Unlocking High-Performance Rust Web Applications
When it comes to building fast and efficient web applications in Rust, the possibilities are endless. In this article, we’ll explore some essential techniques to analyze and improve the performance of your Rust web applications.
Setting Up for Success
To get started, you’ll need a recent Rust installation (1.45+) and a Python3 installation with Locust. Create a new Rust project and add the necessary dependencies to your Cargo.toml file. We’ll use Warp and Tokio to create a small web service.
Building a Minimal Web Service
Let’s create a basic Warp web service with a shared resource and a couple of endpoints to test. We’ll define some types, including a WebResult helper type and a Clients type, which is our shared resource. We’ll wrap it in a Mutex to guard access and use an Arc smart pointer to pass it around safely.
Load Testing with Locust
To test the performance of our web service, we’ll use Locust. Install Locust and create a locust folder in your project. Write a locustfile that defines a class based on HttpUser, which will give us all the Locust helpers within the class. We’ll define a @task called read, which makes a GET request to /read using the HTTP client Locust provides.
Running the Load Test
Run the load test using the command locust -f locustfile.py. Navigate to http://localhost:8089 and set the number of users you want to simulate and how fast they should spawn. This will create some load on the web service, helping us find performance bottlenecks and hot paths in the code.
Optimizing Locking Performance
Reviewing our code, we notice that we’re doing something inefficient when it comes to the Mutex lock. We acquire the lock, access the data, and then hold onto it for the whole duration of our fake DB call. We can optimize this by dropping the lock after we’re done using it and using an RwLock instead of a Mutex.
Flame Graphs
Next, we’ll use the cargo-flamegraph tool to collect performance data and visualize the results in a flame graph. This will help us identify where we’re spending most of our time during the load test. We’ll add a handler that extends the calculation to run inside a long loop, and then run cargo flamegraph to collect profiling stats.
Analyzing the Flame Graph
The resulting flame graph shows us where we’re spending most of our time during the load test. We can trace from the Tokio runtime up to our cpu_handler and the calculation. We notice that we’re spending a lot of time doing allocations due to unnecessary cloning.
Fixing the Allocation Issue
We can fix this by removing the unnecessary .cloned() call. Let’s run the sampling again and analyze the resulting flame graph. We’ll see a significant reduction in allocation time, and our code will be much faster.
Conclusion
In this article, we explored some essential techniques for measuring and improving the performance of Rust web applications. By using tools like Locust and cargo-flamegraph, we can identify performance bottlenecks and optimize our code for maximum efficiency. If you’re interested in diving deeper into performance optimization, check out The Rust Performance Book for more resources and techniques.