Linux Kernel Python API Docs | dltHub

DAMON is a Linux kernel subsystem providing Data Access MONitoring-based Operation Notation (DAMON) APIs for kernel-space programs to monitor memory access patterns and apply memory management actions. The REST API base URL is (no REST API) - DAMON is a kernel C API, not an HTTP/REST service. and no HTTP auth; requires kernel-level privileges (root) and building/using kernel APIs or privileged userspace tools..

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 Linux Kernel data in under 10 minutes.

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What data can I load from Linux Kernel?

Here are some of the endpoints you can load from Linux Kernel:

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How do I authenticate with the Linux Kernel API?

No HTTP authentication — DAMON is used from kernel-space code or privileged user-space interfaces (requires kernel headers and appropriate kernel privileges when interacting via kernel interfaces).

1. Get your credentials

1. There are no HTTP credentials. To use DAMON, build against kernel headers (include/linux/damon.h) and run code in kernel context or use privileged user-space tooling; ensure you have root privileges and a kernel that includes DAMON support.

2. Add them to .dlt/secrets.toml

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[sources.linux_kernel_source]
# none (no HTTP API)

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 Linux Kernel 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 linux_kernel_pipeline.py

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

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Pipeline linux_kernel_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset linux_kernel_data
The duckdb destination used duckdb:/linux_kernel.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 linux_kernel_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 damon_start and damon_call from the Linux Kernel 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 linux_kernel_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "(no REST API) - DAMON is a kernel C API, not an HTTP/REST service.",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            {"name": "damon_start", "endpoint": {"path": "(kernel API) damon_start"}},
            {"name": "damon_call", "endpoint": {"path": "(kernel API) damon_call"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="linux_kernel_pipeline",
        destination="duckdb",
        dataset_name="linux_kernel_data",
    )
    load_info = pipeline.run(linux_kernel_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("linux_kernel_pipeline").dataset()
sessions_df = data.damon_start.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM linux_kernel_data.damon_start LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("linux_kernel_pipeline").dataset()
data.damon_start.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 Linux Kernel 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 / Permissions

DAMON is a kernel API and requires kernel-level privileges. Use root or appropriate capabilities to create contexts or interact with kernel interfaces. There are no HTTP credentials.

No REST interface

There is no REST/HTTP interface exposed by upstream DAMON documentation. All interactions are through kernel headers (include/linux/damon.h), exported kernel symbols, and kernel threads (kdamond). Do not expect HTTP endpoints or JSON responses.

Common kernel errors / return codes

DAMON C functions return 0 on success and negative errno-style values on failure (e.g., -EINVAL, -ENOMEM, -EBUSY). Check kernel logs (dmesg) and return codes from API calls for diagnostics.

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