Top 13 Caching Strategies to Know in 2026

What Is Caching?

Caching strategies define how data is stored, retrieved, and managed in a cache to improve application performance. Common approaches include cache-aside (read from cache, then source), read-through (cache and source managed by a cache provider), write-through (writes to cache and source simultaneously), write-back (writes to cache first, then source later), and write-around (writes to source, then cache on read). The best strategy depends on the data access patterns, with options like stale-while-revalidate for frequently changing content and cache-only for static content.

Read Strategies

These strategies dictate how the application fetches data from the cache.

• Cache-Aside: The application checks the cache first; if the data is found, it’s returned (a cache hit). If not (a cache miss), the application fetches it from the data source, stores it in the cache, and then returns it. This is a common "lazy loading" approach.
• Read-Through: In this approach, the cache provider itself handles the data retrieval. When data is requested, the cache provider checks its own store; if the data isn’t there, the provider fetches it from the data source, stores it in the cache, and then returns it to the application.
• Stale-While-Revalidate: This strategy returns a cached version of the resource quickly while simultaneously fetching an updated version from the network if available. This is suitable for content that changes often, ensuring both speed and freshness.
• Network First, Fallback on Cache: The system first attempts to fetch the latest content from the server; if that fails (e.g., due to being offline), it falls back to a cached version.

Write Strategies

These strategies govern how data is written to both the cache and the data source.

• Write-Through: When data is written, it is simultaneously sent to both the cache and the data source. This ensures both are updated at the same time, guaranteeing consistency but adding a slight write overhead.
• Write-Back: Data is written only to the cache initially. The actual write to the data source occurs later, sometimes in batches, or upon a trigger. This offers faster write performance but introduces a risk of data loss if the cache fails before the data is written to the source.
• Write-Around: Writes go directly to the data source, bypassing the cache. Data is only written to the cache if it’s subsequently requested and a cache miss occurs. This is useful when the written data is not expected to be read again soon.

Cache Management Strategies (Eviction Policies)

These policies determine which items are removed from the cache when it’s full.

• First-In, First-Out (FIFO): Removes the oldest data that was added to the cache
• Least Recently Used (LRU): Removes the data that hasn’t been accessed for the longest time.
• Random Replacement: Removes data at random to make space.
• Most Recently Used (MRU): Removes the most recently accessed item first, assuming it’s less likely to be used again soon.
• Least Frequently Used (LFU): Removes the item with the lowest access count, favoring data that’s accessed more often.
• Last-In, First-Out (LIFO): Removes the most recently added item first, like a stack.

The best caching strategy depends on your specific needs:

• Data and usage patterns: Consider if your data is read-heavy or write-heavy, and how frequently it changes.
• Consistency vs. performance: Decide how critical it is to have the most up-to-date data versus achieving the fastest possible response times.
• Application type: For static content that rarely changes, strategies like Stale-While-Revalidate (returning a cached version of the resource quickly while simultaneously fetching an updated version) can be efficient, for example.

Understanding the Key Caching Strategies

The following table provides a quick summary of the caching strategies. Learn more about each of the strategies below.

Understanding the Key Caching Strategies

Read Strategies

Read strategies define how data is accessed and populated in the cache during read operations. These strategies influence latency, cache freshness, and backend load. Depending on whether the application or cache provider handles misses, and how consistency is maintained, different strategies serve different performance and availability goals.

1. Cache-Aside (Lazy Loading)

Cache-aside, also known as lazy loading, is a strategy where the application code is responsible for loading data into the cache as needed. When data is requested, the system first checks the cache. If the data is not there (a cache miss), the application retrieves it from the underlying data store, stores it in the cache, and then returns it to the requester. This approach ensures only frequently accessed data is cached, helping manage cache size efficiently.

One downside of cache-aside is that it can produce a burst of requests to the backend storage if several users request the same uncached data simultaneously, a problem known as a cache stampede. Additionally, it doesn’t automatically keep the cache updated with changes in the underlying datasource. Separate logic is required for cache invalidation or updates.

2. Read-Through

The read-through strategy delegates cache population to the cache itself rather than to the application logic. Here, the cache is configured with read handlers that automatically fetch data from the data store on a cache miss, insert it into the cache, and return it to the caller. The interface for the application remains simple, just a standard cache get call, with cache population handled transparently in the background.

Read-through caching reduces the complexity for developers and centralizes cache-management concerns. Changes in the source of truth are not automatically reflected in the cache until it is invalidated or expires, making it vulnerable to serving stale data. As with cache-aside, cache stampedes are possible but can sometimes be mitigated more easily since the cache implementation can include built-in locks or request-deduplication mechanisms.

3. Stale-While-Revalidate

Stale-while-revalidate is a strategy that tries to balance fast data delivery with cache freshness. When data is requested, the cache serves it immediately, even if it is slightly outdated (stale). In the background, the cache asynchronously fetches the latest version from the underlying data source, refreshing the cache for subsequent requests. This allows for both low latency and partial consistency in high-read scenarios.

This approach is useful for workloads where slightly outdated data is acceptable for a short time and where it’s critical to avoid delays waiting for slow backend operations. However, there is a risk of increased traffic to the data store if many users are requesting the same data during the refresh window. Developers must carefully configure cache expiration, revalidation intervals, and monitor freshness guarantees to balance speed and consistency.

4. Network First, Fallback on Cache

The network-first, fallback-on-cache approach attempts to retrieve data from the primary source (such as an API or database) for every request. If the network request fails—due to timeouts, errors, or unavailability—the system falls back to serving the data from the local cache. This ensures the latest data is delivered when possible but allows continued operation even during outages.

This approach is often used in progressive web apps and offline-first applications, ensuring high availability and resilience. The main challenge is determining cache expiration logic and handling potentially inconsistent data. Developers also need to decide how to update the cache after network recovery to avoid the long-term serving of outdated data.

Write Strategies

Write strategies determine when and how updates to data are propagated to the cache and the underlying data store. These strategies influence consistency guarantees, write performance, and resilience to failure. Choosing the right one depends on the frequency of writes, tolerance for stale data, and system durability needs.

1. Write-Through

Write-through caching synchronously writes data to both the cache and the underlying data store on every update or write operation. This approach guarantees that the cache always reflects the most current state of the primary data store, removing the risk of stale data during subsequent reads. The immediate propagation of updates to both locations ensures data consistency but can introduce higher write latency, as both operations must succeed before confirming the request.

This strategy is beneficial in systems where read-after-write consistency is crucial and where the slight increase in write latency is acceptable. Write-through cache implementations are simpler to reason about from a consistency perspective but can put added load on the database and cache, especially during write-heavy workloads. Careful monitoring and tuning may be required to prevent performance bottlenecks.

2. Write-Behind (Write-Back)

Write-behind (or write-back) caching separates the process of updating the cache and writing to the data store. When data is written, the update is made only in the cache, and the change is asynchronously propagated to the underlying data store after a delay or in batches. This strategy improves write performance and reduces backend load because multiple writes can be coalesced, and the application does not have to wait for the database to confirm updates.

However, write-behind introduces a window of inconsistency, if the cache fails or loses data before it flushes to the store, changes may be lost. This risk requires careful design, including mechanisms for retrying or persisting pending writes. Write-behind is best where high write performance is needed and eventual consistency is acceptable for the workload.

3. Write-Around

Write-around caching bypasses the cache for write operations, sending new or updated data directly to the underlying data store. The cache is only updated when future read requests occur, causing the data to be loaded and cached at that point. This approach avoids polluting the cache with infrequently read data and ensures that the cache mainly contains hot, frequently accessed items.

The primary drawback is the potential for more read misses after writes since just-updated data won’t be present in the cache until explicitly requested. Write-around is useful when write operations target data that is unlikely to be needed immediately or frequently, and it helps keep the cache focused on high-value data.

Cache Management / Eviction Strategies

Eviction strategies decide which data to remove when cache capacity is reached. The goal is to keep frequently or recently accessed data in memory while discarding less useful items. The right eviction policy depends on access patterns, memory constraints, and application performance targets.

1. Least Recently Used (LRU)

The least recently used (LRU) eviction strategy removes the cache entry that hasn’t been accessed for the longest period whenever the cache reaches its capacity. The idea is that data not used recently is less likely to be accessed again soon, so it makes sense to evict it. LRU is simple to reason about and often matches typical real-world access patterns, making it a default choice for many caching solutions.

Implementing LRU usually involves maintaining a linked list or an ordered map structure, with the most recently accessed items at the front and the least recently used at the end. When items are accessed or updated, they are moved to the head of the list. When the cache is full, the item at the end is evicted. While this offers good performance for many workloads, it can become less effective if usage patterns change or if many items are accessed at similar frequencies.

