Fractal Tree Indexes and Mead – MySQL Meetup
Thanks again to Sheeri Cabral for having me at the Boston MySQL Meetup on Monday for the talk on “Fractal Tree® Indexes – Theoretical Overview and Customer Use Cases.” The crowd was very interactive, and I appreciated that over 50 people signed up for the event and left some very positive comments and reviews.
In addition, the conversation spilled over late into the night as we made our way over to nearby Mead Hall afterwards for a few drinks, some food, and to continue the discussion.
As a brief overview – most databases employ B-trees to achieve a good tradeoff between the ability to update data quickly and to search it quickly. It turns out that B-trees are far from the optimum in this tradeoff space. This led to the development at MIT, Rutgers and Stony Brook of Fractal Tree indexes. Fractal Tree indexes improve MySQL® scalability and query performance by allowing greater insertion rates, supporting rich indexing and offering efficient compression. They can also eliminate operational headaches such as dump/reloads, inflexible schemas and partitions.
The presentation provides an overview on how Fractal Tree indexes work, and then gets into some specific product features, benchmarks, and customer use cases that show where people have deployed Fractal Tree indexes via the TokuDB® storage engine.