In-Memory Cache

What Is an In-Memory Cache?

An in-memory cache is a high-speed data storage layer that keeps frequently accessed data in RAM instead of on disk. It often is deployed between an application and a backing data store (such as a relational database, NoSQL database, or external API) and returns cached results in sub-millisecond to milliseconds, dramatically reducing latency and offloading read and sometimes write load from the underlying systems.

An in-memory cache is a high-speed data storage layer that keeps frequently accessed data in RAM rather than on disk. It is commonly used alongside an application and a backing data store (such as a relational database, NoSQL database, or external API) to serve cached data with sub-millisecond to millisecond latency. By absorbing a large portion of read traffic—and, in some architectures, write traffic—it can significantly reduce latency and alleviate load on underlying systems, at the cost of additional complexity around consistency, eviction, and durability.

Rather than querying the database for every request, the application first looks in the in-memory cache:

• On a cache hit, the cache returns the response immediately.
  On a cache miss, the cache retrieves the data from the database or service, stores it in memory for subsequent requests, and then returns the result to the application.

By serving the “hottest” or most frequently accessed data directly from memory, in-memory caching helps organizations:

• Cache hits are very fast and improve average and tail latency for users
• Protect downstream databases and services from traffic spikes
• Can reduce infrastructure costs by offloading repetitive queries and operations from expensive systems
• Improve scalability and, when designed appropriately, application resilience of mission-critical applications

To see how in-memory caching fits into a broader in-memory architecture, explore theGridGain In-Memory Computing Platform.

How an In-Memory Cache Works

At a high level, many in-memory caches follow a cache-aside or similar access pattern:

1. The application receives a request and computes the key used to look up data in the cache.
2. The cache checks whether the key exists:
   • Cache hit – the item is found in memory and returned immediately.
   • Cache miss – the item is not present or is expired.
     Note: Some caches return expired data with a flag, allow serving stale data while refreshing asynchronously, or don’t strictly enforce TTL at read time.
3. On a cache miss, the cache (read-through) or application (cache-aside) retrieves the data from the backing store (for example, a disk-based database or external service).
4. The data is written into the cache (often with a time-to-live (TTL) setting), and then returned to the application. Note: some caches rely more on explicit invalidation, versioning, or eviction-only policies (LRU/LFU).
5. Subsequent requests for the same key are served directly from the in-memory cache until the data expires or is evicted. In addition, data may also be removed due to manual invalidation, memory pressure, cluster rebalancing, or node failure.

Because random-access memory is orders of magnitude faster than disk-based storage, this pattern can significantly lower tail latency and improve overall throughput for read-heavy workloads.

For workloads that need not only caching but also full database semantics, see the glossary entry forIn-Memory Database.

In-Memory Cache vs. Database

An in-memory cache does not replace a database. Instead, it complements existing data stores:

Database

• System of record
• Durable storage with strong or configurable consistency guarantees
• Optimized for persistence, complex queries, and transactions 

In-Memory Cache

• Primarily a performance layer in front of one or more data stores. Note: some are embedded (in-process), used alongside event streams, or occasionally primary for ephemeral data.
• Holds copies or derived views of data
• Optimized for ultra-fast reads and, in some architectures, for simplified writes

Applications typically treat the database as the ultimate source of truth, while the in-memory cache stores:

• Results of expensive queries
• Reference data and configuration
• Session data or user profiles
• Aggregated or precomputed values for operational or near-real-time analytics and personalization

Note: Some systems temporarily treat the cache as authoritative for sessions, rate limits, and feature flags.

Distributed vs. Non-Distributed In-Memory Caches

Early caching implementations often ran on a single server. For example, a web application might use a local cache or a dedicated caching node to offload its primary database. While this is simple to set up, it is bound by the memory available on that one machine and can become a single point of failure.

Modern applications frequently require far more cached data and much higher availability. In these scenarios, organizations rely on distributed in-memory caches.

