def test_process_status_not_running(self):
'''
This test passes in a status for a server that is leaving the system
and ensures that all the metadata reflects this fact after the
processing.
'''
# Construct a new thread status to prime the policy engine with.
function_name = 'square'
status = ThreadStatus()
status.running = True
status.ip = self.ip
status.tid = 1
status.functions.append(function_name)
status.utilization = 0
key = (status.ip, status.tid)
# Add metadata to the policy engine to make it think that this node
# used to have a pinned function.
self.policy.function_locations[function_name] = {key}
self.policy.thread_statuses[key] = status
# Clear the status' fields to report that it is turning off.
status = ThreadStatus()
status.running = False
status.ip = self.ip
status.tid = 1
# Process the status and check the metadata.
self.policy.process_status(status)
self.assertTrue(key not in self.policy.thread_statuses)
self.assertTrue(key not in self.policy.unpinned_executors)
self.assertEqual(len(self.policy.function_locations[function_name]), 0)
def test_process_status_restart(self):
'''
This tests checks that when we receive a status update from a restarted
executor, we correctly update its metadata.
'''
# Construct a new thread status to pass into the policy engine.
function_name = 'square'
status = ThreadStatus()
status.running = True
status.ip = self.ip
status.tid = 1
status.functions.append(function_name)
status.utilization = 0
key = (status.ip, status.tid)
# Add metadata to the policy engine to make it think that this node
# used to have a pinned function.
self.policy.function_locations[function_name] = {key}
self.policy.thread_statuses[key] = status
# Clear the status' pinned functions (i.e., restart).
status = ThreadStatus()
status.ip = self.ip
status.tid = 1
status.running = True
status.utilization = 0
# Process the status and check the metadata.
self.policy.process_status(status)
self.assertEqual(len(self.policy.function_locations[function_name]), 0)
self.assertTrue(key in self.policy.unpinned_executors)
def test_process_status(self):
'''
This test ensures that when a new status update is received from an
executor, the local server metadata is correctly updated in the normal
case.
'''
# Construct a new thread status to pass into the policy engine.
function_name = 'square'
status = ThreadStatus()
status.running = True
status.ip = self.ip
status.tid = 1
status.functions.append(function_name)
status.utilization = 0.10
# Process the newly created status.
self.policy.process_status(status)
status.tid = 2
status.utilization = 0.90
self.policy.process_status(status)
key = (status.ip, status.tid)
self.assertTrue(key not in self.policy.unpinned_cpu_executors)
self.assertTrue(key in self.policy.function_locations[function_name])
self.assertTrue(key in self.policy.backoff)
def setUp(self):
self.pusher_cache = zmq_utils.MockPusherCache()
self.socket = zmq_utils.MockZmqSocket()
self.pin_socket = zmq_utils.MockZmqSocket()
self.kvs_client = kvs_client.MockAnnaClient()
self.ip = '127.0.0.1'
self.policy = DefaultCloudburstSchedulerPolicy(self.pin_socket,
self.pusher_cache,
self.kvs_client,
self.ip,
random_threshold=0)
# Add an executor to the policy engine by default.
status = ThreadStatus()
status.ip = self.ip
status.tid = 0
self.executor_key = (status.ip, status.tid)
self.policy.unpinned_executors.add(self.executor_key)
def test_metadata_update(self):
'''
This test calls the periodic metadata update protocol and ensures that
the correct metadata is removed from the system and that the correct
metadata is retrieved/updated from the KVS.
'''
# Create two executor threads on separate machines.
old_ip = '127.0.0.1'
new_ip = '192.168.0.1'
old_executor = (old_ip, 1)
new_executor = (new_ip, 2)
old_status = ThreadStatus()
old_status.ip = old_ip
old_status.tid = 1
old_status.running = True
new_status = ThreadStatus()
new_status.ip = new_ip
new_status.tid = 2
new_status.running = True
self.policy.thread_statuses[old_executor] = old_status
self.policy.thread_statuses[new_executor] = new_status
# Add two executors, one with old an old backoff and one with a new
# time.
self.policy.backoff[old_executor] = time.time() - 10
self.policy.backoff[new_executor] = time.time()
# For the new executor, add 10 old running times and 10 new ones.
self.policy.running_counts[new_executor] = set()
for _ in range(10):
time.sleep(.0001)
self.policy.running_counts[new_executor].add(time.time() - 10)
for _ in range(10):
time.sleep(.0001)
self.policy.running_counts[new_executor].add(time.time())
# Publish some caching metadata into the KVS for each executor.
old_set = StringSet()
old_set.keys.extend(['key1', 'key2', 'key3'])
new_set = StringSet()
new_set.keys.extend(['key3', 'key4', 'key5'])
self.kvs_client.put(get_cache_ip_key(old_ip),
LWWPairLattice(0, old_set.SerializeToString()))
self.kvs_client.put(get_cache_ip_key(new_ip),
LWWPairLattice(0, new_set.SerializeToString()))
self.policy.update()
# Check that the metadata has been correctly pruned.
self.assertEqual(len(self.policy.backoff), 1)
self.assertTrue(new_executor in self.policy.backoff)
self.assertEqual(len(self.policy.running_counts[new_executor]), 10)
# Check that the caching information is correct.
self.assertTrue(len(self.policy.key_locations['key1']), 1)
self.assertTrue(len(self.policy.key_locations['key2']), 1)
self.assertTrue(len(self.policy.key_locations['key3']), 2)
self.assertTrue(len(self.policy.key_locations['key4']), 1)
self.assertTrue(len(self.policy.key_locations['key5']), 1)
self.assertTrue(old_ip in self.policy.key_locations['key1'])
self.assertTrue(old_ip in self.policy.key_locations['key2'])
self.assertTrue(old_ip in self.policy.key_locations['key3'])
self.assertTrue(new_ip in self.policy.key_locations['key3'])
self.assertTrue(new_ip in self.policy.key_locations['key4'])
self.assertTrue(new_ip in self.policy.key_locations['key5'])
def executor(ip, mgmt_ip, schedulers, thread_id):
# logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s %(message)s')
logging.basicConfig(filename='log_executor.txt',
level=logging.INFO,
filemode="w",
format='%(asctime)s %(message)s')
# Check what resources we have access to, set as an environment variable.
if os.getenv('EXECUTOR_TYPE', 'CPU') == 'GPU':
exec_type = GPU
else:
exec_type = CPU
context = zmq.Context(1)
poller = zmq.Poller()
pin_socket = context.socket(zmq.PULL)
pin_socket.bind(sutils.BIND_ADDR_TEMPLATE % (sutils.PIN_PORT + thread_id))
unpin_socket = context.socket(zmq.PULL)
unpin_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.UNPIN_PORT + thread_id))
exec_socket = context.socket(zmq.PULL)
exec_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.FUNC_EXEC_PORT + thread_id))
dag_queue_socket = context.socket(zmq.PULL)
dag_queue_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.DAG_QUEUE_PORT + thread_id))
dag_exec_socket = context.socket(zmq.PULL)
dag_exec_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.DAG_EXEC_PORT + thread_id))
self_depart_socket = context.socket(zmq.PULL)
self_depart_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.SELF_DEPART_PORT + thread_id))
pusher_cache = SocketCache(context, zmq.PUSH)
poller = zmq.Poller()
poller.register(pin_socket, zmq.POLLIN)
poller.register(unpin_socket, zmq.POLLIN)
poller.register(exec_socket, zmq.POLLIN)
poller.register(dag_queue_socket, zmq.POLLIN)
poller.register(dag_exec_socket, zmq.POLLIN)
poller.register(self_depart_socket, zmq.POLLIN)
# If the management IP is set to None, that means that we are running in
# local mode, so we use a regular AnnaTcpClient rather than an IPC client.
has_ephe = False
if mgmt_ip:
if 'STORAGE_OR_DEFAULT' in os.environ and os.environ[
'STORAGE_OR_DEFAULT'] == '0':
client = AnnaTcpClient(os.environ['ROUTE_ADDR'],
ip,
local=False,
offset=thread_id)
has_ephe = True
else:
client = AnnaIpcClient(thread_id, context)
# force_remote_anna = 1
# if 'FORCE_REMOTE' in os.environ:
# force_remote_anna = int(os.environ['FORCE_REMOTE'])
# if force_remote_anna == 0: # remote anna only
# client = AnnaTcpClient(os.environ['ROUTE_ADDR'], ip, local=False, offset=thread_id)
# elif force_remote_anna == 1: # anna cache
# client = AnnaIpcClient(thread_id, context)
# elif force_remote_anna == 2: # control both cache and remote anna
# remote_client = AnnaTcpClient(os.environ['ROUTE_ADDR'], ip, local=False, offset=thread_id)
# cache_client = AnnaIpcClient(thread_id, context)
# client = cache_client
# user_library = CloudburstUserLibrary(context, pusher_cache, ip, thread_id, (cache_client, remote_client))
local = False
else:
client = AnnaTcpClient('127.0.0.1', '127.0.0.1', local=True, offset=1)
local = True
user_library = CloudburstUserLibrary(context,
pusher_cache,
ip,
thread_id,
client,
has_ephe=has_ephe)
status = ThreadStatus()
status.ip = ip
status.tid = thread_id
status.running = True
status.type = exec_type
utils.push_status(schedulers, pusher_cache, status)
departing = False
# Maintains a request queue for each function pinned on this executor. Each
# function will have a set of request IDs mapped to it, and this map stores
# a schedule for each request ID.
queue = {}
# Tracks the actual function objects that are pinned to this executor.
function_cache = {}
# Tracks runtime cost of excuting a DAG function.
runtimes = {}
# If multiple triggers are necessary for a function, track the triggers as
# we receive them. This is also used if a trigger arrives before its
# corresponding schedule.
received_triggers = {}
# Tracks when we received a function request, so we can report end-to-end
# latency for the whole executio.
receive_times = {}
# Tracks the number of requests we are finishing for each function pinned
# here.
exec_counts = {}
# Tracks the end-to-end runtime of each DAG request for which we are the
# sink function.
dag_runtimes = {}
# A map with KVS keys and their corresponding deserialized payloads.
cache = {}
# A map which tracks the most recent DAGs for which we have finished our
# work.
finished_executions = {}
# The set of pinned functions and whether they support batching. NOTE: This
# is only a set for local mode -- in cluster mode, there will only be one
# pinned function per executor.
batching = False
# Internal metadata to track thread utilization.
report_start = time.time()
event_occupancy = {
'pin': 0.0,
'unpin': 0.0,
'func_exec': 0.0,
'dag_queue': 0.0,
'dag_exec': 0.0
}
total_occupancy = 0.0
while True:
socks = dict(poller.poll(timeout=1000))
if pin_socket in socks and socks[pin_socket] == zmq.POLLIN:
work_start = time.time()
batching = pin(pin_socket, pusher_cache, client, status,
function_cache, runtimes, exec_counts, user_library,
local, batching)
utils.push_status(schedulers, pusher_cache, status)
elapsed = time.time() - work_start
event_occupancy['pin'] += elapsed
total_occupancy += elapsed
if unpin_socket in socks and socks[unpin_socket] == zmq.POLLIN:
work_start = time.time()
unpin(unpin_socket, status, function_cache, runtimes, exec_counts)
utils.push_status(schedulers, pusher_cache, status)
elapsed = time.time() - work_start
event_occupancy['unpin'] += elapsed
total_occupancy += elapsed
if exec_socket in socks and socks[exec_socket] == zmq.POLLIN:
work_start = time.time()
# logging.info(f'Executor timer. exec_socket recv: {work_start}')
exec_function(exec_socket,
client,
user_library,
cache,
function_cache,
has_ephe=has_ephe)
user_library.close()
utils.push_status(schedulers, pusher_cache, status)
elapsed = time.time() - work_start
event_occupancy['func_exec'] += elapsed
total_occupancy += elapsed
if dag_queue_socket in socks and socks[dag_queue_socket] == zmq.POLLIN:
work_start = time.time()
logging.info(
f'Executor timer. dag_queue_socket recv: {work_start}')
# In order to effectively support batching, we have to make sure we
# dequeue lots of schedules in addition to lots of triggers. Right
# now, we're not going to worry about supporting batching here,
# just on the trigger dequeue side, but we still have to dequeue
# all schedules we've received. We just process them one at a time.
while True:
schedule = DagSchedule()
try:
msg = dag_queue_socket.recv(zmq.DONTWAIT)
except zmq.ZMQError as e:
if e.errno == zmq.EAGAIN:
break # There are no more messages.
else:
raise e # Unexpected error.
schedule.ParseFromString(msg)
fname = schedule.target_function
logging.info(
'Received a schedule for DAG %s (%s), function %s.' %
(schedule.dag.name, schedule.id, fname))
if fname not in queue:
queue[fname] = {}
queue[fname][schedule.id] = schedule
if (schedule.id, fname) not in receive_times:
receive_times[(schedule.id, fname)] = time.time()
# In case we receive the trigger before we receive the schedule, we
# can trigger from this operation as well.
trkey = (schedule.id, fname)
fref = None
# Check to see what type of execution this function is.
for ref in schedule.dag.functions:
if ref.name == fname:
fref = ref
if (trkey in received_triggers and
((len(received_triggers[trkey]) == len(schedule.triggers))
or (fref.type == MULTIEXEC))):
triggers = list(received_triggers[trkey].values())
if fname not in function_cache:
logging.error('%s not in function cache', fname)
utils.generate_error_response(schedule, client, fname)
continue
exec_start = time.time()
# logging.info(f'Executor timer. dag_queue_socket exec_dag: {exec_start}')
# We don't support actual batching for when we receive a
# schedule before a trigger, so everything is just a batch of
# size 1 if anything.
success = exec_dag_function(pusher_cache, client,
[triggers],
function_cache[fname],
[schedule], user_library,
dag_runtimes, cache,
schedulers, batching)[0]
user_library.close()
del received_triggers[trkey]
if success:
del queue[fname][schedule.id]
fend = time.time()
fstart = receive_times[(schedule.id, fname)]
runtimes[fname].append(fend - work_start)
exec_counts[fname] += 1
finished_executions[(schedule.id, fname)] = time.time()
elapsed = time.time() - work_start
event_occupancy['dag_queue'] += elapsed
total_occupancy += elapsed
if dag_exec_socket in socks and socks[dag_exec_socket] == zmq.POLLIN:
work_start = time.time()
# logging.info(f'Executor timer. dag_exec_socket recv: {work_start}')
# How many messages to dequeue -- BATCH_SIZE_MAX or 1 depending on
# the function configuration.
if batching:
count = BATCH_SIZE_MAX
else:
count = 1
trigger_keys = set()
for _ in range(count): # Dequeue count number of messages.
trigger = DagTrigger()
try:
msg = dag_exec_socket.recv(zmq.DONTWAIT)
except zmq.ZMQError as e:
if e.errno == zmq.EAGAIN: # There are no more messages.
break
else:
raise e # Unexpected error.
trigger.ParseFromString(msg)
# We have received a repeated trigger for a function that has
# already finished executing.
if trigger.id in finished_executions:
continue
fname = trigger.target_function
logging.info(
'Received a trigger for schedule %s, function %s.' %
(trigger.id, fname))
key = (trigger.id, fname)
trigger_keys.add(key)
if key not in received_triggers:
received_triggers[key] = {}
if (trigger.id, fname) not in receive_times:
receive_times[(trigger.id, fname)] = time.time()
received_triggers[key][trigger.source] = trigger
# Only execute the functions for which we have received a schedule.
# Everything else will wait.
for tid, fname in list(trigger_keys):
if fname not in queue or tid not in queue[fname]:
trigger_keys.remove((tid, fname))
if len(trigger_keys) == 0:
continue
fref = None
schedule = queue[fname][list(trigger_keys)[0]
[0]] # Pick a random schedule to check.
# Check to see what type of execution this function is.
for ref in schedule.dag.functions:
if ref.name == fname:
fref = ref
break
# Compile a list of all the trigger sets for which we have
# enough triggers.
trigger_sets = []
schedules = []
for key in trigger_keys:
if (len(received_triggers[key]) == len(schedule.triggers)) or \
fref.type == MULTIEXEC:
if fref.type == MULTIEXEC:
triggers = [trigger]
else:
triggers = list(received_triggers[key].values())
if fname not in function_cache:
logging.error('%s not in function cache', fname)
utils.generate_error_response(schedule, client, fname)
continue
trigger_sets.append(triggers)
schedule = queue[fname][key[0]]
schedules.append(schedule)
exec_start = time.time()
# logging.info(f'Executor timer. dag_exec_socket exec_dag: {exec_start}')
# Pass all of the trigger_sets into exec_dag_function at once.
# We also include the batching variaible to make sure we know
# whether to pass lists into the fn or not.
if len(trigger_sets) > 0:
successes = exec_dag_function(pusher_cache, client,
trigger_sets,
function_cache[fname], schedules,
user_library, dag_runtimes,
cache, schedulers, batching)
user_library.close()
del received_triggers[key]
for key, success in zip(trigger_keys, successes):
if success:
del queue[fname][key[0]] # key[0] is trigger.id.
fend = time.time()
fstart = receive_times[key]
average_time = (fend - work_start) / len(trigger_keys)
runtimes[fname].append(average_time)
exec_counts[fname] += 1
finished_executions[(schedule.id, fname)] = time.time()
elapsed = time.time() - work_start
event_occupancy['dag_exec'] += elapsed
total_occupancy += elapsed
if self_depart_socket in socks and socks[self_depart_socket] == \
zmq.POLLIN:
# This message does not matter.
self_depart_socket.recv()
logging.info('Preparing to depart. No longer accepting requests ' +
'and clearing all queues.')
status.ClearField('functions')
status.running = False
utils.push_status(schedulers, pusher_cache, status)
departing = True
# periodically report function occupancy
report_end = time.time()
if report_end - report_start > REPORT_THRESH:
if len(cache) > 100:
extra_keys = list(cache.keys())[:len(cache) - 100]
for key in extra_keys:
del cache[key]
utilization = total_occupancy / (report_end - report_start)
status.utilization = utilization
# Periodically report my status to schedulers with the utilization
# set.
utils.push_status(schedulers, pusher_cache, status)
logging.debug('Total thread occupancy: %.6f' % (utilization))
for event in event_occupancy:
occ = event_occupancy[event] / (report_end - report_start)
logging.debug('\tEvent %s occupancy: %.6f' % (event, occ))
event_occupancy[event] = 0.0
stats = ExecutorStatistics()
for fname in runtimes:
if exec_counts[fname] > 0:
fstats = stats.functions.add()
fstats.name = fname
fstats.call_count = exec_counts[fname]
fstats.runtime.extend(runtimes[fname])
runtimes[fname].clear()
exec_counts[fname] = 0
for dname in dag_runtimes:
dstats = stats.dags.add()
dstats.name = dname
dstats.runtimes.extend(dag_runtimes[dname])
dag_runtimes[dname].clear()
# If we are running in cluster mode, mgmt_ip will be set, and we
# will report our status and statistics to it. Otherwise, we will
# write to the local conf file
if mgmt_ip:
sckt = pusher_cache.get(
sutils.get_statistics_report_address(mgmt_ip))
sckt.send(stats.SerializeToString())
sckt = pusher_cache.get(utils.get_util_report_address(mgmt_ip))
sckt.send(status.SerializeToString())
else:
logging.info(stats)
status.ClearField('utilization')
report_start = time.time()
total_occupancy = 0.0
# Periodically clear any old functions we have cached that we are
# no longer accepting requests for.
del_list = []
for fname in queue:
if len(queue[fname]) == 0 and fname not in status.functions:
del_list.append(fname)
del function_cache[fname]
del runtimes[fname]
del exec_counts[fname]
for fname in del_list:
del queue[fname]
del_list = []
for tid in finished_executions:
if (time.time() - finished_executions[tid]) > 10:
del_list.append(tid)
for tid in del_list:
del finished_executions[tid]
# If we are departing and have cleared our queues, let the
# management server know, and exit the process.
if departing and len(queue) == 0:
sckt = pusher_cache.get(utils.get_depart_done_addr(mgmt_ip))
sckt.send_string(ip)
# We specifically pass 1 as the exit code when ending our
# process so that the wrapper script does not restart us.
sys.exit(1)
def executor(ip, mgmt_ip, schedulers, thread_id):
logging.basicConfig(filename='log_executor.txt',
level=logging.INFO,
format='%(asctime)s %(message)s')
context = zmq.Context(1)
poller = zmq.Poller()
pin_socket = context.socket(zmq.PULL)
pin_socket.bind(sutils.BIND_ADDR_TEMPLATE % (sutils.PIN_PORT + thread_id))
unpin_socket = context.socket(zmq.PULL)
unpin_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.UNPIN_PORT + thread_id))
exec_socket = context.socket(zmq.PULL)
exec_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.FUNC_EXEC_PORT + thread_id))
dag_queue_socket = context.socket(zmq.PULL)
dag_queue_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.DAG_QUEUE_PORT + thread_id))
dag_exec_socket = context.socket(zmq.PULL)
dag_exec_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.DAG_EXEC_PORT + thread_id))
self_depart_socket = context.socket(zmq.PULL)
self_depart_socket.bind(sutils.BIND_ADDR_TEMPLATE %
(sutils.SELF_DEPART_PORT + thread_id))
pusher_cache = SocketCache(context, zmq.PUSH)
poller = zmq.Poller()
poller.register(pin_socket, zmq.POLLIN)
poller.register(unpin_socket, zmq.POLLIN)
poller.register(exec_socket, zmq.POLLIN)
poller.register(dag_queue_socket, zmq.POLLIN)
poller.register(dag_exec_socket, zmq.POLLIN)
poller.register(self_depart_socket, zmq.POLLIN)
# If the management IP is set to None, that means that we are running in
# local mode, so we use a regular AnnaTcpClient rather than an IPC client.
if mgmt_ip:
client = AnnaIpcClient(thread_id, context)
else:
client = AnnaTcpClient('127.0.0.1', '127.0.0.1', local=True, offset=1)
user_library = CloudburstUserLibrary(context, pusher_cache, ip, thread_id,
client)
status = ThreadStatus()
status.ip = ip
status.tid = thread_id
status.running = True
utils.push_status(schedulers, pusher_cache, status)
departing = False
# Maintains a request queue for each function pinned on this executor. Each
# function will have a set of request IDs mapped to it, and this map stores
# a schedule for each request ID.
queue = {}
# Tracks the actual function objects that are pinned to this executor.
function_cache = {}
# Tracks runtime cost of excuting a DAG function.
runtimes = {}
# If multiple triggers are necessary for a function, track the triggers as
# we receive them. This is also used if a trigger arrives before its
# corresponding schedule.
received_triggers = {}
# Tracks when we received a function request, so we can report end-to-end
# latency for the whole executio.
receive_times = {}
# Tracks the number of requests we are finishing for each function pinned
# here.
exec_counts = {}
# Tracks the end-to-end runtime of each DAG request for which we are the
# sink function.
dag_runtimes = {}
# A map with KVS keys and their corresponding deserialized payloads.
cache = {}
# Internal metadata to track thread utilization.
report_start = time.time()
event_occupancy = {
'pin': 0.0,
'unpin': 0.0,
'func_exec': 0.0,
'dag_queue': 0.0,
'dag_exec': 0.0
}
total_occupancy = 0.0
while True:
socks = dict(poller.poll(timeout=1000))
if pin_socket in socks and socks[pin_socket] == zmq.POLLIN:
work_start = time.time()
pin(pin_socket, pusher_cache, client, status, function_cache,
runtimes, exec_counts, user_library)
utils.push_status(schedulers, pusher_cache, status)
elapsed = time.time() - work_start
event_occupancy['pin'] += elapsed
total_occupancy += elapsed
if unpin_socket in socks and socks[unpin_socket] == zmq.POLLIN:
work_start = time.time()
unpin(unpin_socket, status, function_cache, runtimes, exec_counts)
utils.push_status(schedulers, pusher_cache, status)
elapsed = time.time() - work_start
event_occupancy['unpin'] += elapsed
total_occupancy += elapsed
if exec_socket in socks and socks[exec_socket] == zmq.POLLIN:
work_start = time.time()
exec_function(exec_socket, client, user_library, cache,
function_cache)
user_library.close()
utils.push_status(schedulers, pusher_cache, status)
elapsed = time.time() - work_start
event_occupancy['func_exec'] += elapsed
total_occupancy += elapsed
if dag_queue_socket in socks and socks[dag_queue_socket] == zmq.POLLIN:
work_start = time.time()
schedule = DagSchedule()
schedule.ParseFromString(dag_queue_socket.recv())
fname = schedule.target_function
logging.info('Received a schedule for DAG %s (%s), function %s.' %
(schedule.dag.name, schedule.id, fname))
if fname not in queue:
queue[fname] = {}
queue[fname][schedule.id] = schedule
if (schedule.id, fname) not in receive_times:
receive_times[(schedule.id, fname)] = time.time()
# In case we receive the trigger before we receive the schedule, we
# can trigger from this operation as well.
trkey = (schedule.id, fname)
if (trkey in received_triggers and
(len(received_triggers[trkey]) == len(schedule.triggers))):
exec_dag_function(pusher_cache, client,
received_triggers[trkey],
function_cache[fname], schedule,
user_library, dag_runtimes, cache)
user_library.close()
del received_triggers[trkey]
del queue[fname][schedule.id]
fend = time.time()
fstart = receive_times[(schedule.id, fname)]
runtimes[fname].append(fend - fstart)
exec_counts[fname] += 1
elapsed = time.time() - work_start
event_occupancy['dag_queue'] += elapsed
total_occupancy += elapsed
if dag_exec_socket in socks and socks[dag_exec_socket] == zmq.POLLIN:
work_start = time.time()
trigger = DagTrigger()
trigger.ParseFromString(dag_exec_socket.recv())
fname = trigger.target_function
logging.info('Received a trigger for schedule %s, function %s.' %
(trigger.id, fname))
key = (trigger.id, fname)
if key not in received_triggers:
received_triggers[key] = {}
if (trigger.id, fname) not in receive_times:
receive_times[(trigger.id, fname)] = time.time()
received_triggers[key][trigger.source] = trigger
if fname in queue and trigger.id in queue[fname]:
schedule = queue[fname][trigger.id]
if len(received_triggers[key]) == len(schedule.triggers):
exec_dag_function(pusher_cache, client,
received_triggers[key],
function_cache[fname], schedule,
user_library, dag_runtimes, cache)
user_library.close()
del received_triggers[key]
del queue[fname][trigger.id]
fend = time.time()
fstart = receive_times[(trigger.id, fname)]
runtimes[fname].append(fend - fstart)
exec_counts[fname] += 1
elapsed = time.time() - work_start
event_occupancy['dag_exec'] += elapsed
total_occupancy += elapsed
if self_depart_socket in socks and socks[self_depart_socket] == \
zmq.POLLIN:
# This message does not matter.
self_depart_socket.recv()
logging.info('Preparing to depart. No longer accepting requests ' +
'and clearing all queues.')
status.ClearField('functions')
status.running = False
utils.push_status(schedulers, pusher_cache, status)
departing = True
# periodically report function occupancy
report_end = time.time()
if report_end - report_start > REPORT_THRESH:
cache.clear()
utilization = total_occupancy / (report_end - report_start)
status.utilization = utilization
# Periodically report my status to schedulers with the utilization
# set.
utils.push_status(schedulers, pusher_cache, status)
logging.info('Total thread occupancy: %.6f' % (utilization))
for event in event_occupancy:
occ = event_occupancy[event] / (report_end - report_start)
logging.info('\tEvent %s occupancy: %.6f' % (event, occ))
event_occupancy[event] = 0.0
stats = ExecutorStatistics()
for fname in runtimes:
if exec_counts[fname] > 0:
fstats = stats.functions.add()
fstats.name = fname
fstats.call_count = exec_counts[fname]
fstats.runtime.extend(runtimes[fname])
runtimes[fname].clear()
exec_counts[fname] = 0
for dname in dag_runtimes:
dstats = stats.dags.add()
dstats.name = dname
dstats.runtimes.extend(dag_runtimes[dname])
dag_runtimes[dname].clear()
# If we are running in cluster mode, mgmt_ip will be set, and we
# will report our status and statistics to it. Otherwise, we will
# write to the local conf file
if mgmt_ip:
sckt = pusher_cache.get(
sutils.get_statistics_report_address(mgmt_ip))
sckt.send(stats.SerializeToString())
sckt = pusher_cache.get(utils.get_util_report_address(mgmt_ip))
sckt.send(status.SerializeToString())
else:
logging.info(stats)
status.ClearField('utilization')
report_start = time.time()
total_occupancy = 0.0
# Periodically clear any old functions we have cached that we are
# no longer accepting requests for.
del_list = []
for fname in queue:
if len(queue[fname]) == 0 and fname not in status.functions:
del_list.append(fname)
del function_cache[fname]
del runtimes[fname]
del exec_counts[fname]
for fname in del_list:
del queue[fname]
# If we are departing and have cleared our queues, let the
# management server know, and exit the process.
if departing and len(queue) == 0:
sckt = pusher_cache.get(utils.get_depart_done_addr(mgmt_ip))
sckt.send_string(ip)
# We specifically pass 1 as the exit code when ending our
# process so that the wrapper script does not restart us.
os._exit(1)