Tables reference

You can run queries against the tables in your Lightdash project.

Tables are built from dbt models (either one, or many joined together).

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Adding Tables to your project

Tables come from dbt models that have been defined in your dbt project's schema.yml files.

If your dbt model has been defined in a .yml file, it will appear in Lightdash as a table.

For example, if we had this in our schema.yml files in dbt, we'd see a Table called users in Lightdash.

version: 2

models:
  - name: users

You can read more about adding Tables to Lightdash here.

Table configuration

You can customize your Tables in your dbt model's YAML file. Here's an example of the properties used in defining a Table:

version: 2

models:
  - name: my_table
    meta:
      label: 'My Custom Table Name'
      order_fields_by: 'label'
      group_label: 'Revenue'
      sql_filter: ${date_dimension} > '2020-01-01'
      joins:
        - join: my_other_table
          sql_on: ${my_table.column_a} = ${my_other_table.column_a}
      required_attributes:
        is_admin: 'true'

Here are all of the properties you can customize:

Property	Value	Note
label	string	Custom label. This is what you'll see in Lightdash instead of the Table name.
order_fields_by	'index' or 'label'	How the fields will be sorted in the sidebar. Read more about the order rules in here
joins	array	Join logic to join other data models to the Table. Read more about joins in here.
metrics	object	Model metrics. Read more about model metrics in here
group_label	string	Group tables in the sidebar. Read more about the group label in here
sql_filter	string	A permanent filter that will always be applied when querying this table directly. Read more about sql_filter below
sql_where	string	Alias for sql_filter
required_attributes	Object with { user_attribute, value }	Limits access to users with those attributes. Read more about user attributes in here
group_details	Object with { label, description }	Describes the groups for dimensions and metrics
required_filters	Array of {filter field: value}	Dimension filters that will be applied when no other filter on those dimension exists. Read more about required_filters below

If you've added a new dbt model to your project, you need to do dbt run + dbt refresh before it will appear in Lightdash

Lightdash gets information about your data models from dbt. But it gets information about the data generated by those data models from your data warehouse.

This means that if you add a new dbt model to your project or update a model so that you're making changes to the table it generates, then you need to do two things before your changes will appear in Lightdash:

1. Materialize the new table/changes to the table (using dbt run). You basically want the data in your data warehouse to be the new table you're expecting. So you need to do dbt run to update the table from the data model you just changed.

2. Click dbt refresh in Lightdash. This will re-sync your dbt project in Lightdash so that any changes you made to your dbt models is shown in Lightdash (e.g. adding a new table).

Order fields by

By default, the fields in your sidebar for any table will appear alphabetically (order_fields_by: "label"). Sometimes, you might not want your fields to appear alphabetically, but instead, in the same order as they are in your model's dbt .yml file. You can achieve this by setting the order_fields_by parameter in your table's meta tag to index, like this:

version: 2

models:
  - name: my_table
    meta:
      order_fields_by: 'index'
    columns:
      - name: user_id
      - name: product_id
      - name: account_id

So, in the example above, the fields in the sidebar for "My Table" would appear in the order:

• user_id
• product_id
• account_id

Instead of being listed alphabetically.

Here are some other things worth mentioning about the order_fields_by parameter:

• By default, order_fields_by is set to label, which means that your fields will appear in the table listed alphabetically.

• Since metrics can be declared in multiple places within your .yml (as a dbt metric, in the model meta tag, under a dimension's meta), we force the following order on metrics if you set order_fields_by to index:

  • dbt metrics appear first
  • then, metrics defined in the model's meta
  • then, metrics defined in the dimensions' meta

• Group labels inherit the index of the first dimension that use them.

Group label

If you set this property, the table will be grouped in the sidebar with other tables with the same group label.

The tables in your sidebar will appear in the following order:

• Group labels appear first, alphabetically
• Ungrouped tables appear after the grouped tables in the sidebar, alphabetically
• Tables within the groups are also ordered alphabetically

SQL filter (row-level security)

sql_filter adds a filter to the table that cannot be removed in Lightdash. It is automatically added to the compiled SQL when running queries.

