WebDevPro #132: Async Code That Looks Fine but Fails in Production

Crafting the Web: Tips, Tools, and Trends for Developers

Most Spring Boot projects stop at REST APIs

Most Spring Boot developers stop at REST APIs. That’s enough to build demos but not to build systems that survive production.

The real work sits beyond that. Service discovery, resilience, observability, config management are what separate working code from systems that hold up under pressure.

You don’t pick this up from tutorials. It comes from building systems, making trade-offs, and seeing them run.

Tomorrow, you get exactly that.

Live. From scratch. With Simon Martinelli and Josh Long.

Only 6 spots left.

---

Welcome to this week’s issue of WebDevPro!

Have you ever written async code that looked perfectly fine, only for it to behave unpredictably later? It’s one of those things that feels obvious while coding, but starts to fall apart once real conditions come into play.

Asynchronous programming is fundamental to Node.js. The event loop keeps everything moving, delegating work and picking it back up when results return. On paper, it feels straightforward. You write code in sequence, so it should run that way too.

But that’s where things get tricky.

Execution is shaped by timing, scheduling, and resource contention. These are not visible in the code itself. What looks sequential can run concurrently. What feels predictable can change under load. Many real-world issues don’t come from syntax errors, but from how async behavior interacts with shared resources and execution order.

This week’s deep dive breaks down where these assumptions fail and what it takes to make async systems behave reliably.

Before we get into it, here’s this week at a glance:

🟦 TypeScript is preparing for a compiler rewrite

📦 pnpm is redesigning how dependencies are stored

🤖 Next.js is pushing toward AI-first app development

⚡ Claude dropped SSR for speed gains

🧩 Storybook is becoming AI-readable infrastructure

When Async Execution Breaks Assumptions

A common source of failure is incorrect assumptions about execution order. Consider a case where two operations attempt to write to the same file. The code may appear sequential, but without explicit coordination, the operations execute independently.

In such scenarios, the following pattern is often observed:

async function raceCondition() {
const filename = ...
await unlink(filename)

writeFile(filename, ‘Written from first promise\n’, { flag: ‘a’ })
writeFile(filename, ‘Written from second promise\n’, { flag: ‘a’ })
}

At first glance, this code appears correct because two write operations are triggered one after the other. However, the absence of awaiting these operations means they run concurrently. Both operations attempt to access the same file at the same time.

This leads to inconsistent results, where the order of writes varies across executions. This behavior is known as a race condition. The issue is not syntax, but incorrect assumptions about how asynchronous execution works.

Concurrency Does Not Guarantee Order

In asynchronous systems, starting operations sequentially does not guarantee sequential execution. Each asynchronous call creates its own execution path, and these paths are resolved independently based on system timing.

When multiple operations target the same resource, they compete for access. Without explicit coordination, the runtime does not guarantee which operation completes first. The outcome becomes dependent on timing rather than intent.

This variability may not appear during development but becomes more prominent under production load, where multiple operations execute simultaneously.

Coordinating Access: Controlling Async Behavior

To prevent race conditions, access to shared resources must be controlled. One approach is to introduce a mechanism that ensures only one operation interacts with the resource at a time.

The following structure demonstrates this approach:

class FileWriter {
#isWriting = false
static instance = null

constructor() {
if (!FileWriter.instance) {
FileWriter.instance = this
}
return FileWriter.instance
}

async writeFile(filename, data) {
if (this.#isWriting) {
await setTimeout(250)
return this.writeFile(filename, data)
}

this.#isWriting = true
const result = await writeFile(filename, data, { flag: ‘a’ })
this.#isWriting = false
return result
}
}

This implementation introduces a lock mechanism. If a write operation is already in progress, subsequent operations wait before retrying. This ensures that writes occur sequentially rather than concurrently.

With coordination in place, execution becomes predictable. Without it, asynchronous code may behave inconsistently under real conditions.

Callback Hell: When Async Structure Breaks Readability

Asynchronous issues are not limited to execution order. They also affect how code is structured. Deeply nested callbacks create code that is difficult to read and maintain.

An example of this structure is shown below:

stepOne((err, resultOne) => {
stepTwo(resultOne, (err, resultTwo) => {
stepThree(resultTwo, (err, resultThree) => {
console.log(resultThree);
});
});
});

Although the code executes correctly, the nested structure makes it harder to follow the flow of data and control. Error handling is repeated at each level, increasing complexity.

As the number of steps increases, the difficulty of maintaining and debugging the code also increases.

The Event Loop and Hidden Blocking

Node.js relies on non-blocking operations to maintain performance. The event loop processes tasks and delegates work when possible, allowing other operations to continue executing.

However, not all operations are non-blocking. Some APIs perform blocking I/O, which pauses execution of the entire program until completion. This prevents the event loop from handling other tasks.

For example, cryptographic operations can block the main thread when executed synchronously. An asynchronous alternative allows work to be delegated externally:

generateKeyPair(”rsa”, { modulusLength: 1024 }, () => {})

The asynchronous version allows other operations to continue, while the synchronous version blocks execution. Under production load, blocking operations can significantly reduce system responsiveness.

Async Does Not Always Mean Non-Blocking

A common misconception is that wrapping a function in asynchronous code makes it non-blocking. This is not always true. If the underlying operation is blocking, it will still block execution.

In such cases, performance improvements come from reducing how often the operation runs rather than changing how it is invoked.

For example, caching results avoids repeated expensive computations:

let cachedSignature = null;

if (!cachedSignature) {
cachedSignature = signData(...)
}

This approach improves throughput by reducing execution frequency rather than altering execution style.

