Darko’s Thoughts: Traffic Volume Management

When to Use It, Common Patterns, and the Poly Way

Traffic volume management involves controlling, rejecting, or throttling incoming traffic to protect systems, ensure fair usage, and manage costs. Every enterprise with something to lose should employ this strategy. It’s also one of the core selling points of API management software, where configuration is done via a management service, and enforcement occurs at the API Gateway. This article outlines key reasons for using traffic volume management, common patterns to implement it, and a fundamentally new approach inspired by architectural choices at Poly.

Applications of Traffic Volume Management

Traffic volume management is crucial in maintaining system stability and ensuring fair resource allocation. It helps control downstream service or database loads by enforcing rate limits and preventing capacity overload for specific operations like writes. This approach also enables fair allocation of volume entitlements among users, ensuring premium customers receive higher limits than free-tier users. By prioritizing traffic from high-value customers or critical workflows, enterprises can meet SLAs and maintain tier prioritization, ensuring essential operations are not delayed. Additionally, it safeguards systems against unwanted usage, such as mitigating DoS attacks, compromised API keys, or development errors like infinite loops. Lastly, traffic volume management aids in cost control, particularly when managing expensive services, avoiding unexpected charges for third-party APIs with per-request fees.

Common Patterns for Traffic Volume Management

1. Service or Operation Rate Limits: A shared rate limit for a downstream service or specific operations. For example, the service may be limited to handling 1,000 requests per second. This approach can be more sophisticated by linking the rate limit to system health, such as CPU or memory usage, to reject requests preemptively.
2. Client Rate Limits: Limits are set for individual consumers, allowing for differentiated treatment across customer tiers. Free-tier users may be allowed 100 requests per second, while premium users could get 1,000 requests per second. This approach is often used to upsell customers to higher service tiers.
3. Tier Rate Limits: Rate limits are applied to cohorts of users as a group. For example, a free-tier user cohort may collectively be allowed 10,000 requests per second to ensure premium users aren’t crowded out. Aggregated tracking for specific user cohorts can be used defensively to limit usage spikes or dynamically to respond to the current load.
4. Queuing: Instead of rejecting traffic, incoming requests are queued and processed later. When a service hits its rate limit, incoming requests are placed in a queue for deferred processing. This approach is helpful for write operations where an immediate response isn’t required. Multiple queues can be used in parallel, with higher-priority messages processed before lower-priority ones.

Poly’s Approach to Rate Limiting and Queuing

1. Server Function Implementation: One option is implementing rate limiting inside the server function. Here, the rate-limiting logic is embedded in the server function’s code. This approach has the least reusability and could be more efficient if multiple server functions need similar logic.
2. Client Function Implementation: Another option is implementing rate limiting as a client function, where the logic is placed in a reusable client function. While this increases reusability, each function manages its counters, leading to possible duplication of logic and state.
3. Shared Server Function: The most efficient approach is to use a shared server function. This method allows a single server function to manage rate limits for multiple services, environments, and tenants. It provides multi-tenant support and reusability, with the same server function managing limits for different durations and applying various rules simultaneously. This shared function can be applied at the API Gateway or service layer, supporting use cases like cost control, SLA prioritization, and service protection.

The Future of Traffic Volume Management with Poly

Poly’s vision includes two significant enhancements for traffic volume management. The first is environment-specific persistence, where Poly will offer a native persistence layer (likely Redis) for rate-limiting counters. Each environment will have its isolated persistence, ensuring customer data segregation. This advancement will simplify rate-limiting logic for customers, eliminating the need to set up their counters or persistence.

The second enhancement is the introduction of out-of-the-box server functions for rate limiting. These pre-built server functions will support common rate-limit types like “X requests per T interval,” with the flexibility for customers to create custom rate-limiting logic using the Poly platform’s infrastructure. Customers can quickly implement per-minute, per-hour, or custom time-based rate limits without writing custom logic for each case.

These advancements will enable customers to easily configure, operate, and evolve their traffic management strategies, leveraging Poly’s infrastructure to handle persistence and execution.

Closing Thoughts

Effective traffic volume management is essential for API security, cost control, and smooth user experiences. Enterprises can protect their systems and bottom line by using tried-and-true patterns like service rate limits, client rate limits, and queuing. Poly is taking this further by offering shared server functions and environment-specific persistence to simplify and enhance how enterprises approach rate limiting.

If this vision resonates with you or you’re curious about how we’re transforming enterprise integration, reach us at hello@polyapi.io. Together, we can shape the future of integration technology.