Process

About

This describes the full lifecycle of planning, designing, executing, and analyzing a load test. It is written to support teams who want to perform meaningful, repeatable, and scalable load testing as part of their QA or DevOps practices.

1. Define the Objective

Start by answering why you are performing the load test. Without a clear objective, the test may not provide actionable insights.

Common objectives include:

• Validate system behavior under expected traffic

• Confirm response time meets service-level targets

• Identify scalability limits or bottlenecks

• Evaluate performance after infrastructure or code changes

• Validate that recent changes do not degrade throughput

Document your objective. Make it specific and measurable.

2. Understand the System Under Test (SUT)

Gain a complete understanding of what you are testing. This includes:

• APIs, services, databases, and frontends involved

• Third-party dependencies (e.g., payment gateways, auth services)

• Environment details (e.g., staging or pre-prod configuration)

• Any limits, such as API rate limiting or queue size

Establish the scope: are you testing a single component or end-to-end flow?

3. Collect Usage and Load Requirements

Use production logs, analytics, or stakeholder input to determine:

• Expected number of concurrent users or sessions

• Peak vs average load

• Session duration and think times

• Request distribution across endpoints (e.g., 80% GET, 20% POST)

• Historical spikes or seasonal traffic patterns

Without this data, your load test risks being unrealistic or irrelevant.

4. Design Test Scenarios

Define how virtual users will behave during the test.

For example:

• 70% of users log in and fetch dashboard

• 20% download reports

• 10% trigger background jobs

Include:

• Authentication

• Navigation flows

• Data dependencies (e.g., valid user IDs or tokens)

• Wait times between actions

Design scenarios to simulate real-world usage, not just raw request volume.

5. Choose the Right Tool

Select a load testing tool based on:

• Target system type (API, UI, database)

• Team skills (e.g., scripting in JavaScript, Python, DSL)

• Support for distributed execution

• Reporting and integrations

Examples:

• JMeter for GUI-driven configuration and complex scripting

• k6 for developer-centric JavaScript scripting

• Gatling for concise DSL-based tests

• Locust for Python scripting flexibility

6. Prepare the Test Environment

The environment should closely match production:

• Same hardware configuration

• Same database size or indexing

• Same network latency, authentication, and middleware

Ensure:

• Monitoring and logging are enabled

• Autoscaling (if any) is configured as it would be in production

• No other teams are testing or deploying during your test window

7. Generate or Load Test Data

Many scenarios require:

• Auth tokens or API keys

• Valid user accounts or customer records

• Pre-existing data (e.g., product catalog, transaction history)

Options:

• Static CSV data

• Dynamic data generation

• REST API calls in setup phase

• Test-specific database snapshots

Avoid using production data directly to prevent risk of leakage.

8. Script and Configure the Test

Build test scripts that include:

• Defined user flows

• Parameterization of inputs (e.g., user IDs)

• Assertions (e.g., status code = 200)

• Think times and pacing

• Configurable test duration, ramp-up time, and concurrency

Validate scripts locally using small thread counts before scaling.

9. Execute the Test

Start with a dry run or baseline run:

• Check environment readiness

• Validate monitoring and log collection

• Confirm no errors in script logic

Then run the actual load test:

• Start with ramp-up period

• Maintain load for steady-state duration

• Collect results at regular intervals

Avoid making configuration changes during the test run.

10. Monitor System Behavior During Execution

Monitor key components:

• Application servers: CPU, memory, thread pool usage

• Databases: connections, query times, locks

• Queues: backlog size, consumer lag

• API gateways: rate limits, throttling

• Logs: exceptions, timeouts, garbage collection activity

Use dashboards (e.g., Grafana, CloudWatch, Prometheus) to observe in real-time.