In-memory computing promises to deliver faster real-time application performance and scalability by replicating your data to an in-memory data store. Since the data doesn’t need to be moved between disk storage to memory, data processing can be as much as 1000x faster.
By combining in-memory computing and distributed processing, clusters can use massively parallel processing to speed up processing while creating room for faster and easier horizontal scaling.
Three main in-memory platforms dominate the market: Redis, Gigaspaces, and Gridgain. Though they accomplish the same task, they have different ways of accomplishing it, which may make all the difference in terms of speed.
GigaSpaces was one of the first companies to offer in-memory computing services commercially. Building on top of its original 2004 design, the company released its iconic XAP 12 brand, an open-source in-memory computing platform.
XAP 12 is a high-performance data grid that was designed for high throughput and low latency applications. It is a popular solution for industries with intensive I/O operations such as the banking sector (Barclays, Deutsche, and Morgan Stanley) and with networking companies such as Cisco.
The XAP framework is built on top of Spring and OpenSpaces. OpenSpaces is a GigaSpaces-built API that aims to make Space-based development faster, cheaper and scalable.
The OpenSpaces API allows developers to build and configure the program’s main features: caching, parallelization, messaging, real-time processing and full support for rich query semantics, data indexing and transactions. The same API also makes Gigaspaces extensible for both translytical and normal workloads such as caching.
Gigaspaces InsightEdge is the company’s offering of XAP and Apache Spark, both of which pair up together to deliver fast processing and data analytics. Its open-source nature also allows developers to use external ETL tools to load and transform data to a target model.
Gridgain is both an open-source software and the name of the company that offers a hosted version of the same. It is an in-memory computing platform built on top of Apache Ignite. It comes bundled with a distributed in-memory data grid, a key-value in-memory database that supports SQL, an engine for stream processing and data analytics, and a machine learning framework that supports real-time deep learning.
Where Gridgain shines is its power as a shared data and processing framework. It offers support for processing across projects for quick delivery of results through its in-memory data grid.
By quickly sliding between the application and underlying databases without the need for disk reads or writes, companies can deliver high-volume, low-latency analytics and transactions. This enables the running of complex data analytics, and transactions and hybrid transactional and analytical processing (HTAP).
Thanks to its application of the relatively newer and more advanced Apache Ignite, it comes with features that make it faster to deploy and maintain with an easier computing environment to manage.
At the enterprise level, data-intensive applications can scale out to millions of transactions per second with low query times. Its fast processing speeds have found use in telecom, e-commerce, healthcare, and SaaS applications.
Redis is perhaps the best-known in-memory framework in the world. It is well-liked among developers because of its comparatively simple API and interoperability with every computing language.
The underlying framework is written in C, but has been ported to most modern programming languages, including C, Python, C++, Java and Go. Besides, its API is so simple to understand, getting it running on a new or existing project will take a very short time for even relatively new programmers. The concepts behind it are more complex to understand and are perhaps required for tasks like cache invalidation, but most SMBs don’t require them.
The in-memory database supports several data structures, including bitmaps, geospatial indexes, streams, radius queries, and simpler types, including strings, lists, and sets. Its in-memory capability is commonly used as a simple cache but can also be used as a message broker. It also comes with features like built-in replication, LRU cache eviction, and on-disk persistence. The latter feature can be used
Redis data types are optimized to use up less space to improve memory efficiency and promote faster read/write operations. It can be used for real-time applications like gaming, transaction processing, and video streaming.
Its usage has also been promoted by its adoption by popular companies like Snapchat, Pinterest, Github, Stackoverflow and Microsoft.