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While transforming the plan into vectors is interesting, I wish they'd gone into more detail about how the ML model prunes and filters the best plan. It is also

While transforming the plan into vectors is interesting, I wish they'd gone into more detail about how the ML model prunes and filters the best plan. It is also not clear what attributes of a plan the corresponding vector encodes. I do not know much about Databloom, but it looks like this "Learning-Based Query Optimizer" is built for specific use-cases in a Data engineering/analytics setting(like K-means as cited in the article). It might not be a replacement for optimizers in traditional Databases.

BenoitP 4y ago
> not clear what attributes of a plan the corresponding vector encodes Fig 5, page 4 from [1]: > Topology Features > Operator Features > Data Movement Features > Dataset Features For a single logical plan, meaning it will vary in its length for another query. (which is a part I don't get: you learn a new model per query? Can you learn with a variable feature length?) [1] https://conferences.computer.org/icde/2020/pdfs/ICDE2020-5acyuqhpJ6L9P042wmjY1p/290300b489/290300b489.pdf https://conferences.computer.org/icde/2020/pdfs/ICDE2020-5ac...