What is Load Testing?

Load testing is a type of performance testing that evaluates how a system performs under normal and peak load conditions. The goal is to identify performance bottlenecks before the software application goes live.

A load tester tool simulates multiple users accessing the application simultaneously. It measures response times, throughput rates, resource utilization levels, and identifies breaking points. This helps determine if the current infrastructure will sufficiently handle the projected traffic.

Key reasons for load testing include:

• Evaluating if performance meets expectations
• Assessing infrastructure capacity
• Identifying bottlenecks and weak points
• Determining breaking points
• Planning for sufficient capacity

Types of Load Tests

Test Type	Description
Stress Testing	Tests the upper limits of the system by gradually increasing load until it fails. Determines breaking point.
Soak Testing	Tests if the system can handle a sustained high load over an extended time period (hours or days). Identifies memory leaks, resource exhaustion.
Spike Testing	Sends large bursts of traffic to test elasticity and recovery. Useful for evaluating cloud autoscaling.
Isolation Testing	Load tests individual components or services to isolate issues to a specific area of the system.

How Load Testing Works

A load testing tool is used to generate virtual users and send requests to the target system. The process generally involves these steps:

1. Identify key scenarios and workflows to test
2. Determine performance metrics and goals
3. Configure the load test tool
4. Run tests and monitor results
5. Analyze reports and refine tests

Identifying Test Scenarios

Load tests should focus on the most common and business critical user journeys. Examples include:

• Logging in
• Searching for products
• Adding items to a cart
• Completing a purchase

Create user flows that represent a typical path through the application. Also consider negative paths and error handling.

Setting Performance Goals

Establish target metrics for response times, error rates, and resource utilization. Consider these questions:

• What is an acceptable response time for key transactions?
• How many concurrent users need to be supported?
• What is the maximum acceptable error rate?
• Which resources are likely to be exhausted first (CPU, memory, network, disk)?

Configuring the Test

Load testing tools allow specifying the number of virtual users, ramp up time, test duration, and other parameters. The test configuration should align with expected real-world traffic patterns.

Some other considerations:

• Simulate traffic from different geolocations if applicable
• Randomize data to avoid caching effects
• Set an appropriate pacing/think time between requests

Running the Test

During test execution, closely monitor application and infrastructure metrics in real-time. Key stats to watch:

• Response times
• Error rates and failed requests
• CPU and memory utilization
• Network traffic and bandwidth consumption
• Database connections and query performance

Be prepared to abort the test if serious issues arise that could cause an outage or data loss.

Analyzing Load Test Results

Reports and Metrics

Most tools provide out-of-the-box reports with graphs and charts visualizing performance over time. Look for trends and anomalies. Useful views include:

Metric	Description
Active Users	Number of concurrent users at a given point in time.
Response Times	Min, Max, Average, Median, 90th/95th percentile over time.
Requests per Second	Throughput rate. Are you meeting target capacity?
Error Rates	Percent of request failures. Spikes can indicate problems.
Resource Utilization	How much headroom is left? Are you close to exhaustion?

Correlating Results

Drilling down into the details, look for correlations between metrics. Examples:

• Do error rates increase as concurrency rises? The system may struggle under load.
• Do response times jump when CPU utilization exceeds a threshold? It may indicate a compute bottleneck.
• Are slow requests correlated with increased database connections or expensive queries? The database may need optimization.

Refining Tests

Based on the results analysis, identify areas for improvement in the application and infrastructure. Make necessary code optimizations, add caching, adjust thread pools and connection limits, or scale out to meet capacity.

Then re-run the load tests to validate the impact of the changes. Iterate as needed until performance is satisfactory.

Best Practices for Effective Load Testing

• Test early and often in the development lifecycle
• Use realistic data volumes and distributions
• Accurately simulate user behavior and network conditions
• Test in a staging environment that mirrors production
• Coordinate with all teams supporting the application
• Have a remediation plan in place to fix issues found
• Schedule tests during low traffic periods if testing in production
• Monitor real usage and adjust test plans over time

Load Testing Tools

There are many open source and commercial load testing tools available. Here are a few popular options:

Tool	Description	License
Apache JMeter	Fully-featured tool for testing web apps, APIs, databases and more.	Open Source
Locust	Python-based open source tool, good for APIs and microservices.	Open Source
Gatling	Scala-based tool designed for ease of use and maintainability.	Open Source
Tsung	High performance framework for testing web apps. Erlang based.	Open Source
LoadRunner	Comprehensive tool from Micro Focus for enterprise testing.	Commercial
NeoLoad	Popular commercial tool from Neotys with advanced analytics.	Commercial

Selection depends on factors like technology stack, required features, scale, and budget.

Frequently Asked Questions

How many concurrent users should I simulate in a load test?

This depends on your expected real-world traffic. Look at web analytics to determine peak concurrent users, then add a healthy buffer (25-50%) on top of that. Consider future growth projections as well.

How long should I run a load test?

Typical load tests run for 1-2 hours. However, soak tests that evaluate performance over an extended period can run for several hours to days.

What if my application keeps failing under load?

This indicates insufficient resources or bottlenecks in the code/architecture. Evaluate the failure symptoms to identify the weak points. Common culprits include inefficient algorithms, unoptimized database queries, resource leaks, or improper caching. Scale out infrastructure as needed.

Can I run load tests in a production environment?

Ideally, run load tests in a staging environment to avoid impacting real users. If you must test in production, run tests during periods of low traffic and closely monitor for adverse effects. Have a rollback plan in case of issues.

How frequently should load testing be performed?

At a minimum, load test before every major release or change in infrastructure. More frequent testing is better, especially for high traffic applications. Incorporate load testing into your continuous integration process if possible.

Conclusion

Load testing is a crucial practice to ensure your application performs well in production under heavy load. By accurately simulating real-world conditions and analyzing the results, you can find and fix issues before they impact end users.

Use the right tools, follow best practices, and test continuously for the best results. With a robust load testing strategy, you can release with confidence knowing your application can handle peak traffic.