DatasetQueryResults

class lsst.daf.butler.registry.queries.DatasetQueryResults

Bases: collections.abc.Iterable

An interface for objects that represent the results of queries for datasets.

Methods Summary

any(*, execute, exact)	Test whether this query returns any results.
byParentDatasetType()	Group results by parent dataset type.
count(*, exact)	Count the number of rows this query would return.
expanded()	Return a DatasetQueryResults for which DataCoordinate.hasRecords returns True for all data IDs in returned DatasetRef objects.
explain_no_results()	Return human-readable messages that may help explain why the query yields no results.
materialize()	Insert this query’s results into a temporary table.

Methods Documentation

any(*, execute: bool = True, exact: bool = True) → bool

Test whether this query returns any results.

Parameters:

execute : bool, optional

If True, execute at least a LIMIT 1 query if it cannot be determined prior to execution that the query would return no rows.

exact : bool, optional

If True, run the full query and perform post-query filtering if needed, until at least one result row is found. If False, the returned result does not account for post-query filtering, and hence may be True even when all result rows would be filtered out.

Returns:

any : bool

True if the query would (or might, depending on arguments) yield result rows. False if it definitely would not.

byParentDatasetType() → collections.abc.Iterator[lsst.daf.butler.registry.queries._results.ParentDatasetQueryResults]

Group results by parent dataset type.

Returns:

iter : Iterator [ ParentDatasetQueryResults ]

An iterator over DatasetQueryResults instances that are each responsible for a single parent dataset type (either just that dataset type, one or more of its component dataset types, or both).

count(*, exact: bool = True) → int

Count the number of rows this query would return.

Parameters:

exact : bool, optional

If True, run the full query and perform post-query filtering if needed to account for that filtering in the count. If False, the result may be an upper bound.

Returns:

count : int

The number of rows the query would return, or an upper bound if exact=False.

Notes

This counts the number of rows returned, not the number of unique rows returned, so even with exact=True it may provide only an upper bound on the number of deduplicated result rows.

expanded() → lsst.daf.butler.registry.queries._results.DatasetQueryResults

Return a DatasetQueryResults for which DataCoordinate.hasRecords returns True for all data IDs in returned DatasetRef objects.

Returns:

expanded : DatasetQueryResults

Either a new DatasetQueryResults instance or self, if it is already expanded.

Notes

As with DataCoordinateQueryResults.expanded, it may be more efficient to call materialize before expanding data IDs for very large result sets.

explain_no_results() → collections.abc.Iterator[str]

Return human-readable messages that may help explain why the query yields no results.

Returns:

messages : Iterator [ str ]

String messages that describe reasons the query might not yield any results.

Notes

Messages related to post-query filtering are only available if the iterator has been exhausted, or if any or count was already called (with exact=True for the latter two).

This method first yields messages that are generated while the query is being built or filtered, but may then proceed to diagnostics generated by performing what should be inexpensive follow-up queries. Callers can short-circuit this at any time by simplying not iterating further.

materialize() → contextlib.AbstractContextManager[lsst.daf.butler.registry.queries._results.DatasetQueryResults]

Insert this query’s results into a temporary table.

Returns:

context : typing.ContextManager [ DatasetQueryResults ]

A context manager that ensures the temporary table is created and populated in __enter__ (returning a results object backed by that table), and dropped in __exit__. If self is already materialized, the context manager may do nothing (reflecting the fact that an outer context manager should already take care of everything else).