Coverage for python/lsst/pipe/base/execution_reports.py: 28%
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« prev ^ index » next coverage.py v7.4.3, created at 2024-03-14 10:49 -0700
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__ = (
24 "QuantumGraphExecutionReport",
25 "TaskExecutionReport",
26 "DatasetTypeExecutionReport",
27 "lookup_quantum_data_id",
28)
30import dataclasses
31import itertools
32import logging
33import uuid
34from collections.abc import Iterable, Mapping
35from typing import Any
37import networkx
38import yaml
39from lsst.daf.butler import Butler, DataCoordinate, DatasetRef
40from lsst.resources import ResourcePathExpression
42from .graph import QuantumGraph, QuantumNode
43from .pipeline import PipelineDatasetTypes
46@dataclasses.dataclass
47class DatasetTypeExecutionReport:
48 """A report on the number of produced datasets as well as the status of
49 missing datasets based on metadata.
51 A `DatasetTypeExecutionReport` is created for each
52 `~lsst.daf.butler.DatasetType` in a `TaskExecutionReport`.
53 """
55 failed: set[DatasetRef] = dataclasses.field(default_factory=set)
56 """Datasets not produced because their quanta failed directly in this
57 run (`set`).
58 """
60 not_produced: set[DatasetRef] = dataclasses.field(default_factory=set)
61 """Missing datasets which were not produced by successful quanta.
62 """
64 blocked: set[DatasetRef] = dataclasses.field(default_factory=set)
65 """Datasets not produced due to an upstream failure (`set`).
66 """
68 n_produced: int = 0
69 """Count of datasets produced (`int`).
70 """
72 def to_summary_dict(self) -> dict[str, Any]:
73 r"""Summarize the DatasetTypeExecutionReport in a dictionary.
75 Returns
76 -------
77 summary_dict : `dict`
78 A count of the datasets with each outcome; the number of
79 produced, ``failed``, ``not_produced``, and ``blocked``
80 `~lsst.daf.butler.DatasetType`\ s.
81 See above for attribute descriptions.
82 """
83 return {
84 "produced": self.n_produced,
85 "failed": len(self.failed),
86 "not_produced": len(self.not_produced),
87 "blocked": len(self.blocked),
88 }
91@dataclasses.dataclass
92class TaskExecutionReport:
93 """A report on the status and content of a task in an executed quantum
94 graph.
96 Use task metadata to identify and inspect failures and report on output
97 datasets.
99 See Also
100 --------
101 QuantumGraphExecutionReport : Quantum graph report.
102 DatasetTypeExecutionReport : DatasetType report.
103 """
105 failed: dict[uuid.UUID, DatasetRef] = dataclasses.field(default_factory=dict)
106 """A mapping from quantum data ID to log dataset reference for quanta that
107 failed directly in this run (`dict`).
108 """
110 n_succeeded: int = 0
111 """A count of successful quanta.
113 This may include quanta that did not produce any datasets; ie, raised
114 `NoWorkFound`.
115 """
117 blocked: dict[uuid.UUID, DataCoordinate] = dataclasses.field(default_factory=dict)
118 """A mapping of data IDs of quanta that were not attempted due to an
119 upstream failure (`dict`).
120 """
122 output_datasets: dict[str, DatasetTypeExecutionReport] = dataclasses.field(default_factory=dict)
123 """Missing and produced outputs of each `~lsst.daf.butler.DatasetType`
124 (`dict`).
125 """
127 def inspect_quantum(
128 self,
129 quantum_node: QuantumNode,
130 status_graph: networkx.DiGraph,
131 refs: Mapping[str, Mapping[uuid.UUID, DatasetRef]],
132 metadata_name: str,
133 log_name: str,
134 ) -> None:
135 """Inspect a quantum of a quantum graph and ascertain the status of
136 each associated data product.
138 Parameters
139 ----------
140 quantum_node : `QuantumNode`
141 The specific node of the quantum graph to be inspected.
142 status_graph : `networkx.DiGraph`
143 The quantum graph produced by
144 `QuantumGraphExecutionReport.make_reports` which steps through the
145 quantum graph of a run and logs the status of each quantum.
146 refs : `~collections.abc.Mapping` [ `str`,\
147 `~collections.abc.Mapping` [ `uuid.UUID`,\
148 `~lsst.daf.butler.DatasetRef` ] ]
149 The DatasetRefs of each of the DatasetTypes produced by the task.
150 Includes initialization, intermediate and output data products.
151 metadata_name : `str`
152 The metadata dataset name for the node.
153 log_name : `str`
154 The name of the log files for the node.
156 See Also
157 --------
158 QuantumGraphExecutionReport.make_reports : Make reports.
159 """
160 quantum = quantum_node.quantum
161 (metadata_ref,) = quantum.outputs[metadata_name]
162 (log_ref,) = quantum.outputs[log_name]
163 blocked = False
164 if metadata_ref.id not in refs[metadata_name]:
165 if any(
166 status_graph.nodes[upstream_quantum_id]["failed"]
167 for upstream_dataset_id in status_graph.predecessors(quantum_node.nodeId)
168 for upstream_quantum_id in status_graph.predecessors(upstream_dataset_id)
169 ):
170 assert quantum.dataId is not None
171 self.blocked[quantum_node.nodeId] = quantum.dataId
172 blocked = True
173 else:
174 self.failed[quantum_node.nodeId] = log_ref
175 # note: log_ref may or may not actually exist
176 failed = True
177 else:
178 failed = False
179 self.n_succeeded += 1
180 status_graph.nodes[quantum_node.nodeId]["failed"] = failed
182 # Now, loop over the datasets to make a DatasetTypeExecutionReport.
183 for output_ref in itertools.chain.from_iterable(quantum.outputs.values()):
184 if output_ref == metadata_ref or output_ref == log_ref:
185 continue
186 if (dataset_type_report := self.output_datasets.get(output_ref.datasetType.name)) is None:
187 dataset_type_report = DatasetTypeExecutionReport()
188 self.output_datasets[output_ref.datasetType.name] = dataset_type_report
189 if output_ref.id not in refs[output_ref.datasetType.name]:
190 if failed:
191 if blocked:
192 dataset_type_report.blocked.add(output_ref)
193 else:
194 dataset_type_report.failed.add(output_ref)
195 else:
196 dataset_type_report.not_produced.add(output_ref)
197 else:
198 dataset_type_report.n_produced += 1
200 def to_summary_dict(
201 self, butler: Butler, do_store_logs: bool = True, human_readable: bool = False
202 ) -> dict[str, Any]:
203 """Summarize the results of the TaskExecutionReport in a dictionary.
205 Parameters
206 ----------
207 butler : `lsst.daf.butler.Butler`
208 The Butler used for this report.
209 do_store_logs : `bool`
210 Store the logs in the summary dictionary.
211 human_readable : `bool`
212 Store more human-readable information to be printed out to the
213 command-line.
215 Returns
216 -------
217 summary_dict : `dict`
218 A dictionary containing:
220 - outputs: A dictionary summarizing the
221 DatasetTypeExecutionReport for each DatasetType associated with
222 the task
223 - failed_quanta: A dictionary of quanta which failed and their
224 dataIDs by quantum graph node id
225 - n_quanta_blocked: The number of quanta which failed due to
226 upstream failures.
227 - n_succeded: The number of quanta which succeeded.
229 And possibly, if human-readable is passed:
231 - errors: A dictionary of data ids associated with each error
232 message. If `human-readable` and `do_store_logs`, this is stored
233 here. Otherwise, if `do_store_logs`, it is stored in
234 `failed_quanta` keyed by the quantum graph node id.
