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1# This file is part of ctrl_bps_htcondor. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://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 <https://www.gnu.org/licenses/>. 

21 

22"""Interface between generic workflow to HTCondor workflow system. 

23""" 

24 

25__all__ = ["HTCondorService", "HTCondorWorkflow"] 

26 

27 

28import logging 

29import os 

30import re 

31from collections import defaultdict 

32from enum import IntEnum, auto 

33from pathlib import Path 

34 

35import htcondor 

36from lsst.ctrl.bps import ( 

37 BaseWmsService, 

38 BaseWmsWorkflow, 

39 GenericWorkflow, 

40 GenericWorkflowJob, 

41 WmsJobReport, 

42 WmsRunReport, 

43 WmsStates, 

44) 

45from lsst.ctrl.bps.bps_utils import chdir, create_count_summary 

46from lsst.utils.timer import time_this 

47from packaging import version 

48 

49from .lssthtc import ( 

50 MISSING_ID, 

51 HTCDag, 

52 HTCJob, 

53 JobStatus, 

54 NodeStatus, 

55 condor_q, 

56 condor_search, 

57 condor_status, 

58 htc_backup_files, 

59 htc_check_dagman_output, 

60 htc_create_submit_from_cmd, 

61 htc_create_submit_from_dag, 

62 htc_create_submit_from_file, 

63 htc_escape, 

64 htc_submit_dag, 

65 htc_version, 

66 pegasus_name_to_label, 

67 read_dag_info, 

68 read_dag_log, 

69 read_dag_status, 

70 read_node_status, 

71 summary_from_dag, 

72 write_dag_info, 

73) 

74 

75 

76class WmsIdType(IntEnum): 

77 """Type of valid WMS ids.""" 

78 

79 UNKNOWN = auto() 

80 """The type of id cannot be determined. 

81 """ 

82 

83 LOCAL = auto() 

84 """The id is HTCondor job's ClusterId (with optional '.ProcId'). 

85 """ 

86 

87 GLOBAL = auto() 

88 """Id is a HTCondor's global job id. 

89 """ 

90 

91 PATH = auto() 

92 """Id is a submission path. 

93 """ 

94 

95 

96DEFAULT_HTC_EXEC_PATT = ".*worker.*" 

97"""Default pattern for searching execute machines in an HTCondor pool. 

98""" 

99 

100_LOG = logging.getLogger(__name__) 

101 

102 

103class HTCondorService(BaseWmsService): 

104 """HTCondor version of WMS service.""" 

105 

106 def prepare(self, config, generic_workflow, out_prefix=None): 

107 """Convert generic workflow to an HTCondor DAG ready for submission. 

108 

109 Parameters 

110 ---------- 

111 config : `lsst.ctrl.bps.BpsConfig` 

112 BPS configuration that includes necessary submit/runtime 

113 information. 

114 generic_workflow : `lsst.ctrl.bps.GenericWorkflow` 

115 The generic workflow (e.g., has executable name and arguments). 

116 out_prefix : `str` 

117 The root directory into which all WMS-specific files are written. 

118 

119 Returns 

120 ------- 

121 workflow : `lsst.ctrl.bps.wms.htcondor.HTCondorWorkflow` 

122 HTCondor workflow ready to be run. 

123 """ 

124 _LOG.debug("out_prefix = '%s'", out_prefix) 

125 with time_this(log=_LOG, level=logging.INFO, prefix=None, msg="Completed HTCondor workflow creation"): 

126 workflow = HTCondorWorkflow.from_generic_workflow( 

127 config, 

128 generic_workflow, 

129 out_prefix, 

130 f"{self.__class__.__module__}." f"{self.__class__.__name__}", 

131 ) 

132 

133 with time_this( 

134 log=_LOG, level=logging.INFO, prefix=None, msg="Completed writing out HTCondor workflow" 

135 ): 

136 workflow.write(out_prefix) 

137 return workflow 

138 

139 def submit(self, workflow): 

140 """Submit a single HTCondor workflow. 

141 

142 Parameters 

143 ---------- 

144 workflow : `lsst.ctrl.bps.BaseWorkflow` 

145 A single HTCondor workflow to submit. run_id is updated after 

146 successful submission to WMS. 

147 """ 

148 dag = workflow.dag 

149 

150 ver = version.parse(htc_version()) 

151 if ver >= version.parse("8.9.3"): 

152 sub = htc_create_submit_from_dag(dag.graph["dag_filename"], {}) 

153 else: 

154 sub = htc_create_submit_from_cmd(dag.graph["dag_filename"], {}) 

155 

156 # For workflow portability, internal paths are all relative. Hence 

157 # the DAG needs to be submitted to HTCondor from inside the submit 

158 # directory. 

159 with chdir(workflow.submit_path): 

160 _LOG.info("Submitting from directory: %s", os.getcwd()) 

161 schedd_dag_info = htc_submit_dag(sub) 

162 if schedd_dag_info: 

163 write_dag_info(f"{dag.name}.info.json", schedd_dag_info) 

164 

165 _, dag_info = schedd_dag_info.popitem() 

166 _, dag_ad = dag_info.popitem() 

167 

168 dag.run_id = f"{dag_ad['ClusterId']}.{dag_ad['ProcId']}" 

169 workflow.run_id = dag.run_id 

170 else: 

171 raise RuntimeError("Submission failed: unable to retrieve DAGMan job information") 

172 

173 def restart(self, wms_workflow_id): 

174 """Restart a failed DAGMan workflow. 

175 

176 Parameters 

177 ---------- 

178 wms_workflow_id : `str` 

179 The directory with HTCondor files. 

180 

181 Returns 

182 ------- 

183 run_id : `str` 

184 HTCondor id of the restarted DAGMan job. If restart failed, it will 

185 be set to None. 

186 run_name : `str` 

187 Name of the restarted workflow. If restart failed, it will be set 

188 to None. 

189 message : `str` 

190 A message describing any issues encountered during the restart. 

191 If there were no issues, an empty string is returned. 

192 """ 

193 wms_path = Path(wms_workflow_id) 

194 if not wms_path.is_dir(): 

195 return None, None, f"Directory '{wms_path}' not found" 

196 

197 _LOG.info("Restarting workflow from directory '%s'", wms_path) 

198 rescue_dags = list(wms_path.glob("*.dag.rescue*")) 

199 if not rescue_dags: 

200 return None, None, f"HTCondor rescue DAG(s) not found in '{wms_path}'" 

201 

202 _LOG.info("Verifying that the workflow is not already in the job queue") 

203 schedd_dag_info = condor_q(constraint=f'regexp("dagman$", Cmd) && Iwd == "{wms_workflow_id}"') 

204 if schedd_dag_info: 

205 _, dag_info = schedd_dag_info.popitem() 

206 _, dag_ad = dag_info.popitem() 

207 id_ = dag_ad["GlobalJobId"] 

208 return None, None, f"Workflow already in the job queue (global job id: '{id_}')" 

209 

210 _LOG.info("Checking execution status of the workflow") 

211 warn = False 

212 dag_ad = read_dag_status(str(wms_path)) 

213 if dag_ad: 

214 nodes_total = dag_ad.get("NodesTotal", 0) 

215 if nodes_total != 0: 

216 nodes_done = dag_ad.get("NodesDone", 0) 

217 if nodes_total == nodes_done: 

218 return None, None, "All jobs in the workflow finished successfully" 

219 else: 

220 warn = True 

221 else: 

222 warn = True 

223 if warn: 

224 _LOG.warning( 

225 "Cannot determine the execution status of the workflow, continuing with restart regardless" 

226 ) 

227 

228 _LOG.info("Backing up select HTCondor files from previous run attempt") 

229 htc_backup_files(wms_path, subdir="backups") 

230 

231 # For workflow portability, internal paths are all relative. Hence 

232 # the DAG needs to be resubmitted to HTCondor from inside the submit 

233 # directory. 

234 _LOG.info("Adding workflow to the job queue") 

235 run_id, run_name, message = None, None, "" 

236 with chdir(wms_path): 

237 try: 

238 dag_path = next(wms_path.glob("*.dag.condor.sub")) 

239 except StopIteration: 

240 message = f"DAGMan submit description file not found in '{wms_path}'" 

241 else: 

242 sub = htc_create_submit_from_file(dag_path.name) 

243 schedd_dag_info = htc_submit_dag(sub) 

244 

245 # Save select information about the DAGMan job to a file. Use 

246 # the run name (available in the ClassAd) as the filename. 

247 if schedd_dag_info: 

248 dag_info = next(iter(schedd_dag_info.values())) 

249 dag_ad = next(iter(dag_info.values())) 

250 write_dag_info(f"{dag_ad['bps_run']}.info.json", schedd_dag_info) 

251 run_id = f"{dag_ad['ClusterId']}.{dag_ad['ProcId']}" 

252 run_name = dag_ad["bps_run"] 

253 else: 

254 message = "DAGMan job information unavailable" 

255 

256 return run_id, run_name, message 

257 

258 def list_submitted_jobs(self, wms_id=None, user=None, require_bps=True, pass_thru=None, is_global=False): 

259 """Query WMS for list of submitted WMS workflows/jobs. 

260 

261 This should be a quick lookup function to create list of jobs for 

262 other functions. 

263 

264 Parameters 

265 ---------- 

266 wms_id : `int` or `str`, optional 

267 Id or path that can be used by WMS service to look up job. 

268 user : `str`, optional 

269 User whose submitted jobs should be listed. 

270 require_bps : `bool`, optional 

271 Whether to require jobs returned in list to be bps-submitted jobs. 

272 pass_thru : `str`, optional 

273 Information to pass through to WMS. 

274 is_global : `bool`, optional 

275 If set, all job queues (and their histories) will be queried for 

276 job information. Defaults to False which means that only the local 

277 job queue will be queried. 

278 

279 Returns 

280 ------- 

281 job_ids : `list` [`Any`] 

282 Only job ids to be used by cancel and other functions. Typically 

283 this means top-level jobs (i.e., not children jobs). 

284 """ 

285 _LOG.debug( 

286 "list_submitted_jobs params: wms_id=%s, user=%s, require_bps=%s, pass_thru=%s, is_global=%s", 

287 wms_id, 

288 user, 

289 require_bps, 

290 pass_thru, 

291 is_global, 

292 ) 

293 

294 # Determine which Schedds will be queried for job information. 

295 coll = htcondor.Collector() 

296 

297 schedd_ads = [] 

298 if is_global: 

299 schedd_ads.extend(coll.locateAll(htcondor.DaemonTypes.Schedd)) 

300 else: 

301 schedd_ads.append(coll.locate(htcondor.DaemonTypes.Schedd)) 

302 

303 # Construct appropriate constraint expression using provided arguments. 

304 constraint = "False" 

305 if wms_id is None: 

306 if user is not None: 

307 constraint = f'(Owner == "{user}")' 

308 else: 

309 schedd_ad, cluster_id, id_type = _wms_id_to_cluster(wms_id) 

310 if cluster_id is not None: 

311 constraint = f"(DAGManJobId == {cluster_id} || ClusterId == {cluster_id})" 

312 

313 # If provided id is either a submission path or a global id, 

314 # make sure the right Schedd will be queried regardless of 

315 # 'is_global' value. 

316 if id_type in {WmsIdType.GLOBAL, WmsIdType.PATH}: 

317 schedd_ads = [schedd_ad] 

318 if require_bps: 

319 constraint += ' && (bps_isjob == "True")' 

320 if pass_thru: 

321 if "-forcex" in pass_thru: 

322 pass_thru_2 = pass_thru.replace("-forcex", "") 

323 if pass_thru_2 and not pass_thru_2.isspace(): 

324 constraint += f" && ({pass_thru_2})" 

325 else: 

326 constraint += f" && ({pass_thru})" 

327 

328 # Create a list of scheduler daemons which need to be queried. 

329 schedds = {ad["Name"]: htcondor.Schedd(ad) for ad in schedd_ads} 

330 

331 _LOG.debug("constraint = %s, schedds = %s", constraint, ", ".join(schedds)) 

332 results = condor_q(constraint=constraint, schedds=schedds) 

333 

334 # Prune child jobs where DAG job is in queue (i.e., aren't orphans). 

335 job_ids = [] 

336 for schedd_name, job_info in results.items(): 

337 for job_id, job_ad in job_info.items(): 

338 _LOG.debug("job_id=%s DAGManJobId=%s", job_id, job_ad.get("DAGManJobId", "None")) 

339 if "DAGManJobId" not in job_ad: 

340 job_ids.append(job_ad.get("GlobalJobId", job_id)) 

341 else: 

342 _LOG.debug("Looking for %s", f"{job_ad['DAGManJobId']}.0") 

343 _LOG.debug("\tin jobs.keys() = %s", job_info.keys()) 

344 if f"{job_ad['DAGManJobId']}.0" not in job_info: # orphaned job 

345 job_ids.append(job_ad.get("GlobalJobId", job_id)) 

346 

347 _LOG.debug("job_ids = %s", job_ids) 

348 return job_ids 

349 

350 def report(self, wms_workflow_id=None, user=None, hist=0, pass_thru=None, is_global=False): 

351 """Return run information based upon given constraints. 

352 

353 Parameters 

354 ---------- 

355 wms_workflow_id : `str`, optional 

356 Limit to specific run based on id. 

357 user : `str`, optional 

358 Limit results to runs for this user. 

359 hist : `float`, optional 

360 Limit history search to this many days. Defaults to 0. 

361 pass_thru : `str`, optional 

362 Constraints to pass through to HTCondor. 

363 is_global : `bool`, optional 

364 If set, all job queues (and their histories) will be queried for 

365 job information. Defaults to False which means that only the local 

366 job queue will be queried. 

367 

368 Returns 

369 ------- 

370 runs : `list` [`lsst.ctrl.bps.WmsRunReport`] 

371 Information about runs from given job information. 

372 message : `str` 

373 Extra message for report command to print. This could be pointers 

374 to documentation or to WMS specific commands. 

375 """ 

376 if wms_workflow_id: 

377 id_type = _wms_id_type(wms_workflow_id) 

378 if id_type == WmsIdType.LOCAL: 

379 schedulers = _locate_schedds(locate_all=is_global) 

380 run_reports, message = _report_from_id(wms_workflow_id, hist, schedds=schedulers) 

381 elif id_type == WmsIdType.GLOBAL: 

382 schedulers = _locate_schedds(locate_all=True) 

383 run_reports, message = _report_from_id(wms_workflow_id, hist, schedds=schedulers) 

384 elif id_type == WmsIdType.PATH: 

385 run_reports, message = _report_from_path(wms_workflow_id) 

386 else: 

387 run_reports, message = {}, "Invalid job id" 

388 else: 

389 schedulers = _locate_schedds(locate_all=is_global) 

390 run_reports, message = _summary_report(user, hist, pass_thru, schedds=schedulers) 

391 _LOG.debug("report: %s, %s", run_reports, message) 

392 

393 return list(run_reports.values()), message 

394 

395 def cancel(self, wms_id, pass_thru=None): 

396 """Cancel submitted workflows/jobs. 

397 

398 Parameters 

399 ---------- 

400 wms_id : `str` 

401 Id or path of job that should be canceled. 

402 pass_thru : `str`, optional 

403 Information to pass through to WMS. 

404 

405 Returns 

406 ------- 

407 deleted : `bool` 

408 Whether successful deletion or not. Currently, if any doubt or any 

409 individual jobs not deleted, return False. 

410 message : `str` 

411 Any message from WMS (e.g., error details). 

412 """ 

413 _LOG.debug("Canceling wms_id = %s", wms_id) 

414 

415 schedd_ad, cluster_id, _ = _wms_id_to_cluster(wms_id) 

416 

417 if cluster_id is None: 

418 deleted = False 

419 message = "invalid id" 

420 else: 

421 _LOG.debug( 

422 "Canceling job managed by schedd_name = %s with cluster_id = %s", 

423 cluster_id, 

424 schedd_ad["Name"], 

425 ) 

426 schedd = htcondor.Schedd(schedd_ad) 

427 

428 constraint = f"ClusterId == {cluster_id}" 

429 if pass_thru is not None and "-forcex" in pass_thru: 

430 pass_thru_2 = pass_thru.replace("-forcex", "") 

431 if pass_thru_2 and not pass_thru_2.isspace(): 

432 constraint += f"&& ({pass_thru_2})" 

433 _LOG.debug("JobAction.RemoveX constraint = %s", constraint) 

434 results = schedd.act(htcondor.JobAction.RemoveX, constraint) 

435 else: 

436 if pass_thru: 

437 constraint += f"&& ({pass_thru})" 

438 _LOG.debug("JobAction.Remove constraint = %s", constraint) 

439 results = schedd.act(htcondor.JobAction.Remove, constraint) 

440 _LOG.debug("Remove results: %s", results) 

441 

442 if results["TotalSuccess"] > 0 and results["TotalError"] == 0: 

443 deleted = True 

444 message = "" 

445 else: 

446 deleted = False 

447 if results["TotalSuccess"] == 0 and results["TotalError"] == 0: 

448 message = "no such bps job in batch queue" 

449 else: 

450 message = f"unknown problems deleting: {results}" 

451 

452 _LOG.debug("deleted: %s; message = %s", deleted, message) 

453 return deleted, message 

454 

455 

456class HTCondorWorkflow(BaseWmsWorkflow): 

457 """Single HTCondor workflow. 

458 

459 Parameters 

460 ---------- 

461 name : `str` 

462 Unique name for Workflow used when naming files. 

463 config : `lsst.ctrl.bps.BpsConfig` 

464 BPS configuration that includes necessary submit/runtime information. 

465 """ 

466 

467 def __init__(self, name, config=None): 

468 super().__init__(name, config) 

469 self.dag = None 

470 

471 @classmethod 

472 def from_generic_workflow(cls, config, generic_workflow, out_prefix, service_class): 

473 # Docstring inherited 

474 htc_workflow = cls(generic_workflow.name, config) 

475 htc_workflow.dag = HTCDag(name=generic_workflow.name) 

476 

477 _LOG.debug("htcondor dag attribs %s", generic_workflow.run_attrs) 

478 htc_workflow.dag.add_attribs(generic_workflow.run_attrs) 

479 htc_workflow.dag.add_attribs( 

480 { 

481 "bps_wms_service": service_class, 

482 "bps_wms_workflow": f"{cls.__module__}.{cls.__name__}", 

483 "bps_run_quanta": create_count_summary(generic_workflow.quanta_counts), 

484 "bps_job_summary": create_count_summary(generic_workflow.job_counts), 

485 } 

486 ) 

487 

488 _, tmp_template = config.search("subDirTemplate", opt={"replaceVars": False, "default": ""}) 

489 if isinstance(tmp_template, str): 

490 subdir_template = defaultdict(lambda: tmp_template) 

491 else: 

492 subdir_template = tmp_template 

493 

494 # Create all DAG jobs 

495 site_values = {} # cache compute site specific values to reduce config lookups 

496 for job_name in generic_workflow: 

497 gwjob = generic_workflow.get_job(job_name) 

498 if gwjob.compute_site not in site_values: 

499 site_values[gwjob.compute_site] = _gather_site_values(config, gwjob.compute_site) 

500 htc_job = _create_job( 

501 subdir_template[gwjob.label], 

502 site_values[gwjob.compute_site], 

503 generic_workflow, 

504 gwjob, 

505 out_prefix, 

506 ) 

507 htc_workflow.dag.add_job(htc_job) 

508 

509 # Add job dependencies to the DAG 

510 for job_name in generic_workflow: 

511 htc_workflow.dag.add_job_relationships([job_name], generic_workflow.successors(job_name)) 

512 

513 # If final job exists in generic workflow, create DAG final job 

514 final = generic_workflow.get_final() 

515 if final and isinstance(final, GenericWorkflowJob): 

516 if final.compute_site and final.compute_site not in site_values: 

517 site_values[final.compute_site] = _gather_site_values(config, final.compute_site) 

518 final_htjob = _create_job( 

519 subdir_template[final.label], 

520 site_values[final.compute_site], 

521 generic_workflow, 

522 final, 

523 out_prefix, 

524 ) 

525 if "post" not in final_htjob.dagcmds: 

526 final_htjob.dagcmds["post"] = ( 

527 f"{os.path.dirname(__file__)}/final_post.sh" f" {final.name} $DAG_STATUS $RETURN" 

528 ) 

529 htc_workflow.dag.add_final_job(final_htjob) 

530 elif final and isinstance(final, GenericWorkflow): 

531 raise NotImplementedError("HTCondor plugin does not support a workflow as the final job") 

532 elif final: 

533 return TypeError(f"Invalid type for GenericWorkflow.get_final() results ({type(final)})") 

534 

535 return htc_workflow 

536 

537 def write(self, out_prefix): 

538 """Output HTCondor DAGMan files needed for workflow submission. 

539 

540 Parameters 

541 ---------- 

542 out_prefix : `str` 

543 Directory prefix for HTCondor files. 

544 """ 

545 self.submit_path = out_prefix 

546 os.makedirs(out_prefix, exist_ok=True) 

547 

548 # Write down the workflow in HTCondor format. 

549 self.dag.write(out_prefix, "jobs/{self.label}") 

550 

551 

552def _create_job(subdir_template, site_values, generic_workflow, gwjob, out_prefix): 

553 """Convert GenericWorkflow job nodes to DAG jobs. 

554 

555 Parameters 

556 ---------- 

557 subdir_template : `str` 

558 Template for making subdirs. 

559 site_values : `dict` 

560 Site specific values 

561 generic_workflow : `lsst.ctrl.bps.GenericWorkflow` 

562 Generic workflow that is being converted. 

563 gwjob : `lsst.ctrl.bps.GenericWorkflowJob` 

564 The generic job to convert to a HTCondor job. 

565 out_prefix : `str` 

566 Directory prefix for HTCondor files. 

567 

568 Returns 

569 ------- 

570 htc_job : `lsst.ctrl.bps.wms.htcondor.HTCJob` 

571 The HTCondor job equivalent to the given generic job. 

572 """ 

573 htc_job = HTCJob(gwjob.name, label=gwjob.label) 

574 

575 curvals = defaultdict(str) 

576 curvals["label"] = gwjob.label 

577 if gwjob.tags: 

578 curvals.update(gwjob.tags) 

579 

580 subdir = subdir_template.format_map(curvals) 

581 htc_job.subfile = Path("jobs") / subdir / f"{gwjob.name}.sub" 

582 

583 htc_job_cmds = { 

584 "universe": "vanilla", 

585 "should_transfer_files": "YES", 

586 "when_to_transfer_output": "ON_EXIT_OR_EVICT", 

587 "transfer_output_files": '""', # Set to empty string to disable 

588 "transfer_executable": "False", 

589 "getenv": "True", 

590 # Exceeding memory sometimes triggering SIGBUS or SIGSEGV error. Tell 

591 # htcondor to put on hold any jobs which exited by a signal. 

592 "on_exit_hold": "ExitBySignal == true", 

593 "on_exit_hold_reason": 'strcat("Job raised a signal ", string(ExitSignal), ". ", ' 

594 '"Handling signal as if job has gone over memory limit.")', 

595 "on_exit_hold_subcode": "34", 

596 } 

597 

598 htc_job_cmds.update(_translate_job_cmds(site_values, generic_workflow, gwjob)) 

599 

600 # job stdout, stderr, htcondor user log. 

601 for key in ("output", "error", "log"): 

602 htc_job_cmds[key] = htc_job.subfile.with_suffix(f".$(Cluster).{key[:3]}") 

603 _LOG.debug("HTCondor %s = %s", key, htc_job_cmds[key]) 

604 

605 htc_job_cmds.update( 

606 _handle_job_inputs(generic_workflow, gwjob.name, site_values["bpsUseShared"], out_prefix) 

607 ) 

608 

609 # Add the job cmds dict to the job object. 

610 htc_job.add_job_cmds(htc_job_cmds) 

611 

612 htc_job.add_dag_cmds(_translate_dag_cmds(gwjob)) 

613 

614 # Add job attributes to job. 

615 _LOG.debug("gwjob.attrs = %s", gwjob.attrs) 

616 htc_job.add_job_attrs(gwjob.attrs) 

617 htc_job.add_job_attrs(site_values["attrs"]) 

618 htc_job.add_job_attrs({"bps_job_quanta": create_count_summary(gwjob.quanta_counts)}) 

619 htc_job.add_job_attrs({"bps_job_name": gwjob.name, "bps_job_label": gwjob.label}) 

620 

621 return htc_job 

622 

623 

624def _translate_job_cmds(cached_vals, generic_workflow, gwjob): 

625 """Translate the job data that are one to one mapping 

626 

627 Parameters 

628 ---------- 

629 cached_vals : `dict` [`str`, `Any`] 

630 Config values common to jobs with same label. 

631 generic_workflow : `lsst.ctrl.bps.GenericWorkflow` 

632 Generic workflow that contains job to being converted. 

633 gwjob : `lsst.ctrl.bps.GenericWorkflowJob` 

634 Generic workflow job to be converted. 

635 

636 Returns 

637 ------- 

638 htc_job_commands : `dict` [`str`, `Any`] 

639 Contains commands which can appear in the HTCondor submit description 

640 file. 

641 """ 

642 # Values in the job script that just are name mappings. 

643 job_translation = { 

644 "mail_to": "notify_user", 

645 "when_to_mail": "notification", 

646 "request_cpus": "request_cpus", 

647 "priority": "priority", 

648 "category": "category", 

649 "accounting_group": "accounting_group", 

650 "accounting_user": "accounting_group_user", 

651 } 

652 

653 jobcmds = {} 

654 for gwkey, htckey in job_translation.items(): 

655 jobcmds[htckey] = getattr(gwjob, gwkey, None) 

656 

657 # If accounting info was not set explicitly, use site settings if any. 

658 if not gwjob.accounting_group: 

659 jobcmds["accounting_group"] = cached_vals.get("accountingGroup") 

660 if not gwjob.accounting_user: 

661 jobcmds["accounting_group_user"] = cached_vals.get("accountingUser") 

662 

663 # job commands that need modification 

664 if gwjob.number_of_retries: 

665 jobcmds["max_retries"] = f"{gwjob.number_of_retries}" 

666 

667 if gwjob.retry_unless_exit: 

668 jobcmds["retry_until"] = f"{gwjob.retry_unless_exit}" 

669 

670 if gwjob.request_disk: 

671 jobcmds["request_disk"] = f"{gwjob.request_disk}MB" 

672 

673 if gwjob.request_memory: 

674 jobcmds["request_memory"] = f"{gwjob.request_memory}" 

675 

676 if gwjob.memory_multiplier: 

677 # Do not use try-except! At the moment, BpsConfig returns an empty 

678 # string if it does not contain the key. 

679 memory_limit = cached_vals["memoryLimit"] 

680 if not memory_limit: 

681 raise RuntimeError( 

682 "Memory autoscaling enabled, but automatic detection of the memory limit " 

683 "failed; setting it explicitly with 'memoryLimit' or changing worker node " 

684 "search pattern 'executeMachinesPattern' might help." 

685 ) 

686 

687 # Set maximal amount of memory job can ask for. 

688 # 

689 # The check below assumes that 'memory_limit' was set to a value which 

690 # realistically reflects actual physical limitations of a given compute 

691 # resource. 

692 memory_max = memory_limit 

693 if gwjob.request_memory_max and gwjob.request_memory_max < memory_limit: 

694 memory_max = gwjob.request_memory_max 

695 

696 # Make job ask for more memory each time it failed due to insufficient 

697 # memory requirements. 

698 jobcmds["request_memory"] = _create_request_memory_expr( 

699 gwjob.request_memory, gwjob.memory_multiplier, memory_max 

700 ) 

701 

702 # Periodically release jobs which are being held due to exceeding 

703 # memory. Stop doing that (by removing the job from the HTCondor queue) 

704 # after the maximal number of retries has been reached or the job was 

705 # already run at maximal allowed memory. 

706 jobcmds["periodic_release"] = _create_periodic_release_expr( 

707 gwjob.request_memory, gwjob.memory_multiplier, memory_max 

708 ) 

709 jobcmds["periodic_remove"] = _create_periodic_remove_expr( 

710 gwjob.request_memory, gwjob.memory_multiplier, memory_max 

711 ) 

712 

713 # Assume concurrency_limit implemented using HTCondor concurrency limits. 

714 # May need to move to special site-specific implementation if sites use 

715 # other mechanisms. 

716 if gwjob.concurrency_limit: 

717 jobcmds["concurrency_limit"] = gwjob.concurrency_limit 

718 

719 # Handle command line 

720 if gwjob.executable.transfer_executable: 

721 jobcmds["transfer_executable"] = "True" 

722 jobcmds["executable"] = os.path.basename(gwjob.executable.src_uri) 

723 else: 

724 jobcmds["executable"] = _fix_env_var_syntax(gwjob.executable.src_uri) 

725 

726 if gwjob.arguments: 

727 arguments = gwjob.arguments 

728 arguments = _replace_cmd_vars(arguments, gwjob) 

729 arguments = _replace_file_vars(cached_vals["bpsUseShared"], arguments, generic_workflow, gwjob) 

730 arguments = _fix_env_var_syntax(arguments) 

731 jobcmds["arguments"] = arguments 

732 

733 # Add extra "pass-thru" job commands 

734 if gwjob.profile: 

735 for key, val in gwjob.profile.items(): 

736 jobcmds[key] = htc_escape(val) 

737 for key, val in cached_vals["profile"].items(): 

738 jobcmds[key] = htc_escape(val) 

739 

740 return jobcmds 

741 

742 

743def _translate_dag_cmds(gwjob): 

744 """Translate job values into DAGMan commands. 

745 

746 Parameters 

747 ---------- 

748 gwjob : `lsst.ctrl.bps.GenericWorkflowJob` 

749 Job containing values to be translated. 

750 

751 Returns 

752 ------- 

753 dagcmds : `dict` [`str`, `Any`] 

754 DAGMan commands for the job. 

755 """ 

756 # Values in the dag script that just are name mappings. 

757 dag_translation = {"abort_on_value": "abort_dag_on", "abort_return_value": "abort_exit"} 

758 

759 dagcmds = {} 

760 for gwkey, htckey in dag_translation.items(): 

761 dagcmds[htckey] = getattr(gwjob, gwkey, None) 

762 

763 # Still to be coded: vars "pre_cmdline", "post_cmdline" 

764 return dagcmds 

765 

766 

767def _fix_env_var_syntax(oldstr): 

768 """Change ENV place holders to HTCondor Env var syntax. 

769 

770 Parameters 

771 ---------- 

772 oldstr : `str` 

773 String in which environment variable syntax is to be fixed. 

774 

775 Returns 

776 ------- 

777 newstr : `str` 

778 Given string with environment variable syntax fixed. 

779 """ 

780 newstr = oldstr 

781 for key in re.findall(r"<ENV:([^>]+)>", oldstr): 

782 newstr = newstr.replace(rf"<ENV:{key}>", f"$ENV({key})") 

783 return newstr 

784 

785 

786def _replace_file_vars(use_shared, arguments, workflow, gwjob): 

787 """Replace file placeholders in command line arguments with correct 

788 physical file names. 

789 

790 Parameters 

791 ---------- 

792 use_shared : `bool` 

793 Whether HTCondor can assume shared filesystem. 

794 arguments : `str` 

795 Arguments string in which to replace file placeholders. 

796 workflow : `lsst.ctrl.bps.GenericWorkflow` 

797 Generic workflow that contains file information. 

798 gwjob : `lsst.ctrl.bps.GenericWorkflowJob` 

799 The job corresponding to the arguments. 

800 

801 Returns 

802 ------- 

803 arguments : `str` 

804 Given arguments string with file placeholders replaced. 

805 """ 

806 # Replace input file placeholders with paths. 

807 for gwfile in workflow.get_job_inputs(gwjob.name, data=True, transfer_only=False): 

808 if not gwfile.wms_transfer: 

809 # Must assume full URI if in command line and told WMS is not 

810 # responsible for transferring file. 

811 uri = gwfile.src_uri 

812 elif use_shared: 

813 if gwfile.job_shared: 

814 # Have shared filesystems and jobs can share file. 

815 uri = gwfile.src_uri 

816 else: 

817 # Taking advantage of inside knowledge. Not future-proof. 

818 # Temporary fix until have job wrapper that pulls files 

819 # within job. 

820 if gwfile.name == "butlerConfig" and Path(gwfile.src_uri).suffix != ".yaml": 

821 uri = "butler.yaml" 

822 else: 

823 uri = os.path.basename(gwfile.src_uri) 

824 else: # Using push transfer 

825 uri = os.path.basename(gwfile.src_uri) 

826 arguments = arguments.replace(f"<FILE:{gwfile.name}>", uri) 

827 

828 # Replace output file placeholders with paths. 

829 for gwfile in workflow.get_job_outputs(gwjob.name, data=True, transfer_only=False): 

830 if not gwfile.wms_transfer: 

831 # Must assume full URI if in command line and told WMS is not 

832 # responsible for transferring file. 

833 uri = gwfile.src_uri 

834 elif use_shared: 

835 if gwfile.job_shared: 

836 # Have shared filesystems and jobs can share file. 

837 uri = gwfile.src_uri 

838 else: 

839 uri = os.path.basename(gwfile.src_uri) 

840 else: # Using push transfer 

841 uri = os.path.basename(gwfile.src_uri) 

842 arguments = arguments.replace(f"<FILE:{gwfile.name}>", uri) 

843 return arguments 

844 

845 

846def _replace_cmd_vars(arguments, gwjob): 

847 """Replace format-style placeholders in arguments. 

848 

849 Parameters 

850 ---------- 

851 arguments : `str` 

852 Arguments string in which to replace placeholders. 

853 gwjob : `lsst.ctrl.bps.GenericWorkflowJob` 

854 Job containing values to be used to replace placeholders 

855 (in particular gwjob.cmdvals). 

856 

857 Returns 

858 ------- 

859 arguments : `str` 

860 Given arguments string with placeholders replaced. 

861 """ 

862 try: 

863 arguments = arguments.format(**gwjob.cmdvals) 

864 except (KeyError, TypeError): # TypeError in case None instead of {} 

865 _LOG.error( 

866 "Could not replace command variables:\narguments: %s\ncmdvals: %s", arguments, gwjob.cmdvals 

867 ) 

868 raise 

869 return arguments 

870 

871 

872def _handle_job_inputs(generic_workflow: GenericWorkflow, job_name: str, use_shared: bool, out_prefix: str): 

873 """Add job input files from generic workflow to job. 

874 

875 Parameters 

876 ---------- 

877 generic_workflow : `lsst.ctrl.bps.GenericWorkflow` 

878 The generic workflow (e.g., has executable name and arguments). 

879 job_name : `str` 

880 Unique name for the job. 

881 use_shared : `bool` 

882 Whether job has access to files via shared filesystem. 

883 out_prefix : `str` 

884 The root directory into which all WMS-specific files are written. 

885 

886 Returns 

887 ------- 

888 htc_commands : `dict` [`str`, `str`] 

889 HTCondor commands for the job submission script. 

890 """ 

891 htc_commands = {} 

892 inputs = [] 

893 for gwf_file in generic_workflow.get_job_inputs(job_name, data=True, transfer_only=True): 

894 _LOG.debug("src_uri=%s", gwf_file.src_uri) 

895 

896 uri = Path(gwf_file.src_uri) 

897 

898 # Note if use_shared and job_shared, don't need to transfer file. 

899 

900 if not use_shared: # Copy file using push to job 

901 inputs.append(str(uri.relative_to(out_prefix))) 

902 elif not gwf_file.job_shared: # Jobs require own copy 

903 

904 # if using shared filesystem, but still need copy in job. Use 

905 # HTCondor's curl plugin for a local copy. 

906 

907 # Execution butler is represented as a directory which the 

908 # curl plugin does not handle. Taking advantage of inside 

909 # knowledge for temporary fix until have job wrapper that pulls 

910 # files within job. 

911 if gwf_file.name == "butlerConfig": 

912 # The execution butler directory doesn't normally exist until 

913 # the submit phase so checking for suffix instead of using 

914 # is_dir(). If other non-yaml file exists they would have a 

915 # different gwf_file.name. 

916 if uri.suffix == ".yaml": # Single file, so just copy. 

917 inputs.append(f"file://{uri}") 

918 else: 

919 inputs.append(f"file://{uri / 'butler.yaml'}") 

920 inputs.append(f"file://{uri / 'gen3.sqlite3'}") 

921 elif uri.is_dir(): 

922 raise RuntimeError( 

923 "HTCondor plugin cannot transfer directories locally within job " f"{gwf_file.src_uri}" 

924 ) 

925 else: 

926 inputs.append(f"file://{uri}") 

927 

928 if inputs: 

929 htc_commands["transfer_input_files"] = ",".join(inputs) 

930 _LOG.debug("transfer_input_files=%s", htc_commands["transfer_input_files"]) 

931 return htc_commands 

932 

933 

934def _report_from_path(wms_path): 

935 """Gather run information from a given run directory. 

936 

937 Parameters 

938 ---------- 

939 wms_path : `str` 

940 The directory containing the submit side files (e.g., HTCondor files). 

941 

942 Returns 

943 ------- 

944 run_reports : `dict` [`str`, `lsst.ctrl.bps.WmsRunReport`] 

945 Run information for the detailed report. The key is the HTCondor id 

946 and the value is a collection of report information for that run. 

947 message : `str` 

948 Message to be printed with the summary report. 

949 """ 

950 wms_workflow_id, jobs, message = _get_info_from_path(wms_path) 

951 if wms_workflow_id == MISSING_ID: 

952 run_reports = {} 

953 else: 

954 run_reports = _create_detailed_report_from_jobs(wms_workflow_id, jobs) 

955 return run_reports, message 

956 

957 

958def _report_from_id(wms_workflow_id, hist, schedds=None): 

959 """Gather run information using workflow id. 

960 

961 Parameters 

962 ---------- 

963 wms_workflow_id : `str` 

964 Limit to specific run based on id. 

965 hist : `float` 

966 Limit history search to this many days. 

967 schedds : `dict` [ `str`, `htcondor.Schedd` ], optional 

968 HTCondor schedulers which to query for job information. If None 

969 (default), all queries will be run against the local scheduler only. 

970 

971 Returns 

972 ------- 

973 run_reports : `dict` [`str`, `lsst.ctrl.bps.WmsRunReport`] 

974 Run information for the detailed report. The key is the HTCondor id 

975 and the value is a collection of report information for that run. 

976 message : `str` 

977 Message to be printed with the summary report. 

978 """ 

979 dag_constraint = 'regexp("dagman$", Cmd)' 

980 try: 

981 cluster_id = int(float(wms_workflow_id)) 

982 except ValueError: 

983 dag_constraint += f' && GlobalJobId == "{wms_workflow_id}"' 

984 else: 

985 dag_constraint += f" && ClusterId == {cluster_id}" 

986 

987 # With the current implementation of the condor_* functions the query will 

988 # always return only one match per Scheduler. 

989 # 

990 # Even in the highly unlikely situation where HTCondor history (which 

991 # condor_search queries too) is long enough to have jobs from before the 

992 # cluster ids were rolled over (and as a result there is more then one job 

993 # with the same cluster id) they will not show up in the results. 

994 schedd_dag_info = condor_search(constraint=dag_constraint, hist=hist, schedds=schedds) 

995 if len(schedd_dag_info) == 0: 

996 run_reports = {} 

997 message = "" 

998 elif len(schedd_dag_info) == 1: 

999 _, dag_info = schedd_dag_info.popitem() 

1000 dag_id, dag_ad = dag_info.popitem() 

1001 

1002 # Create a mapping between jobs and their classads. The keys will be 

1003 # of format 'ClusterId.ProcId'. 

1004 job_info = {dag_id: dag_ad} 

1005 

1006 # Find jobs (nodes) belonging to that DAGMan job. 

1007 job_constraint = f"DAGManJobId == {int(float(dag_id))}" 

1008 schedd_job_info = condor_search(constraint=job_constraint, hist=hist, schedds=schedds) 

1009 if schedd_job_info: 

1010 _, node_info = schedd_job_info.popitem() 

1011 job_info.update(node_info) 

1012 

1013 # Collect additional pieces of information about jobs using HTCondor 

1014 # files in the submission directory. 

1015 _, path_jobs, message = _get_info_from_path(dag_ad["Iwd"]) 

1016 _update_jobs(job_info, path_jobs) 

1017 

1018 run_reports = _create_detailed_report_from_jobs(dag_id, job_info) 

1019 else: 

1020 ids = [ad["GlobalJobId"] for dag_info in schedd_dag_info.values() for ad in dag_info.values()] 

1021 run_reports = {} 

1022 message = ( 

1023 f"More than one job matches id '{wms_workflow_id}', " 

1024 f"their global ids are: {', '.join(ids)}. Rerun with one of the global ids" 

1025 ) 

1026 return run_reports, message 

1027 

1028 

1029def _get_info_from_path(wms_path): 

1030 """Gather run information from a given run directory. 

1031 

1032 Parameters 

1033 ---------- 

1034 wms_path : `str` 

1035 Directory containing HTCondor files. 

1036 

1037 Returns 

1038 ------- 

1039 wms_workflow_id : `str` 

1040 The run id which is a DAGman job id. 

1041 jobs : `dict` [`str`, `dict` [`str`, `Any`]] 

1042 Information about jobs read from files in the given directory. 

1043 The key is the HTCondor id and the value is a dictionary of HTCondor 

1044 keys and values. 

1045 message : `str` 

1046 Message to be printed with the summary report. 

1047 """ 

1048 messages = [] 

1049 try: 

1050 wms_workflow_id, jobs = read_dag_log(wms_path) 

1051 _LOG.debug("_get_info_from_path: from dag log %s = %s", wms_workflow_id, jobs) 

1052 _update_jobs(jobs, read_node_status(wms_path)) 

1053 _LOG.debug("_get_info_from_path: after node status %s = %s", wms_workflow_id, jobs) 

1054 

1055 # Add more info for DAGman job 

1056 job = jobs[wms_workflow_id] 

1057 job.update(read_dag_status(wms_path)) 

1058 

1059 job["total_jobs"], job["state_counts"] = _get_state_counts_from_jobs(wms_workflow_id, jobs) 

1060 if "bps_run" not in job: 

1061 _add_run_info(wms_path, job) 

1062 

1063 message = htc_check_dagman_output(wms_path) 

1064 if message: 

1065 messages.append(message) 

1066 _LOG.debug( 

1067 "_get_info: id = %s, total_jobs = %s", wms_workflow_id, jobs[wms_workflow_id]["total_jobs"] 

1068 ) 

1069 

1070 # Add extra pieces of information which cannot be found in HTCondor 

1071 # generated files like 'GlobalJobId'. 

1072 # 

1073 # Do not treat absence of this file as a serious error. Neither runs 

1074 # submitted with earlier versions of the plugin nor the runs submitted 

1075 # with Pegasus plugin will have it at the moment. However, once enough 

1076 # time passes and Pegasus plugin will have its own report() method 

1077 # (instead of sneakily using HTCondor's one), the lack of that file 

1078 # should be treated as seriously as lack of any other file. 

1079 try: 

1080 job_info = read_dag_info(wms_path) 

1081 except FileNotFoundError as exc: 

1082 message = f"Warn: Some information may not be available: {exc}" 

1083 messages.append(message) 

1084 else: 

1085 schedd_name = next(iter(job_info)) 

1086 job_ad = next(iter(job_info[schedd_name].values())) 

1087 job.update(job_ad) 

1088 except FileNotFoundError: 

1089 message = f"Could not find HTCondor files in '{wms_path}'" 

1090 _LOG.warning(message) 

1091 messages.append(message) 

1092 wms_workflow_id = MISSING_ID 

1093 jobs = {} 

1094 

1095 message = "\n".join([msg for msg in messages if msg]) 

1096 return wms_workflow_id, jobs, message 

1097 

1098 

1099def _create_detailed_report_from_jobs(wms_workflow_id, jobs): 

1100 """Gather run information to be used in generating summary reports. 

1101 

1102 Parameters 

1103 ---------- 

1104 wms_workflow_id : `str` 

1105 The run id to create the report for. 

1106 jobs : `dict` [`str`, `dict` [`str`, Any]] 

1107 Mapping HTCondor job id to job information. 

1108 

1109 Returns 

1110 ------- 

1111 run_reports : `dict` [`str`, `lsst.ctrl.bps.WmsRunReport`] 

1112 Run information for the detailed report. The key is the given HTCondor 

1113 id and the value is a collection of report information for that run. 

1114 """ 

1115 _LOG.debug("_create_detailed_report: id = %s, job = %s", wms_workflow_id, jobs[wms_workflow_id]) 

1116 dag_job = jobs[wms_workflow_id] 

1117 report = WmsRunReport( 

1118 wms_id=f"{dag_job['ClusterId']}.{dag_job['ProcId']}", 

1119 global_wms_id=dag_job.get("GlobalJobId", "MISS"), 

1120 path=dag_job["Iwd"], 

1121 label=dag_job.get("bps_job_label", "MISS"), 

1122 run=dag_job.get("bps_run", "MISS"), 

1123 project=dag_job.get("bps_project", "MISS"), 

1124 campaign=dag_job.get("bps_campaign", "MISS"), 

1125 payload=dag_job.get("bps_payload", "MISS"), 

1126 operator=_get_owner(dag_job), 

1127 run_summary=_get_run_summary(dag_job), 

1128 state=_htc_status_to_wms_state(dag_job), 

1129 jobs=[], 

1130 total_number_jobs=dag_job["total_jobs"], 

1131 job_state_counts=dag_job["state_counts"], 

1132 ) 

1133 

1134 for job_id, job_info in jobs.items(): 

1135 try: 

1136 if job_info["ClusterId"] != int(float(wms_workflow_id)): 

1137 job_report = WmsJobReport( 

1138 wms_id=job_id, 

1139 name=job_info.get("DAGNodeName", job_id), 

1140 label=job_info.get("bps_job_label", pegasus_name_to_label(job_info["DAGNodeName"])), 

1141 state=_htc_status_to_wms_state(job_info), 

1142 ) 

1143 if job_report.label == "init": 

1144 job_report.label = "pipetaskInit" 

1145 report.jobs.append(job_report) 

1146 except KeyError as ex: 

1147 _LOG.error("Job missing key '%s': %s", str(ex), job_info) 

1148 raise 

1149 

1150 run_reports = {report.wms_id: report} 

1151 _LOG.debug("_create_detailed_report: run_reports = %s", run_reports) 

1152 return run_reports 

1153 

1154 

1155def _summary_report(user, hist, pass_thru, schedds=None): 

1156 """Gather run information to be used in generating summary reports. 

1157 

1158 Parameters 

1159 ---------- 

1160 user : `str` 

1161 Run lookup restricted to given user. 

1162 hist : `float` 

1163 How many previous days to search for run information. 

1164 pass_thru : `str` 

1165 Advanced users can define the HTCondor constraint to be used 

1166 when searching queue and history. 

1167 

1168 Returns 

1169 ------- 

1170 run_reports : `dict` [`str`, `lsst.ctrl.bps.WmsRunReport`] 

1171 Run information for the summary report. The keys are HTCondor ids and 

1172 the values are collections of report information for each run. 

1173 message : `str` 

1174 Message to be printed with the summary report. 

1175 """ 

1176 # only doing summary report so only look for dagman jobs 

1177 if pass_thru: 

1178 constraint = pass_thru 

1179 else: 

1180 # Notes: 

1181 # * bps_isjob == 'True' isn't getting set for DAG jobs that are 

1182 # manually restarted. 

1183 # * Any job with DAGManJobID isn't a DAG job 

1184 constraint = 'bps_isjob == "True" && JobUniverse == 7' 

1185 if user: 

1186 constraint += f' && (Owner == "{user}" || bps_operator == "{user}")' 

1187 

1188 job_info = condor_search(constraint=constraint, hist=hist, schedds=schedds) 

1189 

1190 # Have list of DAGMan jobs, need to get run_report info. 

1191 run_reports = {} 

1192 for jobs in job_info.values(): 

1193 for job_id, job in jobs.items(): 

1194 total_jobs, state_counts = _get_state_counts_from_dag_job(job) 

1195 # If didn't get from queue information (e.g., Kerberos bug), 

1196 # try reading from file. 

1197 if total_jobs == 0: 

1198 try: 

1199 job.update(read_dag_status(job["Iwd"])) 

1200 total_jobs, state_counts = _get_state_counts_from_dag_job(job) 

1201 except StopIteration: 

1202 pass # don't kill report can't find htcondor files 

1203 

1204 if "bps_run" not in job: 

1205 _add_run_info(job["Iwd"], job) 

1206 report = WmsRunReport( 

1207 wms_id=job_id, 

1208 global_wms_id=job["GlobalJobId"], 

1209 path=job["Iwd"], 

1210 label=job.get("bps_job_label", "MISS"), 

1211 run=job.get("bps_run", "MISS"), 

1212 project=job.get("bps_project", "MISS"), 

1213 campaign=job.get("bps_campaign", "MISS"), 

1214 payload=job.get("bps_payload", "MISS"), 

1215 operator=_get_owner(job), 

1216 run_summary=_get_run_summary(job), 

1217 state=_htc_status_to_wms_state(job), 

1218 jobs=[], 

1219 total_number_jobs=total_jobs, 

1220 job_state_counts=state_counts, 

1221 ) 

1222 run_reports[report.global_wms_id] = report 

1223 

1224 return run_reports, "" 

1225 

1226 

1227def _add_run_info(wms_path, job): 

1228 """Find BPS run information elsewhere for runs without bps attributes. 

1229 

1230 Parameters 

1231 ---------- 

1232 wms_path : `str` 

1233 Path to submit files for the run. 

1234 job : `dict` [`str`, `Any`] 

1235 HTCondor dag job information. 

1236 

1237 Raises 

1238 ------ 

1239 StopIteration 

1240 If cannot find file it is looking for. Permission errors are 

1241 caught and job's run is marked with error. 

1242 """ 

1243 path = Path(wms_path) / "jobs" 

1244 try: 

1245 subfile = next(path.glob("**/*.sub")) 

1246 except (StopIteration, PermissionError): 

1247 job["bps_run"] = "Unavailable" 

1248 else: 

1249 _LOG.debug("_add_run_info: subfile = %s", subfile) 

1250 try: 

1251 with open(subfile, "r", encoding="utf-8") as fh: 

1252 for line in fh: 

1253 if line.startswith("+bps_"): 

1254 m = re.match(r"\+(bps_[^\s]+)\s*=\s*(.+)$", line) 

1255 if m: 

1256 _LOG.debug("Matching line: %s", line) 

1257 job[m.group(1)] = m.group(2).replace('"', "") 

1258 else: 

1259 _LOG.debug("Could not parse attribute: %s", line) 

1260 except PermissionError: 

1261 job["bps_run"] = "PermissionError" 

1262 _LOG.debug("After adding job = %s", job) 

1263 

1264 

1265def _get_owner(job): 

1266 """Get the owner of a dag job. 

1267 

1268 Parameters 

1269 ---------- 

1270 job : `dict` [`str`, `Any`] 

1271 HTCondor dag job information. 

1272 

1273 Returns 

1274 ------- 

1275 owner : `str` 

1276 Owner of the dag job. 

1277 """ 

1278 owner = job.get("bps_operator", None) 

1279 if not owner: 

1280 owner = job.get("Owner", None) 

1281 if not owner: 

1282 _LOG.warning("Could not get Owner from htcondor job: %s", job) 

1283 owner = "MISS" 

1284 return owner 

1285 

1286 

1287def _get_run_summary(job): 

1288 """Get the run summary for a job. 

1289 

1290 Parameters 

1291 ---------- 

1292 job : `dict` [`str`, `Any`] 

1293 HTCondor dag job information. 

1294 

1295 Returns 

1296 ------- 

1297 summary : `str` 

1298 Number of jobs per PipelineTask label in approximate pipeline order. 

1299 Format: <label>:<count>[;<label>:<count>]+ 

1300 """ 

1301 summary = job.get("bps_job_summary", job.get("bps_run_summary", None)) 

1302 if not summary: 

1303 summary, _ = summary_from_dag(job["Iwd"]) 

1304 if not summary: 

1305 _LOG.warning("Could not get run summary for htcondor job: %s", job) 

1306 _LOG.debug("_get_run_summary: summary=%s", summary) 

1307 

1308 # Workaround sometimes using init vs pipetaskInit 

1309 summary = summary.replace("init:", "pipetaskInit:") 

1310 

1311 if "pegasus_version" in job and "pegasus" not in summary: 

1312 summary += ";pegasus:0" 

1313 

1314 return summary 

1315 

1316 

1317def _get_state_counts_from_jobs(wms_workflow_id, jobs): 

1318 """Count number of jobs per WMS state. 

1319 

1320 Parameters 

1321 ---------- 

1322 wms_workflow_id : `str` 

1323 HTCondor job id. 

1324 jobs : `dict` [`str`, `Any`] 

1325 HTCondor dag job information. 

1326 

1327 Returns 

1328 ------- 

1329 total_count : `int` 

1330 Total number of dag nodes. 

1331 state_counts : `dict` [`lsst.ctrl.bps.WmsStates`, `int`] 

1332 Keys are the different WMS states and values are counts of jobs 

1333 that are in that WMS state. 

1334 """ 

1335 state_counts = dict.fromkeys(WmsStates, 0) 

1336 

1337 for jid, jinfo in jobs.items(): 

1338 if jid != wms_workflow_id: 

1339 state_counts[_htc_status_to_wms_state(jinfo)] += 1 

1340 

1341 total_counted = sum(state_counts.values()) 

1342 if "NodesTotal" in jobs[wms_workflow_id]: 

1343 total_count = jobs[wms_workflow_id]["NodesTotal"] 

1344 else: 

1345 total_count = total_counted 

1346 

1347 state_counts[WmsStates.UNREADY] += total_count - total_counted 

1348 

1349 return total_count, state_counts 

1350 

1351 

1352def _get_state_counts_from_dag_job(job): 

1353 """Count number of jobs per WMS state. 

1354 

1355 Parameters 

1356 ---------- 

1357 job : `dict` [`str`, `Any`] 

1358 HTCondor dag job information. 

1359 

1360 Returns 

1361 ------- 

1362 total_count : `int` 

1363 Total number of dag nodes. 

1364 state_counts : `dict` [`lsst.ctrl.bps.WmsStates`, `int`] 

1365 Keys are the different WMS states and values are counts of jobs 

1366 that are in that WMS state. 

1367 """ 

1368 _LOG.debug("_get_state_counts_from_dag_job: job = %s %s", type(job), len(job)) 

1369 state_counts = dict.fromkeys(WmsStates, 0) 

1370 if "DAG_NodesReady" in job: 

1371 state_counts = { 

1372 WmsStates.UNREADY: job.get("DAG_NodesUnready", 0), 

1373 WmsStates.READY: job.get("DAG_NodesReady", 0), 

1374 WmsStates.HELD: job.get("JobProcsHeld", 0), 

1375 WmsStates.SUCCEEDED: job.get("DAG_NodesDone", 0), 

1376 WmsStates.FAILED: job.get("DAG_NodesFailed", 0), 

1377 WmsStates.MISFIT: job.get("DAG_NodesPre", 0) + job.get("DAG_NodesPost", 0), 

1378 } 

1379 total_jobs = job.get("DAG_NodesTotal") 

1380 _LOG.debug("_get_state_counts_from_dag_job: from DAG_* keys, total_jobs = %s", total_jobs) 

1381 elif "NodesFailed" in job: 

1382 state_counts = { 

1383 WmsStates.UNREADY: job.get("NodesUnready", 0), 

1384 WmsStates.READY: job.get("NodesReady", 0), 

1385 WmsStates.HELD: job.get("JobProcsHeld", 0), 

1386 WmsStates.SUCCEEDED: job.get("NodesDone", 0), 

1387 WmsStates.FAILED: job.get("NodesFailed", 0), 

1388 WmsStates.MISFIT: job.get("NodesPre", 0) + job.get("NodesPost", 0), 

1389 } 

1390 try: 

1391 total_jobs = job.get("NodesTotal") 

1392 except KeyError as ex: 

1393 _LOG.error("Job missing %s. job = %s", str(ex), job) 

1394 raise 

1395 _LOG.debug("_get_state_counts_from_dag_job: from NODES* keys, total_jobs = %s", total_jobs) 

1396 else: 

1397 # With Kerberos job auth and Kerberos bug, if warning would be printed 

1398 # for every DAG. 

1399 _LOG.debug("Can't get job state counts %s", job["Iwd"]) 

1400 total_jobs = 0 

1401 

1402 _LOG.debug("total_jobs = %s, state_counts: %s", total_jobs, state_counts) 

1403 return total_jobs, state_counts 

1404 

1405 

1406def _htc_status_to_wms_state(job): 

1407 """Convert HTCondor job status to generic wms state. 

1408 

1409 Parameters 

1410 ---------- 

1411 job : `dict` [`str`, `Any`] 

1412 HTCondor job information. 

1413 

1414 Returns 

1415 ------- 

1416 wms_state : `WmsStates` 

1417 The equivalent WmsState to given job's status. 

1418 """ 

1419 wms_state = WmsStates.MISFIT 

1420 if "JobStatus" in job: 

1421 wms_state = _htc_job_status_to_wms_state(job) 

1422 elif "NodeStatus" in job: 

1423 wms_state = _htc_node_status_to_wms_state(job) 

1424 return wms_state 

1425 

1426 

1427def _htc_job_status_to_wms_state(job): 

1428 """Convert HTCondor job status to generic wms state. 

1429 

1430 Parameters 

1431 ---------- 

1432 job : `dict` [`str`, `Any`] 

1433 HTCondor job information. 

1434 

1435 Returns 

1436 ------- 

1437 wms_state : `lsst.ctrl.bps.WmsStates` 

1438 The equivalent WmsState to given job's status. 

1439 """ 

1440 _LOG.debug( 

1441 "htc_job_status_to_wms_state: %s=%s, %s", job["ClusterId"], job["JobStatus"], type(job["JobStatus"]) 

1442 ) 

1443 job_status = int(job["JobStatus"]) 

1444 wms_state = WmsStates.MISFIT 

1445 

1446 _LOG.debug("htc_job_status_to_wms_state: job_status = %s", job_status) 

1447 if job_status == JobStatus.IDLE: 

1448 wms_state = WmsStates.PENDING 

1449 elif job_status == JobStatus.RUNNING: 

1450 wms_state = WmsStates.RUNNING 

1451 elif job_status == JobStatus.REMOVED: 

1452 wms_state = WmsStates.DELETED 

1453 elif job_status == JobStatus.COMPLETED: 

1454 if ( 

1455 job.get("ExitBySignal", False) 

1456 or job.get("ExitCode", 0) 

1457 or job.get("ExitSignal", 0) 

1458 or job.get("DAG_Status", 0) 

1459 or job.get("ReturnValue", 0) 

1460 ): 

1461 wms_state = WmsStates.FAILED 

1462 else: 

1463 wms_state = WmsStates.SUCCEEDED 

1464 elif job_status == JobStatus.HELD: 

1465 wms_state = WmsStates.HELD 

1466 

1467 return wms_state 

1468 

1469 

1470def _htc_node_status_to_wms_state(job): 

1471 """Convert HTCondor status to generic wms state. 

1472 

1473 Parameters 

1474 ---------- 

1475 job : `dict` [`str`, `Any`] 

1476 HTCondor job information. 

1477 

1478 Returns 

1479 ------- 

1480 wms_state : `lsst.ctrl.bps.WmsStates` 

1481 The equivalent WmsState to given node's status. 

1482 """ 

1483 wms_state = WmsStates.MISFIT 

1484 

1485 status = job["NodeStatus"] 

1486 if status == NodeStatus.NOT_READY: 

1487 wms_state = WmsStates.UNREADY 

1488 elif status == NodeStatus.READY: 

1489 wms_state = WmsStates.READY 

1490 elif status == NodeStatus.PRERUN: 

1491 wms_state = WmsStates.MISFIT 

1492 elif status == NodeStatus.SUBMITTED: 

1493 if job["JobProcsHeld"]: 

1494 wms_state = WmsStates.HELD 

1495 elif job["StatusDetails"] == "not_idle": 

1496 wms_state = WmsStates.RUNNING 

1497 elif job["JobProcsQueued"]: 

1498 wms_state = WmsStates.PENDING 

1499 elif status == NodeStatus.POSTRUN: 

1500 wms_state = WmsStates.MISFIT 

1501 elif status == NodeStatus.DONE: 

1502 wms_state = WmsStates.SUCCEEDED 

1503 elif status == NodeStatus.ERROR: 

1504 # Use job exist instead of post script exit 

1505 if "DAGMAN error 0" in job["StatusDetails"]: 

1506 wms_state = WmsStates.SUCCEEDED 

1507 else: 

1508 wms_state = WmsStates.FAILED 

1509 

1510 return wms_state 

1511 

1512 

1513def _update_jobs(jobs1, jobs2): 

1514 """Update jobs1 with info in jobs2. 

1515 

1516 (Basically an update for nested dictionaries.) 

1517 

1518 Parameters 

1519 ---------- 

1520 jobs1 : `dict` [`str`, `dict` [`str`, `Any`]] 

1521 HTCondor job information to be updated. 

1522 jobs2 : `dict` [`str`, `dict` [`str`, `Any`]] 

1523 Additional HTCondor job information. 

1524 """ 

1525 for jid, jinfo in jobs2.items(): 

1526 if jid in jobs1: 

1527 jobs1[jid].update(jinfo) 

1528 else: 

1529 jobs1[jid] = jinfo 

1530 

1531 

1532def _wms_id_type(wms_id): 

1533 """Determine the type of the WMS id. 

1534 

1535 Parameters 

1536 ---------- 

1537 wms_id : `str` 

1538 WMS id identifying a job. 

1539 

1540 Returns 

1541 ------- 

1542 id_type : `lsst.ctrl.bps.htcondor.WmsIdType` 

1543 Type of WMS id. 

1544 """ 

1545 try: 

1546 int(float(wms_id)) 

1547 except ValueError: 

1548 wms_path = Path(wms_id) 

1549 if wms_path.exists(): 

1550 id_type = WmsIdType.PATH 

1551 else: 

1552 id_type = WmsIdType.GLOBAL 

1553 except TypeError: 

1554 id_type = WmsIdType.UNKNOWN 

1555 else: 

1556 id_type = WmsIdType.LOCAL 

1557 return id_type 

1558 

1559 

1560def _wms_id_to_cluster(wms_id): 

1561 """Convert WMS id to cluster id. 

1562 

1563 Parameters 

1564 ---------- 

1565 wms_id : `int` or `float` or `str` 

1566 HTCondor job id or path. 

1567 

1568 Returns 

1569 ------- 

1570 schedd_ad : `classad.ClassAd` 

1571 ClassAd describing the scheduler managing the job with the given id. 

1572 cluster_id : `int` 

1573 HTCondor cluster id. 

1574 id_type : `lsst.ctrl.bps.wms.htcondor.IdType` 

1575 The type of the provided id. 

1576 """ 

1577 coll = htcondor.Collector() 

1578 

1579 schedd_ad = None 

1580 cluster_id = None 

1581 id_type = _wms_id_type(wms_id) 

1582 if id_type == WmsIdType.LOCAL: 

1583 schedd_ad = coll.locate(htcondor.DaemonTypes.Schedd) 

1584 cluster_id = int(float(wms_id)) 

1585 elif id_type == WmsIdType.GLOBAL: 

1586 constraint = f'GlobalJobId == "{wms_id}"' 

1587 schedd_ads = {ad["Name"]: ad for ad in coll.locateAll(htcondor.DaemonTypes.Schedd)} 

1588 schedds = [htcondor.Schedd(ad) for ad in schedd_ads.values()] 

1589 queries = [schedd.xquery(requirements=constraint, projection=["ClusterId"]) for schedd in schedds] 

1590 results = { 

1591 query.tag(): dict(ads[0]) 

1592 for query in htcondor.poll(queries) 

1593 if (ads := query.nextAdsNonBlocking()) 

1594 } 

1595 if results: 

1596 schedd_name = next(iter(results)) 

1597 schedd_ad = schedd_ads[schedd_name] 

1598 cluster_id = results[schedd_name]["ClusterId"] 

1599 elif id_type == WmsIdType.PATH: 

1600 try: 

1601 job_info = read_dag_info(wms_id) 

1602 except (FileNotFoundError, PermissionError, IOError): 

1603 pass 

1604 else: 

1605 schedd_name = next(iter(job_info)) 

1606 job_id = next(iter(job_info[schedd_name])) 

1607 schedd_ad = coll.locate(htcondor.DaemonTypes.Schedd, schedd_name) 

1608 cluster_id = int(float(job_id)) 

1609 else: 

1610 pass 

1611 return schedd_ad, cluster_id, id_type 

1612 

1613 

1614def _create_periodic_release_expr(memory, multiplier, limit): 

1615 """Construct an HTCondorAd expression for releasing held jobs. 

1616 

1617 The expression instruct HTCondor to release any job which was put on hold 

1618 due to exceeding memory requirements back to the job queue providing it 

1619 satisfies all of the conditions below: 

1620 

1621 * number of run attempts did not reach allowable number of retries, 

1622 * the memory requirements in the last failed run attempt did not reach 

1623 the specified memory limit. 

1624 

1625 Parameters 

1626 ---------- 

1627 memory : `int` 

1628 Requested memory in MB. 

1629 multiplier : `float` 

1630 Memory growth rate between retires. 

1631 limit : `int` 

1632 Memory limit. 

1633 

1634 Returns 

1635 ------- 

1636 expr : `str` 

1637 A string representing an HTCondor ClassAd expression for releasing jobs 

1638 which have been held due to exceeding the memory requirements. 

1639 """ 

1640 is_retry_allowed = "NumJobStarts <= JobMaxRetries" 

1641 was_below_limit = f"min({{int({memory} * pow({multiplier}, NumJobStarts - 1)), {limit}}}) < {limit}" 

1642 

1643 # Job ClassAds attributes 'HoldReasonCode' and 'HoldReasonSubCode' are 

1644 # UNDEFINED if job is not HELD (i.e. when 'JobStatus' is not 5). 

1645 # The special comparison operators ensure that all comparisons below will 

1646 # evaluate to FALSE in this case. 

1647 # 

1648 # Note: 

1649 # May not be strictly necessary. Operators '&&' and '||' are not strict so 

1650 # the entire expression should evaluate to FALSE when the job is not HELD. 

1651 # According to ClassAd evaluation semantics FALSE && UNDEFINED is FALSE, 

1652 # but better safe than sorry. 

1653 was_mem_exceeded = ( 

1654 "JobStatus == 5 " 

1655 "&& (HoldReasonCode =?= 34 && HoldReasonSubCode =?= 0 " 

1656 "|| HoldReasonCode =?= 3 && HoldReasonSubCode =?= 34)" 

1657 ) 

1658 

1659 expr = f"{was_mem_exceeded} && {is_retry_allowed} && {was_below_limit}" 

1660 return expr 

1661 

1662 

1663def _create_periodic_remove_expr(memory, multiplier, limit): 

1664 """Construct an HTCondorAd expression for removing jobs from the queue. 

1665 

1666 The expression instruct HTCondor to remove any job which was put on hold 

1667 due to exceeding memory requirements from the job queue providing it 

1668 satisfies any of the conditions below: 

1669 

1670 * allowable number of retries was reached, 

1671 * the memory requirements during the last failed run attempt reached 

1672 the specified memory limit. 

1673 

1674 Parameters 

1675 ---------- 

1676 memory : `int` 

1677 Requested memory in MB. 

1678 multiplier : `float` 

1679 Memory growth rate between retires. 

1680 limit : `int` 

1681 Memory limit. 

1682 

1683 Returns 

1684 ------- 

1685 expr : `str` 

1686 A string representing an HTCondor ClassAd expression for removing jobs 

1687 which were run at the maximal allowable memory and still exceeded 

1688 the memory requirements. 

1689 """ 

1690 is_retry_disallowed = "NumJobStarts > JobMaxRetries" 

1691 was_limit_reached = f"min({{int({memory} * pow({multiplier}, NumJobStarts - 1)), {limit}}}) == {limit}" 

1692 

1693 # Job ClassAds attributes 'HoldReasonCode' and 'HoldReasonSubCode' are 

1694 # UNDEFINED if job is not HELD (i.e. when 'JobStatus' is not 5). 

1695 # The special comparison operators ensure that all comparisons below will 

1696 # evaluate to FALSE in this case. 

1697 # 

1698 # Note: 

1699 # May not be strictly necessary. Operators '&&' and '||' are not strict so 

1700 # the entire expression should evaluate to FALSE when the job is not HELD. 

1701 # According to ClassAd evaluation semantics FALSE && UNDEFINED is FALSE, 

1702 # but better safe than sorry. 

1703 was_mem_exceeded = ( 

1704 "JobStatus == 5 " 

1705 "&& (HoldReasonCode =?= 34 && HoldReasonSubCode =?= 0 " 

1706 "|| HoldReasonCode =?= 3 && HoldReasonSubCode =?= 34)" 

1707 ) 

1708 

1709 expr = f"{was_mem_exceeded} && ({is_retry_disallowed} || {was_limit_reached})" 

1710 return expr 

1711 

1712 

1713def _create_request_memory_expr(memory, multiplier, limit): 

1714 """Construct an HTCondor ClassAd expression for safe memory scaling. 

1715 

1716 Parameters 

1717 ---------- 

1718 memory : `int` 

1719 Requested memory in MB. 

1720 multiplier : `float` 

1721 Memory growth rate between retires. 

1722 limit : `int` 

1723 Memory limit. 

1724 

1725 Returns 

1726 ------- 

1727 expr : `str` 

1728 A string representing an HTCondor ClassAd expression enabling safe 

1729 memory scaling between job retries. 

1730 """ 

1731 # The check if the job was held due to exceeding memory requirements 

1732 # will be made *after* job was released back to the job queue (is in 

1733 # the IDLE state), hence the need to use `Last*` job ClassAds instead of 

1734 # the ones describing job's current state. 

1735 # 

1736 # Also, 'Last*' job ClassAds attributes are UNDEFINED when a job is 

1737 # initially put in the job queue. The special comparison operators ensure 

1738 # that all comparisons below will evaluate to FALSE in this case. 

1739 was_mem_exceeded = ( 

1740 "LastJobStatus =?= 5 " 

1741 "&& (LastHoldReasonCode =?= 34 && LastHoldReasonSubCode =?= 0 " 

1742 "|| LastHoldReasonCode =?= 3 && LastHoldReasonSubCode =?= 34)" 

1743 ) 

1744 

1745 # If job runs the first time or was held for reasons other than exceeding 

1746 # the memory, set the required memory to the requested value or use 

1747 # the memory value measured by HTCondor (MemoryUsage) depending on 

1748 # whichever is greater. 

1749 expr = ( 

1750 f"({was_mem_exceeded}) " 

1751 f"? min({{int({memory} * pow({multiplier}, NumJobStarts)), {limit}}}) " 

1752 f": max({{{memory}, MemoryUsage ?: 0}})" 

1753 ) 

1754 return expr 

1755 

1756 

1757def _locate_schedds(locate_all=False): 

1758 """Find out Scheduler daemons in an HTCondor pool. 

1759 

1760 Parameters 

1761 ---------- 

1762 locate_all : `bool`, optional 

1763 If True, all available schedulers in the HTCondor pool will be located. 

1764 False by default which means that the search will be limited to looking 

1765 for the Scheduler running on a local host. 

1766 

1767 Returns 

1768 ------- 

1769 schedds : `dict` [`str`, `htcondor.Schedd`] 

1770 A mapping between Scheduler names and Python objects allowing for 

1771 interacting with them. 

1772 """ 

1773 coll = htcondor.Collector() 

1774 

1775 schedd_ads = [] 

1776 if locate_all: 

1777 schedd_ads.extend(coll.locateAll(htcondor.DaemonTypes.Schedd)) 

1778 else: 

1779 schedd_ads.append(coll.locate(htcondor.DaemonTypes.Schedd)) 

1780 return {ad["Name"]: htcondor.Schedd(ad) for ad in schedd_ads} 

1781 

1782 

1783def _gather_site_values(config, compute_site): 

1784 """Gather values specific to given site. 

1785 

1786 Parameters 

1787 ---------- 

1788 config : `lsst.ctrl.bps.BpsConfig` 

1789 BPS configuration that includes necessary submit/runtime 

1790 information. 

1791 compute_site : `str` 

1792 Compute site name. 

1793 

1794 Returns 

1795 ------- 

1796 site_values : `dict` [`str`, `Any`] 

1797 Values specific to the given site. 

1798 """ 

1799 site_values = {"attrs": {}, "profile": {}} 

1800 search_opts = {} 

1801 if compute_site: 

1802 search_opts["curvals"] = {"curr_site": compute_site} 

1803 

1804 # Determine the hard limit for the memory requirement. 

1805 found, limit = config.search("memoryLimit", opt=search_opts) 

1806 if not found: 

1807 search_opts["default"] = DEFAULT_HTC_EXEC_PATT 

1808 _, patt = config.search("executeMachinesPattern", opt=search_opts) 

1809 del search_opts["default"] 

1810 

1811 # To reduce the amount of data, ignore dynamic slots (if any) as, 

1812 # by definition, they cannot have more memory than 

1813 # the partitionable slot they are the part of. 

1814 constraint = f'SlotType != "Dynamic" && regexp("{patt}", Machine)' 

1815 pool_info = condor_status(constraint=constraint) 

1816 try: 

1817 limit = max(int(info["TotalSlotMemory"]) for info in pool_info.values()) 

1818 except ValueError: 

1819 _LOG.debug("No execute machine in the pool matches %s", patt) 

1820 if limit: 

1821 config[".bps_defined.memory_limit"] = limit 

1822 

1823 _, site_values["bpsUseShared"] = config.search("bpsUseShared", opt={"default": False}) 

1824 site_values["memoryLimit"] = limit 

1825 

1826 found, value = config.search("accountingGroup", opt=search_opts) 

1827 if found: 

1828 site_values["accountingGroup"] = value 

1829 found, value = config.search("accountingUser", opt=search_opts) 

1830 if found: 

1831 site_values["accountingUser"] = value 

1832 

1833 key = f".site.{compute_site}.profile.condor" 

1834 if key in config: 

1835 for key, val in config[key].items(): 

1836 if key.startswith("+"): 

1837 site_values["attrs"][key[1:]] = val 

1838 else: 

1839 site_values["profile"][key] = val 

1840 

1841 return site_values