Production Performance & Reliability Diagnostic

Who it is for

Teams dealing with slow APIs, PostgreSQL pressure, Redis growth, timeouts, recurring incidents, batch or queue issues, or unexplained application load.

Common symptoms

• • APIs are slow or time out under ordinary or peak load
• • PostgreSQL pressure, locking, or expensive queries keep returning
• • Redis memory or key growth is rising without a clear retention story
• • Queues, workers, or batch jobs amplify load or miss windows
• • Incidents recur without a shared, evidence-backed diagnosis

What is reviewed

• • API request paths and hot code paths
• • SQL and query plans, indexes, locking, and connection pools
• • Cache behavior, TTL, and key growth
• • Queue workers, query amplification, and batch design
• • Infrastructure saturation signals available for review

Deliverables

• • Evidence-backed findings
• • Remediation priorities
• • Implementation considerations
• • Baseline and validation plan

What is not included by default

• • Diagnosis and remediation planning are in scope by default
• • Implementation, on-call coverage, and ongoing operations support are agreed separately
• • No guaranteed latency, uptime, or capacity outcome is promised without an agreed baseline and follow-through

How to start

Useful context for the first conversation:

• • Recent incidents and the business impact
• • Relevant logs, metrics, query plans, or traces when available
• • Workload patterns and which outcomes matter most

1. 1. Discuss the performance problem and its business impact
2. 2. Triage symptoms and available evidence
3. 3. Investigate critical paths across app, data, cache, and jobs
4. 4. Read out findings, priorities, and the validation plan

Relevant evidence

• • Production-oriented investigation experience in a centralized multi-school ERP context — separating observed symptoms, recorded actions, and unmeasured follow-up.
• • Related work: Investigating Performance and Reliability in an Enterprise ERP.