235 """
236 failed_quanta = {}
237 for node_id, log_ref in self.failed.items():
238 data_ids = dict(log_ref.dataId.required)
239 quantum_info: dict[str, Any] = {"data_id": data_ids}
240 if do_store_logs:
241 try:
242 log = butler.get(log_ref)
243 except LookupError:
244 quantum_info["error"] = []
245 except FileNotFoundError:
246 quantum_info["error"] = None
247 else:
248 quantum_info["error"] = [
249 record.message for record in log if record.levelno >= logging.ERROR
250 ]
251 if human_readable:
252 failed_quanta["data_id"] = data_ids
253 return {
254 "outputs": {name: r.to_summary_dict() for name, r in self.output_datasets.items()},
255 "failed_quanta": failed_quanta,
256 "n_quanta_blocked": len(self.blocked),
257 "n_succeeded": self.n_succeeded,
258 "errors": quantum_info,
259 }
260 else:
261 failed_quanta[str(node_id)] = quantum_info
262 return {
263 "outputs": {name: r.to_summary_dict() for name, r in self.output_datasets.items()},
264 "failed_quanta": failed_quanta,
265 "n_quanta_blocked": len(self.blocked),
266 "n_succeeded": self.n_succeeded,
267 }
269 def __str__(self) -> str:
270 """Return a count of the failed and blocked tasks in the
271 TaskExecutionReport.
272 """
273 return f"failed: {len(self.failed)}\nblocked: {len(self.blocked)}\n"
276@dataclasses.dataclass
277class QuantumGraphExecutionReport:
278 """A report on the execution of a quantum graph.
280 Report the detailed status of each failure; whether tasks were not run,
281 data is missing from upstream failures, or specific errors occurred during
282 task execution (and report the errors). Contains a count of expected,
283 produced DatasetTypes for each task. This report can be output as a
284 dictionary or a yaml file.
286 Attributes
287 ----------
288 tasks : `dict`
289 A dictionary of TaskExecutionReports by task label.
291 See Also
292 --------
293 TaskExecutionReport : A task report.
294 DatasetTypeExecutionReport : A dataset type report.
295 """
297 tasks: dict[str, TaskExecutionReport] = dataclasses.field(default_factory=dict)
298 """A dictionary of TaskExecutionReports by task label (`dict`)."""
300 def to_summary_dict(
301 self, butler: Butler, do_store_logs: bool = True, human_readable: bool = False
302 ) -> dict[str, Any]:
303 """Summarize the results of the `QuantumGraphExecutionReport` in a
304 dictionary.
306 Parameters
307 ----------
308 butler : `lsst.daf.butler.Butler`
309 The Butler used for this report.
310 do_store_logs : `bool`
311 Store the logs in the summary dictionary.
312 human_readable : `bool`
313 Store more human-readable information to be printed out to the
314 command-line.
316 Returns
317 -------
318 summary_dict : `dict`
319 A dictionary containing a summary of a `TaskExecutionReport` for
320 each task in the quantum graph.
321 """
322 return {
323 task: report.to_summary_dict(butler, do_store_logs=do_store_logs, human_readable=human_readable)
324 for task, report in self.tasks.items()
325 }
327 def write_summary_yaml(self, butler: Butler, filename: str, do_store_logs: bool = True) -> None:
328 """Take the dictionary from
329 `QuantumGraphExecutionReport.to_summary_dict` and store its contents in
330 a yaml file.
332 Parameters
333 ----------
334 butler : `lsst.daf.butler.Butler`
335 The Butler used for this report.
336 filename : `str`
337 The name to be used for the summary yaml file.
338 do_store_logs : `bool`
339 Store the logs in the summary dictionary.
340 """
341 with open(filename, "w") as stream:
342 yaml.safe_dump(self.to_summary_dict(butler, do_store_logs=do_store_logs), stream)
344 @classmethod
345 def make_reports(
346 cls,
347 butler: Butler,
348 graph: QuantumGraph | ResourcePathExpression,
349 ) -> QuantumGraphExecutionReport:
350 """Make a `QuantumGraphExecutionReport`.
352 Step through the quantum graph associated with a run, creating a
353 `networkx.DiGraph` called status_graph to annotate the status of each
354 quantum node. For each task in the quantum graph, use
355 `TaskExecutionReport.inspect_quantum` to make a `TaskExecutionReport`
356 based on the status of each node. Return a `TaskExecutionReport` for
357 each task in the quantum graph.
359 Parameters
360 ----------
361 butler : `lsst.daf.butler.Butler`
362 The Butler used for this report. This should match the Butler used
363 for the run associated with the executed quantum graph.
364 graph : `QuantumGraph` | `ResourcePathExpression`
365 Either the associated quantum graph object or the uri of the
366 location of said quantum graph.
368 Returns
369 -------
370 report: `QuantumGraphExecutionReport`
371 The `TaskExecutionReport` for each task in the quantum graph.
372 """
373 refs = {} # type: dict[str, Any]
374 status_graph = networkx.DiGraph()
375 if not isinstance(graph, QuantumGraph):
376 qg = QuantumGraph.loadUri(graph)
377 else:
378 qg = graph
379 assert qg.metadata is not None, "Saved QGs always have metadata."
380 collection = qg.metadata["output_run"]
381 report = cls()
382 task_defs = list(qg.iterTaskGraph())
383 pipeline_dataset_types = PipelineDatasetTypes.fromPipeline(task_defs, registry=butler.registry)
384 for dataset_type in itertools.chain(
385 pipeline_dataset_types.initIntermediates,
386 pipeline_dataset_types.initOutputs,
387 pipeline_dataset_types.intermediates,
388 pipeline_dataset_types.outputs,
389 ):
390 if (component := dataset_type.component()) is not None:
391 # Work around the fact that component support has been phased
392 # out of daf_butler queries but not pipe_base's QGs. This
393 # should go away on DM-40441.
394 parent_dataset_type = dataset_type.makeCompositeDatasetType()
395 refs[dataset_type.name] = {
396 ref.id: ref.makeComponentRef(component)
397 for ref in butler.registry.queryDatasets(
398 parent_dataset_type.name, collections=collection, findFirst=False
399 )
400 }
401 else:
402 refs[dataset_type.name] = {
403 ref.id: ref
404 for ref in butler.registry.queryDatasets(
405 dataset_type.name, collections=collection, findFirst=False
406 )
407 }
408 for task_def in qg.iterTaskGraph():
409 for node in qg.getNodesForTask(task_def):
410 status_graph.add_node(node.nodeId)
411 for ref in itertools.chain.from_iterable(node.quantum.outputs.values()):
412 status_graph.add_edge(node.nodeId, ref.id)
413 for ref in itertools.chain.from_iterable(node.quantum.inputs.values()):
414 status_graph.add_edge(ref.id, node.nodeId)
416 for task_def in qg.iterTaskGraph():
417 task_report = TaskExecutionReport()
418 if task_def.logOutputDatasetName is None:
419 raise RuntimeError("QG must have log outputs to use execution reports.")
420 for node in qg.getNodesForTask(task_def):
421 task_report.inspect_quantum(
422 node,
423 status_graph,
424 refs,
425 metadata_name=task_def.metadataDatasetName,
426 log_name=task_def.logOutputDatasetName,
427 )
428 report.tasks[task_def.label] = task_report
429 return report
431 def __str__(self) -> str:
432 return "\n".join(f"{tasklabel}:{report}" for tasklabel, report in self.tasks.items())
435def lookup_quantum_data_id(
436 graph_uri: ResourcePathExpression, nodes: Iterable[uuid.UUID]
437) -> list[DataCoordinate | None]:
438 """Look up a dataId from a quantum graph and a list of quantum graph
439 nodeIDs.
441 Parameters
442 ----------
443 graph_uri : `ResourcePathExpression`
444 URI of the quantum graph of the run.
445 nodes : `~collections.abc.Iterable` [ `uuid.UUID` ]
446 Quantum graph nodeID.
448 Returns
449 -------
450 data_ids : `list` [ `lsst.daf.butler.DataCoordinate` ]
451 A list of human-readable dataIDs which map to the nodeIDs on the
452 quantum graph at graph_uri.
453 """
454 qg = QuantumGraph.loadUri(graph_uri, nodes=nodes)
455 return [qg.getQuantumNodeByNodeId(node).quantum.dataId for node in nodes]