Caching

What is Caching?#

Caching is the process of storing copies of data or computational results in a cache, a temporary storage location. When a request for data is made, the system first checks if the data is available in the cache. If it is, this is known as a cache hit, and the data is retrieved much faster than if it had to be fetched from the original source (e.g., a database or a remote server). If the data is not in the cache, known as a cache miss, the system fetches it from the source, stores a copy in the cache for future requests, and delivers it to the client.

Benefits of Caching#

In distributed systems, caching plays a crucial role in reducing latency, improving throughput, and ensuring scalability. Let’s break down these benefits:

1. Reduced Latency: By storing frequently accessed data closer to the user, caches reduce the time it takes to retrieve that data. This is particularly important in distributed systems, where data might be spread across multiple nodes or even geographic locations.

2. Improved Throughput: Caching reduces the load on primary data sources, such as databases. By handling more requests directly from the cache, the system can process a higher volume of requests in the same period, leading to better overall performance.

3. Scalability: Distributed systems often need to scale to handle growing amounts of data and user requests. Caching helps manage this by preventing bottlenecks that can occur when too many requests are sent to a single source. By distributing the load across cache layers, systems can scale more effectively.

Types of Caching#

1. Client-Side Caching: This type of caching happens on the user’s device or close to the user. It is often implemented in web browsers or applications to store data that doesn’t change frequently, such as images, scripts, or web pages.

2. Server-Side Caching: In this approach, caches are placed on the server side, closer to the data sources like databases or API servers. Examples include database query caching and application-level caching where computed results are stored.

3. Edge Caching: Edge caching is implemented in Content Delivery Networks (CDNs). Data is cached at the “edge” of the network, closer to the end-users. This is particularly useful for static content and is a common strategy for improving the performance of globally distributed systems.

4. Distributed Caching: This involves using a distributed cache that spans multiple servers or nodes. Data is cached across these nodes, ensuring that no single point of failure exists and that the cache itself is scalable. Technologies like Redis, Memcached, and Apache Ignite are popular choices for distributed caching.

Caching Pattern#

1. Write-Through Caching: In write-through caching, every time data is written to the cache, it is also written to the underlying data source. This ensures that the cache and the source are always in sync, but it can introduce latency during write operations.

2. Write-Behind (Write-Back) Caching: Here, data is written to the cache first and then asynchronously to the data source. This improves write performance but can lead to inconsistencies if the system crashes before the data is written back.

Cache Invalidation#

• Time-to-Live (TTL) Invalidation

  Time-to-Live is a common and straightforward cache invalidation strategy. Each cached item is associated with a TTL value, which is a predefined period after which the cache entry is automatically considered stale and removed or refreshed. For example, if a web page’s TTL is set to 60 seconds, the cache will invalidate that page’s data after 60 seconds, ensuring that fresh data is loaded for subsequent requests.

• Write-Through Caching with Invalidation

  In write-through caching, every update to the cache also triggers an update to the underlying data store, ensuring that the cache and the data store are always in sync. Cache invalidation in this context means that any update operation automatically invalidates the corresponding cache entry, ensuring that subsequent reads will retrieve the latest data.

• Event-Driven Invalidation

  In event-driven invalidation, cache invalidation is triggered by specific events that indicate changes in the underlying data. For instance, if a product’s price is updated in a database, an event is emitted to invalidate the corresponding cache entry. This approach is often used in systems where changes are infrequent but need immediate reflection in the cache.

• Manual Invalidation

  Manual invalidation involves explicitly removing or updating cache entries based on specific triggers or actions initiated by users or administrators. This method is typically used in scenarios where cache updates are infrequent or where automated invalidation strategies are not feasible.

Cache Eviction#

When the cache reaches its storage limit, some data must be evicted to make room for new data.

• Least Recently Used (LRU)

  LRU is one of the most popular cache eviction strategies. It works on the principle that data that hasn’t been accessed recently is less likely to be needed in the near future. When the cache reaches its capacity, LRU evicts the least recently used data, making space for new entries.

• Most Recently Used (MRU)

  MRU is the opposite of LRU. It assumes that the most recently used data is less likely to be accessed again soon. MRU evicts the most recently used data first, making room for new entries.

• Least Frequently Used (LFU)

  LFU keeps track of how often each data entry in the cache is accessed. When eviction is needed, the data that has been accessed the least frequently is removed first.

• First In, First Out (FIFO)

  FIFO evicts the oldest data first, regardless of how frequently it has been accessed. This method operates on a simple queue basis, where the first item added to the cache is the first one to be evicted when space is needed.

• Random Replacement (RR)

  As the name suggests, RR evicts data at random when the cache is full. While this might seem inefficient, in some cases, it can perform surprisingly well, particularly when there is no clear access pattern.

Challenges and Considerations#

While caching can significantly improve the performance of distributed systems, it also introduces challenges:

1. Consistency: Ensuring that the cached data is consistent with the source data is critical, especially in systems where data changes frequently.

2. Cache Coherency: In distributed caches, coherency involves keeping different cache nodes in sync. Without proper management, different nodes might return different data for the same query.

3. Cache Invalidations: Improper invalidation strategies can lead to stale data being served to users, potentially causing errors or inconsistencies in the system.

4. Scalability of the Cache Itself: As the system grows, the cache system must also scale to handle the increasing volume of data and requests. This often requires careful planning of cache architecture.

Key Takeaways#

• Caching in distributed systems reduces latency and improves throughput by storing frequently accessed data closer to users.
• Different types of caching, such as client-side, server-side, edge, and distributed caching, serve various use cases.
• Choosing the right caching strategy—whether write-through, write-behind, or a combination—is essential for system performance and data consistency.
• Challenges in caching include maintaining consistency, managing cache coherency, and planning for the scalability of the cache system itself.

FAQs#