Design a Rate Limiter

1. Problem Understanding (What is a Rate Limiter?)

A Rate Limiter is a mechanism used to control the rate of incoming requests to a system. By enforcing limits on the access frequency for specific dimensions (such as IP address, user ID, or API path), it ensures system stability and fair resource allocation. When requests exceed a predefined threshold, the system rejects them, typically with an HTTP 429 (Too Many Requests) response.

Why Do We Need a Rate Limiter?

Rate limiters address several practical challenges:

1. Protecting System Stability
   • Prevent system overloads caused by traffic spikes or malicious attacks.
   • Avoid cascading failures to ensure high service availability.
   • Shield backend databases or services from excessive pressure.
2. Ensuring Fair Resource Allocation
   • Guarantee equitable resource access for different users or tenants.
   • Restrict high-frequency users from consuming a disproportionate amount of resources.
   • Support quota management for multi-tenant platforms or public APIs.
3. Technical Characteristics
   • Support for multi-dimensional rate limiting (e.g., by IP, user ID, or composite keys).
   • Low-latency decisions (P 99 latency < 10 ms).
   • High scalability to handle high QPS (e.g., 1 million QPS).

Illustrative Example

Consider a social media platform API that limits a user to 100 requests per minute. An original request might look like this:

POST https://api.socialmedia.com/v1/posts?userId=12345&content=hello

If the user sends too many requests in a short period, the rate limiter will track the frequency, trigger the limit, and return:

HTTP/1.1 429 Too Many Requests
Retry-After: 30
Content-Type: application/json

{
  "error": "RATE_LIMIT_EXCEEDED",
  "message": "Rate limit exceeded. Please try again in 30 seconds."
}

This mechanism not only protects the server but also offers flexibility through dynamic rules, such as relaxing limits for VIP users.

2. Requirements

Functional Requirements

1. Users or services must be able to configure request rate limits based on specific dimensions (e.g., IP, User ID, API Path) to ensure traffic does not exceed a predefined threshold (e.g., 100 requests per minute).
2. When a request exceeds the limit, the system must return a standard HTTP 429 Too Many Requests response, including headers like Retry-After and rate limit status.
3. The system must support dynamic configuration of rate limiting rules (e.g., thresholds, window sizes) in real-time without requiring a system restart.
4. Whitelisting functionality must be provided to allow specific users or IPs to bypass limits or have higher quotas.

Non-Functional Requirements

1. The system must guarantee 99.99% high availability, with a P 99 latency for rate-limiting decisions under 10 milliseconds.
2. The system must be scalable to handle a peak load of 1 million QPS and store data for 100 million active rate-limiting keys per day (approx. 700 GB of data with a 7-day retention period).
3. Rate limiting rules and state must be secure to prevent malicious bypasses (e.g., IP rotation) or resource exhaustion attacks (e.g., high-cardinality key attacks).
4. Counting accuracy in a distributed environment must be ensured. Consistency is preferred, but minor inaccuracies may be acceptable in high-throughput scenarios.
5. A RESTful API must be provided for managing rate limiting rules and for real-time monitoring and analytics (e.g., rejection rates, hot key statistics).

3. Estimation and Constraints

Traffic

Assuming the rate limiter serves a global API system where all requests undergo a check, with a read-to-write ratio of 100:1 (checks are reads, rule updates are writes) and 100 million active keys per day.

Daily Read/Write Volume

• Read Volume (Rate Limit Checks): 100 × 100 million = 10 billion/day
• Write Volume (State Updates): 1 × 100 million = 100 million/day

Requests Per Second (RPS)

• Daily state updates converted to QPS:
• 100 million / (24 hrs × 3600 seconds) ≈ 1,157 QPS
• With a 100:1 read-to-write ratio, the check requests are:
• 100 × 1,157 ≈ 115,700 QPS (with a peak assumption of 1 million QPS)

Bandwidth

• For Write Requests (Inbound traffic, updating state):
• Assuming each request is 1 KB (key, timestamp, etc.).
• 1,157 QPS × 1 KB ≈ 1.16 MB/s
• For Read Requests (Outbound traffic, check responses):
• 115,700 QPS × 1 KB ≈ 116 MB/s
• Throttled responses (e.g., 429) are smaller, say 500 bytes, and account for 10% of requests: - 0.1 × 115,700 × 500 bytes ≈ 5.8 MB/s
• Total Outbound Bandwidth: 116 + 5.8 ≈ 122 MB/s

Storage

• Assuming a 7-day retention period for rate limit state:
• 100 million active keys per day, with each key consuming ~1 KB (for a ZSET or counter):
• 100 million × 1 KB = 100 GB/day
• Total Storage (7 days):
• 100 GB × 7 = 700 GB
• For rule storage, assuming 1,000 rules at 1 KB each:
• 1,000 × 1 KB = 1 MB (negligible)

Cache

• Applying the Pareto principle (80/20 rule), 80% of requests target 20% of hot keys.
• Daily requests:
• 115,700 QPS × 24 hrs × 3600 seconds ≈ 10 billion requests/day
• Required cache memory (for 20% hot keys):
• 20% × 100 million keys × 1 KB ≈ 20 GB/day
• For local caches (e.g., Guava/Caffeine), with each instance caching 1% of hot keys:
• Assuming 1,000 instances: 1% of 20 GB is 200 MB per instance.

High-Level Estimates

4. Clarifying Questions

To ensure the design is comprehensive and accurate, the following questions could be used to clarify key constraints and business scenarios with an interviewer:

1. Deployment Environment:
   • Is the system deployed in a single or multi-datacenter environment? If multi-DC, is global rate limiting (e.g., consistent quotas across regions) required?
   • Is preliminary rate limiting at the edge (e.g., on a CDN) a requirement?
2. Algorithm Precision:
   • What level of precision is required? For instance, do financial transactions need per-second accuracy, or is some tolerance acceptable (e.g., for social media)?
   • Are approximate algorithms (like the sliding window counter) acceptable in exchange for better performance?
3. Service Dependencies:
   • Is there existing infrastructure we can leverage, such as Redis, Etcd, or another distributed storage/configuration system?
   • Do we need to support alternative storage solutions (e.g., DynamoDB, Memcached) as a fallback?
4. Fault Tolerance Requirements:
   • If the core storage (e.g., Redis) fails, is graceful degradation (e.g., falling back to local cache-based limiting) acceptable? Is the associated loss of precision tolerable?
   • Should there be default rate limiting rules in place if dependency services are unavailable?
5. Business Use Cases:
   • What are the specific use cases? E.g., preventing brute-force login attacks, managing e-commerce flash sales, enforcing API quotas, or anti-abuse for social media.
   • Are there special rule requirements, such as higher quotas for specific user groups (VIPs) or location-based limiting?
6. Security and Anti-Abuse:
   • Are additional anti-bypass mechanisms needed, such as detecting IP rotation, forged user IDs, or device fingerprinting?
   • How should we handle high-cardinality key attacks (large numbers of random keys causing memory exhaustion)?
7. Monitoring and Analytics:
   • Do we need detailed statistics on rate-limiting events (e.g., rejection rates per key or rule)?
   • Is real-time alerting required (e.g., for high rejection rates or spikes in hot keys)?

5. API Design

Note: The following are administrative APIs for system administrators or operations platforms to configure and manage rate limiting rules. End-user rate limit status is typically conveyed via response headers (X-RateLimit-*) on their normal API requests, not through a dedicated status-checking API.

1. Create Rate Limit Rule API

Description: This API allows administrators to create a new rate limiting rule, specifying dimensions, thresholds, and time windows.

Request:

• Method: POST /rate-limits
• Content-Type: application/json
• Body:

{
  "rule_id": "api-global-default",
  "path_pattern": "/api/v1/**",
  "key_type": "ip",
  "limit": 100,
  "window_seconds": 60,
  "algorithm": "SlidingWindowCounter",
  "enabled": true
}

Response:

• Status Code: 201 Created
• Content-Type: application/json
• Body:

{
  "rule_id": "api-global-default",
  "path_pattern": "/api/v1/**",
  "key_type": "ip",
  "limit": 100,
  "window_seconds": 60,
  "algorithm": "SlidingWindowCounter",
  "enabled": true,
  "created_at": "2023-08-25T10:00:00Z"
}

2. Check Rate Limit Status API

Description: This API checks the current rate limit status for a specific key (e.g., user ID or IP), returning the remaining quota and reset time.

Request:

• Method: GET /rate-limits/{rule_id}/{key}
• URL Example: GET /rate-limits/user-login-attempt/john_doe

Response:

• Status Code: 200 OK
• Content-Type: application/json
• Body:

{
  "rule_id": "user-login-attempt",
  "key": "john_doe",
  "limit": 5,
  "remaining": 3,
  "window_seconds": 300,
  "reset_time": "2023-08-25T10:05:00Z"
}

3. Update Rate Limit Rule API

Description: This API allows administrators to update the properties of an existing rule, such as its threshold, window size, or enabled status.

Request:

• Method: PUT /rate-limits/{rule_id}
• Content-Type: application/json
• Body:

{
  "limit": 150,
  "window_seconds": 60,
  "enabled": true
}

Response:

• Status Code: 200 OK
• Content-Type: application/json
• Body:

{
  "rule_id": "api-global-default",
  "path_pattern": "/api/v1/**",
  "key_type": "ip",
  "limit": 150,
  "window_seconds": 60,
  "algorithm": "SlidingWindowCounter",
  "enabled": true,
  "updated_at": "2023-08-25T11:00:00Z"
}

4. Delete Rate Limit Rule API

Description: This API allows administrators to delete a rate limiting rule that is no longer needed.

Request:

• Method: DELETE /rate-limits/{rule_id}
• URL Example: DELETE /rate-limits/user-login-attempt

Response:

• Status Code: 200 OK (or 204 No Content)
• Body:

{
  "message": "Rate limit rule 'user-login-attempt' deleted successfully."
}

5. Get Rate Limit Statistics API

Description: This API provides statistics for a given rule, including total requests, rejections, and hot keys, for monitoring and analysis.

Request:

• Method: GET /rate-limits/{rule_id}/stats
• URL Example: GET /rate-limits/api-global-default/stats

Response:

• Status Code: 200 OK
• Content-Type: application/json
• Body:

{
  "rule_id": "api-global-default",
  "total_requests": 10000,
  "rejected_requests": 500,
  "rejection_rate": 0.05,
  "hot_keys": [
    {
      "key": "192.168.1.1",
      "request_count": 1200,
      "rejection_count": 100
    },
    {
      "key": "192.168.1.2",
      "request_count": 800,
      "rejection_count": 50
    }
  ],
  "last_updated": "2023-08-25T12:00:00Z"
}

6. Database Schema Design

Given the need for high-concurrency request handling and flexible rule management, a NoSQL document database like MongoDB is a suitable choice for its high throughput and horizontal scalability. Below is the schema for the core collections.

RateLimitRules Collection

Collection Name: rate_limit_rules

Example Document:

{
  "_id": "64e5f2c3d5b6b9a01d2f4e12",
  "rule_id": "user-login-attempt",
  "path_pattern": "/auth/login",
  "key_type": "username",
  "limit": 5,
  "window_seconds": 300,
  "algorithm": "SlidingWindowLog",
  "enabled": true,
  "created_at": "2023-08-25T10:00:00Z",
  "updated_at": "2023-08-25T10:00:00Z"
}

RateLimitStates Collection

Collection Name: rate_limit_states (Note: This is for high-precision scenarios or persistent state; typically, Redis handles the real-time data plane)

Example Document:

{
  "_id": "64e5f2c3d5b6b9a01d2f4e34",
  "rule_id": "user-login-attempt",
  "key": "john_doe",
  "timestamps": [
    "2023-08-25T12:00:00Z",
    "2023-08-25T12:00:10Z",
    "2023-08-25T12:00:25Z"
  ],
  "count": 3,
  "last_updated": "2023-08-25T12:00:25Z",
  "expires_at": "2023-08-25T12:05:00Z"
}

RateLimitStats Collection (Optional)

Collection Name: rate_limit_stats

Design Features and Considerations

• High Concurrency Support:
• MongoDB supports horizontal scaling, making it suitable for high QPS scenarios (1 M peak).
• The rate_limit_states collection can be sharded on rule_id and key to distribute write load.
• Index Optimization:
• A unique index on rule_id in rate_limit_rules ensures efficient rule lookups.
• A compound index on rule_id and key in rate_limit_states enables fast state queries.
• An index on rule_id and time_window in rate_limit_stats optimizes analytical queries.
• TTL Index:
• A TTL index on the expires_at field in rate_limit_states automatically purges expired state data, preventing memory bloat.
• A similar TTL index can be used on time_window in rate_limit_stats to retain data for the required period (e.g., 7 days).
• Storage Efficiency:
• The schema is flexible, using an array for timestamps (Sliding Window Log) or a simple counter (Sliding Window Counter) based on the chosen algorithm.
• For 100 million active keys at ~1 KB each, the total storage over 7 days (~700 GB) is managed via sharding and TTL indexes.

Relationship with Redis

• While real-time rate limiting state (the data plane) is typically managed by Redis (using ZSETs and Lua scripts) for maximum performance, MongoDB plays a critical control plane role in this architecture. It serves as the persistent store and source of truth for rules and can also store offline, aggregated statistical data.
• In the event of a Redis failure, the state data in MongoDB could serve as a basis for recovery or a fallback mechanism.

7. High-Level Design

1. Rate Limiting Algorithms

Designing a distributed rate limiter requires balancing precision, performance, memory usage, and consistency to meet the demands of 1 M QPS, low latency (<10 ms), and high availability (99.99%).

Algorithm Analysis and Derivation

1. Token Bucket
   • Mechanism: A bucket of fixed capacity is refilled with tokens at a constant rate. Each request consumes a token; if the bucket is empty, the request is rejected.
   • Pros: Handles bursty traffic, simple to implement, low memory footprint.
   • Cons: Limited precision; distributed implementation requires synchronizing token state, leading to race conditions.
   • Applicability: Ideal for coarse-grained limiting at the edge (e.g., by IP) to quickly mitigate DDoS attacks.
2. Leaky Bucket
   • Mechanism: Requests are processed at a fixed rate, smoothing out traffic. Excess requests are queued or dropped.
   • Pros: Smooths traffic flow, ensuring a stable load on downstream services.
   • Cons: Does not handle bursts; queueing introduces high latency, making it unsuitable for real-time APIs.
3. Fixed Window Counter
   • Mechanism: Counts requests within a fixed time window. The count resets at the end of each window.
   • Pros: Extremely simple to implement, minimal memory usage.
   • Cons: Boundary issues: A burst of traffic at the edge of two windows could allow up to twice the limit, resulting in low precision.
4. Sliding Window Log
   • Mechanism: Stores a timestamp for each request. The count is the number of timestamps within the current sliding window.
   • Pros: High precision, no boundary issues. Ideal for critical scenarios like financial transactions or anti-abuse.
   • Cons: High memory usage (stores all timestamps), leading to performance overhead.
5. Sliding Window Counter
   • Mechanism: Divides the time window into smaller segments, each with its own counter. The total count is the sum of counts in the segments within the sliding window.
   • Pros: An excellent balance of precision and performance. Smoother than the fixed window and far more memory-efficient than the log algorithm.
   • Cons: Involves a minor approximation error (it smooths out intra-segment bursts), but this is acceptable for most use cases.
   • Applicability: The best general-purpose choice for most real-time API scenarios that require a balance of precision and performance.

Final Choice: Sliding Window Counter + A Layered Strategy

• Core Algorithm: The Sliding Window Counter is chosen as the primary algorithm for fine-grained, service-level rate limiting, offering the best trade-off between precision, performance, and memory.
• Layered Strategy:
• Edge Layer (Token Bucket): Use a token bucket at the API Gateway or CDN (e.g., Cloudflare) for coarse-grained IP-based limiting (e.g., 1000 RPS) to block DDoS attacks.
• Service Layer (Sliding Window Counter): Apply precise limiting based on user ID, API Key, etc., to enforce business logic accurately.
• High-Precision Scenarios (Sliding Window Log): Reserve the Sliding Window Log algorithm for use cases that demand the highest precision, such as login attempt limits, payments, and anti-scraping.

Algorithm Comparison Table

Architecture Diagram

This diagram illustrates the request flow from the client to backend services, showing the interaction between the API Gateway, Rate Limiter Service, and its dependencies.

2. Core Implementation: Redis + Lua for Sliding Window Log

To illustrate a high-precision implementation, here is how the Sliding Window Log can be realized using Redis and Lua to ensure atomicity and performance.

Redis Data Structure (Sorted Set - ZSET)

• Key: rate_limit:<rule_id>:<key> (e.g., rate_limit: user-login-attempt:john_doe).
• Score: The request timestamp in milliseconds.
• Member: A unique request ID (e.g., timestamp:random_suffix) to prevent collisions within the same millisecond.

Lua Script

A Lua script ensures that the “read-check-write” operation is atomic, preventing race conditions in a distributed environment.

-- KEYS[1]: The ZSET key (e.g., rate_limit: user-login-attempt:john_doe)
-- ARGV[1]: Current timestamp (in milliseconds)
-- ARGV[2]: Window size (in milliseconds)
-- ARGV[3]: Maximum number of requests (the limit)
-- ARGV[4]: A unique ID for the current request
Local key = KEYS[1]
Local now = tonumber (ARGV[1])
Local window_size = tonumber (ARGV[2])
Local limit = tonumber (ARGV[3])
Local request_id = ARGV[4]

-- Remove records that are outside the current window
Redis.Call ("ZREMRANGEBYSCORE", key, 0, now - window_size)

-- Get the current count within the window
Local current_count = redis.Call ("ZCARD", key)

-- Check if the limit has been reached
If current_count < limit then
    -- Add the new request to the sorted set
    Redis.Call ("ZADD", key, now, request_id)
    -- Set an expiration on the key to clean it up after it's no longer needed
    -- The buffer (e.g., 60 s) prevents premature expiry
    Redis.Call ("PEXPIRE", key, window_size + 60000)
    Return {1, limit - current_count - 1}  -- {Allowed, RemainingQuota}
Else
    Return {0, 0}  -- {Rejected, RemainingQuota}
End

Workflow

1. Request Arrival: The Rate Limiter Service receives a request and extracts the rule ID (e.g., user-login-attempt) and key (e.g., john_doe).
2. Rule Lookup: It retrieves the rule configuration (e.g., limit=5, window=300 s) from a local cache or Consul/Etcd.
3. Local Pre-Check: It checks a local Caffeine cache (with a short TTL, e.g., 1 s) to quickly reject obviously excessive requests.
4. Redis Check: It constructs the ZSET key and executes the Lua script on Redis.
5. Process Result: Based on the script’s return value, it either allows the request to proceed to the backend or returns an HTTP 429 response.

Sequence Diagram

This diagram visualizes the core atomic operation of the Sliding Window Log algorithm in the data plane.

3. Distributed System Challenges

A distributed rate limiter at a scale of 1 M QPS and 100 M active keys faces several challenges.

Challenge 1: Race Conditions

• Problem: Multiple service instances concurrently updating the counter for the same key (e.g., user:john_doe) can lead to exceeding the limit.
• Solution:
• Redis Lua Scripts: Encapsulate the check-and-increment logic within a Lua script, which Redis executes atomically.
• Local Cache Pre-check: Use a local Caffeine cache (with a short TTL, e.g., 1 s) in the limiter service to quickly reject requests that are clearly over the limit, reducing contention on Redis.

Challenge 2: Data Consistency

• Problem: Redis master-slave replication lag (1-10 ms) can cause reads from a replica to return stale data, potentially leading to incorrect decisions.
• Solution:
• Read/Write from Master: By default, direct all rate-limiting operations to the Redis master node to ensure strong consistency.
• Read from Replicas (High-Throughput): For high-QPS scenarios where minor inaccuracies are acceptable, allow reads from replicas to distribute the load.
• Hash Tags: Use key formats like rate_limit:{user 123}:login, where {user 123} is a hash tag, to ensure all keys for a single user reside on the same Redis shard.
• High Availability & Fallback: Deploy Redis in a Master-Slave + Sentinel or Cluster configuration. If Redis fails, the limiter service can degrade gracefully to local-cache-only mode with a more lenient, pre-configured limit.

Challenge 3: Global Rate Limiting in a Multi-DC Environment

• Problem: Independent limiting in each data center (DC) can lead to the global limit being exceeded. A centralized Redis instance would introduce high cross-region latency.
• Solution (Eventual Consistency):
  1. Local-First Limiting: Each DC enforces a local limit (a fraction of the global limit) to maintain low latency.
  2. Asynchronous Usage Reporting: Each DC reports its usage data to a central service via a message queue like Kafka.
  3. Dynamic Quota Rebalancing: The central service aggregates global usage and dynamically adjusts the quotas for each DC, propagating the new rules via a configuration store like Consul or Etcd.
  4. Effect: This approach trades a small, short-term global overage for extremely low local decision latency.

Challenge 4: Hot Keys and High-Cardinality Keys

• Problem: A hot key can overwhelm a single Redis shard. A high-cardinality key attack (using many random IDs) can exhaust memory.
• Solution:
• Hot Key Handling: Use a local Caffeine cache in the limiter service (with a very short TTL, e.g., 100 ms) to absorb the vast majority of requests for a hot key.
• High-Cardinality Defense: - Pre-validation with Bloom Filter: Place a lightweight Bloom Filter in front of the rate limiter, pre-populated with all valid user IDs. Requests with keys that fail this check can be rejected immediately or subjected to a very strict default rule, preventing them from creating entries in Redis. - Strict Eviction Policy: All Redis keys must have a reasonable EXPIRE time set, and the maxmemory-policy should be configured to allkeys-lru or a similar eviction strategy.

4. Client Communication and Error Handling

Clear feedback and robust client behavior are critical for a successful rate-limiting system.

HTTP 429 Response Design

• Standard 429 Response: Return HTTP 429 Too Many Requests.
• Response Headers:
• Retry-After: The recommended time in seconds to wait before retrying.
• X-RateLimit-Limit: The limit for the current window.
• X-RateLimit-Remaining: The number of remaining requests in the current window.
• X-RateLimit-Reset: The Unix timestamp when the window resets.
• Response Body: Provide a user-friendly error message.

Optional Strategies: Queuing and Degradation

• Request Queuing: For non-critical, asynchronous tasks (e.g., batch data processing), requests exceeding the limit can be placed in a message queue (e.g., Kafka) for later processing by a consumer.
• Degraded Responses: For non-essential read-only APIs, instead of rejecting, a slightly stale, cached response or a generic default response can be served to improve the user experience.

8. Deep Dive into Non-Functional Requirements

1. Scalability

• Problem: How to support 1 M peak QPS, 100 M active keys, and 700 GB of data?
• Solution:
• Horizontal Scaling: Deploy stateless rate limiter service instances behind a load balancer, backed by a sharded Redis Cluster.
• Multi-Level Caching: Employ a caching hierarchy: CDN at the edge -> Service-local cache (Caffeine) -> Distributed cache (Redis).
• Rule Distribution: Use a system like Consul or Etcd to distribute rules, which are then cached locally by each service instance.

2. Fault Tolerance

• Problem: How to guarantee 99.99% availability in the face of component failures?
• Solution:
• No Single Point of Failure: Use Redis in a Master-Slave + Sentinel or Cluster Mode configuration.
• Graceful Degradation: If Redis becomes completely unavailable, the limiter service automatically switches to a fallback mode, using only its local Caffeine cache for limiting.
• Cross-Zone/Region Deployment: Deploy services and databases across multiple availability zones (AZs) or regions.

3. Monitoring

• Problem: How to gain real-time insight into system health and respond quickly to anomalies?
• Solution:
• Metrics: - Request Metrics: Total requests, allowed/rejected counts, rejection rate. - Latency Metrics: P 99, P 95, P 50 latency for limit-checking decisions. - Resource Metrics: CPU/Memory usage for Redis and service instances.
• Alerting: - Alert when the rejection rate for a critical API exceeds a threshold (e.g., 5%). - Alert when latency breaches the SLA (e.g., >10 ms). - Alert on detection of a hot key attack.
• Tool Stack: Use a standard stack like Prometheus for metrics collection, Grafana for visualization, and Alertmanager for alerting.

9. Summary

This document outlines the design of a production-grade, distributed rate limiter. By employing a layered approach (edge-level token bucket + service-level sliding window) and dynamic rule management, the system effectively protects backend services from overload.

• Core Architecture: The design utilizes a separation of the control plane and data plane, balancing the flexibility of rule management with the high performance required for online processing.
• Technical Implementation: The core state is managed in a Redis Cluster, with Lua scripts ensuring atomicity to solve critical challenges in a distributed environment.
• Distributed Considerations: The design thoroughly addresses complex issues such as race conditions, data consistency, multi-DC global limiting, hot keys, and high-cardinality key attacks, providing multi-layered solutions that include local caching, asynchronous message queues, and Bloom filters.
• High Availability and Observability: Through graceful degradation, automatic failover, and a comprehensive monitoring and alerting system, the design meets the 99.99% availability requirement and enables rapid problem response.
• Cost Considerations: The design reflects a clear understanding of cost trade-offs. For example, it defaults to the memory-efficient Sliding Window Counter algorithm, reserving the more costly Sliding Window Log for high-value APIs. Furthermore, by intercepting a large volume of traffic at the CDN and with local caches, the design significantly optimizes the Total Cost of Ownership (TCO) of the expensive centralized Redis cluster.

This design provides a comprehensive framework that balances performance, cost, availability, and maintainability, making it adaptable to a wide range of business scenarios, from high-concurrency internet applications to high-precision financial systems.