Left Join Databricks


                Left Join Databricks

In Databricks, a left join (a left outer join) is a join operation in SQL that combines records from two tables based on a standard column or key. It returns all rows from the left table and the matched rows from the right table. If there is no match, the result set will still include the row from the left table, but the corresponding columns from the right table will have NULL values.

Understanding Left Join

  • Purpose: Left joins are often used when retaining all the information from the left table, even if there are no corresponding matches in the right table.
    • Typical Use Cases: Enriching data: Adding details from one table to another.
    • It identifies missing records: Finding rows in one table that don’t have matches in another.

How to Perform a Left Join in Databricks

You can use standard SQL syntax within Databricks to perform a left join. Here’s the basic structure:


SELECT columns

FROM left_table

LEFT JOIN right_table

ON left_table.column = right_table.column;

  • columns: Specify the columns you want to select from both tables.
  • left_table: The table from which you want to retrieve all rows.
  • right_table: The table you want to join to the left table.
  • ON: Specifies the condition determining how rows from the two tables are matched.


Let’s say you have two tables:

  • Customers: Contains customer ID, name, and city.
  • Orders: Contains order ID, customer ID, and order date.

To get a list of all customers and their orders (including customers who haven’t placed orders), you would use a left join:


SELECT customers.customer_id, customers.name, orders.order_id, orders.order_date

FROM customers

LEFT JOIN orders

ON customers.customer_id = orders.order_id;

Important Considerations

  • Watermarks and Event-Time Constraints: In Databricks, if you’re working with streaming data and performing outer joins (including left joins), you might need to specify watermarks and event-time constraints to ensure correct results. This helps Databricks determine when it’s safe to assume that no future matches will occur for a particular row.

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