I see that you're using sqlalchemy and db.Model to describe your data and queries.

I've been trying to use dataclasses + fquery to do something similar.

dataclasses-sql on sqlalchemy: most recent commits in the repo below have examples.

https://github.com/adsharma/dataclasses-sql/tree/decorator

More on fquery:

https://adsharma.github.io/fquery/ https://github.com/adsharma/fquery

Looking for django/flask projects to test these ideas out.

I noticed that there were some errors in the Japanese localization. This PR aims to improve the Japanese translation by using established terminology.

Output: https://github.com/cclauss/flask-state/actions

Run pytest .
ImportError while loading conftest '/home/runner/work/flask-state/flask-state/tests/conftest.py'.
tests/conftest.py:4: in <module>
    from src.flask_state import DEFAULT_BIND_SQLITE
E   ModuleNotFoundError: No module named 'src'
Error: Process completed with exit code 4.

I tried to improve the English translation while staying true to the original document. I also changed the formatting a little so that this README file conforms to markdown rules and recommendations.

Currently, it is unclear how to use example/, because there's no information available.

Looks like it needs its own guide:

• How to install all the required dependencies?
• How to run the project?
• What should you expect?
• How to simulate the high-load so I can test that the metrics work?

1. https://github.com/yoobool/flask-state/blob/master/setup.cfg#L2
2. https://github.com/yoobool/flask-state/blob/master/setup.py#L8

In my opinion, there should be only a single place to define this data.

Is your feature request related to a problem? Please describe. Change disk usage plot to a pie chart or a disk chart.

Describe the solution you'd like The disk usage plot can be changed to a pie chart which will have a better representation.

Describe the bug When I use log.exception, the Flask-State does not log trace as expected.
It just show the error msg like use log.error

To Reproduce Steps to reproduce the behavior:

1. Run the examples/app.py without change the example Redis config
2. Change the services/host_status in line 94 from log.warning(t) to log.exception(t)
3. When trigger the record events, timeout error will be raised

Expected behavior log.exception will print full stack trace of the error

Actual behavior Simply log imeout connecting to server and with confuse code line like the following output:

FLASK_APP = examples/app.py
FLASK_ENV = development
FLASK_DEBUG = 1

flask-state/env/bin/python -m flask run
 * Serving Flask app "examples/app.py" (lazy loading)
 * Environment: development
 * Debug mode: on
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
 * Restarting with stat
2020-11-22 23:11:20,821 | INFO | manager:record_timer:83 - Acquired file lock successfully. process ID: 29123
 * Debugger is active!
 * Debugger PIN: 161-836-542
127.0.0.1 - - [22/Nov/2020 23:11:41] "GET / HTTP/1.1" 200 -
2020-11-22 23:12:01,012 | ERROR | __init__:exception:1413 - Timeout connecting to server
2020-11-22 23:12:01,025 | INFO | host_status:create_host_status:52 - Insert status

Additional context Commit ID is 11c2ea4b29c40b3fa10cb85b38986eeca0b93ce3

Hi 👋

One of my Pull Requests around fixing Code Quality Issues with DeepSource was merged here: https://github.com/yoobool/flask-state/pull/33

I'd just like to inform you that the issues fixed here were detected by running DeepSource analysis on the repo. If you like, you can activate analysis for your repository to detect such code quality issues/bug risks on the fly for every change made. You can also use the Autofix feature to fix them with one click.

The .deepsource.toml file you merged will only take effect if you activate analysis for this repo.

Here's what you can do if you wish to activate DeepSource to continuously analyze your repository:

• Sign up on DeepSource and activate analysis for this repository.
• Create .deepsource.toml configuration which you can use to configure your analysis settings (My PR already added that, but feel free to edit it anytime).
• Track/Check analysis here.

If you have any doubts or questions, you can check out the docs, or feel free to reach out :)

Real time use of the memory in the code e.g. if you import NumPy then what's the state of the memory. If you create a variable then how much did the memory increase. Increase or change in memory usage as per every line that's present in the code would be really helpful for the programmers.

Is your feature request related to a problem? Please describe. No

Describe the solution you'd like Would be nice to have a network monitor in the dashboard. Something that depicted network information such as packets in/out, data received/sent, etc.

Describe alternatives you've considered No alternative, but I think it would make a good addition to the dashboard.

Additional context Might I recommend looking into the Scapy python library. It is a packet manipulation library that allows you to implement packet sniffers and network monitors.

Flask mentions Implementing API Exceptions in docs, which MAY be better than what we used in current project.

from flask import jsonify

class InvalidUsage(Exception):
    status_code = 400

    def __init__(self, message, status_code=None, payload=None):
        Exception.__init__(self)
        self.message = message
        if status_code is not None:
            self.status_code = status_code
        self.payload = payload

    def to_dict(self):
        rv = dict(self.payload or ())
        rv['message'] = self.message
        return rv

@app.errorhandler(InvalidUsage)
def handle_invalid_usage(error):
    response = jsonify(error.to_dict())
    response.status_code = error.status_code
    return response

@app.route('/foo')
def get_foo():
    """**Just raise and don't need more try-catch code!**"""
    raise InvalidUsage('This view is gone', status_code=410)

I believe the temperature of the machine will be an interesting feature as the machines overheat when running deep learning models locally. Boot time, number of threads can be an interesting addition as well.

GPU stats can be included too

Also temperature of CPU and other internal components and the relation between usage and temperature can be interesting stats to look at...

Besides, if you give interesting stats and analytics to your users and work on this area, it can be amazing and it brings a great advantage to your program. It may worth to inspect machine state monitor desktop programs to discover features that would match your project.

This is more like a question, not actually a feature request.

For me right now it is unclear if I can use this project together with Prometheus stack. It is the default solution right now for monitoring things.

Looks like it should be possible to use it instead of your own frontend to see the metrics. Or are there any problems / unknowns?