Develop Reference

Update single database value on a website with many users

For this question, I'm particularly struggling with how to structure this:
User accesses website
User clicks button
Value x in database increments
My issue is that multiple people could potentially be on the website at the same time and click the button - I want to make sure each user is able to click the button, and update the value and read the incremented value too, but I don't know how to circumvent any synchronisation/concurrency issues.
I'm using flask to run my website backend, and I'm thinking of using MongoDB or Redis to store my single value that needs to be updated.
Please comment if there is any lack of clarity in my question, but this is a problem I've really been struggling with how to solve.
Thanks :)

redis, I think you can use redis hincrby command, or create a distributed lock to make sure there is only one writer at the same time and only the lock holding writer can make the update in your flask framework. Make sure you release the lock after certain period of time or after the writer done using the lock.
mysql, you can start a transaction, and make the update and commit the change to make sure the data is right

To solve this problem I would suggest you follow a micro service architecture.
A service called worker would handle the flask route that's called when the user clicks on the link/button on the website. It would generate a message to be sent to another service called queue manager that maintains a queue of increment/decrement messages from the worker service.
There can be multiple worker service instances running concurrently but the queue manager is a singleton service that takes the messages from each service and adds them to the queue. If the queue manager is busy the worker service will either timeout and retry or return a failure message to the user. If the queue is full a response is sent back to the worker to retry n number of times, and you can count down that n.
A third service called storage manager is run every time the queue is not empty, this service sends the messages to the storage solution (whatever mongo, redis, good ol' sql) and it will ensure the increment/decrement messages are handled in the order they were received in the queue. You could also include a time stamp from the worker service in the message if you wanted to use that to sort the queue.
Generally whatever hosting environment for flask will use gunicorn as the production web server and support multiple concurrent worker instances to handle the http requests, and this would naturally be your worker service.
How you build and coordinate the queue manager and storage manager is down to implementation preference, for instance you could use something like Google Cloud pub/sub system to send messages between different deployed services but that's just off the top of my head. There's a load of different ways to do it, and you're in the best position to decide that.
Without knowing more details about what you're trying to achieve and what's the requirements for concurrent traffic I can't go into greater detail, but that's roughly how I've approached this type of problem in the past. If you need to handle more concurrent users at the website, you can pick a hosting solution with more concurrent workers. If you need the queue to be longer, you can pick a host with more memory, or else write the queue to an intermediate storage. This will slow it down but will make recovering from a crash easier.
You also need to consider handling when messages fail between different services, how to recover from a service crashing or the queue filling up.
EDIT: Been thinking about this over the weekend and a much simpler solution is to just create a new record in a table directly from the flask route that handles user clicks. Then to get your total you just get a count from this table. Your bottlenecks are going to be how many concurrent workers your flask hosting environment supports and how many concurrent connections your storage supports. Both of these can be solved by throwing more resources at them.

Best Way to Handle user triggered task (like import data) in Django

I need your opinion on a challenge that I'm facing. I'm building a website that uses Django as a backend, PostgreSQL as my DB, GraphQL as my API layer and React as my frontend framework. Website is hosted on Heroku. I wrote a python script that logs me in to my gmail account and parse few emails, based on pre-defined conditions, and store the parsed data into Google Sheet. Now, I want the script to be part of my website in which user will specify what exactly need to be parsed (i.e. filters) and then display the parsed data in a table to review accuracy of the parsing task.
The part that I need some help with is how to architect such workflow. Below are few ideas that I managed to come up with after some googling:
generate a graphQL mutation that stores a 'task' into a task model. Once a new task entry is stored, a Django Signal will trigger the script. Not sure yet if Signal can run custom python functions, but from what i read so far, it seems doable.
Use Celery to run this task asynchronously. But i'm not sure if asynchronous tasks is what i'm after here as I need this task to run immediately after the user trigger the feature from the frontend. But i'm might be wrong here. I'm also not sure if I need Redis to store the task details or I can do that on PostgreSQL.
What is the best practice in implementing this feature? The task can be anything, not necessarily parsing emails; it can also be importing data from excel. Any task that is user generated rather than scheduled or repeated task.
I'm sorry in advance if this question seems trivial to some of you. I'm not a professional developer and the above project is a way for me to sharpen my technical skills and learn new techniques.
Looking forward to learn from your experiences.

You can dissect your problem into the following steps:
User specifies task parameters
System executes task
System displays result to the User
You can either do all of these:
Sequentially and synchronously in one swoop; or
Step by step asynchronously.
Synchronously
You can run your script when generating a response, but it will come with the following downsides:
The process in the server processing your request will block until the script is finished. This may or may not affect the processing of other requests by that same server (this will depend on the number of simultaneous requests being processed, workload of the script, etc.)
The client (e.g. your browser) and even the server might time out if the script takes too long. You can fix this to some extent by configuring your server appropriately.
The beauty of this approach however is it's simplicity. For you to do this, you can just pass the parameters through the request, server parses and does the script, then returns you the result.
No setting up of a message queue, task scheduler, or whatever needed.
Asynchronously
Ideally though, for long-running tasks, it is best to have this executed outside of the usual request-response loop for the following advantages:
The server responding to the requests can actually serve other requests.
Some scripts can take a while, some you don't even know if it's going to finish
Script is no longer dependent on the reliability of the network (imagine running an expensive task, then your internet connection skips or is just plain intermittent; you won't be able to do anything)
The downside of this is now you have to set more things up, which increases the project's complexity and points of failure.
Producer-Consumer
Whatever you choose, it's usually best to follow the producer-consumer pattern:
Producer creates tasks and puts them in a queue
Consumer takes a task from the queue and executes it
The producer is basically you, the user. You specify the task and the parameters involved in that task.
This queue could be any datastore: in-memory datastore like Redis; a messaging queue like RabbitMQ; or an relational database management system like PostgreSQL.
The consumer is your script executing these tasks. There are multiple ways of running the consumer/script: via Celery like you mentioned which runs multiple workers to execute the tasks passed through the queue; via a simple time-based job scheduler like crontab; or even you manually triggering the script
The question is actually not trivial, as the solution depends on what task you are actually trying to do. It is best to evaluate the constraints, parameters, and actual tasks to decide which approach you will choose.
But just to give you a more relevant guideline:
Just keep it simple, unless you have a compelling reason to do so (e.g. server is being bogged down, or internet connection is not reliable in practice), there's really no reason to be fancy.
The more blocking the task is, or the longer the task takes or the more dependent it is to third party APIs via the network, the more it makes sense to push this to a background process add reliability and resiliency.
In your email import script, I'll most likely push that to the background:
Have a page where you can add a task to the database
In the task details page, display the task details, and the result below if it exists or "Processing..." otherwise
Have a script that executes tasks (import emails from gmail given the task parameters) and save the results to the database
Schedule this script to run every few minutes via crontab
Yes the above has side effects, like crontab running the script in multiple times at the same time and such, but I won't go into detail without knowing more about the specifics of the task.

Webhook Endpoints - Handling Many Concurrent Requests

This isn't specifically related to programming, more-so infrastructure, but of all the exchange sites StackOverflow seems to be most knowledgeable in departments of RESTful APIs.
I have a single endpoint configured for handling events that could take in up to 1k events within a 3 minute window. I am noticing a lot of events "missed", but I'm not sure that I'm willing to blame over-utilization right away without fully understanding.
The listening endpoint is /users/events?user=2345345 where 2345345 is the user id. From here we perform necessary actions on that particular user, but what if during this the next user, 2895467 performs an action which results in a new event being sent to /users/events?user=2895467 before the first could be processed. What happens?
I intend to alleviate the concern by using celery to signal tasks which would greatly reduce this, but is it fair to assume that events could be missed while this single endpoint remains synchronous?

Real-life behavior depends on approach used for "deployment".
For example if you are using uwsgi with single unthreaded worker behind nginx, then requests will be processed "sequentially": if second request arrives before first is processed, then second will be "queued" (added to backlog).
How long it can be queued and how many requests may be in queue
depends on the configuration of nginx (listen backlog), configuration of uwsgi (concurrency, listen backlog) and even on configuration
of OS kernel (search for net.core.somaxconn,
net.core.netdev_max_backlog). When queue becomes "full" then new
"concurrent" connections will be dropped instead of being added to queue.

Pyro4 kill empty nameserver

I am using Pyro4 in a distributed system. My problem is that after two networks connect there can be many nameserver. It is ok for me but if any of them is empty (has no objects registered) my network can fail .
This is because I am implementing chord and I need to know if there is any chord node running already (if the empty nameserver responds then two chord rings will be created.
Is there any way to kill a nameserver if it is empty?
My code is in python3. Thanks

Pyro's name server assumes it is running all the time: you never know if there will be someone a bit later that wants to register a new name. If you really need it to auto shutdown, you'll have to either wrap the default name server in some custom code, or use some form of watchdog process that kills the name server if it detects that it has 0 entries.
You can get the number of entries via a regular Pyro call to the nameserver: ns.count(). For obvious reasons, it's not possible to remotely shutdown the nameserver via such a Pyro API call, hence the need of a watch dog process.

Data buffering/storage - Python

I am writing an embedded application that reads data from a set of sensors and uploads to a central server. This application is written in Python and runs on a Rasberry Pi unit.
The data needs to be collected every 1 minute, however, the Internet connection is unstable and I need to buffer the data to a non volatile storage (SD-card) etc. whenever there is no connection. The buffered data should be uploaded as and when the connection comes back.
Presently, I'm thinking about storing the buffered data in a SQLite database and writing a cron job that can read the data from this database continuously and upload.
Is there a python module that can be used for such feature?

Is there a python module that can be used for such feature?
I'm not aware of any readily available module, however it should be quite straight forward to build one. Given your requirement:
the Internet connection is unstable and I need to buffer the data to a non volatile storage (SD-card) etc. whenever there is no connection. The buffered data should be uploaded as and when the connection comes back.
The algorithm looks something like this (pseudo code):
# buffering module
data = read(sensors)
db.insert(data)
# upload module
# e.g. scheduled every 5 minutes via cron
data = db.read(created > last_successful_upload)
success = upload(data)
if success:
last_successful_upload = max(data.created)
The key is to seperate the buffering and uploading concerns. I.e. when reading data from the sensor don't attempt to immediately upload, always upload from the scheduled module. This keeps the two modules simple and stable.
There are a few edge cases however that you need to concern yourself with to make this work reliably:
insert data while uploading is in progress
SQLlite doesn't support being accessed from multiple processes well
To solve this, you might want to consider another database, or create multiple SQLite databases or even flat files for each batch of uploads.

If you mean a module to work with SQLite database, check out SQLAlchemy.
If you mean a module which can do what cron does, check out sched, a python event scheduler.
However, this looks like a perfect place to implemet a task queue --using a dedicated task broker (rabbitmq, redis, zeromq,..), or python's threads and queues. In general, you want to submit an upload task, and worker thread will pick it up and execute, while the task broker handles retries and failures. All this happens asynchronously, without blocking your main app.
UPD: Just to clarify, you don't need the database if you use a task broker, because a task broker stores the tasks for you.

This is only database work. You can create a master and slave databases in different locations and if one is not on the network, will run with the last synched info.
And when the connection came back hr merge all the data.
Take a look in this answer and search for master and slave database