Relationships

Relationship are a way of telling Whaly how your datasets are linked together.

It's a great way to:

• Combine data from various source

• Create detailed explorations

How can I configure relationship?

To configure a dataset relationship, go to the information tab and then scroll to the relationship block and click on Add a relationship.

Adding relationships

In order to add a relationship you need to provide:

• A dataset which will be related to the current dataset

• A column from the current dataset

• A column from the related dataset that will match the one selected above in values and in type (example => String can only match String columns)

• A type of relationship

  • Has many

  • Belongs to

  • Has one

Understanding Relationship Type

Let's imagine we have the three following tables:

Orders

| order_id | amount   | user_id  |
|----------|----------|----------|
| 1        | 90       | 1        | 
| 2        | 10       | 1        |
| 3        | 50       | 2        |
| 4        | 30       | 1        |
| 5        | 20       | 2        |

Users

| user_id  | name            | address_id  |
|----------|-----------------|-------------|
| 1        | Rick Sanchez    | 1           | 
| 2        | Archer Sterling | 2           |

Address

| address_id  | name                 |
|-------------|----------------------|
| 1           | 6910 Smith Residence |
| 2           | Penthouse NYC        |

In this example a User has multiple orders therefore when setting up relationships we will do:

Relationship 1:

• From dataset: User

• To dataset: Order

• Type has many

• From Column: user_id

• To column: : user_id

Please note that "has many" is the opposite of "belongs to", therefore this relationship would have been legit too:

Relationship 2:

• From dataset: Order

• To dataset: User

• Type belongs to

• From Column: user_id

• To column: : user_id

Whenever a relationship is created, the symmetric relationship is automatically created on the other dataset.

As we have the same number of rows in both the Users and Address as a User can have a single Address and vice versa, we will create a one to one relationship:

Relationship 3:

• From dataset: User

• To dataset: Address

• Type: has one

• From Column: address_id

• To column: address_id

The "has one" relationship is quite rare in real world, it's relevant when you have exactly the same number of rows in the two related datasets.