A healthy ASP.NET Core application can become unstable without a single bug in its business logic. All it takes is more work arriving than the system can process. Requests accumulate, queues grow, memory usage rises, database connections disappear, latency explodes, and eventually the entire application may fail. Backpressure prevents that chain reaction. Instead of accepting unlimited work, the system deliberately slows producers, limits concurrency, bounds queues, rejects excess requests, or sheds less important work.
In this guide, we'll build a practical backpressure strategy using ASP.NET Core rate limiting, bounded Channel<T> queues, concurrency controls, cancellation, and graceful degradation, while looking closely at the trade-offs that determine whether an overloaded application bends or breaks.
What Is Backpressure?
Imagine a busy restaurant kitchen.
Orders arrive every few seconds.
When the kitchen can prepare 40 meals every 10 minutes and customers order 30, everything works well.
Now 200 orders arrive.
The kitchen cannot magically cook five times faster.
If the restaurant keeps accepting unlimited orders, tickets begin covering the walls. Customers wait longer. Staff make mistakes. Ingredients run out. Eventually, even orders that could have been completed quickly become trapped behind hundreds of others.
Software behaves surprisingly similarly.
Suppose an ASP.NET Core service can reliably process:
500 requests per secondbut receives:
2,000 requests per secondWe have two rates:
Arrival rate: 2,000/sec
Processing rate: 500/secThe missing 1,500 requests do not disappear.
Unless the system deliberately controls them, they wait somewhere.
That is where the trouble begins.
Backpressure is how a system communicates that it cannot safely accept work as quickly as work is arriving.
Overload Is Different from Failure
This distinction is important.
A failed database is unavailable.
An overloaded database may still be perfectly healthy. It simply has more work than it can process efficiently.
Likewise, an ASP.NET Core application can be running normally while becoming progressively overloaded.
At first:
CPU: 45%
Latency: 80 ms
Queue: 10Then:
CPU: 80%
Latency: 450 ms
Queue: 2,000Eventually:
CPU: 100%
Latency: 12 seconds
Queue: 50,000
Memory: criticalBy the time health checks begin failing, the real problem may have started minutes earlier.
Backpressure attempts to stop the system from reaching that final state.
Why Unlimited Queues Are Dangerous
Queues are useful because they separate producers from consumers.
Suppose an API receives image-processing jobs.
Instead of making users wait while every image is processed, the endpoint adds jobs to a queue.
HTTP Request
↓
Job Queue
↓
Background Worker
↓
Image ProcessingThat seems resilient.
But what happens if requests arrive faster than the worker processes them?
If the queue has no meaningful limit:
10 jobs
100 jobs
10,000 jobs
500,000 jobsThe queue may simply move the overload somewhere else.
An in-memory queue consumes memory.
A durable broker consumes storage and increases processing delay.
A database-backed queue grows.
Users may receive an immediate “accepted” response while their job will not actually begin for hours.
A queue absorbs temporary bursts.
It does not create processing capacity.
The Basic Backpressure Equation
Every system has some sustainable throughput.
Conceptually:
Incoming Work <= Processing CapacityWhen this remains true, the system is stable.
When:
Incoming Work > Processing Capacityfor long enough, something has to happen.
We can:
Make producers wait
Queue a limited amount of work
Reject excess work
Drop replaceable work
Reduce the cost of each request
Scale processing capacity
What we should not do is pretend capacity is unlimited.
Backpressure Begins with Knowing Your Limits
Before setting limits, we need to understand what actually constrains the application.
It might be:
CPU
Memory
Database connections
Database throughput
HTTP connections
Thread-pool pressure
External API quotas
Queue consumers
Disk throughputConsider an endpoint that calls a payment provider.
Your ASP.NET Core server might handle thousands of concurrent requests.
But perhaps the payment provider safely supports only 100 concurrent operations.
Your application’s theoretical capacity does not matter.
The dependency is the bottleneck.
A good backpressure strategy protects the scarce resource, not merely the web server.
Concurrency Is Often More Important Than Request Rate
Suppose two endpoints each receive 100 requests per second.
Endpoint A takes 10 milliseconds.
Endpoint B takes 5 seconds.
Their impact is completely different.
Rate alone does not describe pressure.
For expensive work, we often care about how many operations are executing simultaneously.
ASP.NET Core’s current rate-limiting infrastructure includes a concurrency limiter that can restrict the number of simultaneous requests and optionally bound the number waiting for permits.
