Imagine your application is running smoothly.
Then your company launches a flash sale.
Or a marketing campaign goes live.
Suddenly, thousands of users start sending requests to the same endpoint at the same time.
The question is no longer:
“Does my API work?”
It’s:
“Can my API still work under heavy load?”
Handling high concurrency isn’t about a single optimization. It’s about building a system that remains reliable as traffic grows.
Here are some techniques I rely on.
1. Avoid Blocking Threads
In ASP.NET Core, every blocked thread reduces your application’s ability to handle incoming requests.
Prefer asynchronous APIs whenever possible.
await repository.GetOrdersAsync();
Using async and await allows the thread to serve other requests while waiting for I/O operations.
2. Optimize Database Access
The database is often the first bottleneck.
Reduce unnecessary work by:
- Selecting only the required columns.
- Adding proper indexes.
- Using
AsNoTracking()for read-only queries. - Avoiding the N+1 query problem.
A faster query means every request spends less time waiting.
3. Cache Frequently Requested Data
Not every request needs to reach the database.
Cache data that changes infrequently using solutions such as Redis or the in-memory cache.
Reducing database traffic significantly improves throughput.
4. Limit Expensive Operations
Some endpoints perform CPU-intensive or long-running work.
Protect your application with:
- Rate limiting.
- Background processing.
- Queues for non-critical tasks.
This prevents a small number of requests from consuming all available resources.
5. Use Optimistic Concurrency
When multiple users update the same record simultaneously, conflicts can occur.
EF Core supports optimistic concurrency using concurrency tokens such as RowVersion.
This helps prevent accidental overwrites.
6. Scale Horizontally
Sometimes optimization isn’t enough.
Run multiple application instances behind a load balancer so incoming requests are distributed across servers.
This increases capacity while improving availability.
7. Monitor Before You Optimize
Use tools such as:
- Grafana
- Prometheus
- Application Insights
- OpenTelemetry
Measure:
- Response time
- Error rate
- CPU usage
- Database latency
- Throughput
You can’t improve what you don’t measure.
Final Thoughts
High concurrency isn’t just about surviving traffic spikes.
It’s about designing systems that continue to respond quickly, remain reliable, and protect shared resources under pressure.
Performance isn’t achieved through one big optimization.
It’s the result of many small engineering decisions working together.
Leave a Reply