Prioritize Critical Findings in a High-Load Batch Processor Review

# Prioritize Critical Findings in a High-Load Batch Processor Review A Python application crashes under heavy load. A large pull request replaces a synchronous processor that handles one request at a time with a batched processor that handles 40 requests per batch. The old synchronous implementation remains available. You have confirmed two facts: 1. If construction of the batched processor fails, the new factory returns `None` instead of falling back to the synchronous processor. 2. The pull-request description claims one batched instance handles 6,000 requests per second, while the repository's documented peak load is 8,000 requests per second per instance. Review these findings. Classify their severity, explain likely production impact, request the evidence needed to validate them, and propose bounded fixes and tests. The pull request is too large to inspect every line manually, so also describe how you would use an AI review tool without accepting its suggestions blindly. ### Clarifying Questions to Ask - What does the caller do when the processor factory returns `None`? - Which failures can make batch-processor construction fail, and are they transient or configuration errors? - Does the 6,000-per-second measurement include the same payloads, latency target, and hardware as the documented 8,000 peak? - Is load distributed across multiple instances, and what headroom or autoscaling delay is required? ### What a Strong Answer Covers - Treats the `None` fallback regression as a potentially immediate availability defect and traces its caller-visible path. - Distinguishes per-instance capacity, fleet capacity, concurrency, batch size, latency, and sustainable throughput. - Requests benchmark methodology and production traffic distributions before trusting headline numbers. - Proposes focused unit, fault-injection, load, saturation, and rollback tests. - Uses AI to index and prioritize the change, then verifies every critical claim against code, docs, tests, and executable evidence. ### Follow-up Questions 1. What if falling back prevents a crash but overloads the synchronous processor and increases tail latency? 2. How would you roll out the batched processor while preserving a fast disable path? 3. Which AI-generated findings deserve immediate human attention, and which can wait?