DM-45993: Optimize DirectButlerCollections.query_info to avoid too many queries

python/lsst/daf/butler/_butler_collections.py

@@ -30,13 +30,17 @@
 __all__ = ("ButlerCollections", "CollectionInfo")
 
 from abc import ABC, abstractmethod
-from collections.abc import Iterable, Sequence, Set
-from typing import Any, overload
+from collections import defaultdict
+from collections.abc import Iterable, Mapping, Sequence, Set
+from typing import TYPE_CHECKING, Any, overload
 
 from pydantic import BaseModel
 
 from ._collection_type import CollectionType
 
+if TYPE_CHECKING:
+    from ._dataset_type import DatasetType
+
 
 class CollectionInfo(BaseModel):
     """Information about a single Butler collection."""
@@ -275,6 +279,8 @@
         include_chains: bool | None = None,
         include_parents: bool = False,
         include_summary: bool = False,
+        include_doc: bool = False,
+        summary_datasets: Iterable[DatasetType] | None = None,
     ) -> Sequence[CollectionInfo]:
         """Query the butler for collections matching an expression and
         return detailed information about those collections.
@@ -298,6 +304,14 @@
         include_summary : `bool`, optional
             Whether the returned information includes dataset type and
             governor information for the collections.
+        include_doc : `bool`, optional
+            Whether the returned information includes collection documentation
+            string.
+        summary_datasets : `~collections.abc.Iterable` [ `DatasetType` ], \
+                optional
+            Dataset types to include in returned summaries. Only used if
+            ``include_summary`` is `True`. If not specified then all dataset
+            types will be included.
 
         Returns
         -------
@@ -411,3 +425,30 @@
             collection_dataset_types.update(info.dataset_types)
         dataset_types_set = dataset_types_set.intersection(collection_dataset_types)
         return dataset_types_set
+
+    def _group_by_dataset_type(
+        self, dataset_types: Set[str], collection_infos: Iterable[CollectionInfo]
+    ) -> Mapping[str, list[str]]:
+        """Filter dataset types and collections names based on summary in
+        collecion infos.
+
+        Parameters
+        ----------
+        dataset_types : `~collections.abc.Set` [`str`]
+            Set of dataset type names to extract.
+        collection_infos : `~collections.abc.Iterable` [`CollectionInfo`]
+            Collection infos, must contain dataset type summary.
+
+        Returns
+        -------
+        filtered : `~collections.abc.Mapping` [`str`, `list`[`str`]]
+            Mapping of the dataset type name to its corresponding list of
+            collection names.
+        """
+        dataset_type_collections: dict[str, list[str]] = defaultdict(list)
+        for info in collection_infos:
+            if info.dataset_types is None:
+                raise RuntimeError("Can only filter by collections if include_summary was True")
+            for dataset_type in info.dataset_types & dataset_types:
+                dataset_type_collections[dataset_type].append(info.name)
+        return dataset_type_collections

python/lsst/daf/butler/direct_butler/_direct_butler_collections.py

@@ -29,7 +29,8 @@
 
 __all__ = ("DirectButlerCollections",)
 
-from collections.abc import Iterable, Sequence, Set
+from collections.abc import Iterable, Mapping, Sequence, Set
+from typing import TYPE_CHECKING, Any
 
 import sqlalchemy
 from lsst.utils.iteration import ensure_iterable
@@ -39,6 +40,11 @@
 from ..registry._exceptions import OrphanedRecordError
 from ..registry.interfaces import ChainedCollectionRecord
 from ..registry.sql_registry import SqlRegistry
+from ..registry.wildcards import CollectionWildcard
+
+if TYPE_CHECKING:
+    from .._dataset_type import DatasetType
+    from ..registry._collection_summary import CollectionSummary
 
 
 class DirectButlerCollections(ButlerCollections):
@@ -107,20 +113,57 @@ def query_info(
         include_chains: bool | None = None,
         include_parents: bool = False,
         include_summary: bool = False,
+        include_doc: bool = False,
+        summary_datasets: Iterable[DatasetType] | None = None,
     ) -> Sequence[CollectionInfo]:
         info = []
         with self._registry.caching_context():
             if collection_types is None:
                 collection_types = CollectionType.all()
-            for name in self._registry.queryCollections(
-                expression,
-                collectionTypes=collection_types,
-                flattenChains=flatten_chains,
-                includeChains=include_chains,
-            ):
+            elif isinstance(collection_types, CollectionType):
+                collection_types = {collection_types}
+
+            records = self._registry._managers.collections.resolve_wildcard(
+                CollectionWildcard.from_expression(expression),
+                collection_types=collection_types,
+                flatten_chains=flatten_chains,
+                include_chains=include_chains,
+            )
+
+            summaries: Mapping[Any, CollectionSummary] = {}
+            if include_summary:
+                summaries = self._registry._managers.datasets.fetch_summaries(records, summary_datasets)
+
+            docs: Mapping[Any, str] = {}
+            if include_doc:
+                docs = self._registry._managers.collections.get_docs(record.key for record in records)
+
+            for record in records:
+                doc = docs.get(record.key, "")
+                children: tuple[str, ...] = tuple()
+                if record.type == CollectionType.CHAINED:
+                    assert isinstance(record, ChainedCollectionRecord)
+                    children = tuple(record.children)
+                parents: frozenset[str] | None = None
+                if include_parents:
+                    # TODO: This is non-vectorized, so expensive to do in a
+                    # loop.
+                    parents = frozenset(self._registry.getCollectionParentChains(record.name))
+                dataset_types: Set[str] | None = None
+                if summary := summaries.get(record.key):
+                    dataset_types = frozenset([dt.name for dt in summary.dataset_types])
+
                 info.append(
-                    self.get_info(name, include_parents=include_parents, include_summary=include_summary)
+                    CollectionInfo(
+                        name=record.name,
+                        type=record.type,
+                        doc=doc,
+                        parents=parents,
+                        children=children,
+                        dataset_types=dataset_types,
+                    )
                 )
+
         return info
 
     def get_info(

python/lsst/daf/butler/registry/collections/_base.py

@@ -36,6 +36,7 @@
 from typing import TYPE_CHECKING, Any, Generic, Literal, NamedTuple, TypeVar, cast
 
 import sqlalchemy
+from lsst.utils.iteration import chunk_iterable
 
 from ..._collection_type import CollectionType
 from ..._exceptions import CollectionCycleError, CollectionTypeError, MissingCollectionError
@@ -450,13 +451,24 @@ def filter_types(records: Iterable[CollectionRecord[K]]) -> Iterator[CollectionR
 
     def getDocumentation(self, key: K) -> str | None:
         # Docstring inherited from CollectionManager.
-        sql = (
-            sqlalchemy.sql.select(self._tables.collection.columns.doc)
-            .select_from(self._tables.collection)
-            .where(self._tables.collection.columns[self._collectionIdName] == key)
-        )
-        with self._db.query(sql) as sql_result:
-            return sql_result.scalar()
+        docs = self.get_docs([key])
+        return docs.get(key)
+
+    def get_docs(self, keys: Iterable[K]) -> Mapping[K, str]:
+        # Docstring inherited from CollectionManager.
+        docs: dict[K, str] = {}
+        id_column = self._tables.collection.columns[self._collectionIdName]
+        doc_column = self._tables.collection.columns.doc
+        for chunk in chunk_iterable(keys):
+            sql = (
+                sqlalchemy.sql.select(id_column, doc_column)
+                .select_from(self._tables.collection)
+                .where(sqlalchemy.sql.and_(id_column.in_(chunk), doc_column != sqlalchemy.literal("")))
+            )
+            with self._db.query(sql) as sql_result:
+                for row in sql_result:
+                    docs[row[0]] = row[1]
+        return docs
 
     def setDocumentation(self, key: K, doc: str | None) -> None:
         # Docstring inherited from CollectionManager.

