Query Optimization: Techniques Every Data Engineer Should Master
Your data is growing, your warehouse is getting bigger and more complex, parts of your business that once were doing everything manually now depend on your data to make decisions daily. This is the goal, but with it comes concerns. As your warehouse scales to encompass more and more parts of your business, and your tables get larger and larger, you may notice that your queries start running slower, reports refresh at a rate unacceptable to your business partners, and people either start complaining, or worse, revert to old practices.
Today I’m going to give you some tips and tricks to implement along the way to ensure that this doesn’t happen, and if it does you will have the tools to quickly adjust.
1. Understand Your Execution Plan
An execution plan is a route (think google maps) that your query takes to get data. Learning how to read and understand an execution plan will help you to identify bottlenecks in your queries, like table scans or missing indexes. Azure Data Studio or SSMS (SQL Server Management Studio), or the EXPLAIN command can help you visualize your execution plan.
Watch for operations like table scans and nested loops over large tables. These often indicate where improvements can be made. Addressing the issues highlighted in your execution plan will help you transform your sluggish queries into lean, efficient operations!
2. Indexing Wisely!
Indexing is a powerful tool, but with great power comes great responsibility. Adding indexes will make your read operations faster, but the trade off is that it takes longer to write new records.
Those operations look like:
INSERT
MERGE
UPDATE
DELETE
Strike the right balance. Index columns that are frequently used in WHERE and GROUP BY statements as well as your JOIN criteria.
Composite indexes can be helpful for queries that filter or join on multiple columns. However, make sure you are using tools that monitor your indexes to ensure they are being used efficiently, and if not, drop them like the bad habit they have become. Indexing is like putting bookmarks in a large book—it helps you quickly jump to the parts you need, but too many bookmarks make it cumbersome.
3. Optimize Joins
Complex or inefficient joins can be performance killers. When managing large tables, your join order matters. Start with smaller tables and make sure you are using indexed columns where you can. Don’t get in the habit of using left joins when a inner join will do. Inner joins require less overhead because they don’t have to account for non matching items.
Avoid CROSS JOINS unless absolutely necessary! These lead to CARTESIAN products which can bring your warehouse to it’s knees on large datasets. If you do need to use a CROSS JOIN (I’ve done it before to create tables for specific use cases) be sure you know what your output is going to look like and test it on a smaller sample set if you can before you test it on your whole dataset.
4. Use CTEs and Subqueries Thoughtfully
Common Table Expressions (CTEs) and subqueries make your code more readable, but beware! CTEs are a great way to break down complex queries, but they also increase the cost of reprocessing if you have referenced them multiple times.
When you use a CTE or Subquery that will be referenced several times in your query, consider creating temp tables instead. This reduces the overall overhead and will speed up your query. However, as with everything it is about balance CTEs are better than subqueries and depending on how you are using them they can be the best option, just be mindful of what your overall execution looks like.
5. Filter Early
Always filter your data as early as possible in the query process. The fewer rows you pass to the next operation like joins or aggregations, the faster your queries will run.
Use WHERE clauses to limit rows before GROUP BY clauses, and , in some cases it can be a good idea to filter out records in your join criteria as well. Additionally, use window functions carefully. Window functions work on sets of rows and minimizing the input helps optimize performance.
6. Limit Columns You SELECT
The SELECT * syntax is convenient but lazy and will cost you time in the long run. Retrieving all of the columns, especially from tables with a lot of fields, increases the amount of data that the database needs to process and render.
Instead be intentional with what columns you need to return. This change will lead to less memory consumption and faster execution, especially when dealing with huge datasets.
7. Partitioning
When you are working with huge tables, partitioning can help to speed things up. Partitioning breaks the table into smaller chunks, which allows the database to just access the relevant partition instead of doing a full table scan.
For example, time-based partitioning (year, quarter, month) can make date related queries much more efficient. It’s important to make sure your queries coincide with the partition key for the biggest efficiency gain here.
8. Caching and Materialized Views
Sometimes, optimizing a query isn’t just about the SQL itself. If you have queries that run frequently on data that doesn’t change often, consider caching the results or using materialized views.
Materialized views store the query results, which makes future reads significantly faster. Just remember to balance the performance benefits against the overhead of refreshing the views when the underlying data changes.
9. Query Scheduling and Load Management
Sometimes optimization isn't about changing the query but rather choosing when to run it. If your environment allows, schedule resource-intensive queries during off-peak hours when fewer processes compete for resources. Load management and query scheduling can prevent resource contention and ensure your critical reports get processed faster.
Recap
Query optimization is both an art and a science, blending technical knowledge with strategic adjustments. Here’s a quick recap to help you get started:
Understand Your Execution Plan: Learn to read and interpret it to spot bottlenecks.
Index Wisely: Index strategically, but be mindful of the write operation costs.
Optimize Joins: Efficient join orders and types can drastically improve query performance.
Use CTEs and Subqueries Thoughtfully: Know when to replace them with temp tables.
Filter Early: Reduce the number of rows passed between operations to speed things up.
Limit Columns: Only select the columns you need to minimize data load.
Partitioning: Break down large tables to improve query efficiency.
Caching and Materialized Views: Cache frequently used results to save time on repetitive queries.
Query Scheduling: Run heavy queries during off-peak hours to reduce contention.
Optimizing your queries requires careful planning, constant evaluation, and experimentation. By following these tips, you can ensure that your queries remain efficient even as your data scales. If you have any specific challenges or want to dig deeper into any of these techniques, drop a comment below—I’d love to help!