Python Socket.IO Python API Docs | dltHub

Python Socket.IO is a Python implementation of the Socket.IO protocol that enables real-time, event-based bidirectional communication over WebSocket and HTTP long-polling transports. The REST API base URL is Application-defined (example: http://localhost:5000). python-socketio is a library; it does not provide a fixed/base hosted REST API URL. and No built‑in auth — authentication is implemented by the hosting application (e.g., cookies, JWTs, headers)..

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 Python Socket.IO data in under 10 minutes.

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What data can I load from Python Socket.IO?

Here are some of the endpoints you can load from Python Socket.IO:

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How do I authenticate with the Python Socket.IO API?

python-socketio itself does not enforce an auth scheme. Developers implement authentication in their HTTP routes or in the socket connection handshake (environ). Common patterns are HTTP cookies/sessions, Authorization: Bearer , or custom query/headers during connect.

1. Get your credentials

Not applicable — obtain credentials per the host application: e.g., register a user in the app’s dashboard or create a JWT according to that app’s documentation.

2. Add them to .dlt/secrets.toml

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[sources.python_socketio_source]
auth_token = "your_jwt_here"
base_url = "http://your-host:5000"

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 Python Socket.IO 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 python_socketio_pipeline.py

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

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Pipeline python_socketio_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset python_socketio_data
The duckdb destination used duckdb:/python_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 python_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 messages and status from the Python Socket.IO 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 python_socketio_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Application-defined (example: http://localhost:5000). python-socketio is a library; it does not provide a fixed/base hosted REST API URL.",
            "auth": {
                "type": "bearer",
                "token": auth_token,
            },
        },
        "resources": [
            {"name": "messages", "endpoint": {"path": "messages"}},
            {"name": "status", "endpoint": {"path": "status"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="python_socketio_pipeline",
        destination="duckdb",
        dataset_name="python_socketio_data",
    )
    load_info = pipeline.run(python_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("python_socketio_pipeline").dataset()
sessions_df = data.messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM python_socketio_data.messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("python_socketio_pipeline").dataset()
data.messages.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 Python Socket.IO 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 connections are rejected, verify the hosting application's auth mechanism (cookies, JWTs, headers). For socket connections inspect the handshake/environ and any Authorization header or query param used during connect. Fix by matching token format, renewing expired JWTs, or updating server auth checks.

Library vs hosted REST API (no fixed endpoints)

python-socketio is a library; there is no standard REST API or fixed base URL. If you expect GET endpoints, inspect the hosting application's routes (Flask/aiohttp/FastAPI) to find their paths and JSON shapes. Use the application's code or its API docs to determine data selectors.

Transport and version compatibility

Client and server must use compatible Socket.IO protocol versions. Mismatches can cause connection failure; ensure compatible python-socketio and python‑engineio versions or matching JavaScript client/server versions.

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