Performance Best Practice: For optimal query performance, we recommend using wide tables wherever possible and minimising joins in the BI layer. While we offer advanced features like fanout protection to help with complex relationships, handling data transformations and complex logic directly in your SQL models will generally yield better performance than relying heavily on joins at query time. Consider pre-joining related data during your data modeling process rather than joining tables on-the-fly in dashboards and reports.

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Adding joins in your models

Joins are defined at the same level as the model parameters in your YAML file. 
models:
  - name: accounts
    meta:
      primary_key: id
      joins:
        - join: deals
          type: left
          sql_on: ${accounts.id} = ${deals.account_id}
          fields: [unique_deals, new_deals, won_deals, lost_deals, stage]
          relationship: one-to-many
When you open Lightdash, the dimensions and metrics from your joined model will appear below the ones in your selected model.
screenshot-joined-table
  • Omit fields to include all metrics and dimensions defined in the joined table.
  • When joined fields reference other fields, those other fields must be included.
  • The sql_on statement can include references to user attributes for row-level filtering.

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Rename a joined model

When joining tables, you may want to rename for readability. This can be done with the label tag, for example on the messages model it makes sense to rename the joined users table as “Sender”: 
models:
  - name: messages
    meta:
      joins:
        - join: users
          label: Sender
          sql_on: ${messages.sent_by} = ${users.user_id}

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Join the same table multiple times

If you need to join a table multiple times, you can use an alias to distinguish between the different tables. A common use case is joining a user table multiple times to another table depending on the type of user. For example this messages model has both a sender and a recipient: 
models:
  - name: messages
    meta:
      joins:
        - join: users
          alias: sender
          sql_on: ${messages.sent_by} = ${sender.user_id}
        - join: users
          alias: recipient
          sql_on: ${messages.sent_to} = ${recipient.user_id}
Note the following important differences when using alias in joins:
  1. You must reference the fields in the model using the new alias. Notice that the joins above use ${sender.user_id} rather than ${users.user_id}.
  2. Because of the above, any fields in the base model or joined model that reference any field ${users.*} will fail to compile. Be careful of aliasing tables that are used in the base model.
  3. Joined models are automatically relabelled with the alias but you may also customise this using the label: field as above.

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Specify your join type

If you want to specify the type of join to be used in your SQL query, use the type field in your YAML configuration file. Set its value to one of the following: inner, left, right, or full. This will determine how the tables are joined in your query, aligning with SQL’s inner join, left outer join, right outer join, or full outer join respectively. Here’s an example of how to specify a join type: 
models:
  - name: messages
    meta:
      joins:
        - join: users
          type: inner
          sql_on: ${messages.sent_by} = ${users.user_id}
By default, if no type is specified, Lightdash will use a left join.
Here’s a table to help you understand what each join type means and how it translates to SQL:
Join TypeGenerated SQLDescription
innerinner joinReturns rows that have matching values in both tables.
leftleft outer joinReturns all rows from the left table, and the matching rows from the right table. Non-matching rows will have NULL for right table’s columns.
rightright outer joinReturns all rows from the right table, and the matching rows from the left table. Non-matching rows will have NULL for left table’s columns.
fullfull outer joinReturns all rows when there is a match in either the left or right table records. Non-matching rows will have NULL for columns of the table that lacks a match.

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inner

An inner join returns rows that have matching values in both tables. For example, if you have a users table and a subscriptions table, an inner join would return only the users who have a subscription. Here’s an example of how to specify an inner join: 
models:
  - name: users
    meta:
      joins:
        - join: subscriptions
          sql_on: ${users.user_id} = ${subscriptions.user_id}
          type: inner

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left

A left join returns all rows from the left table (i.e. the model where you’re adding the join to), and the matching rows from the right table (i.e. the model you’ve specified in - join:). Non-matching rows will have NULL for right table’s columns. For example, if you have a users table and a subscriptions table, a left join would return all users, and the subscription information for users who have a subscription. Here’s an example of how to specify a left join: 
models:
  - name: users
    meta:
      joins:
        - join: subscriptions
          sql_on: ${users.user_id} = ${subscriptions.user_id}
          type: left # you can omit this, as left is the default
A right join returns all rows from the right table (i.e. the model you’ve specified in - join: ), and the matching rows from the left table (i.e. the model where you’re adding the join to). Non-matching rows will have NULL for left table’s columns. For example, if you have a users table and a subscriptions table, a right join would return all subscriptions, and the user information for users who have a subscription. Here’s an example of how to specify a right join: 
models:
  - name: users
    meta:
      joins:
        - join: subscriptions
          sql_on: ${users.user_id} = ${subscriptions.user_id}
          type: right

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full

A full join returns all rows when there is a match in either the left or right table records. Non-matching rows will have NULL for columns of the table that lacks a match. For example, if you have a users table and a subscriptions table, a full join would return all users and all subscriptions, and the subscription information for users who have a subscription.

