Contents
Why API traffic control is important
Controlling the amount of traffic coming into your API is crucial for several key reasons:
– Protects your systems: It stops your backend servers from getting overloaded, malfunctioning, or even crashing due to too much traffic, especially from malicious attacks.
– Ensures fair access: It makes sure that no single user or client can take up all your resources, so everyone gets a fair chance to use the API.
– Keeps performance predictable: It helps your API maintain a consistent and expected level of performance. This is vital for meeting any agreements you have about service quality and for keeping users happy.
What is rate limiting?
Rate limiting sets a strict maximum on how many times a client can send requests to an API within a specific period (like per second, minute, hour, or day). If a client sends more requests than this allowed amount, any extra requests are blocked. The API will typically respond with an HTTP 429 Too Many Requests error.
Key features:
– Strict enforcement: Once the limit is reached, no more requests are processed.
– User-level control: Limits are usually applied to each individual client or API key.
– Primary uses: It’s mainly used to stop spam, ensure users stick to their allowed data limits, and protect against rapid, repeated attempts to access or attack the API (like brute-force or Denial-of-Service attacks).
What is rate throttling?
Throttling, also known as “rate throttling,” is a more flexible way to manage the flow of requests. Instead of simply blocking requests when a limit is hit, it actively slows down or puts incoming calls into a waiting line (queue).
Core characteristics:
– Adaptive handling: It responds to sudden bursts of requests by intentionally adding delays, queuing requests, or reducing the speed at which requests are processed.
– Graceful degradation: When there are too many requests, the extra ones are handled more slowly instead of being completely rejected.
– System-level control: This is especially useful when your backend systems are under heavy load. Throttling helps maintain the overall flow of requests without completely shutting down the service.
Algorithms & Strategies
When it comes to controlling traffic for APIs through rate limiting or throttling, the core mechanics are built upon a few fundamental algorithms. Each of these algorithms behaves differently in how it handles traffic, sudden bursts, and continuous flow.
Fixed window counter
This is the simplest method. You define a specific time window (for example, one minute) and keep a count of how many requests a client makes within that window. Once the client hits the limit, no more requests are allowed until that time window ends. At the start of the next window, the counter resets.
– Pros: Easy to understand and implement.
– Cons: Can be unfair. If a client makes many requests at the very end of one window and then many more at the very beginning of the next, they effectively get double the allowed requests in a short period around the “window boundary.”
Sliding Window (Log or Counter)
This approach uses a time window that continuously moves, rather than being fixed.
– Sliding window log: This method stores a timestamp for every request a client makes. When a new request comes in, it looks at all the timestamps from the last “window” of time (e.g., the last minute) and counts them. Any timestamps older than the window are removed.
– Sliding window counter: This is a hybrid approach. It divides the main window into smaller sub-windows (e.g., a 1-minute window made of 60 1-second sub-windows). It then calculates the current rate by combining the count from the current sub-window with a weighted average of the previous sub-window(s).
– Pros: Much more precise and fair than the fixed window because it avoids the “boundary spike” problem.
– Cons: The log-based version can use a lot of memory for storing all timestamps, and both are more complex to implement than the fixed window.
Token bucket
This is one of the most flexible algorithms. Imagine a bucket that slowly fills up with “tokens” at a steady rate (e.g., 5 tokens per second) until it reaches its maximum capacity. Each time an incoming request arrives, it “consumes” one token (or more, depending on your setup).
– If tokens are available: The request is allowed to go through.
– If tokens are exhausted (bucket is empty):
The request might be rejected immediately (this is called “policing”).
Or, the request might be queued or delayed until new tokens are added to the bucket (“shaping”).
– Pros: This algorithm allows for “bursts” of traffic. Clients can use up a bunch of accumulated tokens all at once to make many rapid requests, and then they have to wait for the bucket to refill. After an initial burst, the rate of requests generally settles down to the token refill rate. It allows you to define both a long-term rate limit and how large a burst is allowed.
Leaky bucket
The leaky bucket algorithm often has two interpretations:
– Leaky bucket as a meter (similar to token bucket in reverse): Imagine a bucket that has a constant “leak” rate (requests are processed at a steady outflow rate). Incoming requests “fill” the bucket. If an incoming request would cause the bucket to overflow, that request is considered non-compliant and is dropped.
– Leaky bucket as a queue: Incoming requests are added to a queue (the “bucket”) that has a fixed capacity. Requests are then taken from this queue and processed at a steady, fixed rate. If the queue is full when a new request arrives, that request overflows and is discarded.
– Pros: Good for smoothing out bursty traffic into a consistent, uniform output rate.
