设计一个API限流器

难度: medium

设计一个API限流器，通过控制用户提交的请求量来管理用户访问。

Solution

Rate limiting is an essential technique used in software systems to control the rate of incoming requests. It helps to prevent the overloading of servers by limiting the number of requests that can be made in a given time frame. It helps to prevent a high volume of requests from overwhelming a server or API

Why is rate limiting used?

Avoid resource starvation due to a Denial of Service (DoS) attack.

Ensure that servers are not overburdened. Using a rate restriction per user

ensures fair and reasonable use without harming other users.

Control the flow of information, for example, prevent a single worker from

accumulating a backlog of unprocessed items while other workers are idle.

Where to place the Rate Limiter – Client Side or Server Side?

A rate limiter should generally be implemented on the server side rather than on the client side. This is because of the following points:

  1. Placing the rate limiter on the server side ensures centralized control over API access, preventing abuse or unintended spikes in traffic.
  2. Server-side rate limiting enhances security by protecting against malicious attacks and ensuring fair resource allocation among users.
  3. It allows for consistent enforcement of rate limits across various API endpoints, promoting a scalable and reliable system architecture.


System requirements

Functional:

  1. Rate Limiting Rules:
  2. Define the rules for rate limiting, specifying the number of requests allowed per user within a specific time window.
  3. Support configuration of rate limits for different API endpoints or user roles.
  4. User Identification:
  5. Identify users making requests using unique identifiers (e.g., API keys, user IDs).
  6. Request Throttling:
  7. Throttle or limit requests from users once they exceed the defined rate limits.
  8. Implement mechanisms for handling burst requests gracefully.
  9. Expiry Mechanism:
  10. Implement a mechanism to reset or expire rate limits after a certain duration (time window).
  11. Ensure that expired rate limits are recalculated for subsequent requests.
  12. Monitoring and Logging:
  13. Log events related to rate limiting to monitor usage and identify potential issues.
  14. Provide detailed logs with information such as user ID, API endpoint, request timestamp, and rate limit status.
  15. Error Handling:
  16. Provide appropriate error responses when users exceed rate limits.
  17. Clearly communicate rate-limiting errors with specific HTTP status codes and informative messages.
  18. Configuration Management:
  19. Allow dynamic configuration of rate limiting rules without server restart.
  20. Provide APIs or tools for administrators to update rate limits in real-time.
  21. Integration with API Services:
  22. Seamlessly integrate the rate limiter with API services to enforce limits effectively.
  23. Ensure minimal impact on the performance and responsiveness of API services.

Non-Functional:

  1. Scalability:
  2. The system should be scalable to handle a growing number of users and increasing request rates.
  3. Ensure that the rate limiter can scale horizontally to distribute the load.
  4. Performance:
  5. The rate-limiting process should introduce minimal latency to API responses.
  6. Optimize data structures and algorithms for efficient tracking of rate limits.
  7. Reliability:
  8. Ensure high availability and reliability of the rate limiter to prevent service disruptions.
  9. Implement failover mechanisms to handle system failures gracefully.
  10. Security:
  11. Protect against abuse and malicious attacks by implementing secure mechanisms for user identification.
  12. Validate and sanitize inputs to prevent injection attacks.
  13. Logging and Auditing:
  14. Log all rate-limiting events for auditing and troubleshooting purposes.
  15. Support integration with centralized logging systems for comprehensive monitoring.
  16. Configurability:
  17. Provide a user-friendly interface or configuration files for adjusting rate limiting rules.
  18. Ensure that changes to configurations take effect immediately.
  19. Maintainability:
  20. Design the system with modular components for ease of maintenance and updates.
  21. Document APIs, configurations, and system architecture comprehensively.
  22. Adaptability:
  23. Allow the rate limiter to adapt to changing usage patterns and traffic fluctuations.
  24. Implement mechanisms to adjust rate limits dynamically based on system load.

Algorithms for Rate Limiting

Here are 2 algorithms that can be used for the Rate limiter

Token Bucket

The Token Bucket algorithm is another widely used approach for rate limiting that provides flexibility in handling burst traffic.

