Learn how to handle cache invalidation and consistency across multiple servers with caching best practices and pitfalls.

For caches, it is important to understand if you need an in-memory cache on each host or a distributed cache. In-memory caches incur no additional latency but they are harder to sync with the underlying dataset while a distributed cache would add a network call to access the cache.

How do you handle cache invalidation and consistency across multiple servers?

Caching is a common technique to improve the performance and scalability of web applications, but it also introduces some challenges, such as cache invalidation and consistency. How do you ensure that your cached data is up to date and consistent across multiple servers, especially when you have frequent updates, concurrent requests, and distributed architectures? In this article, we will explore some caching best practices and pitfalls, and how to handle cache invalidation and consistency across multiple servers.

1 What is caching and why is it important?

Caching is the process of storing frequently accessed or computationally expensive data in a fast and accessible storage layer, such as memory or disk. Caching can reduce the load on your database, network, or other resources, and improve the response time and user experience of your web application. However, caching also adds complexity and trade-offs to your system design, as you have to balance between the benefits of caching and the costs of maintaining and updating the cached data.

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Murali Srinivasan

Vice Mayor, Sunnyvale
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The other costs of caching which are not apparent are: error identification and debugging costs and also invalidating the cache (in many cases) to restore "system sanity"

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2 What are the common caching strategies and methods?

Depending on your application's needs and requirements, there are various caching strategies and methods available. Cache-aside is one option, where data is only fetched from the cache when needed, and if it is not present then it is fetched from the source and stored in the cache for future use. Read-through avoids the cache miss penalty, but incurs a cache write penalty for each request. Write-through writes data to both the cache and the source synchronously, ensuring consistency but incurring a performance degradation for each write operation. Finally, with write-behind data is written to the cache first and then asynchronously written to the source, improving performance and availability of write operations but risking data loss or inconsistency if either fails.

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3 What are the challenges of cache invalidation and consistency?

Cache invalidation and consistency are two of the main challenges when using caching, where cache invalidation involves removing or updating outdated or invalid data, and cache consistency is the property of having the same data in the cache and the source at any given time. These challenges arise due to user actions, system events, external factors, cache size, and cache distribution. Data changes must be reflected in the cache to avoid serving stale or incorrect data to users. Additionally, an efficient management of the cache size is necessary to prevent wasting resources or evicting useful data from the cache. Finally, caches distributed across multiple servers or regions must be synchronized and coordinated to ensure that they have the same data and respond to the same requests.

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4 How do you handle cache invalidation?

Depending on your caching strategy and method, there are various ways to handle cache invalidation. For instance, you can set a time-to-live (TTL) value for your cached data and invalidate it after it expires. This approach avoids keeping stale data in your cache for too long, but you must choose an appropriate TTL value that balances between freshness and performance. Alternatively, you can invalidate your cached data based on specific events or triggers such as data updates, user actions, or system notifications. This ensures that your cache is always up to date with the source, but you must implement and manage the event-driven logic and communication between your cache and source. Lastly, you can combine both time-based and event-based invalidation, using the one that suits your use case better. For example, you could use time-based invalidation for less critical or less frequently updated data and event-based invalidation for more critical or more frequently updated data.

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Priyanka Singh

Senior Software Engineer at Google
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Another alternative is Active cache invalidation. It's a technique where the data source proactively sends notifications to the cache whenever data changes, ensuring cache consistency and reducing the risk of serving stale data. This is in contrast to event-driven cache invalidation, which relies on various events or conditions from multiple sources within the system to trigger cache updates. Facebook's TAO is a custom-built caching system that employs invalidation mechanisms to maintain consistency across its distributed caching system. TAO uses a combination of active cache invalidation and other techniques to ensure that the cache remains consistent with the underlying data source.

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5 How do you handle cache consistency?

When it comes to cache consistency, there are different ways to handle it depending on your caching strategy and method. Strong consistency ensures that your cache and source are always in sync, but this can sacrifice performance and availability as you have to wait for the cache and source to synchronize. Weak consistency allows some degree of inconsistency between the cache and source, which can improve performance and availability, but may serve stale or incorrect data. Eventual consistency is a compromise between strong and weak consistency, as any read or write operation will eventually reflect the latest state of your data. This way, you can balance performance and availability with consistency and freshness by relying on asynchronous processes and mechanisms to reconcile changes between the cache and source.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Bipin Parmar

Full stack engineer - mobile applications
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For caches, it is important to understand if you need an in-memory cache on each host or a distributed cache. In-memory caches incur no additional latency but they are harder to sync with the underlying dataset while a distributed cache would add a network call to access the cache.

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Bipin Parmar

Full stack engineer - mobile applications
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When you have a multi-region application, the replication of your cache from one region to another region may be necessary. In most applications, the underlying datasets are replicated and if you do not have a way to update your cache from the replicated dataset, you may need to make sure that your caches also replicate from one region to another.

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How do you handle cache invalidation and consistency across multiple servers?

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1 What is caching and why is it important?

2 What are the common caching strategies and methods?

3 What are the challenges of cache invalidation and consistency?

4 How do you handle cache invalidation?

5 How do you handle cache consistency?

6 Here’s what else to consider

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