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2022/06/26

Start developing python flask with uwsgi and vscode's debugger inside docker!

1. Goal

In this blog post, I am trying to setup the vscode such that I can develop my flask project with debugger supported. Literally, I am trying to do following setup:

vscode + docker + uwsgi + flask

Although I am using a flask project I left since 2021 DEC, this methodology fits any project, and any frameworks. It is 2022. We should curse any projects that cannot develop with debugger.

  • TLDR, show me your codes
  • Go section 3 if you just interested on the setup’s explanation
    By the way, during my research time, there is no public solution for my requirements. Hopefully, this is the 1st public solution for vscode / uwsgi + flask + docker :)

1.1 Target Audiences

  • Any new python project
  • Any existing python project

1.2 Contents overview

In section 2, I will, briefly, explain the general idea on configuring the debugger in vscode.
In section 3, I will explain the changes file by file

Side track

A gif from vscode’s debugger introduction for showing how debugger can help our development.

2. General idea on configuring vscode’s debugger

Although I strongly recommend you to spend sometime on reading the vscode’s Debugging documentation, there are some highlights on some critical (I think) ideas.

2.1 launch.json: launch request vs attach request LINK

The best way to explain the difference between launch and attach is to think of a launch configuration
as a recipe for how to start your app in debug mode before VS Code attaches to it, while an attach
configuration is a recipe for how to connect VS Code’s debugger to an app or process
that’s already running.

In my own wording, for those applications that cannot launch in your own local terminal directly, you will need attach. Otherwise, you will need launch. For example, you will need attach for an application hosted by uwsgi (IE, our project in this blog). You will need launch if you can run the flask application locally. (IE, python -m flask run)

2.1.1 launch based environment

For launch based environment, no magic setups are required. You can get the basic configuration by clicking the Add configuration button in launch.json. Then, you can define the necessary env / args for your own application manually. Below is an example of a configuration in launch.json

{
            "name": "Python: Current File",
            "type": "python",
            "request": "launch",
            "program": "${file}",
            "console": "integratedTerminal",
            "justMyCode": false,
            "purpose": ["debug-test"],
            "env": {
                "PYTEST_ADDOPTS": "--no-cov"
            }
        }

2.1.2 attach based environment

Since vscode is not responsible for launching the application, you need to instruct vscode to attach to the application in order to activate the debugger for your application. Read section 3 for a concrete example.

Besides instructing vscode to attach somewhere, you need to configure the pathMappings. Literally, you need to tell vscode how to map the filesystem between your local vscode, and the remote filesystem (the application you attach). For example, ~/git/PROJECT is the project root in your local device while /var/www/PROJECT is where your application lives in production. You will need to specify the filesystem mapping between ~/git/PROJECT & /var/www/PROJECT. Otherwise, debugger cannot stop at a particular line for you.

2.2 tasks.json: The Makefile (Or, script section in package.json) in vscode LINK

  • tasks.json is not REQUIRED in our problem.
  • Without this, you can still make the debugger works.
  • With this, you will just save sometimes on typing just like Makefile or Script in package.json

3. Code explanation

Link to the git diff for related configurations

There are massive code changes. Before explaining the changes, I will state the goals of them:

  • I am running the vscode’s debugger in attach mode
    Since there are limitations (features) from my base docker image, I must run the flask application via uwsgi & superviord. Otherwise, I will runs my debugger in launcher mode. It is much easier.

  • I need to run flask with debugpy CONDTIONALLY
    Since I would like to use the same codebase between development & production, I don’t want to run the debugger in the production. So, I need to make sure the debugpy can be started only if it is a development environment

  • If I need debugger, I need to ensure

    • uwsgi creates one and only one worker for our flask app
    • uwsgi does not kill our worker
    • the debugpy’s server is on the worker thread

According to my tests, the attach mode of debugpy is really bad on multithreading. Since uwsgi must runs our flask in multithreading manner, I need to instruct uwsgi creates only 1 permanent worker. Otherwise, debugpy’s server cannot listen on my desired network port.

In uwsgi, the flask request handler’s codes are run by the workers. If you don’t creates the debugpy’s server on the worker process, the breakpoint functions in request handlers will not work as expected.

Of cause, if you need to debug codes tin both (master & workers) processes, you will need 2 different debugpy’s server. Then, you need 2 vscode’s debugger, for attaching to the servers separately.