  1. Token Bucket Structure: Each virtual token bucket is associated with each user or API key and the tokens are added to the bucket at a fixed rate (token generation rate). Each time a request is made, check if there are sufficient tokens in the bucket to process the request. If there are enough tokens, process the request and decrement the token count, else reject the request.
  2. Token Generation: Tokens are generated in the bucket at a fixed rate, Unused tokens accumulate up to a maximum capacity
  3. Expiry Mechanism: We can implement an expiry mechanism to prevent token hoarding. Tokens that exceed the bucket's capacity are discarded.
  4. Adaptability: We can dynamically adjust the token generation rate based on observed traffic patterns and system load.

Sliding Window Algorithm

The Sliding Window algorithm is a time-based approach that tracks the number of requests made by a user within a fixed time window. a sliding time window that moves forward in fixed intervals, tracking the requests made by each user within the defined time span.

  1. Request Tracking: a counter is maintained for each user or API key to keep track of the number of requests made within the sliding time window. If the request count exceeds the limit, reject or delay the request to enforce rate limits.\
  2. Window Advancement: As time progresses, we advance the sliding window, discarding old request counts and allowing for fresh tracking of recent requests.
  3. Adaptability: we can dynamically adjust the sliding window and the allowed number of requests based on observed traffic patterns and system load for adaptive rate limiting.

API design

Considering a rate limiter design using the Sliding Window algorithm, here are some essential APIs:

  1. Set Rate Limit API:
  2. Description: Set or update the rate limit for a specific user or API endpoint.
  3. Input: User ID or API endpoint identifier, new rate limit value.
  4. Output: Confirmation message or error response.
  5. Get Rate Limit API:
  6. Description: Retrieve the current rate limit for a specific user or API endpoint.
  7. Input: User ID or API endpoint identifier.
  8. Output: Current rate limit value or error response.
  9. Make Request API:
  10. Description: Process a user's API request, checking against the sliding window rate limit.
  11. Input: User ID or API key, requested endpoint.
  12. Output: Success response if within limits, rate-limit-exceeded error otherwise.
  13. Adjust Sliding Window Duration API:
  14. Description: Dynamically modify the duration of the sliding window.
  15. Input: New sliding window duration (e.g., time in seconds).
  16. Output: Confirmation message or error response.
  17. User Activity Metrics API:
  18. Description: Retrieve recent user activity metrics within the sliding window.
  19. Input: User ID.
  20. Output: Request count, timestamps, and other relevant metrics.

These APIs provide the necessary functionalities to manage, monitor, and adjust rate limits using the Sliding Window algorithm. They ensure flexibility and control over rate-limiting parameters while offering insights into user activity.

Database design

For the tables required in this design, refer to the class diagram, the list of classes is not exhaustive but this is a good number of tables to start with.

  • User and Token Data: 
  • CAP Theorem: Consistency and Partition Tolerance.
  • Database Type: Relational Database (e.g., PostgreSQL, MySQL)
  • Reasoning: Relational databases offer strong consistency and are well-suited for scenarios where data integrity is crucial, such as user information and token details. Additionally, they handle partition tolerance effectively.
  • Request Counter Data: 
  • Database Type: NoSQL Database (e.g., Redis, MongoDB):
  • Focus: Availability and Partition Tolerance.
  • Reasoning: NoSQL databases are often chosen for scenarios with high read and write throughput. In the case of request counters, where frequent updates are expected within the sliding window, a NoSQL database can provide high availability and performance.
  • Caching Layer: 
  • Database Type: In-memory Database (e.g., Redis)
  • CAP Theorem: Availability.
  • Reasoning: Caching is critical for quickly retrieving frequently accessed data. An in-memory database like Redis provides low-latency access, making it suitable for storing and quickly updating request counters within the sliding window.
  • Token Verification: Key-Value Store (e.g., Redis):
  • CAP Theorem: Availability.
  • Reasoning: Token validation is a frequent operation and a key-value store like Redis can provide fast and highly available lookups for token values. It helps in efficiently checking the validity of tokens during request processing.
  • Logging and Metrics:
  • Database Type: Log Database (e.g., Elasticsearch for logs, Prometheus for metrics)
  • CAP Theorem: Partition Tolerance.
  • Reasoning: Log and metric data often have a high volume and require efficient storage and retrieval. Databases designed for log and metric data, such as Elasticsearch and Prometheus, prioritize partition tolerance to ensure reliable data storage and querying.

Data Partitioning:

  • Partitioning Strategy:
  • Best Strategy: Hash-Based Partitioning
  • Reasoning: Hash-Based Partitioning can evenly distribute data across partitions, ensuring a balanced workload. This is especially effective for scenarios where there is no clear range-based pattern in the access patterns or the data distribution.
  • Regional or Geographical Partitioning:
  • Best Strategy: Not Recommended
  • Reasoning: Since the system is a rate limiter and the data involves users, tokens, and request counters, there may not be a natural fit for regional or geographical partitioning. Access patterns are likely to be more evenly distributed globally, and implementing regional partitioning might introduce unnecessary complexity.

Sharding:

  • Sharding Strategy:
  • Best Strategy: Horizontal Sharding
  • Reasoning: As the tables grow in size, horizontal sharding allows for splitting the data across multiple database instances, each handling a subset of the data. This can help distribute the load and scale horizontally, accommodating the increased number of users and requests.
  • Sharding Key:
  • Best Strategy: user_id
  • Reasoning: Using the user_id as the sharding key can help group related data together, as requests and tokens are directly associated with individual users. This reduces the need to perform cross-shard operations for most user-centric queries.

Replication:

  • Replication Strategy:
  • Best Strategy: Multi-Datacenter Replication (Active-Active)
  • Reasoning: Active-Active replication allows for data redundancy and load distribution across multiple data centers. This enhances fault tolerance, availability, and provides low-latency access for users globally.
  • Replication Lag Consideration:
  • Best Strategy: Asynchronous Replication with Monitoring
  • Reasoning: In a rate-limiting system, eventual consistency may be acceptable. Asynchronous replication with careful monitoring ensures that replication lag is managed, and system performance is optimized.

Load Balancing:

  • Load Balancing Strategy:
  • Best Strategy: Round Robin or Least Connections
  • Reasoning: A round-robin or least-connections load-balancing strategy can distribute incoming requests evenly across the replicated and sharded database instances. This prevents any single instance from becoming a bottleneck and ensures optimal utilization of resources.

High-level design

  • Client:
  • Represents the external applications or users making API requests.
  • API Gateway:
  • Acts as an entry point for API requests, responsible for routing and load balancing.
  • Authentication Service:
  • Validates user credentials or API keys, ensuring secure access to the system.
  • Rate Limiter Service:
  • Core service implementing the rate-limiting logic.
  • Utilizes the Sliding Window algorithm.
  • Interacts with the Token Service and Request Counter Service.
  • Token Service:
  • Manages user tokens, responsible for token validation.
  • Request Counter Service:
  • Tracks the request counter for each user within the sliding window.
  • Database Cluster:
  • Consists of multiple databases, each containing sharded tables.
  • Employs horizontal sharding for scalable storage.
  • Load Balancer (Database):
  • Distributes database queries across multiple instances to ensure balanced load.
  • Replication Manager:
  • Manages data replication across multiple data centers for high availability.
  • Monitoring and Logging Service:
  • Collects and analyzes logs for monitoring system behavior, identifying potential issues.
  • Configuration Service:
  • Allows dynamic configuration updates for rate limiting rules without server restart.
  • Cache Layer:
  • Optionally, a caching layer to store frequently accessed data for improved performance.
  • Content Delivery Network (CDN):
  • Serves static assets and caches API responses closer to end-users for reduced latency.
flowchart TD
    A(User)
    B(Token_Service)
    C(Counter_Service)
    D(Processing_Service)
    E(Redis_Cache)
    F(Service_Load_Balancer)
    G(Database)
    H(API_Gateway)
    I(DB_Cache_Service)

    A --> |API Request| F
    F --> |Distribute Requests| B
    F --> |Distribute Requests| C
    C --> |Rate Limit Check| D
    D --> |Process Request| I
    I --> |Read/Write| E
    I --> |Read/Write| G
    H --> |Read/Write| E
    H --> |Read/Write| G

Detailed component design

1. API Gateway:

  • Responsibilities:
  • Request Routing: Direct incoming requests to the appropriate services.
  • Authentication: Validate and authenticate users.
  • Load Balancing: Distribute requests across multiple instances for scalability.
  • Logging: Log request metadata for analysis.

