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You have data in one table that you want to combine with data from another table. To do that, you’ll need to use a JOIN to tell your database how the rows from one table relate to the rows in the other table.

For a background on joins, check out Joins in Metabase, which walks through how to create joins using the query builder — no code necessary.

This article shows how to create joins using SQL, and you can follow along in Metabase by clicking on Ask a question -> Native Query, and selecting the Sample Dataset as your data.

Example join using the Sample Dataset

Let’s say we want to ask a question that returns rows with columns from multiple tables. For example, we want to know the order date, ID, and total, but also include some info about the product ordered. Here’s the table we want to produce:

| Order date | Order ID | Product Title       | Product Category | Product Rating | Order total |
| 12/25/2016 | 448      | Rustic Paper Wallet | Gizmo            | 4.6            | 30.86326113 |
| ...        | ...      | ...                 | ...              | ...            | ...         |

Now let’s take a look at the tables in our Sample Dataset. We’ll find that we can get the necessary columns from two tables, the orders table:

| ID  | Person | Product ID | Subtotal    | Tax  | Total       | Discount | Created At | Quantity | Person       | Product ID             |
| 1   | 1      | 14         | 37.64814539 | 2.07 | 39.71814539 |          | 2/11/2019  | 2        | Hudson Borer | Awesome Concrete Shoes |
| ... | ...    | ...        | ...         | ...  | ...         | ...      | ...        | ...      | ...          | ...                    |

and the products table:

| ID  | Ean           | Title               | Category | Vendor                       | Price       | Rating | Created At |
| 1   | 1018947080336 | Rustic Paper Wallet | Gizmo    | Swaniawski, Casper and Hilll | 29.46326113 | 4.6    | 7/19/2017  |
| ... | ...           | ...                 | ...      | ...                          | ...         | ...    | ...        |

Note that in the SQL editor, you can view info about the tables and fields in your database by clicking on the book icon to open up the data reference sidebar. Much of the work of learning how to join tables is getting to know the tables you’re working with and how they relate to each other.

<em>Fig. 1</em>. The <strong>data reference</strong> sidebar in the SQL editor shows info about tables and fields in your database.
Fig. 1. The data reference sidebar in the SQL editor shows info about tables and fields in your database.

We’ll need to grab columns from both of these tables to produce our target results, and we’ll do that using a join.

The query

Here’s the answer up front:

  o.created_at AS "order date", AS "order id",
  p.title AS "product title",
  p.category AS "product category",
  p.rating AS "product rating", AS "order total"
-- joining orders to products
  orders AS o 
  JOIN products AS p ON o.product_id =

Note that the comment (the line starting with --) isn’t necessary; it’s just there to point out the key part of the code: the join statement.

This query tells the database that the rows in the orders table can be combined with the rows in the products table by “lining up” the foreign keys in the orders table to the entity keys (a.k.a. the primary key) of the products table. We’ve also aliased each table (orders AS o and products AS p), so you can tell in the SELECT statement which table each field comes from (o.created_at comes from the orders table, and so on).

By entity key, we mean a column in a table that contains each row’s unique identifier. In the orders table, each row is an order with an ID. The same is true of products in the products table: each row is a product with an ID. By foreign key, we mean a column that references the entity key in another table. In this case, the orders table contains a product_id, which refers to a specific row in the products table. If we check the products table in the data reference sidebar, we’ll see that it has two connections, one to the orders table, and one to the reviews table.

<em>Fig. 2</em>. The <strong>Products</strong> table has connections to the <strong>Orders</strong> and <strong>Reviews</strong> tables.
Fig. 2. The Products table has connections to the Orders and Reviews tables.

We could get the same results by combining every row in products with every row in orders and then filtering with a WHERE clause like this:

  o.created_at as "order date", as "order id",
  p.title as "product title",
  p.category as "product category",
  p.rating as "product rating", as "order total"
-- a join using a WHERE clause to "line up" the entity and foreign keys
  orders AS o, 
  products AS p 
  o.product_id =

Which is saying: combine every row from the orders table with every row from the products table, then filter the results to include only the rows where the product_id in the orders table matches the id in the products table. We recommend that you use the first form (with ON) to avoid confusion: ON always introduces the condition for a join, while WHERE is used for all kinds of filtering. For more, check out Best practices for writing SQL queries.

