This document is for an in-development version 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
import hashlib import logging import random from datetime import datetime from sqlalchemy import func from galaxy import ( model, util ) from galaxy.tool_util.deps.dependencies import ToolInfo 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) tool_info = ToolInfo(tool.containers, tool.requirements, tool.requires_galaxy_python_environment, tool.docker_env_pass_through) 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(util.smart_str(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