superset/docs-v2/docs/installation/sql-templating.mdx

---
title: SQL Templating
hide_title: true
sidebar_position: 10
version: 1
---

## SQL Templating

### Jinja Templates

SQL Lab and Explore supports [Jinja templating](https://jinja.palletsprojects.com/en/2.11.x/) in queries.
To enable templating, the `ENABLE_TEMPLATE_PROCESSING` feature flag needs to be enabled in
`superset_config.py`. When templating is enabled, python code can be embedded in virtual datasets and
in Custom SQL in the filter and metric controls in Explore. By default, the following variables are
made available in the Jinja context:

- `columns`: columns which to group by in the query
- `filter`: filters applied in the query
- `from_dttm`: start `datetime` value from the selected time range (`None` if undefined)
- `to_dttm`: end `datetime` value from the selected time range (`None` if undefined)
- `groupby`: columns which to group by in the query (deprecated)
- `metrics`: aggregate expressions in the query
- `row_limit`: row limit of the query
- `row_offset`: row offset of the query
- `table_columns`: columns available in the dataset
- `time_column`: temporal column of the query (`None` if undefined)
- `time_grain`: selected time grain (`None` if undefined)

For example, to add a time range to a virtual dataset, you can write the following:

```sql
SELECT * from tbl where dttm_col > '{{ from_dttm }}' and dttm_col < '{{ to_dttm }}'
```

To add custom functionality to the Jinja context, you need to to to overload the default Jinja
context in your environment by defining the `JINJA_CONTEXT_ADDONS` in your superset configuration
(`superset_config.py`). Objects referenced in this dictionary are made available for users to use
where the Jinja context is made available.

```python
JINJA_CONTEXT_ADDONS = {
    'my_crazy_macro': lambda x: x*2,
}
```

Besides default Jinja templating, SQL lab also supports self-defined template processor by setting
the `CUSTOM_TEMPLATE_PROCESSORS` in your superset configuration. The values in this dictionary
overwrite the default Jinja template processors of the specified database engine. The example below
configures a custom presto template processor which implements its own logic of processing macro
template with regex parsing. It uses the `$` style macro instead of `{{ }}` style in Jinja
templating.

By configuring it with `CUSTOM_TEMPLATE_PROCESSORS`, a SQL template on a presto database is
processed by the custom one rather than the default one.

```python
def DATE(
    ts: datetime, day_offset: SupportsInt = 0, hour_offset: SupportsInt = 0
) -> str:
    """Current day as a string."""
    day_offset, hour_offset = int(day_offset), int(hour_offset)
    offset_day = (ts + timedelta(days=day_offset, hours=hour_offset)).date()
    return str(offset_day)

class CustomPrestoTemplateProcessor(PrestoTemplateProcessor):
    """A custom presto template processor."""

    engine = "presto"

    def process_template(self, sql: str, **kwargs) -> str:
        """Processes a sql template with $ style macro using regex."""
        # Add custom macros functions.
        macros = {
            "DATE": partial(DATE, datetime.utcnow())
        }  # type: Dict[str, Any]
        # Update with macros defined in context and kwargs.
        macros.update(self.context)
        macros.update(kwargs)

        def replacer(match):
            """Expand $ style macros with corresponding function calls."""
            macro_name, args_str = match.groups()
            args = [a.strip() for a in args_str.split(",")]
            if args == [""]:
                args = []
            f = macros[macro_name[1:]]
            return f(*args)

        macro_names = ["$" + name for name in macros.keys()]
        pattern = r"(%s)\s*\(([^()]*)\)" % "|".join(map(re.escape, macro_names))
        return re.sub(pattern, replacer, sql)

CUSTOM_TEMPLATE_PROCESSORS = {
    CustomPrestoTemplateProcessor.engine: CustomPrestoTemplateProcessor
}
```

SQL Lab also includes a live query validation feature with pluggable backends. You can configure
which validation implementation is used with which database engine by adding a block like the
following to your configuration file:

```python
FEATURE_FLAGS = {
    'SQL_VALIDATORS_BY_ENGINE': {
        'presto': 'PrestoDBSQLValidator',
    }
}
```

The available validators and names can be found in
[sql_validators](https://github.com/apache/superset/tree/master/superset/sql_validators).

### Available Macros

In this section, we'll walkthrough the pre-defined Jinja macros in Superset.

**Current Username**

The `{{ current_username() }}` macro returns the username of the currently logged in user.

