Creating distributed throttling with fairness

Distributed throttling with fairness is a critical concept when you’re managing system resources across multiple services or applications, especially in environments where you need to control the rate of requests or data flow to avoid overloads, but still ensure fair access to resources for all users or clients.

Here’s how you can approach this problem:

1. Understanding Throttling and Fairness

• Throttling is the process of limiting the number of requests that a system will handle within a given time period. This can prevent server overload and maintain service quality.

• Fairness in throttling ensures that no single client or service monopolizes resources, and all clients get equal opportunity to access the system. This is especially important in multi-tenant systems or in distributed environments.

2. Key Concepts of Distributed Throttling

• Rate Limiting: This is the most basic form of throttling where you limit the number of requests per user, IP, or API key within a specific time window (e.g., 100 requests per minute).

• Global Throttling: When you enforce a limit across the entire system or cluster of services, ensuring that no more than a predefined number of requests are processed globally, regardless of source.

• Distributed Locking: Used to synchronize access to shared resources across different services or nodes. This ensures that distributed components are aware of one another’s state and can adjust accordingly to prevent overwhelming the system.

3. How Fairness Plays into Throttling

• Without fairness, high-demand clients might get more resources, causing others to be starved or experience delayed access.

• Fair throttling ensures that all users or services receive a fair share of resources, which is particularly useful in systems that provide a public API or cloud services to multiple clients.

A common method of achieving fairness is using token bucket or leaky bucket algorithms, which allows for dynamic rate limiting while ensuring that each client gets a fair opportunity to make requests.

4. Approaches to Implement Distributed Throttling with Fairness

a. Token Bucket Algorithm

• Basic Idea: Each client is assigned a token bucket. The system issues tokens at a constant rate, and clients consume tokens as they make requests. If there are no tokens available, the request is throttled.

• Fairness Implementation: Each client gets the same rate of token issuance, ensuring that no client is allowed to make more requests than others.

• Distributed Nature: In a distributed system, the token bucket state (number of tokens) can be shared across nodes through a centralized data store like Redis, or via a distributed coordination service like Apache Zookeeper.

b. Leaky Bucket Algorithm

• Basic Idea: Requests flow into the bucket at any rate, but they leak out at a fixed rate. If the bucket overflows, requests are dropped (or delayed).

• Fairness Implementation: Similar to the token bucket, each client has a separate bucket, ensuring fairness in the request rate across clients.

• Distributed Nature: Again, the state of the leaky bucket (water level) is stored in a distributed store like Redis to synchronize between all nodes in the system.

c. Rate Limiting with Redis

• Redis as a Distributed Cache: Redis can be used to manage the rate limiting logic in a distributed system. With features like Redis Sorted Sets or HyperLogLog, you can track request counts across multiple instances.

• Redis Lua Scripting: To ensure atomicity in rate limiting (i.e., to prevent a race condition where multiple requests are handled at the same time), you can implement Lua scripts within Redis. This way, Redis handles the logic and ensures fairness by synchronizing access to the rate limiting counters.

d. Distributed Locking with Zookeeper

• Zookeeper can be used to coordinate throttling in a distributed environment. If your system is made up of multiple microservices or distributed components, you can use Zookeeper to lock access to a resource temporarily, ensuring that only one request is processed at any given time.

• Fair Queueing: Zookeeper can also be used for fair queueing of requests. By maintaining a FIFO (First In, First Out) queue, requests can be processed in the order they arrived, giving all clients equal access to the resource.

5. Handling Hotspots in Distributed Systems

• Hotspots are a common problem in distributed systems where certain parts of the system (like a specific service or API endpoint) are overloaded with requests, causing it to throttle disproportionately compared to others.

• Fairness with Hotspots: To handle this, you can introduce dynamic load balancing along with throttling. Instead of globally limiting the number of requests, you distribute requests more evenly across available resources or servers.

• Additionally, systems can monitor the throughput of each service and dynamically adjust throttling based on the load in real-time.

6. Strategies for Fairness in a Distributed System

• Sliding Window Rate Limiting: This technique allows you to keep track of the requests over a sliding window (e.g., the last 60 seconds). This ensures that clients don’t make bursts of requests at the beginning of a time period but are instead evenly distributed.

• Weighted Fair Queuing (WFQ): This approach assigns different weights to different users or services based on their importance or priority, and throttles resources according to these weights. For example, priority users may have a higher token rate than others.

• Random Early Detection (RED): This strategy detects congestion early and randomly drops packets (or requests) before the system is overloaded. In the case of a distributed system, requests can be dropped or delayed at random to prevent any one client from overwhelming the system.

7. Real-World Example of Distributed Throttling with Fairness

A typical example is a multi-tenant cloud service that provides APIs to users. Suppose the system is handling API requests for clients across different regions. Without throttling, one client could overload the system by making too many requests, leaving others without sufficient resources.

• Solution: Implement a distributed token bucket algorithm using Redis. Each client gets a set number of tokens (for example, 100 tokens per minute). The tokens are stored in a centralized Redis instance, which synchronizes across all service nodes. If a client tries to make a request and they have no tokens left, the system delays or drops the request, ensuring no one client overwhelms the system.

Additionally, the system uses weighted fair queuing, where premium clients can have access to a larger token rate than free-tier clients, but both are still limited to their fair share.

8. Challenges and Trade-offs

• Latency: Distributed throttling, especially when using external systems like Redis or Zookeeper, can introduce latency due to network delays. However, the benefits in terms of fairness and stability often outweigh this.

• Scalability: If not designed properly, the rate limiting mechanism can become a bottleneck, especially in systems with millions of clients. A horizontally scalable architecture, such as using partitioned Redis clusters or sharded databases, can help mitigate this issue.

• Complexity: Ensuring fairness in a distributed environment introduces additional complexity in terms of state synchronization, fault tolerance, and failure handling. Careful design is necessary to ensure that the system remains consistent and responsive.

9. Best Practices

• Centralized State Management: Use centralized systems like Redis for managing rate limits and ensuring consistency across distributed nodes.

• Monitoring and Alerts: Implement robust monitoring to detect when throttling is causing issues or when the system is approaching its limits.

• Backoff Strategies: Introduce exponential backoff to reduce the rate of retries when a request is throttled. This ensures that clients do not keep bombarding the system when it’s under heavy load.

• Granular Rate Limits: Apply rate limits not just at the global level but also at the level of individual users, services, or API endpoints to ensure fairness in a more granular fashion.

By combining these approaches, you can implement distributed throttling with fairness, ensuring that your system is both resilient to overloads and equitable in distributing resources across users.