As in-memory computing (IMDG) becomes more popular with different businesses and industries, distributed caching has seen less exposure and usage in recent years. Both technologies still hold the interest of data-centric organizations, but the IMDG has shown that it can do distributed caching and much more. Whereas distributed caches are simply caches distributed across multiple nodes, an IMDG adds computing power so that collocated processing, distributed databases, and more are also possible.
With the advent of in-memory computing, there are fewer reasons to cache intermediate database results in memory because today’s data sources are adept enough to do that themselves. Even NoSQL databases have shown that it’s possible ”even ideal ”to do in-memory and comprehensive caching without the help of a distributed cache, along with other in-memory capabilities.
Although the distributed cache might arguably be on its way out, it’s important to know the main differences between these two technologies so distributed caching can be put in the right historical context. A distributed cache provided a boost in performance relative to costs in the past, but the landscape is simply different today and the benefits are not that significant.
What is a Distributed Cache?
High availability is the main objective of a distributed cache, and one of the main reasons it came to life in the first place. Distributed caching keeps frequently accessed data in process memory so they are easily accessible without repeated access to the disk.
Caches were typically designed as distributed key/value stores so they can support simple sets of put and get operations. For more complex processes, there was an option to enable read-through and write-through instances so caches could write and read values to and from disk-based storage. A variety of implementations allowed for more advanced features, including replication, eviction policies, active backups, and ACID transactions.
Distributed caching holds a vital place in computing history books because it paved the way for current technologies. Its fundamental data management prowess provided the foundation for technologies that came after it. In-memory data grids were built on top of a distributed cache infrastructure.
What is an In-memory Data Grid?
An in-memory data grid or IMDG is a network of computers that work together while combining and sharing their RAM across the network so applications can use them to share data within the same network. Specialized software is run on each computer to enable the sharing of RAM and computing power and avoid constant access to disk. An IMDG helps minimize data movement within the network and to and from disk, minimizing bottlenecks typically caused by disk-based storage. Synchronizing data within each cluster and across the network addresses a common challenge brought about by the complexity of data updating and retrieval, which allows businesses to speed up application development.
IMDG are more than just storage solutions; they are designed to process complex data at high speeds and for large-scale implementations that require more RAM than usual. To ensure the highest possible performance of applications, IMDG’s work with the combined RAM and computing power of all available computers within the network. It also helps that both the application and its data collocate in the same memory space, minimizing latency and maximizing throughput.
A cost-effective solution for businesses, IMDG does away with the complex processes involved in the handling of disk-based databases. Scaling an IMDG implementation is also as simple as adding a new node to a cluster of server nodes. Because an IMDG isn’t dependent on a centralized server and uses parallelized distributed processing, it allows for shared computer processing to multiple computers across various locations.
In-memory and Into the Future
The future of in-memory data grids is a very interesting one; early adopters readily jumped into it due to the platform’s simplicity and ease of use, but IMDG users are transitioning into power users, where they are now taking advantage of more sophisticated features and have higher expectations on what the the IMDG can deliver.
As analytics and machine learning capabilities continue to develop, it might not be long before AI-powered IMDG’s completely bridge the gap between analytics and transactional processing. Even now, some companies already have a unified transactional/analytical processing (HTAP) system in place, and the IMDG has been instrumental in the development of this and Translytical data platforms. These allow for real-time analytics without the need to replicate operational data so that rear-view mirror analytics is prevented.
The future, in not so many words, is moving away from pure data management and moving into the era of hybrid data and compute management. Rethinking distributed caches led to the addition of distributed SQL and MapReduce-type processing, which disrupted the industry and brought about the dawn of the in-memory data grid revolution.
