Python's message queue package SnakeMQ is used
Using message queues has many advantages in data communication. SnakeMQ is an open source cross-platform MQ library implemented in Python. Well, a preliminary study on the use of Python's message queue package SnakeMQ, here we go:
1. Official introduction about snakemq
SnakeMQ’s GitHub project page: https://github.com/dsiroky/snakemq1. Pure python implementation, cross-platform
2. Automatic restart Connection
3. Reliable sending--configurable message mode and message timeout mode
4. Persistent/temporary two queues
5. Support asynchronous--poll ()
6.symmetrical -- A single TCP connection can be used for duplex communication
7.Multiple database support--SQLite, MongoDB...
8.brokerless - similar The implementation principle of ZeroMQ
9. Extension modules: RPC, bandwidth throttling
The above are all official words. You need to verify them by yourself. I encapsulated them and it feels cute.
2. Description of several major issues
1. Supports automatic reconnection. You don’t need to write the heartbeat logic yourself. Just focus on sending and receiving
2. Support data persistence. If persistence is started, data will be sent automatically after reconnection.
3. Snakemq implements data reception by providing callbacks. You only need to write a receiving method and add it to the callback list.
4. When sending data, all data sent here are bytes type (binary), so conversion is required. What I test in the program are all text strings. I use str.encode('utf-8') to convert them into bytes, and then convert them back when receiving.
5. Terminology explanation, Connector: TcpClient similar to socket, Listener: TcpServer similar to socket. Each connector or listener has an ident identification. When sending and receiving data, you will know whose data it is. .
6. When using sqlite persistence, you need to modify the source code, sqlite3.connect(filename, check_same_thread = False), to solve the problem of multi-threaded access to sqlite. (Will there be a deadlock?)
7. When starting persistence, if the connection is reconnected, it will be sent automatically to ensure reliability.
8. For the purpose of encapsulation, after receiving the data, I send it out through callback.
3. Code
Explanation The custom log module is used in the code
from common import nxlogger import snakemqlogger as logger
can be replaced by logging.
Callback class (callbacks.py):
# -*- coding:utf-8 -*- '''synchronized callback''' class Callback(object): def __init__(self): self.callbacks = [] def add(self, func): self.callbacks.append(func) def remove(self, func): self.callbacks.remove(func) def __call__(self, *args, **kwargs): for callback in self.callbacks: callback(*args, **kwargs)
Connector class (snakemqConnector.py):
# -*- coding:utf-8 -*- import threading import snakemq import snakemq.link import snakemq.packeter import snakemq.messaging import snakemq.message from snakemq.storage.sqlite import SqliteQueuesStorage from snakemq.message import FLAG_PERSISTENT from common.callbacks import Callback from common import nxlogger import snakemqlogger as logger class SnakemqConnector(threading.Thread): def __init__(self, snakemqident = None, remoteIp = "localhost", remotePort = 9090, persistent = False): super(SnakemqConnector,self).__init__() self.messaging = None self.link = None self.snakemqident = snakemqident self.pktr = None self.remoteIp = remoteIp self.remotePort = remotePort self.persistent = persistent self.on_recv = Callback() self._initConnector() def run(self): logger.info("connector start...") if self.link != None: self.link.loop() logger.info("connector end...") def terminate(self): logger.info("connetor terminating...") if self.link != None: self.link.stop() self.link.cleanup() logger.info("connetor terminated") def on_recv_message(self, conn, ident, message): try: self.on_recv(ident, message.data.decode('utf-8'))#dispatch received data except Exception as e: logger.error("connector recv:{0}".format(e)) print(e) '''send message to dest host named destIdent''' def sendMsg(self, destIdent, byteseq): msg = None if self.persistent: msg = snakemq.message.Message(byteseq, ttl=60, flags=FLAG_PERSISTENT) else: msg = snakemq.message.Message(byteseq, ttl=60) if self.messaging == None: logger.error("connector:messaging is not initialized, send message failed") return self.messaging.send_message(destIdent, msg) ''' ''' def _initConnector(self): try: self.link = snakemq.link.Link() self.link.add_connector((self.remoteIp, self.remotePort)) self.pktr = snakemq.packeter.Packeter(self.link) if self.persistent: storage = SqliteQueuesStorage("SnakemqStorage.db") self.messaging = snakemq.messaging.Messaging(self.snakemqident, "", self.pktr, storage) else: self.messaging = snakemq.messaging.Messaging(self.snakemqident, "", self.pktr) self.messaging.on_message_recv.add(self.on_recv_message) except Exception as e: logger.error("connector:{0}".format(e)) finally: logger.info("connector[{0}] loop ended...".format(self.snakemqident))
Listener class (snakemqListener.py):
# -*- coding:utf-8 -*- import threading import snakemq import snakemq.link import snakemq.packeter import snakemq.messaging import snakemq.message from common import nxlogger import snakemqlogger as logger from common.callbacks import Callback class SnakemqListener(threading.Thread): def __init__(self, snakemqident = None, ip = "localhost", port = 9090, persistent = False): super(SnakemqListener,self).__init__() self.messaging = None self.link = None self.pktr = None self.snakemqident = snakemqident self.ip = ip; self.port = port self.connectors = {} self.on_recv = Callback() self.persistent = persistent self._initlistener() ''' thread run ''' def run(self): logger.info("listener start...") if self.link != None: self.link.loop() logger.info("listener end...") ''' terminate snakemq listener thread ''' def terminate(self): logger.info("listener terminating...") if self.link != None: self.link.stop() self.link.cleanup() logger.info("listener terminated") ''' receive message from host named ident ''' def on_recv_message(self, conn, ident, message): try: self.on_recv(ident, message.data.decode('utf-8'))#dispatch received data self.sendMsg('bob','hello,{0}'.format(ident).encode('utf-8')) except Exception as e: logger.error("listener recv:{0}".format(e)) print(e) def on_drop_message(self, ident, message): print("message dropped", ident, message) logger.debug("listener:message dropped,ident:{0},message:{1}".format(ident, message)) '''client connect''' def on_connect(self, ident): logger.debug("listener:{0} connected".format(ident)) self.connectors[ident] = ident self.sendMsg(ident, "hello".encode('utf-8')) '''client disconnect''' def on_disconnect(self, ident): logger.debug("listener:{0} disconnected".format(ident)) if ident in self.connectors: self.connectors.pop(ident) ''' listen start loop ''' def _initlistener(self): try: self.link = snakemq.link.Link() self.link.add_listener((self.ip, self.port)) self.pktr = snakemq.packeter.Packeter(self.link) self.pktr.on_connect.add(self.on_connect) self.pktr.on_disconnect.add(self.on_disconnect) if self.persistent: storage = SqliteQueuesStorage("SnakemqStorage.db") self.messaging = snakemq.messaging.Messaging(self.snakemqident, "", self.pktr, storage) else: self.messaging = snakemq.messaging.Messaging(self.snakemqident, "", self.pktr) self.messaging.on_message_recv.add(self.on_recv_message) self.messaging.on_message_drop.add(self.on_drop_message) except Exception as e: logger.error("listener:{0}".format(e)) finally: logger.info("listener:loop ended...") '''send message to dest host named destIdent''' def sendMsg(self, destIdent, byteseq): msg = None if self.persistent: msg = snakemq.message.Message(byteseq, ttl=60, flags=FLAG_PERSISTENT) else: msg = snakemq.message.Message(byteseq, ttl=60) if self.messaging == None: logger.error("listener:messaging is not initialized, send message failed") return self.messaging.send_message(destIdent, msg)
Test code connector (testSnakeConnector.py):
Read a local 1M file, then send it to the listener, and then the listener sends back a hello message.
from netComm.snakemq import snakemqConnector import time import sys import os def received(ident, data): print(data) if __name__ == "__main__": bob = snakemqConnector.SnakemqConnector('bob',"10.16.5.45",4002,True) bob.on_recv.add(received) bob.start() try: with open("testfile.txt",encoding='utf-8') as f: txt = f.read() for i in range(100): bob.sendMsg("niess",txt.encode('utf-8')) time.sleep(0.1) except Exception as e: print(e) time.sleep(5) bob.terminate() 测试代码listener(testSnakeListener.py): from netComm.snakemq import snakemqListener import time def received(ident, data): filename = "log/recFile{0}.txt".format(time.strftime('%S',time.localtime())) file = open(filename,'w') file.writelines(data) file.close() if __name__ == "__main__": niess = snakemqListener.SnakemqListener("niess","10.16.5.45",4002) niess.on_recv.add(received) niess.start() print("niess start...") time.sleep(60) niess.terminate() print("niess end...")
For more articles related to the use of Python’s message queue package SnakeMQ, please pay attention to the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex
