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✨ source-linkedin-ads: migrate to poetry (#35086)
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alafanechere authored Feb 9, 2024
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146 changes: 44 additions & 102 deletions airbyte-integrations/connectors/source-linkedin-ads/README.md
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# Linkedin Ads Source Connector
# Linkedin-Ads source connector

This is the repository for the Linkedin Ads source connector, written in Python.
For information about how to use this connector within Airbyte, see [the documentation](https://docs.airbyte.io/integrations/sources/linkedin-ads).

This is the repository for the Linkedin-Ads source connector, written in Python.
For information about how to use this connector within Airbyte, see [the documentation](https://docs.airbyte.com/integrations/sources/linkedin-ads).

## Local development

### Prerequisites
**To iterate on this connector, make sure to complete this prerequisites section.**

#### Minimum Python version required `= 3.7.0`
* Python (~=3.9)
* Poetry (~=1.7) - installation instructions [here](https://python-poetry.org/docs/#installation)

#### Build & Activate Virtual Environment and install dependencies
From this connector directory, create a virtual environment:
```
python3 -m venv .venv
```

This will generate a virtualenv for this module in `.venv/`. Make sure this venv is active in your
development environment of choice. To activate it from the terminal, run:
```
source .venv/bin/activate
pip install -r requirements.txt
### Installing the connector
From this connector directory, run:
```bash
poetry install --with dev
```
If you are in an IDE, follow your IDE's instructions to activate the virtualenv.

Note that while we are installing dependencies from `requirements.txt`, you should only edit `setup.py` for your dependencies. `requirements.txt` is
used for editable installs (`pip install -e`) to pull in Python dependencies from the monorepo and will call `setup.py`.
If this is mumbo jumbo to you, don't worry about it, just put your deps in `setup.py` but install using `pip install -r requirements.txt` and everything
should work as you expect.

#### Create credentials
**If you are a community contributor**, follow the instructions in the [documentation](https://docs.airbyte.io/integrations/sources/linkedin-ads)
to generate the necessary credentials. Then create a file `secrets/config.json` conforming to the `source_linkedin_ads/spec.json` file.
### Create credentials
**If you are a community contributor**, follow the instructions in the [documentation](https://docs.airbyte.com/integrations/sources/linkedin-ads)
to generate the necessary credentials. Then create a file `secrets/config.json` conforming to the `source_linkedin_ads/spec.yaml` file.
Note that any directory named `secrets` is gitignored across the entire Airbyte repo, so there is no danger of accidentally checking in sensitive information.
See `integration_tests/sample_config.json` for a sample config file.
See `sample_files/sample_config.json` for a sample config file.

**If you are an Airbyte core member**, copy the credentials in Lastpass under the secret name `source linkedin-ads test creds`
and place them into `secrets/config.json`.

### Locally running the connector
```
python main.py spec
python main.py check --config secrets/config.json
python main.py discover --config secrets/config.json
python main.py read --config secrets/config.json --catalog integration_tests/configured_catalog.json
poetry run source-linkedin-ads spec
poetry run source-linkedin-ads check --config secrets/config.json
poetry run source-linkedin-ads discover --config secrets/config.json
poetry run source-linkedin-ads read --config secrets/config.json --catalog sample_files/configured_catalog.json
```

### Locally running the connector docker image



#### Use `airbyte-ci` to build your connector
The Airbyte way of building this connector is to use our `airbyte-ci` tool.
You can follow install instructions [here](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/README.md#L1).
Then running the following command will build your connector:
### Running unit tests
To run unit tests locally, from the connector directory run:
```
poetry run pytest unit_tests
```

### Building the docker image
1. Install [`airbyte-ci`](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/README.md)
2. Run the following command to build the docker image:
```bash
airbyte-ci connectors --name=source-linkedin-ads build
```
Once the command is done, you will find your connector image in your local docker registry: `airbyte/source-linkedin-ads:dev`.

##### Customizing our build process
When contributing on our connector you might need to customize the build process to add a system dependency or set an env var.
You can customize our build process by adding a `build_customization.py` module to your connector.
This module should contain a `pre_connector_install` and `post_connector_install` async function that will mutate the base image and the connector container respectively.
It will be imported at runtime by our build process and the functions will be called if they exist.

Here is an example of a `build_customization.py` module:
```python
from __future__ import annotations

from typing import TYPE_CHECKING

if TYPE_CHECKING:
# Feel free to check the dagger documentation for more information on the Container object and its methods.
# https://dagger-io.readthedocs.io/en/sdk-python-v0.6.4/
from dagger import Container

An image will be available on your host with the tag `airbyte/source-linkedin-ads:dev`.

