Unleash the Power of oha: Website Performance Testing Made Simple

Website performance is crucial for user experience and SEO. In this guide, you’ll learn how to use oha, a lightweight HTTP load testing tool, to measure your website’s performance under load. Whether you’re a developer or site owner, these tests will help you understand your site’s capabilities.

What is oha?

oha (おはよう) is:

• A modern, lightweight HTTP load testing tool
• Written in Rust for optimal performance
• Perfect for quick website performance testing
• Features real-time visualization of results

Key benefits:

Feature	Benefit
Speed	Fast execution with minimal resource usage
Simplicity	Single command operation
Visual Output	Real-time metrics display
Detailed Reports	Comprehensive performance statistics

Installation

Choose your operating system and follow these simple steps:

Linux:

cargo install oha

MacOS:

brew install oha

Windows:

winget install hatoo.oha

Basic Usage

Simple Test Command:

oha https://yourwebsite.com

Common Options Table:

Option	Description	Example
-n	Total requests	oha -n 200 https://site.com
-c	Concurrent users	oha -c 50 https://site.com
-q	Requests per second	oha -q 100 https://site.com
—no-tui	Disable visual interface	oha —no-tui https://site.com

Understanding Test Results

Let’s analyze a typical output:

Summary:
  Success rate: 100.00%
  Total:        0.6689 secs
  Slowest:      0.4123 secs
  Fastest:      0.0733 secs
  Average:      0.1557 secs
  Requests/sec: 299.0098

Key Metrics Explained:

Metric	What It Means	Good Values
Success rate	Percentage of successful requests	Should be close to 100%
Average time	Mean response time	Under 1 second
Requests/sec	Throughput capacity	Depends on your needs
Slowest	Worst response time	Should not be more than 3x average

Response Time Distribution

The percentile breakdown shows how your site performs across all requests:

• 50th percentile (median): Normal user experience
• 90th percentile: Slower but acceptable responses
• 99th percentile: Worst-case scenarios

Real Output:

╰─❯ oha https://www.bitdoze.com
Summary:
  Success rate: 100.00%
  Total:        0.6689 secs
  Slowest:      0.4123 secs
  Fastest:      0.0733 secs
  Average:      0.1557 secs
  Requests/sec: 299.0098

  Total data:   2.08 MiB
  Size/request: 10.64 KiB
  Size/sec:     3.11 MiB

Response time histogram:
  0.073 [1]   |
  0.107 [144] |■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■
  0.141 [5]   |■
  0.175 [0]   |
  0.209 [0]   |
  0.243 [0]   |
  0.277 [0]   |
  0.311 [5]   |■
  0.345 [15]  |■■■
  0.378 [18]  |■■■■
  0.412 [12]  |■■

Response time distribution:
  10.00% in 0.0803 secs
  25.00% in 0.0847 secs
  50.00% in 0.0935 secs
  75.00% in 0.3010 secs
  90.00% in 0.3617 secs
  95.00% in 0.3813 secs
  99.00% in 0.4108 secs
  99.90% in 0.4123 secs
  99.99% in 0.4123 secs


Details (average, fastest, slowest):
  DNS+dialup:   0.0811 secs, 0.0488 secs, 0.1036 secs
  DNS-lookup:   0.0001 secs, 0.0000 secs, 0.0005 secs

Status code distribution:
  [200] 200 responses

Basic Testing Scenarios

1. Quick Health Check

oha -n 100 https://yoursite.com

Purpose: Quick overview of site performance

2. Load Testing

oha -n 1000 -c 50 https://yoursite.com

Purpose: Simulate multiple concurrent users

3. Stress Testing

oha -n 2000 -c 100 -q 200 https://yoursite.com

Purpose: Find performance limits

Real-World Testing Examples

1. Testing API Endpoints

oha -n 500 -c 50 -m POST -T "application/json" -d '{"key":"value"}' https://api.yoursite.com/endpoint

API Testing Parameters:

Parameter	Description	Usage
-m POST	HTTP method	For API calls
-T	Content type	Specify data format
-d	Request body	Send data

2. Simulating Peak Traffic

oha -n 2000 -c 100 --disable-keepalive https://yoursite.com

Peak Traffic Settings:

• Higher concurrent connections (-c)
• Disabled keepalive for realism
• Larger number of requests (-n)

Interpreting Results

Performance Metrics Table

Metric	Good	Warning	Critical
Response Time	< 1s	1-3s	> 3s
Success Rate	> 99%	95-99%	< 95%
Requests/sec	Site-specific	20% drop	> 30% drop

Common Issues and Solutions

1. High Response Times

• Possible Causes:
  • Server resources maxed out
  • Database bottlenecks
  • Unoptimized code
• Solutions:
  • Implement caching
  • Optimize database queries
  • Scale server resources

2. Failed Requests

• Possible Causes:
  • Server timeout
  • Rate limiting
  • Network issues
• Solutions:
  • Increase timeout values
  • Adjust rate limits
  • Check network configuration

Best Practices for Load Testing

Do’s and Don’ts

✅ Do:

• Start with small tests
• Test during low-traffic periods
• Monitor server resources
• Test regularly
• Document results

❌ Don’t:

• Test production without warning
• Run tests from production servers
• Ignore error rates
• Test single endpoints only

Comparing oha with k6

k6 is a modern load testing tool by Grafana Labs that uses JavaScript for creating test scenarios. Unlike oha’s simple command-line approach, k6 allows you to write complex testing scripts that can simulate real user behaviors.

Feature Comparison

Feature	oha	k6
Ease of Use	★★★★★	★★★☆☆
Scripting Required	No	Yes
Real-time Metrics	Basic	Advanced
Learning Curve	Minimal	Moderate
CI/CD Integration	Limited	Extensive

When to Use Each Tool

Use oha for:

• Quick performance checks
• Simple HTTP testing
• Immediate results
• Command-line operations

Use k6 for:

• Complex user scenarios
• Detailed performance analysis
• CI/CD pipeline integration
• Custom test scripts

Conclusion

Website load testing with oha provides a straightforward and efficient way to measure and understand your site’s performance under various conditions. Through this guide, we’ve explored how to install and use oha, interpret its results, and apply best practices for effective load testing.

Remember that regular testing, careful documentation of results, and gradual scaling of test parameters are key to maintaining optimal website performance. Whether you’re managing a small blog or a complex web application, oha’s simplicity and powerful features make it an excellent choice for routine performance monitoring and load testing. As you implement these testing practices, focus on establishing baseline metrics, monitoring changes over time, and using the insights gained to continuously improve your website’s performance and user experience.