The term “join” is somewhat misleading, as you’re not connecting the tables. You’re taking the rows from two (or more) different tables and returning a new set of rows that combines columns in both tables.

Let’s dial in on that join statement:

-- joining orders to products
  orders AS o 
  JOIN products AS p ON o.product_id =

When you join tables, you use the ON keyword to specify which field in Table A (orders) corresponds to Table B (products), so the database understands how to “line up” the data. o.product_id = is an expression that resolves to true or false; the database will only return rows where the expression is true.

Joining multiple tables

You can chain multiple joins by listing each join type and its condition. Here’s a join that includes fields from the people table:

  orders AS o 
  JOIN products AS p ON o.product_id =
  JOIN people AS u ON o.user_id =

You’ll need to do a lot of horizontal scrolling here, as this query will yield many columns (all columns from each table, in fact). And you’ll see duplicate information row to row. For example, the results will repeat the customer address for each order the customer has placed.

| ID  | USER_ID | PRODUCT_ID | SUBTOTAL    | TAX | TOTAL       | DISCOUNT | CREATED_AT       | QUANTITY | ID  | EAN           | TITLE               | CATEGORY | VENDOR                       | PRICE       | RATING | CREATED_AT      | ID  | ADDRESS                 | EMAIL                  | PASSWORD                             | NAME         | CITY    | LONGITUDE  | STATE | SOURCE | BIRTH_DATE | ZIP   | LATITUDE | CREATED_AT    |
| 448 | 61      | 1          | 29.46326113 | 1.4 | 30.86326113 |          | 12/25/2016 22:19 | 2        | 1   | 1018947080336 | Rustic Paper Wallet | Gizmo    | Swaniawski, Casper and Hilll | 29.46326113 | 4.6    | 7/19/2017 19:44 | 61  | 7100 Hudson Chapel Road | | 2da78e08-2bf7-41b8-a737-1acd815fb99c | Lina Labadie | Catawba | -81.017265 | NC    | Google | 3/28/1984  | 28609 | 35.69917 | 6/5/2016 4:03 |
| ... | ...     | ...        | ...         | ... | ...         | ...      | ...              | ...      | ... | ...           | ...                 | ...      | ...                          | ...         | ...    | ...             | ... | ...                     | ...                    | ...                                  | ...          | ...     | ...        | ...   | ...    | ...        | ...   | ...      | ...           |

In the query above, you can cut down on the number of columns returned by replacing the * in the SELECT statement to specify only the columns you need.

Joins on multiple conditions

You can include multiple true/false expressions in the ON statement to restrict results. These true/false expressions are known as predicates. We’ve already been using predicates to join the tables above, like o.user_id =

Let’s say we want to know:

  • the average price for orders,
  • by product category,
  • that sold at full price.

We’ll need to calculate the unit price using data from the orders table, and grab the product category and listed price from the products table. Here’s our query:

  p.category AS "Product category", 
  AVG(p.price) AS "Average price", 
  COUNT(*) AS "Count of full price orders" 
  orders AS o
  -- first predicate
  JOIN products AS p ON o.product_id = 
  -- second predicate: calculate the unit price 
  -- and see if it corresponds to the product's listed price.
  AND o.subtotal / o.quantity = p.price 
  -- guard against divide-by-zero scenarios
  o.quantity > 0 

Which gives us:

|Product category|Average price|Count of full price orders|
|Widget          |54.96699655  |168                       |
|Gizmo           |51.49700878  |137                       |
|Gadget          |54.87034242  |136                       |
|Doohickey       |51.69197973  |123                       |

Different types of joins

The particular type of join we’ve been using throughout this article is the default type of join, also known as an inner join. There are other ways you can join tables; see our article on SQL join types.

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