async def pre_connector_install(base_image_container: Container) -> Container:
return await base_image_container.with_env_variable("MY_PRE_BUILD_ENV_VAR", "my_pre_build_env_var_value")

async def post_connector_install(connector_container: Container) -> Container:
return await connector_container.with_env_variable("MY_POST_BUILD_ENV_VAR", "my_post_build_env_var_value")
```

#### Build your own connector image
This connector is built using our dynamic built process in `airbyte-ci`.
The base image used to build it is defined within the metadata.yaml file under the `connectorBuildOptions`.
The build logic is defined using [Dagger](https://dagger.io/) [here](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/pipelines/builds/python_connectors.py).
It does not rely on a Dockerfile.

If you would like to patch our connector and build your own a simple approach would be to:

1. Create your own Dockerfile based on the latest version of the connector image.
```Dockerfile
FROM airbyte/source-linkedin-ads:latest

COPY . ./airbyte/integration_code
RUN pip install ./airbyte/integration_code

# The entrypoint and default env vars are already set in the base image
# ENV AIRBYTE_ENTRYPOINT "python /airbyte/integration_code/main.py"
# ENTRYPOINT ["python", "/airbyte/integration_code/main.py"]
```
Please use this as an example. This is not optimized.

2. Build your image:
```bash
docker build -t airbyte/source-linkedin-ads:dev .
# Running the spec command against your patched connector
docker run airbyte/source-linkedin-ads:dev spec
```
#### Run
### Running as a docker container
Then run any of the connector commands as follows:
```
docker run --rm airbyte/source-linkedin-ads:dev spec
Expand All @@ -121,29 +58,34 @@ docker run --rm -v $(pwd)/secrets:/secrets airbyte/source-linkedin-ads:dev disco
docker run --rm -v $(pwd)/secrets:/secrets -v $(pwd)/integration_tests:/integration_tests airbyte/source-linkedin-ads:dev read --config /secrets/config.json --catalog /integration_tests/configured_catalog.json
```

## Testing
### Running our CI test suite
You can run our full test suite locally using [`airbyte-ci`](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/README.md):
```bash
airbyte-ci connectors --name=source-linkedin-ads test
```

### Customizing acceptance Tests
Customize `acceptance-test-config.yml` file to configure tests. See [Connector Acceptance Tests](https://docs.airbyte.com/connector-development/testing-connectors/connector-acceptance-tests-reference) for more information.
Customize `acceptance-test-config.yml` file to configure acceptance tests. See [Connector Acceptance Tests](https://docs.airbyte.com/connector-development/testing-connectors/connector-acceptance-tests-reference) for more information.
If your connector requires to create or destroy resources for use during acceptance tests create fixtures for it and place them inside integration_tests/acceptance.py.

## Dependency Management
All of your dependencies should go in `setup.py`, NOT `requirements.txt`. The requirements file is only used to connect internal Airbyte dependencies in the monorepo for local development.
We split dependencies between two groups, dependencies that are:
* required for your connector to work need to go to `MAIN_REQUIREMENTS` list.
* required for the testing need to go to `TEST_REQUIREMENTS` list
### Dependency Management
All of your dependencies should be managed via Poetry.
To add a new dependency, run:
```bash
poetry add <package-name>
```

Please commit the changes to `pyproject.toml` and `poetry.lock` files.

### Publishing a new version of the connector
## Publishing a new version of the connector
You've checked out the repo, implemented a million dollar feature, and you're ready to share your changes with the world. Now what?
1. Make sure your changes are passing our test suite: `airbyte-ci connectors --name=source-linkedin-ads test`
2. Bump the connector version in `metadata.yaml`: increment the `dockerImageTag` value. Please follow [semantic versioning for connectors](https://docs.airbyte.com/contributing-to-airbyte/resources/pull-requests-handbook/#semantic-versioning-for-connectors).
2. Bump the connector version (please follow [semantic versioning for connectors](https://docs.airbyte.com/contributing-to-airbyte/resources/pull-requests-handbook/#semantic-versioning-for-connectors)):
- bump the `dockerImageTag` value in in `metadata.yaml`
- bump the `version` value in `pyproject.toml`
3. Make sure the `metadata.yaml` content is up to date.
4. Make the connector documentation and its changelog is up to date (`docs/integrations/sources/linkedin-ads.md`).
4. Make sure the connector documentation and its changelog is up to date (`docs/integrations/sources/linkedin-ads.md`).
5. Create a Pull Request: use [our PR naming conventions](https://docs.airbyte.com/contributing-to-airbyte/resources/pull-requests-handbook/#pull-request-title-convention).
6. Pat yourself on the back for being an awesome contributor.
7. Someone from Airbyte will take a look at your PR and iterate with you to merge it into master.

8. Once your PR is merged, the new version of the connector will be automatically published to Docker Hub and our connector registry.
2 changes: 1 addition & 1 deletion airbyte-integrations/connectors/source-linkedin-ads/metadata.yaml
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connectorSubtype: api
connectorType: source
definitionId: 137ece28-5434-455c-8f34-69dc3782f451
dockerImageTag: 0.6.7
dockerImageTag: 0.6.8
dockerRepository: airbyte/source-linkedin-ads
documentationUrl: https://docs.airbyte.com/integrations/sources/linkedin-ads
githubIssueLabel: source-linkedin-ads
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