Non-Distributed (Single-Node) Cache

• Runs on a single machine
• Simple to deploy and reason about
• Limited by the RAM, CPU, and, in some cases, network bandwidth capacity of that node
• Failure of the node typically means a loss of cached data on that node

Distributed In-Memory Cache

• Spans a cluster of nodes that share cache responsibilities
• Data is typically partitioned into shards and distributed across the cluster
• Copies of data can be replicated for higher availability and resilience
• Capacity can scale horizontally by adding more nodes
• Supports use cases requiring very large cache footprints (up to terabytes of data)

In distributed caches, the cluster usually manages how keys are mapped to partitions, where partitions are stored, and how data is replicated, rebalanced, and recovered if a node fails.

To learn more about distributed, in-memory architectures, see the glossary entry forIn-Memory Data Grid.

Common Caching Strategies

Different workloads require different caching strategies. Choosing the right pattern depends on factors such as consistency requirements, update frequency, and tolerance for stale data. Three widely used strategies are cache-aside, read-through, and write-through.

Cache-Aside

Cache-aside (also known as lazy-loading) is a simple and popular pattern:

1. The application first requests data from the cache.
2. On a cache hit, the cached value is returned.
3. On a cache miss, the application reads from the database or service, writes the result to the cache, and then returns it to the caller.

With cache-aside:

• The application owns the logic for fetching and populating the cache.
• Cached data is loaded only when needed.
• Cache entries can be invalidated or expired based on TTL or explicit rules.

Read-Through

Read-through caching also loads data lazily, but places the responsibility for reading from the backing store inside the cache itself:

1. The application always queries the cache.
2. If the data is present, it is returned directly.
3. If the data is missing, the cache transparently fetches it from the data store via a read-through loader, stores it in memory, and returns it.

This pattern:

• Simplifies application code because the cache abstracts access to the data store
• Centralizes the logic for loading and caching data
• Requires configuration of cache loaders and secure access to the backing store

Write-Through

Write-through caching ensures that the cache and backing store are kept in sync on writes:

1. The application writes data to the cache.
2. The cache synchronously writes the same data to the database or service.
3. The write is considered successful only when both cache and data store have been updated.

Write-through helps keep cache and store synchronized, but can:

• Increase write latency, because each write involves both cache and database
• Be best suited for workloads where write volume is moderate and consistency is more important than raw write performance

Additional Patterns: Write-Behind and Write-Around

Two related strategies are often used in conjunction with the above:

• Write-behind (write-back)
  • The application writes to the cache.
  • The cache immediately returns success to the application and batches or asynchronously writes updates to the database.
  • This improves write performance but introduces a small window where data may not yet be persisted.
• Write-around
  • The application writes directly to the database and invalidates or bypasses the cache.
  • Useful when data is rarely read shortly after being written and caching every write would be wasteful.

Eviction Policies and Data Freshness

Because memory is finite, an in-memory cache must decide which entries to keep and which to remove as it fills up. Eviction and expiration policies control how the cache manages its contents and how stale data is handled. Common options include:

• Time-to-live (TTL)
  • Each entry is given an expiration time.
  • After the TTL passes, the entry is considered invalid and is either lazily or proactively removed. However, some caches allow serving stale data briefly, return expired data with metadata, or refresh entries asynchronously.
• Least Recently Used (LRU)
  • The cache tracks or approximates access recency and evicts the entries that have not been used for the longest period.
• Least Frequently Used (LFU)
  • The cache tracks or approximates access frequency and evicts the least frequently requested ones.
• Size-based eviction
  • The cache is configured with memory or entry-count limits.
  • When limits are reached, entries are evicted based on LRU, LFU, or other custom policies.

In practice, caches often combine expiration and eviction policies, using approximations to balance accuracy and performance. By combining eviction policies with appropriate TTL settings, teams can balance freshness, memory usage, and performance.

Real-World Use Cases for In-Memory Caching

In-memory caches are widely used anywhere low latency and high throughput are required. Common scenarios include:

High-Traffic Websites and APIs

• Caching HTML fragments, API responses, product catalogs, and user profiles to keep response times predictably low for cached requests.
• Reducing the load on downstream databases during traffic spikes.

