support batch_line_success_ratio ≥ 0.90 and dollars_per_1k_lines ≤ 8?
3. Did you compare prediction vs transcript?

All responses are required.