LFU vs. LRU: How to choose the right cache eviction policy

Least Frequently Used (LFU) and Least Recently Used (LRU) are two of the most common cache eviction policies for determining which data to evict when a cache fills up. Without a policy in place, the cache can become full, defeating the purpose of building a database system that supports rapid retrieval.

A cache is essential for building high-performance applications. Queries to a traditional database will always be slower than queries to a cache. This principle is simple, but its application is nuanced: Where do you draw the line on what data should be in the cache and what shouldn’t? That’s where LFU and LRU come in.

Without the right policy in place, tech teams can come across numerous issues, all of which can often be traced back to their caching policies. These include:

• Slow response times.
• Unpredictable cache behavior.
• Suboptimal user experiences.
• Repetitive entry of information
• Lack of consistent behavior making your app feel buggy or unreliable
• Cascading issues during peak times

Both LFU and LRU aim to improve cache performance by keeping “hot” data in memory, but they prioritize different signals. Both can be effective, but choosing the right one can make a big difference for your cache’s performance.

LRU vs. LFU at a glance

Businesses operating in environments where speed can differentiate them from other competitors, such as ecommerce, real-time analytics, and AI, have especially high stakes here. With the right cache eviction policies, businesses can suffer from declining user retention, lost revenue, and weakened competitive advantage.

To avoid this risk, figure out the nuances that distinguish LFU and LRU, learn how to flexibly implement and configure these policies, and identify which setup is best for your scenario.

LFU vs. LRU: What's the difference?

A cache eviction policy determines what data gets evicted from the cache when the cache is full. By managing cache space effectively, a cache eviction policy ensures that limited memory is used efficiently. In Redis, eviction only occurs when the cache reaches its configured memory limit. Until then, keys remain in memory, even if their eviction policy is defined, so tuning maxmemory and setting the policy are both required for eviction to take place.

LFU and LRU are the two most common cache eviction policies. LFU favors data with high access frequency, and LRU focuses on recency of access.

LFU evicts items that have been used the least often over time. LFU assumes that infrequently accessed items are less valuable and less likely to need rapid retrieval.

LRU evicts items that have gone unused for the longest period of time. LRU assumes that if the data hasn’t been accessed for a long period of time, it’s unlikely to be needed soon.

When items are tied with the same frequency, recency is often the tiebreaker that determines which item gets evicted. A recently accessed item with low frequency might stay in the cache instead of another infrequently used item that hasn't been accessed in a while.

What is least frequently used (LFU) caching?

LFU favors data with high access frequency, prioritizing data based on how often items are accessed, meaning it evicts items that are accessed the least often.

When an LFU caching policy is in place, a counter tracks how often an item is accessed, and it increments with every request. When the cache evicts items, it chooses the item with the lowest access count.

In Redis, LFU uses a probabilistic counter, not a precise count. It’s a lightweight mechanism that tracks usage with logarithmic counters and decay, which reduces overhead while preserving effectiveness. This design makes LFU practical for real-time caching, but understanding its approximate nature helps set expectations for tuning and behavior.

LFU is often seen as a suitable policy for workloads with stable, predictable data access patterns. The core idea of LFU is to prioritize data with proven, persistent popularity, so it often works when data is relatively predictable.

The pros of LFU include:

• Consistently popular items tend to be available for longer.
• Predictable, stable traffic patterns are well supported.
• Repetitive access patterns are supported with little latency.

The cons of LFU include:

• Implementation complexity.
• Inflexibility when access patterns change rapidly.
• Cache pollution if items with high historical counts persist even when they’re not relevant anymore.

The key to keep in mind is that LFU focuses on frequency, whereas LRU, which we’ll get into next, focuses on recency.

What is least recently used (LRU) caching?

LRU focuses on recency of access and prioritizes evicting items that were last accessed the furthest in the past.

The LRU policy orders items in the cache by recency of use. When an item is accessed, it’s marked as the most recent, and when eviction is necessary, the algorithm removes whatever is oldest–in other words, the item with the longest period between eviction and last access.

Generally, an LRU policy is suitable for workloads that have rapidly changing, dynamic access patterns.

The pros of LRU include:

• Simple implementation.
• Adaptable to recent user behavior changes.
• Low overhead.

The cons of LRU include:

• Risk of evicting items that are consistently useful, even if accessed less recently.
• Poor performance during cyclic workloads.
• Memory overhead for large caches.

Neither policy presents a case for one being better than the other. Choosing requires understanding their impact and your use case.

LFU vs. LRU: How to know which policy is right for you

The decision between LFU and LRU isn't always binary. To find the optimal approach, you need to understand your workload, map out its patterns, and use caching capabilities that allow you to dynamically adapt to changing usage patterns.

When to choose LFU

In general, opt for LFU when popularity is more important than trendiness for supporting data access. The most common scenarios where LFU makes sense include:

• Skewed data access patterns: In workloads with an asymmetric data distribution, which often follow the 80/20 rule (or the Pareto principle), where a small subset of items accounts for the majority of access requests, historical access count (or "popularity") is a strong predictor of future access. In these cases, LFU caching tends to perform well. For example, an ecommerce site might prioritize caching popular category pages, like “best sellers” or “accessories," because users tend to access these in nearly every session and they tend to remain popular over time.
• Static hot data: In some situations, the “hot” data doesn’t change often, and an LFU policy can ensure this data remains cached. Some applications offer a range of APIs, for example, but a select few are often executed much more frequently than others. In these cases, LFU is typically best.
• Systems with popularity rankings: In some cases, most often with recommendation systems, user activity determines the popularity of items and the frequency with which they’re recommended. LFU works well when the popularity of an evergreen item is more important than sheer item recency.

Either policy presents a tradeoff. The LFU tradeoff is right for you when evicting frequently-used items is worse than evicting recently-used but less popular items.

When to choose LRU

In general, opt for LRU when recent usage is the strongest signal of future usage. If your workload benefits from a cache that tracks the data being made available as it’s processed, LRU is likely the right choice. The most common scenarios where LRU makes sense include:

• Rapidly changing access patterns: Sometimes, access patterns shift on hourly and minute-by-minute timescales, and LRU is usually more effective in these cases. A social media news feed, for example, might prioritize recent updates well above old ones. Similarly, a real-time analytics dashboard might benefit from prioritizing recently used metrics as users scan across alerts they’re monitoring.
• Temporal locality: Sometimes, when users access data, the most recent access request needs to be redone as the user completes their tasks. Many applications require users to sign into profiles, for example, so the application might pull from that profile frequently while the user is online. In these cases, the recency is most important until the user logs off.
• Burst patterns: Sometimes, access patterns can be irregular, but for good reason. If an ecommerce application runs a big sale, for example, less frequently viewed items might receive a burst of traffic. LRU ensures those frequently accessed items remain cached because, until the sale is over, recency is most important.

Beyond these examples, LRU is often a good choice when implementation has to be as simple as possible. Generally, LRU is easier to set up and maintain than LFU.

When to consider a hybrid or flexible approach

Most applications contain a mix of data that is needed frequently and data that’s needed because it’s recent. As a result, hybrid strategies make the most sense for many environments.

In a financial application, for example, LRU will be useful because the latest market data will be most effective when it’s cached, but at the same time, LFU will also be useful because popular tickers will benefit from caching, too.

Many scenarios follow the same pattern, where caching needs to be adaptable and access patterns can change dynamically. Hybrid approaches typically use a single cache space and rely on a more complex eviction algorithm to determine which items to remove, rather than managing two separate caches. If you don’t have a flexible approach established, static caching strategies can cause performance degradation or excessive cache misses.

Common LFU and LRU pitfalls and how to avoid them

Choosing between LFU and LRU, even including a hybrid strategy combining the two, is only half the battle. Even when you select the right caching policy, implementation mistakes or oversights can affect performance and limit potential. Fixing these issues in retrospect can be difficult, so if you recognize common pitfalls early and avoid them, you can prevent cache degradation, unnecessary troubleshooting, and performance bottlenecks.

Pitfall #1: Choosing the wrong policy for your workload

Teams often select LFU or LRU based on theoretical advantages, ignoring actual data access patterns and workloads. It’s easy to assume your workload depends more on recency or popularity, and end up picking the wrong one.

