GitHub user dosubot[bot] added a comment to the discussion: Add Support for SQL
Server Analysis Services (SSAS) Connection with MDX Query Execution, Dimension
Management, Pivot Tables, and Domain User Login
<!-- Answer -->
To resolve the "Could not load database driver: SSASEngineSpec" error when
adding support for SQL Server Analysis Services (SSAS) to Apache Superset,
ensure that the `SSASEngineSpec` is correctly registered and loaded. Here are
the steps and configuration changes required:
### 1. Define the SSASEngineSpec Class
Ensure that you have a class `SSASEngineSpec` that extends `BaseEngineSpec` and
is properly defined in a module.
### 2. Register the SSASEngineSpec
Make sure that the `SSASEngineSpec` is included in the list of engine specs
that Superset loads. This is done in the `load_engine_specs` function.
### Example Implementation
#### Define the SSASEngineSpec Class
```python
# superset/db_engine_specs/ssas.py
from typing import Any, Optional
from datetime import datetime
from superset.db_engine_specs.base import BaseEngineSpec
class SSASEngineSpec(BaseEngineSpec):
engine = "ssas"
engine_name = "SQL Server Analysis Services"
@classmethod
def execute(cls, cursor: Any, query: str, **kwargs: Any) -> Any:
cursor.execute(query)
return cursor.fetchall()
@classmethod
def get_dimensions(cls, cursor: Any) -> list[tuple[Any, ...]]:
cursor.execute("SELECT * FROM $system.DISCOVER_DIMENSIONS")
return cursor.fetchall()
@classmethod
def get_databases(cls, cursor: Any) -> list[tuple[Any, ...]]:
cursor.execute("SELECT * FROM $system.DISCOVER_CATALOGS")
return cursor.fetchall()
@classmethod
def convert_dttm(cls, target_type: str, dttm: datetime) -> Optional[str]:
return f"'{dttm.isoformat()}'"
@classmethod
def fetch_data(cls, cursor: Any, limit: Optional[int] = None) ->
list[tuple[Any, ...]]:
if not cursor.description:
return []
return super().fetch_data(cursor, limit)
```
#### Register the SSASEngineSpec
Ensure the `SSASEngineSpec` is loaded in `superset/db_engine_specs/__init__.py`:
```python
# superset/db_engine_specs/__init__.py
from pathlib import Path
import pkgutil
from importlib import import_module
from superset.db_engine_specs.base import BaseEngineSpec
def load_engine_specs() -> list[type[BaseEngineSpec]]:
engine_specs: list[type[BaseEngineSpec]] = []
# Load standard engines
db_engine_spec_dir = str(Path(__file__).parent)
for module_info in pkgutil.iter_modules([db_engine_spec_dir], prefix="."):
module = import_module(module_info.name, package=__name__)
engine_specs.extend(
getattr(module, attr)
for attr in module.__dict__
if is_engine_spec(getattr(module, attr))
)
# Manually add SSASEngineSpec if not using entry points
from superset.db_engine_specs.ssas import SSASEngineSpec
engine_specs.append(SSASEngineSpec)
# Load additional engines from external modules
for ep in entry_points(group="superset.db_engine_specs"):
try:
engine_spec = ep.load()
except Exception: # pylint: disable=broad-except
logger.warning("Unable to load Superset DB engine spec: %s",
ep.name)
continue
engine_specs.append(engine_spec)
return engine_specs
```
### Connection String Format
The connection string format for SSAS should be verified against the specific
driver you are using. A common format might look like:
```
ssas://username:password@hostname/ssas_dbname
```
Ensure that the driver you are using supports this format and that all
necessary parameters are included.
By ensuring that the `SSASEngineSpec` class is defined and properly registered
in the `load_engine_specs` function, you should be able to resolve the "Could
not load database driver: SSASEngineSpec" error
<sup>[[1]](https://github.com/apache/superset/blob/master/superset/db_engine_specs/__init__.py)</sup>.
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GitHub link:
https://github.com/apache/superset/discussions/33093#discussioncomment-12797472
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