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KnowledgeLayer 5Reliability Patterns

Circuit Breakers: Stop One Failure From Becoming Many

A circuit breaker prevents cascade failures by detecting when an external service is failing and temporarily stopping requests. When failures exceed a threshold, it trips open and fails fast instead of waiting for timeouts. For businesses, this means one broken integration cannot take down your entire platform.

Your payment integration times out. Your system retries. And retries. And retries.

200 requests pile up behind the failed one. Your database buckles. Your entire platform goes down.

One broken integration just took down everything. For hours.

The fastest way to recover from failure is to stop trying.

8 min read
intermediate
Relevant If You're
Systems with external API dependencies
AI workflows calling third-party services
Any integration that can fail and cascade

QUALITY & RELIABILITY LAYER - Stop one failure from becoming many.

Where This Sits

Where Circuit Breakers Fit

5
Layer 5

Quality & Reliability

Model Fallback ChainsGraceful DegradationCircuit BreakersRetry StrategiesTimeout HandlingIdempotency
Explore all of Layer 5
What It Is

What Circuit Breakers Actually Do

A safety switch for your integrations

A circuit breaker monitors the health of external services and integrations. When failures cross a threshold, it trips open and stops sending new requests. Instead of hammering a broken service (making things worse), the system fails fast and gracefully.

The pattern comes from electrical engineering. A circuit breaker protects your house when too much current flows. Trip the breaker, and the house stays safe while you fix the problem. Same principle applies to software: trip the breaker, and your system stays healthy while the external service recovers.

Without circuit breakers, a 5-second timeout on one API can become a 5-minute outage for your entire platform. With them, one failure stays contained as one failure.

The Lego Block Principle

Circuit breakers solve a universal problem: how do you stop one point of failure from cascading through an entire system? The same pattern appears anywhere dependent components can drag each other down.

The core pattern:

Monitor requests for failures. When failures exceed a threshold, stop sending new requests. Wait for recovery. Test with occasional requests. When successful, resume normal operation.

Where else this applies:

Communication systems - When email provider times out, stop queuing new emails and switch to backup or queue for later
Report generation - When data source becomes unresponsive, serve cached reports instead of blocking indefinitely
Payment processing - When processor fails, halt new charges and notify ops rather than accumulating failed transactions
External data sync - When API hits rate limits, pause sync and schedule retry window instead of burning through quota
Interactive: Circuit Breakers in Action

Watch one failure cascade through your system

Toggle the external service to "Down" and send requests. Watch your system crash. Then enable circuit breaker and try again.

Click to cycle: healthy / degraded / down

10/10
Connections Free
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Pending
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Success
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Failed
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Rejected (Fast)
System Health100%
Recent Requests
No requests yet. Click "Send Request" to start.
Try this: Toggle the external service to "Down" and send a burst of requests. Watch what happens to your connection pool and system health without circuit breaker protection.
How It Works

How Circuit Breakers Work

Three states that protect your system

Closed (Normal)

Requests flow normally

The circuit is closed. All requests pass through to the external service. The breaker monitors success and failure rates, tracking recent history to detect problems.

Pro: Full functionality, minimal overhead
Con: Must detect failures fast enough to prevent cascade

Open (Tripped)

Requests fail immediately

The circuit is open. New requests fail immediately without contacting the external service. This prevents piling more load on a broken service and protects your system from resource exhaustion.

Pro: Instant fail-fast, no resource drain, enables fallback behavior
Con: Must have graceful degradation ready

Half-Open (Testing)

Probing for recovery

After a timeout period, the breaker allows limited requests through to test if the service has recovered. Success closes the circuit; failure opens it again for another timeout period.

Pro: Automatic recovery detection
Con: Must tune probe frequency and success criteria

How Should You Configure Your Circuit Breaker?

Answer a few questions to get a recommended configuration for your situation.

How many requests per minute to this service?

Connection Explorer

"Why did our entire platform go down for 2 hours?"

The payment provider had a 5-minute outage. But without circuit breakers, every checkout request waited 30 seconds to timeout. Connection pools exhausted. The queue backed up. Other features that share resources went down. A 5-minute external issue became a 2-hour platform outage.