For example:

models:
  - name: sales
    meta:
      sql_filter: ${TABLE}.sales_region = 'EMEA'

Any queries that I run using the Sales table in Lightdash will always have a filter for sales_region = 'EMEA' in their compiled SQL

SELECT
  [...]
FROM `lightdash`.`prod`.`sales`

WHERE sales_region = 'EMEA'

Row-level security using user attributes

Using sql_filter with user attributes allows you to set up row-level security in your tables. You can reference user attributes in your sql_filter using ${lightdash.attributes.my_attribute_name}

For example:

models:
  - name: sales
    meta:
      sql_filter: ${TABLE}.sales_region IN (${lightdash.attributes.sales_region})

sql_filter will only be applied when querying tables directly.

For example:

• Table A is joined to Table B
• Table B has a sql_filter applied to it
• A user queries Table A and adds a field from the joined table (Table B) to their query
• the sql_filter from Table B will not be applied to the query (you would need to add this as a sql_filter to Table A directly for it to apply)

If you reference a dimension from a joined table in your sql_filter, the referenced table will always be joined in your queries.

For example:

• You have Table A which is joined to Table B
• In Table A, you've added a sql_filter: ${TABLE}.sales_region = 'EMEA' OR ${table_b}.sales_region IS NULL
• Table B will always be joined to Table A in your queries (even if there are no fields from Table B selected in your results table)

Required attributes

Lightdash can use user attributes to limit some tables to some users.

In the example below, only users with is_admin attribute true can use the payments table. Users without access to this table will not see it on the tables page or the explore page when joined to other tables.

version: 2
models:
  - name: payments
    meta:
      required_attributes:
        is_admin: "true"

If a user without access to this table runs a query that contains this table, they will get a Forbidden error.

Required Filters

Use required_filters to define filters on Dimensions that will be applied when no other user-defined filter on those Dimensions exists. This allows to enforce a default filter on a Dimension when querying a table. This defined required_filters will be replaced by any user-defined filter on the same Dimension making sure at least one filter is applied to the Dimension.

This is particularly useful when you want to enforce a default filter to save on query time and potential warehouse costs.

info

A required filter's field reference can't be changed, but its operator (is, is not, etc.) and value can be changed when querying the table.

For example, the defined required_filters on the date Dimension will be in place unless there is another user-defined filter on the date Dimension when querying the orders table, like this:

version: 2

models:
  - name: orders
    meta:
      required_filters:
        - date: 'inThePast 14 days'
    columns:
      - name: date
        description: 'Order date'
        meta:
          dimension:
            type: date

If you have many filters in your list, they will be joined using AND

For example:

version: 2
  - name: orders
    meta:
      required_filters:
        - date: 'inThePast 14 days'
        - status: "completed"
    columns:
      - name: date
        description: 'Order date'
        meta:
          dimension:
            type: date
      - name: status
        description: 'Order status - completed, pending, cancelled'
        meta:
          dimension:
            type: string

In the example above, the orders table will have a default filter of date in the past 14 days and status completed.

Available filter types

Type	Example (in English)	Example (as code)
is	User name is equal to katie	user_name: "katie"
is not	User name is not equal to katie	user_name: "!katie"
contains	User name contains katie	user_name: "%katie%"
does not contain	User name does not contain katie	user_name: "!%katie%"
starts with	User name starts with katie	user_name: "katie%"
ends with	User name ends with katie	user_name: "%katie"
is greater than (number)	Number of orders is greater than 4	num_orders: "> 4"
in the past (date) (interval)	Date is before x (days / months / years)	date: "inThePast 14 months"
in the next (date) (interval)	Date is after x (days / months / years)	date: "inTheNext 14 days"
is greater than or equal to	Number of orders is greater than or equal to 4	num_orders: ">= 4"
is less than	Number of orders is less than 4	num_orders: "< 4"
is less than or equal to	Number of orders is less than or equal to 4	num_orders: "<= 4"
is null	Status is NULL	status: "null"
is not null	Status is not NULL	status: "!null"
is [boolean]	Is complete is true	is_complete: "true"
is not [boolean]	Is complete is false or null	is_complete: "!true"