Previously to run a flow client code had to put together the flow,
an engine, logbook, flowdetail, and storage backend. This commit
adds two helper functions, run() and load(), so that simplest usecase
now looks like
taskflow.engines.run(flow)
Client code may also provide configuration for storage and engine if
needed, but if not needed it just works with defaults.
Engines are loaded via stevedore, as drivers in 'taskflow.engines'
backend. Now three entry points are defined in that namespace:
- 'default', for SingleThreadedActionEngine, used by default;
- 'serial', as another synonym for SingleThreadedActionEngine;
- 'parallel', for MultiThreadedActionEngine.
Closes-bug: #1224726
Change-Id: I7f4cb5c8ff7f5f12831ddd0952c202d2fd8cd6ef
178 lines
5.3 KiB
Python
178 lines
5.3 KiB
Python
import logging
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import os
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import random
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import sys
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import time
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import uuid
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logging.basicConfig(level=logging.ERROR)
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top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
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os.pardir,
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os.pardir))
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sys.path.insert(0, top_dir)
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import taskflow.engines
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from taskflow.patterns import graph_flow as gf
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from taskflow import task
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# Simulates some type of persistance.
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MY_DB = {
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'space': {
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'cpus': 2,
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'memory': 8192,
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'disk': 1024,
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},
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'vms': {},
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'places': ['127.0.0.1', '127.0.0.2', '127.0.0.3'],
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}
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# This prints out the transitions a flow is going through.
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def flow_notify(state, details):
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print("'%s' entered state: %s" % (details.get('flow_name'), state))
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# This prints out the transitions a flows tasks are going through.
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def task_notify(state, details):
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print("'%s' entered state: %s" % (details.get('task_name'), state))
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# Simulates what nova/glance/keystone... calls a context
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class Context(object):
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def __init__(self, **items):
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self.__dict__.update(items)
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def __str__(self):
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return "Context: %s" % (self.__dict__)
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# Simulates translating an api request into a validated format (aka a vm-spec)
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# that will later be acted upon.
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class ValidateAPIInputs(task.Task):
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def __init__(self):
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super(ValidateAPIInputs, self).__init__('validates-api-inputs',
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provides='vm_spec')
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def execute(self, context):
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print "Validating api inputs for %s" % (context)
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return {'cpus': 1,
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'memory': 512,
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'disk': 100,
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}
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# Simulates reserving the space for a vm and associating the vm to be with
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# a unique identifier.
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class PeformReservation(task.Task):
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def __init__(self):
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super(PeformReservation, self).__init__('reserve-vm',
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provides='vm_reservation_spec')
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def revert(self, context, vm_spec, result):
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reserved_spec = result
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print("Undoing reservation of %s" % (reserved_spec['uuid']))
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vm_spec = MY_DB['vms'].pop(reserved_spec['uuid'])
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print 'Space before: %s' % (MY_DB['space'])
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# Unreserve 'atomically'
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for (k, v) in vm_spec.items():
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if k in ['scheduled']:
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continue
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MY_DB['space'][k] += v
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print 'Space after: %s' % (MY_DB['space'])
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def execute(self, context, vm_spec):
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print 'Reserving %s for %s' % (vm_spec, context)
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# Reserve 'atomically'
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print 'Space before: %s' % (MY_DB['space'])
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for (k, v) in vm_spec.items():
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if MY_DB['space'][k] < v:
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raise RuntimeError("Not enough %s available" % (k))
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MY_DB['space'][k] -= vm_spec[k]
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print 'Space after: %s' % (MY_DB['space'])
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# Create a fake 'db' entry for the vm
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vm_uuid = str(uuid.uuid4())
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MY_DB['vms'][vm_uuid] = vm_spec
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MY_DB['vms'][vm_uuid]['scheduled'] = False
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return {'uuid': vm_uuid,
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'reserved_on': time.time(),
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'vm_spec': vm_spec,
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}
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# Simulates scheudling a vm to some location
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class ScheduleVM(task.Task):
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def __init__(self):
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super(ScheduleVM, self).__init__('find-hole-for-vm',
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provides=['vm_hole', 'vm_uuid'])
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def revert(self, context, vm_reservation_spec, result):
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vm_uuid = result[1]
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vm_place = result[0]
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print "Marking %s as not having a home at %s anymore" % (vm_uuid,
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vm_place)
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MY_DB['vms'][vm_uuid]['scheduled'] = False
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MY_DB['places'].append(vm_place)
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def execute(self, context, vm_reservation_spec):
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print "Finding a place to put %s" % (vm_reservation_spec)
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vm_uuid = vm_reservation_spec['uuid']
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MY_DB['vms'][vm_uuid]['scheduled'] = True
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# Reserve the place 'atomically'
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vm_place = random.choice(MY_DB['places'])
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print 'Placing %s at %s' % (vm_uuid, vm_place)
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MY_DB['places'].remove(vm_place)
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return vm_place, vm_uuid
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# Fail booting a vm to see what happens.
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class BootVM(task.Task):
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def __init__(self):
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super(BootVM, self).__init__('boot-vm', provides='vm_booted')
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def execute(self, context, vm_reservation_spec, vm_hole):
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raise RuntimeError("Failed booting")
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# Lets try booting a vm (not really) and see how the reversions work.
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flow = gf.Flow("Boot-Fake-Vm").add(
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ValidateAPIInputs(),
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PeformReservation(),
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ScheduleVM(),
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BootVM())
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# Simulates what nova/glance/keystone... calls a context
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context = {
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'user_id': 'xyz',
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'project_id': 'abc',
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'is_admin': True,
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}
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context = Context(**context)
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# Load the flow
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engine = taskflow.engines.load(flow, store={'context': context})
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# Get notified of the state changes the flow is going through.
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engine.notifier.register('*', flow_notify)
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# Get notified of the state changes the flows tasks/runners are going through.
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engine.task_notifier.register('*', task_notify)
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print '-' * 7
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print 'Running'
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print '-' * 7
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try:
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engine.run()
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except Exception as e:
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print 'Flow failed: %r' % e
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print '-' * 11
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print 'All results'
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print '-' * 11
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result = engine.storage.fetch_all()
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for tid in sorted(result):
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print '%s => %s' % (tid, result[tid])
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