Coverage for python/lsst/pipe/base/executionButlerBuilder.py: 10%
141 statements
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1# This file is part of pipe_base.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
21from __future__ import annotations
23__all__ = ("buildExecutionButler",)
25import io
26import itertools
27from collections import defaultdict
28from typing import Callable, DefaultDict, Iterable, List, Mapping, Optional, Set, Tuple, Union
30from lsst.daf.butler import Butler, Config, DataCoordinate, DatasetRef, DatasetType, Registry
31from lsst.daf.butler.core.repoRelocation import BUTLER_ROOT_TAG
32from lsst.daf.butler.registry import ConflictingDefinitionError, MissingDatasetTypeError
33from lsst.daf.butler.transfers import RepoExportContext
34from lsst.resources import ResourcePath, ResourcePathExpression
35from lsst.utils.introspection import get_class_of
37from .graph import QuantumGraph
38from .pipeline import PipelineDatasetTypes
40DataSetTypeMap = Mapping[DatasetType, Set[DataCoordinate]]
43def _validate_dataset_type(
44 candidate: DatasetType, previous: dict[Union[str, DatasetType], DatasetType], registry: Registry
45) -> DatasetType:
46 """Check the dataset types and return a consistent variant if there are
47 different compatible options.
49 Parameters
50 ----------
51 candidate : `lsst.daf.butler.DatasetType`
52 The candidate dataset type.
53 previous : `dict` [Union[`str`, `DatasetType`], `DatasetType`]
54 Previous dataset types found, indexed by name and also by
55 dataset type. The latter provides a quick way of returning a
56 previously checked dataset type.
57 registry : `lsst.daf.butler.Registry`
58 Main registry whose dataset type registration should override the
59 given one if it exists.
61 Returns
62 -------
63 datasetType : `lsst.daf.butler.DatasetType`
64 The dataset type to be used. This can be different from the
65 given ``candidate`` if a previous dataset type was encountered
66 with the same name and this one is compatible with it.
68 Raises
69 ------
70 ConflictingDefinitionError
71 Raised if a candidate dataset type has the same name as one
72 previously encountered but is not compatible with it.
74 Notes
75 -----
76 This function ensures that if a dataset type is given that has the
77 same name as a previously encountered dataset type but differs solely
78 in a way that is interchangeable (through a supported storage class)
79 then we will always return the first dataset type encountered instead
80 of the new variant. We assume that the butler will handle the
81 type conversion itself later.
82 """
83 # First check that if we have previously vetted this dataset type.
84 # Return the vetted form immediately if we have.
85 checked = previous.get(candidate)
86 if checked:
87 return checked
89 # Have not previously encountered this dataset type.
90 name = candidate.name
91 if prevDsType := previous.get(name):
92 # Check compatibility. For now assume both directions have to
93 # be acceptable.
94 if prevDsType.is_compatible_with(candidate) and candidate.is_compatible_with(prevDsType):
95 # Ensure that if this dataset type is used again we will return
96 # the version that we were first given with this name. Store
97 # it for next time and return the previous one.
98 previous[candidate] = prevDsType
99 return prevDsType
100 else:
101 raise ConflictingDefinitionError(
102 f"Dataset type incompatibility in graph: {prevDsType} not compatible with {candidate}"
103 )
105 # We haven't seen this dataset type in this graph before, but it may
106 # already be in the registry.
107 try:
108 registryDsType = registry.getDatasetType(name)
109 previous[candidate] = registryDsType
110 return registryDsType
111 except MissingDatasetTypeError:
112 pass
113 # Dataset type is totally new. Store it by name and by dataset type so
114 # it will be validated immediately next time it comes up.
115 previous[name] = candidate
116 previous[candidate] = candidate
117 return candidate
120def _accumulate(
121 butler: Butler,
122 graph: QuantumGraph,
123 dataset_types: PipelineDatasetTypes,
124) -> Tuple[Set[DatasetRef], DataSetTypeMap]:
125 # accumulate the DatasetRefs that will be transferred to the execution
126 # registry
128 # exports holds all the existing data that will be migrated to the
129 # execution butler
130 exports: Set[DatasetRef] = set()
132 # inserts is the mapping of DatasetType to dataIds for what is to be
133 # inserted into the registry. These are the products that are expected
134 # to be produced during processing of the QuantumGraph
135 inserts: DefaultDict[DatasetType, Set[DataCoordinate]] = defaultdict(set)
137 # It is possible to end up with a graph that has different storage
138 # classes attached to the same dataset type name. This is okay but
139 # must we must ensure that only a single dataset type definition is
140 # accumulated in the loop below. This data structure caches every dataset
141 # type encountered and stores the compatible alternative.
