How does Kylin handle the issue of data skewness?

The following measures can be taken by Kylin to handle data skew situations:

1. Data preprocessing: Before loading data into Kylin, it is possible to preprocess the data by methods such as data bucketing and sharding to prevent data skew.
2. Data skew detection: Kylin is able to detect data skew through various tools or built-in features, enabling timely identification and resolution of issues.
3. Adjusting table structure: If data skew is significant, consider making adjustments to the table structure, such as adding partitions or shards, to optimize data distribution.
4. Use appropriate partition key: When creating a Cube, you can choose an appropriate partition key to distribute data and reduce the chances of data skew.
5. Adjusting the data distribution: By redistributing or reorganizing the data, the distribution of data can be adjusted to reduce the impact of data skewness.

In conclusion, addressing data skew in Kylin requires a comprehensive approach that includes data preprocessing, data skew detection, table structure adjustments, partition key selection, and data distribution adjustments, in order to resolve the performance issues caused by data skew.