| 摘要 |
In one embodiment, an elastic, massively parallel processing (MPP) data warehouse leveraging a cloud computing system is disclosed. Queries received via one or more API endpoints are decomposed into parallelizable subqueries and executed across a heterogenous set of demand-instantiable computing units. Available computing units vary in capacity, storage, memory, bandwidth, and hardware; the specific mix of computing units instantiated is determined dynamically according to the specifics of the query. Better performance is obtained by modifying the mix of instantiated computing units according to a machine learning algorithm. |
