feat: query datasets from SQL Lab (#15241)

* feat: Jinja2 macro for querying datasets

* Add docs

* Address comments

docs/docs/installation/sql-templating.mdx

@@ -273,3 +273,27 @@ Here's a concrete example:
     superiors
     order by lineage, level
 ```
+
+**Datasets**
+
+It's possible to query physical and virtual datasets using the `dataset` macro. This is useful if you've defined computed columns and metrics on your datasets, and want to reuse the definition in adhoc SQL Lab queries.
+
+To use the macro, first you need to find the ID of the dataset. This can be done by going to the view showing all the datasets, hovering over the dataset you're interested in, and looking at its URL. For example, if the URL for a dataset is https://superset.example.org/superset/explore/table/42/ its ID is 42.
+
+Once you have the ID you can query it as if it were a table:
+
+```
+SELECT * FROM {{ dataset(42) }} LIMIT 10
+```
+
+If you want to select the metric definitions as well, in addition to the columns, you need to pass an additional keyword argument:
+
+```
+SELECT * FROM {{ dataset(42, include_metrics=True) }} LIMIT 10
+```
+
+Since metrics are aggregations, the resulting SQL expression will be grouped by all non-metric columns. You can specify a subset of columns to group by instead:
+
+```
+SELECT * FROM {{ dataset(42, include_metrics=True, columns=["ds", "category"]) }} LIMIT 10
+```

superset/connectors/base/models.py

@@ -449,7 +449,7 @@ class BaseDatasource(
     def get_query_str(self, query_obj: QueryObjectDict) -> str:
         """Returns a query as a string
 
-        This is used to be displayed to the user so that she/he can
+        This is used to be displayed to the user so that they can
         understand what is taking place behind the scene"""
         raise NotImplementedError()
 

superset/jinja_context.py

@@ -38,6 +38,7 @@ from sqlalchemy.engine.interfaces import Dialect
 from sqlalchemy.types import String
 from typing_extensions import TypedDict
 
+from superset.datasets.commands.exceptions import DatasetNotFoundError
 from superset.exceptions import SupersetTemplateException
 from superset.extensions import feature_flag_manager
 from superset.utils.core import convert_legacy_filters_into_adhoc, merge_extra_filters
@@ -490,6 +491,7 @@ class JinjaTemplateProcessor(BaseTemplateProcessor):
                 "cache_key_wrapper": partial(safe_proxy, extra_cache.cache_key_wrapper),
                 "filter_values": partial(safe_proxy, extra_cache.filter_values),
                 "get_filters": partial(safe_proxy, extra_cache.get_filters),
+                "dataset": partial(safe_proxy, dataset_macro),
             }
         )
 
@@ -602,3 +604,34 @@ def get_template_processor(
     else:
         template_processor = NoOpTemplateProcessor
     return template_processor(database=database, table=table, query=query, **kwargs)
+
+
+def dataset_macro(
+    dataset_id: int,
+    include_metrics: bool = False,
+    columns: Optional[List[str]] = None,
+) -> str:
+    """
+    Given a dataset ID, return the SQL that represents it.
+
+    The generated SQL includes all columns (including computed) by default. Optionally
+    the user can also request metrics to be included, and columns to group by.
+    """
+    # pylint: disable=import-outside-toplevel
+    from superset.datasets.dao import DatasetDAO
+
+    dataset = DatasetDAO.find_by_id(dataset_id)
+    if not dataset:
+        raise DatasetNotFoundError(f"Dataset {dataset_id} not found!")
+
+    columns = columns or [column.column_name for column in dataset.columns]
+    metrics = [metric.metric_name for metric in dataset.metrics]
+    query_obj = {
+        "is_timeseries": False,
+        "filter": [],
+        "metrics": metrics if include_metrics else None,