2. Load Balancer:

  • Responsibilities:
  • Distribute Load: Balance incoming traffic across instances of the Processing Service.
  • Health Checking: Regularly check the health of each instance to ensure proper functionality.
  • Fault Tolerance: Redirect traffic away from unhealthy instances.

3. Processing Service:

  • Responsibilities:
  • Token Validation: Validate the user's token for authenticity.
  • Rate Limit Check: Verify if the user has exceeded the rate limit using the Counter Service.
  • Request Processing: Execute the business logic associated with the API request.
  • Database Interaction: Communicate with the Database Cluster for data retrieval and updates.
  • Caching: Utilize the Redis Cache for frequently accessed data.
  • Response Generation: Generate appropriate responses based on the request processing.

4. Token Service:

  • Responsibilities:
  • Token Validation: Validate the authenticity and expiration of user tokens.
  • Error Handling: Handle token-related errors gracefully.

5. Counter Service:

  • Responsibilities:
  • Rate Limit Logic: Implement the logic for rate limiting based on the sliding window.
  • Counter Updates: Update and maintain the request counters for users.
  • Error Handling: Handle rate limit exceeded errors.

6. Database Cluster:

  • Responsibilities:
  • Data Sharding: Store user, token, and request counter data based on sharding strategy.
  • Transaction Management: Ensure data consistency and integrity through transactions.
  • Scaling: Scale horizontally as data grows.
  • Backups and Recovery: Implement mechanisms for data backups and recovery.

7. Redis Cache:

  • Responsibilities:
  • Caching Strategy: Implement a caching strategy to store frequently accessed data.
  • Expiration Policies: Define policies for data expiration to ensure cache freshness.
  • Cache Invalidation: Handle invalidation of cached data when necessary.

Trade offs/Tech choices

  • Simplicity in Current Design:
  • The current database design employs a straightforward relational schema for ease of transaction management and maintenance.
  • Simplicity is prioritized to facilitate straightforward queries, ensuring a reliable and consistent approach.
  • Hybrid Approach with SQL and NoSQL:
  • Future enhancements could involve adopting a hybrid approach, integrating both SQL and NoSQL databases into the system architecture.
  • Leveraging NoSQL databases for specific tasks, such as caching or managing less structured data, would enhance scalability and flexibility.
  • Optimizing Performance and Flexibility:
  • Utilizing NoSQL databases for certain functionalities allows the system to optimize performance and adapt to varying data structures.
  • Retaining the relational database for transactional integrity ensures structured data storage, striking a balance between relational consistency and NoSQL flexibility.

Future improvements

  1. Dynamic Rate Limit Adjustment:
  2. Idea: Implement an adaptive rate limiting mechanism that dynamically adjusts rate limits based on real-time user behavior and system load.
  3. Rationale: This enhances the system's ability to handle varying traffic patterns, optimizing user experience and resource utilization.
  4. Asynchronous Processing for Database Interactions:
  5. Idea: Introduce asynchronous processing for non-critical database interactions, such as logging or secondary data updates, to minimize request processing time.
  6. Rationale: By decoupling these operations, the system can improve responsiveness, allowing the Processing Service to focus on critical tasks without being hindered by non-essential database interactions.
  7. Global Rate Limiting Policies:
  8. Idea: Implement global rate limiting policies that can be applied across different regions or user segments, providing centralized control for managing access thresholds.
  9. Rationale: This facilitates easier administration of rate limits, especially in scenarios where different user groups or geographical regions may have distinct rate-limiting requirements.
  10. Machine Learning-based Anomaly Detection:
  11. Idea: Integrate machine learning algorithms to analyze user behavior and detect anomalous patterns that may indicate potential security threats or unexpected spikes in traffic.
  12. Rationale: By proactively identifying and responding to unusual activities, the system can enhance security measures and improve its ability to handle unforeseen challenges.


得分: 10