Dealing with API rate limits can be frustrating, but there are several ways to avoid or work around hitting the limit. In this comprehensive guide, we’ll explore common causes of rate limiting, strategies to prevent hitting the threshold, and techniques to recover once you’ve gone over the API call quota.
What causes API rate limits?
APIs enforce rate limits to prevent abuse and ensure fair usage across all users. The specific thresholds and algorithms vary between providers, but some common causes of hitting rate limits include:
- Making too many requests in a short period of time
- Sending requests too frequently without proper pacing
- Having multiple client applications all accessing the API concurrently
- Sudden spikes in traffic that exceed normal usage
- Bugs or inefficient code that result in redundant API calls
Many APIs have both an overall request limit as well as a per-endpoint or per-method restriction. Going over either threshold can trigger rate limiting.
Strategies to avoid rate limits
Here are some proactive strategies to prevent hitting API rate limits:
Understand the specific rate limits
Carefully read through the API provider’s documentation to understand the specific rate limits for that service. Look for details on:
- The overall request quota – how many requests are allowed per day, hour, minute etc.
- Limits for specific endpoints or methods
- How counts reset – per day, hour, rolling time period etc.
Knowing the exact thresholds will help you throttle requests appropriately.
Throttle and pace your requests
Don’t bombard the API with a flurry of concurrent requests. Pace out calls gradually using delay/wait mechanisms to stay under the request limit.
Some tips:
- Add a delay of 1+ seconds between requests
- Use exponential backoff retries for failed requests
- Random jitter delays to avoid traffic spikes
- Concurrent request limits if using multiple threads
This both paces your app and avoids triggering abuse detection mechanisms.
Use caching and request optimization
Avoid duplicate requests by caching data locally whenever possible. Here are some optimization tips:
- Cache frequently needed resources/references
- Set far future cache headers to reuse data
- Use conditional GETs to fetch updated resources
- Consolidate multiple requests into a single batch call
This reduces the number of API requests made overall.
Distribute requests across multiple IPs
If your app is making requests from a single outbound IP, consider distributing across multiple servers or IP pools.
This prevents all requests coming from the same source, which can trigger abuse detection much faster.
Monitor usage and stay below limits
Keep a close eye on your application’s API usage to make sure you stay well below the overall thresholds. Monitor metrics like:
- Requests per second/minute
- Peak concurrent requests
- Requests by endpoint and method
- Overall percentage of quota used
Having visibility into these metrics helps you throttle your app to avoid crossing the limits.
Handling rate limit exceptions
Even with good strategies, you may still hit an API’s rate limit during sudden traffic spikes or unexpected errors. Here are some ways to handle rate limit exceptions:
Exponential backoff
When you get a rate limit error, use exponential backoff to pause retries longer and longer. This gives the API time to reset counters and quotas.
For example, wait 2^1 seconds, then 2^2, 2^3 and so on before retrying.
Retry after delay indicated
Some APIs return a Retry-After header that tells you how long to wait before retrying. Respect this delay before sending the request again.
Queue and buffer
If requests are not urgent, queue them locally and slowly replay from the buffer when the API opens back up.
Degrade functionality
During severe outages, degrade functionality gracefully by disabling non-critical features that use the API.
Notify users
Let users know if service is limited due to API rationing. This sets proper expectations.
Recovering from rate limits
Once you’ve triggered a rate limit, here are some steps to get back in good standing:
Understand your violation
Review the rate limit error details closely to understand which limit was violated – overall quota, endpoint-specific, etc.
Check reset time
See when the counter or quota resets based on the API’s rules – often 24 hours or per UTC day.
Stop all requests
Immediately stop sending any and all requests until the reset period has passed.
Adjust your usage
Double check your throttling, pacing and caching to keep usage well under limits going forward.
Request higher quotas
If necessary, contact the API provider to request higher rate limits commensurate with your actual needs.
Advanced rate limiting strategies
Here are some more advanced tactics when working within restrictive rate limits:
Burst requests
Make occasional controlled bursts of higher rate requests to maximize your allowed usage. Useful for batched synchronization tasks that need to complete as fast as possible.
IP diversity
Distribute requests across multiple geographic regions and cloud providers to avoid consolidated limits.
Load balancer worker node
Use a load balancer to consolidate and pace requests before dispatching to backend worker nodes.
Queue prioritization
Prioritize high value requests first in the queue as quota opens up.
Adaptive concurrency
Dynamically tune the level of concurrency based on real-time throughput.
API Request Strategies and Best Practices
Here are some overarching API request strategies and best practices to avoid hitting rate limits:
Follow caching best practices
Leverage caching aggressively per REST API best practices – use ETags, set cache control headers, cache repeated reference data, etc.
Understand your usage patterns
Analyze traffic to plan and optimize request volume and concurrency.
Review frequently changing endpoints
Avoid polling endpoints with constantly changing data unless absolutely required.
Minimize endpoints called per user action
Consolidate multiple endpoints into single requests to reduce total volume.
Set quotas across all application nodes
Enforce rate limits and quotas evenly across all backend nodes rather than per node.
Tune retries for transient failures
Prevent retry amplification – use techniques like jittered backoff.
Isolate and throttle third-party APIs separately
Applying a separate throttling policy for external APIs prevents interference with core app functions.
Have fallback plan for crashed endpoints
Design graceful degradation plan for sudden failed endpoints – use stale caches etc.
Rigorously monitor usage
Measure metrics like peak throughput, concurrency, requests per time window etc.
Load test appropriately
Simulate traffic at higher volumes during load testing to uncover and fix bottlenecks.
Example Rate Limiting Pseudocode
Here is some example pseudocode illustrating API rate limiting techniques:
MAX_REQUESTS_PER_HOUR = 1000
requestsThisHour = 0
lastResetTime = getCurrentUTCHourStart()
on APIRequest():
currentTime = getCurrentTime()
if currentTime >= lastResetTime + 1hour:
requestsThisHour = 0
lastResetTime = currentTime
requestsThisHour++
if requestsThisHour
This limits requests to 1000 per rolling 60 minute window, preventing spikes and staying under the quota.
API Rate Limiting Tools
In addition to programmatic throttling, these tools can help prevent hitting rate limits:
API Management Platforms
Services like Apigee can enforce quotas and pacing across all API clients.
Load Testing Tools
Stress test tools like Loader.io can validate performance before release.
API Monitoring Software
APIs.guru monitors usage metrics to prevent breaching limits.
Request Queuing Libraries
Libraries like BottleneckJS smooth Traffic spikes.
| Tool | Description |
|---|---|
| Apigee | API management platform with quota enforcement |
| Loader.io | Load testing to validate API performance |
| APIs.guru | Monitor API usage metrics in real-time |
| BottleneckJS | Request queueing library for Node.js |
Conclusion
Rate limits are an inevitable factor when working with APIs. Careful request pacing, caching, monitoring, and having a fallback plan are all essential to stay within quota. Always thoroughly read the documentation to understand the specific rate limits for the provider's API.
With techniques like throttling, optimization, and graceful degradation, you can robustly build applications on rate-limited APIs while providing a good user experience.
API usage will only continue to grow over time, so mastering request throttling is a vital skill for any developer. Implementing these industry best practices will help you avoid disruptive interruptions from hitting request limits.
How have you handled rate limiting in your projects? What other strategies have worked for you? Please share your insights and advice!