Sudden and unusual spikes in traffic are common in Node.js applications. This is where rate limiting comes in. Basically, it ensures security, stability, and fair use of server resources. This process is a safeguard against malicious cyberattacks and accidental client errors.
In fact, a resilient implementation of rate limiting includes efficient in-process mechanisms. These help provide immediate protection through external, scalable solutions. These help handle the distributed load.
For instance, in insurance platforms, traffic spikes aren’t hypothetical. Rather, they happen during seasonal campaigns, mass policy renewals, or aggregator-driven quote requests.
A single misconfigured endpoint can trigger thousands of downstream calls to rating engines, document generators, and SMS gateways. Rate limiting ensures these workflows stay predictable and cost-controlled.
Therefore, read on to get a better idea of how to work with rate limiting in Node.js.
What Does Rate Limiting Mean?
At the outset, rate limiting means your requests are counted, and blocking occurs after a threshold. But in Node.js, edge cases show up fast:
- Multiple instances behind a load balancer
- IPv6 quirks
- “trust proxy” misconfiguration
- Bursty clients that are legitimate
- Time windows have boundaries you can exploit
The moment horizontal scaling kicks in, in-memory counters start lying because each instance enforces its own reality.
In fact, rate limiting is not only about stopping attackers. Rather, it is also about cost control and service health. For an insurance broker CRM, endpoints like “get quote” or “generate policy document” often fan out to multiple third-party services. This underwrites APIs, compliance checks, and payment gateways.
Without sensible limits, a bot or even a misbehaving script can dramatically inflate operational costs, turning a simple quote request into a chain of expensive calls.
Why Rate Limiting Becomes a Security Issue?
The OWASP API Security Top 10 bluntly frames the issue that APIs often fail to enforce consumption limits. Also, exploitation can be as simple as repeated requests, sometimes leading to denial-of-service attacks or direct cost spikes when a backend calls paid providers.
This vulnerability keeps showing up because teams ship features first and guardrails later. Hence, the fix is not one magical library. Rather, it is choosing an enforcement model that matches how traffic behaves and how infrastructure is shaped.
When a limiter is chosen, trade-offs are being made:
- accuracy vs. overhead
- simplicity vs. fairness
- local speed vs. distributed correctness
Fixed windows are easy but can be gamed at boundaries. Meanwhile, sliding windows are fairer but heavier. Moreover, token buckets handle bursts nicely, but can feel abstract when the product asks, “Why did this user get blocked right now?” And if it can’t be explained, it tends to get disabled.
In insurance, attackers may exploit open-quote APIs to scrape premium data or flood claim-submission endpoints, leading to denial-of-service attacks or inflated reinsurance costs.
OWASP’s warning about unrestricted resource consumption is especially relevant when each request can trigger paid services such as credit checks or fraud scoring.
Common Approaches to Rate Limiting
The following are the common approaches that show up in production teams:
|
Approach |
Where state lives |
Good at |
Typical failure mode |
Best fit |
|
In-memory fixed window |
Single Node.js process |
Simplicity, low latency |
Breaks with multiple instances, resets on restart |
Internal tools, single-instance apps |
|
Distributed fixed window (Redis counter + TTL) |
Shared Redis |
Horizontal scaling, easy ops |
Boundary bursts, “double-dip” around window edges |
Basic public APIs, quick protection |
|
Sliding window (Redis sorted set or counter approximation) |
Shared Redis |
Fairness, fewer boundary exploits |
More Redis work, careful atomicity needed |
Auth endpoints, expensive operations |
|
Middleware library (Express ecosystem) |
Depends on the store |
Plug-and-play, sane defaults |
Misconfiguration around proxies, wrong keying |
Most Express apps that want a baseline |
The “middleware library” line is worth calling out. For instance, express-rate-limit is a common baseline in Express projects. In fact, it is designed to limit repeated requests to endpoints like password resets, supports standard rate-limit headers, and can use external stores beyond memory.
