Rate Limit
Rate limiting defines the maximum number of requests an application can make to the API within a specified time period. This mechanism is crucial for maintaining the overall health and reliability of the services.
Why we use rate limiting
Rate limiting serves several important purposes:
API stability and performance
Prevents any single client from overwhelming our servers with too many requests
Ensures consistent response times for all users
Protects backend services from traffic spikes
Maintains predictable load patterns on the infrastructure
Security benefits
Defends against brute force attacks
Mitigates distributed denial-of-service (DDoS) attacks
Reduces the impact of poorly implemented client applications
Limits damage from compromised API credentials
Fair resource allocation
Ensures equitable access to shared API resources across all clients
Prevents aggressive clients from degrading service for other users
Helps prioritize traffic according to business needs
Encourages efficient API usage patterns
How rate limiting works
Each endpoint may have different limits, defined by:
Time window (per minute, hour, or day)
Application type (standard or enterprise)
Operation type (higher limits for read operations, lower for writes)
When you exceed your rate limit, you receive a HTTP 429
(Too Many Requests) response.
Example rate limit exceeded response
When you exceed the rate limit, you'll receive a response like this:
{
"message": "Too Many Requests"
}
Handling 429 too many requests errors
When you receive a 429
error, implement these strategies to handle it gracefully:
Implement exponential backoff with jitter
Exponential backoff is a retry strategy where you progressively increase the waiting time between retries:
// Pseudo-code example of exponential backoff with jitter
function fetchWithBackoff(url, maxRetries = 5) {
let retries = 0;
while (retries < maxRetries) {
try {
const response = await fetch(url);
if (response.status !== 429) {
return response; // Success or different error
}
// Calculate backoff time with jitter
const baseWaitTime = Math.pow(2, retries) * 1000; // 1s, 2s, 4s, 8s, 16s
const jitter = Math.random() * 0.5 * baseWaitTime;
const waitTime = baseWaitTime + jitter;
console.log(`Rate limited. Waiting ${waitTime/1000} seconds before retry ${retries + 1}/${maxRetries}`);
await sleep(waitTime);
retries++;
} catch (error) {
// Handle other errors
throw error;
}
}
throw new Error("Maximum retry attempts reached");
}
Review application behavior
Audit your code to identify inefficient API usage patterns
Look for unintended loops or redundant API calls
Consider batching multiple operations into single requests where applicable
Distribute your requests more evenly over time instead of sending them in bursts
Implement caching
Cache frequently accessed and rarely changing data
Implement proper cache invalidation strategies
Use appropriate cache timeouts based on data volatility
Consider using ETags or Last-Modified headers for conditional requests
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