In-memory databases (IMDBs) have changed the way data is processed. In short, it works by keeping data in a computer’s main memory (RAM) instead of using slower, disk-based storage. After all, RAM speeds are often around 25,000 MB/s, compared to SSD’s 3,500 MB/s.
IMDBs offer incredible speed and performance, making them perfect for real-time data processing and complex analytics with lightning-quick response times.
Recent advances in 64-bit computing and falling RAM costs have made in-memory databases much more accessible. This progress means that organizations can now handle large volumes of data with very low latency. So, real-time, data-driven decisions can be made much more conveniently.
Since IMDBs skip the need to fetch data from disks, they can provide microsecond response times and handle high volumes of traffic smoothly. This level of performance is essential for industries like finance, e-commerce, and gaming. But how exactly does it work, and how does a company go about implementing it?
How In-Memory Databases Work
In-memory databases keep data directly in a computer’s RAM. When an In-memory database starts, it loads the entire dataset into RAM, making it possible to access and work with data without the slowdowns linked to disk I/O. Data can be stored in a compressed, non-relational format designed for in-memory use, though it can also be relational. The result is boosted memory efficiency and faster querying.
Not storing sensitive data on a hard drive may sound like an unusual risk. To ensure data is safe and can be recovered, IMDBs use a few different techniques like transaction logging and snapshotting.
Transaction logs track all changes to the database, helping with recovery if something goes wrong. Snapshotting means saving the data to disk at regular intervals, which are essentially backups. Both of these, of course, are stored onto hard drives, meaning there is a secure backup if the RAM fails.
Unsurprisingly, many businesses do not want to invest in all this RAM with a new infrastructure. Instead, they access a cloud server which is intended specifically for IMDB. This brings us to replication methods, another backup technique, and it involves things like multi-master, where data is spread across multiple servers to ensure reliability and high availability.
IMDBs also use advanced memory-saving methods, like columnar storage and compression, to make the best use of memory and improve query speed. By storing data in columns instead of rows, IMDBs can access information faster. Again, real-time analytics then becomes that little bit more efficient.
Key Benefits of In-Memory Databases
The main benefit of in-memory databases is, as discussed, their performance. It’s not necessarily the cheapest option, but by keeping data in RAM, IMDBs have much faster query execution than traditional disk-based databases. There’s no I/O slowdown. This leads to microsecond response times and high throughput and is particularly impressive under heavy use.
Due to their speed, IMDBs are suited for applications that need real-time data processing or analysis. In fintech, IMDBs support quick transaction processing and real-time risk assessments to help prevent fraud. In the past, this got in the way of user convenience, but today both speed and safety are both achievable. Companies Uber and Google use IMDB and caching solutions to provide real-time traffic information and driver pricing.
E-commerce and retail businesses also gain from these techniques. By holding customer data in memory, IMDBs provide rapid product recommendations that are truly personalized. This means that customers can see more relevant suggestions and conversion rates will improve. With the increase in accessible data, such as through IoT and smart city design, even local governments can benefit from investing in IMDBs.
Just because of the RAM use, it doesn’t mean IMDBs can’t handle large amounts of data, as we’ve seen big tech firms do. It can also withstand high levels of activity. They can scale both horizontally and vertically to manage growing datasets and workloads. Through distributed setups, IMDBs spread data across several nodes so parallel processing can occur.
Drawbacks and Considerations with IMDBs
While In-memory databases offer major speed benefits, they do come with some drawbacks. One of the main concerns is the high cost, as RAM is much more expensive than traditional disk storage. As data grows, scaling an IMDB can become costly. However, scaling is easy in the sense that it can be done with a cloud provider, and the costs should be easy to forecast (i.e. predict future prices based on expected data sizes).
One risk though is data loss due to RAM’s volatile nature. If there’s a power failure or system crash, data in memory can be lost if it hasn’t been saved to disk or replicated elsewhere. To prevent this, the aforementioned techniques need to be reliably practiced. This may be tricky for self-hosting, but cloud providers usually perform this on your behalf.
When Companies Need an In-Memory Database
Deciding if an In-memory database is right depends on the company’s needs. It’s best to forecast how much faster your real-time processing is going to be. For some services, it will be essential. For others, the gain in speed may not be too important to the end user. The key is to assess how much will be gained from the extra speed of querying because the costs should be relatively easy to quantify. Alternatively, it may be useful for those simply looking to overcome traffic spike issues.
Industries like finance and gaming benefit greatly. In some cases, a hybrid approach combining IMDBs with traditional databases may offer the best balance of speed and cost.