+        "columns": columns,
+    }
+    sqla_query = dataset.get_query_str_extended(query_obj)
+    sql = sqla_query.sql
+    return f"({sql}) AS dataset_{dataset_id}"

tests/unit_tests/jinja_context_test.py

@@ -14,8 +14,15 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+# pylint: disable=invalid-name, unused-argument
 
-from superset.jinja_context import where_in
+import json
+
+import pytest
+from pytest_mock import MockFixture
+
+from superset.datasets.commands.exceptions import DatasetNotFoundError
+from superset.jinja_context import dataset_macro, where_in
 
 
 def test_where_in() -> None:
@@ -25,3 +32,95 @@ def test_where_in() -> None:
     assert where_in([1, "b", 3]) == "(1, 'b', 3)"
     assert where_in([1, "b", 3], '"') == '(1, "b", 3)'
     assert where_in(["O'Malley's"]) == "('O''Malley''s')"
+
+
+def test_dataset_macro(mocker: MockFixture, app_context: None) -> None:
+    """
+    Test the ``dataset_macro`` macro.
+    """
+    # pylint: disable=import-outside-toplevel
+    from superset.connectors.sqla.models import SqlaTable, SqlMetric, TableColumn
+    from superset.models.core import Database
+
+    columns = [
+        TableColumn(column_name="ds", is_dttm=1, type="TIMESTAMP"),
+        TableColumn(column_name="num_boys", type="INTEGER"),
+        TableColumn(column_name="revenue", type="INTEGER"),
+        TableColumn(column_name="expenses", type="INTEGER"),
+        TableColumn(
+            column_name="profit", type="INTEGER", expression="revenue-expenses"
+        ),
+    ]
+    metrics = [
+        SqlMetric(metric_name="cnt", expression="COUNT(*)"),
+    ]
+
+    dataset = SqlaTable(
+        table_name="old_dataset",
+        columns=columns,
+        metrics=metrics,
+        main_dttm_col="ds",
+        default_endpoint="https://www.youtube.com/watch?v=dQw4w9WgXcQ",  # not used
+        database=Database(database_name="my_database", sqlalchemy_uri="sqlite://"),
+        offset=-8,
+        description="This is the description",
+        is_featured=1,
+        cache_timeout=3600,
+        schema="my_schema",
+        sql=None,
+        params=json.dumps(
+            {
+                "remote_id": 64,
+                "database_name": "examples",
+                "import_time": 1606677834,
+            }
+        ),
+        perm=None,
+        filter_select_enabled=1,
+        fetch_values_predicate="foo IN (1, 2)",
+        is_sqllab_view=0,  # no longer used?
+        template_params=json.dumps({"answer": "42"}),
+        schema_perm=None,
+        extra=json.dumps({"warning_markdown": "*WARNING*"}),
+    )
+    DatasetDAO = mocker.patch("superset.datasets.dao.DatasetDAO")
+    DatasetDAO.find_by_id.return_value = dataset
+
+    assert (
+        dataset_macro(1)
+        == """(SELECT ds AS ds,
+       num_boys AS num_boys,
+       revenue AS revenue,
+       expenses AS expenses,
+       revenue-expenses AS profit
+FROM my_schema.old_dataset) AS dataset_1"""
+    )
+
+    assert (
+        dataset_macro(1, include_metrics=True)
+        == """(SELECT ds AS ds,
+       num_boys AS num_boys,
+       revenue AS revenue,
+       expenses AS expenses,
+       revenue-expenses AS profit,
+       COUNT(*) AS cnt
+FROM my_schema.old_dataset
+GROUP BY ds,
+         num_boys,
+         revenue,
+         expenses,
+         revenue-expenses) AS dataset_1"""
+    )
+
+    assert (
+        dataset_macro(1, include_metrics=True, columns=["ds"])
+        == """(SELECT ds AS ds,
+       COUNT(*) AS cnt
+FROM my_schema.old_dataset
+GROUP BY ds) AS dataset_1"""
+    )
+
+    DatasetDAO.find_by_id.return_value = None
+    with pytest.raises(DatasetNotFoundError) as excinfo:
+        dataset_macro(1)
+    assert str(excinfo.value) == "Dataset 1 not found!"