For example:
using System.Threading.RateLimiting;
builder.Services.AddRateLimiter(options =>
{
options.AddConcurrencyLimiter(
"expensive",
limiter =>
{
limiter.PermitLimit = 20;
limiter.QueueLimit = 10;
limiter.QueueProcessingOrder =
QueueProcessingOrder.OldestFirst;
});
});Then:
app.UseRateLimiter();
app.MapPost("/reports", GenerateReport)
.RequireRateLimiting("expensive");Now only 20 requests can perform that expensive work concurrently.
Another 10 may wait.
Beyond that, requests are rejected.
That rejection is not necessarily a failure of the architecture.
It may be exactly what keeps the architecture alive.
Rate Limiting and Backpressure Are Related, but Different
Rate limiting asks:
How much work may arrive over a period of time?
Backpressure asks:
What should happen when work arrives faster than we can safely process it?
Rate limiting is therefore one tool for implementing overload protection.
ASP.NET Core supports several rate-limiting approaches, including fixed-window, sliding-window, token-bucket, and concurrency limiters. Policies can be global or attached to selected endpoints. Microsoft recommends load testing rate-limiting configurations before production deployment.
The correct limiter depends on what we are protecting.
Different Endpoints Need Different Limits
A common mistake is setting one global number for the entire API.
Consider:
GET /products
POST /orders
POST /reports
GET /healthThese endpoints do dramatically different amounts of work.
A cached product request may cost almost nothing.
Generating a complex report might use significant CPU, memory, database time, and storage.
Treating them equally wastes capacity.
Instead, policies should reflect resource cost.
Products → generous limit
Orders → moderate limit
Reports → strict concurrency limit
Health → lightweight separate pathBackpressure becomes much more effective when it understands workload classes.
Protect Downstream Dependencies
Imagine:
ASP.NET Core API
↓
Payment ProviderYour API receives 2,000 requests per second.
The provider safely handles 200.
Without outbound control, your application becomes an amplifier.
It accepts huge amounts of work and sends the overload downstream.
A better architecture deliberately limits calls to the dependency.
.NET’s current resilience libraries support strategies including rate limiting and concurrency limiting as part of resilience pipelines. Microsoft distinguishes inbound rate control from outbound concurrency limiting, which can protect dependencies from excessive parallel work.
Conceptually:
2,000 incoming
↓
ASP.NET Core
↓
Concurrency Control
↓
200 safe operations
↓
DependencyProtecting your own service while destroying the next service is not resilience.
Bounded Channels
For in-process producer-consumer workloads, System.Threading.Channels gives us an excellent backpressure primitive.
Instead of:
Channel.CreateUnbounded<Job>();we can create:
var channel = Channel.CreateBounded<Job>(
new BoundedChannelOptions(100)
{
FullMode = BoundedChannelFullMode.Wait
});The queue can contain at most 100 items.
When it fills, producers do not continue adding unlimited work. With Wait, asynchronous writes wait until capacity becomes available. Microsoft explicitly describes this situation as the writer experiencing backpressure when producers outrun consumers.
That small difference changes the stability characteristics of the entire system.
A Practical Background Queue
Let’s create a simple queue.
public sealed record ProcessingJob(
Guid Id,
string FileName);
public sealed class JobQueue
{
private readonly Channel<ProcessingJob> _channel;
public JobQueue()
{
_channel =
Channel.CreateBounded<ProcessingJob>(
new BoundedChannelOptions(100)
{
FullMode =
BoundedChannelFullMode.Wait,
SingleReader = true,
SingleWriter = false
});
}
public ValueTask QueueAsync(
ProcessingJob job,
CancellationToken cancellationToken)
{
return _channel.Writer.WriteAsync(
job,
cancellationToken);
}
public IAsyncEnumerable<ProcessingJob>
ReadAllAsync(
CancellationToken cancellationToken)
{
return _channel.Reader.ReadAllAsync(
cancellationToken);
}
}A worker consumes the queue:
public sealed class ProcessingWorker : BackgroundService
{
private readonly JobQueue _queue;
public ProcessingWorker(JobQueue queue)
{
_queue = queue;
}
protected override async Task ExecuteAsync(
CancellationToken stoppingToken)
{
await foreach (
var job in _queue.ReadAllAsync(stoppingToken))
{
await ProcessAsync(job, stoppingToken);
}
}
private Task ProcessAsync(
ProcessingJob job,
CancellationToken cancellationToken)
{
// Perform the real work.
return Task.CompletedTask;
}
}Now the producer and consumer have an explicit capacity relationship.
What Does “Wait” Really Mean?
Suppose the queue contains 100 items.
Another producer calls:
await queue.QueueAsync(job, cancellationToken);With BoundedChannelFullMode.Wait, that write waits asynchronously until space is available.
This propagates pressure backward.
Worker slow
↓
Queue fills
↓
Producer waits
↓
Request slowsThat may sound undesirable.
But compare it with:
Worker slow
↓
Queue grows forever
↓
Memory rises
↓
GC pressure rises
↓
Everything slows
↓
Process diesWaiting can be healthier than pretending everything is fine.
Waiting Forever Is Not Backpressure
There is another trap.
Suppose the queue is full and an HTTP request waits 15 minutes for capacity.
Technically, we bounded the queue.
Operationally, the user experience is terrible.
Backpressure needs limits at multiple levels.
For example:
using var timeout =
CancellationTokenSource.CreateLinkedTokenSource(
httpContext.RequestAborted);
timeout.CancelAfter(TimeSpan.FromSeconds(2));
try
{
await queue.QueueAsync(job, timeout.Token);
return Results.Accepted();
}
catch (OperationCanceledException)
{
return Results.StatusCode(
StatusCodes.Status503ServiceUnavailable);
}Now the system gives the queue a short opportunity to recover.
If it cannot, we stop waiting.
Sometimes Dropping Work Is Correct
Not every piece of work deserves to wait.
BoundedChannelFullMode supports several behaviors when capacity is exhausted:
Wait
DropNewest
DropOldest
DropWriteMicrosoft’s current Channels documentation defines these modes explicitly. Wait blocks asynchronous producers until capacity becomes available, while the drop modes discard particular items when the channel is full.
Which one should we use?
It depends entirely on what the data means.
Never Drop Business-Critical Commands Casually
Imagine the queue contains:
ChargeCustomer
ShipOrder
CancelSubscriptionDropping one silently is unacceptable.
For durable business operations, we may need a persistent message broker, durable queue, or transactional design instead of an in-memory channel.
Now imagine the queue contains:
Refresh dashboard temperature
Refresh dashboard temperature
Refresh dashboard temperatureIf the dashboard only needs the latest reading, keeping stale updates may be pointless.
Dropping old telemetry can be completely reasonable.
Backpressure is not just a technical decision.
It is a business semantics decision.
Freshness Can Matter More Than Completeness
Consider a live monitoring dashboard.
Updates arrive:
10:01:01 CPU 40%
10:01:02 CPU 42%
10:01:03 CPU 44%
...
10:01:30 CPU 91%If the consumer falls behind, displaying every historical intermediate value may mean the user sees conditions from 30 seconds ago.
Dropping older updates could be better.
For financial transactions, that would be disastrous.
For live telemetry, it might be exactly right.
Ask:
Does the consumer need every event, or does it need the latest state?
The answer changes the backpressure policy.
Load Shedding
Sometimes the safest response to overload is simply:
No.
This is load shedding.
Instead of accepting work we are unlikely to complete successfully, we reject it early.
For an HTTP API, that might mean returning an appropriate overload response such as 429 Too Many Requests for rate-limited traffic or, depending on the failure mode and API contract, 503 Service Unavailable.
ASP.NET Core’s rate-limiting middleware provides rejection handling and can expose retry information for limiter algorithms that can estimate when permits will become available.
Early rejection protects resources.
Late failure wastes them.
Reject Before Expensive Work Begins
Consider this sequence:
Authenticate
↓
Load 50 MB payload
↓
Query database
↓
Allocate large objects
↓
Discover system overloaded
↓
RejectWe have already spent substantial resources.
Where possible, overload controls should operate before expensive work begins.
Request
↓
Cheap validation
↓
Capacity check
↓
Accepted?
├── No → Reject
└── Yes → Expensive workThis is one reason middleware and endpoint-level limiting can be effective.
Queueing Has a Latency Cost
Suppose an operation normally takes:
200 msWe allow 1,000 requests to queue.
Under overload, the 1,000th request may wait far longer than 200 milliseconds before processing even begins.
The service may technically be completing every request.
But the latency distribution becomes terrible.
This is why queue size should not be chosen simply based on available memory.
Ask:
How long are we willing to make a user wait?
A useful queue capacity follows from acceptable waiting time and processing throughput.
Little’s Law Gives Us a Useful Mental Model
Queueing theory provides a useful relationship:
Items in system
≈
Arrival rate × Time in systemIf throughput is 100 jobs per second and we allow 10 seconds of waiting, hundreds or thousands of operations can accumulate depending on the workload.
The exact mathematics can become sophisticated, but the practical lesson is simple:
Large queues create large latency.
A queue of 100,000 is not automatically safer than a queue of 1,000.
It may merely hide overload longer.
Cancellation Is Part of Backpressure
Suppose a client disconnects while an expensive request is still running.
If the result is no longer useful, continuing the work wastes capacity.
ASP.NET Core exposes request cancellation through:
HttpContext.RequestAbortedPass cancellation tokens through the application.
app.MapGet(
"/report",
async (
HttpContext context,
ReportService reports) =>
{
return await reports.GenerateAsync(
context.RequestAborted);
});Then continue passing the token to operations that support cancellation.
Client leaves
↓
Request cancelled
↓
Database/API work cancelled where supported
↓
Capacity becomes availableCancellation is not merely a convenience.
During overload, it can reclaim scarce resources.
Timeouts Also Release Capacity
A downstream call that normally takes 100 milliseconds begins taking 30 seconds.
Without a timeout, requests pile up.
Request 1 waiting
Request 2 waiting
Request 3 waiting
...
Request 5,000 waitingTimeouts establish a limit on how long resources can remain committed to work that is no longer useful.
This connects directly with the resilience patterns we covered earlier.
Retries, however, require special caution.
Retries Can Make Overload Worse
Suppose a dependency is overloaded.
Requests fail.
Every caller retries three times.
Now:
1,000 original requestscan become thousands of attempts.
The system is already struggling, and retries generate even more work.
This is sometimes called a retry storm.
Our earlier Advanced Retry Strategies article discussed exponential backoff and jitter. During overload, those ideas become especially important.
Retries should be:
Limited
Delayed
Used only for appropriate transient failures
Combined with cancellation and circuit breaking
Resilience patterns must cooperate.
A retry policy that ignores system capacity can reduce resilience instead of improving it.
Graceful Degradation
Rejecting requests is not our only option.
Sometimes we can make requests cheaper.
Imagine an e-commerce homepage normally includes:
Product catalog
Personal recommendations
Live inventory
Reviews
Recently viewed
PromotionsDuring extreme load, perhaps recommendations are temporarily unavailable.
The application can still return:
Product catalog
Live inventory
PromotionsThe experience is reduced, but useful.
This is graceful degradation.
Define Critical and Optional Work
A useful architecture explicitly distinguishes:
Critical
Important
OptionalFor checkout:
Payment Critical
Inventory reservation Critical
Recommendation update Optional
Analytics event OptionalWhen resources become scarce, optional work should not compete equally with payment processing.
That might mean:
Separate queues
Separate concurrency limits
Lower-priority processing
Dropping non-critical telemetry
Temporarily disabling expensive enhancements
A system that knows what matters most can degrade intelligently.
Bulkheads Prevent One Workload from Consuming Everything
Imagine two operations sharing the same application:
Checkout
ReportingReporting becomes extremely busy.
If both workloads share every resource without limits, reporting may consume all available capacity.
Checkout slows down.
A bulkhead-style design isolates capacity.
Checkout
↓
Dedicated capacity
Reporting
↓
Limited capacity.NET’s resilience tooling includes rate-limiting and concurrency-limiting strategies that can help constrain workloads and dependencies.
The principle is similar to watertight compartments on a ship.
One flooded compartment should not sink everything.
Backpressure Across Service Boundaries
Now imagine:
API
↓
Orders
↓
Payments
↓
Fraud
↓
External ProviderFraud slows down.
Payments begins waiting.
Orders begins waiting.
The API begins waiting.
Pressure travels backward through the dependency chain.
That is actually useful information.
The dangerous alternative is allowing every layer to create an enormous queue.
API queue
↓
Orders queue
↓
Payments queue
↓
Fraud queueNow nobody knows where the real bottleneck is, and requests may spend minutes moving between queues.
Distributed systems need an intentional overload strategy across boundaries.
