class Master(object):
"""
Master is the server that controls the cluster. There must be one and only one master
per cluster. It will direct and delegate work taking place on the Nodes and Workers
"""
def __init__(self):
logger.info('====== starting master ======')
#locks
self._lock = Lock() #general lock, use when multiple shared resources are touched
self._lock_queue = Lock() #for access to _queue
#load rsa crypto
self.pub_key, self.priv_key = load_crypto('./master.key')
self.rsa_client = RSAClient(self.priv_key, self.pub_key, callback=self.init_node)
#load tasks queue
self._running = list(TaskInstance.objects.running())
self._running_workers = {}
self._queue = list(TaskInstance.objects.queued())
#task statuses
self._task_statuses = {}
self._next_task_status_update = datetime.datetime.now()
#cluster management
self.workers = {}
self.nodes = self.load_nodes()
self.known_nodes = set()
self._workers_idle = []
self._workers_working = {}
#connection management
self.connecting = True
self.reconnect_count = 0
self.attempts = None
self.reconnect_call_ID = None
#load tasks that are cached locally
#the master won't actually run the tasks unless there is also
#a node running locally, but it will be used to inform the controller what is available
self.task_manager = TaskManager()
self.task_manager.autodiscover()
self.available_tasks = self.task_manager.registry
self.connect()
self.host = 'localhost'
self.autodiscovery()
def autodiscovery(self, callback=None):
"""
set up the dbus loop, and add the callbacks for adding nodes on the fly
based on http://avahi.org/wiki/PythonBrowseExample
"""
def service_resolved(*args):
# at this point we have all the info about the node we need
if pydraSettings.multicast_all:
# add the node (without the restart)
Node.objects.create(host=args[7], port=args[8])
self.connect()
else:
self.known_nodes.add((args[7], args[8]))
def print_error(*args):
logger.info("Couldn't resolve avahi name: %s" % str(args))
def node_found(interface, protocol, name, stype, domain, flags):
if flags & avahi.LOOKUP_RESULT_LOCAL:
# local service, skip
pass
server.ResolveService(interface, protocol, name, stype,
domain, avahi.PROTO_UNSPEC, dbus.UInt32(0),
reply_handler=service_resolved, error_handler=print_error)
# initialize dbus stuff needed for discovery
loop = DBusGMainLoop()
bus = dbus.SystemBus(mainloop=loop)
server = dbus.Interface( bus.get_object(avahi.DBUS_NAME, '/'),
'org.freedesktop.Avahi.Server')
sbrowser = dbus.Interface(bus.get_object(avahi.DBUS_NAME,
server.ServiceBrowserNew(avahi.IF_UNSPEC,
avahi.PROTO_UNSPEC, '_pydra._tcp', 'local', dbus.UInt32(0))),
avahi.DBUS_INTERFACE_SERVICE_BROWSER)
sbrowser.connect_to_signal("ItemNew", node_found)
def get_services(self):
"""
Get the service objects used by twistd
"""
# setup cluster connections
realm = MasterRealm()
realm.server = self
# setup worker security - using this checker just because we need
# _something_ that returns an avatarID. Its extremely vulnerable
# but thats ok because the real authentication takes place after
# the worker has connected
self.worker_checker = checkers.InMemoryUsernamePasswordDatabaseDontUse()
p = portal.Portal(realm, [self.worker_checker])
#setup AMF gateway security
checker = checkers.InMemoryUsernamePasswordDatabaseDontUse()
checker.addUser("controller", "1234")
#setup controller connection via AMF gateway
# Place the namespace mapping into a TwistedGateway:
from pyamf.remoting.gateway.twisted import TwistedGateway
from pyamf.remoting import gateway
interface = AMFInterface(self, checker)
gw = TwistedGateway({
"controller": interface,
}, authenticator=interface.auth)
# Publish the PyAMF gateway at the root URL:
root = resource.Resource()
root.putChild("", gw)
#setup services
from twisted.internet.ssl import DefaultOpenSSLContextFactory
try:
context = DefaultOpenSSLContextFactory('ca-key.pem', 'ca-cert.pem')
except:
logger.critical('Problem loading certificate required for ControllerInterface from ca-key.pem and ca-cert.pem. Generate certificate with gen-cert.sh')
sys.exit()
controller_service = internet.SSLServer(pydraSettings.controller_port, server.Site(root), contextFactory=context)
worker_service = internet.TCPServer(pydraSettings.port, pb.PBServerFactory(p))
return controller_service, worker_service
def load_nodes(self):
"""
Load node configuration from the database
"""
logger.info('loading nodes')
nodes = Node.objects.all()
node_dict = {}
for node in nodes:
node_dict[node.id] = node
logger.info('%i nodes loaded' % len(nodes))
return node_dict
def connect(self):
"""
Make connections to all Nodes that are not connected. This method is a single control
for connecting to nodes. individual nodes cannot be connected to. This is to ensure that
only one attempt at a time is ever made to connect to a node.
