Load Gerrit Code Review data in Python using dltHub

Build a Gerrit Code Review-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Gerrit Code Review data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def gerrit_code_review_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8080/changes/myProject~master~I8473b95934b5732ac55d26311a706c9c2bde9940",
            "auth": {
                "type": "query",
                "token": "access_token",
            },
        },
        "resources": [
            suggest_review, suggest_reviewers
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='gerrit_code_review_pipeline',
        destination='duckdb',
        dataset_name='gerrit_code_review_data', 
    )
    # Load the data
    load_info = pipeline.run(gerrit_code_review_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from gerrit_code_review’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Changes Management: Create, retrieve, update, and delete changes/reviews in projects
  • Documentation: Access API documentation and help resources
  • Review Suggestions: Get suggestions for reviewers and review recommendations for changes
  • Project Operations: Manage changes across different projects and branches

You will then debug the Gerrit Code Review pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with Gerrit Code Review support.

    dlt init dlthub:gerrit_code_review duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Gerrit Code Review API, as specified in @gerrit_code_review-docs.yaml 
Start with endpoint(s) suggest_review and suggest_reviewers and skip incremental loading for now. 
Place the code in gerrit_code_review_pipeline.py and name the pipeline gerrit_code_review_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python gerrit_code_review_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Access tokens are passed as query parameters in the request URL. In the example shown, the access_token parameter is set to "secret" and must be included in the query string (e.g., ?access_token=secret). This is the authentication mechanism for API requests to this service.

    To get the appropriate API keys, please visit the original source at gerrit-review.googlesource.com. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python gerrit_code_review_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline gerrit_code_review load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset gerrit_code_review_data
The duckdb destination used duckdb:/gerrit_code_review.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline gerrit_code_review_pipeline show

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("gerrit_code_review_pipeline").dataset()
# get suggest_review table as Pandas frame
data.suggest_review.df().head()

Extra resources:

Next steps