In-memory cache

Your app needs to be fast to be usable. Most slow apps aren’t slow because they’re poorly designed; they’re waiting on data. In-memory data caching cuts data access time, making your app faster and more responsive.

Common performance problems that caching helps solve include:

• Slow response times caused by repeated database queries
• Backend errors and timeouts when the database is overloaded
• High infrastructure costs from scaling databases just to handle reads
• Spiky traffic patterns that the backend can't absorb quickly enough
• Excess load on primary databases from duplicate or frequent queries
• Poor user experience due to delayed access to critical data
• Low request throughput when apps are bottlenecked on data access
• Wasted compute resources spent fetching the same data repeatedly

The list can go on. All these problems don’t just make life harder for developers. Slow response times, for example, immediately affect end-users downstream. Meeting this pace requires understanding caching, learning why some solutions excel over others, and how to get started with your own caching strategy.

What is an in-memory cache?

An in-memory cache is a high-speed data storage layer that keeps data in volatile memory rather than on persistent disk. Essentially, it’s a cache that lives entirely in RAM, sitting between your application and its primary database, and it serves up frequently used data with extremely low latency.

An in-memory cache drastically cuts down the time needed to retrieve data for subsequent requests. Applications can access data in microseconds because it’s stored in memory, much faster than applications could ever access data stored on SSDs or HDDs.

With an in-memory cache available, the process for a request goes like this:

• First, the system checks the cache (in RAM) for a given data request.
• If it’s present (a cache hit), it’s returned immediately from memory.
• If it’s not present (a cache miss), it falls back, querying the primary database.
• If it queries the primary database, it stores a copy in the memory cache for the next time.

Subsequent queries are then faster because the same query will lead to a cache hit, leading to improved throughput and reduced load on the primary database.

The value of an in-memory cache isn’t just in sheer speed but in what that speed allows. When applications can retrieve frequently called data at this pace, the user experience improves (because it’s more responsive and smooth), performance rises (because the primary database faces less pressure), and real-time analytics become possible (because applications can’t reach those speeds otherwise).

In-memory systems aren’t a cure-all. Because they store data in volatile memory, they require more careful failure handling. And not every application needs this level of speed. Workloads with large storage needs and low query rates may be better off with disk-based solutions.

When should you use an in-memory cache?

Not every application needs an in-memory cache. In some cases, reading directly from a database is fast enough, especially when queries are infrequent or performance isn’t critical. But it's important to make that decision early. Retrofitting a caching layer later can be complex and costly. Planning ahead gives you a caching strategy that scales when you need it. Common signs your application needs caching.

Your application won’t flash a banner that says “Caching needed!” Instead, you’ll see symptoms that, together, form a diagnosis that calls for caching.

• Frequent database slowdowns and bottlenecks: If you notice your primary database is struggling under load, that’s a strong signal you need caching. Typical relational databases can become a bottleneck when many users repeatedly request the same data or when heavy queries run often. If query latency is increasing during peak usage, or you’re seeing timeouts and slow queries piling up, you might have hit a bottleneck.
• High error rates: If your application experiences spikes in errors during high traffic, caching might be the cure. Offloading reads from the primary database allows caches to reduce contention on database connections and prevent the cascade of failures that occur when the primary database can’t keep up.
• High resource utilization of the primary database: If you monitor your primary database and see CPU near max or read IOPS through the roof, your system is likely using a lot of resources just to return the same data repeatedly. If your primary database’s CPU, memory, or disk I/O is consistently high, that’s a sign that caching could reduce the load and costs by serving popular or recent data from RAM.
• Traffic spikes that your current infrastructure cannot handle effectively: If your application experiences periodic surges in traffic (from, for example, successful marketing campaigns or seasonal peaks) and it slows to a crawl or crashes, caching can help absorb those spikes.
• Increased costs from unnecessary database load: If you notice your infrastructure costs ballooning primarily due to read-heavy workload scaling, caching is a likely remedy that lowers unnecessary database load and cost. In some situations, applications can meet demand by scaling the primary database vertically or horizontally, but this tends to be costlier than using in-memory caches to meet scalability through increased read throughput.
• Slow response times affect customer satisfaction and retention. If response times are slow and users are complaining or bouncing, you should consider caching. By caching frequently queried results, such as product listings in an ecommerce application, users get the page almost instantly, which greatly improves the perceived performance.

All these signs point toward using an in-memory cache, but the biggest sign is often opportunity cost: Not adopting an in-memory cache can mean missing many potential benefits.

Benefits of using an in-memory cache

An in-memory cache isn't just a fix for performance problems. It improves speed, scalability, reliability, and cost efficiency, which provides both technical and user experience improvements.

On the technical side, benefits include:

• Enhanced scalability to handle traffic spikes: In-memory caches can handle enormous request volumes on even modest hardware. They operate in memory with simple data structures, which allows them to serve hundreds of thousands to millions of operations per second on a single node. They can also scale out horizontally, allowing you to add cache nodes and shard data among them, further increasing capacity.
• Reduced load on primary databases, lowering operational costs: By offloading frequent reads, caches dramatically reduce the query burden on your main database. This means your database can perform better for the queries that do hit it since it’s handling fewer total operations, and less load translates to lower costs.
• Increased reliability and availability of applications due to reduced database dependency: Despite its reputation for volatility, an in-memory database can actually improve your system’s reliability by reducing dependency on the primary database for every request. The overall system can withstand partial outages better.

On the user experience side, benefits include:

• Improved application speed and response times: The primary benefit of an in-memory cache is drastically faster data retrieval. By serving content from RAM, applications can respond with sub-millisecond latency, whereas hitting a disk-based store might take tens or hundreds of milliseconds. This can cut page load times and API responses down significantly, which benefits users as well as the business.
• Better user experience, increasing customer satisfaction and retention: Faster response times and higher reliability directly improve the user experience. Pages that load quickly and consistently will keep users on your app; slow or error-prone pages will drive them away. With caching, you remove a lot of the performance issues that frustrate users.
• More robust session management: Many apps use caching to store session data like login state and recent activity. This improves speed, consistency, and overall user experience.

Of course, these benefits often overlap. Few users will ever say “Loved the scalability!”, but users will benefit from it implicitly when traffic spikes and the system handles it without failure or latency. Often, the best benefits are invisible.

Application types that benefit from caching

In-memory caching improves performance in systems where speed, scale, or repeated access to data is critical. Here are some examples:

Ecommerce platforms

Ecommerce applications use in-memory caches to store product catalog data, pricing information, inventory status, shopping cart contents, user session data, and more.

With an in-memory cache, ecommerce applications can better handle big sales or seasonal spikes in traffic by serving the most popular or most recent data from memory. This prevents the database from failing or slowing down under a heavy read load.

For example, Ulta Beauty uses Redis Cloud’s caching features to improve inventory management. "Everything is in-memory, so there’s no need for cold storage,” says Omar Koncobo, IT Director at Ulta Beauty.

Gaming and interactive applications

Online games, betting platforms, and other platforms with leaderboards, matchmaking data, and in-game statistics often use in-memory caches to retrieve and update their interfaces during gameplay quickly.

Etermax, for example, used Redis Cloud’s auto-sharding and auto-scaling capabilities to support and scale its gaming servers. “It would have been impossible to scale to 25 million daily users with any solution other than Redis Cloud,” says Gonzalo Garcia, Former CTO at Etermax.

Financial services and trading

Banks, trading platforms, and fintech applications often use in-memory caching to achieve the ultra-low latency necessary for trading and risk management.

A stock trading platform, for example, might cache the latest market data (including prices and order book info) in memory, so that traders can get updates with minimal delay. Risk systems could cache current exposures for quick recalculation when new trades come in.

