16 bookmarks. First posted by rcrowley january 2018.
Does all this mean that learned indexes are a bad idea? Not at all: the paper makes a great observation that, when cycles are cheap relative to memory accesses, compute-intensive function approximations can be beneficial for lookups, and ML models may be better at approximating some functions than existing data structures. The idea of self-tuning data structures is also exciting. Our main message, though, is that there are many other ideas that tackle the indexing problem in creative ways, and we should make sure to take advantage of these insights, too. In the case of hashing, cuckoo hash tables are asymptotically better due to a deep algorithmic insight, the power of two choices, that greatly improves load balance. In other domains, ideas such as adaptive indexing, database cracking, perfect hashing, data-dependent hashing, function approximation and sketches also provide powerful tools to system designers, e.g., by adapting to the query distribution in addition to the data distribution. It will be exciting to compare and combine these ideas with tools from machine learning and the latest hardware.algorithm data-structure machine-learning performance
march 2018 by brandon.w.barry