– Cons: If used as a queue, it can have unused capacity during idle periods. The “meter” version doesn’t allow for bursts like the Token bucket.
Adaptive throttling & priority-based handling
These strategies go beyond static algorithms by dynamically adjusting how traffic is handled:
– Dynamic delays/queuing: The system can change how long it delays requests or how long requests wait in queues based on the current load on the system. If the server is very busy, delays increase; if it’s idle, delays decrease.
– Priority tiers: Different types of users or requests can be given different priorities. For example, premium users’ requests might be processed first, while requests from lower-tier users might experience delays during peak times.
– Circuit breakers or feedback loops: These mechanisms can monitor the health of downstream systems (like databases or other microservices). If a downstream system is under stress, the throttling mechanism can reduce the flow of requests to protect it from being overwhelmed, acting like a “circuit breaker” to prevent cascading failures.
Use cases & when to use each
Understanding the best situations for using rate limiting versus throttling will help you build an API that is reliable, secure, and fair.
Rate limiting: When to choose it
Key use cases:
– Security & abuse prevention: Use strict rate limits on login pages, authentication endpoints, or any public-facing part of your API. This protects against brute-force attacks (rapidly guessing passwords), data scraping, or large-scale Denial-of-Service (DDoS) attacks by setting firm caps per user or IP address.
– Quota enforcement & pricing tiers: If you offer different service plans (free, professional, enterprise), rate limiting is perfect for enforcing usage limits. For example, a free tier might allow 1,000 calls per day, while an enterprise plan gets 100,000. Hitting these limits results in an HTTP 429 Too Many Requests error.
– Resource-intensive operations: Limit how often users can perform actions that consume a lot of server power, like bulk data exports, generating complex reports, or processing large video files. This prevents these operations from overwhelming your backend systems.
Why rate limit?
– It guarantees predictable usage of your system.
– Blocking excess requests immediately improves your defensive posture against attacks.
– It’s generally simple to set up and easy for users to understand their limits.
Throttling: When it’s the right tool
Typical scenarios:
– Traffic spikes & flash events: During special promotions, product launches, or viral events, APIs can experience sudden, massive increases in traffic. Throttling helps by delaying or queuing requests to maintain overall service quality, rather than simply rejecting legitimate user requests.
– Tier-based prioritisation: You can use throttling to give better service to certain users. For instance, premium users or critical actions (like payment transactions) might get higher priority or faster processing, while less critical requests are intentionally slowed down during busy periods.
– Shared or multi-tenant systems: In environments where multiple customers share the same API infrastructure, throttling prevents one “noisy neighbor” (a user making a lot of requests) from using up all the resources and negatively impacting other users.
Why throttle?
– It smooths out unpredictable traffic patterns into a more manageable flow.
– It enhances the availability of your service during peak times by processing requests slowly instead of completely denying them.
– It delivers a better user experience by allowing requests to be served (albeit slower) rather than outright rejecting them.
Hybrid approach: Combining rate limiting & throttling
Why combine them?
This is often the most effective strategy, as it uses the strengths of both:
– You can use strict rate limits to stop malicious behavior and enforce baseline usage rules.
– You then use throttling to gracefully handle legitimate but excessive bursts of traffic, ensuring your service remains accessible and responsive even during high demand.
Example use case: Ride-sharing app (eg Uber)
– Rate limit: You might limit each user to 100 ride requests per hour to prevent misuse (like bots constantly requesting rides).
– Throttle: During peak times like rush hour or a special event, the system could slow down or queue incoming ride requests. This protects the backend systems from being overwhelmed while still eventually processing most traffic, even if there’s a slight delay for users.
Best practices for implementing rate limiting & throttling
These best practices, based on current industry standards, will help you create API traffic controls that are both effective and user-friendly.
Understand traffic & usage patterns
– Analyse your data: Look at past and real-time API traffic to find out when your busiest times are, how many requests you get, how quickly your usage is growing, and when you see sudden spikes. Use this real usage data to set your rate limits and throttling thresholds.
– Anticipate bursts: Use your data to predict periods of high activity, seasonal demand, and how individual users typically behave.
Choose the right algorithm & layer
Select the best algorithm:
– Token buckets or sliding windows are good if your API needs to handle sudden bursts of requests smoothly.
– A fixed window is simpler and good for basic, predictable limits.
– Leaky bucket is useful when you need to ensure a smooth, consistent flow of requests, evening out bursts.
Where to enforce: Apply these controls at the most effective point in your system:
– API Gateway: For centralised control that can handle a lot of traffic.
– Web server (e.g., Nginx): For simpler setups.