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Defining join relationships

You can define the relationship between tables in your joins to help Lightdash show warnings and generate the appropriate SQL. This is especially useful for preventing SQL fanouts issues described in the SQL fanouts section. To define a relationship, add the relationship field to your join configuration. 
models:
  - name: users
    meta:
      primary_key: user_id
      joins:
        - join: orders
          sql_on: ${users.user_id} = ${orders.user_id}
          relationship: one-to-many
Make sure that you consider the direction of the join when defining the relationship. If you incorreclty define the join relationship, your will be affected by fanouts.
The following join relationships are supported:
  • one-to-many - Starting table has 1 record, joined table has many matches
  • many-to-one - Starting table has many records, joined table has 1 match
  • one-to-one - Starting table has 1 record, joined table has 1 match
  • many-to-many - Multiple records in the starting table match multiple records in the joined table

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Always join a table

If you need a table to always be joined, you can set the always field to true. 
models:
  - name: messages
    meta:
      joins:
        - join: users
          sql_on: ${messages.sent_by} = ${users.user_id}
          always: true
This will make sure that even when you don’t select any of the fields from the joined table it will still be joined in the query.

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Only select a subset of fields from a join

Use the fields tag to select a subset of fields from a join. This is useful if you want to join a model but only a few of its fields are useful in the joined context. For example this messages model only needs the name andemail fields from the users model. Note we must also include the user_id field since it’s needed for the join. 
models:
  - name: messages
    meta:
      joins:
        - join: users
          sql_on: ${messages.sent_by} = ${users.user_id}
          fields: [user_id, email, name]

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Using joined dimensions or metrics in your YAML

Once you’ve joined a model, you can reference the metrics and dimensions from your joined model in your configurations. For example, I can filter one of my metrics using a dimension from my joined model, like this: 
models:
  - name: users
    meta:
      joins:
        - join: web_sessions
          sql_on: ${web_sessions.user_id} = ${users.user_id}
        - join: subscriptions
          sql_on: ${subscriptions.user_id} = ${users.user_id} AND ${subscriptions.is_active}

    columns:
      - name: user_id
        meta:
          metrics:
            num_unique_premium_user_ids:
              type: count_distinct
              filters:
                - subscriptions.plan: premium
You can also reference joined metrics and dimensions in custom sql, like this: 
models:
  - name: users
    meta:
      joins:
        - join: web_sessions
          sql_on: ${web_sessions.user_id} = ${users.user_id}
        - join: subscriptions
          sql_on: ${subscriptions.user_id} = ${users.user_id} AND ${subscriptions.is_active}

    columns:
      - name: user_id
        meta:
          dimension:
            sql: IF(${subscriptions.plan} IS NULL, NULL, ${user_id})
          metrics:
            num_unique_premium_user_ids:
              type: count_distinct
              sql: IF(${subscriptions.plan} = 'premium', ${user_id}, NULL)
Every joined field that you reference in a YAML file adds a dependency that you’ll have to include in all future references to that model.For example, you might define deals.unique_enterprise_deals by using a joined field in a filter: ${accounts.segment} = 'Enterprise'.Then later you need to join deals to a marketing_attribution model.The unique_enterprise_deals metric must be excluded from the join unless you also join accounts to get the accounts.segment field.Check out our dimensions and metrics reference docs to see all of the other configurations you can use with your joined fields.

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Hide joined tables

Sometimes, you need to use an intermediate model to join two models together and don’t want to show its columns in the list of available metrics/dimensions. You can add a hidden: true tag to joins, like this to hide these intermediate models: 
models:
  - name: users
    meta:
    joins:
      - join: map_users_organizations
        sql_on: users.user_id = map_users_organizations.user_id
        hidden: true
      - join: organizations
        sql_on: organizations.organization_id = map_users_organizations.organization_id
Using just the hidden tag in the join and changing nothing else will remove the joined table from the sidebar of metrics/dimensions in users, but it will still appear in the Tables list as a table to explore on its own.
You can adjust which tables show up in the Tables list by adjusting your tables configuration under project settings, read the guide here.

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SQL fanouts

When joining tables in SQL, it can accidentally inflate your metrics if those tables have one-to-many relationships. Understanding when this happens helps you know which metrics need special handling. When dealing with joins that might cause metric inflation, certain types of calculations remain safe to use.

