In this day and age, the need for constant communication and easy access to information is a foregone conclusion. The constantly growing amounts of data have pushed organizations to rethink the way they manage data to come up with an approach that can provide high-speed processing without sacrificing performance. The value of big data is no longer confined to analysts and has now become a focus of every forward-thinking organization. Management has been pushing IT teams to come up with ways or develop tooIs that will provide what’s needed to address business needs and customer demands.
In-memory data grids (IMDGs) provide a viable solution for organizations because they present an approach that’s different from the way organizations usually manage their data. Big data has progressed with great speed through the years, but the price of this progress is increasing workloads and more demanding customers. As the data that needs to be processed and analyzed gets larger, organizations also have to contend with its growing complexity. In-memory data grids can address these issues by providing super fast data processing at a competitive cost.
What are In-memory Data Grids?
IMDGs provide the fast data processing speeds needed today by using RAM as its main storage component. They run specialized software on each computer within a cluster to allow applications to share data easily while minimizing latency and maximizing throughput. By limiting data movement to and from disk and within the network, bottlenecks are avoided and data integrity is preserved. IMDGs are more than just storage solutions that use RAM, they are designed for large-scale applications that require more RAM than is usually found in a server. By combining the compute capabilities of a multitude of computers within a network or grid, an IMDG ensures the highest possible application performance.
Although an IMDG uses several computers, each computer has its very own data structures, the view of which is shared across the network. The specialized software run by the IMDG keeps track of the data in each node to ensure seamless access and sharing to applications or other nodes. This ensures synchronicity of data within each cluster and across the entire network, which addresses the challenges related to complex data updating and retrieval. This, in turn, allows organizations to speed up application development while also increasing overall system efficiency.
Old Tech, New Approach
In-memory computing is by no means a new technology or approach. RAM has been used for years to increase the workload performance of applications and other services, and it has been part of computing architectures since the mainframe era. Distributed caches and in-memory caching have also been around for more than 20 years. More than accelerating storage I/O, leveraging in-memory solutions not only boosts application performance but also allows it to scale at less the cost. Ironically, maximizing the platform to gain these benefits can be expensive and complex. New solutions are designed to address these challenges, and you should look for one that provides a good balance of being cost-effective and providing data efficacy and safety even when using virtualized environments or the cloud.
Data replication is also a consideration when choosing an in-memory solution because it ensures low latency even in large computer grids, allowing for quick access to and easy sharing of data. This also protects data from component failure and allows for additional features like failover, high availability, and data synchronization between clients. Working with an in-memory data grid is a cost-effective solution because it also brings with it the following benefits:
- Fast transactions (writes)
To avoid data consistency issues and performance drawbacks, in-memory data grids provide the capability to optimize transactions to support ACID (atomicity, consistency, isolation, durability) properties.
- Simple lookups (reads)
Occurring more frequently especially as a common method of data access for microservices, lookups or reads retrieve small amounts of data from the larger dataset. An in-memory data grid ensures that this is a seamless process by minimizing the need to access disk-based storage, allowing for high throughput and low latency overall.
- Session state data
In-memory data grids are known for their high availability and easy scalability, and session state data helps in scaling a data grid up or down through the addition or removal of instances of microservices. It has a short lifespan that ends when each session ends, but it also has the capability to create new instances that can start where a failed one left off.
- MapReduce-style processing
MapReduce executes code to the same location where data resides to reduce data movement and eliminate the need to move data first before processing and analysis. The local execution of code through appropriate data partitions also allows parallel processing that helps in speeding up date processing.
Ultimately, IT infrastructure productivity should be at the highest level while keeping the level of complexity as low as possible. The in-memory data grid is a cost-effective performance architecture because it keeps the active data of applications as close as possible to the CPU’s and provides optimal input-output processors (IOP’s) for virtualized computing environments.