How LIMIT helps you save time in BigQuery

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.
Here's another rather rare instance where I've used ANY_VALUE inBigQuery. It's basically an aggregation function like SUM or COUNT except it retrieves a arbitrary value from the grouping. I've posted about ANY_VALUE before, but today's query was a bi...
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

Here's a basic thing that can save you a bit of time when analyzing data or validating data transformations.
So I've previously posted about how in BigQuery using LIMIT for query output does not yield any cost saving as it has no effect on amount on data being processed - just how many results are returned to you.
But there are still cases where I use LIMIT.

Say I'm validating some data and I want to check an assumption I have about the data. For instance, knowing that even a few duplicate records exist indicates me that the problem exists and provides an example to investigate.
I do not need to know all the possible duplicates in the table, therefore I use LIMIT to get at least one observation that will contradict what I'm expecting.
And even with LIMIT, if I don't get anything back, it means that the query hasn't found any matching rows which validates my initial hypothesis.
On a big enough table, one could notice the query execution time difference between using LIMIT and not using it. Again, there is no cost difference, but your time also costs 😁.
P.S. This is not to say that LIMIT is completely irrelevant to performance in BigQuery. Check out this post for a case where LIMIT does make a difference!
Found it useful? Subscribe to my Analytics newsletter at notjustsql.com.
Enjoyed this? Here are some related articles you might find useful: