# Syntax

Entities within the data model (e.g., cubes, views, etc.) should be placed under
the `model` [folder][self-folder-structure], follow [naming
conventions][self-naming], and be defined using a supported
[syntax][self-syntax].

## Folder structure

Data model files should be placed inside the `model` folder. You can use the
[`schema_path` configuration option][ref-config-model-path] to override the
folder name or the [`repository_factory` configuration
option][ref-config-repository-factory] to dynamically define the folder name and
data model file contents.

It's recommended to place each cube or view in a separate file, in `model/cubes`
and `model/views` folders, respectively. Example:

```tree
model
├── cubes
│   ├── orders.yml
│   ├── products.yml
│   └── users.yml
└── views
    └── revenue.yml
```

## Model syntax

Cube supports two ways to define data model files: with [YAML][wiki-yaml] or
JavaScript syntax. YAML data model files should have the `.yml` extension,
whereas JavaScript data model files should end with `.js`. You can mix YAML and
JavaScript files within a single data model.

<CodeTabs>

```javascript
cube(`orders`, {
  sql: `
    SELECT *
    FROM orders, line_items
    WHERE orders.id = line_items.order_id
  `
})
```

```yaml
cubes:
  - name: orders
    sql: |
      SELECT *
      FROM orders, line_items
      WHERE orders.id = line_items.order_id

```

</CodeTabs>

You can define the data model statically or build [dynamic data
models][ref-dynamic-data-models] programmatically. YAML data models use
[Jinja and Python][ref-dynamic-data-models-jinja] whereas JavaScript data
models use [JavaScript][ref-dynamic-data-models-js].

It is recommended to default to YAML syntax because of its
simplicity and readability. However, JavaScript might provide more flexibility
for dynamic data modeling.

<ReferenceBox>

See [Cube style guide][ref-style-guide] for more recommendations on syntax and structure.

</ReferenceBox>

## Naming

Common rules apply to names of entities within the data model. All names must:

- Start with a letter.
- Consist of letters, numbers, and underscore (`_`) symbols only.
- Not be a [reserved keyword in Python][link-python-reserved-words], e.g., `from`, `return`, or `yield`.
- When using the DAX API, not clash with the names of columns in [date hierarchies][ref-dax-api-date-hierarchies].

It is also recommended that names use [snake case][wiki-snake-case].

Good examples of names:

- `orders`, `stripe_invoices`, or `base_payments` (cubes)
- `opportunities`, `cloud_accounts`, or `arr` (views)
- `count`, `avg_price`, or `total_amount_shipped` (measures)
- `name`, `is_shipped`, or `created_at` (dimensions)
- `main`, `orders_by_status`, or `lambda_invoices` (pre-aggregations)

## SQL expressions

When defining cubes, you would often provide SQL snippets in `sql` and
`sql_table` parameters.

Provided SQL expressions should match your database SQL dialect, e.g.,
to aggregate a list of strings, you would probably pick the [`LISTAGG`
function][link-snowflake-listagg] in Snowflake and the [`STRING_AGG`
function][link-bigquery-stringagg] in BigQuery.

<CodeTabs>

```yaml
cubes:
  - name: orders
    sql_table: orders

    measures:
      - name: statuses
        sql: "STRING_AGG(status)"
        type: string

    dimensions:
      - name: status
        sql: "UPPER(status)"
        type: string
```

```javascript
cube(`orders`, {
  sql_table: `orders`,

  measures: {
    statuses: {
      sql: `STRING_AGG(status)`,
      type: `string`
    }
  },

  dimensions: {
    status: {
      sql: `UPPER(status)`,
      type: `string`
    }
  }
})
```

</CodeTabs>

<ReferenceBox>

Currently, Cube does not wrap your SQL snippets in parentheses during SQL
generation. In case of non-trivial snippets, this may lead to unexpected results.
Please [track this issue](https://github.com/cube-js/cube/issues/6373).

</ReferenceBox>

### User-defined functions

If you have created a [user-defined function][link-sql-udf] (UDF) in your data
source, you can use it in the `sql` parameter as well.

### Case sensitivity

If your database uses case-sensitive identifiers, make sure to properly
quote table and column names. For example, here's how you can reference
a Postgres table that contains uppercase letters in its name:

<CodeTabs>

```yaml
cubes:
  - name: orders
    sql_table: 'public."Orders"'
```

```javascript
cube(`orders`, {
  sql_table: `public."Orders"`
})
```

</CodeTabs>

## References

To write versatile data models, it is important to be able to reference
members of cubes and views, such as measures or dimensions, as well as
table columns. Cube supports the following syntax for references.