For insurance apps, sliding-window algorithms are ideal for sensitive flows like OTP verification during policy purchase or claim filing. These endpoints require fairness and resilience because they often involve identity checks and fraud prevention systems.
A Baseline Implementation with “express-rate-limit.”
If something immediately usable is needed, express-rate-limit works as the outer guardrail. This is because it buys time and reduces noise.
The core configuration looks like the following:
- Choose a window
- Choose a limit
- Decide how headers behave
- Cecide whether proxy headers are trusted.
The package explicitly supports a built-in memory store and external stores, which is the bridge to “works in a cluster.”
import express from "express";
import { rateLimit } from "express-rate-limit";
const app = express();
// If you're behind a reverse proxy, configure trust proxy properly.
// app.set("trust proxy", 1);
const limiter = rateLimit({
windowMs: 15 * 60 * 1000,
limit: 100,
standardHeaders: "draft-8",
legacyHeaders: false,
// store: … // Use Redis/Memcached store when you scale out
});
app.use("/api/", limiter);Understand that a limiter is only as good as its key. Moreover, IP-based keys are fine until NAT, mobile networks, or shared office gateways come into play, and legitimate users get punished. Then the shift happens toward API keys, user IDs, or a hybrid model.
Where Do Fixed Windows Fall Apart?
Fixed windows have a specific issue: they reset at a clear boundary. That means a client can sometimes squeeze in a burst at the end of one window and immediately again at the start of the next.
In fact, Redis tutorials explain this plainly when contrasting fixed vs. sliding behavior. In real apps, this shows up as short spikes that still overload downstream dependencies, even with “rate limiting” in place.
If an app has endpoints that are computationally expensive, like report generation, PDF rendering, image conversion, or third-party verification calls, boundary bursts are not theoretical.
OWASP’s discussion of unrestricted resource consumption is essentially a reminder that requests consume CPU, memory, bandwidth, and sometimes paid-for provider resources.
Sliding Window with Redis
Sliding window rate limiting is a serious option. In fact, Redis itself demonstrates the idea: sliding windows reduce boundary exploits by evaluating a rolling time window rather than a hard bucket reset, and Redis sorted sets, along with Lua scripts, are a common approach to atomicity.
Timestamps are stored, old entries are pruned, what remains is counted, and then allow vs. block is decided. Although it sounds simple, the details matter under concurrency.
|
// Pseudocode-ish: sliding window log with Redis ZSET // Key: rl:{userOrIp}:{route} // 1) ZADD key now now // 2) ZREMRANGEBYSCORE key 0 (now - windowMs) // 3) ZCARD key -> count // 4) EXPIRE key windowSeconds // 5) if count > limit -> block |
It can be made more efficient with Lua, so steps 1 through 4 are atomic, but even the high-level flow is enough to reason about trade-offs. However, if operations are at a large scale, the sorted set approach can be more expensive.
In general, Redis works per-request rather than on a simple INCR. So, it is typically reserved for endpoints that need fairness most: login, password reset, OTP verification, and anything that fans out to expensive downstream services.
Practical Guidance
A limiter design is not complete unless blocking behavior is defined beyond “return 429.” Returning 429 is correct, but observability is also needed: logs, metrics, maybe even a small trace attribute that says which policy triggered. Otherwise, abuse tends to surface via angry user reports.
So, keep it restrained, though, no noisy log storms. In this case, two small rules that stay useful:
- Rate limit sensitive endpoints separately from general read traffic, because authentication and expensive operations have different risk profiles.
- Use distributed storage when more than one app instance exists, because local counters do not represent the global truth.
However, proxies still need to be configured correctly. If traffic is keyed by IP and proxy headers are misinterpreted, limiter keys can collapse into a single shared identity. That is when everyone gets blocked, and the limiter becomes the villain.
Insurance platforms should apply separate rate limits for high-risk operations. These include claims filing, KYC verification, and payment initiation. Also, it is important to keep broader read-only endpoints more permissive. This segmentation prevents fraud attempts from degrading customer experience for legitimate users.
