Simplify and Conquer: Writing Efficient Queries in Snowflake

by Brian Chong

Use Query advisor to improve query writing.

Keeping it Simple

Complex queries can be difficult to read and understand, and can slow down performance, making it essential to keep your queries as simple and concise as possible.

Use Simple Syntax

Use simple syntax in your queries to make them easier to read and understand. Avoid using complex joins or subqueries unless they are necessary for the analysis. This will make your queries more accessible to team members who may not be as familiar with SQL, and make them easier to debug if issues arise. Using descriptive table and column names can make your queries easier to read and understand. Instead of using vague names like "Table A" or "Column 1" use more descriptive names like "Product_Data" or "Customer_Name." This will make it easier for you and your team members to understand the purpose of the query, and help avoid confusion.

Break Up Complex Queries into Smaller Parts

If you have a complex query that performs multiple operations, consider breaking it up into smaller, more manageable parts. This can make it easier to understand and optimize the query, and can lead to better performance.

Avoid Using Redundant Code

Redundant code can make your queries longer and harder to read, and can slow down performance. Make sure to remove any unnecessary code or repeated operations to keep your queries as concise as possible.

By keeping your queries simple and concise, you can optimize their performance and make them easier to read and understand. This will help you get the most out of your data warehousing solution, and achieve better results in less time.

Avoid Using SELECT *

SELECT * is a common query practice, but it can lead to unnecessary data processing and reduced performance in Snowflake. This is because SELECT * retrieves all the columns in a table, including those that may not be needed for the analysis. This can lead to slower query execution times, increased network traffic, and reduced performance.

Here are some tips to help you avoid using SELECT * in Snowflake:

Specify the Columns You Need to Retrieve

Instead of using SELECT *, be more specific about the columns you need to retrieve. This can help reduce the amount of data that needs to be processed, leading to improved performance. For example, if you only need the customer name and the total sales amount, specify those columns explicitly in your SELECT statement.

Use Table Aliases to Simplify Your Queries

Table aliases can make your queries easier to read and understand, especially when working with multiple tables. They also reduce the amount of code you need to write, making your queries more concise. For example, instead of writing:

SELECT Sales_Data.Customer_Name, Sales_Data.Sales_Amount FROM Sales_Data, you can use the alias "SD" and write SELECT SD.Customer_Name, SD.Sales_Amount FROM Sales_Data SD

Avoid Using SELECT * in Subqueries

Using SELECT * in subqueries can lead to reduced performance, as the subquery will retrieve all the columns in the table. Instead, specify the columns you need to retrieve explicitly in the subquery. For example, instead of writing:

SELECT * FROM Sales_Data WHERE Customer_Name IN (SELECT Customer_Name FROM Customers), write SELECT Customer_Name, Sales_Amount FROM Sales_Data WHERE Customer_Name IN (SELECT Customer_Name FROM Customers)

By avoiding SELECT * in your queries, you can optimize query performance, reduce network traffic, and achieve better results in less time. By being more specific about the columns you need to retrieve, using table aliases, and avoiding SELECT * in subqueries, you can create more efficient queries that are optimized for performance.

Use Proper Filtering and Sorting

Proper filtering and sorting techniques can greatly improve query performance in Snowflake. By using these techniques, you can limit the amount of data that needs to be processed, making queries faster and more efficient. Here are some tips to help you use proper filtering and sorting techniques in Snowflake:

Use WHERE Clauses to Filter Data

WHERE clauses are used to filter data in SQL, and they can greatly improve query performance. By specifying conditions in the WHERE clause, you can limit the amount of data that needs to be processed. For example, if you only need data for the year 2022, you can use a WHERE clause to filter out all other years.

Use Appropriate Comparison Operators

Appropriate comparison operators are essential to proper filtering. In Snowflake, you can use comparison operators like "=", "<>", "<", ">", "<=", and ">=". Using the appropriate comparison operator can help you filter data more efficiently.

Use INDEXED BY Clauses to Optimize Sorting

INDEXED BY clauses are used to specify how data should be sorted in SQL. By using INDEXED BY clauses, you can optimize query performance by reducing the amount of data that needs to be sorted. For example, if you are sorting a large table by a specific column, you can use an INDEXED BY clause to sort only the data in that column, instead of sorting the entire table.

Use ORDER BY Clauses to Sort Data

ORDER BY clauses are used to sort data in SQL, and they can greatly improve query performance. By using ORDER BY clauses, you can specify the order in which data should be sorted, which can help you retrieve data more efficiently.

Use LIMIT Clauses to Limit Results

LIMIT clauses are used to limit the number of results returned in SQL. By using LIMIT clauses, you can reduce the amount of data that needs to be processed, which can improve query performance. For example, if you only need the top 10 results, you can use a LIMIT clause to limit the query results to those 10 rows.

By using proper filtering and sorting techniques, you can optimize query performance in Snowflake. By using WHERE clauses to filter data, appropriate comparison operators to compare data, INDEXED BY clauses to optimize sorting, ORDER BY clauses to sort data, and LIMIT clauses to limit results, you can create more efficient queries that are optimized for performance.