APScheduler Python API Docs | dltHub

Flask-APScheduler is a Flask extension that provides a REST API to manage scheduled jobs for the APScheduler library. The REST API base URL is http://localhost:5000/scheduler and all requests can be protected using HTTP Basic Auth via the flask_apscheduler.auth.HTTPBasicAuth class.

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 add "dlt[hub]" and start loading APScheduler data in under 10 minutes.

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What data can I load from APScheduler?

Here are some of the endpoints you can load from APScheduler:

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How do I authenticate with the APScheduler API?

Authentication is handled by the Flask-APScheduler extension using HTTP Basic Auth, which requires a username and password checked against a custom provider function. Headers follow the standard HTTP Basic format 'Authorization: Basic <base64_encoded_credentials>'.

1. Get your credentials

APScheduler itself is a library and does not have a native REST API or dashboard. If you are using the popular Flask-APScheduler extension, you configure authentication in your Flask application code. Instantiate an authentication handler (e.g., HTTPBasicAuth), set it to your scheduler instance via 'scheduler.auth', and define an 'authenticate' function using the '@scheduler.authenticate' decorator to validate credentials against your own logic.

2. Add them to .dlt/secrets.toml

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[sources.apscheduler_source]
apscheduler_api_key = "your_secret_api_key_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 APScheduler 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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uv run python apscheduler_pipeline.py

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

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Pipeline apscheduler_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset apscheduler_data
The duckdb destination used duckdb:/apscheduler.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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uv run dlthub 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 /jobs and /scheduler from the APScheduler 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 apscheduler_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:5000/scheduler",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": auth},
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "scheduler/jobs"}},
            {"name": "job", "endpoint": {"path": "scheduler/jobs/<job_id>"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apscheduler_pipeline",
        destination="duckdb",
        dataset_name="apscheduler_data",
    )
    load_info = pipeline.run(apscheduler_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("apscheduler_pipeline").dataset()
sessions_df = data.jobs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM apscheduler_data.jobs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("apscheduler_pipeline").dataset()
data.jobs.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 APScheduler 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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Next steps

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

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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