Correlated columns are useful in many situations where you need to retrieve data from multiple tables and use data from one table to filter data in another. Some common use cases include:
- Joining tables on non-unique keys: If you have two tables that have a non-unique key, you can use a correlated subquery to join the tables based on the values in the key.
- Data normalization: Correlated subqueries can be used to retrieve data from a normalized table and join it with data from a denormalized table, making it easier to maintain data integrity and avoid data duplication.
- Dynamic filtering: If you need to filter data based on the values in another table, you can use a correlated subquery to perform the filtering dynamically.
- Data aggregation: If you need to aggregate data from multiple tables, you can use a correlated subquery to retrieve data from one table and aggregate it, and then join the aggregated data with data from another table.
- Handling missing data: If you have data in one table that is not present in another table, you can use a correlated subquery to retrieve the missing data and join it with the data that is present.
How are correlated columns implemented in ClickHouse?
In ClickHouse, correlated columns can be implemented using a subquery in the SELECT statement. The subquery can reference columns from the outer query and use them in its own WHERE clause to filter data. For example:
SELECT outer_table.col1, (SELECT inner_table.col2 FROM inner_table WHERE inner_table.col3 = outer_table.col3 ) AS correlated_col FROM outer_table
In this example, the subquery references the col3 column from the outer query and uses it to filter data from the inner_table. The result of the subquery is then used as a correlated column in the outer query’s SELECT statement.