### `column`

Most commonly, you would use bare column names in the `sql` parameter of
measures or dimensions. In the following example, `name` references
the respective column of the `users` table.

<CodeTabs>

```yaml
cubes:
  - name: users
    sql_table: users

    dimensions:
      - name: name
        sql: name
        type: string
```

```javascript
cube(`users`, {
  sql_table: `users`,

  dimensions: {
    name: {
      sql: `name`,
      type: `string`
    }
  }
})
```

</CodeTabs>

This syntax works great for simple use cases. However, if your cubes have
joins and joined cubes have columns with the same name, the generated SQL
query might become ambiguous. See below how to work around that.

### `{member}`

When defining measures and dimensions, you can also reference other members
of the same cube by wrapping their names in curly braces. In the following
example, the `full_name` dimension references `name` and `surname` dimensions
of the same cube.

<CodeTabs>

```yaml
cubes:
  - name: users
    sql_table: users

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

      - name: surname
        sql: "UPPER(surname)"
        type: string

      - name: full_name
        sql: "CONCAT({name}, ' ', {surname})"
        type: string
```

```javascript
cube(`users`, {
  sql_table: `users`,

  dimensions: {
    name: {
      sql: `name`,
      type: `string`
    },

    surname: {
      sql: `UPPER(surname)`,
      type: `string`
    },

    full_name: {
      sql: `CONCAT(${name}, ' ', ${surname})`,
      type: `string`
    }
  }
})
```

</CodeTabs>

This syntax works great for simple use cases. However, there are cases
(like [subquery][ref-subquery]) when you'd like to reference members of
other cubes. See below how to do that.

### `{time_dimension.granularity}`

When referencing a [time dimension][ref-time-dimension], you can specificy a
granularity to refer to a time value with that specific granularity. It can be
one of the [default granularities][ref-default-granularities] (e.g., `year` or
`week`) or a [custom granularity][ref-custom-granularities]:

<CodeTabs>

```yaml
cubes:
  - name: users
    sql_table: users

    dimensions:
      - name: created_at
        sql: created_at
        type: time

        granularities:
          - name: sunday_week
            interval: 1 week
            offset: -1 day

      - name: created_at__year
        sql: "{created_at.year}"
        type: time

      - name: created_at__sunday_week
        sql: "{created_at.sunday_week}"
        type: time
```

```javascript
cube(`users`, {
  sql_table: `users`,

  dimensions: {
    created_at: {
      sql: `created_at`,
      type: `time`,

      granularities: {
        sunday_week: {
          interval: `1 week`,
          offset: `-1 day`
        }
      }
    },

    created_at__year: {
      sql: `${created_at.year}`,
      type: `time`
    },

    created_at__sunday_week: {
      sql: `${created_at.sunday_week}`,
      type: `time`
    }
  }
})
```

</CodeTabs>

### `{cube}.column`, `{cube.member}`

You can qualify column and member names with the name of a cube to remove
the ambiguity when cubes are joined and reference members of other cubes.

<CodeTabs>

```yaml
cubes:
  - name: users
    sql_table: users

    joins:
      - name: contacts
        sql: "{users}.contact_id = {contacts.id}"
        relationship: one_to_one

    dimensions:
      - name: id
        sql: "{users}.id"
        type: number
        primary_key: true

      - name: name
        sql: "COALESCE({users.name}, {contacts.name})"
        type: string

  - name: contacts
    sql_table: contacts

    dimensions:
      - name: id
        sql: "{contacts}.id"
        type: number
        primary_key: true

      - name: name
        sql: "{contacts}.name"
        type: string
```

```javascript
cube(`users`, {
  sql_table: `users`,

  joins: {
    contacts: {
      sql: `${users}.contact_id = ${contacts.id}`,
      relationship: `one_to_one`
    }
  },

  dimensions: {
    id: {
      sql: `${users}.id`,
      type: `number`,
      primary_key: true
    },

    name: {
      sql: `COALESCE(${users}.name, ${contacts.name})`,
      type: `string`
    }
  }
})

cube(`contacts`, {
  sql_table: `contacts`,

  dimensions: {
    id: {
      sql: `${contacts}.id`,
      type: `number`,
      primary_key: true
    },

    name: {
      sql: `${contacts}.name`,
      type: `string`
    }
  }
})
```

</CodeTabs>

In production, using fully-qualified names is generally encouraged since it
removes the ambiguity and keeps data model code maintainable as it grows.
However, always referring to the current cube by its name leads to code
repetition and violates the DRY principle. See below how to solve that.

