SurrealDB, the ultimate multi-model database, is debuting the next iteration of its database solution, centered on further simplifying the lives of developers. SurrealDB 2.0 adds a series of new functionalities, incorporating both user requests and advancements in stability, performance, security, and data management capabilities.
SurrealDB blends the strengths of a variety of different database models which can all be leveraged at once with an easy-to-use query language, SurrealQL. By consolidating the power of multiple different database platforms, SurrealDB aims to afford developers the necessary flexibility and simplicity of a multi-model database without sacrificing performance or stability.
The debut of SurrealDB 2.0 continues this mission, specifically empowering developers to build complex, real-time applications faster, more cost-effectively, and at scale across teams, data volumes, and diverse use cases, according to the company. This latest iteration of SurrealDB supports the needs of modern enterprises by ensuring consistent performance across all database operations, forwarding greater stability and reliability for enterprise-level applications.
“What we're seeing with the users and the organizations who are using us is they're using us in this very unique way, which combines all of these models together, [with] potentially [a] more analytical focus than just transactional. And when you're running queries, a lot of it is real-time analytics—as opposed to analytics that takes hours to compute—[and] you need [these] queries to respond in a kind of predictive way,” explained Tobie Morgan Hitchcock, co-founder and CEO of SurrealDB. “So, to make these models work efficiently, the right performance is important, especially as an organization is using more of these models in one application.”
In coordination with the effort to improve performance, SurrealDB 2.0 has rebuilt its SurrealQL parser to be faster, more light-weight, and more resilient. This serves to improve memory handling, better support deep recursive queries, and deliver a more robust transaction layer with advanced caching, according to SurrealDB. SurrealQL also features new statements—such as UPSERT, ALTER, REBUILD, and DEFINE ACCESS—to help enterprises deal with large amounts of data that need to be indexed concurrently in the background.
Index performance also sees improvement in this release, enhancing hashing mechanisms as well as the addition of the HNSW algorithm for AI-driven searches and the option for asynchronous indexing to ensure fast, reliable data access at scale.
With this announcement, SurrealDB 2.0 introduces SurrealKV, its native key-value storage engine built entirely in Rust. SurrealKV is an embedded, ACID-compliant engine with built-in versioning that supports historical and temporal querying, offering powerful, efficient data storage and retrieval, according to the company.
“The idea for SurrealKV comes from two points: The first is we want to be in complete control of our database as a whole. That means writing our own storage engine so that SurrealDB works in the ways that we expect it to work, whether that's running embedded, a single node, or a large distributed system,” said Morgan Hitchcock. “In addition to that, we're not just rewriting it to make it better; we're actually rewriting it to have more functionality that does not exist out there in the world at all, and that functionality is the temporal, historical querying aspect. It enables users who use SurrealKV and within SurrealDB to query the data that they have in their database with a fourth dimension—which is time—and that enables them to go back historically, whether they're using graph or time series.”
Another functionality of SurrealDB 2.0 is SurrealML, a powerful engine that enables users to store and execute machine learning (ML) models directly within SurrealDB, adjacent to their data. By integrating ML directly into the database to store, load, and execute ML models alongside data, workflows become more streamlined by enabling real-time processing and analysis.
Finally, enhanced SDK support for SurrealDB 2.0 improves performance, flexibility, and offers a smoother, more efficient experience for developers. This also delivers enhancements for data integrity and type safety, driving better data handling, reducing bundle sizes, and delivering new embedding capabilities.
“We're continuing to deliver on the promise of trying to improve the developer experience when working with data and working with databases, delivering on better performance, better stability, and more functionality for querying and working with your data,” concluded Morgan Hitchcock. “We're trying to simplify developers’ lives…to those organizations who are using us, this has become apparent, because they are able to build so much quicker and so much more on SurrealDB than they were previously.”
To learn more about SurrealDB, please visit https://surrealdb.com/.