The GridGain in-memory computing platform built on Apache Ignite powers the scaling of web applications and other data-intensive applications. Cloud computing, big data, social, and mobility are four industry megatrends driven by millions and billions of users and their devices. End users expect fast and consistent response times from their applications, no matter where they are or what device they are using. Application performance is critical. Hence, architects and developers of next generation apps need to think about how to speed up their apps, increase data throughput and decrease latency.
Unlike traditional, centralized scale-up enterprise systems, infrastructure for modern applications that can scale globally is characterized by a distributed in-memory scale-out architecture. The architecture can take advantage of cost-effective, high volume, commodity hardware that maximizes compute power efficiently. The use of commodity hardware and open source in-memory computing software and tools ensures that new projects can launch quickly without the usual complexity of licensing terms and budgets.
In-Memory Computing for Scaling Applications
In-memory computing is defined by data stored in RAM across a scale-out cluster of computers. This contrasts with traditional centralized database management systems (DBMS) that utilize disk as their primary storage mechanism. Applications using in-memory computing can typically process data orders of magnitude faster than traditional DBMS by utilizing highly parallelized system memory rather than spinning disks. RAM can deliver much faster response times and greater capacity while significantly improving reliability and dramatically reducing power consumption. More importantly, disk performance has not kept up with performance advances in CPU and memory speed so I/O and network limitations often become the performance bottleneck for high-performance, hyperscale applications.
Developers, architects, devops and datacenter managers want freedom when designing and running high-performance, hyperscale application for their environment. To address this need, in-memory solutions must be more than just a caching layer or data grid, bolted on a single, existing database. Instead, a comprehensive data fabric offers tremendous flexibility with respect to the development language for the application, the types of data stores the application can talk to, and the data processing QoS levels (i.e., whether the application is real-time/streaming, interactive or batch-oriented like in today’s Hadoop clusters). Only a comprehensive in-memory data fabric that is flexible, manageable, secure and extensible can meet the needs of modern hyperscale applications.
The GridGain In-Memory Data Fabric for Scaling Applications
GridGain empowers scaling of web applications and other data-intensive applications by providing the leading enterprise-grade GridGain In-Memory Data Fabric. It powers high-volume transactional applications, real-time analytics applications, and the new breed of hybrid transactional-analytical applications. Using the GridGain In-Memory Data Fabric, applications scale up and out without compromising end-user response times. They use less hardware, power, cooling and data center space. Application response times and throughput are dramatically improved by caching the most frequently requested data. As a result, service level agreements (SLAs) can easily be met for tier-1 business-critical applications with a significant increase in application performance across the board.