142 datasetTypes: dict[Union[str, DatasetType], DatasetType] = {}
144 # Add inserts for initOutputs (including initIntermediates); these are
145 # defined fully by their DatasetType, because they have no dimensions.
146 # initInputs are part of Quantum and that's the only place the graph stores
147 # the dataset IDs, so we process them there even though each Quantum for a
148 # task has the same ones.
149 for dataset_type in itertools.chain(dataset_types.initIntermediates, dataset_types.initOutputs):
150 dataset_type = _validate_dataset_type(dataset_type, datasetTypes, butler.registry)
151 inserts[dataset_type].add(DataCoordinate.makeEmpty(dataset_type.dimensions.universe))
153 # Output references may be resolved even if they do not exist. Find all
154 # actually existing refs.
155 check_refs: Set[DatasetRef] = set()
156 for quantum in (n.quantum for n in graph):
157 for attrName in ("initInputs", "inputs", "outputs"):
158 attr: Mapping[DatasetType, Union[DatasetRef, List[DatasetRef]]] = getattr(quantum, attrName)
159 for type, refs in attr.items():
160 # This if block is because init inputs has a different
161 # signature for its items
162 if not isinstance(refs, list):
163 refs = [refs]
164 for ref in refs:
165 if ref.id is not None:
166 # We could check existence of individual components,
167 # but it should be less work to check their parent.
168 if ref.isComponent():
169 ref = ref.makeCompositeRef()
170 check_refs.add(ref)
171 exist_map = butler.datastore.knows_these(check_refs)
172 existing_ids = set(ref.id for ref, exists in exist_map.items() if exists)
173 del exist_map
175 for quantum in (n.quantum for n in graph):
176 for attrName in ("initInputs", "inputs", "outputs"):
177 attr = getattr(quantum, attrName)
179 for type, refs in attr.items():
180 if not isinstance(refs, list):
181 refs = [refs]
182 # iterate over all the references, if it exists and should be
183 # exported, if not it should be inserted into the new registry
184 for ref in refs:
185 # Component dataset ID is the same as its parent ID, so
186 # checking component in existing_ids works OK.
187 if ref.id is not None and ref.id in existing_ids:
188 # If this is a component we want the composite to be
189 # exported.
190 if ref.isComponent():
191 ref = ref.makeCompositeRef()
192 # Make sure we export this with the registry's dataset
193 # type, since transfer_from doesn't handle storage
194 # class differences (maybe it should, but it's not
195 # bad to be defensive here even if that changes).
196 type = _validate_dataset_type(type, datasetTypes, butler.registry)
197 if type != ref.datasetType:
198 ref = ref.overrideStorageClass(type.storageClass)
199 assert ref.datasetType == type, "Dataset types should not differ in other ways."
200 exports.add(ref)
201 else:
202 if ref.isComponent():
203 # We can't insert a component, and a component will
204 # be part of some other upstream dataset, so it
205 # should be safe to skip them here
206 continue
207 type = _validate_dataset_type(type, datasetTypes, butler.registry)
208 inserts[type].add(ref.dataId)
209 return exports, inserts
212def _discoverCollections(butler: Butler, collections: Iterable[str]) -> set[str]:
213 # Recurse through any discovered collections to make sure all collections
214 # are exported. This exists because I ran into a situation where some
215 # collections were not properly being discovered and exported. This
216 # method may be able to be removed in the future if collection export
217 # logic changes
218 collections = set(collections)
219 while True:
220 discoveredCollections = set(
221 butler.registry.queryCollections(collections, flattenChains=True, includeChains=True)
222 )
223 if len(discoveredCollections) > len(collections):
224 collections = discoveredCollections
225 else:
226 break
227 return collections
230def _export(butler: Butler, collections: Optional[Iterable[str]], inserts: DataSetTypeMap) -> io.StringIO:
231 # This exports relevant dimension records and collections using daf butler
232 # objects, however it reaches in deep and does not use the public methods
233 # so that it can export it to a string buffer and skip disk access. This
234 # does not export the datasets themselves, since we use transfer_from for
235 # that.
236 yamlBuffer = io.StringIO()
237 # Yaml is hard coded, since the class controls both ends of the
238 # export/import
239 BackendClass = get_class_of(butler._config["repo_transfer_formats", "yaml", "export"])
240 backend = BackendClass(yamlBuffer, universe=butler.registry.dimensions)
241 exporter = RepoExportContext(butler.registry, butler.datastore, backend, directory=None, transfer=None)
243 # Need to ensure that the dimension records for outputs are
244 # transferred.