Real-time analytics

Analytics systems that provide real-time insights often rely on an in-memory cache to support the retrieval of frequently requested metrics with little to no delay.

A real-time dashboard that shows website metrics (such as visitors, clicks, and conversions), for example, might use the latest computed aggregates cached in memory so that each dashboard refresh doesn’t recompute everything from scratch.

Challenges with operating and scaling an in-memory cache

While in-memory caching can greatly boost performance, it introduces its own set of challenges. Data consistency, scalability, and reliability issues can all crop up. For each issue, however, there’s a way to address it, so with the right plan, you can maximize the benefits and minimize the tradeoffs.

Managing cache invalidation strategies

Phil Karlton once said, “There are only two hard things in Computer Science: cache invalidation and naming things.” Keeping cached data in sync with the source of truth has always been a little tricky, but caching tools have gotten better at making it easier. Still, the core question remains: If the underlying database record changes, how do we ensure the cache isn’t serving stale data?

Two of the best approaches are implementing time-to-live (TTL) expirations and adopting cache invalidation techniques.

Assign a TTL to each cache entry so that it automatically expires after a certain time. This ensures stale data doesn’t live forever.

You can also adopt cache invalidation techniques triggered by specific database updates. This is when your system removes data from a system’s cache as soon as that data is no longer valid or useful. This ensures that the cache can maintain consistency, which prevents errors.

Redis Data Integration (RDI) makes these approaches simpler to implement and significantly more effective. With RDI, you can ensure data is always in sync, eliminate cache misses, remove stale data, and overcome the limitations of traditional, manual approaches – all with configuration, instead of code.

Maintaining performance during peak usage periods

Caches have limited memory, by definition, so what happens when they fill up? That’s the challenge behind maintaining performance during peak traffic periods – the times when you likely need performance the most.

Caches must evict some data to make room, but choosing what to evict is a nuanced choice. Most caches use a cache eviction policy like Least Recently Used (LRU) by default, but solutions like Redis provide multiple policies (LRU, LFU, and random eviction). The right policies can help you avoid memory errors and degraded app performance.

Redis clustering allows you to solve the same problem by distributing the load efficiently across multiple nodes, ensuring periods of high traffic are a reason to celebrate, not worry. Similarly, Redis also provides load balancing options to help you evenly distribute requests and optimize resource utilization.

Ensuring high availability, failover, and disaster recovery

In-memory caches are volatile, so if a cache node goes down, all the data on it is lost. A failed cache can be disruptive to your systems, even if it’s not as catastrophic as a primary database failure, because recovering all that data from the primary database can cause a cache stampede.

To mitigate this, you want high availability (HA) in your caching layer. Redis Sentinel, for example, can be configured to support automatic failover and monitoring. Similarly, you can implement Redis replication to maintain data availability and durability.

In-memory cache options: Side-by-side comparison

In-memory caching has been around for a long time, and there are numerous options to consider. The trouble is that many caching options can support your primary use case in theory, but fail in practice as you scale. Use a side-by-side comparison to see how different features stack up and contrast how different options scale in real-world conditions.

Redis vs. Memcached

Redis and Memcached are both fast, in-memory key-value stores that support caching use cases. Memcached was developed in 2003 and remains a simpler cache, but over the years, Redis has evolved into a feature-rich in-memory data store.

Here are three key differences between Memcached and Redis:

• Data structures: Memcached only deals with simple string keys and string values, whereas Redis supports a wide range of data structures, including strings, hashes, lists, sets, sorted sets, bitmaps, streams, and more. This means Redis can support features like queues, pub/sub messaging, and time-series data.
• Persistence capabilities: Memcached is purely in-memory and has no persistence mechanism, meaning that if it restarts, all data is gone. Redis was designed as an in-memory store with optional persistence, allowing you to use it as an in-memory database that can recover state after a reboot or be backed up for durability.
• Clustering and scalability: Both Memcached and Redis can scale horizontally, but Memcached scaling is typically handled at the client level, whereas Redis Cluster allows a set of Redis nodes to automatically shard data among themselves and failover if one goes down.

Overall, Memcached can often serve basic caching needs well, but Redis additionally supports features like session management and real-time data operations, making it a better option for companies that need more.

Redis vs. Amazon ElastiCache

Amazon ElastiCache is AWS’s managed service for caching, and it supports Redis, Valkey and Memcached engines. Technically, ElastiCache can be run with Redis up to Redis 7.2, but there are notable differences comparing the service (ElastiCache) to a self-managed Redis instance or an instance provided by Redis itself.

Here are three key differences between ElastiCache and Redis:

• Scalability: ElastiCache frequently has issues with slow scaling, sometimes even resulting in a 1-hour scaling delay. Redis Cloud, as proven by benchmarks, can go from 10M ops/sec with 6 AWS EC2 instances to 30M ops/sec with 18 AWS EC2 instances. If that’s not enough, Redis Cloud can scale even further by scaling shards, nodes, and proxies.
• Latency: Since ElastiCache doesn’t support new versions of Redis, you can’t read and write to multiple Redis instances at the same time, which causes latency as data moves between regions. Redis’s Active-Active multi-region architecture, in contrast, delivers <1ms response times and much greater scalability than what you can get through ElastiCache (even when you’re using ElastiCache for Redis).
• Lock-in: Using ElastiCache often means getting locked into using AWS, which can be a risky strategy given the rise in hybrid and multicloud deployments. Redis maintains six official clients in five different programming languages, giving developers much more flexibility.

For example, when Plivo wanted to improve scalability, Amazon ElastiCache wasn’t enough: "We wanted to ensure we could meet uptime and scalability requirements through Active-Active Redis,” says Rajat Dwivedi, Director of API Engineering at Plivo.

Redis vs. Google Memorystore

Google Cloud Memorystore is GCP’s managed service for Redis, Valkey and Memcached. Google Cloud Memorystore is roughly equivalent to ElastiCache for Redis. It offers a managed Redis instance, but does not support the latest Redis versions and features.

Here are three key differences between Google Memorystore and Redis:

• Deployment flexibility: Google Memorystore can only be deployed on Google Cloud, which can be a huge limitation if you want to pursue a multi-cloud deployment strategy. Redis supports AWS, Azure, and Google Cloud, allowing you to deploy where you want and avoid lock-in.
• High availability: Google Memorystore provides 99.9% uptime through Memorystore for Redis and 99.99% uptime through Memorystore for Redis Cluster & Memorystore for Valkey. Redis Enterprise, in contrast, provides 99.99% uptime through multi availability zones and 99.999% through active-active architectures.
• Replication: Google Memorystore lacks the same replication features as Redis, potentially leading to data inconsistencies. Redis offers active-active geo replication to support simultaneous reads and writes to multiple geographically distributed nodes, which provides replication without sacrificing data consistency.

When Niantic, the developers behind Pokémon Go, wanted to improve performance and reliability during high load periods, they decided against Memorystore: “Adding Redis clusters is less expensive than deploying additional Google Cloud servers,” said Da Xing, Staff Software Engineer.

From caching to real-time data platform

Caching makes your app faster. But making your entire system real-time and ready to scale takes more than that. Redis gives developers a complete platform with the data structures, features, and ecosystem needed to power modern applications.

With Redis, caching is just the beginning. You can:

• Search and filter structured data in memory
• Expand your cache capacity cost-effectively with Redis Flex and SSD-based storage
• Keep data fresh and in sync with your system of record using Redis Data Integration

These capabilities turn Redis into more than a performance boost. They give you a real-time layer that works across use cases—from personalization and analytics to transactions and messaging.

If you're ready to move beyond basic caching and build for real-time, Redis is ready for you.

Ready to experience real-time application performance? Try Redis free or book a meeting today.