In-Memory Database

What is an In-Memory Database?

An in-memory database (IMDB) is a data management system that stores data primarily in the computer’s main memory. This is unlike traditional relational databases that rely on solid-state drives (SSDs) or spinning disks for data storage. IMDBs allow mission-critical applications to benefit from faster response times than disk-based databases. People occasionally refer to an IMDB as an in-memory database management system (IMDBMS).

Traditional relational databases are ubiquitous, but their disk-based technology is an obstacle to reaching real-time responses. Some in-memory computing approaches such as data grids achieve notable speed but lack full SQL support. Their implementation requires changes in existing applications designed for relational databases. IMDBs excel in providing the best of data grids and relational databases, combining RAM speeds with full ANSI SQL support.

ANSI-99 SQL and ACID Transactions

SQL is a standard introduced in 1986 by the American National Standards Institute (ANSI). It was last updated in 2016. The standard ANSI-99 consolidated many of the specifications that form the core of most relational databases used today. These specifications include commands for a data definition language (DDL), a data modification language (DML), a query syntax, and transactional commands.

IMDBs are also ACID compliant, meaning they enforce strong data consistency through the four critical properties of a transaction: atomicity, consistency, isolation, and durability (ACID). In practice, an ACID-compliant IMDB ensures that the data distributed across clusters remains consistent, even after a power failure, network problem, or validation errors.

Why are In-Memory Databases Useful?

IMDBs are best suited for new, high-performance applications designed to query and update data with the power of the SQL language and existing applications that rely on a traditional relational database but require an upgrade to real-time data access technology.

Real-World Use

Use cases that require low latency, real-time responses, and a relational IMDB include:

• Streaming apps for environments where various sources generate continuous data flow, which streaming platforms must process, store, and analyze in real-time. Streaming applications use real-time computing to perform analytics and high-performance queries to find events of interest.
• Call center apps to deliver great customer experiences. Contact center management solutions can benefit from an IMDBMS performing key metrics tracking, intelligent routing, live chat, and real-time monitoring.
• Reservation apps that use IMDBMS to help reservation applications make real-time decisions using low latency queries and massive data sets based on current bookings and customer absences.

Apache Ignite and GridGain as In-Memory Database

Apache Ignite is a distributed database for high-performance computing with in-memory speed. Application developers frequently choose this solution for their in-memory database use cases.

Like many distributed in-memory databases, Ignite supports the familiar SQL language that lets developers leverage their experience with queries and DDL commands to manipulate the database schema. Ignite also supports key-value queries and other essential APIs for data processing. As well, developers can configure Ignite to work in a pure in-memory mode for the IMDB use case.

Unlike typical distributed in-memory databases, Ignite native persistence is implemented similarly to classical relational databases, letting you scale beyond available memory capacity. The disk tier keeps your fully operational data set on disk while putting only a subset of data in memory. Organizations can use a combination of memory and disk space to take advantage of hybrid transactional and analytical processing while adjusting the amount of data in memory to balance costs and performance.

The open-source Apache Ignite project provides the foundation for GridGain. The enterprise-grade GridGain in-memory computing platform enables developers to deploy Ignite securely on a global scale, without any downtime.

Conclusion

The rapid reduction of memory costs accelerated the adoption of in-memory computing technologies, such as caches, data grids, and IMDBs. But, as developers, we understand that technology alone is not enough for a business to succeed. We need to stay informed about what architecture, tools, and services are best to address our company’s IMDB needs. We have to be aware of new approaches that deliver real-time experiences without disrupting internal systems or requiring us to switch to an entirely new technical stack.

As the leading edge of in-memory computing, Apache Ignite and GridGain help us address those needs. To learn more about how to leverage the power of Apache Ignite, try GridGain Nebula for free and discover how to solve your database challenges to become production-ready and cloud-native.