Backend Performance & Data Consistency Audit

Evidence-based diagnostics for Java, Spring, PostgreSQL and Kafka systems.

Production backend problems are often difficult because the visible symptom is not where the actual problem originates.

A slow API may originate in SQL, locking, connection pool exhaustion, application code or an external dependency. A duplicated business operation may originate in retry behaviour, transaction boundaries, Kafka delivery semantics or missing idempotency.

The purpose of the audit is to move from symptoms and assumptions to evidence, defensible findings, root causes where they can be established, and actionable remediation.

Performance Audit

Suitable when a backend system is slow, unstable under load, increasingly expensive to operate or difficult to diagnose.

• p95 / p99 latency investigation
• slow endpoints and request paths
• SQL and query-plan analysis
• missing or ineffective indexes
• N+1 and ORM-related performance problems
• database locks and contention
• long-running transactions
• connection-pool exhaustion
• thread-pool and application bottlenecks
• JVM profiling and allocation / GC investigation
• load-related degradation

Data Consistency & Reliability Audit

Suitable for systems where correctness across requests, transactions or asynchronous processing is critical.

• transaction boundary review
• concurrency and race conditions
• idempotency
• duplicate processing
• lost or partially processed operations
• Kafka delivery and consumer behaviour
• retry and failure handling
• outbox / inbox patterns
• event ordering assumptions
• consistency between database and messaging systems

Observability Baseline

If the system does not currently expose enough information to diagnose production behaviour, observability can be established before or as part of the investigation.

• application and JVM metrics
• HTTP latency and error metrics
• database visibility
• connection-pool metrics
• distributed tracing
• correlation / trace IDs
• focused dashboards for backend diagnosis

How the Audit Works

Deliverables

A typical audit produces a technical report containing:

• executive summary
• observed system behaviour
• confirmed findings
• supporting evidence
• root-cause analysis where established
• risk and impact assessment
• prioritised remediation recommendations
• areas requiring further investigation

Principles

• Evidence over intuition. No finding without supporting data.
• Read-only where practical. Production systems are primarily observed, not experimented on.
• No unnecessary complexity. The objective is to remove problems, not introduce fashionable architecture.
• Measurable outcomes. Performance changes should be benchmarked where possible.
• Clear uncertainty. If available evidence is insufficient to establish a root cause, the report should say so explicitly.

Optional Remediation

The audit can end with the technical report and remediation plan.

If useful, implementation of selected changes can be scoped separately. Remediation is followed by verification using the same relevant measurements used to establish the original baseline.

Technology Focus

Java · Spring Boot · Hibernate · PostgreSQL · Apache Kafka · SQL · Linux · Docker · metrics · tracing · profiling

Discuss a Problem

If your backend is slow, behaves unpredictably under load, or you are concerned about data consistency, send me a short description of the system and the problem.

Michal Navratil · Backend Reliability & Performance