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Databricks, a leader in big data analytics, offers a powerful platform to perform data science operations at scale. One of the main features of Databricks is its ability to write and execute SQL queries on massive datasets, which makes SQL an important part of your data science toolkit. Below, we will discuss some best practices and tips for writing efficient SQL code in Databricks.
Understanding Query Execution
Before delving into the best practices and tips, understanding how a query is executed will help in optimizing your SQL code. When a query is executed, Databricks translate your SQL query into a series of transformations on DataFrame/Dataset.
Here’s an example of a simple SQL statement:
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SELECT * FROM sales WHERE sales_amount > 5000; |
Tip #1: Limit the Number of Output Rows
It can be tempting to use SELECT * to bring back all rows from a table. However, returning a large number of rows will consume memory and may slow down the responsiveness of your Databricks notebook. If you only need a subset of rows for analysis or testing purposes, use the LIMIT clause.
Here’s how you can do it:
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SELECT * FROM sales LIMIT 100; |
Tip #2: Use Where Clauses to Filter Data Early
By filtering data with a WHERE clause as early as possible, you will be reducing the amount of data that needs to be processed in the later stages of the query. This can significantly improve the performance of your SQL queries.
Here’s an example:
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SELECT * FROM sales WHERE sales_amount > 5000 LIMIT 100; |
Tip #3: Avoid Using Subqueries
While subqueries can be handy, they can also negatively impact the performance of your SQL queries because they must be run for each result of the outer query. Instead, consider using JOINs whenever possible.
Here’s an anti-pattern:
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SELECT a.* FROM sales a WHERE a.sales_id NOT IN (SELECT b.sales_id FROM returns b); |
Instead, this could be rewritten as:
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SELECT a.* FROM sales a LEFT JOIN returns b ON a.sales_id = b.sales_id WHERE b.sales_id IS NULL; |
Conclusion
These are just a few tips and best practices to get you started with writing efficient SQL code in Databricks. Always remember the key to performance optimization lays in understanding the data, the workings of your SQL engine, and applying sound practices around data modeling and SQL coding. Happy querying!