Traditional databases may efficiently store data on disk but the problem is that they are slow. With all the data generated today, new solutions are needed to cope with storing, retrieving and processing large data volumes at speed.
If you’ve played online games, purchased from a major e-commerce store or used a credit card, you have already used in-memory technology. The technology moves data completely into memory to avoid the latency associated with accessing information stored in a disk-based database.
Storing data in main memory
A change from disk storage to storing data in main memory means it becomes quick and easy to access, manipulate and analyze. Its technological advances and a reduction in the costs of main memory costs that now allow for the storage of large data amounts in main memory.
Every time users query data or update it, they can do so directly from the main memory, which is much faster than using the disk. There is no need to access secondary memory and navigate the whole stack when wanting to read or write records. Eliminating the need to access slower secondary storage allows for the use of algorithms in an in-memory database that wouldn’t be feasible for a disk-based database.
An in-memory database (IMDB) is not the only approach to storing information for instant access. An in memory data grid (IMDG) is a distributed system that can store and process data in memory to boost the speed and scalability of an application without making changes to the existing database. It allows scaling simply by adding new RAM, which is the fastest, easiest way to increase capacity without significantly changing system architecture.
There are a number of low-level technical differences between an IMDB and an IMDG which is designed to handle intensive data processing applications. IMDB applications usually process smaller data blocks at a time since applications need to read data from the IMDB and write it back after processing.
Distributed data infrastructure
Traditional databases store structured data which is well organized with concrete records. A weakness is a lack of adaptability and difficulty in the storage and processing of large data amounts. In-memory database architectures require a management system that uses the computer’s main memory as the primary location to store and access data.
An in-memory database has a distributed data infrastructure. A cluster of computers working in parallel means more storage, the better transfer speed of unstructured data and quicker processing. Managing and controlling unstructured data is an increasing challenge for many companies today and an in-memory database provides a solution.
Latency is an issue in today’s high-speed 5G environments. It is the lag between a user action and the response of an application to the action. Disk latency is measured in milliseconds, whereas in-memory latency is measured in nanoseconds. An in-memory database is essential for applications that need low latency and real-time performance.
Analytics that previously took hours to run can now be completed in seconds which enables real-time business decisions before data loses its value. This can help to prevent revenue leakage and unlock hidden revenue sources.
Data is ready to use
Data in an in-memory database is in a directly usable format, unlike traditional in-disk databases that use encrypted or compressed data that isn’t immediately usable.
In-memory databases are also structured to allow for efficient navigation independent of disk blocking issues. It allows for direct navigation from index to row, row to row or column to column without slowing down. Changes are implemented by rearranging points and allocating memory blocks.
Supports ACID transactions
In-memory databases commonly support all the other ACID transactions: atomic, consistent, and isolated, but durability is a problem. Immediate transactional consistency means applications can make accurate decisions involving shared resources at a massive scale, which is especially helpful in 5G environments.
As in-memory databases store all data in volatile memory, a power outage or RAM crash can cause data loss. This makes data non-durable but it is possible to mitigate this problem in various ways, such as using a flash drive or persisting data to a disk. If a database is opened using the persistent in-memory mode, changed content is automatically written to secondary storage when the database is closed.
Applications of an in-memory database
Using an in-memory database is best when data persistence isn’t a high priority. In-memory databases are frequently used in banking, online gaming, mobile advertising and telecommunications.
Retail, advertising and e-commerce often make use of in-memory databases. An example would be a high-traffic e-commerce site that stores shopping cart contents for thousands of customers at any given time. Response times at the scale would be too slow for a traditional database. An in-memory database is able to keep up and ensure a positive customer experience.
Another example use case would be when using business intelligence analytics, where data is retrieved and presented in a dashboard. Using an in-memory database allows users to quickly access data so they spend less time waiting for the system to respond and more time analyzing data and making decisions.
In-memory databases are also used to detect data anomalies as they occur and block fraudulent traffic before it overwhelms a network.
Applications requiring real-time data such as call center apps, streaming apps, travel apps, reservations apps and use of an LMS also work well when using in-memory database management systems.
The cloud and an IMDB
Combining cloud and in-memory computing offers a great way to maximize the i-memory benefits. A cloud environment allows organizations the opportunity to access large amounts of RAM and it can also help to make in-memory storage more reliable.
The bottom line
A database is a vital part of any data platform, and one equipped with an in-memory database is a powerful tool to unlock the value of data in real-time. They are extremely useful when there’s a need to quickly and frequently access data and are ideal for environments that demand real-time responses when handling large amounts of traffic and unplanned spikes in usage.