Load Flask-SocketIO data to DuckDB

Flask-SocketIO is a Flask extension that provides bi-directional real-time event-based communication using Socket.IO and Engine.IO protocols. Everything needed to build a working Flask-SocketIO → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Flask-SocketIO to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Flask-SocketIO to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Flask-SocketIO API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Flask-SocketIO API at a glance

These values come from the Flask-SocketIO API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Flask-SocketIO API?

Flask-SocketIO does not have a native REST API; it operates over a WebSocket/Engine.IO connection. Authentication can be performed by passing an 'auth' dictionary in the Socket.IO connection client, or by using query parameters or headers (if supported by the client) and validating them in the 'connect' event handler.

1. Get your credentials

Flask-SocketIO does not provide a standard REST API dashboard for managing API keys. Authentication is implemented by the application developer. Common patterns include using an 'auth' dictionary in the Socket.IO connection packet, standard HTTP headers (e.g., Authorization: Bearer ), or query parameters. To implement this, generate a token within your identity provider or application's internal user management system, then pass it during the Socket.IO client connection handshake (e.g., io('url', {auth: {token: '...'}})).

2. Add them to .dlt/secrets.toml

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[sources.flask_socketio_source]
api_key = "your_secret_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Flask-SocketIO data can I load into DuckDB?

These are the Flask-SocketIO endpoints dlt can load into DuckDB:

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How do I load only new Flask-SocketIO records?

The Flask-SocketIO API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "socket_io", "endpoint": {
    "path": "socket.io",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Flask-SocketIO pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading socket.io and rooms from the Flask-SocketIO API into DuckDB:

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

@dlt.source
def flask_socketio_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "No REST API; Socket.IO endpoint is mounted at the Flask app path (default '/socket.io'), e.g. https://{your-host}/socket.io",
            "auth": {"type": "bearer", "token": auth},
        },
        "resources": [
            {"name": "socket_io", "endpoint": {"path": "socket.io"}},
            {"name": "app_defined_routes", "endpoint": {"path": "/"}}
        ],
    }
    yield from rest_api_resources(config)


def load_flask_socketio_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="flask_socketio_pipeline",
        destination="duckdb",
        dataset_name="flask_socketio_data",
    )
    load_info = pipeline.run(flask_socketio_source())
    print(load_info)


if __name__ == "__main__":
    load_flask_socketio_to_duckdb()

Run it with python flask_socketio_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Flask-SocketIO data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("flask_socketio_pipeline").dataset()
df = data.socket_io.df()
print(df.head())

SQL:

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SELECT * FROM flask_socketio_data.socket_io LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Flask-SocketIO to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Flask-SocketIO loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Flask-SocketIO data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack