Throttling controls or slows down requests when a system is receiving more traffic than it can safely process.
flowchart LR
C["Clients"] --> T["Throttler"]
T -->|"Normal Load"| API["Backend"]
T -->|"High Load"| Q["Delay / Queue / Limit"]
Q --> API1. Why Do We Need Throttling?
- Protect overloaded services
- Prevent cascading failures
- Control resource consumption
- Handle sudden traffic spikes
- Protect databases and downstream services
2. How Throttling Works
When traffic is within the system's capacity, requests are processed normally. When load becomes too high, the system can slow, queue, or reject requests.
flowchart LR
C["Incoming Requests"] --> T["Throttling"]
T -->|"Capacity Available"| API["Process Request"]
T -->|"Capacity Exceeded"| D["Delay / Queue / Reject"]3. Throttling vs Rate Limiting
| Rate Limiting | Throttling |
|---|---|
| Defines how many requests are allowed | Controls traffic when resources are constrained |
| Often rejects requests after the limit | Can slow, queue, or reject requests |
| Usually policy-based | Can be based on current system load |
4. Throttling with a Queue
Instead of immediately rejecting excess requests, the system can place them into a queue and process them at a controlled rate.
flowchart LR
C["Clients"] --> T["Throttler"]
T --> Q["Message Queue"]
Q --> W["Workers"]
W --> S["Backend Service"]This is especially useful for asynchronous workloads such as notifications, report generation, image processing, and background jobs.
5. Throttling Based on System Load
flowchart LR
C["Clients"] --> T["Throttler"]
M["System Load"] --> T
T -->|"Healthy"| API["Backend"]
T -->|"Overloaded"| Q["Queue / Delay"]A system can use signals such as CPU usage, queue depth, connection count, latency, or downstream capacity to decide how aggressively to throttle.
6. Example
Suppose a payment service can safely process 1,000 requests per second, but traffic suddenly reaches 5,000 requests per second.
flowchart LR
C["5000 Requests/sec"] --> T["Throttler"]
T -->|"1000/sec"| P["Payment Service"]
T -->|"Remaining"| Q["Queue / Controlled Response"]Without protection, the sudden load could increase latency, exhaust connections, overload the database, and potentially cause failures.
7. Where Can Throttling Be Implemented?
- API Gateway
- Load balancer / proxy layer
- Application service
- Message queue / worker layer
- Downstream dependency protection
Important Interview Questions
- What is throttling?
- Why do we need throttling?
- Rate limiting vs throttling?
- How do you handle sudden traffic spikes?
- How can a queue help with throttling?
- What happens when the queue becomes full?
- Where would you implement throttling in a microservices system?
- How can system load influence throttling?
Key Takeaways
- Rate limiting controls how many requests are allowed.
- Throttling controls or slows traffic when capacity is constrained.
- Throttling can delay, queue, or reject requests.
- Queues are useful for smoothing traffic spikes.
- Throttling protects backend services and downstream dependencies.
Conclusion
For interviews, remember: Traffic Spike → Throttling → Control Load → Protect Backend.