Payment Processing and Financial Services

• Maintaining reference data such as FX rates, risk scores, and customer entitlements in memory to support low-latency decisioning.
• Caching results of fraud checks and authorization decisions to accelerate repeat transactions.
• Such cached decisions are typically short-lived and carefully scoped.

Session and Identity Management

• Storing user sessions, tokens, and authentication metadata in a distributed in-memory cache to support stateless application servers and fast logins.

Analytics and Business Intelligence

• Caching precomputed aggregates or materialized views so that dashboards and BI reports load quickly, even when queries are complex.
• Using caches to accelerate access to historical data that changes infrequently.

Microservices and Event-Driven Architectures

• Sharing configuration, reference data, and computed results across microservices without repeatedly calling downstream systems.
• Reducing chattiness and latency in distributed applications.

To see how caching is used as part of broader real-time architectures, explore use cases such asDigital Integration Hub.

In-Memory Cache vs. In-Memory Database vs. In-Memory Data Grid

“In-memory cache,” “in-memory database,” and “in-memory data grid” are closely related but represent different architectural concepts.

In-Memory Cache

• Focuses on improving performance by storing copies of frequently accessed data in RAM
• Typically used as a layer in front of existing databases or services
• Often key-value oriented, optimized for fast reads and writes

In-Memory Database

• Stores primary data primarily in memory, with optional persistence to disk
• Provides database capabilities such as SQL, indexing, transactions, and schema management
• Designed for ultra-low-latency transactional or analytical workloads

Learn more in the In-Memory Database glossary article.

In-Memory Data Grid

• Distributes data and computations across a cluster of nodes
• May offer an in-memory key-value store plus capabilities like compute, services, and streaming APIs
• Frequently used to build high-performance, scalable applications that process large data sets in memory

See the In-Memory Data Grid glossary article for a deeper dive.

A distributed in-memory cache can be implemented using an in-memory data grid or an in-memory computing platform, which provides richer functionality such as collocated compute, SQL queries, transactions, persistence, and integration with streaming systems.

In-Memory Caching with Apache Ignite and GridGain

Apache Ignite is an open-source distributed database and in-memory computing platform that can be used as a powerful in-memory cache, an in-memory data grid, or a full-featured distributed SQL database. It supports:

• Key-value APIs
• SQL and JDBC/ODBC connectivity
• ACID transactions
• Compute and machine learning capabilities
• Persistence and durable memory options

Because Ignite is designed for high-performance in-memory operations and clustering, many teams adopt it as a distributed in-memory cache for existing databases and services. It can act as a cache in front of relational databases, NoSQL stores, mainframes, and other systems of record, while also enabling advanced processing close to the data.

TheGridGain In-Memory Computing Platform is built on the Apache Ignite project and adds enterprise-grade capabilities such as:

• Advanced security, monitoring, and management
• Rolling upgrades and zero-downtime operations
• Disaster recovery and multi-datacenter replication
• Enhanced tooling, support, and training
• Cloud-native deployment options and Kubernetes integrations

By combining Apache Ignite with GridGain’s enterprise features, organizations can deploy a distributed in-memory caching layer that:

• Delivers ultra-low latency at scale
• Integrates with existing databases and applications
• Supports mixed transactional, analytical, and streaming workloads
• Helps teams modernize legacy architectures without a complete rewrite

For SaaS-style deployments, seeGridGain Nebula, a managed service based on the same in-memory computing technology.

Conclusion

An in-memory cache is a foundational building block for modern, low-latency applications. By keeping frequently accessed data in RAM and distributing it across a cluster, organizations can dramatically improve response times, increase throughput, and reduce the burden on existing databases and services.

Platforms such as Apache Ignite and the GridGain In-Memory Computing Platform make it possible to deploy in-memory caching, data grid, and in-memory database capabilities together, helping teams build resilient, real-time systems without abandoning their current investments.

To explore how in-memory caching fits into your architecture and how GridGain can help:

• Learn more about the GridGain In-Memory Computing Platform
• Explore additional resources in the GridGain Resource Center

Visit the Developer Hub for Apache Ignite for tutorials, guides, and examples