"""
#lock for two reasons:
# 1) connect() cannot be called more than once at a time
# 2) if a node fails while connecting the reconnect call will block till
# connections are finished
with self._lock:
self.connecting=True
# make sure the various states are in sync
for i in Node.objects.all():
if i.id not in self.nodes:
self.nodes[i.id] = i
if (i.host, i.port) in self.known_nodes:
self.known_nodes.discard((i.host, i.port))
"Begin the connection process"
connections = []
self.attempts = []
for id, node in self.nodes.items():
#only connect to nodes that aren't connected yet
if not node.ref:
factory = NodeClientFactory(node, self)
reactor.connectTCP(node.host, node.port, factory)
# SSH authentication is not currently supported with perspectiveBroker.
# For now we'll perform a key handshake within the info/init handshake that already
# occurs. Prior to the handshake completing access will be limited to info which
# is informational only.
#
# Note: The first time connecting to the node will accept and register whatever
# key is passed to it. This is a small trade of in temporary insecurity to simplify
# this can be avoided by manually generating and setting the keys
#
#credential = credentials.SSHPrivateKey('master', 'RSA', node.pub_key, '', '')
credential = credentials.UsernamePassword('master', '1234')
deferred = factory.login(credential, client=self)
connections.append(deferred)
self.attempts.append(node)
defer.DeferredList(connections, consumeErrors=True).addCallbacks(
self.nodes_connected, errbackArgs=("Failed to Connect"))
# Release the connection flag.
self.connecting=False
def nodes_connected(self, results):
"""
Called with the results of all connection attempts. Store connections and retrieve info from node.
The node will respond with info including how many workers it has.
"""
# process each connected node
failures = False
for result, node in zip(results, self.attempts):
#successes
if result[0]:
# save reference for remote calls
node.ref = result[1]
d = node.ref.callRemote('get_key')
d.addCallback(self.check_node, node)
#failures
else:
logger.error('node:%s:%s - failed to connect' % (node.host, node.port))
node.ref = None
failures = True
#single call to reconnect for all failures
if failures:
self.reconnect_nodes()
else:
self.reconnect_count = 0
def check_node(self, key, node):
# node.pub_key is set only for paired nodes, make sure we don't attempt
# to pair with a known pub key
duplicate = ''.join(key) in [i.pub_key for i in self.nodes.values()]
if duplicate and not node.pub_key:
logger.info('deleting %s:%s - duplicate' % (node.host, node.port))
node.delete()
return
# Authenticate with the node
pub_key = node.load_pub_key()
self.rsa_client.auth(node.ref, self.receive_key_node, server_key=pub_key, node=node)
logger.info('node:%s:%s - connected' % (node.host, node.port))
def reconnect_nodes(self, reset_counter=False):
"""
Called to signal that a reconnection attempt is needed for one or more nodes. This is the single control
for requested reconnection. This single control is used to ensure at most
one request for reconnection is pending.
