# ADR-0001 - Use Architectural Decision Records

## Date

2023-12-01

## Context

In a distributed team with many subteams, the best solution to communicate decisions is to use a format accessible by everyone in charge of development.

We use *Specification* and *Discovery* documents stored in Google Drive, but they present some downsides:

# ADR-0004 - Use a platform-oriented architecture for Gradle

## Date

2024-02-07

## Context

The Gradle code base is essentially a large monolith, without strong internal boundaries.
This has a number of negative effects on productivity, including:

- Unclear ownership of code.
- Difficult to focus on one particular area.
- Unintended coupling between areas of the code, including tests.

## Decision

```gradle
dependencies {
  // Pick one:

  // 1. Use Guava in your implementation only:
  implementation("com.google.guava:guava:33.2.0-jre")

  // 2. Use Guava types in your public API:
  api("com.google.guava:guava:33.2.0-jre")

  // 3. Android - Use Guava in your implementation only:
  implementation("com.google.guava:guava:33.2.0-android")

  // 4. Android - Use Guava types in your public API:

    The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

That would generate a `dict` with only the data that was set when creating the `item` model, excluding default values.

Then you can use this to generate a `dict` with only the data that was set (sent in the request), omitting default values:

## Decision

We do not use Java serialization.
Instead, we use custom serialization where we explicitly describe how data objects should be serialized and deserialized.

For internal purposes, we use binary formats for their brevity.
We use the `Serializer` abstraction to separate the actual implementation of serialization from its uses.

When sharing data with external tools, we use JSON.

## Status

ACCEPTED

But don't worry, you can show it as you wish to your final users in the frontend.

And your database models can use any other names you want.

But for the login *path operation*, we need to use these names to be compatible with the spec (and be able to, for example, use the integrated API documentation system).

The spec also states that the `username` and `password` must be sent as form data (so, no JSON here).

* `--workers`: The number of worker processes to use, each will run a Uvicorn worker, in this case, 4 workers.

* `--worker-class`: The Gunicorn-compatible worker class to use in the worker processes.
    * Here we pass the class that Gunicorn can import and use with:

        ```Python
        import uvicorn.workers.UvicornWorker
        ```

To declare a **request** body, you use <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> models with all their power and benefits.

!!! info
    To send data, you should use one of: `POST` (the more common), `PUT`, `DELETE` or `PATCH`.

SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

By using a pre-made container image it's very easy to **combine** and use different tools. For example, to try out a new database. In most cases, you can use the **official images**, and just configure them with environment variables.

That way, in many cases you can learn about containers and Docker and re-use that knowledge with many different tools and components.