245 for _, dataIds in inserts.items():
246 exporter.saveDataIds(dataIds)
248 # Look for any defined collection, if not get the defaults
249 if collections is None:
250 collections = butler.registry.defaults.collections
252 # look up all collections associated with those inputs, this follows
253 # all chains to make sure everything is properly exported
254 for c in _discoverCollections(butler, collections):
255 exporter.saveCollection(c)
256 exporter._finish()
258 # reset the string buffer to the beginning so the read operation will
259 # actually *see* the data that was exported
260 yamlBuffer.seek(0)
261 return yamlBuffer
264def _setupNewButler(
265 butler: Butler,
266 outputLocation: ResourcePath,
267 dirExists: bool,
268 datastoreRoot: Optional[ResourcePath] = None,
269) -> Butler:
270 """Set up the execution butler
272 Parameters
273 ----------
274 butler : `Butler`
275 The original butler, upon which the execution butler is based.
276 outputLocation : `ResourcePath`
277 Location of the execution butler.
278 dirExists : `bool`
279 Does the ``outputLocation`` exist, and if so, should it be clobbered?
280 datastoreRoot : `ResourcePath`, optional
281 Path for the execution butler datastore. If not specified, then the
282 original butler's datastore will be used.
284 Returns
285 -------
286 execution_butler : `Butler`
287 Execution butler.
288 """
289 # Set up the new butler object at the specified location
290 if dirExists:
291 # Remove the existing table, if the code got this far and this exists
292 # clobber must be true
293 executionRegistry = outputLocation.join("gen3.sqlite3")
294 if executionRegistry.exists():
295 executionRegistry.remove()
296 else:
297 outputLocation.mkdir()
299 # Copy the existing butler config, modifying the location of the
300 # registry to the specified location.
301 # Preserve the root path from the existing butler so things like
302 # file data stores continue to look at the old location.
303 config = Config(butler._config)
304 config["root"] = outputLocation.geturl()
305 config["allow_put_of_predefined_dataset"] = True
306 config["registry", "db"] = "sqlite:///<butlerRoot>/gen3.sqlite3"
308 # Remove any namespace that may be set in main registry.
309 config.pop(("registry", "namespace"), None)
311 # record the current root of the datastore if it is specified relative
312 # to the butler root
313 if datastoreRoot is not None:
314 config["datastore", "root"] = datastoreRoot.geturl()
315 elif config.get(("datastore", "root")) == BUTLER_ROOT_TAG and butler._config.configDir is not None:
316 config["datastore", "root"] = butler._config.configDir.geturl()
317 config["datastore", "trust_get_request"] = True
319 # Requires that we use the dimension configuration from the original
320 # butler and not use the defaults.
321 config = Butler.makeRepo(
322 root=outputLocation,
323 config=config,
324 dimensionConfig=butler.registry.dimensions.dimensionConfig,
325 overwrite=True,
326 forceConfigRoot=False,
327 )
329 # Return a newly created butler
330 return Butler(config, writeable=True)
333def _import(
334 yamlBuffer: io.StringIO,
335 newButler: Butler,
336 inserts: DataSetTypeMap,
337 run: Optional[str],
338 butlerModifier: Optional[Callable[[Butler], Butler]],
339) -> Butler:
340 # This method takes the exports from the existing butler, imports
341 # them into the newly created butler, and then inserts the datasets
342 # that are expected to be produced.
344 # import the existing datasets using "split" mode. "split" is safe
345 # because execution butler is assumed to be able to see all the file
346 # locations that the main datastore can see. "split" supports some
347 # absolute URIs in the datastore.
348 newButler.import_(filename=yamlBuffer, format="yaml", reuseIds=True, transfer="split")
350 # If there is modifier callable, run it to make necessary updates
351 # to the new butler.
352 if butlerModifier is not None:
353 newButler = butlerModifier(newButler)
355 # Register datasets to be produced and insert them into the registry
356 for dsType, dataIds in inserts.items():
357 # Storage class differences should have already been resolved by calls
358 # _validate_dataset_type in _export, resulting in the Registry dataset
359 # type whenever that exists.
360 newButler.registry.registerDatasetType(dsType)
361 newButler.registry.insertDatasets(dsType, dataIds, run)
363 return newButler
366def buildExecutionButler(
367 butler: Butler,
368 graph: QuantumGraph,
369 outputLocation: ResourcePathExpression,
370 run: Optional[str],
371 *,
372 clobber: bool = False,
373 butlerModifier: Optional[Callable[[Butler], Butler]] = None,
374 collections: Optional[Iterable[str]] = None,
375 datastoreRoot: Optional[ResourcePathExpression] = None,
376 transfer: str = "auto",
377) -> Butler:
378 r"""buildExecutionButler is a function that is responsible for exporting
379 input `QuantumGraphs` into a new minimal `~lsst.daf.butler.Butler` which
380 only contains datasets specified by the `QuantumGraph`. These datasets are
381 both those that already exist in the input `~lsst.daf.butler.Butler`, and
382 those that are expected to be produced during the execution of the
383 `QuantumGraph`.
385 Parameters
386 ----------
387 butler : `lsst.daf.butler.Bulter`
388 This is the existing `~lsst.daf.butler.Butler` instance from which
389 existing datasets will be exported. This should be the
390 `~lsst.daf.butler.Butler` which was used to create any `QuantumGraphs`
391 that will be converted with this object.
392 graph : `QuantumGraph`
393 Graph containing nodes that are to be exported into an execution
394 butler
395 outputLocation : convertible to `ResourcePath`
396 URI Location at which the execution butler is to be exported. May be
397 specified as a string or a `ResourcePath` instance.
398 run : `str`, optional
399 The run collection that the exported datasets are to be placed in. If
400 None, the default value in registry.defaults will be used.
401 clobber : `bool`, Optional
402 By default a butler will not be created if a file or directory
403 already exists at the output location. If this is set to `True`
404 what is at the location will be deleted prior to running the
405 export. Defaults to `False`
406 butlerModifier : `~typing.Callable`, Optional
407 If supplied this should be a callable that accepts a
408 `~lsst.daf.butler.Butler`, and returns an instantiated
409 `~lsst.daf.butler.Butler`. This callable may be used to make any
410 modifications to the `~lsst.daf.butler.Butler` desired. This
411 will be called after importing all datasets that exist in the input
412 `~lsst.daf.butler.Butler` but prior to inserting Datasets expected
413 to be produced. Examples of what this method could do include
414 things such as creating collections/runs/ etc.
415 collections : `~typing.Iterable` of `str`, Optional
416 An iterable of collection names that will be exported from the input
417 `~lsst.daf.butler.Butler` when creating the execution butler. If not
418 supplied the `~lsst.daf.butler.Butler`\ 's `~lsst.daf.butler.Registry`
419 default collections will be used.
420 datastoreRoot : convertible to `ResourcePath`, Optional
421 Root directory for datastore of execution butler. If `None`, then the
422 original butler's datastore will be used.
423 transfer : `str`
424 How (and whether) the input datasets should be added to the execution
425 butler datastore. This should be a ``transfer`` string recognized by
426 :func:`lsst.resources.ResourcePath.transfer_from`.
427 ``"auto"`` means to ``"copy"`` if the ``datastoreRoot`` is specified.
429 Returns
430 -------
431 executionButler : `lsst.daf.butler.Butler`
432 An instance of the newly created execution butler
434 Raises
435 ------
436 FileExistsError
437 Raised if something exists in the filesystem at the specified output
438 location and clobber is `False`
439 NotADirectoryError
440 Raised if specified output URI does not correspond to a directory
441 """
442 # We know this must refer to a directory.
443 outputLocation = ResourcePath(outputLocation, forceDirectory=True)
444 if datastoreRoot is not None:
445 datastoreRoot = ResourcePath(datastoreRoot, forceDirectory=True)
447 # Do this first to Fail Fast if the output exists
448 if (dirExists := outputLocation.exists()) and not clobber:
449 raise FileExistsError("Cannot create a butler at specified location, location exists")
450 if not outputLocation.isdir():
451 raise NotADirectoryError("The specified output URI does not appear to correspond to a directory")
453 # Gather all DatasetTypes from the Python and check any that already exist
454 # in the registry for consistency. This does not check that all dataset
455 # types here exist, because they might want to register dataset types
456 # later. It would be nice to also check that, but to that we would need to
457 # be told whether they plan to register dataset types later (DM-30845).
458 dataset_types = PipelineDatasetTypes.fromPipeline(graph.iterTaskGraph(), registry=butler.registry)
460 exports, inserts = _accumulate(butler, graph, dataset_types)
461 yamlBuffer = _export(butler, collections, inserts)
463 newButler = _setupNewButler(butler, outputLocation, dirExists, datastoreRoot)
465 newButler = _import(yamlBuffer, newButler, inserts, run, butlerModifier)
467 if transfer == "auto" and datastoreRoot is not None:
468 transfer = "copy"
470 # Transfer the existing datasets directly from the source butler.
471 newButler.transfer_from(
472 butler,
473 exports,
474 transfer=transfer,
475 skip_missing=False, # Everything should exist.
476 register_dataset_types=True,
477 transfer_dimensions=True,
478 )
480 return newButler