3.1 Related to Vscode configuration

In this section, I will explain changes related to vscode configurations. As described on section 2, I am just instructing the vscode to attach to the application I want.

launch.json

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Attach to docker container",
            "type": "python",
            "request": "attach",
            "connect": { "host": "127.0.0.1", "port": 5678 },
            "pathMappings": [{ "localRoot": "${workspaceFolder}", "remoteRoot": "/workspace" }],
            "justMyCode": false
        }
    ]
}
  • As explained in section 2, we need attach based configuration.
    • We need to instruct the vscode to connect to 127.0.0.1:5678. This will be the debugger port that the application opened because our later configurations.
  • Since /workspace is the file root of our application inside the docker container, we instruct vscode to map our ${workspaceFolder} to /workspace. Checkout variable references from vscode if you are not familiar with them.
  • Rest of the options are not that important. Please read this manual for detail if you are interested

tasks.json

        {
            "type": "shell",
            "label": "local - docker-compose up -d with debugpy",
            "command": "docker-compose",
            "args": [
                "--env-file", "docker/setup_debugger.env",
                "-f", "docker/docker-compose-local-dev.yml",
                "up", "-d"
            ],
            "presentation": {
                "reveal": "always",
                "panel": "shared"
            }
        }
  • The only 1 important command in this file. It runs following command
    • docker-compose --env-file docker/setup_debugger.env -f docker/docker-compose-local-dev.yml up -d
    • This environment file docker/setup_debugger.env instructs how application to launch a debugger for vscode to attach. Refer to the explanations in below section

requirement-text.txt

  • Install debugpy, which is a library for vscode’s debugger

3.2 Related to the uwsgi configuration

app/uwsgi_dev.ini

[uwsgi]
module = app.app.main:createFlaskApp()
enable-threads = true
worker-reload-mercy = 999999999
cheaper-algo = backlog
  • worker-reload-mercy & cheaper-algo. are important options for instructing uwsgi not killing our worker.
  • enable-threads fixes warning from uwsgi
  • For all option’s detail, please refer to uwsgi option document

docker/docker-compose-local-dev.yaml

  • I split the environment variables in different files (services.CoreServer.env_file)
    • IMO, it is easier for maintaining them in different environments
  • services.CoreServer.environment allows me to run debugpy CONDITIONALLY
  • Without services.CoreServer.ports, the vscode debugger cannot attach to our debugpy’s server
  • Without stop_grace_period, I need to wait 10 seconds for stopping a container. For development, I want to restart it faster.

docker/xxx.env

As the file name suggested, I group environment variables accordingly.
In order to run a debugger successfully, please make sure you set option cheaper to 1. Otherwise, uwsgi will runs more than 1 worker. It will fails our setup

Note: As I told earlier, I am using some features (limitations) from the base docker image. That’s how they expect developers to do the configurations. Therefore, in your own setup, you don’t really need UWSGI_XXX. You can write all required UWSGI options in your uwsgi.ini.

3.3 Related to the flask application

app/app/main.py

  • createFlaskApp is how uwsgi build our application. So, we need to keep this
  • MyFlaskAppFactory keeps rest of the logic for building a flask application
  • DebugpyWsgiMiddleware runs the debugpy & wait for the debugger to attach before processing any wsgi requests
    • In uwsgi, you need to tell debugpy where is the python interpreter by using debugpy.configure({"python": pythonExecutablePath})
  • MyFlaskAppFactory._initializeFlaskWithDebugpy
    • shows how I stop debugpy being made in production environment
    • Then, I inject my DebugpyWsgiMiddleware on flaskApp.wsgi_app in order to make sure the debugger starts on the worker thread instead of the master thread

Note:
Other blog setup does not fit my case since I don’t really know the pid of the master process due to my special base docker image.

4. How to run the application with vscode debugger

After the configuration above, let’s run the flask application with debugger.

  • Run the docker-compose command as illustrated in section 3.1 for starting all the docker containers
  • curl any api in the server. For example curl http://127.0.0.1:56733/
  • Expect to see the output of the curl being stalled
    • Go to Run & Debug & run Attach to docker container
  • Expect to see the server output from the curl above
  • Now, you can setup any breakpoints, and debug whatever you want with that debugger

5. [Bonus] Enhance CICD with code coverage & junit report

In the old blog here, I show you how to add coverage report in the cicd in detail. To recap in short, all you need is following yaml

coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'

In 2022, gitlab provides much more on the coverage & testing. For detail, you can refer to following links:

  script:
      - flake8
      - coverage run -m pytest --junit-xml=junit.xml
      - coverage report
      - coverage xml
  coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'
  artifacts:
    reports:
      coverage_report:
        coverage_format: cobertura
        path: coverage.xml
      junit: junit.xml

Then, in the cicd pipeline, it will generates junit.xml & coverage.xml for the gitlab builtin features as listed above

6. References

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2021/12/13

Learning Flask 1: Bootstrap with flask

Background

This is a follow up blog post of previous NETCONF blog. Below diagram recalls the application I am going to build.

In the last blog post, I have built a server for running netconfd effectively. In this blog post, I will focus on building the CoreServer (Frontend layer) with flask.
By the way, the full source code of this application is here

Why flask?

I do not have much experiences on python flaks. Actually, I am more familiar with Django instead. Django, however, is not fit in this application since it is sort of overkill in my requirement. I need something small, and easy to code at the first place.
After some research from this website, I pick flask from the list. By the way, Django is classified as full-stack framework while flask is simply a micro framework in this site.

Start the application with a boilerplate

Nowadays, as long as you are using well-known frameworks / libraries etc, usually, there are multiple open sources boilerplates for your framework / libraries. Then, you can save much efforts on building the ground.
Personally, I have picked this boilerplate for my application. This boilerplate provides the docker image, the docker-compose file, the folder structure.

The usage of docker image + docker-compose

They are essential for me to build the application in a docker manner, and they save my time on building the CI/CD pipeline easily

The folder structure

It saves my time for figuring out how to organise files in flaks manner.

Setup CI / CD pipeline

I strongly recommend readers to setup a CI / CD pipeline asap especially on a new project. After having a CI / CD pipeline, code’s quality is kind of guaranteed. For example, coding style, some basic tests etc

  • Below is the yml file for running a CI / CD pipeline in gitlab CI.
# You can override the included template(s) by including variable overrides
# SAST customization: https://docs.gitlab.com/ee/user/application_security/sast/#customizing-the-sast-settings
# Secret Detection customization: https://docs.gitlab.com/ee/user/application_security/secret_detection/#customizing-settings
# Dependency Scanning customization: https://docs.gitlab.com/ee/user/application_security/dependency_scanning/#customizing-the-dependency-scanning-settings
# Note that environment variables can be set in several places
# See https://docs.gitlab.com/ee/ci/variables/#cicd-variable-precedence
include:
  - template: Security/SAST.gitlab-ci.yml

stages:
  - build
  - test

variables:
  PIPELINE_REGISTRY_DOCKER_IMAGE: "${CI_REGISTRY_IMAGE}:pipeline"
  PIPELINE_RUNTIME_DOCKER_IMAGE: "hands-on-core-server:latest"
  DOCKER_COMPOSE_CI_YML_FILE_NAME: "docker-compose-ci.yml"

buildDockerImage:
  image: docker
  services:
      - docker:dind
  stage: build
  variables:
    DOCKER_BUILDKIT: 0
  script:
      - apk add --no-cache docker-compose
      - docker login -u "gitlab-ci-token" -p "$CI_JOB_TOKEN" "$CI_REGISTRY"
      - docker-compose -f "docker/${DOCKER_COMPOSE_CI_YML_FILE_NAME}" build CoreServer
      - docker tag "${PIPELINE_RUNTIME_DOCKER_IMAGE}" "${PIPELINE_REGISTRY_DOCKER_IMAGE}"
      - docker push "${PIPELINE_REGISTRY_DOCKER_IMAGE}"

sast:
  stage: test

runTests:
  image: ${PIPELINE_REGISTRY_DOCKER_IMAGE}
  stage: test
  script:
      - flake8
  • The sast task is provided by gitlab. Just ignore it
  • Pipeline’s contents are trivial. Literally,
    • I compose a docker image which holding all my boilerplates, and dependencies.
    • I setup a stage runTests which runs with the docker image above, and run lint check with flake8 only at this moment
  • Below is the Dockerfile for ci
FROM tiangolo/uwsgi-nginx-flask:python3.8

WORKDIR /workspace
COPY . /workspace

RUN \
    pip install -r requirements-test.txt && \
    pip install -r requirements.txt

Conclusions

Now, I have a good ground to build up my application in flask. There is a CI-CD pipeline to make sure my codes will be in good qualities in some sense. There is a flask-style folder structure for me to organise my codes.
The next step of the development will be writing some trivial tests, and make sure the pipeline will run them on each commit. They will be covered in the next blog

Useful links

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