To avoid this pitfall, perform a careful analysis of your real-world access patterns before selecting a cache eviction policy. Look at your access patterns across a long period of time and consider how select events, such as holiday seasonality, might change those patterns. Often, you might assume one or the other, and the data reveals a mix, meaning a hybrid or adaptive caching approach is best.

Pitfall #2: Underestimating complexity in LFU implementation

LFU is complex, and teams often implement it incorrectly, leading to performance issues. The combination of complexity and the risk of poor performance leads many teams to avoid it altogether.

To avoid this pitfall, look for vendors that provide caching capabilities with simple, streamlined LFU implementation options. Some even offer algorithms that track frequency and adjustable decay counters.

Pitfall #3: Ignoring cache size and infrastructure limits

The best caching strategy can only do so much if your cache is too small. Inadequate sizing or ignored infrastructure constraints can lead to unexpected cache evictions or performance degradation, even with what would have been the right strategy otherwise.

To avoid this pitfall, regularly monitor your cache hit ratio and adjust your cache size as your data set or traffic volume grows. If it all becomes too burdensome to monitor, consider finding a caching provider that offers auto-tiering or other infrastructure efficiency optimizations.

Pitfall #4: Overlooking the importance of flexibility

Rigid caching implementations can make sense in one moment but suffer in the next. Seasonal shifts or sudden spikes can lead to firefighting as important items are evicted right when you need them most, degrading performance during peak times.

To avoid this pitfall, evaluate caching solutions that support hybrid eviction strategies and adaptive configurations. With these features, you can make dynamic workload adjustments so that spikes don’t overwhelm you. Similarly, you’ll want to test your cache policies against different scenarios regularly. If you can’t adapt on your own, consider getting vendor support.

Pitfall #5: Not accounting for regional or global scaling needs

Teams often fail to plan for multi-region or global scaling requirements. A caching strategy suitable in one region can fail when you need to expand to the next, leading to latency spikes, cache misses, and downtime.

To avoid this pitfall, look for caching solutions with multi-region active-active architectures, which will ensure reliable performance and availability across regions. Parallel to vendor selection, make sure to plan your caching strategy alongside your business growth plans. The two should change hand-in-hand.

Pitfall #6: Ignoring cost and operational efficiency

It’s often easy for teams to underestimate the operational costs associated with inefficient caching solutions. Troubleshooting can be frequent, and maintenance can be high, especially when it accumulates over time.

To avoid this pitfall, find caching solutions with proven operational efficiency, especially ones that can provide case studies proving cost savings and lower management overhead. Solutions with performance analytics make this even better, allowing you to reduce overhead as you scale.

What to look for in a caching solution

Choosing the right cache eviction policy is just one part of building a holistic data strategy and optimizing performance across the databases you’re using. Ultimately, choosing the right providers and selecting the right caching solution matters just as much.

Flexible eviction strategies

Modern businesses don’t always have the luxury of stable, predictable workloads, making flexibility a key feature. When you can adapt your eviction strategy across situations, you should be able to keep up with your data access patterns, even as they evolve.

Hybrid approaches to eviction can dynamically blend LFU and LRU characteristics, giving you the best of both worlds, but these approaches only work best when your system offers configurable eviction parameters, such as adjustable decay counters, sample sizes, or eviction pools.

In Redis, eviction policies are configured per database, not per key. In Redis Software and Redis Cloud however, muti-tenancy allows multiple databases to share infrastructure while maintaining separate eviction strategies. This is not possible in Redis Open Source, Valkey or most cloud caching layer where a single eviction policy applies. As a result, companies can:

• Tailor eviction policies to each workload, ensuring they don’t have to generalize a policy across too many workloads.
• Maintain strong isolation under memory pressure, ensuring that a spike in one tenant won’t evict data from another tenant.
• Fine-tune eviction behavior, ensuring different decay rates, sampling sizes, and maxmemory settings are configured for each tenant.
• Operationalize for efficiency, ensuring, for example, lower memory and faster TTLs for certain use cases without those settings affecting other tenants.

This flexibility helps companies avoid a one-size-fits-all strategy because, when it comes to eviction strategies, one size typically fits none.

Real-time performance and low latency

In the nuances around caching strategies and eviction policies, it can be easy to lose sight of the key priorities: Reducing latency and maintaining consistently high performance. As a corollary, the goal is to do both even during periods when the workload is heavy.

With this goal in mind, look for caching solutions that can offer proven low-latency benchmarks, ideally benchmarks that show sub-millisecond response times. Similarly, effective caching solutions should be able to document the ability to deliver predictable performance under heavy workloads or spikes in demand.

Organizations with hundreds of millions of customers use Redis to keep performance levels high. “We’re always prepared for unexpected spikes, from viral clips to breaking news,” says Prachi Sharma, associate vice president at JioCinema.

Proven scalability and reliability

The difference between caching that works at a small scale and caching that works at a large scale can be enormous. Choosing a solution that works well but can’t scale is risking future problems that are hard to undo.

When you shop for caching solutions, look for features that strengthen scalability, such as multi-region active-active architectures that can help maintain reliability and uptime. To ensure these features work in practice, as well as theory, look for providers that have data-backed case studies.

Redis, for example, has worked with enterprises like Ulta Beauty to help them build modern, scalable software architecture. Ulta required rapid inventory lookup functionality to support a smooth customer experience that extended from an online storefront to a multitude of physical locations. With LFU policies implemented via Redis Cloud, Ulta was able to increase revenue by 40% increase after migrating to Redis, and reduce the time for inventory call operations from 2 seconds to 1 millisecond – a 99% time reduction.

“It only took four weeks to develop a strategy to open our entire retail chain to curbside pickup,” says Omar Koncobo, IT director on Ulta Beauty’s innovation team. “As we expanded the curbside pickup service, we didn’t have to add additional capacity. Everything scaled perfectly.”

Ease of implementation and management

Even a great caching solution can only be as effective as it is easy to implement. If the solution is hard to configure, monitor, and adjust, it can fail to reach its potential despite the best efforts of the developers implementing it.

When you shop for solutions, look for providers that offer user-friendly management interfaces and built-in monitoring and analytics tools that simplify troubleshooting and tuning. Redis, for example, helped Wizz scale across 300 million active users despite its small tech team.

“We need to simplify management, and we don’t want to bother with scaling our infrastructure,” says Gautier Gédoux, chief technology officer at Wizz. “Redis Enterprise is very stable and easy to use.”

Cost efficiency and ROI

Caching solutions can, themselves, pose different costs, but caching solutions can also carry implications for other infrastructure costs. If a caching solution doesn’t keep the right data in cache, for example, other costs might climb if more queries than necessary have to go to the primary database.

As a result, look for solutions that reduce infrastructure costs through efficient resource utilization. Auto-tiering, for example, can automatically move data across different storage tiers based on access patterns. Features like these should play out in real-life ROI, too, as demonstrated by customer success stories that demonstrate measurable business impacts.

Redis, for example, supported Etermax, a game developer, as they pursued auto-sharding and auto-scaling capabilities. As a result, Etermax was able to reduce its AWS infrastructure costs by 70%.

Level up your caching strategy with Redis

From the outside in, cache eviction policies seem like a detail that lies deep within your infrastructure. But this detail, like the foundation to a house, has a critical impact on everything you build on top of it. Poor caching decisions can lead to slow response times, frustrated users, lost revenue, and wasted technical resources – all of which can cascade into tech debt.

Choosing between LFU and LRU is just the first step. True caching success depends on using advanced, flexible solutions that intelligently adapt to real-world workloads. Caching is a lever for adjusting latency, reliability, and cost optimization, and the right solution enables you to pull the lever with the force required to support your infrastructure.

Redis is a proven, industry-leading solution uniquely designed to help teams implement and optimize advanced caching strategies. Redis offers flexible LFU and LRU implementations that simplify complexity, hybrid caching strategies that dynamically adapt to evolving workloads, and proven scalability and sub-millisecond real-time performance documented by industry leaders, such as JioCinema, Ulta Beauty, Wizz, and Etermax.

Ready to take your cache management strategy to the next level? Try Redis free or book a demo today.