How to use and update same variable across multiple modules in python independently?

Question

I have 3 files and I want to do something like below

[1] conf.py

var = 10  # Intialized with 10 (start)

[2] file_1.py

import conf
print(conf.var)   # Prints 10
conf.var = 1000   # Updated to 1000

[3] file_2.py

import conf
print(conf.var)   # Prints 1000
conf.var = 9999   # Updated to 9999

I want something like this. Assume that the files, file_1 and file_2 will be running and will stay in memory unless pressed CTRL+C. How can I change var in other 2 files and persist the value for it? Final value for var should be 9999 if we were to use it in file_3 like other 2 files. [It should print 9999] and so on.

Execution order file_1.py -> file_2.py.

Help me with identifying some way to do it or some module/package that can handle this.

Thanks! :)

Answer 1

I worked on this problem. I will suggest to use shared memory cache providers like Redis or memcached. Both start a server instance that we need to connect and use it like a key-value store. Note than values should be of type str or bytes.

Memcached

Install memcached

sudo apt install memcached

Start Server with default settings

You need to start the server before using it or you can set it as a service in linux

sudo service memcached start

Install python memcached client

pip install pymemcache

Sample Code

from pymemcache.client import base

client = base.Client(('localhost', 11211))
client.set('some_key', 'stored value')

client.get('some_key')  # Returns "stored value"

Redis

The process is exact same for redis

Install redis

sudo apt install redis

Start Server with default settings

You need to start the server before using it or you can set it as a service in linux

sudo service redis start

Install python redis client

pip install redis

Sample Code

import redis

client = redis.Redis()  # Default settings/credentials
client.set('some_key', 'stored value')

client.get('some_key')  # Returns "stored value"

Usage

• Redis gives more safety and fault tolerance.
• Both has some memory limits but is configurable. If your data is more in size, use files or db suitable to your problem.
• Both are easy to configure