"""
#lock - Blocking here ensures that connect() cannot happen while requesting
# a reconnect.
with self._lock:
#reconnecting flag ensures that connect is only called a single time
#it's possible that multiple nodes can have problems at the same time
#reset_counter overrides this
if not self.connecting or reset_counter:
self.connecting = True
#reset the counter, useful when a new failure occurs
if reset_counter:
#cancel existing call if any
if self.reconnect_call_ID:
try:
self.reconnect_call_ID.cancel()
# There is a slight chance that this method can be called
# and receive the lock, after connect() has been called.
# in that case reconnect_call_ID will point to an already called
# item. The error can just be ignored as the locking will ensure
# the call we are about to make does not start
# until the first one does.
except AlreadyCalled:
pass
self.reconnect_count = 0
reconnect_delay = 5*pow(2, self.reconnect_count)
#let increment grow exponentially to 5 minutes
if self.reconnect_count < 6:
self.reconnect_count += 1
logger.debug('reconnecting in %i seconds' % reconnect_delay)
self.reconnect_call_ID = reactor.callLater(reconnect_delay, self.connect)
def receive_key_node(self, key, node=None, **kwargs):
"""
Receives the public key from the node
"""
logger.debug("saving public key from node: %s" % node)
node.pub_key = key
node.save()
def init_node(self, node):
"""
Start the initialization sequence with the node. The first
step is to query it for its information.
"""
d = node.ref.callRemote('info')
d.addCallback(self.add_node, node=node)
def add_node(self, info, node):
"""
Process Node information. Most will just be stored for later use. Info will include
a list of workers. The master will then connect to all Workers.
"""
# save node's information in the database
node.cores = info['cores']
node.cpu_speed = info['cpu']
node.memory = info['memory']
node.save()
#node key to be used by node and its workers
node_key_str = '%s:%s' % (node.host, node.port)
# add all workers
for i in range(node.cores):
worker_key = '%s:%i' % (node_key_str, i)
self.worker_checker.addUser(worker_key, '1234')
# we have allowed access for all the workers, tell the node to init
d = node.ref.callRemote('init', self.host, pydraSettings.port, node_key_str)
d.addCallback(self.node_ready, node)
def node_ready(self, result, node):
"""
Called when a call to initialize a Node is successful
"""
logger.info('node:%s - ready' % node)
def worker_authenticated(self, worker_avatar):
"""
Callback when a worker has been successfully authenticated
"""
#request status to determine what this worker was doing
deferred = worker_avatar.remote.callRemote('status')
deferred.addCallback(self.add_worker, worker=worker_avatar, worker_key=worker_avatar.name)
def add_worker(self, result, worker, worker_key):
"""
Add a worker avatar as worker available to the cluster. There are two possible scenarios:
1) Only the worker was started/restarted, it is idle
2) Only master was restarted. Workers previous status must be reestablished
The best way to determine the state of the worker is to ask it. It will return its status
plus any relevent information for reestablishing it's status
"""
# worker is working and it was the master for its task
if result[0] == WORKER_STATUS_WORKING:
logger.info('worker:%s - is still working' % worker_key)
#record what the worker is working on
#self._workers_working[worker_key] = task_key
# worker is finished with a task
elif result[0] == WORKER_STATUS_FINISHED:
logger.info('worker:%s - was finished, requesting results' % worker_key)
#record what the worker is working on
#self._workers_working[worker_key] = task_key
#check if the Worker acting as master for this task is ready
if (True):
#TODO
pass
#else not ready to send the results
else:
#TODO
pass
#otherwise its idle
else:
with self._lock:
self.workers[worker_key] = worker
# worker shouldn't already be in the idle queue but check anyway
if not worker_key in self._workers_idle:
self._workers_idle.append(worker_key)
logger.info('worker:%s - added to idle workers' % worker_key)
def remove_worker(self, worker_key):
"""
Called when a worker disconnects
"""
with self._lock:
# if idle, just remove it. no need to do anything else
if worker_key in self._workers_idle:
logger.info('worker:%s - removing worker from idle pool' % worker_key)
self._workers_idle.remove(worker_key)
#worker was working on a task, need to clean it up
else:
removed_worker = self._workers_working[worker_key]
#worker was working on a subtask, return unfinished work to main worker
if removed_worker[3]:
logger.warning('%s failed during task, returning work unit' % worker_key)
task_instance = TaskInstance.objects.get(id=removed_worker[0])
main_worker = self.workers[task_instance.worker]
if main_worker:
d = main_worker.remote.callRemote('return_work', removed_worker[3], removed_worker[4])
d.addCallback(self.return_work_success, worker_key)
d.addErrback(self.return_work_failed, worker_key)
else:
#if we don't have a main worker listed it probably already was disconnected
#just call successful to clean up the worker
self.return_work_success(None, worker_key)
#worker was main worker for a task. cancel the task and tell any
#workers working on subtasks to stop. Cannot recover from the
#main worker going down
else:
#TODO
pass
def return_work_success(self, results, worker_key):
"""
Work was sucessful returned to the main worker
"""
with self._lock:
del self._workers_working[worker_key]
def return_work_failed(self, results, worker_key):
"""
A worker disconnected and the method call to return the work failed
"""
pass
def select_worker(self, task_instance_id, task_key, args={}, subtask_key=None, workunit_key=None):
"""
Select a worker to use for running a task or subtask
"""
#lock, selecting workers must be threadsafe
with self._lock:
if len(self._workers_idle):
#move the first worker to the working state storing the task its working on
worker_key = self._workers_idle.pop(0)
self._workers_working[worker_key] = (task_instance_id, task_key, args, subtask_key, workunit_key)
#return the worker object, not the key
return self.workers[worker_key]
else:
return None
def queue_task(self, task_key, args={}, subtask_key=None):
"""
Queue a task to be run. All task requests come through this method. It saves their
information in the database. If the cluster has idle resources it will start the task
immediately, otherwise it will queue the task until it is ready.
"""
logger.info('Task:%s:%s - Queued: %s' % (task_key, subtask_key, args))
#create a TaskInstance instance and save it
task_instance = TaskInstance()
task_instance.task_key = task_key
task_instance.subtask_key = subtask_key
task_instance.args = simplejson.dumps(args)
task_instance.save()
#queue the task and signal attempt to start it
with self._lock_queue:
self._queue.append(task_instance)
self.advance_queue()
return task_instance
def cancel_task(self, task_id):
"""
Cancel a task. This function is used to cancel a task that was scheduled.
If the task is in the queue still, remove it. If it is running then
send signals to all workers assigned to it to stop work immediately.
"""
task_instance = TaskInstance.objects.get(id=task_id)
logger.info('Cancelling Task: %s' % task_id)
with self._lock_queue:
if task_instance in self._queue:
#was still in queue
self._queue.remove(task_instance)
logger.debug('Cancelling Task, was in queue: %s' % task_id)
else:
logger.debug('Cancelling Task, is running: %s' % task_id)
#get all the workers to stop
for worker_key, worker_task in self._workers_working.items():
if worker_task[0] == task_id:
worker = self.workers[worker_key]
logger.debug('signalling worker to stop: %s' % worker_key)
worker.remote.callRemote('stop_task')
self._running.remove(task_instance)
task_instance.completion_type = STATUS_CANCELLED
task_instance.save()
return 1
def advance_queue(self):
"""
Advances the queue. If there is a task waiting it will be started, otherwise the cluster will idle.
This should be called whenever a resource becomes available or a new task is queued
"""
logger.debug('advancing queue: %s' % self._queue)
with self._lock_queue:
try:
task_instance = self._queue[0]
except IndexError:
#if there was nothing in the queue then fail silently
logger.debug('No tasks in queue, idling')
return False
if self.run_task(task_instance.id, task_instance.task_key, simplejson.loads(task_instance.args), task_instance.subtask_key):
#task started, update its info and remove it from the queue
logger.info('Task:%s:%s - starting' % (task_instance.task_key, task_instance.subtask_key))
task_instance.started = datetime.datetime.now()
task_instance.completion_type = STATUS_RUNNING
task_instance.save()
del self._queue[0]
self._running.append(task_instance)
else:
# cluster does not have idle resources.
# task will stay in the queue
logger.debug('Task:%s:%s - no resources available, remaining in queue' % (task_instance.task_key, task_instance.subtask_key))
return False
def run_task(self, task_instance_id, task_key, args={}, subtask_key=None, workunit_key=None):
"""
Run the task specified by the task_key. This shouldn't be called directly. Tasks should
be queued with queue_task(). If the cluster has idle resources it will be run automatically
This function is used internally by the cluster for parallel processing work requests. Work
requests are never queued. If there is no resource available the main worker for the task
should be informed and it can readjust its count of available resources. Any type of resource
sharing logic should be handled within select_worker() to keep the logic organized.
"""
# get a worker for this task
worker = self.select_worker(task_instance_id, task_key, args, subtask_key, workunit_key)
# determine how many workers are available for this task
available_workers = len(self._workers_idle)+1
if worker:
logger.debug('Worker:%s - Assigned to task: %s:%s %s' % (worker.name, task_key, subtask_key, args))
d = worker.remote.callRemote('run_task', task_key, args, subtask_key, workunit_key, available_workers)
d.addCallback(self.run_task_successful, worker, task_instance_id, subtask_key)
return worker
# no worker was available
# just return 0 (false), the calling function will decided what to do,
# depending what called run_task, different error handling will apply
else:
logger.warning('No worker available')
return None
def run_task_successful(self, results, worker, task_instance_id, subtask_key=None):
#save the history of what workers work on what task/subtask
#its needed for tracking finished work in ParallelTasks and will aide in Fault recovery
#it might also be useful for analysis purposes if one node is faulty
if subtask_key:
#TODO, update model and record what workers worked on what subtasks
pass
else:
task_instance = TaskInstance.objects.get(id=task_instance_id)
task_instance.worker = worker.name
task_instance.save()
def send_results(self, worker_key, results, workunit_key):
"""
Called by workers when they have completed their task.
Tasks runtime and log should be saved in the database
"""
logger.debug('Worker:%s - sent results: %s' % (worker_key, results))
with self._lock:
task_instance_id, task_key, args, subtask_key, workunit_key = self._workers_working[worker_key]
logger.info('Worker:%s - completed: %s:%s (%s)' % (worker_key, task_key, subtask_key, workunit_key))
# release the worker back into the idle pool
# this must be done before informing the
# main worker. otherwise a new work request
# can be made before the worker is released
del self._workers_working[worker_key]
self._workers_idle.append(worker_key)
#if this was the root task for the job then save info. Ignore the fact that the task might have
#been canceled. If its 100% complete, then mark it as such.
if not subtask_key:
with self._lock_queue:
task_instance = TaskInstance.objects.get(id=task_instance_id)
task_instance.completed = datetime.datetime.now()
task_instance.completion_type = STATUS_COMPLETE
task_instance.save()
#remove task instance from running queue
try:
self._running.remove(task_instance)
except ValueError:
# was already removed by cancel
pass
else:
#check to make sure the task was still in the queue. Its possible this call was made at the same
# time a task was being canceled. Only worry about sending the reults back to the Task Head
# if the task is still running
task_instance = TaskInstance.objects.get(id=task_instance_id)
with self._lock_queue:
if task_instance in self._running:
#if this was a subtask the main task needs the results and to be informed
task_instance = TaskInstance.objects.get(id=task_instance_id)
main_worker = self.workers[task_instance.worker]
logger.debug('Worker:%s - informed that subtask completed' % task_instance.worker)
main_worker.remote.callRemote('receive_results', results, subtask_key, workunit_key)
else:
logger.debug('Worker:%s - returned a subtask but the task is no longer running. discarding value.' % worker_key)
#attempt to advance the queue
self.advance_queue()
def task_failed(self, worker_key, results, workunit_key):
"""
Called by workers when the task they were running throws an exception
"""
with self._lock:
task_instance_id, task_key, args, subtask_key, workunit_key = self._workers_working[worker_key]
logger.info('Worker:%s - failed: %s:%s (%s)' % (worker_key, task_key, subtask_key, workunit_key))
# cancel the task and send notice to all other workers to stop
# working on this task. This may be partially recoverable but that
# is not included for now.
with self._lock_queue:
# release the worker back into the idle pool
del self._workers_working[worker_key]
self._workers_idle.append(worker_key)
task_instance = TaskInstance.objects.get(id=task_instance_id)
task_instance.completed = datetime.datetime.now()
task_instance.completion_type = STATUS_FAILED
task_instance.save()
for worker_key, worker_task in self._workers_working.items():
if worker_task[0] == task_instance_id:
worker = self.workers[worker_key]
logger.debug('signalling worker to stop: %s' % worker_key)
worker.remote.callRemote('stop_task')
#remove task instance from running queue
try:
self._running.remove(task_instance)
except ValueError:
# was already removed
pass
#attempt to advance the queue
self.advance_queue()
def fetch_task_status(self):
"""
updates the list of statuses. this function is used because all
workers must be queried to receive status updates. This results in a
list of deferred objects. There is no way to block until the results
are ready. instead this function updates all the statuses. Subsequent
calls for status will be able to fetch the status. It may be delayed
by a few seconds but thats minor when considering a task that could run
for hours.
For now, statuses are only queried for Main Workers. Including
statuses of subtasks requires additional logic and overhead to pass the
intermediate results to the main worker.
"""
# limit updates so multiple controllers won't cause excessive updates
now = datetime.datetime.now()
if self._next_task_status_update < now:
workers = self.workers
for key, data in self._workers_working.items():
if not data[3]:
worker = workers[key]
task_instance_id = data[0]
deferred = worker.remote.callRemote('task_status')
deferred.addCallback(self.fetch_task_status_success, task_instance_id)
self.next_task_status_update = now + datetime.timedelta(0, 3)
def fetch_task_status_success(self, result, task_instance_id):
"""
updates task status list with response from worker used in conjunction
with fetch_task_status()
"""
self._task_statuses[task_instance_id] = result
def task_statuses(self):
"""
Returns the status of all running tasks. This is a detailed list
of progress and status messages.
"""
# tell the master to fetch the statuses for the task.
# this may or may not complete by the time we process the list
self.fetch_task_status()
statuses = {}
for instance in self._queue:
statuses[instance.id] = {'s':STATUS_STOPPED}
for instance in self._running:
start = time.mktime(instance.started.timetuple())
# call worker to get status update
try:
progress = self._task_statuses[instance.id]
except KeyError:
# its possible that the progress does not exist yet. because
# the task has just started and fetch_task_status is not complete
pass
progress = -1
statuses[instance.id] = {'s':STATUS_RUNNING, 't':start, 'p':progress}
return statuses
def worker_stopped(self, worker_key):
"""
Called by workers when they have stopped due to a cancel task request.
"""
with self._lock:
logger.info(' Worker:%s - stopped' % worker_key)
# release the worker back into the idle pool
# this must be done before informing the
# main worker. otherwise a new work request
# can be made before the worker is released
del self._workers_working[worker_key]
self._workers_idle.append(worker_key)
#attempt to advance the queue
self.advance_queue()
def request_worker(self, workerAvatar, subtask_key, args, workunit_key):
"""
Called by workers running a Parallel task. This is a request
for a worker in the cluster to process a workunit from a task
"""
#get the task key and run the task. The key is looked up
#here so that a worker can only request a worker for the
#their current task.
