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:

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JavaScript

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:

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JavaScript

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:

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JavaScript

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.

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JavaScript

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:

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JavaScript

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:

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JavaScript

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:

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JavaScript

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.

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JavaScript

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:

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JavaScript

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.

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JavaScript

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.

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JavaScript

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 companies through the companies_to_users cube:

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JavaScript

cubes:
  - name: users
    sql: >
      SELECT 1 AS id, 'Ali' AS name UNION ALL
      SELECT 2 AS id, 'Bob' AS name UNION ALL
      SELECT 3 AS id, 'Eve' AS name
 
    measures:
      - name: count
        type: count
 
    dimensions:
      - name: id
        sql: id
        type: string
        primary_key: true
 
  - name: companies
    sql: >
      SELECT 11 AS id, 'Acme Corporation' AS name UNION ALL
      SELECT 12 AS id, 'Stark Industries' AS name
 
    dimensions:
      - name: id
        sql: id
        type: string
        primary_key: true
 
      - name: name
        sql: name
        type: string
 
  - name: companies_to_users
    sql: >
      SELECT 11 AS company_id, 1 AS user_id UNION ALL
      SELECT 11 AS company_id, 2 AS user_id UNION ALL
      SELECT 12 AS company_id, 3 AS user_id
 
    joins:
      - name: users
        sql: "{CUBE}.user_id = {users.id}"
        relationship: one_to_many
 
      - name: companies
        sql: "{CUBE}.company_id = {companies.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}.company_id)"
        type: string
        primary_key: true

With this data model, querying users.count as a measure and companies.name as a dimension would yield the following error: Can't find join path to join 'users', 'companies'.

The root cause is that joins are directed and there's no join path that goes by join definitions in the data model from users to companies or in the opposite direction.

In Data Graph, you can see that both users and companies are to the right of companies_to_users, meaning that there's no way to go from users to companies moving left to right or right to left:

One possible solution is to move one of two joins from the companies_to_users cube to either users or companies cube. Please note that it would affect the query semantics and thus the final result:

YAML

JavaScript

cubes:
  - name: users
 
    joins:
      - name: companies_to_users
        sql: "{CUBE}.id = {companies_to_users}.user_id"
        relationship: one_to_many
 
    # ...
 
  - name: companies_to_users
 
    joins:
      # - name: users
      #   sql: "{CUBE}.user_id = {users.id}"
      #   relationship: one_to_many
 
      - name: companies
        sql: "{CUBE}.company_id = {companies.id}"
        relationship: one_to_many
 
    # ...

Now there's a join path that goes by join definitions in the data model from users to companies_to_users to companies. Data Graph also shows that you can reach companies from cubes by going left to right.

Data blending Syntax