Background

I was recently made to work with Hackernews API using python (Django). There were several challenges faced by me during the project but I didn't give in. This post tends to document the application.

Source code

The code for the project is on GitHub.

...def get_max_item_id():    max_item_id = requests.get(f"{BASE_API_URL}/maxitem.json")    return max_item_id.json()@shared_taskdef store_latest_stories():    max_item_id = get_max_item_id()    for sid in reversed(range(max_item_id)):        story_response = get_item(sid)        ...

Latest update

Changed the algorithm used in fetching the stories from the API. Instead of getting only the new (latest) stories, I now get the maximum or largest (latest) item ID, then walk backwards to fetch subsequent ones:

...def get_max_item_id():    max_item_id = requests.get(f"{BASE_API_URL}/maxitem.json")    return max_item_id.json()@shared_taskdef store_latest_stories():    max_item_id = get_max_item_id()    for sid in reversed(range(max_item_id)):        story_response = get_item(sid)        ...

Also, the UI now allows only the item types available in the database for filtering. As soon as a new item type is fetched from the API, it will automatically be added to the filters.

Update

It is live on Heroku at newhackernews.herokuapp.com.

This application tends to make it easier to navigate Hackernews by utilizing its public API. It provides a better UX and interactivity courtesy its real-time searching and filtering capabilities, beautiful UI, and lazy-loading. Custom API was also incorporated for ease of accessibility, though POST requests require token-based authentication. Though it depends on hackernews API, all the available data are stored in a separate database and hosted on this platform thereby providing parallel storage and preventing a single source of failure. Hence more reliable. It looks like:

Data are being consumed every 5 minutes via a background task powered by Celery with redis as the broker. This presents performance gains as users do not need to wait for such actions' completions.

The application also provides an in-depth text searching with search term highlighting. Though it is not an enterprise grade full-text search functionality but it is close to that. PostgreSQL could have been used as database to facilitate full-text search but it was later dropped during development. It ended up being used in production.

Local setup

The application was built on a Linux machine (Pop!_OS 20.04 LTS) with Python 3.8.10, git, and Redis installed. Virtual environment was managed by pipenv though you can opt for other virtual environment tools (requirements.txt is included). Ensure your machine has all these tools to locally run this web application.

A typical setup to the app up and running locally is stated below:

• Get the program source files: You can clone it from GitHub via:
  

  git clone https://github.com/Sirneij/Hackernews.git

• Change directory into the source code folder:
  

  cd Hackernews

• Activate virtual environment (pipenv is used here but you can use virtualenv, venv, poetry, or conda):
  

  pipenv shell

• Install the web application's dependencies (again, pipenv is used here but you are at liberty to use any other tool. requirements.txt is included):
  

  pipenv install

• Create migrations (migrations folder already populated. If you prefer to start afresh, delete all the files in the migrations folder of each major app accounts, and news except the __init__.py files. Then, in your terminal, execute python manage.py makemigrations):
  

  python manage.py migrate

• You can opt to create super user by executing:
  

  python manage.py createsuperuser

  Provide the details requested by the prompts that follow.

• Run the application. You will need a second terminal to start the Celery tasks. In one terminal, start the application:
  

  python manage.py runserver

  You can optionally provide a port as the default port is 8000. To provide a port, the command above becomes:
  

  python manage.py runserver port_number

  You can now visit your browser and navigate to http://localhost:8000/ or http://localhost:port_number/ as the case may be.

  In the second terminal, start the celery tasks by (ensure your virtual environment is activated):
  

  celery -A hackernews worker -l info -B

About the Web application

The application was built using Django web framework, Tailwind CSS, SQLite database (PostgreSQL in production or on heroku) and Redis as a broker for celery. Lazy loading, real-time search and filtering were fascinated by jQuery ajax including infinite scroll capabilities. It has the following structure:

. accounts    admin.py    apps.py    __init__.py    migrations       0001_initial.py       __init__.py    models.py    tests.py    views.py api    admin.py    apps.py    __init__.py    migrations       __init__.py    permissions.py    serializers.py    tests.py    urls.py    views.py celerybeat-schedule db.sqlite3 detail.png hackernews    asgi.py    celery.py    __init__.py    settings       base.py       development.py       __init__.py       production.py    urls.py    wsgi.py home.png manage.py news    admin.py    apps.py    __init__.py    migrations       0001_initial.py       0002_alter_comment_story.py       __init__.py    models.py    tasks.py    templatetags       custom_tags.py       __init__.py    tests.py    urls.py    utils.py    views.py Pipfile Pipfile.lock Procfile pyproject.toml README.md requirements.txt runtime.txt setup.cfg static    admin       css          autocomplete.css          ...       fonts          LICENSE.txt          ...       img          calendar-icons.svg          ...       js           actions.js           ...    css       all.min.css       style.css       tailwind.min.css    images       useravatar.png    js       jquery.min.js       mark.min.js       scripts.js    webfonts        fa-brands-400.eot        ... templates     base.html     includes        _header.html     news         comments.html         commons            _filters.html         detail.html         index.html         stories.html32 directories, 210 files

The major apps are news and accounts. While the former handles almost all the physical functionalities of the system, the latter only does user stuff. The api app exposes the data for consumption. It is documented using drf-yasg, a Swagger generation tool implemented without using the schema generation provided by Django Rest Framework. The api has a token-based authentication which requires that all POST requests must provide a token to be accepted, otherwise a not too interesting response:

{    "detail": "Authentication credentials were not provided."}

will be given. To get a token, you can use the following:

http POST http://127.0.0.1:8000/api/api-token-auth/ username=your_username password=your_password

Or in your terminal:

python manage.py drf_create_token user_name

If the user account is available, you should have a response like:

HTTP/1.1 200 OKAllow: POST, OPTIONSContent-Length: 52Content-Type: application/jsonDate: Fri, 24 Sep 2021 08:30:18 GMTReferrer-Policy: same-originServer: WSGIServer/0.2 CPython/3.8.10X-Content-Type-Options: nosniffX-Frame-Options: DENY{    "token": "55868c9d71901f4bb09059eb0a669485511586f7"}

or (for terminal):

Generated token 55868c9d71901f4bb09059eb0a669485511586f7 for user sirneij

You can then use the token for requests such as:

http POST http://localhost:8000/api/latest-stories/ 'Authorization: Token 55868c9d71901f4bb09059eb0a669485511586f7' title="Just testing" story_type="story" text="Just some text" dead=false story_url="http://localhost:8000/api/latest-stories/"

which should give you something like:

HTTP/1.1 201 CreatedAllow: GET, POST, HEAD, OPTIONSContent-Length: 399Content-Type: application/jsonDate: Fri, 24 Sep 2021 08:44:03 GMTLocation: http://localhost:8000/api/latest-stories/dd6fdcdd-da6f-45c8-a3bb-e883b5a16419/Referrer-Policy: same-originServer: WSGIServer/0.2 CPython/3.8.10Vary: AcceptX-Content-Type-Options: nosniffX-Frame-Options: DENY{    "author": "sirneij",    "created_by": "sirneij",    "dead": false,    "descendants": null,    "id": "dd6fdcdd-da6f-45c8-a3bb-e883b5a16419",    "score": 0,    "slug": "just-testing",    "story_type": "story",    "story_url": "http://localhost:8000/api/latest-stories/",    "text": "Just some text",    "time": "2021-09-24T08:44:03.688895Z",    "title": "Just testing",    "url": "http://localhost:8000/api/latest-stories/dd6fdcdd-da6f-45c8-a3bb-e883b5a16419/"}

If you need to make this request in a python application using the requests library, you can do something like:

import requestsurl = 'http://localhost:8000/api/latest-stories/'payload = {    "title": "Title of the story",    "story_type": "story",    "text": "some text",    "dead": true,    "story_url": "http://example.com"}headers = {'Authorization': 'Token 55868c9d71901f4bb09059eb0a669485511586f7'}r = requests.post(url, headers=headers, data=payload)

You can achieve same with JavaScript's fetch, or axios.

That is pretty much it!!!

Suggestions to make it better are welcome.*

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