Next Generation Caching & In-Memory Searching

When Traditional Databases Reach Their Limits

Enterprise applications frequently face performance bottlenecks that stem from the underlying persistence layer. Despite decades of database evolution, many core applications still struggle with latency, throughput, and architectural complexity. These issues are particularly evident in systems that depend on complex join operations across multiple tables, need to process unstructured data, or must execute cross-database queries spanning disparate systems to fulfill analytical workflows.

In such environments, it is common to see a combination of various technologies being deployed in tandem: a traditional relational database, a distributed cache, a NoSQL database, and sometimes a dedicated search server such as Elasticsearch. While each of these systems addresses a specific technical challenge, their co-existence leads to architectural fragmentation, high infrastructure costs, duplicated data models, and increased development and maintenance efforts. Moreover, the overall latency and resource usage often remain suboptimal due to inherent I/O-bound limitations and serialization overheads between systems.

Eclipse Data Grid addresses these challenges through a Java-native in-memory data layer that is positioned between applications and their underlying databases. Its primary function is to offload complex data processing from the database tier and execute it within memory, thereby significantly improving performance while reducing infrastructure load and database licensing costs. Acting as a general-purpose, distributed in-memory data grid, Eclipse Data Grid enables low-latency, high-throughput data access and processing capabilities using plain Java.

A General-Purpose In-Memory Data Grid for Java

Eclipse Data Grid is a distributed, Java-based in-memory data processing platform that supports a broad range of use cases, from traditional caching scenarios to advanced in-memory computation. Unlike conventional caches, which are generally limited to key-value lookups and basic TTL-based eviction strategies, Eclipse Data Grid provides developers with a programmable data layer. This allows for the execution of custom business logic and complex data operations directly within memory, using the full expressive power of the Java programming language.

Support for Common Caching Use Cases

At its foundation, Eclipse Data Grid supports standard caching requirements. It can function as a distributed key-value cache, conforming to familiar APIs such as JCache (JSR 107), enabling straightforward adoption for applications that already rely on caching abstractions. For these use cases, it offers robust performance, high availability, and horizontal scalability across JVM nodes.

Next-Generation In-Memory Data Processing

Beyond traditional caching, Eclipse Data Grid introduces a novel approach to in-memory computing by empowering developers to build full-featured, Java-native applications directly on top of the grid. Developers can work with the native Java object model, avoiding the need for object-relational mapping, JSON serialization, or schema translations. Complex object graphs, including circular references, polymorphic structures, and nested collections, are supported without compromise.

With support for Java Streams, including parallel streams, developers can implement expressive queries, aggregation pipelines, and graph traversals that operate directly on in-memory data structures. The Lucene integration enables fulltext search. Any other Java libraries can also be seamlessly integrated for advanced search capabilities. These features make Eclipse Data Grid an ideal platform for processing large data volumes, performing real-time analytics, and executing business-specific algorithms without the overhead of moving data between systems.

A key differentiator is the off-heap bitmap indexing engine, which enables sub-millisecond search across billions of Java objects. These indexes operate independently of the JVM heap, providing both performance and memory efficiency.

In this architecture, Java assumes the role that stored procedures or proprietary scripting languages play in traditional databases. Developers can implement any business logic, algorithm, or transformation directly in Java, using familiar paradigms and tooling.

ACID-Compliant Persistence with EclipseStore

Persistence is handled by EclipseStore, a companion Eclipse Foundation project that provides ACID-compliant, object-graph-oriented persistence. Unlike traditional caching systems, which often rely on naive snapshot-based persistence, EclipseStore provides transactional consistency, journaling, and delta-based storage. This ensures that only modified objects are persisted, reducing write amplification and enabling fine-grained rollback and recovery capabilities.

By integrating EclipseStore, Eclipse Data Grid offers consistent, durable storage semantics across JVM nodes, and a convenient schema migration concept without object-flattening procedures. The system supports lazy loading of object graphs, fine-grained locking on object graph in memory, and optimized data formats tailored to the Java runtime.

This architectural model also addresses one of the major limitations of traditional distributed caches: their dependence on high volumes of RAM. In common caching solutions, all cached data must reside entirely in RAM to ensure low-latency access. If an application requires 128 GB of cached data, the infrastructure must provision at least 128 GB of free memory – often more due to meta data overhead and replication requirements. Furthermore, to avoid data loss during node failures, traditional caches employ sharding with replication, which further multiplies RAM needs. For instance, a replication factor of two would double the required RAM to 256 GB.

Eclipse Data Grid overcomes this limitation through its native integration with EclipseStore and the use of GigaMap, a high-performance, Java-native structure with built-in lazy loading. Data is automatically persisted and can be loaded into memory on demand. This means that only frequently accessed (hot) data needs to be kept in RAM, while the rest can remain on disk or in a BLOB storage like S3. Consequently, applications can work with datasets far larger than available memory – for example, managing 128 GB of data with nodes provisioned with just 16 GB of RAM each. Since EclipseStore avoids costly ORM mapping and operates on native object serialization, access to persisted data remains highly performant.

