def compute_func(in_streams, out_streams):
merged_stream = Stream('merge of two ntp server offsets')
averaged_stream = Stream('sliding window average of offsets')
blend(func=lambda x: x,
in_streams=in_streams,
out_stream=merged_stream)
map_window(func=average_of_list,
in_stream=merged_stream,
out_stream=averaged_stream,
window_size=2,
step_size=1)
stream_to_file(in_stream=averaged_stream, filename='average.dat')
def run_Paxos():
proposers = [
blend(func=proposer_behavior,
in_streams=daemons_proposers_out_streams,
out_stream=proposer_out_streams[id],
state=(id, 0, random_number(), {}),
name='proposer_' + str(id)) for id in range(num_proposers)
]
acceptors = [
map_element(func=acceptor_behavior,
in_stream=daemons_acceptors_out_streams[id],
out_stream=acceptor_out_streams[id],
state=(id, -1, -1, -1, -1),
name='acceptor_' + str(id)) for id in range(num_acceptors)
]
time_step_agent = blend(func=time_step_behavior,
in_streams=in_streams_for_time_step,
out_stream=time_step_stream,
state=({}, None),
name='time step agent')
daemons_acceptors = [
blend(func=delete,
in_streams=proposer_out_streams,
out_stream=daemons_acceptors_out_streams[id],
name='daemons_acceptors_' + str(id))
for id in range(num_acceptors)
]
daemons_proposers = [
blend(func=delete,
in_streams=acceptor_out_streams,
out_stream=daemons_proposers_out_streams[id],
name='daemons_proposers_' + str(id))
for id in range(num_proposers)
]
#STEP 4. START COMPUTATION
# Get the scheduler and execute a step.
scheduler = Stream.scheduler
# Start the computation by putting a value into the
# time step stream
time_step_stream.append(('time_step', 1)) # Start the scheduler.
scheduler.step()
def compute(in_streams, out_streams):
merged_stream = Stream('merge of two ntp server offsets')
blend(func=identity, in_streams=in_streams, out_stream=merged_stream)
stream_to_file(in_stream=merged_stream, filename='offsets.dat')
def g(in_streams, out_stream, **kwargs):
return blend(func, in_streams, out_stream, **kwargs)