python/lsst/daf/butler/registry/interfaces/_collections.py

@@ -36,7 +36,7 @@
 ]
 
 from abc import abstractmethod
-from collections.abc import Iterable, Set
+from collections.abc import Iterable, Mapping, Set
 from typing import TYPE_CHECKING, Any, Generic, Self, TypeVar
 
 import sqlalchemy
@@ -570,6 +570,23 @@ def getDocumentation(self, key: _Key) -> str | None:
         """
         raise NotImplementedError()
 
+    @abstractmethod
+    def get_docs(self, key: Iterable[_Key]) -> Mapping[_Key, str]:
+        """Retrieve the documentation string for multiple collections.
+
+        Parameters
+        ----------
+        key : `~collections.abc.Iterable` [ _Key ]
+            Internal primary key value for the collection.
+
+        Returns
+        -------
+        docs : `~collections.abc.Mapping` [ _Key, `str`]
+            Documentation strings indexed by collection key. Only collections
+            with non-empty documentation strings are returned.
+        """
+        raise NotImplementedError()
+
     @abstractmethod
     def setDocumentation(self, key: _Key, doc: str | None) -> None:
         """Set the documentation string for a collection.

python/lsst/daf/butler/remote_butler/_remote_butler_collections.py

@@ -36,6 +36,7 @@
 from .._collection_type import CollectionType
 
 if TYPE_CHECKING:
+    from .._dataset_type import DatasetType
     from ._registry import RemoteButlerRegistry
 
 
@@ -79,6 +80,8 @@ def query_info(
         include_chains: bool | None = None,
         include_parents: bool = False,
         include_summary: bool = False,
+        include_doc: bool = False,
+        summary_datasets: Iterable[DatasetType] | None = None,
     ) -> Sequence[CollectionInfo]:
         # This should become a single call on the server in the future.
         if collection_types is None:

python/lsst/daf/butler/script/exportCalibs.py

@@ -138,6 +138,7 @@ def exportCalibs(
         collections_query,
         flatten_chains=True,
         include_chains=True,
+        include_doc=True,
         collection_types={CollectionType.CALIBRATION, CollectionType.CHAINED},
     ):
         log.info("Checking collection: %s", collection.name)

python/lsst/daf/butler/script/queryDataIds.py

@@ -177,27 +177,24 @@ def queryDataIds(
         if datasets:
             # Need to constrain results based on dataset type and collection.
             query_collections = collections or "*"
-            collections_info = butler.collections.query_info(query_collections, include_summary=True)
+            collections_info = butler.collections.query_info(
+                query_collections, include_summary=True, summary_datasets=dataset_types
+            )
             expanded_collections = [info.name for info in collections_info]
-            filtered_dataset_types = list(
-                butler.collections._filter_dataset_types([dt.name for dt in dataset_types], collections_info)
+            dataset_type_collections = butler.collections._group_by_dataset_type(
+                {dt.name for dt in dataset_types}, collections_info
             )
-            if not filtered_dataset_types:
+            if not dataset_type_collections:
                 return (
                     None,
                     f"No datasets of type {datasets!r} existed in the specified "
                     f"collections {','.join(expanded_collections)}.",
                 )
 
-            sub_query = query.join_dataset_search(
-                filtered_dataset_types.pop(0), collections=expanded_collections
-            )
-            for dt in filtered_dataset_types:
-                sub_query = sub_query.join_dataset_search(dt, collections=expanded_collections)
+            for dt, dt_collections in dataset_type_collections.items():
+                query = query.join_dataset_search(dt, collections=dt_collections)
 
-            results = sub_query.data_ids(dimensions)
-        else:
-            results = query.data_ids(dimensions)
+        results = query.data_ids(dimensions)
 
         if where:
             results = results.where(where)

python/lsst/daf/butler/script/queryDatasets.py

@@ -240,31 +240,39 @@
             Dataset references matching the given query criteria grouped
             by dataset type.
         """
-        datasetTypes = self._dataset_type_glob or ...
+        datasetTypes = self._dataset_type_glob
         query_collections: Iterable[str] = self._collections_wildcard or ["*"]
 
