How does Kylin achieve response times in seconds for queries?
Kylin achieves sub-second response times by utilizing a multidimensional data analysis engine and precomputation techniques. Specifically, Kylin achieves high-performance query responses through the following methods:
- Multi-dimensional data analysis engine: Kylin utilizes an Online Analytical Processing (OLAP) engine that allows for efficient querying and analyzing of multi-dimensional data. This engine can quickly aggregate, filter, and group operations on large data sets, enabling fast query responses.
- Precomputation technology: Kylin precalculates and stores various aggregate metrics during the Cube construction process, including summaries, averages, counts, etc. This allows Kylin to directly retrieve results from the precomputed data when a user initiates a query, eliminating the need for real-time calculations and greatly improving query performance.
- Kylin stores data in a column-based storage engine, which has a clear advantage when processing aggregate queries, improving query performance.
In conclusion, Kylin uses various technologies such as multidimensional data analysis engine, precomputed techniques, and column storage to achieve high-performance query response, enabling complex queries on large-scale datasets to be completed within seconds. This makes Kylin one of the ideal tools for handling big data analysis tasks.
