What is a Distributed Cache?

Distributed caches are crucial tools for enhancing the dependability and speed of applications. By storing frequently accessed data across several servers and closer to the point of demand, distributed caches lower latency and decrease the strain on backend systems. The definition, operation, and importance of distributed caches for modern applications will all be discussed in this article.

What is a Distributed Cache?

A distributed cache is a cache with data spread across multiple nodes in a cluster and multiple clusters across multiple data centers worldwide. A distributed cache is a system that pools together the random-access memory (RAM) of multiple networked computers into a single in-memory data store used as a data cache to provide fast access to data.

• While most caches are traditionally in one physical server or hardware component, a distributed cache can grow beyond the memory limits of a single computer by linking together multiple computers–referred to as a distributed architecture and increased processing power.
• Distributed caches are especially useful in environments with high data volume and load.

How Distributed Cache Works?

Below is how a Distributed Cache typically works:

• Data Storage: Each node or server has a specific amount of memory set aside by the distributed cache system for the storage of cached data. Read and write operations can be performed more quickly due to this memory's generally faster access than disk storage.
• Data Replication: To ensure high availability and fault tolerance, the distributed cache system replicates cached data across multiple nodes or servers.
• Cache Invalidation: Cached data needs to be invalidated or updated periodically to reflect changes in the underlying data source.
• Cache Coherency: Maintaining cache coherency ensures that all nodes in the distributed cache system have consistent copies of cached data.
• Cache Access: Applications interact with the distributed cache system through a cache API, which provides methods for storing, retrieving, and updating cached data.
• Cache Eviction: To prevent the cache from consuming too much memory, distributed cache systems implement eviction policies to remove least recently used (LRU) or least frequently used (LFU) data from the cache when it reaches its capacity limit.

Key components of Distributed Caching

The key components of distributed Caching include:

1. Cache Servers or Nodes
   • Cache servers are the primary components in a distributed caching system. They store temporary data across multiple machines or nodes, ensuring that the data is available close to where it’s needed.
2. Cache Data
   • This is the actual data stored in the distributed cache system. It can include frequently accessed objects, database query results, or any other data that benefits from being stored in memory for fast access.
3. Cache Client
   • Cache clients are used by applications to communicate with the distributed cache system. They offer an interface for saving, retrieving, and modifying cached data.
   • The cache client makes it simpler for developers to include caching into their applications by abstracting away the complexities of cache management and communication with cache nodes.
4. Cache API
   • The cache API defines the methods and operations available for interacting with the distributed cache system. This includes commands for reading, writing, invalidating, and evicting cached data.
5. Cache Manager
   • The cache manager is responsible for coordinating cache operations and managing the overall behavior of the distributed cache system.

Benefits of Distributed Cache

These are some of the core benefits of using a distributed cache methodology:

• Maintains data that is often retrieved in memory, improving user experience and application speed of response.
• It is appropriate for applications needing high throughput since it can accommodate large data requests and add more nodes to the cluster, allowing applications to scale horizontally without affecting performance.
• Can replicate data across multiple nodes, ensuring that data is always available even if one or more nodes fail.
• Distributed cache improves efficiency and lowers network traffic by storing data in memory instead of requiring network requests to retrieve data from a database or file system.
• Reduces the need for expensive hardware upgrades or additional database lisences, making it a cost-effective solution for scaling applications.
• Can store user session data, improving the performance and scalability of web applications.

Popular Use Cases of Distributed Cache

There are many use cases for which an application developer may include a distributed cache as part of their architecture. These include:

• Application acceleration:
  • Applications that rely on disk-based relational databases can’t always meet today’s increasingly demanding transaction performance requirements.
  • By storing the most frequently accessed data in a distributed cache, you can highly reduce the I/O bottleneck of disk-based systems.
• Storing web session data:
  • A site may store user session data in a cache to serve as inputs for shopping carts and recommendations.
  • With a distributed cache, you can have a large number of concurrent web sessions that can be accessed by any of the web application servers that are running the system.
• Decreasing network usage/costs:
  • By caching data in multiple places in your network, including on the same computers as your application, you can reduce network traffic and leave more bandwidth available for other applications that depend on the network.
• Reducing the impact of interruptions :
  • Depending on the architecture, a cache may be able to answer data requests even when the source database is unavailable. This adds another level of high availability to your system.
• Extreme scaling :
  • Some applications request significant volumes of data. By leveraging more resources across multiple machines, a distributed cache can answer those requests.

Steps for Implementing Distributed Caching

From selecting the best caching solution to customizing and implementing it in a distributed setting, there are multiple processes involved in setting up a distributed cache. The general step-by-step instructions are provided below:

• Step 1: Select a suitable distributed caching solution based on application requirements and infrastructure.
• Step 2: Install and configure the caching software on each node or server in the distributed system.
• Step 3: Define data partitioning and replication strategies to ensure efficient data distribution and high availability.
• Step 4: Integrate the caching solution with the application, ensuring that data reads and writes are directed to the cache.
• Step 5: Monitor and fine-tune the cache performance, adjusting configurations as needed for optimal results.

Popular Distributed Caching solutions

Below are some of the popular distributed caching solutions:

1. Redis: A highly popular in-memory data store, Redis supports caching, databases, and message brokering. It’s known for speed and flexibility, and works well for distributed caching with built-in data replication and persistence options.
2. Memcached: A lightweight, in-memory key-value store. Memcached is widely used for caching frequently accessed data and is easy to set up, though it lacks some advanced features like persistence and replication.
3. Amazon ElastiCache: A fully managed service by AWS, it supports both Redis and Memcached, allowing you to use caching in a distributed cloud environment without managing the infrastructure yourself.
4. Apache Ignite: An in-memory computing platform that offers distributed caching with advanced features like transactions and real-time streaming. It’s designed for high-performance computing scenarios.
5. Hazelcast: A scalable in-memory data grid that provides distributed caching, data partitioning, and failover features. It’s often used in highly scalable enterprise applications.

Distributed Caching Challenges

Some of the challenges with distributed caching are:

• Data Consistency: Keeping cache data in sync across multiple servers is hard. If one cache is updated but others aren’t, you get out of date or conflicting data.
• Cache Invalidation: Deciding when to remove or update cache is hard. If data stays in cache too long users get out of date info.
• Scalability: As you grow the system managing and coordinating cache across many servers gets harder and slows things down.
• Network Latency: Getting data from a distributed cache across multiple locations can introduce latency especially if servers are far apart.
• Fault Tolerance: If one cache server goes down the system needs to handle it without impacting performance or data availability. Balancing this across many servers is hard.

Conclusion

Distributed cache is an essential component in modern web applications that can help improve application performance, scalability, and user experience. For example, it can reduce application latency, improve response times, and enable faster data access by storing frequently accessed data in memory.