How platform engineers can simulate production without losing performance

As the complexity of cloud-native systems continues to grow, platform engineers are increasingly faced with the challenge of testing and validating new code before it hits production. Simulating production environments is a critical part of this process, allowing engineers to foresee issues that might not be visible in a standard development or staging environment. However, maintaining performance while replicating production environments for testing purposes has proven difficult.

This blog explores how modern platform engineering practices, like environment replication and automation, can help teams accurately simulate production environments while preserving system performance.

The importance of accurate environment replication

In traditional software development lifecycles, separate environments for development, staging, and production are an everyday occurrence. However, one of the most significant limitations of this approach is the inability to ensure that staging environments perfectly replicate production. This discrepancy often results in unexpected issues during deployment, even if all tests pass in the staging phase.

The reasons for this gap include:

• Inconsistent data: Staging environments often don’t reflect the volume and diversity of data found in production.
• Third-party dependencies: Many applications depend on third-party APIs or services, which are difficult to simulate accurately in a staging environment.
• Performance issues: Even when staging environments mirror production closely, they might have different infrastructure and performance characteristics, leading to inaccurate test results.

For platform engineers, simulating production is more than just testing features—it’s about ensuring that the application’s performance, scalability, and reliability remain intact under real-world conditions. Without an accurate production simulation, performance issues may remain hidden until the application is live, resulting in costly downtime and loss of user trust.

4 Key challenges in simulating production environments

Simulating a production environment that accurately mirrors the conditions under which applications run is challenging. Some of the primary challenges include:

1. Infrastructure and resource demands: A vital issue with replicating production is the sheer scale and resource intensity required. Cloud-native applications are often highly distributed, meaning that simulating an environment with the same infrastructure, network, and service configurations as production can be resource-intensive and costly.
2. Data fidelity: Real-world applications generate massive amounts of data. Capturing and replicating this data in a staging environment requires storage capacity and the ability to recreate real-world traffic patterns. Traditional data replication methods often result in incomplete datasets, making it difficult to identify performance bottlenecks and issues related to data handling.
3. Third-party API rate limits and costs: Many applications rely on third-party services for critical functions like payments, user authentication, or messaging. While these services typically offer sandbox environments for testing, they don’t always behave the same way as production APIs, which can introduce risks when deploying updates.
4. Hidden dependencies: Complex applications often have hidden dependencies between services, databases, and third-party tools. Identifying and replicating these dependencies in a test environment can be incredibly difficult, leading to missed issues during the testing phase.

Despite these challenges, platform engineers must simulate production as closely as possible to prevent catastrophic failures when code is deployed. Fortunately, new tools and practices, like Speedscale’s environment replication capabilities, are emerging to address these issues.

Speedscale’s approach to simulating production without performance loss

Speedscale tackles the problem of environment replication by combining service virtualization with network-level observability. The solution enables platform engineers to create accurate, lightweight simulations of production environments without replicating every single detail of the live environment.

Here’s how Speedscale manages to simulate production without sacrificing performance:

1. Capturing real-world traffic: One of Speedscale’s primary innovations is its ability to capture real-time traffic from production environments. Instead of replicating an entire production database, Speedscale monitors network traffic and captures data over a specified period—typically 15 minutes to an hour. This enables platform engineers to test their applications with real-world traffic patterns, ensuring that the simulation accurately reflects the usage patterns found in production.
2. Environment replication: Rather than relying on traditional staging environments, Speedscale uses environment replication to create lightweight, ephemeral environments that simulate production. These environments contain the necessary infrastructure, network configurations, and application data to mirror production but are scalable and cost-effective, minimizing the resource overhead often associated with running large-scale simulations.
3. Automated service virtualization: Speedscale can automatically generate mocks of backend services and third-party APIs. This is crucial for replicating external dependencies in a test environment. By virtualizing services, Speedscale can mimic real-world interactions between the application and its dependencies. It allows platform engineers to test how the application will perform under various conditions without relying on the actual third-party service. This also prevents issues like API rate limits or sandbox environment limitations from impacting the accuracy of the simulation.
4. Resource optimization: Speedscale’s significant advantage is its focus on minimizing resource use while providing a high-fidelity production simulation. Instead of running a full-scale replica of output at all times, Speedscale allows engineers to spin up temporary environments on demand. These environments can be scaled down once the tests are complete, resulting in considerable savings in cloud infrastructure costs and reducing the performance hit associated with maintaining large test environments.

Addressing performance issues with environment replication

While Speedscale’s environment replication capabilities provide a significant advantage, ensuring that these replicated environments are performant and do not degrade the application’s performance during testing is essential. By focusing on traffic and service simulation rather than attempting to replicate every aspect of production, Speedscale reduces the likelihood of performance bottlenecks in test environments.

Moreover, limiting the timeframe for data replication (e.g., simulating only 15 minutes of production traffic) allows engineers to conduct performance tests in a controlled manner without overwhelming the system. This is particularly valuable when dealing with high-traffic applications where running a full-scale simulation could be prohibitively expensive and resource-intensive.

Another benefit of this approach is the ability to conduct performance testing in a safe, controlled environment. By simulating production traffic and infrastructure without the risk of affecting live users, engineers can identify potential performance bottlenecks, latency issues, or failures early in the development lifecycle.

Leveraging observability to improve performance testing

One of the most compelling aspects of Speedscale’s solution is its integration with observability. Traditional performance testing tools like Grafana, Datadog, or New Relic provide valuable insights into system performance but often lack the granularity needed for environment replication. Speedscale’s network-level observability goes a step further, allowing engineers to monitor every aspect of the system, from API calls to database queries, in real-time.

By combining observability with environment replication, Speedscale provides a holistic view of the application’s performance under real-world conditions. Engineers can monitor how the application responds to simulated traffic and identify the areas where performance degrades.

Suppose a service responds slowly to a particular API request. In that case, Speedscale’s observability tools can pinpoint the exact moment and source of the issue, allowing engineers to optimize the service before it reaches production. This proactive approach to performance testing ensures that applications are fully optimized for production, reducing the risk of performance issues once deployed.

Simulating production without sacrificing performance

Today’s fast-paced development environments demand platform engineers to find ways to test and validate code under real-world conditions without compromising performance. Simulating production environments has always been a challenge. Still, tools like Speedscale are making it easier than ever to replicate real-world traffic and infrastructure while maintaining performance and reducing costs.

By capturing real-world traffic, virtualizing services, and leveraging network-level observability, Speedscale enables platform engineers to test their applications thoroughly before deployment. This reduces the risk of performance issues in production and helps teams move faster and deploy confidently.

As the complexity of cloud-native applications continues to grow, solutions like Speedscale will become increasingly vital for ensuring that platform engineers can simulate production environments without losing performance. The ability to test at scale, optimize resource use, and identify performance bottlenecks early will be crucial for businesses that want to stay competitive in an ever-evolving digital landscape.

This blog is based on a recent webinar with Matthew Laray, co-founder and CTO of Speedscale. The guest discussed how platform engineers can simulate production without losing performance. 

Watch the full video here.