In-memory cache for today’s applications

Today’s applications demand fast data access. An in-memory cache is one way to get this speed. By keeping frequently used data in high-speed memory rather than on disk, in-memory caching reduces response times and relieves pressure on backend databases. But what is it? How does it work? What are its benefits and challenges? When do you use it? Why is it essential in today’s data architectures?

What is an in-memory cache?

An in-memory cache is a high-performance data store that keeps frequently accessed information in a system’s main memory for quick retrieval. The primary purpose of a cache is to avoid repeated access to a slower underlying storage layer, such as a disk-based database, by temporarily storing copies of the data in RAM. Because RAM access is much faster than disk access, reading data from an in-memory cache typically takes under a millisecond, versus several milliseconds or more from disk.

In practical terms, an in-memory cache often sits between an application and its primary database. When the application needs a piece of data, it first checks the cache. If the data is found, it is called a “cache hit” and returned from memory, thereby speeding up the response. If the data is not in the cache, called a “cache miss,” the application fetches it from the database, then stores it in the cache for next time. This way, subsequent requests for the same data are faster because they come from memory. This is known as cache-aside or lazy loading, and it stores only the data that is frequently requested, keeping the cache content relevant.

In-memory caches are commonly implemented using key-value stores or similar structures. Caches may be local, stored (in-memory within an application process, or distributed across multiple servers. Distributed in-memory caches let multiple applications or microservices share a common fast data tier, handling large data volumes and high query rates.

Ways to improve in-memory caching 

The cache is typically structured as a lookup table, often hash-map-like, using keys to reference values, residing in RAM. Applications interact with the cache using simple get/put operations. 

The tradeoff with standard caching is that the first request for a given item still incurs the full database latency, because the cache isn’t populated until after a miss, which slightly increases the initial response time. However, there are several techniques that address this problem.

Write-through caching

Write-through caching updates the cache at the same time the primary database is updated. Whenever the application writes new data or updates existing data in the database, it simultaneously writes that data to the cache. This means the cache is always fresh, increasing the likelihood of cache hits for reads. The benefit is improved read performance consistency because the cache is up-to-date, so reads rarely miss, and reduced read load on the database. The downside is that even infrequently used data will be stored in the cache, using memory for items that may not be read often. For this reason, write-through is often combined with cache-aside plus an expiration policy so seldom-used data eventually gets deleted from the cache.

Caching eviction policy

Because memory is finite, caches have limited capacity and cannot store unlimited data. When the cache gets full, older or less-used items must be deleted to make room for new data. The cache uses an eviction policy to decide which data to remove. A common approach is Least Recently Used (LRU): the cache tracks access recency and deletes the item that hasn’t been used for the longest time once the cache hits its limit. The assumption is that if data hasn’t been used in a while, it’s less likely to be needed soon, making it a good candidate for removal. Other policies include Least Frequently Used (LFU), First-In-First-Out (FIFO), or random eviction, but LRU or its variants are widely used due to their simplicity and effectiveness in many scenarios. By evicting stale or cold entries, the cache frees space for more relevant data and maintains high-speed access to the “hot” data that is retrieved more often.

Distributed caching

In larger systems, caches are often distributed across multiple nodes. Instead of one machine’s memory, a distributed cache cluster pools the memory of many machines. Data is partitioned or sharded across the nodes, so the cache’s total capacity and throughput grow almost linearly as you add more nodes. Distributed caches also replicate data to some degree, depending on the configuration, such as by keeping a copy of each cached item on two nodes, so that if one node fails, the data is still available on another node. This approach provides high availability, reducing the cache as a single point of failure, and it supports the needs of large-scale, high-concurrency applications by scaling out horizontally.

By combining lookup strategies, eviction policies, and distribution/replication, in-memory caching systems keep the most relevant data in memory where it is retrieved quickly. The result is faster reads and the ability to run more queries than a typical disk-based database could handle.

Benefits of in-memory caching

An in-memory cache improves performance and scalability for an application architecture. Here are some of the advantages.

Fast data access with low latency

Memory access is faster than disk or network calls. Serving data out of RAM typically takes microseconds to a few milliseconds, while reading from a traditional disk-based database might take tens or hundreds of milliseconds. This difference is game-changing: Reading data from an in-memory cache is often a sub-millisecond operation. By avoiding disk I/O and network hops to a database, caches reduce latency, improving application response times and throughput. 

