Distance Matrix Python API Docs | dltHub

Distancematrix.ai is a location‑data platform providing distance matrix and geocoding REST APIs for computing travel distances and times and converting addresses to/from coordinates. The REST API base URL is https://api.distancematrix.ai and all requests require an API key passed as the key query parameter..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Distance Matrix data in under 10 minutes.

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What data can I load from Distance Matrix?

Here are some of the endpoints you can load from Distance Matrix:

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How do I authenticate with the Distance Matrix API?

Authentication is via a single API key included as the key query parameter on all GET requests.

1. Get your credentials

1. Visit https://distancematrix.ai and sign up / log in.
2. Open the Dashboard and navigate to the API Keys or Developers section.
3. Click “Create new key” or copy an existing key.
4. Save the key; optionally configure IP or referrer restrictions and billing settings.

2. Add them to .dlt/secrets.toml

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[sources.distance_matrix_source]
api_key = "YOUR_DISTANCEMATRIX_API_KEY"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Distance Matrix API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python distance_matrix_pipeline.py

If everything is configured correctly, you'll see output like this:

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

Inspect your pipeline and data:

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dlt pipeline distance_matrix_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads distance_matrix and geocoding from the Distance Matrix API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def distance_matrix_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.distancematrix.ai",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "distance_matrix", "endpoint": {"path": "maps/api/distancematrix/json", "data_selector": "rows"}},
            {"name": "geocoding", "endpoint": {"path": "maps/api/geocode/json", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="distance_matrix_pipeline",
        destination="duckdb",
        dataset_name="distance_matrix_data",
    )
    load_info = pipeline.run(distance_matrix_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("distance_matrix_pipeline").dataset()
sessions_df = data.distance_matrix.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM distance_matrix_data.distance_matrix LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("distance_matrix_pipeline").dataset()
data.distance_matrix.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Distance Matrix data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 403 or responses indicating "request denied" or "invalid key", verify that your api_key is correct, not expired, and passed as the key query parameter. Ensure the key is enabled in the Distancematrix.ai dashboard and that any IP or referrer restrictions are properly configured.

Rate limits / Quota exceeded

The API enforces per‑plan rate limits; a 429 response means the limit was reached. Apply exponential backoff, reduce the number of origins/destinations per request, or upgrade your plan.

Response format & pagination

Distance Matrix responses follow Google’s JSON schema: top‑level fields include status, origin_addresses, destination_addresses, and rows (array). Each rows[n] contains an elements array with per‑destination details. Geocoding returns a results array. There is no built‑in pagination; split large batches into multiple calls.

Other common errors

• 400 Bad Request – malformed parameters.
• 401/403 – missing or invalid API key.
• 429 Too Many Requests – rate limit exceeded.
• 500/503 – server errors; retry with backoff.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack