This document is for an old release of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.
Source code for galaxy.jobs.rule_helper
from datetime import datetime import hashlib import random from sqlalchemy import func from galaxy import model from galaxy import util import logging log = logging.getLogger(__name__) VALID_JOB_HASH_STRATEGIES = ["job", "user", "history", "workflow_invocation"][docs]class RuleHelper(object): """ Utility to allow job rules to interface cleanly with the rest of Galaxy and shield them from low-level details of models, metrics, etc.... Currently focus is on figuring out job statistics for a given user, but could interface with other stuff as well. """[docs] def __init__(self, app): self.app = app[docs] def supports_docker(self, job_or_tool): """ Job rules can pass this function a job, job_wrapper, or tool and determine if the underlying tool believes it can be containered. """ # Not a ton of logic in this method - but the idea is to shield rule # developers from the details and they shouldn't have to know how to # interrogate tool or job to figure out if it can be run in a # container. if hasattr(job_or_tool, 'containers'): tool = job_or_tool elif hasattr(job_or_tool, 'tool'): # Have a JobWrapper-like tool = job_or_tool.tool else: # Have a Job object. tool = self.app.toolbox.get_tool(job_or_tool.tool_id, tool_version=job_or_tool.tool_version) # Can't import at top because circular import between galaxy.tools and galaxy.jobs. import galaxy.tools.deps.containers tool_info = galaxy.tools.deps.containers.ToolInfo(tool.containers, tool.requirements, tool.requires_galaxy_python_environment) container_description = self.app.container_finder.find_best_container_description(["docker"], tool_info) return container_description is not None[docs] def job_count( self, **kwds ): query = self.query(model.Job) return self._filter_job_query(query, **kwds).count()[docs] def sum_job_runtime( self, **kwds ): # TODO: Consider sum_core_hours or something that scales runtime by # by calculated cores per job. query = self.metric_query( select=func.sum(model.JobMetricNumeric.table.c.metric_value), metric_name="runtime_seconds", plugin="core", ) query = query.join(model.Job) return float(self._filter_job_query(query, **kwds).first())[docs] def metric_query(self, select, metric_name, plugin, numeric=True): metric_class = model.JobMetricNumeric if numeric else model.JobMetricText query = self.query(select) query = query.filter(metric_class.table.c.plugin == plugin) query = query.filter(metric_class.table.c.metric_name == metric_name) return query[docs] def query(self, select_expression): return self.app.model.context.query(select_expression)def _filter_job_query( self, query, for_user_email=None, for_destination=None, for_destinations=None, for_job_states=None, created_in_last=None, updated_in_last=None, ): if for_destination is not None: for_destinations = [for_destination] query = query.join(model.User) if for_user_email is not None: query = query.filter(model.User.table.c.email == for_user_email) if for_destinations is not None: if len(for_destinations) == 1: query = query.filter(model.Job.table.c.destination_id == for_destinations) else: query = query.filter(model.Job.table.c.destination_id.in_(for_destinations)) if created_in_last is not None: end_date = datetime.now() start_date = end_date - created_in_last query = query.filter(model.Job.table.c.create_time >= start_date) if updated_in_last is not None: end_date = datetime.now() start_date = end_date - updated_in_last log.info(end_date) log.info(start_date) query = query.filter(model.Job.table.c.update_time >= start_date) if for_job_states is not None: # Optimize the singleton case - can be much more performant in my experience. if len(for_job_states) == 1: query = query.filter(model.Job.table.c.state == for_job_states) else: query = query.filter(model.Job.table.c.state.in_(for_job_states)) return query[docs] def should_burst(self, destination_ids, num_jobs, job_states=None): """ Check if the specified destinations ``destination_ids`` have at least ``num_jobs`` assigned to it - send in ``job_state`` as ``queued`` to limit this check to number of jobs queued. See stock_rules for an simple example of using this function - but to get the most out of it - it should probably be used with custom job rules that can respond to the bursting by allocating resources, launching cloud nodes, etc.... """ if job_states is None: job_states = "queued,running" from_destination_job_count = self.job_count( for_destinations=destination_ids, for_job_states=util.listify(job_states) ) # Would this job push us over maximum job count before requiring # bursting (roughly... very roughly given many handler threads may be # scheduling jobs). return (from_destination_job_count + 1) > int(num_jobs)[docs] def choose_one(self, lst, hash_value=None): """ Choose a random value from supplied list. If hash_value is passed in then every request with that same hash_value would produce the same choice from the supplied list. """ if hash_value is None: return random.choice(lst) if not isinstance(hash_value, int): # Convert hash_value string into index as_hex = hashlib.md5(hash_value).hexdigest() hash_value = int(as_hex, 16) # else assumed to be 'random' int from 0-~Inf random_index = hash_value % len(lst) return lst[random_index][docs] def job_hash(self, job, hash_by=None): """ Produce a reproducible hash for the given job on various criteria - for instance if hash_by is "workflow_invocation,history" - all jobs within the same workflow invocation will receive the same hash - for jobs outside of workflows all jobs within the same history will receive the same hash, other jobs will be hashed on job's id randomly. Primarily intended for use with ``choose_one`` above - to consistent route or schedule related jobs. """ if hash_by is None: hash_by = ["job"] hash_bys = util.listify(hash_by) for hash_by in hash_bys: job_hash = self._try_hash_for_job(job, hash_by) if job_hash: return job_hash # Fall back to just hashing by job id, should always return a value. return self._try_hash_for_job(job, "job")def _try_hash_for_job(self, job, hash_by): """ May return False or None if hash type is invalid for that job - e.g. attempting to hash by user for anonymous job or by workflow invocation for jobs outside of workflows. """ if hash_by not in VALID_JOB_HASH_STRATEGIES: message = "Do not know how to hash jobs by %s, must be one of %s" % (hash_by, VALID_JOB_HASH_STRATEGIES) raise Exception(message) if hash_by == "workflow_invocation": return job.raw_param_dict().get("__workflow_invocation_uuid__", None) elif hash_by == "history": return job.history_id elif hash_by == "user": user = job.user return user and user.id elif hash_by == "job": return job.id