### `{cube1.cube2.member}`

You can also qualify member names with more than one cube name, separated by a dot, to
provide a [join path][ref-join-paths] and remove the ambiguity of join resolution. Join
paths can be used in [calculated members][ref-calculated-members], [views][ref-views],
and [pre-aggregation][ref-preaggs] definitions.

In the following example, four cubes are joined together, forming a [_diamond
subgraph_][ref-diamond-subgraphs] in the join tree: `a` joins to `b` and `c`, and both
`b` and `c` join to `d`. As you can see, join paths in the `d_via_b` and `d_via_c`
dimensions of `a` are then used to specify which intermediate cube to use when resolving
the dimension from `d`.

<CodeTabs>

```yaml
cubes:
  - name: a
    sql: |
      SELECT 1 AS id UNION ALL
      SELECT 2 AS id UNION ALL
      SELECT 3 AS id

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

      - name: d_via_b
        sql: "{b.d.id}"
        type: number

      - name: d_via_c
        sql: "{c.d.id}"
        type: number

    joins:
      - name: b
        sql: "{a.id} = {b.id}"
        relationship: one_to_one

      - name: c
        sql: "{a.id} = {c.id}"
        relationship: one_to_one

  - name: b
    sql: |
      SELECT 1 AS id UNION ALL
      SELECT 2 AS id UNION ALL
      SELECT 3 AS id

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

    joins:
      - name: d
        sql: "{b.id} = {d.id}"
        relationship: one_to_one

  - name: c
    sql: |
      SELECT 1 AS id UNION ALL
      SELECT 2 AS id UNION ALL
      SELECT 3 AS id

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

    joins:
      - name: d
        sql: "{c.id} = {d.id}"
        relationship: one_to_one

  - name: d
    sql: |
      SELECT 1 AS id UNION ALL
      SELECT 2 AS id UNION ALL
      SELECT 3 AS id

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

```javascript
cube(`a`, {
  sql: `
    SELECT 1 AS id UNION ALL
    SELECT 2 AS id UNION ALL
    SELECT 3 AS id
  `,

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primary_key: true
    },

    d_via_b: {
      sql: `${b.d.id}`,
      type: `number`
    },

    d_via_c: {
      sql: `${c.d.id}`,
      type: `number`
    }
  },

  joins: {
    b: {
      sql: `${a.id} = ${b.id}`,
      relationship: `one_to_one`
    },

    c: {
      sql: `${a.id} = ${c.id}`,
      relationship: `one_to_one`
    }
  }
})

cube(`b`, {
  sql: `
    SELECT 1 AS id UNION ALL
    SELECT 2 AS id UNION ALL
    SELECT 3 AS id
  `,

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primary_key: true
    }
  },

  joins: {
    d: {
      sql: `${b.id} = ${d.id}`,
      relationship: `one_to_one`
    }
  }
})

cube(`c`, {
  sql: `
    SELECT 1 AS id UNION ALL
    SELECT 2 AS id UNION ALL
    SELECT 3 AS id
  `,

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primary_key: true
    }
  },

  joins: {
    d: {
      sql: `${c.id} = ${d.id}`,
      relationship: `one_to_one`
    }
  }
})

cube(`d`, {
  sql: `
    SELECT 1 AS id UNION ALL
    SELECT 2 AS id UNION ALL
    SELECT 3 AS id
  `,

  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primary_key: true
    }
  }
})
```

</CodeTabs>

### `{CUBE}` variable

You can use a handy `{CUBE}` [context variable][ref-context-variables]
(mind the uppercase) to reference the current cube so you don't have to
repeat the its name over and over. It works both for column and member
references.

<CodeTabs>