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Rate Limiting
Timeout Handling
Retry Strategies
Circuit Breakers
You Are Here
Graceful Degradation
Model Fallback Chains
Contained Failure
Outcome
React Flow
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Foundation
Quality & Reliability
Outcome

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Upstream (Requires)

Retry StrategiesTimeout HandlingRate Limiting

Downstream (Enables)

Graceful DegradationModel Fallback Chains
See It In Action

Same Pattern, Different Contexts

This component works the same way across every business. Explore how it applies to different situations.

Notice how the core pattern remains consistent while the specific details change

Common Mistakes

What breaks when circuit breakers go wrong

Setting thresholds too high

You configure the breaker to trip after 50 failures in a minute. But with 100 requests per second, by the time it trips, 3,000 failed requests have already piled up and your database connection pool is exhausted.

Instead: Set thresholds based on acceptable impact, not arbitrary numbers. 5-10 failures in 30 seconds is often enough to detect a problem.

Not having fallback behavior

The circuit breaker trips and starts returning errors immediately. But your application does not know what to do with those errors. Users see blank screens or cryptic error messages.

Instead: Design fallback behavior before implementing circuit breakers. Cached data, degraded functionality, or clear user messaging.

Using the same breaker for unrelated services

You use one circuit breaker for all external APIs. When the payment service fails, it trips the breaker and blocks your email sending, analytics, and everything else.

Instead: One circuit breaker per external dependency. Each service fails independently.

Frequently Asked Questions

Common Questions

What is a circuit breaker in software?

A circuit breaker is a design pattern that prevents cascade failures by monitoring external service health. When failures exceed a threshold, the circuit trips open and fails requests immediately instead of waiting for timeouts. This protects your system from resource exhaustion when dependencies fail. The pattern mirrors electrical circuit breakers that trip to prevent house fires.

When should I use circuit breakers?

Use circuit breakers whenever your system depends on external services that can fail: payment processors, email providers, third-party APIs, AI services. If a single service timing out could exhaust your connection pool or thread pool, you need a circuit breaker. They are especially critical for high-volume services where failures compound quickly.

What are the three states of a circuit breaker?

Circuit breakers have three states: Closed (normal operation, requests pass through), Open (tripped, requests fail immediately), and Half-Open (testing recovery with limited requests). Failures transition from Closed to Open. A timeout transitions from Open to Half-Open. Success in Half-Open returns to Closed; failure returns to Open.

How do I configure circuit breaker thresholds?

Start with 5 failures in 30 seconds for most services. High-volume services (hundreds of requests per second) can use tighter windows like 5 failures in 10 seconds. Critical services may trip after just 2-3 failures. The key is detecting problems before cascade effects, not waiting until damage is done. Tune based on observed failure patterns.

What is the difference between circuit breakers and retry logic?

Retry logic attempts the same request multiple times hoping for success. Circuit breakers stop all requests when failure is detected. They work together: retries handle transient failures, circuit breakers prevent retries from hammering a truly failed service. Without circuit breakers, retry logic makes cascade failures worse by multiplying load on failing services.

How do circuit breakers enable graceful degradation?

When a circuit breaker trips, your application knows the service is unavailable and can activate fallback behavior: serve cached data, switch to backup services, or present reduced functionality. Without circuit breakers, your code just sees timeouts and errors. The circuit breaker gives you a clear signal to trigger degradation logic.

Have a different question? Let's talk

Getting Started

Where Should You Begin?

Choose the path that matches your current situation

Starting from zero

You have no failure isolation between services

Your first action

Add a circuit breaker to your most critical external dependency. Start with 5 failures in 30 seconds.

Have the basics

You have circuit breakers but no fallback behavior

Your first action

Implement graceful degradation for each tripped circuit. Cached data, degraded mode, or clear messaging.

Ready to optimize

Circuit breakers work but configuration is guesswork

Your first action

Add monitoring for breaker state changes. Tune thresholds based on observed failure patterns.
What's Next

Now that you understand circuit breakers

You have learned how to stop failures from cascading through your system. The natural next step is understanding how to maintain partial functionality when components fail.

Recommended Next

Graceful Degradation

Maintaining partial functionality when components fail

Timeout HandlingRetry StrategiesModel Fallback Chains
Explore Layer 5Learning Hub
Last updated: January 2, 2026
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Part of the Operion Learning Ecosystem