๐Ÿ—ป James Van Dyne

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  • Unit Testing django-graphene GraphQL endpoints with pytest

    Nov 13, 2019
    by James

    On a project at work I've been learning GraphQL. I'm in charge of both developing the backend ( using the wonderful graphene-django package) and the frontend ( using Typescript / Vue.js / axios ) for this specific feature.

    After wrapping my head around GraphQL and getting a basic endpoint working I wanted to write some tests to ensure that filtering and other requirements are working as expected.

    For a proper End-to-end test I'd usually setup a client and post a json dictionary or such. With End-to-end GraphQL tests you need to send actual GraphQL queries, which makes sense but it feels a bit like SQL Injection.

    Below is how I've settled organizing and writing my GraphQL tests.

    tests/
    โ”œโ”€โ”€ __init__.py
    โ”œโ”€โ”€ conftest.py
    โ”œโ”€โ”€ test_myapp/
    โ”‚ย ย  โ””โ”€โ”€ test_schema.py

    Because the graphql client will be reused across the django site, I added a global fixture that will automatically load mty project's schema.

    # tests/conftest.py
    @pytest.fixture
    def graphql_client():
    from myproject.schema import schema
    from graphene.test import Client
    return Client(schema)

    In this example I'm testing that I'm filtering data as expected when passing a search parameter.

    For setup, first I write a query as its own fixture so I can re-use it throughout the test and it's clear exactly what is going to be run. Second, I make sure the query uses variables instead of hard-coded values when querying so I can change the input depending on the test. Third, setup a model_bakery fixture for data that I expect to find.

    import pytest
    from model_bakery import baker

    @pytest.mark.django_db
    class TestMyModelData:
    @pytest.fixture
    def query(self):
    return """
    query testGetMyModel($searchParam: String!){
    myModelData(searchParam: $searchParam) {
    totalCount
    }
    }"""

    @pytest.fixture
    def my_model(self):
    baker.make(
    "myapp.MyModel",
    total_count="20", # Decimal field
    )

    def test_none_response(self, graphql_client, query, my_model):
    executed = graphql_client.execute(query, variables={"searchParam": "skittles"})
    assert executed == {"data": {"myModelData": None}}

    def test_filters_usage(self, graphql_client, query, my_model):
    params = {"searchParam": "skittles"}
    executed = graphql_client.execute(query, variables=params)
    assert executed == {
    "data": {
    "myModelData": {
    "totalCount": 20
    }
    }
    }

    Executing each test I simply pass my query and required variables for the test/query. In this I'm testing the same query twice: once with and one without a searchParameter. My expectation is that I get no results without a search term and data when to my graphql_client fixture.

    As the return value from our client is a dictionary, I can simply assert my expecte results with the actual results. If something changes I'll know immediately.

    Using the techniques above I can easily add new tests for my GraphQL endpoint as the available changes or bugs are found.

    ๐Ÿ”—permalink
  • Nov 12, 2019
    by James

    Fuji looking grand tonight. Autumn Fuji is the best.

    ๐Ÿ”—permalink
  • Checkin to KUA`AINA

    KUA`AINA 35.4525303291549 139.643101809736
    Nov 10, 2019
    by James
    in Yokohama, Kanagawa, Japan
    Avocado Cheddar burger!
    ๐Ÿ”—permalink
  • Nov 10, 2019
    by James

    Checked in at Kua 'Aina (KUA`AINA). Avocado Cheddar burger!

    ๐Ÿ”—permalink
  • Nov 07, 2019
    by James

    Did a presentation at work today about editing PDFs with Python. Mixing and matching libraries is fast, but youโ€™ll run into a bunch of inefficiencies that result in slow processing and huge files. I should write a book. For serious.

    ๐Ÿ”—permalink
  • Nov 04, 2019
    by James

    Checked in at COPEN LOCAL BASE KAMAKURA. Burritos!

    ๐Ÿ”—permalink
  • Checkin to COPEN LOCAL BASE KAMAKURA

    COPEN LOCAL BASE KAMAKURA 35.31605084574534 139.5499748699059
    Nov 04, 2019
    by James
    in Kamakura, Kanagawa, Japan
    Burritos!
    ๐Ÿ”—permalink
  • Nov 04, 2019
    by James

    Checked in at Tsurugaoka Hachimangu (้ถดๅฒกๅ…ซๅนกๅฎฎ)

    ๐Ÿ”—permalink
  • Checkin to Tsurugaoka Hachimangu (้ถดๅฒกๅ…ซๅนกๅฎฎ)

    Tsurugaoka Hachimangu (้ถดๅฒกๅ…ซๅนกๅฎฎ) 35.32494983672849 139.5557226240635
    Nov 04, 2019
    by James
    in Kamakura, Kanagawa, Japan
    ๐Ÿ”—permalink
  • Always Solve for X

    Nov 03, 2019
    by James

    Reflecting back on the products I've released over the years I noticed a key difference between the successful products and the non-successful products. The difference wasn't in market or craftsmanship. The difference started before I wrote a single line of code.

    With [ImageXY ](/past-projects/imagexy) we were focused on designing a solution to real estate agents uploading photos that were the wrong size or too large into a crm. When building it we concentrated on that single use-case and kept our solution focused. The result was a batch image resizing app that didn't have all the complexities of its competitors. And it turned out a lot of non-real estate agents _also_ had the same problem and our solution worked for them as well.

    Other products, like [Byoyomi](/past-projects/byoyomi), a timer application that let you save and run multiple timers on your Mac, were flops. The application was stable and worked as advertised, but it was more of a solution in search of a problem.

    As developers its easy to dive straight into writing code or designing interfaces the moment we have an idea that we think might work. And as a developer taking those random ideas and turning them into a reality is _fun_. But the chances of those inspiration-turned-hacking sessions turning into a successful product are slim.

    Instead of diving into code the moment we have an idea, it's critical that you do the hard work before your hacking sessions. The work that isn't necessarily fun and _feels_ like work. Take a step-back form Sketch and define, with words, the problem you're trying to solve.

    If you can't clearly articulate the problem and who's having that problem, any solution you build will be equally muddled.

    > When designing anything, you first need to define the problem that you're trying to solve.

    The major difference between my successful products and those that were just that. The successful products had a clear problem that they were solving for a specific niche while the unsuccessful ones did not. It's much easier to solve for X when you've looked at the rest of the equation.

    ๐Ÿ”—permalink
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Web developer living in Japan.