How does Kylin handle complex JOIN operations?

Kylin is an OLAP engine based on Hadoop that can handle intricate JOIN operations. Within Kylin, JOIN operations can be achieved by defining the relationships between different tables in the data model. Kylin supports various JOIN types, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

When performing a JOIN operation in Kylin, the following points need to be considered:

1. Data Model Design: Using data models in Kylin to define the relationship between data tables helps reduce the complexity of JOIN operations.
2. Query optimization: Kylin has an optimizer to improve query plans, enabling it to choose the appropriate JOIN strategy based on query conditions and data models to enhance query performance.
3. Data sharding: Kylin supports data sharding, which allows data to be stored in shards based on a certain field, reducing the amount of data needed for JOIN operations.
4. Index optimization: Kylin supports defining indexes in the data model, which can improve the speed of JOIN operations.

Overall, Kylin can effectively handle complex JOIN operations, but in practical applications, it is necessary to design data models, optimize queries, and data storage structures appropriately to improve query performance.