#!/usr/bin/env python3
import zmq
import time
import pickle
import logging
logger = logging.getLogger(__name__)
class CommandClient(object):
""" CommandClient
"""
def __init__(self, ip_address, port_range):
"""
Parameters
----------
ip_address: str
IP address of the client (where Parsl runs)
port_range: tuple(int, int)
Port range for the comms between client and interchange
"""
self.context = zmq.Context()
self.zmq_socket = self.context.socket(zmq.DEALER)
self.zmq_socket.set_hwm(0)
self.port = self.zmq_socket.bind_to_random_port("tcp://{}".format(ip_address),
min_port=port_range[0],
max_port=port_range[1])
def run(self, message):
""" This function needs to be fast at the same time aware of the possibility of
ZMQ pipes overflowing.
The timeout increases slowly if contention is detected on ZMQ pipes.
We could set copy=False and get slightly better latency but this results
in ZMQ sockets reaching a broken state once there are ~10k tasks in flight.
This issue can be magnified if each the serialized buffer itself is larger.
"""
self.zmq_socket.send_pyobj(message, copy=True)
reply = self.zmq_socket.recv_pyobj()
return reply
def close(self):
self.zmq_socket.close()
self.context.term()
class TasksOutgoing(object):
""" Outgoing task queue from the executor to the Interchange
"""
def __init__(self, ip_address, port_range):
"""
Parameters
----------
ip_address: str
IP address of the client (where Parsl runs)
port_range: tuple(int, int)
Port range for the comms between client and interchange
"""
self.context = zmq.Context()
self.zmq_socket = self.context.socket(zmq.DEALER)
self.zmq_socket.set_hwm(0)
self.port = self.zmq_socket.bind_to_random_port("tcp://{}".format(ip_address),
min_port=port_range[0],
max_port=port_range[1])
self.poller = zmq.Poller()
self.poller.register(self.zmq_socket, zmq.POLLOUT)
def put(self, message, max_timeout=1000):
""" This function needs to be fast at the same time aware of the possibility of
ZMQ pipes overflowing.
The timeout increases slowly if contention is detected on ZMQ pipes.
We could set copy=False and get slightly better latency but this results
in ZMQ sockets reaching a broken state once there are ~10k tasks in flight.
This issue can be magnified if each the serialized buffer itself is larger.
Parameters
----------
message : py object
Python object to send
max_timeout : int
Max timeout in milliseconds that we will wait for before raising an exception
Raises
------
zmq.EAGAIN if the send failed.
"""
timeout_ms = 0
current_wait = 0
logger.info("Putting task into queue")
while current_wait < max_timeout:
socks = dict(self.poller.poll(timeout=timeout_ms))
if self.zmq_socket in socks and socks[self.zmq_socket] == zmq.POLLOUT:
# The copy option adds latency but reduces the risk of ZMQ overflow
self.zmq_socket.send_pyobj(message, copy=True)
return
else:
timeout_ms += 1
logger.debug("Not sending due to full zmq pipe, timeout: {} ms".format(timeout_ms))
current_wait += timeout_ms
# Send has failed.
logger.debug("Remote side has been unresponsive for {}".format(current_wait))
raise zmq.error.Again
def close(self):
self.zmq_socket.close()
self.context.term()
class ResultsIncoming(object):
""" Incoming results queue from the Interchange to the executor
"""
def __init__(self, ip_address, port_range):
"""
Parameters
----------
ip_address: str
IP address of the client (where Parsl runs)
port_range: tuple(int, int)
Port range for the comms between client and interchange
"""
self.context = zmq.Context()
self.results_receiver = self.context.socket(zmq.DEALER)
self.results_receiver.set_hwm(0)
self.port = self.results_receiver.bind_to_random_port("tcp://{}".format(ip_address),
min_port=port_range[0],
max_port=port_range[1])
def get(self, block=True, timeout=None):
return self.results_receiver.recv_multipart()
def request_close(self):
status = self.results_receiver.send(pickle.dumps(None))
time.sleep(0.1)
return status
def close(self):
self.results_receiver.close()
self.context.term()
