New blogpost up via databloom.ai:
https://engineering.databloom.ai/2022/03/the-missing-piece-in-ml-based-query.html
Machine Learning (ML) has not only become omnipresent in our everyday lives (with self-driving cars, digital personal assistants, chatbots etc.) but has also started spreading to our core technological systems, such as databases and operating systems. In the area of databases, there is a large amount of works aiming at optimizing data management components, from index building, knob tuning to query optimization. Just in query optimization, ML is used in the place of many optimizer components, such as cardinality estimation, cost model, and join enumeration. In this blog post, we focus on the case of using an ML in the place of a cost model and go from the traditional cost-based query optimization to the newly proposed ML-based query optimization.
https://engineering.databloom.ai/2022/03/the-missing-piece-in-ml-based-query.html
Machine Learning (ML) has not only become omnipresent in our everyday lives (with self-driving cars, digital personal assistants, chatbots etc.) but has also started spreading to our core technological systems, such as databases and operating systems. In the area of databases, there is a large amount of works aiming at optimizing data management components, from index building, knob tuning to query optimization. Just in query optimization, ML is used in the place of many optimizer components, such as cardinality estimation, cost model, and join enumeration. In this blog post, we focus on the case of using an ML in the place of a cost model and go from the traditional cost-based query optimization to the newly proposed ML-based query optimization.