Backpressure and Message Brokers
Durable brokers such as RabbitMQ or Azure Service Bus can absorb larger bursts than an in-memory queue.
But the same principle remains.
Suppose producers generate:
10 million messages/hourwhile consumers process:
5 million messages/hourThe backlog grows by five million every hour.
A durable queue prevents immediate memory exhaustion, but the system is still unsustainable.
Monitor:
Queue depth
Oldest message age
Enqueue rate
Completion rate
Consumer utilization
Failure rateQueue age is particularly important.
A queue of 10,000 messages may be fine if they are processed in five seconds.
A queue of 500 may be disastrous if the oldest message has been waiting six hours.
Observability Makes Backpressure Measurable
We should know when the system is applying pressure.
Useful metrics include:
Current queue depth
Queue capacity
Rejected requests
Dropped items
Time waiting for capacity
Active operations
Available concurrency permits
Request latency
Dependency latency
Cancellation countASP.NET Core’s rate-limiting middleware exposes built-in metrics that can help monitor how limiting affects application behavior.
These signals tell us not just that the application is overloaded, but how it is protecting itself.
Watch Percentiles, Not Just Averages
Suppose average latency is:
250 msThat sounds healthy.
But perhaps:
p50 = 100 ms
p95 = 900 ms
p99 = 8 secondsA small but meaningful group of users is having a terrible experience.
Queueing often appears in tail latency before averages look alarming.
Watch p95 and p99 latency alongside queue depth and rejection rates.
Those signals can reveal pressure much earlier.
Autoscaling Is Not a Substitute for Backpressure
A common response to overload is:
Add more instances.
Scaling is useful.
But it is not instantaneous, unlimited, or always effective.
Suppose ten API instances all connect to the same database.
Adding another 50 instances may simply generate more database traffic.
More API instances
↓
More database connections
↓
Database overloadBackpressure remains necessary even in autoscaled systems.
Scaling increases capacity.
Backpressure protects capacity.
We often need both.
Backpressure at the Edge
Our recent Edge Computing article introduced workloads operating close to users and devices.
Backpressure becomes especially important there because edge hardware often has fixed resources.
A small edge computer may have:
4 CPU cores
8 GB RAM
Limited storage
Intermittent connectivityThousands of sensors cannot be allowed to create unlimited local work.
The edge service might:
Sample telemetry
Drop stale readings
Batch updates
Bound local queues
Prioritize alarms
Delay cloud synchronization
Again, the policy depends on the meaning of the data.
A safety alarm must not be treated like a routine temperature sample.
Backpressure and Dynamic Configuration
Our previous article explored Configuration at Scale in ASP.NET Core.
Many overload controls are natural candidates for configuration:
QueueCapacity
MaximumConcurrency
RequestTimeout
BatchSizeBut changing them dynamically requires care.
Suppose:
QueueCapacity = 10,000changes to:
QueueCapacity = 100while 8,000 items already exist.
What happens?
Configuration can change a number.
It cannot automatically define the transition semantics.
As we discussed in the previous article, dynamic settings need safe operational boundaries.
A Complete Overload Strategy
Let’s imagine a document-processing API.
Users upload documents that require expensive analysis.
A robust architecture might look like:
Client
↓
ASP.NET Core
↓
Request Rate Limit
↓
Validation
↓
Bounded Job Queue
↓
Worker Pool
↓
Concurrency Limit
↓
Document ProcessorNow suppose traffic spikes.
First, the queue absorbs a small burst.
Then it reaches its configured capacity.
Producers begin waiting briefly.
If capacity does not return quickly enough, new work is rejected.
Workers remain at controlled concurrency.
The processor never receives more parallel work than it can safely handle.
Metrics show increasing queue depth and rejection rates.
Autoscaling may add consumers if infrastructure allows it.
The system becomes slower or rejects some work.
But it remains alive.
That is successful overload handling.
What Failure Looks Like Without Backpressure
Now remove the limits.
Traffic spike
↓
Unlimited requests accepted
↓
Unlimited jobs queued
↓
Memory increases
↓
Workers increase concurrency
↓
Database connections exhausted
↓
Latency increases
↓
Requests time out
↓
Clients retry
↓
More traffic
↓
Application collapsesNotice the feedback loop.
Slow systems create retries.
Retries create additional load.
Additional load makes systems slower.
Backpressure breaks that loop before it becomes catastrophic.
