Designing a Cache System: A Comprehensive Approach to Building a Redis-like Solution
Introduction
In today's digital landscape, speed and responsiveness are paramount to delivering exceptional user experiences. When applications need to process millions of requests per second with minimal latency, cache systems become critical components of modern architectures. A cache system like Redis serves as a high-performance data storage layer that sits between applications and their primary databases, significantly reducing access times and server loads.
Cache systems have become increasingly important as businesses scale their digital operations globally. They are essential for handling traffic spikes, reducing database load, and maintaining consistent performance across distributed systems. Companies like Facebook, Twitter, and Amazon rely heavily on sophisticated caching mechanisms to deliver seamless experiences to their massive user bases.
What is a Cache System?
A cache system is an intermediary high-speed data storage layer that stores a subset of data, typically transient, so future requests for that data can be served faster. Unlike primary storage systems designed for persistence and durability, caches prioritize speed and are typically memory-based to achieve microsecond response times.
Modern cache systems like Redis offer rich functionality beyond simple key-value storage, including:
Data structures support (lists, sets, sorted sets, hashes)
Pub/sub messaging capabilities
Transaction support
Scripting facilities
High availability through replication
Automatic partitioning with cluster mode
These systems operate on the principle that frequently accessed data should be kept as close as possible to the processing units, minimizing the time and resources required to retrieve information.
Requirements and Goals of the System
Functional Requirements
Fast Data Access: Provide sub-millisecond data retrieval times
Support for Multiple Data Types: Store strings, lists, sets, sorted sets, hashes
Configurable Eviction Policies: Allow specification of how data is removed when memory limits are reached (LRU, LFU, etc.)
Expiration Mechanism: Support time-to-live (TTL) for cached items
Concurrency Control: Handle multiple concurrent read/write operations
Scalability Support: Allow horizontal scaling through sharding or clustering
Basic Persistence: Provide options to persist data to disk periodically
Connection Management: Handle thousands of client connections simultaneously
Non-Functional Requirements
High Performance: Achieve response times under 1 millisecond
Reliability: Maintain availability during component failures
Scalability: Scale horizontally to handle increasing loads
Consistency Model: Provide configurable consistency levels (eventual vs. strong)
Resource Efficiency: Optimize memory usage and CPU utilization
Monitoring Capabilities: Enable real-time monitoring of cache performance
Security: Provide authentication and authorization mechanisms
Capacity Estimation and Constraints
Traffic Estimates
Assume our cache system needs to handle 100,000 requests per second
With a peak of 200,000 requests during high traffic periods
80% read and 20% write operations (typical for cache workloads)
Storage Requirements
Average size of key: 64 bytes
Average size of value: 1 KB
Total number of unique items: 10 million
Total raw storage needed: 10 million × (64 bytes + 1 KB) ≈ 10.6 TB
Memory Constraints
Since caches are primarily memory-based, we need to estimate RAM requirements
Not all data needs to be cached (typically 20% of data serves 80% of requests)
Required cache storage: 20% of 10.6 TB ≈ 2.12 TB
With replication factor of 2 for high availability: 4.24 TB total RAM needed
Bandwidth Requirements
For 100,000 requests/second with 1KB average response size
Read bandwidth: 80,000 × 1 KB = 80 MB/s
Write bandwidth: 20,000 × 1 KB = 20 MB/s
Total bandwidth: 100 MB/s (800 Mbps)
System APIs
Our cache system should expose simple, intuitive APIs for basic operations:
// Set a key with a value
SET(key: string, value: string, expiry: int = -1): boolean
// Parameters:
// - key: identifier for the cached item
// - value: data to be stored
// - expiry: optional TTL in seconds, -1 means no expiration
// Returns: true if successful, false otherwise
// Get a value by key
GET(key: string): string
// Parameters:
// - key: identifier for the cached item
// Returns: cached value or null if not found
// Delete a key
DEL(key: string): boolean
// Parameters:
// - key: identifier to be removed
// Returns: true if deleted, false if key didn't exist
// Check if a key exists
EXISTS(key: string): boolean
// Parameters:
// - key: identifier to check
// Returns: true if key exists, false otherwise
// Increment a counter stored at key
INCR(key: string, increment: int = 1): int
// Parameters:
// - key: identifier for counter
// - increment: amount to increase (default 1)
// Returns: new value after increment
For data structures, additional specialized APIs would be provided:
// List operations
LPUSH(key: string, value: string): int
RPOP(key: string): string
// Hash operations
HSET(key: string, field: string, value: string): boolean
HGET(key: string, field: string): string
// Set operations
SADD(key: string, member: string): boolean
SISMEMBER(key: string, member: string): boolean
Database Design
Cache systems typically don't employ traditional database designs but rather specialized in-memory data structures optimized for speed. However, we still need to define our data organization:
Primary Data Structures
Dictionary/Hash Table: The core mapping structure linking keys to values
Provides O(1) average lookup time
Implemented using optimized hash tables with collision resolution
Expiration Dictionary: Maps keys to their expiration timestamps
Enables efficient TTL management
Typically implemented as a minimum heap or sorted data structure
LRU/LFU Tracking Structure: Maintains access patterns for eviction policies
LRU (Least Recently Used): Doubly-linked list for tracking access order
LFU (Least Frequently Used): Frequency counter with aging mechanism
Persistence Structures (Optional)
For systems requiring durability:
Append-Only File (AOF): Records all write operations for replay during restart
Point-in-time Snapshots (RDB): Periodic full-memory dumps to disk
Memory Management
Unlike traditional databases that use disk storage, our cache system will primarily use RAM:
Memory Allocator: Custom allocator to reduce fragmentation
Slab Allocation: Pre-allocated memory chunks of different sizes to reduce waste
Compression: Optional in-memory compression for values to increase effective capacity
High-Level System Design
┌─────────────────────────────────────────────────────────────────┐
│ Client Applications │
└───────────────────────────────┬─────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Load Balancer/Proxy │
└───────────────────────────────┬─────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Cache Cluster │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Cache Node 1│ │ Cache Node 2│ │ Cache Node n│ │
│ │ (Primary) │◄──►│ (Replica) │◄──►│ │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
└───────────────────────────────┬─────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Configuration & Management │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Monitoring │ │ Cluster │ │ Admin │ │
│ │ System │ │ Management │ │ Interface │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────────┘
The high-level architecture consists of:
Client Applications: Services consuming the cache
Load Balancer/Proxy: Distributes requests across cache nodes
Cache Cluster: A collection of cache nodes storing the actual data
Configuration & Management: Handles monitoring, administration, and cluster management
Service-Specific Block Diagrams
Cache Node Internal Architecture
┌─────────────────────────────────────────────────────────────┐
│ Cache Node │
│ │
│ ┌─────────────┐ ┌─────────────────────────────────┐ │
│ │ Connection │ │ Command Processor │ │
│ │ Manager │◄───┤ - Parser │ │
│ └─────────────┘ │ - Request Handler │ │
│ │ │ - Response Formatter │ │
│ ▼ └─────────────────┬───────────────┘ │
│ ┌─────────────┐ │ │
│ │ Client │ ▼ │
│ │ Connections │ ┌────────────────────┐ │
│ └─────────────┘ │ Data Access Layer │ │
│ └────────┬───────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────┐ ┌────────────────────┐ │
│ │ Eviction │◄───────┤ In-Memory Store │ │
│ │ Manager │ │ - Hash Tables │ │
│ └──────────────┘ │ - Data Structures │ │
│ └────────┬───────────┘ │
│ ┌──────────────┐ │ │
│ │ Expiration │◄────────────────┘ │
│ │ Manager │ │
│ └──────────────┘ │
│ │
│ ┌──────────────┐ ┌────────────────────┐ │
│ │ Replication │◄───────┤ Persistence │ │
│ │ Manager │ │ Manager (AOF/RDB) │ │
│ └──────────────┘ └────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
Each cache node contains:
Connection Manager: Handles client connections using multiplexed I/O
Command Processor: Parses and executes client commands
Data Access Layer: Mediates access to the actual data store
In-Memory Store: Core data structures holding cached items
Eviction Manager: Implements policies for removing data when memory limits are reached
Expiration Manager: Tracks TTLs and removes expired entries
Replication Manager: Handles data synchronization with replica nodes
Persistence Manager: Manages optional durability mechanisms (AOF/RDB)
Cluster Management Service
┌───────────────────────────────────────────────────────────┐
│ Cluster Management Service │
│ │
│ ┌────────────────┐ ┌────────────────────┐ │
│ │ Configuration │ │ Node Registry │ │
│ │ Store │◄───────┤ - Node Discovery │ │
│ │ (etcd/ZooKeeper│ │ - Health Tracking │ │
│ └────────────────┘ └────────┬───────────┘ │
│ ▲ │ │
│ │ ▼ │
│ ┌──────┴───────┐ ┌────────────────────┐ │
│ │ Config │ │ Shard Manager │ │
│ │ Manager │ │ - Hash Ring │ │
│ └──────────────┘ │ - Rebalancing │ │
│ └────────┬───────────┘ │
│ │ │
│ ┌────────────────┐ ▼ │
│ │ Failover │ ┌────────────────────┐ │
│ │ Controller │◄───────┤ Monitoring System │ │
│ │ │ │ - Metrics │ │
│ └────────────────┘ │ - Alerts │ │
│ └────────────────────┘ │
└───────────────────────────────────────────────────────────┘
The cluster management service includes:
Configuration Store: Distributed configuration system (etcd/ZooKeeper)
Node Registry: Tracks available nodes and their health
Shard Manager: Handles data distribution across nodes
Monitoring System: Collects performance metrics
Failover Controller: Manages primary-replica transitions during failures
Config Manager: Handles configuration changes and distribution
Deep Dive into Specific Components
Memory Management and Data Structures
The heart of any cache system is its memory management and data structures. For a Redis-like system, we need efficient implementations that balance speed, memory usage, and flexibility.
Key-Value Store Implementation
For our core key-value storage, we'll use a hash table with separate chaining for collision resolution. This provides O(1) average lookup time.
Why hash tables over alternatives:
Speed: Hash tables provide near-constant time access, significantly faster than tree-based structures (O(log n))
Simplicity: Simpler to implement and maintain than complex data structures like B-trees
Memory Efficiency: Properly sized hash tables have minimal overhead
Financial services firms like JPMorgan and Goldman Sachs prefer hash-based caching for their trading platforms due to predictable sub-millisecond access times, which is critical for real-time decision making.
Memory Allocation Strategy
For efficient memory management, we'll implement a slab allocator:
Pre-allocated Slabs: Memory is divided into slabs of different sizes (64B, 128B, 256B, etc.)
Size Classes: Each object is stored in the smallest slab that can fit it
Slabs Management: Track free and used chunks within each slab
This approach has significant advantages over general-purpose allocators:
Reduced Fragmentation: Similar-sized objects are grouped together
Faster Allocation: Pre-allocated chunks eliminate expensive malloc/free calls
Better Memory Utilization: Minimizes wasted space
E-commerce platforms like Shopify and social media platforms like Instagram use slab-based allocation in their caching layers to handle billions of small objects with minimal overhead.
Eviction Policies
When memory limits are reached, the cache must decide which items to remove. We'll support multiple eviction policies:
LRU (Least Recently Used):
Implementation: Doubly-linked list + hash map for O(1) operations
Use Case: General-purpose caching where recency predicts future access
LFU (Least Frequently Used):
Implementation: Frequency counters with aging mechanism
Use Case: When access frequency is more important than recency
Random Eviction:
Implementation: Simple random selection from all keys
Use Case: Very high throughput systems where policy computation overhead is a concern
TTL-based Eviction:
Implementation: Min-heap ordered by expiration time
Use Case: Time-sensitive data where staleness is critical
The choice between these policies depends on workload characteristics:
Content delivery networks (CDNs) like Cloudflare typically favor LFU for static assets as popularity is often more important than recency
Session stores in web applications typically use LRU since recent sessions are more likely to be accessed again
Financial data caches often use TTL-based eviction to ensure data freshness
Persistence Mechanisms
While caches are primarily in-memory systems, persistence options provide durability and warm startup capabilities:
RDB (Redis Database):
Point-in-time snapshots of the entire dataset
Advantages: Compact files, faster recovery for large datasets
Disadvantages: Potential data loss between snapshots
AOF (Append-Only File):
Logs every write operation for replay during recovery
Advantages: Minimal data loss, easier to understand
Disadvantages: Larger files, slower recovery
Hybrid Approach:
Combines periodic RDB snapshots with AOF logs
Advantages: Balances durability and recovery speed
Disadvantages: More complex implementation
E-commerce platforms typically use hybrid persistence approaches for their caching layers to balance performance with the need to preserve shopping cart data across system restarts.
Data Partitioning
As data volume grows beyond the capacity of a single node, we need strategies to distribute it across multiple machines:
Hash-Based Partitioning
The most common approach uses consistent hashing:
┌───────────────────────────────────────────────────────────┐
│ Consistent Hash Ring │
│ │
│ Node 1 Node 4 │
│ ▲ ▲ │
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │
│ ────┴──────────────────────────────────────┴──────── │
│ / \ │
│ \ / │
│ ────┬──────────────────────────────────────┬──────── │
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │
│ ▼ ▼ │
│ Node 2 Node 3 │
│ │
└───────────────────────────────────────────────────────────┘
Why consistent hashing over alternative partitioning strategies:
Minimal Redistribution: When nodes are added or removed, only a fraction of keys need to be remapped
Scalability: Easily accommodates cluster expansion or contraction
Balance: Virtual nodes ensure even distribution despite node heterogeneity
Amazon DynamoDB's caching layer uses consistent hashing to distribute billions of items across thousands of nodes while maintaining sub-millisecond access times.
