Caching Strategies: Understand Write-Through, Write-Behind, Read-Through, and Cache Aside

📆 · ⏳ 6 min read · ·


Greetings, fellow developers! Today, we’re diving into understanding the caching strategies, where we’ll be covering four powerful techniques: Write-Through, Write-Behind, Read-Through, and Cache Aside.

Caching has long been the secret weapon to optimize data access, reduce latency, and boost application performance. Let’s learn how it can help us to level up our application’s responsiveness and scalability.

Write-Through Caching

Think of Write-Through caching as a real-time data saver. When new data is written, it’s simultaneously stored in both the cache and the underlying data source (such as a database).

This ensures that the cache and the data source remain in sync, minimizing the risk of data loss.

Here’s how Write-Through caching works

  • Write Operation Request: When a write (insert, update, or delete) operation is initiated, it first goes to the cache.

    The cache immediately updates the data and forwards the write request to the underlying data store, such as a database.

  • Data Store Update: The data store processes the write request, making the corresponding changes to the data. Once the data store acknowledges the update, it is considered committed.

  • Maintaining Consistency: Write-Through caching ensures that the data in the cache is always consistent with the data in the data store.

    This means that when you retrieve data from the cache, you’ll always get the most up-to-date version because any changes made to the data store are reflected in the cache.


Write-Through is a valuable choice when maintaining data integrity is critical, but you should carefully consider its impact on latency and resource usage in your specific use case.

Write-Behind Caching

Picture Write-Behind caching as your helpful assistant who saves you time. When new data is written, it’s initially stored only in the cache, and the write to the underlying data source is deferred.

This deferred write helps to optimize write-heavy workloads by reducing the frequency of write operations to the data source.

However, it’s essential to handle cache evictions and ensure that eventually, the data is written back to the data source to maintain data consistency.

Here’s how Write-Behind caching works

  • Write Operation Request: When a write operation (insert, update, or delete) is initiated, it first goes to the cache.

    Instead of immediately updating the data store like Write-Through caching, the cache quickly acknowledges the write and stores it in a queue or buffer.

  • Deferred Data Store Update: After storing the write operation in the queue, the cache doesn’t immediately update the underlying data store.

    Instead, it waits for an opportune time to batch-process these write operations and apply them to the data store.

  • Optimized Performance: Write-Behind caching significantly improves write operation performance.

    Since writes are initially stored in the cache and then asynchronously propagated to the data store, the application experiences lower write latency.

  • Maintaining Consistency: While there’s a brief period when the cache and data store are out of sync due to deferred updates, Write-Behind caching ensures eventual consistency.

    The cache processes the queued writes, eventually bringing the data store in line with the cache.


Write-Behind is an excellent choice when optimizing write speed is crucial and temporary data inconsistency is tolerable for your application’s use case.

Read-Through Caching

Meet Read-Through caching, your swift data fetcher. When a read request is made, the cache first checks if the data is available. If not, it fetches the data from the underlying data source, stores it in the cache, and then serves the request.

Here’s how Read-Through caching works

  • Read Request: When a read request is made, the cache is the first stop. The cache checks whether the requested data is already present.

    If it is, the cache delivers the data directly to the requester, offering a substantial boost in speed and reducing the load on the underlying data store.

  • Cache Miss: If the cache doesn’t contain the requested data, a cache miss occurs. In this case, the cache doesn’t return an error but rather takes the responsibility of retrieving the data from the data store.

    It actively goes to the data store to fetch the requested data.

  • Data Store Retrieval: The cache contacts the underlying data store, retrieves the requested data, and stores it locally for future read requests.

    Once the data is fetched, it is not only provided to the requester but also cached for subsequent access, improving future read performances.

  • Data Consistency: Read-Through caching maintains data consistency by ensuring that the cache always delivers up-to-date information.

    If the data in the data store changes, the next read request for that data will trigger an update of the cache with the fresh data.


This technique optimizes read operations, as frequently accessed data remains in the cache. Hence, this approach is particularly valuable for systems that prioritize the speed of retrieval while ensuring data accuracy.

Cache Aside Caching

Imagine Cache-Aside caching as giving your application the power to choose what to cache. In this strategy, the application code takes the lead in loading and updating data in the cache.

Here’s how Cache-Aside caching works:

  • Data Fetching Logic: The application code is the mastermind behind fetching data. Before accessing data, it checks the cache to see if the data is already there.

  • Cache Check: The application first looks into the cache. If the required data is found (a cache hit), it’s retrieved from the cache, saving the need to access the underlying data source.

  • Cache Population: If there’s a cache miss (the data is not in the cache), the application fetches the data from the underlying data source, whether it’s a database or another storage system.

  • Manual Cache Update: After fetching the data from the data source, the application manually populates the cache with the new data, ensuring it’s available for future requests.


Cache-Aside provides flexibility for the application to decide what to cache, making it suitable for scenarios where the application has specific knowledge about data access patterns and can make informed decisions about caching.

This approach grants the application control over the cache, but it also means that the application needs to handle cache management explicitly. It’s a strategy that shines when the application has insights into data access patterns and can make strategic decisions about what to cache and when.


We’ve explored the world of Write-Through, Write-Behind, Read-Through, and Cache Aside caching strategies. Armed with these powerful techniques, you’re now equipped to fine-tune your applications, striking the perfect balance between data consistency, read and write performance, and efficient cache utilization.

As you venture into the world of caching, remember to carefully select the right caching strategy that aligns with your application’s unique needs. Happy caching, and may your applications perform at their best!

You may also like

  • Building a Read-Heavy System: Key Considerations for Success

    In this article, we will discuss the key considerations for building a read-heavy system and how to ensure its success.

  • Building a Write-Heavy System: Key Considerations for Success

    In this article, we'll discuss crucial considerations that can guide you towards success in building a write-heavy system and help you navigate the complexities of managing high volumes of write operations.

  • Tackling Thundering Herd Problem effectively

    In this article, we will discuss what is the thundering herd problem and how you can tackle it effectively when designing a system.