```javascript
cube(`users`, {
  sql_table: `users`,

  joins: {
    contacts: {
      sql: `${CUBE}.contact_id = ${contacts.id}`,
      relationship: `one_to_one`
    }
  },

  dimensions: {
    id: {
      sql: `${CUBE}.id`,
      type: `number`,
      primary_key: true
    },

    name: {
      sql: `COALESCE(${CUBE}.name, ${contacts.name})`,
      type: `string`
    }
  }
})

cube(`contacts`, {
  sql_table: `contacts`,

  dimensions: {
    id: {
      sql: `${CUBE}.id`,
      type: `number`,
      primary_key: true
    },

    name: {
      sql: `${CUBE}.name`,
      type: `string`
    }
  }
})
```

```yaml
cubes:
  - name: users
    sql_table: users

    joins:
      - name: contacts
        sql: "{CUBE}.contact_id = {contacts.id}"
        relationship: one_to_one

    dimensions:
      - name: id
        sql: "{CUBE}.id"
        type: number
        primary_key: true

      - name: name
        sql: "COALESCE({CUBE.name}, {contacts.name})"
        type: string

  - name: contacts
    sql_table: contacts

    dimensions:
      - name: id
        sql: "{CUBE}.id"
        type: number
        primary_key: true

      - name: name
        sql: "{CUBE}.name"
        type: string
```

</CodeTabs>

Check the `{users.name}` dimension. Referencing another cube in the
dimension definition instructs Cube to make an implicit join to that cube.
For example, using the data model above, we can make the following query:

```json
{
  "dimensions": ["users.name"]
}
```

The resulting generated SQL query would look like this:

```sql
SELECT COALESCE("users".name, "contacts".name) "users__name"
FROM users "users"
LEFT JOIN contacts "contacts"
  ON "users".contact_id = "contacts".id
```

### `{cube.sql()}` function

When defining a cube, you can reference the `sql` parameter of another cube,
effectively reusing the SQL query it's defined on. This is particularly useful
when defining [polymorphic cubes][ref-polymorphism] or using [data blending][ref-data-blending].

Consider the following data model:

<CodeTabs>

```javascript
cube(`organisms`, {
  sql_table: `organisms`
})

cube(`animals`, {
  sql: `
    SELECT *
    FROM ${organisms.sql()}
    WHERE kingdom = 'animals'
  `
})

cube(`dogs`, {
  sql: `
    SELECT *
    FROM ${animals.sql()}
    WHERE species = 'dogs'
  `,

  measures: {
    count: {
      type: `count`
    }
  }
})
```

```yaml
cubes:
  - name: organisms
    sql_table: organisms

  - name: animals
    sql: |
      SELECT *
      FROM {organisms.sql()}
      WHERE kingdom = 'animals'

  - name: dogs
    sql: |
      SELECT *
      FROM {animals.sql()}
      WHERE species = 'dogs'

    measures:
      - name: count
        type: count
```

</CodeTabs>

If you query for `dogs.count`, Cube will generate the following SQL:

```sql
SELECT count(*) "dogs__count"
FROM (
  SELECT *
  FROM (
    SELECT *
    FROM organisms
    WHERE kingdom = 'animals'
  )
  WHERE species = 'dogs'
) AS "dogs"
```

### Curly braces and escaping

As you can see in the examples above, within [SQL expressions][self-sql-expressions],
curly braces are used to reference cubes and members.

In YAML data models, use `{reference}`:

```yaml
cubes:
  - name: orders
    sql: |
      SELECT id, created_at
      FROM {other_cube.sql()}

    dimensions:
      - name: status
        sql: status
        type: string

      - name: status_x2
        sql: "{status} || ' ' || {status}"
        type: string
```

In JavaScript data models, use `${reference}` in [JavaScript template
literals][link-js-template-literals] (mind the dollar sign):

```javascript
cube(`orders`, {
  sql: `
    SELECT id, created_at
    FROM ${other_cube.sql()}
  `,

  dimensions: {
    status: {
      sql: `status`,
      type: `string`
    },

    status_x2: {
      sql: `${status} || ' ' || ${status}`,
      type: `string`
    }
  }
})
```

If you need to use literal, non-referential curly braces in YAML, e.g.,
to define a JSON object, you can escape them with a backslash:

```yaml
cubes:
  - name: json_object_in_postgres
    sql: SELECT CAST('\{"key":"value"\}'::JSON AS TEXT) AS json_column

  - name: csv_from_s3_in_duckdb
    sql: |
      SELECT *
      FROM read_csv(
        's3://bbb/aaa.csv',
        delim = ',',
        header = true,
        columns=\{'time':'DATE','count':'NUMERIC'\}
      )
```

### Non-SQL references

Outside [SQL expressions][self-sql-expressions], `column` is not recognized
as a column name; it is rather recognized as a member name. It means that,
outside `sql` and `sql_table` parameters, you can skip the curly braces and
reference members by their names directly: `member`, `cube_name.member`, or
`CUBE.member`.


<CodeTabs>

```yaml
cubes:
  - name: orders
    sql_table: orders

    dimensions:
      - name: status
        sql: status
        type: string

    measures:
      - name: count
        type: count

    pre_aggregations:
      - name: orders_by_status
        dimensions:
          - CUBE.status
        measures:
          - CUBE.count
```

```javascript
cube(`orders`, {
  sql_table: `orders`,

  dimensions: {
    status: {
      sql: `status`,
      type: `string`
    }
  },

  measures: {
    count: {
      type: `count`
    }
  },

  pre_aggregations: {
    orders_by_status: {
      dimensions: [CUBE.status],
      measures: [CUBE.count]
    }
  }
})
```

</CodeTabs>

## Context variables

In addition to the `CUBE` variable, you can also use a few more [context
variables][ref-context-variables] within your data model. They are
generally useful for two purposes: optimizing generated SQL queries and
defining dynamic data models.

## Troubleshooting

### `Can't parse timestamp`

Sometimes, you might come across the following error message: `Can't parse timestamp:
2023-11-07T14:33:23.16.000`.

It indicates that the data source was unable to recognize the value of a [time
dimension][ref-time-dimension] as a timestamp. Please check that the [SQL
expression](#sql-expressions) of this time dimension evaluates to a `TIMESTAMP` type.

<ReferenceBox>

Check [this recipe][ref-recipe-string-time-dimensions] to see how you can work around
string values in time dimensions.

</ReferenceBox>


[self-folder-structure]: #folder-structure
[self-naming]: #naming
[self-syntax]: #code-syntax
[self-sql-expressions]: #sql-expressions
[ref-dynamic-data-models]: /product/data-modeling/dynamic
[ref-dynamic-data-models-jinja]: /product/data-modeling/dynamic/jinja
[ref-dynamic-data-models-js]: /product/data-modeling/dynamic/javascript
[ref-context-variables]: /product/data-modeling/reference/context-variables
[ref-config-model-path]: /product/configuration/reference/config#schemapath
[ref-config-repository-factory]:
  /product/configuration/reference/config#repositoryfactory
[ref-subquery]: /product/data-modeling/concepts/calculated-members#subquery-dimensions
[wiki-snake-case]: https://en.wikipedia.org/wiki/Snake_case
[wiki-yaml]: https://en.wikipedia.org/wiki/YAML
[link-snowflake-listagg]: https://docs.snowflake.com/en/sql-reference/functions/listagg
[link-bigquery-stringagg]: https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#string_agg
[link-sql-udf]: https://en.wikipedia.org/wiki/User-defined_function#Databases
[ref-time-dimension]: /product/data-modeling/reference/types-and-formats#time
[ref-default-granularities]: /product/data-modeling/concepts#time-dimensions
[ref-custom-granularities]: /product/data-modeling/reference/dimensions#granularities
[ref-style-guide]: /product/data-modeling/recipes/style-guide
[ref-polymorphism]: /product/data-modeling/concepts/polymorphic-cubes
[ref-data-blending]: /product/data-modeling/concepts/data-blending
[link-js-template-literals]: https://developer.mozilla.org/en-US/docs/Learn_web_development/Core/Scripting/Strings#embedding_javascript
[link-python-reserved-words]: https://docs.python.org/3/reference/lexical_analysis.html#keywords
[ref-dax-api-date-hierarchies]: /product/apis-integrations/dax-api#date-hierarchies
[ref-time-dimension]: /product/data-modeling/concepts#time-dimensions
[ref-recipe-string-time-dimensions]: /product/data-modeling/recipes/string-time-dimensions
[ref-views]: /product/data-modeling/concepts#views
[ref-preaggs]: /product/data-modeling/concepts#pre-aggregations
[ref-join-paths]: /product/data-modeling/concepts/working-with-joins#join-paths
[ref-calculated-members]: /product/data-modeling/concepts/calculated-members
[ref-diamond-subgraphs]: /product/data-modeling/concepts/working-with-joins#diamond-subgraphs