2. Most Recently Used (MRU)

The most recently used (MRU) strategy evicts the most recently accessed item first, in direct contrast with LRU. MRU can be valuable in scenarios where recently accessed data becomes less likely to be needed again, such as certain types of media streaming or one-time query caches. It’s less common than LRU but can outperform LRU with workloads that demonstrate this behavior.

MRU is implemented similarly to LRU, but the logic is inverted so the newest records are evicted first when the cache fills up. The risk is that if the data access pattern does not favor MRU, this eviction policy can increase cache misses and reduce performance. Therefore, MRU is best suited for special cases where older data is preferred and should be chosen only after careful analysis of application workload characteristics.

3. Least Frequently Used (LFU)

Least frequently used (LFU) eviction tracks how often each cache entry is accessed and removes the item with the lowest access count when space is needed. LFU works well for workloads where some data is consistently more popular over time, as these will be retained in the cache, while rarely accessed items are evicted.

Implementing LFU is more complex than LRU or MRU since it requires maintaining access counts and efficient lookup and update mechanisms. It is also sensitive to changing access patterns; occasionally accessed data may linger indefinitely unless aging or decay algorithms reset counters to prevent stale data from persisting in the cache. LFU is best when long-term popularity is a strong signal of future access.

4. First-In-First-Out (FIFO)

First-in-first-out (FIFO) eviction treats the cache as a simple queue, always removing the oldest item when space is needed. FIFO is easy to implement and fast to operate, as it just requires tracking the insertion order. For data with predictable access patterns, like certain batches of jobs, FIFO can offer good results.

However, FIFO can evict frequently accessed data simply because it entered the cache earlier than others, even if it’s still being requested. This can lead to reduced hit rates in workloads where data popularity is not strictly time-based. Despite its simplicity, FIFO is best for cases where access frequency does not matter as much as strict ordering.

5. Last-In-First-Out (LIFO)

Last-in-first-out (LIFO) eviction is the reverse of FIFO—the newest cache entry is the first to be evicted when the cache becomes full. LIFO works like a stack, so each new entry pushes the previous ones down, and the latest one is removed first if eviction is needed. LIFO is rarely used in general caching due to its poor alignment with typical data access patterns.

Frequently, the latest data is also the most likely to be reused soon, so evicting it immediately can decrease cache efficiency. LIFO is mainly applicable to very specific cases, like undo operations, where the stack-like behavior is beneficial.

6. Random Replacement

Random replacement chooses a cache entry at random for eviction when the cache is full. The idea is to spread evictions evenly across the cache and avoid the complexity of tracking recency or frequency. It is notably simple to implement, requiring no extra metadata per entry.

This strategy is often used as a baseline for performance comparisons rather than in production systems. It can work reasonably well for uniform, unpredictable access patterns but usually underperforms compared to more targeted policies like LRU or LFU. Random replacement is best considered when simplicity and minimal resource use are the top priorities, and cache misses are tolerable.

How to Choose the Right Cache Strategy

Workload and Access Pattern

Understanding the characteristics of your application’s data access patterns is essential when choosing a cache strategy. For example, if your workload consists of many repeated accesses to the same items (high temporal locality), an LRU-based eviction policy may yield excellent results. Alternatively, if some data stays consistently popular, LFU could keep your cache populated with the most valuable entries.

Practical tips:

• Analyze read/write ratios to determine if the workload is read-heavy or write-heavy
• Identify hot data and access bursts to choose an eviction policy like LRU or LFU
• Use metrics like hit/miss rates and eviction counts to validate assumptions
• Simulate cache performance under realistic workloads before choosing a strategy
• Leverage built-in instrumentation in cache systems to monitor usage and behavior

Consistency and Freshness Requirements

Different applications impose different requirements for how fresh or consistent cached data must be. For example, financial platforms typically demand strong consistency, where every read reflects the latest committed state. In such cases, write-through or network-first strategies may be necessary, even at the expense of higher latency. Stale-while-revalidate is a good choice for workloads tolerant of temporary inconsistency but highly sensitive to delays.

