System design is a critical skill in software and data engineering roles, especially in more senior roles and at larger tech companies. In today’s world of distributed systems and large-scale applications, managing data efficiently is crucial for delivering high performance, scalability, and reliability. One of the key tools in achieving these goals is a distributed cache. For this reason, it’s a common component that tech companies like to see used in the system design interview. Some of the more complex system design interview problems will require the use of distributed caching and the interviewer will want to see that the candidate has a thorough understanding of how to design and use them, including how to choose the right technologies and effectively handle cache invalidation and evictions strategies.
This article explores distributed caches in detail: what they are, why they are needed, common use cases, and best practices for designing them.
What is a Distributed Cache?
A distributed cache is a system that stores frequently accessed data across multiple nodes in a distributed environment. It acts as an intermediary between the application and the primary data source (e.g., a database), providing quick access to data that is otherwise expensive to fetch or compute.
Unlike traditional caching mechanisms that operate on a single machine, a distributed cache is spread across multiple servers, enabling it to handle larger data volumes with high concurrency and low latency. These systems ensure that cached data is available and localized across the entire application infrastructure, irrespective of where the request is made.
Understanding the core concepts of the distributed cache is crucial in the system design process for software and data engineers, especially in designing modern distributed systems.
Characteristics of a Distributed Cache
A distributed cache is a system that stores frequently accessed data in-memory across multiple nodes, improving application performance, scalability, and fault tolerance. Unlike traditional caching solutions that run on a single machine, distributed caches distribute data across multiple servers, ensuring high availability and low-latency access at scale.
1. Scalability
- Horizontal Scalability: Distributed caches scale by adding more nodes rather than upgrading a single machine (vertical scaling). This allows handling increased workloads efficiently.
- Sharding (Partitioning): Data is distributed across multiple nodes using hashing or consistent hashing techniques to prevent bottlenecks.
2. High Availability & Fault Tolerance
Use replication to ensure fault tolerance and data availability. However, consider trade-offs between replication and latency, as replication can increase write times. Consider replication strategies for how the leaders handle acknowledgments and how it affects latency, availability, and consistency. For example a leader could replicate the data to multiple nodes, but not wait for any acknowledgements that the replicated nodes committed these changes and go ahead and commit them and move on. This would result in a low latency replication strategy, but could affect consistency and availability if the leader fails and not all of the nodes have committed the change, leaving the replicas in different states. On the other hand, if you set the acknowledgments to all then the leader would have to wait for the changes to be committed on all of the replicas and receive confirmation responses before moving on, causing potential higher latencies. Acknowledgements can be tweaked from 0 to all and anything in between to balance the latency vs. consistency depending on the use case.
- Data Replication: Distributed caches ensure redundancy by replicating cached data across multiple nodes. If one node fails, another can take over seamlessly.
- Automatic Failover: Mechanisms such as leader-election (in Redis Sentinel) and quorum-based replication ensure that failures do not disrupt the cache layer.
3. Low Latency & High Throughput
- In-Memory Storage: Distributed caches store data in RAM or flash memory rather than disks, reducing access time to microseconds instead of milliseconds.
- Efficient Data Retrieval: Often employs O(1) complexity operations (like in Redis and Memcached) for ultra-fast lookups.
4. Data Consistency Models
Choose a consistency model based on your use case. For a deeper understanding of Consistency models read Chapter 9: Consistency and Consensus in Martin Kleppmann’s book: Designing Data-Intensive Applications and read more about the Read more about CAP or PACELC theory. The two main consistency models are:
- Strong Consistency: Ensures all nodes have the latest data but can increase latency. As the name suggests, this model provides stronger consistency guarantees, but at the cost of higher latency.
- Eventual Consistency: Offers better performance but risks serving stale data momentarily. This is considered a weak consistency guarantee, but provides low-latency.
5. Cache Invalidation Strategies
Implement eviction policies to manage memory constraints. As the cache space fills you have to make decisions on how to what data in the cache gets moved out to create space for new cached data. Common strategies include:
- Least Recently Used (LRU): Evicts the least recently accessed data.
- Least Frequently Used (LFU): Removes the least accessed items over time.
- Window TinyLFU (W-TinyLFU): Combines LRU and LFU strategies considering both how recent and how frequently the data is accessed.
- Time-to-Live (TTL): Automatically expires data after a specified duration.
6. Distributed Hashing & Load Balancing
- Consistent Hashing: Distributes cache entries efficiently across multiple nodes and minimizes data movement when nodes are added or removed and prevents hot spots. Hot spots are when data is unevenly distributed in nodes causing some nodes to experience a high load while others are idle or with a much lighter load. Even with a very large cluster with many nodes, if the data is not partitioned and balanced efficiently there will be bottlenecks and performance degradation..
- Load Balancing: Prevents any single cache node from becoming overloaded by dynamically routing requests.
7. Security & Access Control
- Authentication & Authorization: Caches like Redis provide ACLs (Access Control Lists) and authentication mechanisms to protect against unauthorized access.
- Data Encryption: Sensitive data in distributed caches can be encrypted at rest and in transit using TLS/SSL.
8. Multi-Region & Geo-Distributed Caching
Distribute the cache across different geographical regions to ensure consistent fast response times for users in different locations.
- Edge Caching (CDN Integration): Platforms like Cloudflare, AWS CloudFront, and Akamai use distributed caching to serve content from edge locations closer to users.
- Geo-Replication: Ensures that users across different geographical locations receive fast responses.
Why Do We Need a Distributed Cache?
1. Performance Optimization
Database queries, especially on large datasets, can be slow and resource-intensive. A distributed cache reduces the load on databases by storing precomputed or frequently accessed data closer to the application. This dramatically improves response times.
2. Scalability
As applications grow, databases can become bottlenecks. By offloading frequent reads to a distributed cache, you can scale your system to handle more requests without overburdening your database.
3. Cost Efficiency
Fetching data from a database incurs compute and storage costs. Distributed caching reduces the frequency of such expensive operations, lowering the overall infrastructure costs.
4. High Throughput
Applications like e-commerce platforms, social media, or gaming systems require handling thousands or millions of requests per second. Distributed caches enable these systems to achieve high throughput without compromising on latency.
Examples of How Distributed Caches Are Used
1. Web Application Performance Optimization
Scenario: High-Traffic Websites (Amazon, Facebook, Twitter)
- Websites use distributed caches to store frequently accessed content, reducing database queries and response times.
- Example: Using Memcached to cache user profiles and news feeds, handling billions of requests per second.
- Technology: Redis, Memcached
2. Session Management in Scalable Applications
Scenario: Large-Scale Authentication Systems
- Distributed caches store user session data to enable seamless authentication across multiple servers.
- Example: Using Redis for managing session tokens, ensuring fast authentication without database hits.
- Technology: Redis Cluster
3. Content Delivery & Edge Caching
Scenario: Content Distribution Networks (CDNs)
- Distributed caching enables fast content delivery by storing static assets (images, videos, HTML, CSS, JS) at edge locations close to users.
- Example: Cloudflare and AWS CloudFront use distributed caches to serve millions of requests with low latency.
- Technology: AWS CloudFront, Redis, Cloudflare Workers
4. Machine Learning Feature Store
Scenario: AI/ML Model Caching (Recommendation Systems)
- Distributed caches store precomputed ML features, allowing real-time model inference.
- Example: Building a distributed inference cache with NVIDIA Inference Server and Redis.
- Technology: RedisAI, Apache Ignite, Amazon DynamoDB Accelerator (DAX)
5. Stock Trading & Financial Services
Scenario: Low-Latency Trade Execution
- Distributed caches store real-time stock prices, reducing database load and ensuring fast trade execution.
- Example: Building a real-time trading platform with Redis.
- Technology: Redis Cluster, Apache Ignite
Example: Designing a Distributed Cache for an E-Commerce Platform
Scenario: An e-commerce platform experiences high traffic during sales events. Customers frequently search for products, which requires querying a large database.
Design Steps:
- Cache Frequently Queried Data:
Cache product catalogs, pricing, and inventory details. Use a TTL policy to ensure data freshness. - Partition Data:
Use consistent hashing to distribute product data across multiple cache nodes. - Enable Replication:
Replicate cache data across nodes to ensure availability during node failures. - Eviction Policy:
Apply an LRU eviction policy to remove older, less accessed products from the cache. - Monitoring:
Track cache hit rates and monitor performance using Prometheus and Grafana.
Result: The distributed cache reduces database load, ensures low-latency responses during high traffic, and improves user satisfaction.
Conclusion
Distributed caching is a cornerstone of modern data architecture and system design, providing the performance, scalability, and cost efficiency needed for today’s high-demand applications. By understanding its principles, use cases, and design strategies, teams can harness its potential to build robust and efficient systems.
Understanding how a distributed cache can be added to a system design is essential for software and data engineers to master as they advance in their careers.
Check out other system design concepts and learning resources to help you master the concepts.