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feat: semantic layer extension (#37815)

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This commit is contained in:
Beto Dealmeida
2026-05-05 12:07:46 -04:00
committed by GitHub
parent 9e91ae8cff
commit cb53745d43
141 changed files with 18851 additions and 667 deletions
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33
superset/utils/core.py
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@@ -96,7 +96,6 @@ from superset.exceptions import (
SupersetException,
SupersetTimeoutException,
)
from superset.explorables.base import Explorable
from superset.sql.parse import sanitize_clause
from superset.superset_typing import (
AdhocColumn,
@@ -115,7 +114,7 @@ from superset.utils.hashing import hash_from_dict, hash_from_str
from superset.utils.pandas import detect_datetime_format
if TYPE_CHECKING:
from superset.connectors.sqla.models import TableColumn
from superset.explorables.base import ColumnMetadata, Explorable
from superset.models.core import Database
logging.getLogger("MARKDOWN").setLevel(logging.INFO)
@@ -200,6 +199,7 @@ class DatasourceType(StrEnum):
QUERY = "query"
SAVEDQUERY = "saved_query"
VIEW = "view"
SEMANTIC_VIEW = "semantic_view"
class LoggerLevel(StrEnum):
@@ -1732,15 +1732,12 @@ def get_metric_type_from_column(column: Any, datasource: Explorable) -> str:
:return: The inferred metric type as a string, or an empty string if the
column is not a metric or no valid operation is found.
"""
from superset.connectors.sqla.models import SqlMetric
metric: SqlMetric = next(
(metric for metric in datasource.metrics if metric.metric_name == column),
SqlMetric(metric_name=""),
metric = next(
(m for m in datasource.metrics if m.metric_name == column),
None,
)
if metric.metric_name == "":
if metric is None:
return ""
expression: str = metric.expression
@@ -1786,7 +1783,7 @@ def extract_dataframe_dtypes(
generic_types: list[GenericDataType] = []
for column in df.columns:
column_object = columns_by_name.get(column)
column_object = columns_by_name.get(str(column))
series = df[column]
inferred_type: str = ""
if series.isna().all():
@@ -1816,11 +1813,17 @@ def extract_dataframe_dtypes(
return generic_types
def extract_column_dtype(col: TableColumn) -> GenericDataType:
if col.is_temporal:
def extract_column_dtype(col: ColumnMetadata) -> GenericDataType:
# Check for temporal type
if hasattr(col, "is_temporal") and col.is_temporal:
return GenericDataType.TEMPORAL
if col.is_numeric:
if col.is_dttm:
return GenericDataType.TEMPORAL
# Check for numeric type
if hasattr(col, "is_numeric") and col.is_numeric:
return GenericDataType.NUMERIC
# TODO: add check for boolean data type when proper support is added
return GenericDataType.STRING
@@ -1834,9 +1837,7 @@ def get_time_filter_status(
applied_time_extras: dict[str, str],
) -> tuple[list[dict[str, str]], list[dict[str, str]]]:
temporal_columns: set[Any] = {
(col.column_name if hasattr(col, "column_name") else col.get("column_name"))
for col in datasource.columns
if (col.is_dttm if hasattr(col, "is_dttm") else col.get("is_dttm"))
col.column_name for col in datasource.columns if col.is_dttm
}
applied: list[dict[str, str]] = []
rejected: list[dict[str, str]] = []