If you have caching enabled in your Superset configuration, then by defaul the the `username` value will be used
by Superset when calculating the cache key. A cache key is a unique identifer that determines if there's a
cache hit in the future and Superset can retrieve cached data.

You can disable the inclusion of the `username` value in the calculation of the
cache key by adding the following parameter to your Jinja code:

```
{{ current_username(add_to_cache_keys=False) }}
```

**Current User ID**

The `{{ current_user_id()}}` macro returns the user_id of the currently logged in user.

If you have caching enabled in your Superset configuration, then by defaul the the `user_id` value will be used
by Superset when calculating the cache key. A cache key is a unique identifer that determines if there's a
cache hit in the future and Superset can retrieve cached data.

You can disable the inclusion of the `user_id` value in the calculation of the
cache key by adding the following parameter to your Jinja code:

```
{{ current_user_id(add_to_cache_keys=False) }}
```

**Custom URL Parameters**

The `{{ url_param('custom_variable') }}` macro lets you define arbitrary URL
parameters and reference them in your SQL code.

Here's a concrete example:

- You write the following query in SQL Lab:

  ```
  SELECT count(*)
  FROM ORDERS
  WHERE country_code = '{{ url_param('countrycode') }}'
  ```

- You're hosting Superset at the domain www.example.com and you send your
  coworker in Spain the following SQL Lab URL `www.example.com/superset/sqllab?countrycode=ES`
  and your coworker in the USA the following SQL Lab URL `www.example.com/superset/sqllab?countrycode=US`
- For your coworker in Spain, the SQL Lab query will be rendered as:

  ```
  SELECT count(*)
  FROM ORDERS
  WHERE country_code = 'ES'
  ```

- For your coworker in the USA, the SQL Lab query will be rendered as:

  ```
  SELECT count(*)
  FROM ORDERS
  WHERE country_code = 'US'
  ```

**Explicitly Including Values in Cache Key**

The `{{ cache_key_wrapper() }}` function explicitly instructs Superset to add a value to the
accumulated list of values used in the the calculation of the cache key.

This function is only needed when you want to wrap your own custom function return values
in the cache key. You can gain more context
[here](https://github.com/apache/superset/blob/efd70077014cbed62e493372d33a2af5237eaadf/superset/jinja_context.py#L133-L148).

Note that this function powers the caching of the `user_id` and `username` values
in the `current_user_id()` and `current_username()` function calls (if you have caching enabled).

**Filter Values**

You can retrieve the value for a specific filter as a list using `{{ filter_values() }}`.

This is useful if:

- you want to use a filter component to filter a query where the name of filter component column doesn't match the one in the select statement
- you want to have the ability for filter inside the main query for performance purposes

Here's a concrete example:

```
SELECT action, count(*) as times
FROM logs
WHERE
    action in ({{ "'" + "','".join(filter_values('action_type')) + "'" }})
GROUP BY action
```

**Filters for a Specific Column**

The `{{ get_filters() }}` macro returns the filters applied to a given column. In addition to
returning the values (similar to how `filter_values()` does), the `get_filters()` macro
returns the operator specified in the Explore UI.

This is useful if:

- you want to handle more than the IN operator in your SQL clause
- you want to handle generating custom SQL conditions for a filter
- you want to have the ability to filter inside the main query for speed purposes

Here's a concrete example:

```
 WITH RECURSIVE
    superiors(employee_id, manager_id, full_name, level, lineage) AS (
    SELECT
        employee_id,
        manager_id,
        full_name,
    1 as level,
    employee_id as lineage
    FROM
        employees
    WHERE
    1=1

    {# Render a blank line #}
    {%- for filter in get_filters('full_name', remove_filter=True) -%}

    {%- if filter.get('op') == 'IN' -%}
        AND
        full_name IN ( {{ "'" + "', '".join(filter.get('val')) + "'" }} )
    {%- endif -%}

    {%- if filter.get('op') == 'LIKE' -%}
        AND
        full_name LIKE {{ "'" + filter.get('val') + "'" }}
    {%- endif -%}

    {%- endfor -%}
    UNION ALL
        SELECT
            e.employee_id,
            e.manager_id,
            e.full_name,
    s.level + 1 as level,
    s.lineage
        FROM
            employees e,
        superiors s
        WHERE s.manager_id = e.employee_id
    )

    SELECT
    employee_id, manager_id, full_name, level, lineage
    FROM
    superiors
    order by lineage, level
```