Flask-SocketIO Python API Docs | dltHub

Flask‑SocketIO is a Flask extension that adds Socket.IO (bi‑directional WebSocket/Engine.IO) support to Flask applications for real‑time event‑based communication. The REST API base URL is No REST API; Socket.IO endpoint is mounted at the Flask app path (default '/socket.io'), e.g. https://{your-host}/socket.io and No built‑in REST auth — authenticate before Socket.IO connect or pass auth dict in the Socket.IO connection..

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 Flask-SocketIO data in under 10 minutes.

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What data can I load from Flask-SocketIO?

Here are some of the endpoints you can load from Flask-SocketIO:

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How do I authenticate with the Flask-SocketIO API?

Authentication is implemented by the application: perform traditional HTTP authentication (session/cookie) before opening the Socket.IO connection, or include an auth dictionary (e.g., token) when the client connects; the server receives this dict in the connect handler and can accept or reject the connection.

1. Get your credentials

1. Implement your app's normal user authentication (e.g., login route) to produce a session or token. 2) If using token‑based auth, retrieve the token after login. 3) When establishing the Socket.IO connection from the client, include the token in the auth dictionary (or in headers/query string) so the server's connect handler can validate it. (Flask‑SocketIO itself does not issue credentials.)

2. Add them to .dlt/secrets.toml

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[sources.flask_socketio_source]
token = "your_token_here"

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 Flask-SocketIO 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 flask_socketio_pipeline.py

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

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Pipeline flask_socketio_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset flask_socketio_data
The duckdb destination used duckdb:/flask_socketio.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 flask_socketio_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 socket_io and rooms from the Flask-SocketIO 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 flask_socketio_source(token=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": "custom",
                "token": token,
            },
        },
        "resources": [
            {"name": "socket_io", "endpoint": {"path": "socket.io"}},
            {"name": "rooms", "endpoint": {"path": "(server-callable)/rooms"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> 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)

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

SQL (DuckDB example):

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SELECT * FROM flask_socketio_data.socket_io LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("flask_socketio_pipeline").dataset()
data.socket_io.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 Flask-SocketIO 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 reject the connection in the connect handler (return False or raise ConnectionRefusedError), the client will receive a connection error packet. Validate tokens in the connect handler and raise ConnectionRefusedError with a clear message.

Timeouts when awaiting acknowledgements

Using SocketIO.call() waits for a client callback; if the client doesn't acknowledge before the timeout (default 60 s) a TimeoutError is raised. Increase the timeout or ensure the client sends an acknowledgement.

No REST GET endpoints available

Flask‑SocketIO is event‑driven; do not expect REST GET endpoints in the library. Expose any HTTP GET endpoints via your Flask app routes and use those for REST‑style access.

Multi‑process message queue configuration

When running multiple workers, configure a message_queue (Redis, RabbitMQ) so events are delivered across processes. Misconfiguration may cause missing broadcasts.

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