Traditional databases can efficiently store data on disk, but the problem is that they are slow. With all of the data that is generated today, new solutions are required to quickly store, access and process large amounts of data.
If you’ve played online games, bought from a major e-commerce store, or used a credit card, you have already used in-memory technology. The technology moves data entirely into memory to avoid the latency of accessing information stored in a disk-based database.
Store data in main memory
Switching from disk storage to storing data in main memory enables quick and easy access, manipulation and analysis. Its technological advances and a reduction in main memory costs now enable large amounts of data to be stored in main memory.
Every time users query or update data, they can do so directly from main memory, which is much faster than using the hard drive. There is no need to access secondary storage and navigate the entire stack when reading or writing records. The elimination of the need to access slower secondary storage enables algorithms to be used in an in-memory database that a disk-based database would not.
An in-memory database (IMDB) is not the only approach to storing information for immediate access. A in the storage data grid (IMDG) is a distributed system that can store and process data in memory to increase the speed and scalability of an application without making changes to the existing database. It allows you to scale up simply by adding new RAM, which is the fastest and easiest way to increase capacity without significantly changing the system architecture.
There are a number of low-level technical differences between an IMDB and an IMDG designed for heavy data processing applications. IMDB applications usually process smaller blocks of data at the same time, as applications have to read data from the IMDB and write it back after processing.
Distributed data infrastructure
Traditional databases store structured data that is well organized with specific data sets. A weakness is a lack of adaptability and difficulties in storing and processing large amounts of data. In-memory database architectures require a management system that uses the computer’s main memory as the primary location for storing and accessing data.
An in-memory database has a distributed data infrastructure. A cluster of computers working in parallel means more storage space, better transmission speed of unstructured data and faster processing. The management and control of unstructured data is an increasing challenge for many companies today and an in-memory database offers a solution.
Minimal latency
Latency is an issue in this day and age High speed 5G environments. It is the delay between a user action and an application responding to the action. Hard drive latency is measured in milliseconds, while in-memory latency is measured in nanoseconds. An in-memory database is essential for applications that require low latency and real-time performance.
Real-time performance
Analytics that used to take hours can now be completed in seconds, enabling real-time business decisions before data loses its value. This can help prevent lost revenue and unlock hidden revenue streams.
Data is ready to use
Data in an in-memory database is in an out-of-the-box format, unlike traditional in-disk databases that use encrypted or compressed data that are not immediately usable.
In-memory databases are also structured to allow efficient navigation regardless of hard drive locking issues. It allows direct navigation from index to row, row to row or column to column without slowing down. Changes are implemented by rearranging points and assigning blocks of memory.
Supports ACID transactions
In-memory databases generally support all other ACID transactions: atomic, consistent, and isolated, but durability is an issue. Instant transaction consistency means that large-scale applications can make accurate decisions with shared resources, which is particularly useful in 5G environments.
Since in-memory databases store all data in volatile memory, a power failure or RAM crash can lead to data loss. This does not make data permanent, but it is possible to mitigate this problem in a number of ways, such as: By using a flash drive or storing data on a hard drive. If a database is opened in persistent in-memory mode, changed contents are 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 is not a high priority. In-memory databases are widely used in banking, online gaming, mobile advertising, and telecommunications.
Retail, advertising and e-commerce often use in-memory databases. An example would be a busy e-commerce site that stores the contents of shopping carts for thousands of customers at one point in time. Response times on the order of magnitude would be too slow for a conventional database. An in-memory database keeps pace and ensures a positive customer experience.
Another use case would be the use of business intelligence analytics, where data is retrieved and presented in a dashboard. Using an in-memory database allows users to access data quickly, 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 instantly detect data anomalies and block fraudulent traffic before it overwhelms a network.
Applications that require real-time data such as call center apps, streaming apps, travel apps, reservation apps, and Use of an LMS also work well when using in-memory database management systems.
The cloud and an IMDB
The combination of cloud and in-memory computing is a great way to maximize the benefits of i-Memory. A cloud environment gives businesses the ability to access large amounts of RAM and can also help make in-memory storage more reliable.
The bottom line
A database is an important part of any data platform, and one with an in-memory database is a powerful tool for unlocking the value of data in real time. They are extremely useful when data needs to be accessed quickly and frequently, and are ideal for environments that require real-time responses when processing large amounts of data and unplanned usage spikes.