worker = self._workers_working[workerAvatar.name]
task_instance = TaskInstance.objects.get(id=worker[0])
logger.debug('Worker:%s - request for worker: %s:%s' % (workerAvatar.name, subtask_key, args))
# lock queue and check status of task to ensure no lost workers
# due to a canceled task
with self._lock_queue:
if task_instance in self._running:
self.run_task(worker[0], worker[1], args, subtask_key, workunit_key)
else:
logger.debug('Worker:%s - request for worker failed, task is not running' % (workerAvatar.name))
class Worker(pb.Referenceable):
"""
Worker - The Worker is the workhorse of the cluster. It sits and waits for Tasks and SubTasks to be executed
Each Task will be run on a single Worker. If the Task or any of its subtasks are a ParallelTask
the first worker will make requests for work to be distributed to other Nodes
"""
def __init__(self, master_host, master_port, node_key, worker_key):
self.id = id
self.__task = None
self.__task_instance = None
self.__results = None
self.__stop_flag = None
self.__lock = Lock()
self.__lock_connection = Lock()
self.master = None
self.master_host = master_host
self.master_port = master_port
self.node_key = node_key
self.worker_key = worker_key
self.reconnect_count = 0
# load crypto for authentication
# workers use the same keys as their parent Node
self.pub_key, self.priv_key = load_crypto("./node.key")
self.master_pub_key = load_crypto("./node.master.key", False, both=False)
self.rsa_client = RSAClient(self.priv_key)
# load tasks that are cached locally
self.task_manager = TaskManager()
self.task_manager.autodiscover()
self.available_tasks = self.task_manager.registry
logger.info("Started Worker: %s" % worker_key)
self.connect()
def connect(self):
"""
Make initial connections to all Nodes
"""
import fileinput
logger.info("worker:%s - connecting to master @ %s:%s" % (self.worker_key, self.master_host, self.master_port))
factory = MasterClientFactory(self.reconnect)
reactor.connectTCP(self.master_host, self.master_port, factory)
deferred = factory.login(credentials.UsernamePassword(self.worker_key, "1234"), client=self)
deferred.addCallbacks(self.connected, self.reconnect, errbackArgs=("Failed to Connect"))
def reconnect(self, *arg, **kw):
with self.__lock_connection:
self.master = None
reconnect_delay = 5 * pow(2, self.reconnect_count)
# let increment grow exponentially to 5 minutes
if self.reconnect_count < 6:
self.reconnect_count += 1
logger.debug("worker:%s - reconnecting in %i seconds" % (self.worker_key, reconnect_delay))
self.reconnect_call_ID = reactor.callLater(reconnect_delay, self.connect)
def connected(self, result):
"""
Callback called when connection to master is made
"""
with self.__lock_connection:
self.master = result
self.reconnect_count = 0
logger.info("worker:%s - connected to master @ %s:%s" % (self.worker_key, self.master_host, self.master_port))
# Authenticate with the master
self.rsa_client.auth(result, None, self.master_pub_key)
def connect_failed(self, result):
"""
Callback called when conenction to master fails
"""
self.reconnect()
def run_task(self, key, args={}, subtask_key=None, workunit_key=None, available_workers=1):
"""
Runs a task on this worker
"""
# Check to ensure this worker is not already busy.
# The Master should catch this but lets be defensive.
with self.__lock:
if self.__task:
return "FAILURE THIS WORKER IS ALREADY RUNNING A TASK"
self.__task = key
self.__subtask = subtask_key
self.__workunit_key = workunit_key
self.available_workers = available_workers
logger.info("Worker:%s - starting task: %s:%s %s" % (self.worker_key, key, subtask_key, args))
# create an instance of the requested task
if not self.__task_instance or self.__task_instance.__class__.__name__ != key:
self.__task_instance = object.__new__(self.available_tasks[key])
self.__task_instance.__init__()
self.__task_instance.parent = self
# process args to make sure they are no longer unicode
clean_args = {}
if args:
for key, arg in args.items():
clean_args[key.__str__()] = arg
return self.__task_instance.start(clean_args, subtask_key, self.work_complete, errback=self.work_failed)
def stop_task(self):
"""
Stops the current task
"""
logger.info("%s - Received STOP command" % self.worker_key)
if self.__task_instance:
self.__task_instance._stop()
def status(self):
"""
Return the status of the current task if running, else None
"""
# if there is a task it must still be running
if self.__task:
return (WORKER_STATUS_WORKING, self.__task, self.__subtask)
# if there are results it was waiting for the master to retrieve them
if self.__results:
return (WORKER_STATUS_FINISHED, self.__task, self.__subtask)
return (WORKER_STATUS_IDLE,)
def work_complete(self, results):
"""
Callback that is called when a job is run in non_blocking mode.
"""
stop_flag = self.__task_instance.STOP_FLAG
self.__task = None
if stop_flag:
# stop flag, this task was canceled.
with self.__lock_connection:
if self.master:
deferred = self.master.callRemote("stopped")
deferred.addErrback(self.send_stopped_failed)
else:
self.__stop_flag = True
else:
# completed normally
# if the master is still there send the results
with self.__lock_connection:
if self.master:
deferred = self.master.callRemote("send_results", results, self.__workunit_key)
deferred.addErrback(self.send_results_failed, results, self.__workunit_key)
# master disapeared, hold results until it requests them
else:
self.__results = results
def work_failed(self, results):
"""
Callback that there was an exception thrown by the task
"""
self.__task = None
with self.__lock_connection:
if self.master:
deferred = self.master.callRemote("failed", results, self.__workunit_key)
logger.error("Worker - Task Failed: %s" % results)
# deferred.addErrback(self.send_failed_failed, results, self.__workunit_key)
# master disapeared, hold failure until it comes back online and requests it
else:
# TODO implement me
pass
def send_results_failed(self, results, task_results, workunit_key):
"""
Errback called when sending results to the master fails. resend when
master reconnects
"""
with self.__lock_connection:
# check again for master. the lock is released so its possible
# master could connect and be set before we set the flags indicating
# the problem
if self.master:
# reconnected, just resend the call. The call is recursive from this point
# if by some odd chance it disconnects again while sending
logger.error("results failed to send by master is still here")
# deferred = self.master.callRemote("send_results", task_results, task_results)
# deferred.addErrback(self.send_results_failed, task_results, task_results)
else:
# nope really isn't connected. set flag. even if connection is in progress
# this thread has the lock and reconnection cant finish till we release it
self.__results = task_results
self.__workunit_key = workunit_key
def send_stopped_failed(self, results):
"""
failed to send the stopped message. set the flag and wait for master to reconnect
"""
with self.__lock_connection:
# check again for master. the lock is released so its possible
# master could connect and be set before we set the flags indicating
# the problem
if master:
# reconnected, just resend the call. The call is recursive from this point
# if by some odd chance it disconnects again while sending
logger.error("STOP FAILED BUT MASTER STILL HERE")
# deferred = self.master.callRemote("stopped")
# deferred.addErrBack(self.send_stopped_failed)
else:
# nope really isn't connected. set flag. even if connection is in progress
# this thread has the lock and reconnection cant finish till we release it
self.__stop_flag = True
def task_status(self):
"""
Returns status of task this task is performing
"""
if self.__task_instance:
return self.__task_instance.progress()
def receive_results(self, results, subtask_key, workunit_key):
"""
Function called to make the subtask receive the results processed by another worker
"""
logger.info("Worker:%s - received REMOTE results for: %s" % (self.worker_key, subtask_key))
subtask = self.__task_instance.get_subtask(subtask_key.split("."))
subtask.parent._work_unit_complete(results, workunit_key)
def request_worker(self, subtask_key, args, workunit_key):
"""
Requests a work unit be handled by another worker in the cluster
"""
logger.info("Worker:%s - requesting worker for: %s" % (self.worker_key, subtask_key))
deferred = self.master.callRemote("request_worker", subtask_key, args, workunit_key)
def return_work(self, subtask_key, workunit_key):
subtask = self.__task_instance.get_subtask(subtask_key.split("."))
subtask.parent._worker_failed(workunit_key)
def get_worker(self):
"""
Recursive function so tasks can find this worker
"""
return self
def get_key(self):
"""
recursive task key generation function. This stops the recursion
"""
return None
def remote_status(self):
return self.status()
def remote_task_list(self):
return self.available_tasks.keys()
def remote_run_task(self, key, args={}, subtask_key=None, workunit_key=None, available_workers=1):
return self.run_task(key, args, subtask_key, workunit_key, available_workers)
def remote_stop_task(self):
return self.stop_task()
def remote_receive_results(self, results, subtask_key, workunit_key):
return self.receive_results(results, subtask_key, workunit_key)
def remote_return_work(self, subtask_key, workunit_key):
"""
Called by the Master when a Worker disconnected while working on a task.
The work unit is returned so another worker can finish it.
"""
return self.return_work(subtask_key, workunit_key)
def remote_task_status(self):
return self.task_status()