feat: improve GoodPandas perf

Noticed that it is not necessary to call heavy `get_full_catalog`. `get_attributes_catalog` can be called instead.

JIRA: PSDK-205
risk: low

gooddata-pandas/gooddata_pandas/data_access.py

@@ -6,7 +6,7 @@
 from gooddata_sdk import (
     Attribute,
     AttributeFilter,
-    CatalogWorkspaceContent,
+    CatalogAttribute,
     ExecutionDefinition,
     ExecutionResponse,
     Filter,
@@ -16,6 +16,7 @@
     ObjId,
     TableDimension,
 )
+from gooddata_sdk.utils import IdObjType
 
 from gooddata_pandas.utils import (
     ColumnsDef,
@@ -319,27 +320,34 @@ def _extract_for_metrics_only(response: ExecutionResponse, cols: list, col_to_me
     return data
 
 
-def _typed_result(catalog: CatalogWorkspaceContent, attribute: Attribute, result_values: list[Any]) -> list[Any]:
+def _find_attribute(attributes: list[CatalogAttribute], id_obj: IdObjType) -> Union[CatalogAttribute, None]:
+    for attribute in attributes:
+        if attribute.find_label(id_obj) is not None:
+            return attribute
+    return None
+
+
+def _typed_result(attributes: list[CatalogAttribute], attribute: Attribute, result_values: list[Any]) -> list[Any]:
     """
     Internal function to convert result_values to proper data types.
 
     Args:
-        catalog (CatalogWorkspaceContent): The catalog workspace content.
+        attributes (list[CatalogAttribute]): The catalog of attributes.
         attribute (Attribute): The attribute for which the typed result will be computed.
         result_values (list[Any]): A list of raw values.
 
     Returns:
         list[Any]: A list of converted values with proper data types.
     """
-    catalog_attribute = catalog.find_label_attribute(attribute.label)
+    catalog_attribute = _find_attribute(attributes, attribute.label)
     if catalog_attribute is None:
         raise ValueError(f"Unable to find attribute {attribute.label} in catalog")
     return [_typed_attribute_value(catalog_attribute, value) for value in result_values]
 
 
 def _extract_from_attributes_and_maybe_metrics(
     response: ExecutionResponse,
-    catalog: CatalogWorkspaceContent,
+    attributes: list[CatalogAttribute],
     cols: list[str],
     col_to_attr_idx: dict[str, int],
     col_to_metric_idx: dict[str, int],
@@ -382,12 +390,12 @@ def _extract_from_attributes_and_maybe_metrics(
         for idx_name in index:
             rs = result.get_all_header_values(attribute_dim, safe_index_to_attr_idx[idx_name])
             attribute = index_to_attribute[idx_name]
-            index[idx_name] += _typed_result(catalog, attribute, rs)
+            index[idx_name] += _typed_result(attributes, attribute, rs)
         for col in cols:
             if col in col_to_attr_idx:
                 rs = result.get_all_header_values(attribute_dim, col_to_attr_idx[col])
                 attribute = col_to_attribute[col]
-                data[col] += _typed_result(catalog, attribute, rs)
+                data[col] += _typed_result(attributes, attribute, rs)
             elif col_to_metric_idx[col] < len(result.data):
                 data[col] += result.data[col_to_metric_idx[col]]
         if result.is_complete(attribute_dim):
@@ -440,10 +448,10 @@ def compute_and_extract(
     if not exec_def.has_attributes():
         return _extract_for_metrics_only(response, cols, col_to_metric_idx), dict()
     else:
-        catalog = sdk.catalog_workspace_content.get_full_catalog(workspace_id)
+        attributes = sdk.catalog_workspace_content.get_attributes_catalog(workspace_id, include=["labels", "datasets"])
         return _extract_from_attributes_and_maybe_metrics(
             response,
-            catalog,
+            attributes,
             cols,
             col_to_attr_idx,
             col_to_metric_idx,

gooddata-sdk/gooddata_sdk/catalog/workspace/content_service.py

@@ -103,21 +103,28 @@ def get_full_catalog(self, workspace_id: str, inject_valid_objects_func: bool =
 
         return CatalogWorkspaceContent.create_workspace_content_catalog(valid_obj_fun, datasets, attributes, metrics)
 
-    def get_attributes_catalog(self, workspace_id: str) -> list[CatalogAttribute]:
+    def get_attributes_catalog(self, workspace_id: str, include: Optional[list[str]] = None) -> list[CatalogAttribute]:
         """Retrieve all attributes in a given workspace.
 
         Args:
             workspace_id (str):
                 Workspace identification string e.g. "demo"
+            include (list[str]):
+                Entities to include.
+                Available: datasets, labels, attributeHierarchies, dataset, defaultView, ALL
 
         Returns:
             list[CatalogAttribute]:
                 List of all attributes in a given workspace.
         """
+        available_includes = {"datasets", "labels", "attributeHierarchies", "dataset", "defaultView", "ALL"}
+        include = include if include is not None else ["labels"]
+        if not set(include).issubset(available_includes):
+            raise ValueError(f"Invalid include parameter. Available values: {available_includes}, got: {include}")
         get_attributes = functools.partial(
             self._entities_api.get_all_entities_attributes,
             workspace_id,
-            include=["labels"],
+            include=include,
             _check_return_type=False,
         )
         attributes = load_all_entities(get_attributes)