Improved speed isn’t just a luxury; it may make the difference for user retention and experience. For example, reducing page load times by even a second increases user engagement. Conversely, more than half of users will leave if a site is sluggish beyond a few seconds. In-memory caching supports ultra-fast interactions such as page loads, real-time recommendations, or live analytics that today’s users and systems expect.

High read throughput and predictable performance 

In-memory caches handle very high query volumes and bursty traffic much better than disk-bound systems. Because operations in memory are so fast, one cache server serves more requests per second, often hundreds of thousands of read operations per second from one cache instance. By spreading data across multiple cache nodes, throughput continues to grow. This helps applications support many simultaneous users and sudden spikes in load without performance degradation. 

For example, an e-commerce site during Black Friday or a social network during a major event might see traffic surges that would normally hammer the database with read requests. With a caching layer absorbing most of those reads, the system maintains predictable, low latency even under peak load. The cache acts as a high-speed shock absorber for the database, smoothing peaks and providing consistently fast reads as the application grows. 

Reduced database load and infrastructure cost

Every cache hit is a query that no longer needs to go to the primary database. Offloading frequent reads to an in-memory cache reduces the workload on the database. This saves money because if the database isn’t handling as many operations, you may be able to use a smaller or less expensive database cluster, or avoid scaling it up further as demand grows. In fact, one cache server, done right, may replace several database servers’ worth of read traffic. This translates to cost savings, especially for cloud databases that charge by throughput or for expensive legacy systems; organizations have seen “dozens of percentage points” reduction in infrastructure costs by using caches to cut down on database usage. 

Moreover, in-memory caching helps reduce database hotspots. In many applications, a small subset of records, such as a popular product or a trending profile, might generate most of the database traffic, causing bottlenecks on those rows or tables. Caching these hot items means the database no longer has to handle an excessive load for them, so you don’t need to overprovision database hardware just to handle spikes on a few popular items. The result is a more efficient use of back-end resources: Size the database for the average load rather than peak reads, and the cache handles the rest, saving money and improving efficiency.

Horizontal scalability and high availability

An in-memory cache deployed as a distributed cluster adds not only speed, but also scalability and reliability to the architecture. Scaling a distributed cache is typically as simple as adding more nodes to the cluster; the cache repartitions or adds new partitions to use the additional memory and network capacity, increasing the total cache size and throughput almost linearly as you grow. Horizontal scaling means the caching layer grows in response to your application’s demand without requiring a major redesign. 

Additionally, today’s distributed caches offer features such as data replication and failover. The cached data is copied to multiple nodes so that if one node goes down, another node serves its data. Some caching systems use consistent hashing and redundancy so even if a server is lost, only a portion of the data becomes temporarily unavailable, and it is often recovered or repopulated quickly. This makes the caching layer available and fault-tolerant, required for applications that cannot afford downtime or extended slowness. In summary, a well-designed caching tier not only speeds reads but also provides a robust, scalable buffer that helps insulate the application from database outages or capacity limits, making the overall system more resilient.

Common scenarios for in-memory caching

In-memory caching is helpful in many situations. Here are some of the most common. 

Content caching

Frequently requested static content, such as images, videos, and HTML fragments or computed results, is stored in memory to serve users quickly. By keeping popular media or page fragments in an in-memory cache, applications reduce calls to storage or databases and deliver content faster. This improves load times for users without changing where the content is stored. 

Session store and user profiles

Web applications often cache user session data, profiles, or recent activity in memory. This supports fast lookups for personalization. For example, storing user profile attributes or game scores in an in-memory cache supports real-time personalization in recommendation engines and leaderboards, because they fetch and update data quickly. In this example, the cache may serve as the authoritative store for session state, with periodic persistence or redundancy to avoid data loss, or it may simply hold a copy of profile data that also resides in a database.

Database query acceleration / caching read-heavy queries

Caching is widely used to offload read pressure from slow back-end datastores such as legacy relational databases, mainframes, or data warehouses. These systems might not be designed for high request rates at web scale and can become a bottleneck as usage grows. An in-memory cache layer in front of the database stores the results of common queries or recent data, so the application serves repeated requests from memory rather than going to the database each time. 

This is particularly useful for expensive queries, such as complex analytics or reports, and for increasing the throughput of systems that serve the same data repeatedly to many users. By returning cached results, the application not only reduces latency for those requests but also prevents overload on the back-end. In effect, the cache acts as a high-speed intermediary between the app and a slower system, so the legacy database is not overwhelmed even when many people are using it. 

In-memory caches are also used in areas such as API response caching, microservice communication by caching results of service calls, edge caching in CDNs or reverse proxies, and real-time analytics by caching recent computations. Anywhere that data is read frequently but changes infrequently, or where a slight lag in data freshness is acceptable, a cache likely improves performance.

Challenges and considerations with in-memory caching

While in-memory caching is beneficial, it also introduces challenges and tradeoffs that architects and developers must consider.

Data staleness and cache invalidation

One of the thorniest issues in caching is keeping the data in the cache fresh and consistent with the primary database. Because the cache holds a copy, there is the inevitable possibility of stale data: the data in cache might have been updated in the database but not yet refreshed in the cache, which might return outdated information to the application. 

To mitigate this, cache entries are often given a time-to-live (TTL). After a set time, cached data expires and is deleted, so the next request fetches the latest version from the database. Another approach is explicit invalidation, where the application or database signals the cache to purge or update an entry when underlying data changes. 

However, coordinating cache invalidation is famously difficult because it is hard to predict when a piece of data becomes stale, especially in complex, distributed systems. If the cache isn’t properly invalidated, the application may read incorrect or outdated data, which leads to correctness issues. On the other hand, invalidating too aggressively or with too short a TTL reduces the effectiveness of the cache because entries get deleted before they’ve delivered much benefit. 

Striking the right balance and strategy for each dataset is important. In summary, maintaining cache coherence with the database is a challenge; developers must design a strategy using TTL expiry, cache-aside validation on access, and write-through updates that keep data reasonably fresh in the cache without losing the performance gains.

Memory limitations and eviction impact

By nature, an in-memory cache is constrained by the amount of RAM available. This means not all application data fits; typically, you cache the most frequently used items rather than everything. Deciding what to cache and what to evict when the cache is full is important. Caches use eviction policies such as LRU to remove old or infrequently used items. 

But eviction has performance implications: If a suddenly needed item is evicted, the application experiences a cache miss and has to fetch from the database again, incurring that latency cost. In pathological cases, thrashing can occur, where an item is evicted and then immediately requested again, creating constant loads on the database. Tuning cache size and eviction policy helps fix this, but you need to understand the access patterns of your application. 

Another consideration is memory overhead, because storing data in memory, especially structured or serialized objects, uses more space than raw data on disk. Large caches become expensive in terms of RAM consumption. Techniques such as data compression in cache or storing only certain fields or a computed summary help use less memory. 

Ultimately, memory is a scarce and costly resource, so it’s important to monitor cache hit rates and memory utilization. If you find that your cache is frequently evicting useful data or not providing a good hit ratio, you may need to allocate more memory, make the cache cluster bigger, or adjust what data is cached to make it more effective. 

Added complexity in architecture

Introducing a caching layer means your architecture now has an additional moving part. Deploying and managing a distributed cache cluster alongside your primary database is more complex. From an operational standpoint, you’ll need to monitor the cache servers, handle their scaling, and make sure they’re available. This is effectively another database to maintain. 

In development, using a cache also makes data flow more complex because developers have to implement cache access logic and decide when to read from or write to the cache versus the source. Bugs in cache usage lead to issues such as inconsistent data if the cache isn’t updated properly or harder-to-reproduce bugs that appear only when certain data is cached. Caching also complicates transactions; for example, if multiple updates need to be atomic, involving a cache gets tricky unless the cache supports transactional operations. 

Moreover, during cache outages or when a cache is just started up and empty, the sudden load on the database spikes. This phenomenon, sometimes called a cache stampede, occurs when many requests simultaneously miss an empty cache and all hit the database, potentially overwhelming it. Some techniques help mitigate stampedes, such as request coalescing or background warming of the cache, but they make the system more complex. In summary, a cache trades higher complexity for higher performance. Teams adopting caching need to be prepared for the operational and design considerations that come with running a distributed in-memory system in tandem with the primary database.

