System Requirements

Functional:

1. Cache Storage - Efficiently store frequently accessed content to reduce response time
2. Cache Invalidation - Implement a mechanism to invalidate or update cached content when the original data changes
3. Cache Retrieval - Provide a method to retrieve cached content for user requests
4. Cache Eviction - Implement a strategy to evict least recently used content to make room for new cached content
5. Cache Expiration - Implement a time based expiration mechanism to remove stale content from the cache
6. Cache Persistence - Ensure cached data is persistent to handle server restarts without losing cached content
7. Cache Configuration - Allow configuration of cache size, expiration times and eviction policies

Non-Functional:

1. Scalability - The system should be able to handle increasing load by scaling horizontally
2. Efficiency - Cache operations should be fast and consume minimal system resources
3. Consistency - Cached content should reflect changes made to the original data source
4. Reliability - System should be robust and maintain data integrity even during failures
5. Security - Implement measures to prevent unauthorized access to cached content
6. Maintainability - System should be easy to maintain and troubleshoot
7. Performance - Cache hits should significantly reduce response time for users
8. Flexibility - Allow for easy integration with different web servers and configs

Capacity Estimation

1. We assume that our cache stores 1 million key value pairs in a day
2. We assume that each key value pair takes 1 megabyte of data
3. Thus, we store 10^6 * 10^6 bytes of data = 1 TB data in a day
4. If our cache persists for a month, we would end up storing 30 TB of data in our cache in the worst case

API Design

1. Cache Management APIs
   1. put(key, value, expiration_time) - Adds or updates an item in the cache with the specified key, value and expiration time
   2. get(key) - Gets the value associated with the key from the cache, not found error otherwise
   3. delete(key) - Deletes key-value pair from the cache
   4. invalidate(key) - Invalidates key in the cache and forces a refresh from the original data source
   5. clear() - Clears the entire cache
2. Configuration APIs
   1. set_cache_size(size) - Sets the maximum size of the cache
   2. set_eviction_policy(policy) - Sets the eviction policy of the cache
   3. set_expiration_time(key, expiration_time) - Sets the expiration time for a specific cached item
   4. get_configuration() - Gets the current eviction policy of the cache
3. Monitoring APIs
   1. get_cache_stats() - Retrieves stats about the cache such as hit rate, miss rate and cache usage
   2. get_cache_content() - Retrieves a list of all key value pairs in the cache currently
4. Cache Invalidation APIs
   1. register_invalidation_listener(listener) - Registers a listener to receive notifications about changes to the original data source, allowing for automatic cache invalidation
5. Lifecycle Management APIs
   1. initialize_cache() - Initialize the cache system, setting up any necessary resources and configs
   2. shutdown_cache() - Gracefully shuts down the cache system, releasing any allocated resources

Database Design

classDiagram class CachedContent { +string key +string content +dateTime expirationTime +string metadata } class CacheMetadata { +string url +string cacheKey +dateTime lastAccessedTime +int size } class CacheConfiguration { +int cacheSize +string evictionPolicy +int expirationTime } class CacheInvalidation { +string cacheKey +dateTime invalidationTime } class Logs { +dateTime timestamp +string message } CachedContent --|> CacheMetadata CachedContent --|> CacheInvalidation

1. Cached Content
   1. For this we use an in memory non relational database like Redis
   2. Redis will provide fast reads and writes due to it being in memory
2. Cached Metadata
   1. For this we use a relational database like PostgreSQL since it is suitable for storing structured data like metadata
3. Cache Configurations
   1. Here we use a non-relational document store like MongoDB since a document store will be able to represent the content related to a key in the form of a JSON which is highly configurable
4. Partition Strategy
   1. Efficient partitioning strategies like consistent hashing can be used to evenly data across data nodes
   2. Consistent hashing will help us in retrieving key value pairs effectively as well
5. Replication
   1. After choosing the destination server for a key value pair, we can replicate the data to the next N servers for redundancy, where N is a configurable parameter
6. Scaling Strategy
   1. Horizontal scaling is typically preferred for scaling a web cache system to handle increasing traffic and data volume
   2. We can add more cache nodes or instances to the system to distribute the workload and accommodate growth
   3. Load balancing techniques can be employed to evenly distribute traffic across multiple cache nodes, ensuring efficient utilization of resources

High-Level Design

graph TD subgraph Web Server WS[Web Server] end subgraph Cache Proxy CP[Cache Proxy] end subgraph User Interface UI[User Interface] end subgraph Cache Manager CM[Cache Manager] end subgraph Cache Storage CS[Cache Storage] end subgraph Cache Invalidation Mechanism CIM[Cache Invalidation Mechanism] end subgraph Configuration Management CMG[Configuration Management] end subgraph Monitoring and Logging ML[Monitoring and Logging] end subgraph Content Delivery Network CDN[Content Delivery Network] end subgraph Load Balancer LB[Load Balancer] end subgraph Security SEC[Security] end WS --> CP CP --> WS CP --> CS CP --> CM UI --> CMG CMG --> CM CM --> CS CM --> CIM CIM --> CS CIM --> CP CIM --> CDN CIM --> LB CIM --> SEC ML --> CM

In the high-level design of the web cache system, several components are required to solve the problem effectively from end to end. These components interact with each other to provide caching functionality, manage cache operations, and ensure seamless integration with the web server. Here are the key components:

• User Interface (UI):
  • Provides a user interface for configuring cache settings, monitoring cache performance, and accessing cached content.
• Web Server:
  • Hosts the original web content and handles user requests.
  • Interfaces with the cache system to retrieve cached content when available.
• Cache Proxy:
  • Acts as a reverse proxy between the web server and the user.
  • Intercepts incoming requests and serves cached content if available, reducing response time and server load.
• Cache Manager:
  • Manages cache operations such as storing, retrieving, updating, and evicting cached content.
  • Implements cache policies and ensures efficient utilization of cache resources.
• Cache Storage:
  • Provides the underlying storage mechanism for storing cached data.
  • Can be implemented using in-memory cache, disk cache, or distributed cache solutions like Redis or Memcached.
• Cache Invalidation Mechanism:
  • Handles the invalidation of cached content when the original data changes.
  • Monitors changes to the original data source and triggers cache invalidation accordingly.
• Configuration Management:
  • Allows for dynamic configuration of cache settings such as cache size, eviction policies, and expiration times.
  • Ensures flexibility and adaptability of the cache system to changing requirements.
• Monitoring and Logging:
  • Monitors cache performance, hit rates, and system health metrics.
  • Logs cache operations and events for troubleshooting and auditing purposes.
• Content Delivery Network (CDN) Integration:
  • Integrates with CDNs to distribute cached content closer to end users, reducing latency and improving content delivery speed.
• Load Balancer:
  • Distributes incoming traffic across multiple cache nodes for load balancing and fault tolerance.
  • Ensures scalability and high availability of the cache system.
• Security:
  • Implements security measures to protect cached content and prevent unauthorized access.
  • Includes authentication, authorization, and encryption mechanisms as necessary.

These components work together to enhance the performance, scalability, and reliability of the web cache system, providing efficient caching of frequently accessed web content and optimizing resource utilization.

Request flows

Detailed Component Design

Cache Eviction Policy

Designing a cache eviction policy involves finding a balance between optimizing storage utilization, access speed, and maintaining consistency with the original data source. Several factors should be considered when deciding the eviction strategy for different types of content in the cache:

1. Access Frequency: Items that are accessed frequently should be prioritized for retention in the cache to improve access speed and user experience. A Least Recently Used (LRU) or Least Frequently Used (LFU) eviction policy can be effective in this regard.
2. Content Importance: Some content may be more critical or frequently requested by users than others. Prioritize caching of important or high-priority content to optimize access speed and user satisfaction.
3. Content Size: Larger items consume more storage space in the cache. Implementing a size-based eviction policy ensures efficient storage utilization by evicting larger items when the cache reaches its capacity limit.
4. Expiration Time: Items with shorter expiration times may be evicted more aggressively to make room for newer content. Implement a time-based eviction policy to remove stale content from the cache and maintain freshness.
5. Content Type: Different types of content may have varying access patterns and importance. Tailor the eviction strategy based on the characteristics of each content type. For example, static assets like images or CSS files may have longer cache lifetimes compared to dynamic content generated by user requests.
6. Adaptability: Periodically analyze cache usage patterns and adjust the eviction policy dynamically based on changing requirements, traffic patterns, and content popularity.

Consistent Hashing:

Consistent hashing is a technique used to distribute data across multiple servers or nodes in a distributed system while ensuring that the distribution remains stable even when nodes are added or removed. In the context of a cache system, consistent hashing allows for efficient distribution of cached content across cache nodes.

Here's how it works:

• Each cache key is hashed to determine which cache node it should be stored on.
• A hash function is used to map each key to a range of values.
• Each cache node is assigned a range of values within the hash space.
• When a cache request is received, the key is hashed, and the corresponding cache node responsible for that key's range is determined.
• If a cache node is added or removed, only a fraction of the keys need to be remapped, minimizing the impact on cache invalidation and ensuring a balanced distribution of load across nodes.

Consistent hashing helps maintain a balanced load distribution and minimizes cache invalidation when nodes are added or removed from the cache system. It provides scalability and fault tolerance by allowing the cache system to adapt dynamically to changes in the number of cache nodes.

Replication and Consistency Protocols:

Replication techniques and consistency protocols are used to ensure that cached content remains consistent across distributed cache nodes. In a distributed cache system, multiple replicas of cached data are maintained across different nodes to provide fault tolerance and redundancy.

Here's how it works:

• Cached data is replicated across multiple cache nodes to provide redundancy and fault tolerance.
• Consistency protocols, such as quorum-based consistency or eventual consistency, are employed to ensure that updates to cached data are propagated consistently across all replicas.
• Quorum-based consistency ensures that a majority of replicas must agree on an update before it is considered successful, ensuring consistency and fault tolerance.
• Eventual consistency allows for temporary inconsistencies between replicas but ensures that eventual convergence is reached, providing fault tolerance and scalability.

By replicating cached data across multiple nodes and employing consistency protocols, the cache system ensures that cached content remains consistent and available even in the event of node failures or network partitions. This approach provides high availability, fault tolerance, and data integrity in a distributed cache environment.

Trade offs/Tech choices

Failure scenarios/bottlenecks

Future improvements