Distinguishing Eclipse Data Grid from Traditional Caching Solutions

Traditional distributed caches are often limited by their reliance on key-value semantics and their inability to process complex object graphs in memory. These systems typically store data in a serialized form, often as JSON or binary blobs, which breaks object references and requires developers to manage serialization and deserialization manually. This not only introduces performance penalties but also limits the expressiveness and type safety of in-memory operations.

In contrast, Eclipse Data Grid retains the full structure of Java object graphs in memory. Object integrity is preserved, reference cycles are supported, and collections behave as expected. This native representation allows for rich querying and data manipulation without leaving the Java type system.

Moreover, the use of Java Streams and third-party libraries enables more sophisticated querying and indexing mechanisms than traditional caches offer. While distributed caches might support rudimentary SQL-like query languages or map-reduce APIs, they generally fall short in supporting real-world business logic that demands flexibility, integration, and developer productivity.

By moving business logic directly into the in-memory layer, Eclipse Data Grid reduces reliance on heavyweight database engines, minimizes data movement, and eliminates the impedance mismatch between the object-oriented application and the underlying storage.

Architecture and Cluster Design

Eclipse Data Grid follows a writer-reader cluster model. At the core is a single writer node responsible for executing all write operations. This includes the execution of EclipseStore store methods, which persist object changes to a durable storage medium. Write operations are strictly ACID-compliant and isolated from read operations, which are delegated to one or more reader nodes.

The writer node acts as the authoritative source of truth. Once a store operation is executed, the updated object or object graph is serialized and published to a Kafka stream. Reader nodes consume this stream and merge the updates into their own local in-memory object graphs. This design ensures that readers operate on an eventually consistent view of the data, while maintaining full consistency on the writer node and local consistency on each reader.

All data access operations, including search, computation, and transformation, are performed on reader nodes. These nodes can be horizontally scaled to serve read-heavy workloads and are stateless with respect to write operations.

External applications and services interact with the data grid via RESTful APIs. Each node exposes a set of REST endpoints that can be generated from templates, allowing developers to implement complex operations in the in-memory layer and expose them without requiring custom client libraries. This approach promotes interoperability, enabling applications written in Python, JavaScript, Go, or other languages to leverage the grid without integrating a Java-based SDK.

The infrastructure can be deployed on-premises, in any cloud environment, or as a managed service. The only requirement for deployment is a Kubernetes cluster, which can be provisioned using Helm charts. MicroStream also provides a SaaS offering for evaluation, development, and testing, with support for elastic scaling and automated configuration.

Getting Started with Eclipse Data Grid

Adopting Eclipse Data Grid requires familiarity with core Java features for in-memory data processing. Developers should be proficient in designing domain models, working with Java Streams, and utilizing modern concurrency features such as virtual threads where applicable.

The first step is understanding EclipseStore and its persistence model. Developers should learn how to configure object graph storage, perform CRUD operations, implement indexing strategies, and utilize lazy loading and locking APIs. Once this foundation is established, the next step is to understand the architecture of the Eclipse Data Grid cluster, including the roles of writer and reader nodes, Kafka integration, and REST endpoint generation.

A strong understanding of the Java language, its standard library, and available open-source tools for data processing and search will significantly improve the effectiveness of in-memory applications built on this platform.

Target Use Cases and Application Domains

Eclipse Data Grid is designed to address a broad set of performance- and complexity-related challenges in enterprise applications. It is particularly well-suited for systems experiencing performance issues due to ORM overheads, complex SQL joins, or latency introduced by multiple distributed components. Applications built with Hibernate or JPA that suffer from N+1 query problems, excessive serialization, or lack of control over fetch behavior can benefit significantly from the grid’s native object graph handling.

In scenarios where NoSQL databases are used but schema flexibility and consistency guarantees are still needed, Eclipse Data Grid offers a compelling alternative by enabling flexible object modeling with strong transactional semantics.

The platform also excels in use cases involving data analytics, real-time reporting, session management, and micro-batch or event-driven processing. Its ability to execute custom logic in memory makes it particularly valuable for pre-processing data in machine learning pipelines, simulating complex workflows, or performing fine-grained filtering on incoming data streams.

Finally, from a cost and maintenance perspective, Eclipse Data Grid simplifies application architecture by reducing the number of external systems required. By consolidating caching, persistence, and computation into a unified, Java-native layer, it eliminates the need for separate caches, search engines, and data transformation services, thereby lowering both operational complexity and licensing costs.

Conclusion

Eclipse Data Grid represents a new generation of in-memory data processing platforms tailored for Java developers. By enabling complex, application-specific logic to run entirely within memory and by supporting ACID-compliant persistence of full Java object graphs, it bridges the gap between the performance requirements of modern applications and the limitations of traditional database technologies. Its architecture supports scalable, distributed deployments, facilitates integration through REST APIs, and empowers developers to leverage the Java ecosystem to its fullest. For organizations facing challenges in data performance, architectural sprawl, or database cost management, Eclipse Data Grid offers a robust and future-proof solution.

Eclipse Data Grid: Getting Started
https://microstream.one/blog/2025/07/09/eclipse-data-grid-getting-started/