Coverage for python/lsst/analysis/tools/interfaces/datastore/_dispatcher.py: 13%
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1# This file is part of analysis_tools.
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/>.
22from __future__ import annotations
24__all__ = ("SasquatchDispatchPartialFailure", "SasquatchDispatchFailure", "SasquatchDispatcher")
26"""Sasquatch datastore"""
27import calendar
28import datetime
29import json
30import logging
31import math
32import re
33from collections.abc import Mapping, MutableMapping, Sequence
34from dataclasses import dataclass
35from typing import TYPE_CHECKING, Any, cast
36from uuid import UUID, uuid4
38import requests
39from lsst.daf.butler import DatasetRef
40from lsst.resources import ResourcePath
41from lsst.utils.packages import getEnvironmentPackages
43if TYPE_CHECKING: 43 ↛ 44line 43 didn't jump to line 44, because the condition on line 43 was never true
44 from .. import MetricMeasurementBundle
47log = logging.getLogger(__name__)
49# Constants assocated with SasquatchDispatcher
50PARTITIONS = 1
51REPLICATION_FACTOR = 3
53IDENTIFIER_KEYS = [
54 "detector",
55 "patch",
56 "skymap",
57 "visit",
58 "tract",
59 "physical_filter",
60 "instrument",
61 "band",
62 "exposure",
63]
66class SasquatchDispatchPartialFailure(RuntimeError):
67 """This indicates that a Sasquatch dispatch was partially successful."""
69 pass
72class SasquatchDispatchFailure(RuntimeError):
73 """This indicates that dispatching a
74 `~lsst.analysis.tool.interface.MetricMeasurementBundle` failed.
75 """
77 pass
80def _tag2VersionTime(productStr: str) -> tuple[str, float]:
81 """Determine versions and dates from the string returned from
82 getEnvironmentPackages.
84 The `~lsst.utils.packages.genEnvironmentPackages` function returns the
85 setup version associated with a product, along with a list of tags that
86 have been added to it.
88 This method splits up that return string, and determines the earliest date
89 associated with the setup package version.
91 Parameters
92 ----------
93 productStr : `str`
94 The product string returned from a lookup on the result of a call to
95 `~lsst.utils.packages.getEnvironmentPackages`.
97 Returns
98 -------
99 result : `tuple` of `str`, `datetime.datetime`
100 The first `str` is the version of the package, and the second is the
101 datetime object associated with that released version.
103 Raises
104 ------
105 ValueError
106 Raised if there are no tags which correspond to dates.
107 """
108 times: list[datetime.datetime] = []
109 version = productStr.split()[0]
110 tags: str = re.findall("[(](.*)[)]", productStr)[0]
111 for tag in tags.split():
112 numDots = tag.count(".")
113 numUnder = tag.count("_")
114 separator = "_"
115 if numDots > numUnder:
116 separator = "."
117 match tag.split(separator):
118 # Daily tag branch.
119 case ("d", year, month, day):
120 dt = datetime.datetime(year=int(year), month=int(month), day=int(day))
121 # Weekly tag branch.
122 case ("w", year, week):
123 iyear = int(year)
124 iweek = int(week)
125 # Use 4 as the day because releases are available starting
126 # on Thursday
127 dayOfWeek = 4
129 # Find the first week to contain a thursday in it
130 cal = calendar.Calendar()
131 cal.setfirstweekday(6)
132 i = 0
133 for i, iterWeek in enumerate(cal.monthdatescalendar(iyear, 1)):
134 if iterWeek[dayOfWeek].month == 1:
135 break
136 # Handle fromisocalendar not being able to handle week 53
137 # in the case were the date was going to subtract 7 days anyway
138 if i and iweek == 53:
139 i = 0
140 iweek = 52
141 delta = datetime.timedelta(days=7 * i)
143 # Correct for a weekly being issued in the last week of the
144 # previous year, as Thursdays don't always line up evenly in
145 # a week / year split.
146 dt = datetime.datetime.fromisocalendar(iyear, iweek, dayOfWeek) - delta
147 # Skip tags that can't be understood.
148 case _:
149 continue
150 times.append(dt)
151 if len(times) == 0:
152 raise ValueError("Could not find any tags corresponding to dates")
153 minTime = min(times)
154 minTime.replace(tzinfo=datetime.timezone.utc)
155 return version, minTime.timestamp()
158@dataclass
159class SasquatchDispatcher:
160 """This class mediates the transfer of MetricMeasurementBundles to a
161 Sasquatch http kafka proxy server.
162 """
164 url: str
165 """Url of the Sasquatch proxy server"""
167 token: str
168 """Authentication token used in communicating with the proxy server"""
170 namespace: str = "lsst.dm"
171 """The namespace in Sasquatch in which to write the uploaded metrics"""
173 def __post_init__(self) -> None:
174 match ResourcePath(self.url).scheme:
175 case "http" | "https":
176 pass
177 case _:
178 raise ValueError("Proxy server must be locatable with either http or https")
180 self._cluster_id: str | None = None
182 @property
183 def clusterId(self) -> str:
184 """ClusterId of the Kafka proxy
186 Notes
187 -----
188 The cluster Id will be fetched with a network call if it is not
189 already cached.
190 """
191 if self._cluster_id is None:
192 self._populateClusterId()
193 return cast(str, self._cluster_id)
195 def _populateClusterId(self) -> None:
196 """Get Sasquatch kafka cluster ID."""
198 headers = {"content-type": "application/json"}
199 r = requests.get(f"{self.url}/v3/clusters", headers=headers)
201 if r.status_code == requests.codes.ok:
202 cluster_id = r.json()["data"][0]["cluster_id"]
204 self._cluster_id = str(cluster_id)
205 else:
206 log.error("Could not retrieve the cluster id for the specified url")
207 raise SasquatchDispatchFailure("Could not retrieve the cluster id for the specified url")
209 def _create_topic(self, topic_name: str) -> bool:
210 """Create a kafka topic in Sasquatch.
212 Parameters
213 ----------
214 topic_name : `str`
215 The name of the kafka topic to create
217 Returns
218 -------
219 status : `bool`
220 If this does not encounter an error it will return a True success
221 code, else it will return a False code.
223 """
225 headers = {"content-type": "application/json"}
227 topic_config = {
228 "topic_name": f"{self.namespace}.{topic_name}",
229 "partitions_count": PARTITIONS,
230 "replication_factor": REPLICATION_FACTOR,
231 }
233 r = requests.post(
234 f"{self.url}/v3/clusters/{self.clusterId}/topics", json=topic_config, headers=headers
235 )
237 if r.status_code == requests.codes.created:
238 log.debug("Created topic %s.%s", self.namespace, topic_name)
239 return True
240 elif r.status_code == requests.codes.bad_request:
241 log.debug("Topic %s.%s already exists.", self.namespace, topic_name)
242 return True
243 else:
244 log.error("Uknown error occured creating kafka topic %s %s", r.status_code, r.json())
245 return False
247 def _generateAvroSchema(self, metric: str, record: MutableMapping[str, Any]) -> tuple[str, bool]:
248 """Infer the Avro schema from the record payload.
250 Parameters
251 ----------
252 metric : `str`
253 The name of the metric
254 record : `MutableMapping`
255 The prepared record for which a schema is to be generated
257 Returns
258 -------
259 resultSchema : `str`
260 A json encoded string of the resulting avro schema
261 errorCode : bool
262 A boolean indicating if any record fields had to be trimmed because
263 a suitable schema could not be generated. True if records were
264 removed, False otherwise.
265 """
266 schema: dict[str, Any] = {"type": "record", "namespace": self.namespace, "name": metric}
268 # Record if any records needed to be trimmed
269 resultsTrimmed = False
271 fields = list()
272 # If avro schemas cant be generated for values, they should be removed
273 # from the records.
274 keysToRemove: list[str] = []
275 for key in record:
276 value = record[key]
277 avroType: Mapping[str, Any]
278 if "timestamp" in key:
279 avroType = {"type": "double"}
280 else:
281 avroType = self._python2Avro(value)
282 if len(avroType) == 0:
283 continue
284 if avroType.get("error_in_conversion"):
285 keysToRemove.append(key)
286 resultsTrimmed = True
287 continue
288 fields.append({"name": key, **avroType})
290 # remove any key that failed to have schema generated
291 for key in keysToRemove:
292 record.pop(key)
294 schema["fields"] = fields
296 return json.dumps(schema), resultsTrimmed
298 def _python2Avro(self, value: Any) -> Mapping:
299 """Map python type to avro schema
301 Parameters
302 ----------
303 value : `Any`
304 Any python parameter.
306 Returns
307 -------
308 result : `Mapping`
309 Return a mapping that represents an entry in an avro schema.
310 """
311 match value:
312 case float() | None:
313 return {"type": "float", "default": 0.0}
314 case str():
315 return {"type": "string", "default": ""}
316 case int():
317 return {"type": "int", "default": 0}
318 case Sequence():
319 tmp = {self._python2Avro(item)["type"] for item in value}
320 if len(tmp) == 0:
321 return {}
322 if len(tmp) > 1:
323 log.error(
324 "Sequence contains mixed types: %s, must be homogeneous for avro conversion "
325 "skipping record",
326 tmp,
327 )
328 return {"error_in_conversion": True}
329 return {"type": "array", "items": tmp.pop()}
330 case _:
331 log.error("Unsupported type %s, skipping record", type(value))
332 return {}
334 def _handleReferencePackage(self, meta: MutableMapping, bundle: MetricMeasurementBundle) -> None:
335 """Check to see if there is a reference package.
337 if there is a reference package, determine the datetime associated with
338 this reference package. Save the package, the version, and the date to
339 the common metric fields.
341 Parameters
342 ----------
343 meta : `MutableMapping`
344 A mapping which corresponds to fields which should be encoded in
345 all records.
346 bundle : `MetricMeasurementBundle`
347 The bundled metrics
348 """
349 package_version, package_timestamp = "", 0.0
350 if ref_package := getattr(bundle, "reference_package", ""):
351 ref_package = bundle.reference_package
352 packages = getEnvironmentPackages(True)
353 if package_info := packages.get(ref_package):
354 try:
355 package_version, package_timestamp = _tag2VersionTime(package_info)
356 except ValueError:
357 # Could not extract package timestamp leaving empty
358 pass
359 # explicit handle if None was set in the bundle for the package
360 meta["reference_package"] = ref_package or ""
361 meta["reference_package_version"] = package_version
362 meta["reference_package_timestamp"] = package_timestamp
364 def _handleTimes(self, meta: MutableMapping, bundle: MetricMeasurementBundle, run: str) -> None:
365 """Add times to the meta fields mapping.
367 Add all appropriate timestamp fields to the meta field mapping. These
368 will be added to all records.
370 This method will also look at the bundle to see if it defines a
371 preferred time. It so it sets that time as the main time stamp to be
372 used for this record.
374 Parameters
375 ----------
376 meta : `MutableMapping`
377 A mapping which corresponds to fields which should be encoded in
378 all records.
379 bundle : `MetricMeasurementBundle`
380 The bundled metrics
381 run : `str`
382 The `~lsst.daf.butler.Butler` collection where the
383 `MetricMeasurementBundle` is stored.
384 """
385 # Determine the timestamp associated with the run, if someone abused
386 # the run collection, use the current timestamp
387 if re.match(r"\d{8}T\d{6}Z", stamp := run.split("/")[-1]):
388 run_timestamp = datetime.datetime.strptime(stamp, r"%Y%m%dT%H%M%S%z")
389 else:
390 run_timestamp = datetime.datetime.now()
391 meta["run_timestamp"] = run_timestamp.timestamp()
393 # If the bundle supports supplying timestamps, dispatch on the type
394 # specified.
395 if hasattr(bundle, "timestamp_version") and bundle.timestamp_version:
396 match bundle.timestamp_version:
397 case "reference_package_timestamp":
398 if not meta["reference_package_timestamp"]:
399 log.error("Reference package timestamp is empty, using run_timestamp")
400 meta["timestamp"] = meta["run_timestamp"]
401 else:
402 meta["timestamp"] = meta["reference_package_timestamp"]
403 case "run_timestamp":
404 meta["timestamp"] = meta["run_timestamp"]
405 case "current_timestamp":
406 timeStamp = datetime.datetime.now()
407 meta["timestamp"] = timeStamp.timestamp()
408 case "dataset_timestamp":
409 log.error("dataset timestamps are not yet supported, run_timestamp will be used")
410 meta["timestamp"] = meta["run_timestamp"]
411 case str(value) if "explicit_timestamp" in value:
412 try:
413 _, splitTime = value.split(":")
414 except ValueError as excpt:
415 raise ValueError(
416 "Explicit timestamp must be given in the format 'explicit_timestamp:datetime', "
417 "where datetime is given in the form '%Y%m%dT%H%M%S%z"
418 ) from excpt
419 meta["timestamp"] = datetime.datetime.strptime(splitTime, r"%Y%m%dT%H%M%S%z").timestamp()
420 case _:
421 log.error(
422 "Timestamp version %s is not supported, run_timestamp will be used",
423 bundle.timestamp_version,
424 )
425 meta["timestamp"] = meta["run_timestamp"]
426 # Default to using the run_timestamp.
427 else:
428 meta["timestamp"] = meta["run_timestamp"]
430 def _handleIdentifier(
431 self,
432 meta: MutableMapping,
433 identifierFields: Mapping[str, Any] | None,
434 datasetIdentifier: str | None,
435 bundle: MetricMeasurementBundle,
436 ) -> None:
437 """Add an identifier to the meta record mapping.
439 If the bundle declares a dataset identifier to use add that to the
440 record, otherwise use 'Generic' as the identifier. If the
441 datasetIdentifier parameter is specified, that is used instead of
442 anything specified by the bundle.
444 This will also add any identifier fields supplied to the meta record
445 mapping.
447 Together these values (in addition to the timestamp and topic) should
448 uniquely identify an upload to the Sasquatch system.
450 Parameters
451 ----------
452 meta : `MutableMapping`
453 A mapping which corresponds to fields which should be encoded in
454 all records.
455 identifierFields: `Mapping` or `None`
456 The keys and values in this mapping will be both added as fields
457 in the record, and used in creating a unique tag for the uploaded
458 dataset type. I.e. the timestamp, and the tag will be unique, and
459 each record will belong to one combination of such.
460 datasetIdentifier : `str` or `None`
461 A string which will be used in creating unique identifier tags.
462 bundle : `MetricMeasurementBundle`
463 The bundle containing metric values to upload.
464 """
465 identifier: str
466 if datasetIdentifier is not None:
467 identifier = datasetIdentifier
468 elif hasattr(bundle, "dataset_identifier") and bundle.dataset_identifier is not None:
469 identifier = bundle.dataset_identifier
470 else:
471 identifier = "Generic"
473 meta["dataset_tag"] = identifier
475 if identifierFields is None:
476 identifierFields = {}
477 for key in IDENTIFIER_KEYS:
478 value = identifierFields.get(key, "")
479 meta[key] = f"{value}"
481 def _prepareBundle(
482 self,
483 bundle: MetricMeasurementBundle,
484 run: str,
485 datasetType: str,
486 timestamp: datetime.datetime | None = None,
487 id: UUID | None = None,
488 identifierFields: Mapping | None = None,
489 datasetIdentifier: str | None = None,
490 extraFields: Mapping | None = None,
491 ) -> tuple[Mapping[str, list[Any]], bool]:
492 """Encode all of the inputs into a format that can be sent to the
493 kafka proxy server.
495 Parameters
496 ----------
497 bundle : `MetricMeasurementBundle`
498 The bundle containing metric values to upload.
499 run : `str`
500 The run name to associate with these metric values. If this bundle
501 is also stored in the butler, this should be the butler run
502 collection the bundle is stored in the butler.
503 datasetType : `str`
504 The dataset type name associated with this
505 `MetricMeasurementBundle`
506 timestamp : `datetime.datetime`, optional
507 The timestamp to be associated with the measurements in the ingress
508 database. If this value is None, timestamp will be set by the run
509 time or current time.
510 id : `UUID`, optional
511 The UUID of the `MetricMeasurementBundle` within the butler. If
512 `None`, a new random UUID will be generated so that each record in
513 Sasquatch will have a unique value.
514 identifierFields: `Mapping`, optional
515 The keys and values in this mapping will be both added as fields
516 in the record, and used in creating a unique tag for the uploaded
517 dataset type. I.e. the timestamp, and the tag will be unique, and
518 each record will belong to one combination of such.
519 datasetIdentifier : `str`, optional
520 A string which will be used in creating unique identifier tags.
521 extraFields: `Mapping`, optional
522 Extra mapping keys and values that will be added as fields to the
523 dispatched record.
525 Returns
526 -------
527 result : `Mapping` of `str` to `list`
528 A mapping of metric name of list of metric measurement records.
529 status : `bool`
530 A status boolean indicating if some records had to be skipped due
531 to a problem parsing the bundle.
532 """
533 if id is None:
534 id = uuid4()
535 sid = str(id)
536 meta: dict[str, Any] = dict()
538 # Add other associated common fields
539 meta["id"] = sid
540 meta["run"] = run
541 meta["dataset_type"] = datasetType
543 # Check to see if the bundle declares a reference package
544 self._handleReferencePackage(meta, bundle)
546 # Handle the various timestamps that could be associated with a record
547 self._handleTimes(meta, bundle, run)
549 # Always use the supplied timestamp if one was passed to use.
550 if timestamp is not None:
551 meta["timestamp"] = timestamp.timestamp()
553 self._handleIdentifier(meta, identifierFields, datasetIdentifier, bundle)
555 # Add in any other fields that were supplied to the function call.
556 if extraFields is not None:
557 meta.update(extraFields)
559 metricRecords: dict[str, list[Any]] = dict()
561 # Record if any records needed skipped
562 resultsTrimmed = False
564 # Look at each of the metrics in the bundle (name, values)
565 for metric, measurements in bundle.items():
566 # Create a list which will contain the records for each measurement
567 # associated with metric.
568 metricRecordList = metricRecords.setdefault(metric, list())
570 record: dict[str, Any] = meta.copy()
572 # loop over each metric measurement within the metric
573 for measurement in measurements:
574 # need to extract any tags, package info, etc
575 note_key = f"{measurement.metric_name.metric}.metric_tags"
576 record["tags"] = dict(measurement.notes.items()).get(note_key, list())
578 # Missing values are replaced by 0 in sasquatch, see RFC-763.
579 name = ""
580 value = 0.0
581 match measurement.json:
582 case {"metric": name, "value": None}:
583 pass
584 case {"metric": name, "value": value}:
585 if math.isnan(value):
586 log.error(
587 "Measurement %s had a value that is a NaN, dispatch will be skipped",
588 measurement,
589 )
590 resultsTrimmed = True
591 continue
592 pass
593 case {"value": _}:
594 log.error("Measurement %s does not contain the key 'metric'", measurement)
595 resultsTrimmed = True
596 continue
597 case {"metric": _}:
598 log.error("Measurement %s does not contain the key 'value'", measurement)
599 resultsTrimmed = True
600 continue
601 record[name] = value
603 metricRecordList.append({"value": record})
604 return metricRecords, resultsTrimmed
606 def dispatch(
607 self,
608 bundle: MetricMeasurementBundle,
609 run: str,
610 datasetType: str,
611 timestamp: datetime.datetime | None = None,
612 id: UUID | None = None,
613 datasetIdentifier: str | None = None,
614 identifierFields: Mapping | None = None,
615 extraFields: Mapping | None = None,
616 ) -> None:
617 """Dispatch a `MetricMeasurementBundle` to Sasquatch.
619 Parameters
620 ----------
621 bundle : `MetricMeasurementBundle`
622 The bundle containing metric values to upload.
623 run : `str`
624 The run name to associate with these metric values. If this bundle
625 is also stored in the butler, this should be the butler run
626 collection the bundle is stored in the butler. This will be used
627 in generating uniqueness constraints in Sasquatch.
628 datasetType : `str`
629 The dataset type name associated with this
630 `MetricMeasurementBundle`.
631 timestamp : `datetime.datetime`, optional
632 The timestamp to be associated with the measurements in the ingress
633 database. If this value is None, timestamp will be set by the run
634 time or current time.
635 id : `UUID`, optional
636 The UUID of the `MetricMeasurementBundle` within the Butler. If
637 `None`, a new random UUID will be generated so that each record in
638 Sasquatch will have a unique value.
639 datasetIdentifier : `str`, optional
640 A string which will be used in creating unique identifier tags. If
641 `None`, a default value will be inserted.
642 identifierFields: `Mapping`, optional
643 The keys and values in this mapping will be both added as fields
644 in the record, and used in creating a unique tag for the uploaded
645 dataset type. I.e. the timestamp, and the tag will be unique, and
646 each record will belong to one combination of such. Examples of
647 entries would be things like visit or tract.
648 extraFields: `Mapping`, optional
649 Extra mapping keys and values that will be added as fields to the
650 dispatched record.
652 Raises
653 ------
654 SasquatchDispatchPartialFailure
655 Raised if there were any errors in dispatching a bundle.
656 """
657 if id is None:
658 id = uuid4()
660 # Prepare the bundle by transforming it to a list of metric records
661 metricRecords, recordsTrimmed = self._prepareBundle(
662 bundle=bundle,
663 run=run,
664 datasetType=datasetType,
665 timestamp=timestamp,
666 id=id,
667 datasetIdentifier=datasetIdentifier,
668 identifierFields=identifierFields,
669 extraFields=extraFields,
670 )
672 headers = {"content-type": "application/vnd.kafka.avro.v2+json"}
673 data: dict[str, Any] = dict()
674 partialUpload = False
675 uploadFailed = []
677 for metric, record in metricRecords.items():
678 # create the kafka topic if it does not already exist
679 if not self._create_topic(metric):
680 log.error("Topic not created, skipping dispatch of %s", metric)
681 continue
682 recordValue = record[0]["value"]
683 # Generate schemas for each record
684 data["value_schema"], schemaTrimmed = self._generateAvroSchema(metric, recordValue)
685 data["records"] = record
687 if schemaTrimmed:
688 partialUpload = True
690 r = requests.post(f"{self.url}/topics/{self.namespace}.{metric}", json=data, headers=headers)
692 if r.status_code == requests.codes.ok:
693 log.debug("Succesfully sent data for metric %s", metric)
694 uploadFailed.append(False)
695 else:
696 log.error(
697 "There was a problem submitting the metric %s: %s, %s", metric, r.status_code, r.json()
698 )
699 uploadFailed.append(True)
700 partialUpload = True
702 # There may be no metrics to try to upload, and thus the uploadFailed
703 # list may be empty, check before issuing failure
704 if len(uploadFailed) > 0 and all(uploadFailed):
705 raise SasquatchDispatchFailure("All records were unable to be uploaded.")
707 if partialUpload or recordsTrimmed:
708 raise SasquatchDispatchPartialFailure("One or more records may not have been uploaded entirely")
710 def dispatchRef(
711 self,
712 bundle: MetricMeasurementBundle,
713 ref: DatasetRef,
714 timestamp: datetime.datetime | None = None,
715 extraFields: Mapping | None = None,
716 datasetIdentifier: str | None = None,
717 ) -> None:
718 """Dispatch a `MetricMeasurementBundle` to Sasquatch with a known
719 `DatasetRef`.
721 Parameters
722 ----------
723 bundle : `MetricMeasurementBundle`
724 The bundle containing metric values to upload.
725 ref : `DatasetRef`
726 The `Butler` dataset ref corresponding to the input
727 `MetricMeasurementBundle`.
728 timestamp : `datetime.datetime`, optional
729 The timestamp to be associated with the measurements in the ingress
730 database. If this value is None, timestamp will be set by the run
731 time or current time.
732 extraFields: `Mapping`, optional
733 Extra mapping keys and values that will be added as fields to the
734 dispatched record if not None.
735 datasetIdentifier : `str`, optional
736 A string which will be used in creating unique identifier tags. If
737 None, a default value will be inserted.
739 Raises
740 ------
741 SasquatchDispatchPartialFailure
742 Raised if there were any errors in dispatching a bundle.
743 """
744 # Parse the relevant info out of the dataset ref.
745 serializedRef = ref.to_simple()
746 id = serializedRef.id
747 if serializedRef.run is None:
748 run = "<unknown>"
749 else:
750 run = serializedRef.run
751 dstype = serializedRef.datasetType
752 datasetType = dstype.name if dstype is not None else ""
753 dataRefMapping = serializedRef.dataId.dataId if serializedRef.dataId else None
755 self.dispatch(
756 bundle,
757 run=run,
758 timestamp=timestamp,
759 datasetType=datasetType,
760 id=id,
761 identifierFields=dataRefMapping,
762 extraFields=extraFields,
763 datasetIdentifier=datasetIdentifier,
764 )