Revisiting Why SQL’s Order of Execution Matters

Senior Data Engineer • Contractor / Freelancer • GCP & AWS Certified
Search for a command to run...

Senior Data Engineer • Contractor / Freelancer • GCP & AWS Certified
No comments yet. Be the first to comment.
Short, practical posts on SQL and BigQuery — from core language features to advanced query patterns. A reference for data practitioners at every level.
Modern SQL engines have a wealth of aggregation functions. Here's a quick example that makes use ofBigQuery STRING_AGG. What does it do? It aggregates all the values in a grouping, joined by a separator of our choice, creating a string of those joine...
Here's a useful Dataform concept: pre_operations and post_operations. As the name implies, these represent a set of actions that run before and after the main operation (table, view, or SQL operations

BigQuery has always been a SQL engine for tabular data. Object tables add an interesting twist to that. Instead of rows containing values, an object table gives you one row per file — pointing at da

Query your data lake with warehouse-grade security and performance — without moving a single file.

Ever run a heavy BigQuery SQL query, processed gigabytes of data — and then accidentally closed the tab or forgot to save the results? 😬 Don't re-run it. Your results are still there. BigQuery automa

You can use query parameters in BigQuery hashtag#SQL (now in the console as well!) — but how are they different from variables, and when should you use each? Both parameters and variables act as place

A few days ago I thought that the following SQL query would not work— I expected the window function result would be summed multiple times.
🚨 Turns out, I was wrong.
This was a great reminder of why understanding SQL’s order of execution is crucial!
I expected SUM(SUM(val)) OVER (PARTITION BY id) to accumulate incorrectly, but SQL’s execution order ensures that:
1️⃣ The GROUP BY clause first aggregates SUM(val) at the id grain.
2️⃣ Then, the window function is applied to the grouped result—not the raw data. Since there’s only one row per id, the window function correctly returns the expected value.
SQL doesn’t “re-sum” the window function like I feared. Instead, it partitions over the already-aggregated values—exactly as it should.
🔍 Have you ever misjudged a query’s behavior?
Found it useful? Subscribe to my Analytics newsletter at notjustsql.com.
Enjoyed this? Here are some related articles you might find useful: