How to optimize Cube design in Kylin?
In Kylin, you can optimize Cube design using the following methods:
- Simplify dimensions and metrics: only include the most commonly used dimensions and metrics, avoiding unnecessary ones, which can reduce the size and computational complexity of the cube.
- Utilize a dimension dictionary: map dimension values to a dictionary table to avoid storing duplicate dimension values in the cube, reducing the size of the cube.
- Opt for efficient storage formats like Parquet and ORC which are suitable for data querying and compression purposes, as they can help reduce storage space and increase query speed.
- Utilize hierarchical aggregation: by aggregating the Cube hierarchically, cache the calculation results of higher-level Cubes to reduce the computational load during querying.
- Utilize precomputed metrics: precalculate metrics for frequent queries and store the results in a Cube to reduce the calculation time during queries.
- Use appropriate data partitioning: Set up data partitions based on the characteristics of the data and query requirements in order to make queries more efficient.
- Regular maintenance of the cube: It is important to optimize and maintain the cube regularly, which includes tasks such as data cleaning, data compression, and index rebuilding, to ensure the stability and reliability of the cube’s performance.