What Good Overload Looks Like
This sounds strange, but a well-designed overloaded system may intentionally return errors.
Imagine:
95% requests succeed normally
5% rejected quicklyCompare that with:
100% requests accepted
100% become extremely slow
70% eventually time outThe first system may appear less friendly because it says “no.”
In reality, it protects the majority of users.
Fast rejection is often better than false acceptance.
Choosing the Right Strategy
There is no universal backpressure policy.
For each workload, ask:
Can the producer wait?
If yes, bounded waiting may work.
Can the work be dropped?
If yes, choose what can safely be discarded.
Must the work eventually complete?
Use durable storage or messaging rather than relying solely on memory.
Is freshness more important than completeness?
Consider dropping stale updates.
Is the dependency concurrency-sensitive?
Limit simultaneous operations.
Can functionality degrade?
Disable or simplify optional work.
Can the caller retry later?
Reject quickly and communicate that clearly.
These are architectural decisions, not just framework settings.
Test the Breaking Point
You cannot design overload protection entirely from assumptions.
Load testing should answer questions such as:
At what throughput does latency rise sharply?
Which dependency saturates first?
How large can queues become?
When do requests begin timing out?
Does memory remain stable?
Does rejection happen early enough?
Does the application recover when load falls?Microsoft specifically recommends stress testing ASP.NET Core rate-limiting policies before deploying them to production.
That last question is especially important.
A resilient system should not merely survive overload.
It should recover cleanly afterward.
Recovery Matters
Suppose traffic spikes for five minutes and then returns to normal.
If the application accepted a massive backlog during those five minutes, it may remain overloaded for another hour.
The incident continues after the traffic spike ends.
With bounded queues and controlled admission:
Traffic spike
↓
Capacity reached
↓
Excess rejected
↓
Traffic normalizes
↓
Small backlog clears
↓
Normal operation resumesBounded systems recover more predictably because the amount of accumulated work is itself bounded.
Do Not Optimize for Zero Rejections
A production system that never rejects work may simply have enormous unused capacity.
Or it may be accepting work it cannot complete.
The goal is not:
Reject nothingThe goal is:
Provide the best useful service
within safe operating limitsDuring normal conditions, users should rarely encounter backpressure.
During abnormal conditions, backpressure should prevent a localized capacity problem from becoming a total outage.
How This Fits Our ASP.NET Core Journey
Our recent articles have been moving steadily from building individual ASP.NET Core features toward operating entire production systems.
.NET Aspire showed us how distributed resources can be orchestrated, discovered, and observed together.
Configuration at Scale showed us how operational behavior can change safely without redeploying every service.
Backpressure introduces another essential production concern.
Even a perfectly orchestrated, correctly configured application has finite capacity.
Eventually, some resource reaches its limit.
At that moment, the architecture needs to know what to do.
Wait?
Queue?
Reject?
Drop?
Degrade?
Scale?
The worst answer is to have no answer at all.
Coming Next
In the next article, we’ll explore Graceful Shutdown and Application Lifecycle in ASP.NET Core: Deploy Without Dropping Work.
Backpressure protects an application while it is running under load. But production systems also need to stop safely.
We’ll examine what happens to active HTTP requests, background jobs, queues, database operations, and external calls when an ASP.NET Core instance receives a shutdown signal, and how cancellation tokens, readiness changes, draining, shutdown timeouts, and application lifecycle hooks can help deployments complete without silently losing work.
Final Thoughts
Backpressure is ultimately about accepting that every system has limits.
CPU is finite.
Memory is finite.
Database connections are finite.
External APIs have limits.
Workers can process only so much work at once.
Ignoring those limits does not make them disappear. It simply delays the point where they become visible, usually until the application is already in trouble.
ASP.NET Core and modern .NET give us strong building blocks for handling overload deliberately. Rate limiting can control incoming traffic. Concurrency limiting can protect expensive resources. Bounded channels can prevent in-process queues from growing indefinitely. Cancellation and timeouts can release capacity that is no longer useful. Load shedding and graceful degradation can preserve critical functionality when full service is impossible.
The most important change, however, is architectural.
Do not ask only:
How much traffic can our application handle?
Ask:
What exactly will our application do when traffic exceeds that amount?
If the answer is deliberate, measurable, and tested, overload becomes something the system can manage rather than something that unexpectedly takes it down.
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