         # Currently need to use old interface to get all the matching
         # dataset types and loop over the dataset types executing a new
         # query each time.
-        dataset_types: set[str] = {d.name for d in self.butler.registry.queryDatasetTypes(datasetTypes)}
+        dataset_types = set(self.butler.registry.queryDatasetTypes(datasetTypes or ...))
         n_dataset_types = len(dataset_types)
+        if n_dataset_types == 0:
+            _LOG.info("The given dataset type, %s, is not known to this butler.", datasetTypes)
+            return
 
         # Expand the collections query and include summary information.
-        query_collections_info = self.butler.collections.query_info(query_collections, include_summary=True)
+        query_collections_info = self.butler.collections.query_info(
+            query_collections,
+            include_summary=True,
+            flatten_chains=True,
+            include_chains=False,
+            summary_datasets=dataset_types,
+        )
         expanded_query_collections = [c.name for c in query_collections_info]
         if self._find_first and set(query_collections) != set(expanded_query_collections):
             raise RuntimeError("Can not use wildcards in collections when find_first=True")
         query_collections = expanded_query_collections
 
         # Only iterate over dataset types that are relevant for the query.
-        dataset_types = set(
-            self.butler.collections._filter_dataset_types(dataset_types, query_collections_info)
+        dataset_type_names = {dataset_type.name for dataset_type in dataset_types}
+        dataset_type_collections = self.butler.collections._group_by_dataset_type(
+            dataset_type_names, query_collections_info
         )
 
-        if (n_filtered := len(dataset_types)) != n_dataset_types:
+        if (n_filtered := len(dataset_type_collections)) != n_dataset_types:
             _LOG.info("Filtered %d dataset types down to %d", n_dataset_types, n_filtered)
-        elif n_dataset_types == 0:
-            _LOG.info("The given dataset type, %s, is not known to this butler.", datasetTypes)
         else:
             _LOG.info("Processing %d dataset type%s", n_dataset_types, "" if n_dataset_types == 1 else "s")
 
@@ -278,7 +286,7 @@
             # possible dataset types to query.
             warn_limit = True
             limit = abs(limit) + 1  # +1 to tell us we hit the limit.
-        for dt in sorted(dataset_types):
+        for dt, collections in sorted(dataset_type_collections.items()):
             kwargs: dict[str, Any] = {}
             if self._where:
                 kwargs["where"] = self._where
@@ -288,7 +296,7 @@
             _LOG.debug("Querying dataset type %s with %s", dt, kwargs)
             results = self.butler.query_datasets(
                 dt,
-                collections=query_collections,
+                collections=collections,
                 find_first=self._find_first,
                 with_dimension_records=True,
                 order_by=self._order_by,

python/lsst/daf/butler/script/queryDimensionRecords.py

@@ -83,21 +83,21 @@ def queryDimensionRecords(
 
         if datasets:
             query_collections = collections or "*"
-            collections_info = butler.collections.query_info(query_collections, include_summary=True)
-            expanded_collections = [info.name for info in collections_info]
-            dataset_types = [dt.name for dt in butler.registry.queryDatasetTypes(datasets)]
-            dataset_types = list(butler.collections._filter_dataset_types(dataset_types, collections_info))
-
-            if not dataset_types:
+            dataset_types = butler.registry.queryDatasetTypes(datasets)
+            collections_info = butler.collections.query_info(
+                query_collections, include_summary=True, summary_datasets=dataset_types
+            )
+            dataset_type_collections = butler.collections._group_by_dataset_type(
+                {dt.name for dt in dataset_types}, collections_info
+            )
+
+            if not dataset_type_collections:
                 return None
 
-            sub_query = query.join_dataset_search(dataset_types.pop(0), collections=expanded_collections)
-            for dt in dataset_types:
-                sub_query = sub_query.join_dataset_search(dt, collections=expanded_collections)
+            for dt, dt_collections in dataset_type_collections.items():
+                query = query.join_dataset_search(dt, collections=dt_collections)
 
-            query_results = sub_query.dimension_records(element)
-        else:
-            query_results = query.dimension_records(element)
+        query_results = query.dimension_records(element)
 
         if where:
             query_results = query_results.where(where)