Working with joins

A join creates a relationship between two cubes in your Cube project. Cube supports three types of join relationships often found in SQL databases:

one_to_one
one_to_many
many_to_one

To use an example, let's use two cubes, customers and orders:

YAML

cubes:
  - name: customers
    # ...

    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

      - name: company
        sql: company
        type: string

  - name: Orders
    # ...

    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

      - name: customer_id
        sql: customer_id
        type: number

We could add a join to the customers cube:

YAML

cubes:
  - name: customers
    # ...

    joins:
      - name: orders
        relationship: one_to_many
        sql: "{CUBE}.id = {orders.customer_id}"

The join above means a customer has many orders. Let's send the following JSON query:

{
  "dimensions": ["orders.status", "customers.company"],
  "measures": ["orders.count"],
  "timeDimensions": [{
    "dimension": "orders.created_at"
  }],
  "order": {"customers.company": "asc"}
}

The query above will generate the following SQL:

SELECT
  "orders".status "orders__status",
  "customers".company "customers__company",
  count("orders".id) "orders__count"
FROM
  public.customers AS "customers"
  LEFT JOIN public.orders AS "orders"
    ON "customers".id = "orders".customer_id
GROUP BY 1, 2
ORDER BY 2 ASC
LIMIT 10000

However, if we have guest checkouts, that would mean we would have orders with no matching customer. Looking back at the one_to_many relationship and its' resulting SQL, any guest checkouts would be excluded from the results. To remedy this, we'll remove the join from the customers cube and instead define a join with a many_to_one relationship on the orders cube:

YAML

cubes:
  - name: orders
    # ...

    joins:
      - name: customers
        relationship: many_to_one
        sql: "{CUBE}.customer_id = {customers.id}"

In the above data model, our orders cube defines the relationship between itself and the customer cube. The same JSON query now results in the following SQL query:

SELECT
  "orders".status "orders__status",
  "customers".company "customers__company",
  count("orders".id) "orders__count"
FROM
  public.orders AS "orders"
  LEFT JOIN public.customers AS "customers"
    ON "orders".customer_id = "customers".id
GROUP BY 1, 2
ORDER BY 2 ASC
LIMIT 10000

As we can see, the base table in the query is orders, and customers is in the LEFT JOIN clause; this means any orders without a customer will also be retrieved.

In Cube, joins only need to be defined from one direction. In the above example, we explicitly removed the one_to_many relationship from the customer cube; not doing so would cause the query to fail as Cube would be unable to determine a valid join path. Click here to learn more about how the direction of joins affects query results.

Many-to-many joins

A many-to-many relationship occurs when multiple records in a cube are associated with multiple records in another cube.

For example, let's say we have two cubes, topics and posts, pointing to the topics and posts tables in our database, respectively. A post can have more than one topic, and a topic may have more than one post.

In a database, you would most likely have an associative table (also known as a junction table or cross-reference table). In our example, this table name might be post_topics.

You can jump to this section if you don't have an associative table in your database.

The diagram below shows the tables posts, topics, post_topics, and their relationships.

Many-to-Many Entity Diagram for posts, topics and post_topics

In the same way the post_topics table was specifically created to handle this association in the database, we need to create an associative cube post_topics, and declare the relationships from it to topics cube and from posts to post_topics.

The following example uses the one_to_many relationship on the post_topics cube; this causes the direction of joins to be posts → post_topics → topics. Read more about direction of joins here.

YAML

cubes:
  - name: posts
    sql_table: posts

    joins:
      - name: post_topics
        relationship: many_to_one
        sql: "{CUBE}.id = {post_topics.post_id}"

  - name: topics
    sql_table: topics

    dimensions:
      - name: post_id
        sql: id
        type: string
        primary_key: true

  - name: post_topics
    sql_table: post_topics

    joins:
      - name: topic
        relationship: one_to_many
        sql: "{CUBE}.topic_id = {topics.id}"

    dimensions:
      - name: post_id
        sql: post_id
        type: string

In scenarios where a table doesn't define a primary key, one can be generated using SQL:

YAML

cubes:
  - name: post_topics
    # ...

    dimensions:
      - name: id
        sql: "CONCAT({CUBE}.post_id, {CUBE}.topic_id)"
        type: number
        primary_key: true

Using virtual associative cube

Sometimes there is no associative table in the database, when in reality, there is a many-to-many relationship. In this case, the solution is to extract some data from existing tables and create a virtual (not backed by a real table in the database) associative cube.

Let’s consider the following example. We have tables emails and transactions. The goal is to calculate the amount of transactions per campaign. Both emails and transactions have a campaign_id column. We don’t have a campaigns table, but data about campaigns is part of the emails table.