Replication Within Partitions
Each partition typically has primary and replica nodes:
┌────────────────┐ ┌────────────────┐ ┌────────────────┐
│ Partition 1 │ │ Partition 2 │ │ Partition 3 │
│ ┌──────────┐ │ │ ┌──────────┐ │ │ ┌──────────┐ │
│ │ Primary │ │ │ │ Primary │ │ │ │ Primary │ │
│ └────┬─────┘ │ │ └────┬─────┘ │ │ └────┬─────┘ │
│ │ │ │ │ │ │ │ │
│ ▼ │ │ ▼ │ │ ▼ │
│ ┌──────────┐ │ │ ┌──────────┐ │ │ ┌──────────┐ │
│ │ Replica │ │ │ │ Replica │ │ │ │ Replica │ │
│ └──────────┘ │ │ └──────────┘ │ │ └──────────┘ │
└────────────────┘ └────────────────┘ └────────────────┘
This approach provides:
High Availability: If a primary fails, a replica can be promoted
Read Scalability: Replicas can serve read requests
Disaster Recovery: Data is preserved even if nodes fail
Consistency and Replication
Cache systems must balance consistency, availability, and partition tolerance. For a Redis-like system, we'll focus on:
Replication Modes
Synchronous Replication:
Primary waits for replica acknowledgment before responding to clients
Advantages: Strong consistency guarantees
Disadvantages: Higher latency for write operations
Asynchronous Replication:
Primary responds immediately, replicating to secondaries in background
Advantages: Lower latency for write operations
Disadvantages: Potential data loss during failures
The financial industry often uses synchronous replication for critical caching layers despite the performance impact because data consistency is paramount. In contrast, social media platforms typically favor asynchronous replication to prioritize user experience and responsiveness.
Handling Replication Conflicts
When network partitions or node failures occur, conflicts may arise:
Primary-Based Resolution:
Always treat the primary as the source of truth
Simple but can lose updates if the primary fails
Vector Clocks:
Track causal relationships between updates
More complex but preserves update history
Last-Writer-Wins (LWW):
Use timestamps to determine which update to keep
Simple but may lose updates
Most production cache systems like Redis implement primary-based resolution due to its simplicity and predictability, with failover mechanisms to minimize downtime during primary failures.
Identifying and Resolving Bottlenecks
Despite careful design, cache systems can develop bottlenecks:
Network Bottlenecks
Problem: High-volume traffic exhausting network bandwidth Solution:
Implement client-side batching to reduce request overhead
Use binary protocols instead of text-based ones (like RESP in Redis)
Deploy cache nodes in the same network segment as application servers
Memory Pressure
Problem: Running out of memory despite eviction Solution:
Implement data compression (LZF, Snappy)
Employ more aggressive TTL policies
Introduce sampling-based eviction for extremely large datasets
Thundering Herd
Problem: Cache misses causing database overload Solution:
Implement staggered expiration times
Use probabilistic early expiration
Add request coalescing for concurrent cache misses
Financial trading platforms often implement request coalescing in their cache layers to prevent database overload during market-moving events when many traders request the same data simultaneously.
Hot Keys
Problem: Few keys receiving disproportionate access Solution:
Implement key-specific replication factors
Create dedicated nodes for hot keys
Use local L1 caches in application servers
Security and Privacy Considerations
Cache systems often contain sensitive data and must be protected:
Authentication
Implement strong password authentication
Support for SSL/TLS encrypted connections
Optional LDAP or OAuth integration for enterprise deployments
Authorization
ACL (Access Control Lists) to restrict commands
Read/write separation permissions
Key pattern-based restrictions
Encryption
Encrypt data at rest for persistence files
Encrypt communication between nodes
Optional encrypted values for sensitive data
Healthcare organizations that cache patient data typically implement all these security measures to comply with regulations like HIPAA, with particular emphasis on encryption both in transit and at rest.
Monitoring and Maintenance
Effective operation requires comprehensive monitoring:
Key Metrics to Track
Hit Rate: Percentage of requests served from cache
Latency: Response time distribution
Memory Usage: Overall and per-data-type
Eviction Rate: Items being removed due to memory pressure
Network Traffic: Bandwidth consumption
Maintenance Operations
Scaling: Adding/removing nodes to accommodate changing workloads
Rebalancing: Redistributing data after topology changes
Upgrades: Performing rolling upgrades without downtime
Backup: Regular persistence snapshots for disaster recovery
E-commerce platforms typically increase their cache capacity by 3-5x before major shopping events like Black Friday, then scale back afterward to optimize costs.
Conclusion
Designing a cache system like Redis requires balancing many complex factors:
Performance vs. Durability
Consistency vs. Availability
Simplicity vs. Feature Richness
Memory Efficiency vs. Computational Overhead
The design decisions outlined in this article reflect the trade-offs that have proven effective in production environments for diverse workloads, from high-throughput social media platforms to latency-sensitive financial systems.
A well-designed cache system can reduce database load by 80-95%, improve application response times by an order of magnitude, and enable applications to scale to millions of users without proportional infrastructure costs.
By understanding these design principles and trade-offs, you can implement or leverage cache systems effectively in your own architectures, tailoring the approach to your specific requirements and constraints.