Fractal Tree Indexes and Mead – MySQL Meetup
From the post:
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.
Whether you are just browsing or seriously looking for better performance, I think you will like this presentation.
Performance of data stores is an issue for topic maps whether you store a final “merged” result or simply present “merged” results to users.