Async Behavior Under Load

Many asynchronous issues only become visible under load. In controlled development environments, operations often execute in predictable sequences, and resource contention is minimal. As a result, code that appears stable during testing can behave differently when multiple operations are triggered at the same time.

Race conditions become more apparent when concurrent requests attempt to access or modify the same resource. What may appear as an occasional inconsistency during development can become a frequent issue when the same code is executed repeatedly under higher traffic. The lack of coordination between asynchronous operations leads to unpredictable results, making these issues harder to reproduce and debug.

Blocking operations also have a more pronounced impact under load. When a synchronous task runs on the main thread, it prevents the event loop from processing other incoming requests. In low-traffic scenarios, this delay may not be noticeable. Under production conditions, where many requests arrive simultaneously, blocking behavior can cause cascading delays, reducing overall responsiveness.

Repeated execution of expensive operations further amplifies the problem. When the same computation is performed for every request without caching or reuse, system resources are consumed unnecessarily. This reduces throughput and increases response times, especially when multiple requests trigger the same operation concurrently.

Another important factor is timing variability. Asynchronous execution depends on system scheduling, resource availability, and workload distribution. Under load, these factors fluctuate more significantly, increasing the likelihood of inconsistent outcomes. Code that relies on implicit ordering or timing assumptions becomes less reliable as concurrency increases.

These issues highlight that asynchronous behavior is not only about writing non-blocking code, but also about understanding how that code behaves when multiple operations interact simultaneously. Without coordination, control over execution order, and careful management of shared resources, asynchronous systems can produce inconsistent results under real-world conditions.

Final words

Asynchronous programming enables Node.js to handle multiple operations efficiently, but it also introduces complexity in execution order, resource access, and performance.

Many failures are caused by incorrect assumptions about how asynchronous code behaves. These issues often remain hidden during development and only surface under production conditions.

Understanding how asynchronous code interacts with the event loop, shared resources, and system constraints is essential for building reliable applications.

This Week in the News

• 🟦 TypeScript 6.0 is really about TypeScript 7.0: TypeScript 6.0 quietly sets the stage for a bigger shift. This release moves the ecosystem closer to a Go-powered native compiler planned for TypeScript 7.0, with clear signals that performance and build speed are about to take a serious leap. It focuses less on surface-level features and more on groundwork that could reshape how large codebases compile and scale.

• 📦 pnpm 11 Beta just changed how dependencies are stored: pnpm 11 Beta offers a glimpse into where package management is heading. The shift to a SQLite-powered store improves lookup speed and reliability, while a broader config overhaul simplifies how projects define and share settings. Stricter build security is now enabled by default, reflecting a growing focus on supply chain safety. This release feels less like an incremental update and more like a rethink of how dependencies are stored and secured.

• 🤖 Next.js just made AI apps feel native: Next.js 16.2 leans deeper into AI-native development. The update reshapes how developers connect model output to real interfaces, tightening the loop between prompting and product. The direction is becoming clear. Next.js is evolving into a foundation for AI-powered applications, not just a frontend framework.

• ⚡ Why Claude dropped SSR for a Vite-powered setup: Anthropic’s team shared how they made Claude and its desktop apps meaningfully faster by moving away from SSR to a static setup using Vite and TanStack Router. The shift highlights a growing pattern where speed and responsiveness win over traditional rendering models, especially for AI-heavy interfaces that demand instant feedback.

• 🧩 Storybook MCP brings AI into your UI workflow: Storybook’s latest update introduces an MCP server that lets coding agents understand your components at a deeper level. Instead of guessing structure, AI can now access metadata, generate stories, write tests, and even help fix bugs with more context. It signals a shift where component libraries are no longer just for developers, but also for the tools assisting them.

Beyond the Headlines

• 🧠 Fix your Next.js errors without exposing your code: Debugging production errors in Next.js often means staring at unreadable stack traces. This guide walks through setting up source maps with Sentry so errors point back to your actual code, not minified chunks. The key detail is balance. You get full visibility in Sentry while keeping source maps out of the browser, which protects your code and improves debugging at the same time.

• 📊 Most open source projects are already accepting AI code: Phil Eaton surveyed 112 major source-available projects to understand how they handle AI-assisted contributions. The results show a clear trend. Most projects already allow or have accepted AI-generated code, with only a handful enforcing outright bans. The takeaway is not just policy, but reality. AI is already part of how modern open source evolves, and governance is still catching up

• ⚛️ The React quirks you hate are actually fundamental: Some of React’s most disliked patterns, like deferred state updates and dependency arrays, are not accidental complexity. This piece argues they reflect deeper constraints of asynchronous UI systems. Once you look past the friction, they reveal problems every framework eventually has to solve, even the ones trying to replace React.

• 🧩 Small programming tricks shape how your code scales: Tiny habits compound. This piece explores how small implementation choices, often dismissed as style or preference, quietly influence readability, maintainability, and long-term velocity. It is a reminder that good engineering is rarely about big rewrites. It is built through consistent, low-level decisions that add up over time.

Tool of the Week

✍️ Draw it once, animate it instantly

Stroke turns rough sketches into production-ready animations. You draw directly in the browser, and it generates Motion-based code you can drop into a React or Next.js component. Under the hood, it converts your strokes into SVG paths and animates them without manual setup, making it ideal for signatures, logos, or playful UI details.

That’s all for this week. Have any ideas you want to see in the next article? Hit Reply!

Cheers!

Editor-in-chief,

Kinnari Chohan

---

👋 Advertise with us
Interested in sponsoring this newsletter and reaching a highly engaged audience of tech professionals? Simply reply to this email, and our team will get in touch with the next steps.