What is In-Memory Database (IMDBs), how does it work and the advantages?
What is an in-memory database?
An in-memory database is a data storage system that holds all of its data in the main memory of the host. The main difference between a traditional database and an in-memory database relies upon where the data is stored. Even when compared with solid-state drives (SSD), random access memory (RAM) is exponentially faster than disk access. Because an in-memory database uses the latter for storage, access to the data is much faster than with a traditional database using disk operations. An in-memory database may be relational or non-relational in nature.
How does an in-memory database work?
An in-memory database is a data storage system that holds all of its data in the main memory of the host. The main difference between a traditional database and an in-memory database relies upon where the data is stored. Even when compared with solid-state drives (SSD), random access memory (RAM) is exponentially faster than disk access.
Features and Advantages of In-Memory Database:
- Lightning Speed:
The most significant feature of IMDBs is their speed. By keeping all data in the system’s main memory, data access and processing times are drastically reduced, resulting in very low latency responses.
- Real-time value:
Due to their low latency and high processing speeds, IMDBs are ideal for applications that require real-time or near-real-time responses.
- High availability:
An in-memory database simplifies availability and redundancy, which eliminates downtime and the resulting revenue loss.
- Lightning Speed:
The most significant feature of IMDBs is their speed. By keeping all data in the system’s main memory, data access and processing times are drastically reduced, resulting in very low latency responses.
- Data persistence:
In addition to storing data in memory, some IMDBs have features to ensure data persistence and recovery. These features include asynchronous disk writes, snapshotting, and disk-based backups.
- Compression:
IMDBs often support data compression to reduce the memory footprint and optimize storage.
- Scalability:
IMDBs can be scaled up (adding more RAM) or scaled out (distributed over multiple systems) to handle large data volumes.
Disadvantages with In-Memory Database:
Since RAM is volatile, means they are not real storage the data will be lost during a power failure or a restart. However we can add replication techniques to copy data to other nodes to prevent the data lose. We can also add a real in-disk database behind the in-memory to store the data periodically. However we can’t rely entirely with in-memory database if you need a huge storage of data. Also when your database is bigger you can’t invest much on the RAMs where the in-disk storage option is much better on cost wise.
Example scenarios where in-memory database can be effective.
In-memory databases are ideal for environments that demand real-time responses even as they handle voluminous traffic and unplanned usage spikes. This makes them a very good choice for industries such as telecommunications, banking, gaming, mobile advertising, and over-the-top media. In-memory databases can find their place in many different scenarios. Typically a mix like In-Memory on top of In-Disk Storage is an excellent approach for both faster performance and data reliability. Some of the typical use cases could include:
IoT data: IoT sensors can provide large amounts of data. An in-memory database could be used for storing and computing data to later be stored in a traditional database.
E-commerce: Some parts of e-commerce applications, such as the shopping cart, can be stored in an in-memory database for faster retrieval on each page view, while the product catalog could be stored in a traditional database.
Real-Time Analytics: Real-time analytics is critical for many industries, such as healthcare, finance, and telecommunications. These databases can quickly process vast volumes of data expected in real-time analytic processing. This allows users to extract insights quickly and make better decisions.
Gaming: Leaderboards require quick updates and fast reads when millions of players are accessing a game at the same time. In-memory databases can help to sort the results more quickly than traditional databases.
High-Speed Transactions: Industries like financial services need to process high volumes of transactions quickly. With their high-speed data access, in-memory databases can effectively support such high-speed transactions.
Caching: In-memory db are frequently used as caching systems since they provide low-latency access to critical data. This benefits web-based applications or services requiring quick data retrieval to ensure smooth user experiences.
Session management: In stateful web applications, a session is created to keep track of a user identity and recent actions. Storing this information in an in-memory database avoids a round trip to the central database with each web request.
MS SQL In-Memory OLTP, SQLite, Apache Ignite, VoltDB and Aerospike are some popular in-memory databases we often use. Redis and Memcached are 2 excellent in-memory databases specific to caching systems.