Today there is a wide range of data generated from all kinds of sources like mobile devices and this data can be structured or unstructured. Data management needs new solutions for coping with the challenges of data volumes and processing data in real-time.
Data analysis using traditional databases is not sufficient to store, retrieve and process large data sets at speed. An in-memory database system (IMDS) is an answer to the challenges and is able to process massive data much faster than traditional databases. When traditional databases reach their limits, in-memory databases are useful.
Traditional database
Traditional databases store all data on disk and disk I/0 is needed to move data into main memory as required. Traditional data structures are designed to store tables and indices efficiently on disk. Database size is virtually unlimited and a traditional database can support a range of workloads.
The problem is that a traditional database is slow. There have been attempts to improve on this by introducing various caching techniques to cache frequently accessed data. This comes at the cost of synchronization of cache with disk and vice versa. Having to implement various complex ways to manage transactions and resources is in itself a limitation to performance.
In-memory database
An in-memory database is also referred to as an in-memory database system (IMDS) or a real-time database (RTDB). The in-memory database system concept has changed the whole architecture paradigm when it comes to data management systems. Storing data entirely in main memory offers easy access to data and the ability to manipulate and analyze it speedily.
With decreasing cost of main memory and advanced technological innovations, it becomes feasible to store a large amount of data in main memory. This drastically improves the speed of reading and writing data. Having all the data in main memory means that every time someone queries or updates data in the database, only the main memory needs to be accessed and there is no disk involved. This is a good thing as main memory is much faster than disk.
The system loads the whole dataset from the hard disk into working memory so no data needs to be loaded while the database is running. The database reviews and adjusts the data often, so if data changes, it remains up to date. Current changes are stored in transaction logs and if an error occurs, it is possible to restore the database to the time before the error occurred. Data is continuously copied from the database to a computer or server as a backup.
An in-memory data grid (IMDG) is different from an in-memory database. It can boost an application’s speed and scalability by tweaking the application without making changes to an existing database.
Some comparisons
A traditional database retrieves data from disk drives while an in-memory database stores data in the main memory of a computer. This means it is optimized for fast performance as there is no necessity to perform disk I/O to query or update data.
Where data in a disk block of a traditional database may be encrypted, flattened, compressed or encoded, data in-memory is in directly usable format. Its structure allows for direct navigation, and changes can be implemented by allocating memory blocks or rearranging pointers.
Both traditional and in-memory databases are processed according to ACID principles of atomicity, consistency, isolation, and durability.
The volume of data stored in a traditional database is generally larger than what can be stored in an in-memory database.
It is a misconception that traditional database systems can offer the same performance by changing the datastore from disk to main memory and that an in-memory database is nothing but a traditional database with the data store in RAM only. This isn’t true. It is not only data access operations that are faster with main memory-based design, but many other optimizations are also possible.
Traditional databases store only structured data. Structured data is clearly organized and defined in concrete data records. A weakness is the difficulty in storing and processing large amounts of data and a lack of adaptability.
Today it is possible to evaluate structured and unstructured data from any system with an in-memory database. This is due to a distributed data infrastructure. A cluster of computers work in parallel and data is distributed, resulting in more storage, quicker processing, and better transfer speed of unstructured data.
Managing and controlling unstructured data is one of the most challenging data security issues for companies. How to hire the best cybersecurity assessment services is a common concern.
Applications
The type of memory used with an in-memory database system isn’t persistent. When an application closes, the data stored in an in-memory database is lost. Several approaches, such as replication and persistence, can help to mitigate the loss. For applications that need a high degree of data persistence between runs, a traditional database may be more suitable.
One of the potential hurdles with in-memory databases is that RAM is volatile. In-memory databases are the right way to go if data persistence is not of the highest priority and the possibility of the loss of data is workable. In-memory databases are essential for applications that require real-time performance and low latency.
Some common uses include:
- Trading applications and banking
- Online interactive gaming
- Weather forecasting
- Medical device analytics
- Geospatial processing
- Retail and advertising
- eCommerce applications
- Processing of streaming sensor data
- Applications in transport systems, network switches and routers
- Telecom switching
Conclusion
There are many advantages that come from using an in-memory database. An in-memory database offers real-time access and can handle structured and unstructured data. If companies work with big data, an in-memory database is essential. In-memory databases are also useful if a company needs frequent and fast access to data and existing database servers or management systems are overloaded. However, effective use of in-memory databases requires standardized systems for data backup to be integrated into the in-memory database processes.