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Safe vs. risky metrics

These metrics stay accurate even when the data contains duplicates:
  • min and max: min(order_total) and max(order_total) give you the same answer whether you have duplicates or not, because they’re looking for the extreme values, not counting or adding things up. The maximum value in a set doesn’t change just because that value appears multiple times.
  • Aggregate functions with distinct: count(distinct user_id) ensures each user is counted only once, regardless of how many times they appear in the joined result.
These metrics can give you the wrong answers:
  • sum, count, avg without using distinct
  • Custom calculations that treat duplicated rows as separate data points

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Common scenarios and what gets affected

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Handling fanouts

Lightdash can automatically handle deduplicating metrics that are inflated due to fanouts if join relationships are specified in YAML as described in Defining join relationships, above. For example: 
models:
  - name: users
    meta:
      primary_key: ${user_id} 
      joins:
        - join: organizations 
          sql_on: ${organizations.organization_id} = ${users.organization_id}
          relationship: many-to-one 
        - join: orders
          sql_on: ${users.user_id} = ${orders.user_id}
          relationship: one-to-many
Specifying a primary_key and the join relationship allows Lightdash to:
  • Understand the intended row-level granularity
  • Prevent metric inflation by deduplicating joined rows
Once you’ve included primary keys and a join relationship, Lightdash will add CTEs to the compiled SQL query that ensure metrics are not inflated.

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How Lightdash solves SQL fanouts

Lightdash uses a pattern of Common Table Expressions (CTEs) to solve the fanout problem. Here’s how it works:
  1. cte_keys: Contains dimensions (like payment_method, order_id) that define the grain of your final results and the primary keys. Any field you want to GROUP BY in your final output should be included here.
  2. cte_metrics: Performs calculations on metrics while maintaining the correct grain established by the keys CTE. This prevents double-counting when aggregating across related tables.
  3. cte_unaffected: Calculates all metrics that are not affected by fanouts. This includes metrics that exclude duplicates by definition (i.e. MIN(), MAX() and COUNT(DISTINCT)) as well as metrics that are calculated on the table that is on the many side of an one-to-many or many-to-one join relationship. For example, if you have joined accounts to deals using a one-to-many join relationship, SUM(deals.amount) would be calculated in this unaffected_cte because the deals data is not susceptible to fanouts.
  4. final: Join the metrics CTEs together to create the complete result set.
Lightdash creates a separate cte_keys and cte_metrics for each table that contains metrics with fanouts. This is why you’ll see names like “cte_keys_orders” and “cte_metrics_orders” in the example below, indicating they’re specific to the orders table.
Examples
# Step 1: Create cte_keys that determine the final grain of your results i.e. whatever we will group by.
# Exclude fields that we use in aggregations to calculate metrics e.g. `"orders".amount`
WITH cte_keys_orders AS (
    SELECT DISTINCT
      "orders".status AS "orders_status", -- grouping dimension
      "orders".order_id AS "pk_order_id"  -- primary key
    FROM "postgres"."jaffle"."payments" AS "payments"
    LEFT OUTER JOIN "postgres"."jaffle"."orders" AS "orders"
      ON ("orders".order_id) = ("payments".order_id)
),
# Step 2: Calculate metrics that are affected by fanouts
cte_metrics_orders AS (
    SELECT
      cte_keys_orders."orders_status",
      SUM("orders".amount) AS "orders_total_order_amount" -- order metric (affected by fanout)
    FROM cte_keys_orders
    LEFT JOIN "postgres"."jaffle"."orders" AS "orders" ON cte_keys_orders."pk_order_id" = "orders".order_id -- join with primary keys
    GROUP BY 1 -- Note orders_status are grouping dimensions
),
# Step 3: Calculate metrics that are not affected by fanouts
cte_unaffected AS (
    SELECT
      "orders".status AS "orders_status",
      COUNT(DISTINCT "payments".payment_id) AS "payments_unique_payment_count" -- payment metric (NOT affected by fanout)
    FROM "postgres"."jaffle"."payments" AS "payments"
    LEFT OUTER JOIN "postgres"."jaffle"."orders" AS "orders"
      ON ("orders".order_id) = ("payments".order_id)
    GROUP BY 1 -- Note orders_status are grouping dimensions
)
# Step 4: Join the metrics CTEs together to create the final result with properly calculated metrics
SELECT
  cte_unaffected.*,
  cte_metrics_orders."orders_total_order_amount" AS "orders_total_order_amount"
FROM cte_unaffected
INNER JOIN cte_metrics_orders ON (
  cte_unaffected."orders_status" = cte_metrics_orders."orders_status" OR ( cte_unaffected."orders_status" IS NULL AND cte_metrics_orders."orders_status" IS NULL )
)
ORDER BY "orders_total_order_amount" DESC
LIMIT 500

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Known limitations

There are a few situations where Lightdash doesn’t currently handle inflated metrics: