So I have a single twisted socket server that serves clients and eventually I'll need to add more servers. The problem is that connections to the server are unique and unable to be shared among multiple server instances.
This makes a problem if the servers are behind a load balancer, or if multiple users from a single chat are across multiple server instances, because a message to a chat won't successfully send to everyone.
How would I resolve this?
It may be a difficult task as balancing load can be improved according to the underlying protocol (like http for web servers).
Are you trying to design a load balancing system for basically any socket based application ? What I mean is that it is one thing to dispatch messages between multiples servers, ensuring correct synchronization, it is another thing to build a dynamic self-balancing system for any communication protocol.
To build your loadbalancer, you can use a "TCP proxy" like HAProxy (http://www.haproxy.org/)
To handle the communication between your application server instances (behind the load balancing server), you can use messaging like zeromq (http://zeromq.org/) or rabbitmq (http://www.rabbitmq.com/). You'll find some common architecture pattern there.
There are python libs for both zeromq and rabbitmq so the implementation within your twisted-based server is not too hard.
Related
Our use case involves one class that has to remotely initialize several instances of another class (each on a different IoT device) and has to get certain results from each of these instances. At most, we would need to receive 30 messages a second from each remote client, with each message being relatively small. What type of architecture would you all recommend to solve this?
We were thinking that each class that is located on the IoT device will serve as a server and the class that receives the results would be the client, so should we create a server, each with its own channel, for each IoT device? Or is it possible to have each IoT device use the same service on the same server (meaning there would be multiple instances of the same service on the same server but on different devices)?
The question would benefit from additional detail to help guide an answer.
gRPC (and its use of HTTP/2) are 'heavier' protocols than e.g. MQTT. MQTT is more commonly used with IoT devices as it has a smaller footprint. REST/HTTP (even though heavier than MQTT) may also have benefits for you over gRPC/HTTP2.
If you're committed to gRPC, I wonder whether it would not be better to invert your proposed architecture and have the IoT device be the client? This seems to provide additional security in that the clients initiate communications with your servers rather than expose services. Either way (and if you decide to use MQTT), hopefully you'll be using mTLS. I assume (!?) client implementations are smaller than server implementations.
Regardless of the orientation, clients and servers can (independently) stream messages. The IoT devices (client or server) could stream the 30 messages/second. The servers could stream management|control messages.
I've no experience managing fleets of IoT devices but, remote management|monitoring and over-the-air upgrades|patching are, I assume, important requirements for you. gRPC does not limit any of these capabilities but debugging can be more challenging. With e.g. REST/HTTP, it is trivial to curl endpoints but with gRPC (even with the excellent grpcurl) you'll be constrained to the services implemented. Yes, you can't call a non-existent REST API either but I find remote-debugging gRPC services more challenging than REST.
I am implementing a small distributed system (in Python) with nodes behind firewalls. What is the easiest way to pass messages between the nodes under the following restrictions:
I don't want to open any ports or punch holes in the firewall
Also, I don't want to export/forward any internal ports outside my network
Time delay less than, say 5 minutes, is acceptable, but closer to real time would be nice, if possible.
1+2 → I need to use a third party, accessible by all my nodes. From this follows, that I probably also want to use encryption
Solutions considered:
Email - by setting up separate or a shared free email accounts (e.g. Gmail) which each client connects to using IMAP/SMTP
Google docs - using a shared online spreadsheet (e.g. Google docs) and some python library for accessing/changing cells using a polling mechanism
XMPP using connections to a third party server
IRC
Renting a cheap 5$ VPS and setting up a Zero-MQ publish-subscribe node (or any other protocol) forwarded over SSH and having all nodes connect to it
Are there any other public (free) accessible message queues available (or platforms that can be misused as a message queue)?
I am aware of the solution of setting up my own message broker (RabbitMQ, Mosquito) etc and make it accessible to my nodes somehow (ssh-forwardning to a third host etc). But my questions is primarily about any solution that doesn't require me to do that, i.e. any solutions that utilizes already available/accessible third party infrastructure. (i.e. are there any public message brokers I can use?)
How about Mosquitto: message broker that implements the MQ Telemetry Transport protocol versions 3.1 and 3.1.1. MQTT provides a lightweight method of carrying out messaging using a publish/subscribe model. This makes it suitable for "machine to machine" messaging. It supports encryption. Time to setup: approximatively 15 mins you should be up and running. Since it is a message broker, you can write your own code to ensure you can communicate with 3rd party solutions. Also, it achieves soft real-time, but depending on your setup you can achieve hard real-time. After you look into Mosquitto have a look at Paho, which is a port of Mosquito to Eclipse Foundation.
Paho also provides a Python Client, which offers support for both MQTT v3.1 and v3.1.1 on Python 2.7 or 3.x. It also provides some helper functions to make publishing one off messages to an MQTT server very straightforward. Plenty of documentation and examples to get you up and running.
I would recommend RabbitMQ or Redis (RabbitMQ preferred because it is a very mature technology and insanely reliable). ZMQ is an option if you want a single hop messaging system instead of a brokered messaging system such as RabbitMQ but ZMQ is harder to use than RabbitMQ. Depending on how you want to utilize the message passing (is it a task dispatch in which case you can use Celery or if you need a slightly more low-level access in which case use Kombu with librabbitmq transport )
Found https://www.cloudamqp.com/ which offers a free plan with a cloud based installation of RabbitMQ. I will try that and see if it fulfill my needs.
The scenario is
I have multiple local computers running a python application. These are on separate networks waiting for data to be sent to them from a web server. These computers are on networks without a static IP and generally behind firewall and proxy.
On the other hand I have web server which gets updates from the user through a form and send the update to the correct local computer.
Question
What options do I have to enable this. Currently I am sending csv files over ftp to achieve this but this is not real time.
The application is built on python and using django for the web part.
Appreciate your help
Use a REST API. Then you can post information to your Django app over HTTP, using an authentication key if necessary.
http://www.django-rest-framework.org/ should help you get started quickly
Sounds like you need a message queue.
You would run a separate broker server which is sent tasks by your web app. This could be on the same machine. On your two local machines you would run queue workers which connect to the broker to receive tasks (so no inbound connection required), then notify the broker in real time when they are complete.
Examples are RabbitMQ and Oracle Tuxedo. What you choose will depend on your platform & software.
Status Quo:
I have two python apps (frontend-server and data-collector, a database is 'between' them).
Currently using redis as db and its publish/subscribe protocol to notify the frontend when new data is available.
But may I want to use a different database (and don't want to keep redis on the system just for the pub/sub).
Are there any simple alternatives to notify my frontend if the data-collector has transacted new data to the database (without using an external message queue like beanstalkd or redis)?
ZeroMQ is a good option. It has good Python bindings, and it makes communicating between processes on the same machine and processes on different machines look almost identical.
Start by reading the guide: http://zguide.zeromq.org/page:all
As I mentioned in my comment, if you want something that is going across a network then other than setting up a web service (flask app?), or writing your own INET socket server there is nothing built in to the operating system to communicate between machines. Beanstalk has a very simple API in Python and I've used it for this kind of thing very successfully.
try:
beanstalk = beanstalkc.Connection(host="my.host.com")
beanstalk.watch("update_queue")
except:
print "Error connecting to beanstalk"
while True:
job = beanstalk.reserve()
do_something_with_job(job)
If you are only going to be working on the same machine, then read up on linux IPC. A socket connection between processes is very fast and has practically zero overhead. They can also be a part of an asynchronous program when you take advantage of epoll call backs.
I am developing a testbed for cloud computing environment. I want to establish multiple client connection to a server. What I want is that, server first of all send a data to all the clients specifying sending_interval and then all the clients will keep on sending their data with a time gap of that time_interval (as specified by the server). Please help me out, how can I do the same using python socket program. (i.e. I want multiple client to single server connectivity and also client sending data with the time gap specified by server). Will be great-full if anyone can help me. Thanks in advance.
This problem is easily solved by the ZeroMQ socket library. It is production stable. It allows you to define publisher-subscriber relationships, where a publishing process will publish data on a port regardless of how many (0 to infinite) listening processes there are. They call this the PUB-SUB model; it's in their docs (link below).
It sounds like you want to set up a bunch of clients that are all publishers. They can subscribe to a controlling channel, which which will send updates to their configuration (how often to write). They also act as publishers, pushing out their own data at an interval specified by default/config channel/socket.
Then, you have one or more listening processes that listen to all the clients' published messages. Perhaps you could even have two listening processes, one for backup or DR, or whatever.
We're using ZeroMQ and loving the simplicity it gives; there's no connection errors because the publisher doesn't care if anyone is listening, and the subscriber can start before the publisher and if there's nothing there to listen to, it can just loop around and wait until there is.
Bindings are available in ALL languages (it's freaky). The Python binding isn't pure-python, it does require a C compiler, but is frighteningly fast, and the pub/sub example is a cut/paste, 'golly, it works!' experience.
Link: http://zeromq.org
There are MANY other methods available with this library, including message queues, etc. They have relatively complete documentation, too.
Multi-Client and Single server Socket programming can be achieved by Multithreading in Socket Programming. I have implemented both the method:
Single Client and Single Server
Multiclient and Single Server
In my GitHub Repo Link: https://github.com/shauryauppal/Socket-Programming-Python
What is Multi-threading Socket Programming?
Multithreading is a process of executing multiple threads simultaneously in a single process.
To understand well you can visit Link: https://www.geeksforgeeks.org/socket-programming-multi-threading-python/, written by me.