Ancelus: an Intriguing New Type of Database

There are many different DBMS types and products on the market these days. We have relational, key/value, document, wide column stores, graph, in-memory, multi-model, and even pre-relational DBMS types. It is rare to see anything truly new and interesting in the realm of DBMS offerings, but I think you will agree than Ancelus is indeed something new and interesting.

Billed as the nanosecond database, Ancelus has been able to achieve incredible performance benchmark results that are orders of magnitude faster than comparable relational database management system results. Better and faster performance is always at the top of the list of what organizations want from their database systems. And Ancelus can deliver. Benchmark results show constant read/write latency under 100 nanoseconds regartdless of the amount of data, up to 16 exabytes. Ancelus ran a billion-row search in 2.4 microseconds; and it adds only about 500 nanoseconds for each 10x increase in database size. Basically, queries that run for hours or days on typical database implementations can run in microseconds on Ancelus.

A database system capable of such speed is intriguing for many applications such as financial analysis, options trading, AI/ML, and many others.

Sometimes databases and applications sacrifice data integrity for speed, but not so with Ancelus. It is ACID-compliant while delivering extreme speed, complexity handling, and massive scale all within a single system.

Furthermore, Ancelus works efficiently for both transactions and analytics, supporting HTAP (Hybrid Transaction Analytical Processing) and translytical environments. Ancelus is not a SQL database system; data is typically accessed by a native API. However, Ancelus does offer TQL (Threaded Query Language), which converts Ancelus schema structures into SQL-readable formats for ease of integration.

What Makes Ancelus Work

The Ancelus database uses a patented algorithmic approach to data storage that replaces predefined storage structures with an algorithm that determines the physical storage location of data. This algorithmic approach decides where to put data based on efficiency of access to it. This means that physical data storage and retrieval are independent of any predefined data layout.

Ancelus couples this storage approach with in-memory data access techniques which are also based on patented memory management methods. By combining algorithmic data storage and in-memory data management, Ancelus has achieved constant performance even for very large databases and complex applications.

There is no duplication of data in an Ancelus database. Each data element is stored only once and data is linked using pointers to its data dictionary. A consequence of this design is that referential integrity is not optional—it is designed right into the database … and it does not impede performance.

With this architecture, users can implement multiple logical schemas over the data at the same time, to access the data as it is in relational tables (even though it is not). The actual physical schema is not exposed to users.

Ancelus can be run on Linux, UNIX, and Windows platforms.

Consider taking a look at Ancelus if you are in the market for a high-performance database system to power your complex applications. You may be able to reduce cost, speed up development, and improve operations using Ancelus. Indeed, some applications that may have been perceived as impractical or too costly may become achievable with Ancelus.

About craig@craigsmullins.com

I'm a strategist, researcher, and consultant with nearly three decades of experience in all facets of database systems development.
This entry was posted in data, DBMS, performance. Bookmark the permalink.

1 Response to Ancelus: an Intriguing New Type of Database

  1. CHRISTOPHER FINEGAN says:

    Sounds very much like “in-memory” or “multi-temperature-storage” management layer sitting on top of Cross-Marriage of RM/T model of E.F. Codd(and RM/D C.J. Date as RM/T v2) and fully-inverted DBMS such as “Sand” IMHO.

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