Let’s take a look at the emails cube first:

YAML

cubes:
  - name: emails
    sql_table: emails

    measures:
      - name: count
        type: count

    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

      - name: campaign_name
        sql: campaign_name
        type: string

      - name: campaign_id
        sql: campaign_id
        type: number

We can extract campaigns data into a virtual campaigns cube:

YAML

cubes:
  - name: campaigns
    sql: >
      SELECT
        campaign_id,
        campaign_name,
        customer_name,
        MIN(created_at) AS started_at
      FROM emails
      GROUP BY 1, 2, 3

    measures:
      - name: count
        type: count

    dimensions:
      - name: id
        sql: campaign_id
        type: string
        primary_key: true

      - name: name
        sql: campaign_name
        type: string

The following diagram shows our data model with the Campaigns cube:

Many-to-Many Entity Diagram for emails, campaigns and transactions

The last piece is to finally declare a many-to-many relationship. This should be done by declaring a one_to_many relationship on the associative cube, campaigns in our case.

YAML

cubes:
  - name: emails
    sql_table: emails

    joins:
      - name: campaigns
        relationship: many_to_one
        sql: >
          {CUBE}.campaign_id = {campaigns.id} AND
          {CUBE}.customer_name = {campaigns.customer_name}

    measures:
      - name: count
        type: count

    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

      - name: campaign_name
        sql: campaign_name
        type: string

      - name: campaign_id
        sql: campaign_id
        type: number

  - name: campaigns

    joins:
      - name: transactions
        relationship: one_to_many
        sql: >
          {CUBE}.customer_name = {transactions.customer_name} AND
          {CUBE}.campaign_id = {transactions.campaign_id}
    
    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

      - name: customer_name
        sql: customer_name
        type: string

Directions of joins

The direction of joins greatly influences the final result set. It can be explicitly controlled on a view level.

As an example, let's take two cubes, orders and customers:

YAML

cubes:
  - name: orders
    sql_table: orders

    measures:
      - name: count
        sql: id
        type: count

      - name: total_revenue
        sql: revenue
        type: sum
    
    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true
    
      - name: customer_id
        sql: customer_id
        type: number

  - name: customers
    sql_table: customers

    measures:
      - name: count
        sql: id
        type: count
    
    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true

With the given data model, we have two valid analytics use cases that require different join directions.

The first case is to calculate the total revenue per customer. To do this, we'll use the total_revenue measure that is defined on the orders cube. We need to be aware that orders can be placed without customer registration (anonymous customers/guest checkouts). Therefore, we should start the join from the orders cube onto the customers cube to ensure that we do not lose data from anonymous orders.

YAML

views:
  - name: total_revenue_per_customer
    description: Total revenue per customer

    cubes:
      - join_path: orders
        includes:
          - total_revenue
          - created_at

      - join_path: orders.customers
        includes:
          - company

We can query this view as follows:

{
  "dimensions": ["total_revenue_per_customer.company"],
  "measures": ["total_revenue_per_customer.total_revenue"],
  "timeDimensions": [{
    "dimension": "total_revenue_per_customer.created_at"
  }]
}

The second case is to find customers who have not placed any orders. We will use the count measure on the customers cube for that. In this case, we should join the customers cube with the orders cube to find customers with zero orders placed. The reverse order of joins would result in a dataset without data for customers with no orders.

YAML

views:
  - name: customers_without_orders
    description: Customers without orders

    cubes:
      - join_path: customers
        includes: 
          - company

      - join_path: customers.orders
        prefix: true
        includes: 
          - created_at
          - count

We can then query the cube as follows:

{
  "dimensions": ["customers_without_orders.company"],
  "timeDimensions": [{
    "dimension": "customers_without_orders.orders_created_at"
  }],
  "filters": [{
    "member": "customers_without_orders.orders_count",
    "operator": "equals",
    "values": ["0"]
  }]
}

Transitive join pitfalls

Let's consider an example where we have a many-to-many relationship between users and organizations through an organization_users cube:

YAML

cubes:
  - name: users
    # ...
    
    measures:
      - name: count
        type: count
    
    dimensions:
      - name: id
        sql: id
        type: string
        primary_key: true

  - name: organization_users
    # ...

    joins:
      - name: users
        sql: "{CUBE}.user_id = {users.id}"
        relationship: one_to_many

      - name: organizations
        sql: "{CUBE}.organization_id = {organizations.id}"
        relationship: one_to_many

    dimensions:
      - name: id
        # Joins require a primary key, so we'll create one on-the-fly
        sql: "CONCAT({CUBE}.user_id, ':', {CUBE}.organization_id)"
        type: string
        primary_key: true

  - name: organizations
    # ...

    dimensions:
      - name: id
        sql: id
        type: string
        primary_key: true

      - name: name
        sql: category
        type: string

Let's try and execute a query:

{
  "measures": ["users.count"],
  "dimensions": ["organizations.name"]
}

You'll get an error: Error: Can't find join path to join 'users', 'organizations'. The problem is that joins are directed and if we try to connect users and organizations there's no path from users to organizations or either from organizations to users. One possible solution is to move the users → organization_users join from organization_users cube to users, although this affects the query semantics and thus the final results:

YAML

cubes:
  - name: Users
    # ...

    joins:
      - name: organization_users
        sql: "{organization_users.user_id} = {users.id}"
        relationship: one_to_many

    measures:
      - name: type
        type: count

  - name: organization_users
    # ...

    joins:
      - name: organizations
        sql: "{organization_users.organization_id} = {organizations.id}"
        relationship: one_to_many

Did you find this page useful?