– Application-level logic: When you need very specific, fine-grained control over how limits are applied.
– CDNs or WAFs (like Cloudflare or Cloudfront): For early protection at the network edge, especially against large-scale attacks like DDoS.
Apply granular control & tiered policies
– Key-level limits: Set different limits for each API key or client. These limits can vary based on user tiers, subscription levels, or the type of API endpoint being accessed.
– Resource-specific limits: Set stricter limits for critical or resource-heavy endpoints (like uploading large files or exporting data) and more flexible limits for less demanding ones (like reading public data).
Provide transparent client feedback
– Use HTTP 429: Always return an HTTP 429 Too Many Requests status code when a client exceeds their limit.
– Include informative headers: Provide clear headers in the response so clients understand their current status:
– X-Rate-Limit-Limit: The total number of requests allowed in the current time window.
– X-Rate-Limit-Remaining: How many requests are left in the current window.
– X-Rate-Limit-Reset: The time (often in seconds or as a timestamp) when the current limit will reset.
– Retry-After: For throttled requests, indicates how many seconds the client should wait before trying again.
– Clear documentation: Provide clear instructions in the response body or your API documentation on why the limit was hit and how long the client needs to wait.
Support adaptive & dynamic controls
– Dynamic adjustments: Implement controls that can change limits in real-time based on your system’s current load, error rates, latency, or resource usage. This helps prevent overload while keeping the API available.
– Priority tiers: Set up different priority levels, so for example, premium clients get immediate processing, while lower-tier requests might be queued or delayed during peak times.
Use graceful handling & retry mechanics
– Client-side retry strategies: Teach your clients how to retry requests smartly:
– Implement exponential backoff: When a 429 error occurs, clients should wait for increasingly longer periods between retries (e.g., 1 second, then 2, then 4, etc.).
– Honor Retry-After headers: Clients should always respect the time specified in the Retry-After header instead of just retrying immediately.
– Buffer zones: Allow for small bursts beyond the hard limit to prevent punishing legitimate users for minor, temporary spikes in requests.
Monitor & adjust continuously
– Log metrics: Keep records of:
– How many requests are rejected or throttled.
– The distribution of latency.
– Client usage patterns.
– Review and adjust: Regularly check for unusual activity and adjust your policies as your API traffic changes. Use dashboards or analytics tools for ongoing fine-tuning.
Balance security and availability
– Protect critical endpoints: Apply stricter limits to sensitive parts of your API (like login attempts or payment processing). For example, set tighter caps on repeated login attempts or payment operations.
– DDoS/Bot protection: For large-scale attacks, consider combining rate and throttle controls with other security measures like IP reputation filtering or CAPTCHA challenges.
Document your policies
– Clear documentation: Always clearly write down your API’s rate limit and throttling rules in your API documentation.
– Include guidance on:
– Request quotas for different user tiers.
– What happens when limits are hit (e.g., HTTP 429, Retry-After header).
– Recommendations for how clients should retry requests.
Sportmonks and API rate control
At Sportmonks, we use clear rate limiting policies for all our sports APIs. We also strongly encourage developers to follow best practices for throttling their requests to ensure smooth integration.
Rate limits per Entity
In the Sportmonks API (version 3), each major category of data (called an “entity,” like teams, fixtures, or players) has its own separate rate limit. On typical plans, this limit is 3,000 calls per entity per hour. If you make more calls for a specific entity within that hour, any extra requests will receive an HTTP 429 Too Many Requests error until the one-hour window resets.
Response metadata for limits
Every successful API response you get from Sportmonks includes a rate_limit object (or sometimes an x-ratelimit-remaining header). This tells you exactly how many calls you have left for that specific entity and when your limit will reset. This clear feedback helps your application avoid suddenly hitting limits and getting rejected.
Developer guidance & best practices
Sportmonks’ documentation explicitly advises developers to “familiarise yourself with our API rate limits and throttle your requests accordingly.” This is to prevent hitting limits and potentially having their access temporarily suspended. We encourage clients to implement their own “rate throttling” logic to avoid overwhelming our API.
Handling exceeded limits
If you go over your assigned limit, Sportmonks will respond with a 429 Too Many Requests status. Your access to that specific entity will then be unavailable until the one-hour window for that limit resets.
Build smarter, scale safer with Sportmonks
Whether you’re handling live scores, team stats, or match fixtures, Sportmonks APIs come with clear rate limits and guidance to help you integrate responsibly. Monitor usage, avoid bottlenecks, and keep your apps running smoothly, even during peak demand.
Faqs about rate limiting and rate throttling
