"How can I implement measures with one-to-many joins?" is a classic problem in dimensional modeling. Let's consider a simple example. Suppose we work with the dimensional model in the image above.

There are two measures in this model: sum(orders.total_cost) and sum(order_items.quantity). The "fact table" is orders, but we're using the term "fact table" loosely because orders has one-to-many relationships with order_items and returns. Suppose we want to answer the question:

- For each state…
- ...what is the total cost of all orders?
- ...and the total number of ordered items?

Put another way, we would like to construct the following pivot table:

Naively, we might use the following SQL query to answer this question:

However, this SQL query would produce a result that violates our intuition about the meaning of these measures. To understand why, suppose our tables are populated with the following data:

**ORDERS**

**ORDER_ITEMS**

**CUSTOMERS**

The result of our naive SQL query would be:

The sum(total_cost) for Alaska is $20, despite the fact that there was only one order in Alaska with a total cost of $10. This occurs because of the one-to-many join from orders to order_items; this single order was counted twice because it had two items in it.

Looker solves this problem using a clever trick called symmetric aggregates. (It is so clever they have patented it.) Looker would generate a query of the form:

The clever part is replacing sum(total_cost) with:

This funky-looking SQL ensures that each row of orders only contributes to the sum(total_cost) measure once. This approach works, but it has several disadvantages:

- It's complicated to write this SQL by hand.
- If you have multiple one-to-many joins, it can generate huge intermediate results.
- Distinct aggregates are slower than their simple counterparts.

If you're trying to accomplish the same thing with hand-written SQL, there is an alternative approach that solves these problems:

We have turned the one-to-many relationship into a one-to-one relationship by doing multiple levels of aggregation using a subquery. Subqueries have a bad reputation among analysts because older database systems execute them using inefficient recursive evaluation, but any modern data warehouse will generate an efficient plan from the above query.

As a bonus, we've avoided the use of more costly distinct aggregates, and there's no possibility of a "combinatorial explosion" producing huge intermediate results. To my knowledge, no BI tool currently uses this technique, but if you're writing SQL queries by hand, this is a useful trick.