What is an in-memory database?

What is an in-memory database?

An in-memory database (IMDB) is a type of computer database that stores data in the computer’s main memory (RAM), as opposed to disks or other permanent storage media. This enables in-memory databases to process vast amounts of data quickly and with very low latency, making them useful in applications where real-time performance is critical.

In-memory databases are alternatives to disk-based databases, where data is stored in nonvolatile media such as hard disks or magnetic tapes. The primary advantage of IMDBs is their fast access times and high throughput. By storing data in RAM instead of a slower disk storage medium, in-memory databases can provide better query performance than traditional disk-based systems.

How do they work?

In-memory databases use in-memory data structures (such as arrays, linked lists, and hash tables) to store and process data. This helps them achieve faster response times and higher throughput. In addition to in-memory data structures, they employ several functions to optimize queries, such as:

• ​Caching: Storing frequently used data in RAM to speed up access and minimize disk input and output (I/O).
• ​Indexing: Using specialized index structures to look up data in large datasets quickly.
• ​Compression: Compressing and decompressing data in RAM to reduce disk space requirements.
• ​In-place updates: Updating data in their in-memory data structure, eliminating the need for disk I/O.

In-memory data structures usually organize information in a hierarchy of levels, with each level storing increasingly larger amounts of data. This hierarchical structure allows IMDBs to process queries more efficiently by quickly locating relevant data without searching through the entire in-memory data structure.

What are the benefits?

In-memory databases offer several advantages over disk-based databases, including:

• ​Real-time performance: With in-memory data structures and low latency, IMDBs can process large amounts of data in real time, making them useful in applications where speed is critical.
• ​Faster query processing: They provide faster query processing than disk-based databases, offering a better real-time experience for users.
• ​Reduced dependency on disk I/O: They reduce organizations’ reliance on expensive and time-consuming disk I/O operations.

How are in-memory databases used in business intelligence?

For modern organizations that leverage business intelligence (BI) technologies, the ability to quickly access and analyze large amounts of data in real time is critical. With in-memory databases, companies can gain valuable insights from their data in a fraction of the time it takes disk-based systems.

However, one downside of solutions that rely exclusively on in-memory engines is that data is compressed, meaning queries are not fully leveraging all the underlying data. In contrast, a direct query approach enables people to issue queries directly against a data source and receive results in real time, allowing analysis to take place on all data.

IMDBs are also critical to the success of modern decision intelligence (DI) platforms, which represent the next evolutionary stage of business intelligence.

How can Pyramid Analytics help?

Pyramid Analytics’ next-generation DI platform leverages in-memory databases so that users in any part of an organization can quickly access and analyze large amounts of data in real time. The Decision Intelligence Platform also provides direct querying capabilities, allowing it to query data directly at the source. Because these platforms provide nontechnical business users with direct access to insights from analytics on a self-service basis, IMDBs are essential to providing users with the insights they need to make better decisions faster.