Practical tips:

• Define how much staleness is acceptable for your data (e.g., seconds, minutes)
• Choose write-through or network-first if strong consistency is a must
• Use stale-while-revalidate where freshness is nice but not critical
• Assess the business impact of serving outdated or inconsistent data
• Clearly document data freshness requirements to guide architecture decisions

Latency and Performance Requirements

Reducing response time is the primary motivation for caching, but the choice of strategy can significantly affect observed latency. In scenarios where low, predictable latency is critical—such as real-time analytics or online gaming—it may be necessary to keep strict control over cache population and eviction. Strategies such as read-through or write-through can help guarantee that the most up-to-date data is always at hand, although at a cost to backend throughput.

Practical tips:

• Set response time targets and latency budgets for each application path
• Use read-through or cache-aside to minimize response time for frequent reads
• Consider stale-while-revalidate or cache fallback for low-latency availability
• Prioritize cache hit rates in latency-sensitive workflows
• Profile end-to-end latency including cache misses and backend fetch costs

Resource Constraints

Caching consumes memory, CPU resources, and sometimes storage, so your strategy has to fit within operational constraints. LFU and LRU can require extra metadata or data structures, which can become an issue in very memory-constrained environments like embedded devices. If your system scales horizontally across many nodes, cache coherence and replication costs add complexity.

Practical tips:

• Choose simple eviction policies like FIFO or random when memory is tight
• Avoid metadata-heavy strategies like LFU on constrained devices
• Account for overhead from write amplification in write-through strategies
• Use write-back to offload pressure from a slow backend, but with safeguards
• Tune cache size and expiration policies to avoid out-of-memory conditions

Ease of Implementation and Maintenance

Finally, the complexity of implementing and managing a caching strategy should be considered. Write-back and LFU, while powerful, often require custom coding and ongoing tuning to avoid consistency issues and performance problems. Simpler strategies, such as cache-aside or FIFO eviction, can often be deployed with minimal changes to application architecture and are less likely to introduce subtle bugs.

Practical tips:

• Start with simple strategies (e.g., cache-aside, FIFO) before optimizing further
• Avoid strategies that require complex invalidation unless absolutely needed
• Monitor cache behavior continuously to detect regressions or inefficiencies
• Choose solutions with built-in observability and tooling support
• Prefer strategies that align with your team’s operational capabilities and expertise

Dragonfly: The Next-Generation In-Memory Data Store

Dragonfly is a modern, source-available, multi-threaded, Redis-compatible in-memory data store that stands out by delivering unmatched performance and efficiency. Designed from the ground up to disrupt existing legacy technologies, Dragonfly redefines what an in-memory data store can achieve.

In terms of usage, just like Redis/Valkey, Dragonfly doesn’t act as an automatic cache provider/agent. Thus, the read-through strategy cannot be used directly without additional automation tools. As a general-purpose in-memory data store, developers are expected to manage key-values stored in Dragonfly within their application with nice features within Dragonfly, such as automatic expiration and eviction policy, to achieve their application requirements.

Unlike Redis/Valkey, which has 8 eviction policies (noeviction, allkeys-lru, allkeys-lfu, allkeys-random, volatile-lru, volatile-lfu, volatile-random, and volatile-ttl), Dragonfly has only two: cache_mode=false/true. When cache mode is disabled, keys without an expiration don’t evict, and developers are responsible for monitoring the overall memory usage. On the other hand, when cache mode is enabled, Dragonfly employs a sophisticated yet highly efficient strategy, namely 2Q, which is a combination of LRU and LFU. Based on theory and practice, we believe that Dragonfly’s cache mode is adaptive to most use cases without developers thinking hard to pick the "proper" eviction strategy.

Key Advancements of Dragonfly

• Multi-Threaded Architecture: Efficiently leverages modern multi-core processors to maximize throughput and minimize latency.
• Unmatched Performance: Achieves 25x better performance than Redis, ensuring your applications run with extremely high throughput and consistent latency.
• Cost Efficiency: Reduces hardware and operational costs without sacrificing performance, making it an ideal choice for budget-conscious enterprises.
• Redis API Compatibility: Offers seamless integration with existing applications and frameworks running on Redis while overcoming its limitations.
• Innovative Design: Built to scale vertically and horizontally, providing a robust solution for rapidly growing data needs.

Dragonfly Cloud is a fully managed service from the creators of Dragonfly, handling all operations and delivering effortless scaling so you can focus on what matters without worrying about in-memory data infrastructure anymore.