Asynchronous programming has been pivotal in transforming software development. This approach’s parallel programming benefits have Improved the user experience. Simply put, it allows you to parallel-process requests and reduces the lag users may experience due to system limitations. However, async programming does have some bottlenecks that hamper software performance.
For example, an async/await loop for asynchronous programming can impact software performance. The state machine’s deep nesting of async call stacks causes this issue. In other words, your system works overtime to process multiple requests, hitting the software’s performance hard.
While this is not the only bottleneck, identifying it and finding solutions is crucial for your software’s performance. This article will allow you to identify critical async backend bottlenecks in your software and how to solve them.
Async Backend Bottlenecks You Need to Resolve
Asynchronous programming has been the go-to approach for many programmers to enable a high-end user experience. Developers prefer async programming to allow web applications to handle more traffic. This is especially important for eCommerce web apps that often see massive traffic during the sales season.
However, bottlenecks in your backend using async programming inadvertently impact the web app’s performance.
Callback Hell
Programmers often leverage NodeJS to build a robust backend but must realize the complexity. Usually wrapped in async functions, code pieces can become tangled and unreadable!
The culprit here is “Inherent complexity.”
Let’s understand this by comparing the sync and async codes.
Each process in sync code has a sequence. For example, if you process A, B, C, and D requests in sync code, they will be A, B, C, and D in the processed sequence.
But, if it’s async code, the sequence will be [A and B], [B and C], [C, D and A], and [A, D and B]. There is an inherent order where each process request follows another, causing a complex scenario.
In other words, your request execution flow is not a sequence. Therefore, you must ensure that each process needs a callback, which increases complexity.
How do you solve “callback hell?”
You can use indicators like “Promises” to ensure clean syntax. Promises help you handle asynchronous codes better than nested callbacks. You can chain multiple asynchronous operations using .then() instead of nesting the callbacks.
function fetchData() {
return fetch("https://mydomain.org/api/users/2").then((response) =>
response.json()
);
}
fetchData().then((data) => {
console.log(data);
});Another way to avoid callback hells is by using async/await. Software programmers can leverage this method to make asynchronous code like sync codes. It is like sugarcoating the promises approach.
async function fetchData() {
const response = await fetch("https://mydomain.org/api/users/2");
return response.json();
}
fetchData().then((data) => {
console.log(data);
});All the ‘async’ functions return a ‘Promise,’ and ‘await’ pauses the execution until a promise is resolved. Lastly, you can simply split the callbacks into smaller and separate functions, which are easy to maintain.
Async Rust
Async rust does provide significant advantages for programmers, such as reduced CPU usage, high performance without dynamic dispatch, and manageable concurrency. However, your web app can experience backend bottlenecks due to async rust.
For example, handling several concurrent connections in the async trust can be challenging and stressful for resources. Here, the problem lies in scale.
Async rust does reduce the overhead on your CPU usage, but as you scale, too many concurrent connections can impact performance and significantly impact network throughput.
Due to several concurrent connections, network throughput reduces, leading to increased latency.
So, should you avoid async rusts?
No, instead, you can use these strategies:
- Use memory pools to ensure re-usage of the resources
- Manually adjust the total concurrent connections to avoid CPU overhead
- Leverage IO multiplexing to process multiple IO events with a single thread to improve the network throughput.
Cache Stampede
Async backends can cause a cache stampede due to intense strain on the caching mechanism. This can impact web application performance and lead to backend services’ failure.
Multiple concurrent attempts to write the same data to a cache are the main causes of a caching stampede. This can lead to server overload and severely impact web app performance.
So, how can you resolve a caching stampede? You can use external recomputation to resolve the stampede. Generate caches independently to reduce concurrency and manage a surge in the total number of requests.
Event Loop Overload
Event loop overload is a backend bottleneck in which too many requests overwhelm the system. One key reason for event loop overload is the excessive use of timers and callbacks.
When you overuse timers and callbacks, several tasks can accumulate in the event loop, overloading the system. Suboptimal resource management and uncontrolled concurrency are other significant reasons for a backend bottleneck in your web application.
The best way to resolve event loop overload is to ensure efficient resource usage through caching, load balancing, and management. Further, you can leverage simplified data structures like arrays or linked lists to optimize the web app performance.
Error Handling
Any exception will be easier to handle if you use synchronous code for your web applications. However, with asynchronous code, there is a sequence for the requests to process, which makes exception handling difficult.
To resolve this, you can leverage a centralized error-handling mechanism applied across the codebase. Further, you can implement end-to-end logging and monitoring solutions to improve error handling.
Now that you know what type of async bottlenecks your backend can encounter, here are some significant impacts it can have on your system.
Impact Async Backend Bottlenecks on Software Performance
Asynchronous programming has a significant impact on web app performance.
- Database queries
- External API calls
- File I/O
These backend services become slower due to different issues in the backend, causing delays. Such delays can lead to slower responses and impact user experience. According to a Forbes report, 53% of consumers prefer a brand’s experience over a product or service. So, if you compromise on the user experience, the entire purpose of async programming becomes obsolete.
Further, these backend bottlenecks can limit your scaling capability. Yes, async codes can be overwhelmed by too many requests being processed in parallel. Not to forget, async function issues have cascading effects on other system components. It’s more of a domino effect that is causing issues for your web application.
All the issues in your backend do have solutions. However, you need more than solutions to individual bottlenecks to ensure seamless web app performance Best Practices.
Best Practices for Async Backend Performance
Backend issues vary depending on your programming language. However, some are common, like CPU overhead or event loop overhead. Solutions can be specific to each programming language, but here are some best practices to help you resolve most of the async backend bottlenecks.
1. Find Your Hot Code Path!
Hot code paths are execution paths where code is executed. They are specific zones in a code where execution occurs. They take a significant amount of time to execute, slowing the process. Identifying and optimizing such hot code paths is crucial to avoiding async bottlenecks.
This is where finding a software development company with experience in profiling tools becomes crucial for businesses. Profiling tools can identify hot code paths by sampling the stack traces during code execution. Further, you can leverage flame graphs to better visualize the hot code paths. Minimizing the allocation of large objects on hot code paths and caching can help optimize it.
2. Scramble Data Across Small Pages
A common practice for all web app developers is ensuring that a large amount of data is not loaded simultaneously. This can happen if you depend on one web-page for loading large objects. Especially returning a collection of large objects can lead to performance issues.
The best practice is to add pagination and reduce thread pool starvation. You can ensure parietal results are returned by leveraging the page size and index parameters. If an exhaustive result is needed, pagination can be used asynchronously, returning batches of results.
3. Parallel Process Promises
When you run multiple “Promises” and process their results ASAP, it can boost async performance. There are some possible “Promise combinators” that you can run in parallel.
- Promise.all() takes an array of promises as an argument and returns a single promise. It either resolves an array of promises or rejects with the reason of the first rejected promise,
- Promise.race() is similar to Promise.all(), but instead of keeping the reason for the first rejected promise, it keeps the reason for the first promise that settles.
You can use this promise when you need to return the result of the first promise that settles, whether it resolves or rejects.
Conclusion
Asynchronous programming is one of the crucial aspects of modern web development techniques. Developers in programming languages like Python, ASP.NET, Javascript, and #C use it to build high-performance web apps.
However, there are caveats leading to overhead CPU consumption, slower performance, and problems like callback hell. Using Promises and other best practices like async/await, you can reduce the impact of async backend bottlenecks.