1. PERFORMANCE ENGINEERING

This covers the entire SDLC and can be used as a formal artifact in engineering teams.

A. Requirements Phase

✔ Performance Requirements Defined

SLAs, SLOs, SLIs documented (e.g., P95 < 300ms)
Target throughput (TPS/RPS) defined
Concurrency / active user count defined
Peak vs average load described
Workload profiles + user journeys defined
Data volume and growth projections
Performance success/failure criteria documented

✔ Non-Functional Requirements (NFRs)

Latency thresholds
Error budget
Capacity & scaling expectations
Availability targets (99.9%, 99.99%, etc.)

B. Architecture & Design

✔ Design Review

Scalability approach (horizontal/vertical)
Stateless service design
Caching strategy (CDN, Redis, query cache, etc.)
Failover / redundancy
Data partitioning or sharding strategy
Database replication, indexing, schema decisions
Message queue selection (Kafka, SQS, RabbitMQ)
Load balancer configuration
API rate limiting / throttling patterns
Event-driven or async model where needed

✔ Performance Risks Identified

High-latency external dependencies
Heavy synchronous operations
Large data transfer points
Long-running jobs

C. Development Phase

✔ Code-Level Optimization

Code profiling performed (CPU, memory)
Hot path optimized
Avoid N+1 queries
Batch processing instead of repeated calls
Pagination used for large results
Efficient data structures chosen
Connection pooling implemented
Logging reduced in critical path
Threading & concurrency issues evaluated

✔ Static Analysis & Quality Gates

Complexity analysis
Memory allocation analysis
Linting/coverage checks

D. Performance Testing Phase

✔ Test Strategy & Plan

Workload model completed
Test environment ready & production-like
Baseline established

✔ Test Types Executed

Load Test
Stress Test
Spike Test
Endurance (soak) Test
Scalability Test
Volume/Data Test

✔ KPIs Captured

P50/P90/P95/P99 latencies
Throughput (TPS/RPS)
Error rate
Resource utilization (CPU/mem/I/O/network/disk)
GC behavior (if applicable)

E. Bottleneck Analysis

✔ Identified Issues

CPU saturation
Memory leaks or high GC time
DB bottlenecks (slow queries, locks, missing indexes)
Network latency issues
I/O bottlenecks
Thread pool exhaustion
Cache misses/high eviction rates

✔ Fixes Implemented

Optimizations validated with re-tests
Architecture/design updated as needed

F. Deployment & Capacity Planning

✔ Infrastructure Tuning

Load balancer tuned
Autoscaling rules set (CPU/RPS/Queue depth)
Container & pod resource requests/limits defined
Cluster/node sizing validated
Production capacity model prepared

G. Production Monitoring & Observability

✔ Monitoring Coverage

Real User Monitoring (RUM)
Synthetic monitors
APM (New Relic, Datadog, AppDynamics)
Distributed tracing
Logs centralized (ELK, Loki)

✔ Alerts & Dashboards

Latency (P95/P99) alerts
Error rate alerts
CPU/memory saturation alerts
Traffic & anomaly detection
SLA/SLO reporting dashboards

✨ This checklist is complete and formal enough for production use.

2. SAMPLE PERFORMANCE TEST PLAN

You can use this as-is in real projects.

PERFORMANCE TEST PLAN

Project Name: XYZ System
Prepared By: Performance Engineering Team
Version: 1.0

1. Introduction

This document outlines the approach, scope, objectives, environment, workload model, KPIs, entry/exit criteria, and execution plan for performance testing of the XYZ system.

2. Objectives

Validate the XYZ system’s ability to meet performance requirements.
Identify scalability limits and system bottlenecks.
Ensure stability under prolonged load.
Verify readiness for production.

3. Scope

In Scope

API response times
Throughput under expected & peak load
Database performance
End-to-end latency
Server resource utilization
Failover behavior

Out of Scope

UI usability tests
Security tests (covered separately)

4. Performance Test Types

Test Type	Purpose
Baseline Test	Establish initial metrics
Load Test	Validate expected normal load
Stress Test	Find breaking point
Spike Test	Evaluate system reaction to sudden load
Endurance Test	Identify memory leaks or degradation
Scalability Test	Measure performance with incremental load
Volume Test	Validate performance with large data sets

5. Workload Model

User Journeys

Login → View Dashboard
Search → Filter → View Details
Add to Cart → Checkout
Admin operations

Traffic Distribution

Journey	% of Traffic
Login	10%
View Dashboard	25%
Search	40%
Checkout	15%
Admin	10%

Load Levels

Normal load: 2,000 concurrent users
Peak load: 5,000 concurrent users
Stress target: 10,000+ users

Think Time

Average: 3 seconds

6. Entry Criteria

Test environment stable & production-like
API endpoints finalized
Monitoring configured
Test data prepared
Build deployed and smoke-tested

7. Exit Criteria

All planned tests executed
P95 latency meets SLA
No critical or high-severity issues open
System stable for ≥ 8-hour endurance test
Bottlenecks analyzed and addressed

8. Test Environment

Hardware

Load Generators: 3 × 8 CPU / 16 GB RAM
Application Servers: Kubernetes cluster (3 nodes)
Database: PostgreSQL 14, high availability setup

Tools

Load tool: k6 / JMeter / Gatling
Monitoring: Prometheus + Grafana
APM: Datadog
Logging: ELK stack

9. KPIs

Response Times

P50 < 100ms
P95 < 300ms
P99 < 600ms

Throughput

Minimum: 5,000 req/sec sustained

Error Rate

< 1% at peak

Resource Utilization

CPU < 75% average
Memory < 80% usage
GC pause < 200ms

10. Execution Plan

Execute baseline tests
Run load test for 1 hour
Run spike tests with instant load surges
Run stress test until failure point
Perform 8–12 hour endurance test
Collect logs, metrics, traces
Analyze results and document findings

11. Reporting

Deliverables:

Performance test summary
Charts & graphs (latency, throughput, resource usage)
Bottleneck analysis
Recommendations for improvement
Final go/no-go report

Also Read: How to become performance engineer .

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