This article explores the best alternatives to traffic shadowing for conducting efficient load testing and refactoring. Traffic shadowing has been a popular technique, however, it’s not without its challenges and limitations. We delve into several innovative methods that promise to provide deeper insights, improved accuracy, and greater efficiency. These alternatives are designed to help developers ensure their applications can handle traffic at scale while maintaining a high-quality user experience, highlighting the strengths and weaknesses of each approach to help you make informed decisions in your testing strategy.

What is Traffic Shadowing?

Traffic shadowing, also called traffic mirroring, is an approach for software testing that utilizes recorded user traffic as the source of data for later replay against a service. This duplication of production traffic allows for effective testing and refactoring with data that is based on the real world, sidestepping some of the significant drawbacks of trying to emulate data.

Notably, this approach does not affect typical data flow, and when properly sanitized and managed, also does not affect user privacy or generate other production data woes. This allows for real world data use with minimized real world data exposure.

Real-World Applications of Traffic Shadowing

Traffic shadowing has a ton of use cases, offering a strong test environment across different disciplines. This is especially true when you’re using the same traffic across all of your shadow testing, allowing for a continuity of testing throughout the production environment.

Traffic shadowing is particularly useful for:

• Load testing and performance optimization – shadow traffic can help with any load test, replicating the users and processes in the data pathway. This allows for a focus on the data that is actually processed as opposed to data that is emulated, mirroring the reality of actual network traffic for more accurate optimization.
• Refactoring and testing new features – by using real data, you can test customer response to a new feature, a new version, or new environments. This allows for relatively safe product iteration without having to push changes to your actual production client.
• Infrastructure scaling and performance testing – real data allows you to test any system or environment with real data. This approach grants significant control over both the environment and backend you aim to scale, while simultaneously managing potential frontend user experiences, facilitating a wide range of customizable testing scenarios. This ultimately results in higher capability and a more robust system output.
• API testing and validation – API testing and validation requires a holistic view of actual use and routing, especially when this data is going to be used to enable the development and iteration of a new service. Accordingly, using real data can have huge implications for this process.
• Real-time monitoring and analysis – when you capture user data, you are necessarily engaging in a monitoring and analysis process. This enables a wide variety of functions, including the ability to filter and compare traffic, to implement AB testing at scale, to test new critical path or feature toggles, and even to simulate novel request handling and route management.

Benefits of Traffic Shadowing

Traffic shadow has some major benefits compared to other solutions.

First, shadowing allows for the testing of new features using production traffic before you release them to production. This is a huge step up, as it eliminates a lot of the guess work and theoretical modeling that has traditionally gone into pre-development efforts.

Shadowing also allows for more substantial and detailed analysis and monitoring of traffic patterns and user paradigms. This method enables more effective error detection and resolution without impacting end users, allowing for errors to be validated and addressed before they affect the user experience. This extends to generate service and feature testing as well, allowing for zero impact across the user experience while unlocking substantial testing and iteration.

This strategy enables in-depth testing and development of infrastructure in ways unmatched by other solutions. Leveraging real-world data and user behavior allows for the realistic simulation of infrastructure scaling and performance, resulting in highly relevant and targeted tests. This approach is particularly beneficial for evaluating aspects such as autoscaling parameters and network rule sets, leading to more practical and effective outcomes and systems.

This recorded traffic can also be used to test fail states that have not yet occurred. Testing circuit breaker patterns or failovers without actual failure on the network means you can prepare for the future without having to provide a poor experience to the users of today, which is a huge benefit of this process.

Finally, employing the shadowing technique reduces the need to create extensive manual tests. By utilizing real user data, you can bypass much of the manual testing process. Errors in test design can lead to significant issues over time, such as setting wrong expectations for your customers, misinterpreting their requests, or failing to align with their fundamental needs and use cases. Leveraging actual traffic circumvents these problems, as it reflects genuine user intent and application usage for replay purposes.

Top Traffic Shadowing Alternatives

Now that we have a strong understanding of what traffic shadowing is, let’s look at some alternatives across the industry for implementing this process.

Speedscale

Speedscale is a highly effective solution focused on enabling real-time API monitoring and replay. It has substantial support and integration for Kubernetes, allowing for automated load testing, production simulation, and application performance and scalability validation across a wide variety of deployment environments.

Key Features

• Real-time API monitoring and replay
• Automation for load testing and production simulation
• Kubernetes integration for cloud-native applications

Benefits

• Rapid performance insights
• Accurate pre-production testing
• Improves resilience and scalability

GoReplay

GoReplay is an open-source tool that allows you to capture and replay HTTP traffic. It is relatively lightweight, and is relatively scalable. It does have a bit of a learning curve; that being said, its plugin-based flexibility makes it usable in a large range of use cases.

Key Features

• Captures and replays HTTP traffic
• Lightweight and scalable
• Open-source with custom plugin support

Benefits

• Low-cost traffic replay solution
• Adaptable for different testing environments
• Ideal for load testing without heavy infrastructure

JMeter

JMeter is a versatile load testing tool supporting various protocols like HTTP, FTP, and JDBC. It generates comprehensive reporting and has significant scripting capabilities. This does introduce some complexity, however it remains popular for very complex testing needs.

Key Features

• Load testing and performance analysis
• Protocol support (HTTP, FTP, JDBC, etc.)
• Detailed reporting and scripting capabilities

Benefits

• Versatile load testing across protocols
• High-level customizability for complex tests
• Comprehensive test metrics and insights

K6

K6 is a tool focused on load and reliability. It’s best used for test scenarios that are highly detailed, as its cloud-native scalability and testing infrastructure allows it to scale effectively depending on the complexity of each environment and testing regime.

Key Features

• Scripting in JavaScript for test scenarios
• Focused on load and reliability testing
• Scalable cloud execution

Benefits

• Developer-friendly scripting environment
• Effective for cloud-native testing
• Enables real-time performance feedback

NeoLoad

NeoLoad is a performance and load testing tool that offers automated testing. It integrates well with CI/CD pipelines, and is particularly effective for distributed and API-driven applications.

Key Features

• Automates performance and load testing
• Supports APIs and microservices
• Integrates with CI/CD pipelines

Benefits

• Simplifies performance monitoring in CI/CD
• Suitable for complex distributed environments
• Reduces testing time with automation

Gatling

Gatling is a high-performance load testing tool built with Scala. It’s relatively efficient for what it does, allowing for more complex load balancing and real-time monitoring without additional resource costs at scale.

Key Features

• Code-based load testing in Scala
• Provides real-time metrics visualization
• High performance with low resource consumption

Benefits

• Efficient testing for high-performance applications
• Offers a developer-friendly interface
• Scalable for large load tests without excessive resources

Conclusion

Traffic shadowing is a powerful tool for efficient load testing and refactoring. It allows for testing new features using production traffic before releasing them to production, which grants huge benefits at scale. By choosing the right tool and implementing best practices, developers can ensure that their application is performing as expected and is ready for production.

Sign up for a free trial of Speedscale today to begin experiencing the benefits of traffic replay to improve customer experience and platform scalability.