Solving the Top Challenges of Legacy RDBMS Architectures with GridGain 9

Introduction 

Relational Database Management Systems (RDBMS) have dominated enterprise IT data infrastructure for decades, serving as the backbone for countless applications. Today, many applications still run on legacy RDBMS, which face significant challenges in meeting the demands of today's data-driven world. As enterprises increasingly require real-time analytics, artificial intelligence (AI), and the ability to handle ever-growing data volumes, the limitations of traditional RDBMS become apparent. The GridGain Unified Real-Time Data Platform offers a solution to these challenges, significantly accelerating legacy systems and addressing their limitations. The new version of the platform, GridGain 9, makes it easier than ever to overcome the constraints of legacy RDBMS with improved SQL capabilities, stronger consistency, and better developer experience.

Challenges of Legacy RDBMS

Legacy RDBMS face several significant challenges in the modern data landscape:

1. Scalability: Horizontal scaling of legacy RDBMS is notoriously difficult. As data volumes and transaction loads increase, traditional RDBMS struggle to keep up, leading to performance bottlenecks and increased latency.
2. Real-time Processing: Modern applications demand ultra-low latency, often on the scale of milliseconds or even faster. Traditional RDBMS architectures are not optimized for such rapid data processing, making it hard to meet these real-time requirements.
3. Multi-model and Hybrid Workloads: Contemporary systems frequently need to integrate and process various types of data, combining transactional processing, historical analytics, stream processing, and AI. Legacy RDBMS are typically not equipped to handle such diverse and complex workloads efficiently.
4. Cloud Transformation: Moving legacy RDBMS to the cloud, especially in a multi-cloud setup, is a complex and resource-intensive process. These systems are often tightly coupled with on-premise infrastructure, making cloud adoption challenging.

How GridGain Addresses Challenges of Legacy RDBMS

GridGain offers innovative solutions to the inherent limitations of legacy RDBMS:

1. Horizontally Scalable Distributed Store and Compute Grid: GridGain’s architecture is designed for horizontal scalability, enabling seamless expansion to accommodate increasing data volumes and transaction loads without sacrificing performance.
2. In-Memory Speed with On-Disk Persistence: By leveraging a memory-centric storage approach, GridGain offers rapid data access and processing while maintaining durability of an on-disk store. This ensures both speed and reliability.
3. Support for SQL and NoSQL Data Models: GridGain enables a wide range of data processing capabilities, supporting both SQL and NoSQL models. This versatility allows for transactional applications, batch and streaming analytics, and AI model execution.
4. Flexibility in Deployment: GridGain can be deployed across various environments, including public clouds, private clouds, multi-cloud setups, on-premise systems, and even mainframes. This flexibility facilitates IT transformation and cloud adoption.

No Rip-and-Replace Database Acceleration with GridGain

GridGain enables the acceleration of existing workloads without requiring a rip-and-replace migration from legacy systems. It can function both as an in-memory cache or as a standalone system of record.

1. In-Memory Cache: When operating as an in-memory cache, GridGain acts as a layer between applications and existing RDBMS, utilizing the RDBMS as the persistence layer. This setup accelerates applications without impacting all legacy systems simultaneously.
2. Standalone System of Record: When functioning as a standalone system of record, GridGain leverages its own on-disk persistence capabilities for data durability. This integration fits seamlessly into existing architectures while eliminating the limitations of legacy tools.

GridGain’s caching and system-of-record capabilities support an iterative approach to evolving enterprise IT infrastructure. Initially, caching can be used to gradually transition performance-sensitive applications to the new platform. Once all applications are ready, the legacy RDBMS can be entirely replaced with GridGain’s persistence layer.

How GridGain Enables Acceleration & Modernization of Legacy Applications

Figure 1. GridGain Deployment Options For RDBMS Acceleration.

GridGan can accelerate legacy RDBMS with gradual modernization instead of a rip-and-replace change. This is because GridGain can operate as a standalone system-of-record or as an in-memory cache between applications and existing RDBMS. By using GridGain as a cache, applications can benefit from GridGain without the need to completely replace the existing RDBMS.

Whether GridGain is deployed as a standalone system-of-record or as a cache, legacy applications can use it without architecture changes thanks to GridGain’s capabilities that match and expand RDBMS functionality:

1. SQL Compliance: Unlike other distributed systems that offer a SQL-like language, GridGain supports ANSI SQL, ensuring compatibility with existing SQL applications and simplifying migration.
2. Durability and Consistency: GridGain provides on-disk persistence with ACID transactions, offering true durability and a strictly serializable transactional model—the highest level of data consistency. This contrasts with other systems that may only claim transactional support without providing full consistency.
3. Server-side Computations: GridGain’s colocated compute capabilities surpass traditional stored procedures, offering powerful data processing tools that enhance performance and functionality.

How GridGain 9 Makes RDBMS Acceleration Easier Than Ever

GridGain 9 introduces several enhancements to streamline the acceleration of legacy RDBMS:

1. Upgraded SQL Support: GridGain 9 supports ANSI SQL 2016, facilitating the migration of advanced SQL applications that utilize modern SQL features. This compatibility enhances the ease of transitioning to GridGain.
2. Strictly Serializable Transactions: Transparent use of strictly serializable transactions across SQL and NoSQL APIs allows for seamless migration of transactional SQL applications, ensuring data consistency and reliability.
3. Enhanced Performance: GridGain 9 introduces a new SQL engine and upgraded storage engines, delivering better performance with less tuning. This enhancement simplifies the optimization process and boosts overall system efficiency.
4. Improved Scalability: New core distributed algorithms in GridGain 9 enable on-demand scaling, ensuring stability both on-premise and in the cloud. This improvement supports seamless scalability to meet growing business needs.
5. Enhanced Developer Experience: GridGain 9 offers updated installation packages, modernized APIs, and a new interactive command-line interface, improving the overall developer experience and accelerating development and deployment processes.

Conclusion

The GridGain Platform, with its latest version, GridGain 9, represents a significant leap forward in accelerating legacy RDBMS systems. By integrating in-memory computing capabilities, GridGain addresses critical performance, scalability, and real-time processing challenges faced by traditional databases. It provides a clear path for easy and gradual modernization with its powerful distributed SQL and no rip-and-replace approach. For organizations looking to maintain their competitive edge while maximizing the value of their existing IT investments, GridGain offers a compelling solution that drives both operational efficiency and strategic growth.