Volatility and data durability

By design, most in-memory caches are volatile, which means if the process or server restarts, the cached data disappears. This is acceptable when the cache is purely a performance layer, because the data is also stored permanently in the database, but it means that after a crash or reboot, the cache will be cold and less effective until it repopulates. In cases where the cached data is not stored elsewhere, such as certain session data or transient computed stats, losing the cache could mean losing that data unless you have taken special measures. 

Some caching solutions offer persistence options, such as writing a snapshot to disk or using append-only logs to recreate state, as well as replication to guard against data loss. However, adding persistence makes the cache slower and blurs the line between a cache and a database. If you choose to use an in-memory store as a primary data store, not just as a cache of another database, you must plan for backup, recovery, and replication, essentially treating it like you would a database in terms of data durability requirements. 

For example, you’d need to consider what the Recovery Point Objective (RPO) is, or how much data you could afford to lose if the cache failed, and a Recovery Time Objective (RTO), or how quickly you could get it back up with data loaded. In many cases, losing the cached data isn’t catastrophic because it just causes higher latency until the cache refills, but it’s important to evaluate this relative to your application. If the data in the cache is essential and not stored elsewhere, then your cache needs features such as persistence and replication to be a reliable store. This effectively turns it into an in-memory database. 

Thus, teams must decide whether the cache is a transient performance booster, in which case volatility is acceptable, or if it’s expected to maintain state reliably. In the latter case, you need additional measures to prevent data loss, which reduces the simplicity and speed that caching provides.

Given these challenges, it’s clear that caching is not a panacea. It solves problems such as speed and scale at the cost of new considerations such as consistency, memory use, and complexity. The good news is that many of these issues are manageable with best practices and today’s tools. Proper monitoring of cache hit rates, intelligent invalidation strategies, and choosing the right caching technology all go a long way.

In fact, newer hybrid solutions combine the benefits of caching and persistent storage, allowing architects to simplify their stack. Rather than managing a separate caching layer and database, one approach is to use an in-memory database or a high-performance data platform that serves data at cache-like speeds while also offering durability and consistency. This reduces common cache sync issues by having one database as fast as a cache. 

In-memory caching with Aerospike

In-memory caching has become a cornerstone of today’s high-performance applications, providing the ultra-low latency and throughput users demand. However, running a cache alongside a database is more complex in terms of data consistency, operations, and cost. Aerospike is a real-time data platform that addresses these challenges by blending the speed of an in-memory cache with the robustness of a database. 

Aerospike’s patented architecture provides cache-level performance with sub-millisecond data access together with built-in persistence, strong consistency options, and automated clustering. It offers the speed of a cache with the persistence and resiliency of an always-on database, delivering sustained high throughput at sub-millisecond latencies in distributed environments. Aerospike is designed for efficiency; it uses techniques such as in-line data compression and a hybrid memory model by storing indexes in RAM and data on flash/NVMe for high performance with less hardware. This means organizations often consolidate separate caching tiers and database systems into a unified platform.

In fact, companies adopting Aerospike as a caching database have simplified their infrastructure and reduced costs. For example, a global advertising technology firm replaced multiple cache layers of Redis/Memcached and a disk-based NoSQL store with Aerospike, reducing their cache server count from 3,200 nodes to 800 nodes while still handling the same load.

Similarly, Sony’s PlayStation Network shifted user personalization and data retrieval workloads to Aerospike and handles more than 100 billion daily events at under 10ms latency on a relatively small cluster, which would have required many times more servers with a traditional setup. These results show how Aerospike’s real-time database serves as both an in-memory cache and a system of record for high performance and operational simplicity.

If your organization is struggling with the limitations of separate caching solutions or needs next-level performance at scale, it may be time to consider Aerospike. Aerospike’s real-time data platform is purpose-built for high-speed, high-volume workloads, from caching use cases to large-scale transaction processing, with enterprise-grade reliability. By adopting a solution that unifies cache and database, you can reduce your server footprint, lower maintenance complexity, and meet stringent latency and throughput SLAs.