RonDB was a pure in-memory database engine in its origin. The main reason for this was to support low latency applications in the telecom business. However already in 2005 we presented a design at VLDB in Trondheim for the introduction of columns stored on disk. These columns cannot be indexed, but is very suitable for columns with large sizes.
RonDB is currently targeting Feature Store applications. These applications often access data through a set of primary key lookups where each row can have hundreds of columns with varying size.
In RonDB 21.04 the support for disk columns uses a fixed size disk row. This works very well to support handling small files in HopsFS. HopsFS is a distributed file system that can handle petabytes of storage in an efficient manner. On top of it Hopsworks build the offline Feature Store applications.
The small files are stored in a set of fixed size rows in RonDB with suitable sizes. YCSB benchmarks have shown that RonDB can handle writes of up to several GBytes per second. Thus the disk implementation of RonDB is very efficient.
Applications using the online Feature Store will however store much of its data in variable sized columns. These work perfectly well in the in-memory columns. They work also in the disk columns in RonDB 21.04. However to make storage more efficient we are designing a new version of RonDB where the row parts on disk are stored on variable sized disk pages.
These pages use the same data structure as the in-memory variable sized pages. So the new format only affects handling free space, handling of recovery. This design has now reached a state where it is passing our functional test suites. We will still add more tests, perform system tests and search for even more problems before we release for production usage.
One interesting challenge that can happen with a variable sized rows is that one might have to use more space in a data page. If this space isn't available we have to find a new page where space is available. It becomes an interesting challenge when taking into account that we can abort operations on a row while still committing other operations on the same row. The conclusion here is that one can never release any allocated resources until you fully commit or fully abort the transaction.
This type of challenge is one reason why it is so interesting to work with the internals of a distributed database engine. After 30 years of education, development and support, there are still new interesting challenges to handle.
Another challenge we faced was that we need to page in multiple data pages to handle an operation on the row. This means that we have to ensure that while paging in one data page, that other pages that we already paged in won't be paged out before we have completed our work on the row. This work also prepares the stage for handling rows that span over multiple disk pages. RonDB already supports rows that span multiple in-memory pages and one disk page.
If you want to learn more about RonDB requirements, LATS properties, use cases and internal algorithms, join us on Monday CMU Vaccin database presentation. Managed RonDB is supported on AWS, Azure and GCP and on-prem.
If you like to join the effort to develop RonDB and a managed RonDB version we have open positions at Hopsworks AB. Contact me at LinkedIn if you are interested.