HdfsCLI Python API Docs | dltHub

HdfsCLI is a Python client library and CLI that provides access to HDFS via the WebHDFS REST API. The REST API base URL is http[s]://<NAMENODE_HOST>:<HTTP_PORT>/webhdfs/v1 and Supports user.name, delegation tokens, Kerberos SPNEGO and optional OAuth2..

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 HdfsCLI data in under 10 minutes.

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What data can I load from HdfsCLI?

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

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How do I authenticate with the HdfsCLI API?

When security is off, pass the user via the user.name query parameter or the InsecureClient user argument. For secured clusters use a delegation token (delegation query parameter or TokenClient) or Kerberos SPNEGO via KerberosClient.

1. Get your credentials

1. For delegation token: obtain a Hadoop delegation token from your Hadoop admin or via the WebHDFS GETDELEGATIONTOKEN operation, which returns a Token JSON object containing the token string. 2) For Kerberos: acquire a Kerberos principal/keytab and run kinit; then configure KerberosClient in hdfscli. 3) For unsecured clusters: no credentials needed; set the user in ~/.hdfscli.cfg or pass the user argument to InsecureClient.

2. Add them to .dlt/secrets.toml

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[sources.hdfs_cli_source]
token = "your_hadoop_delegation_token_urlstring_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 HdfsCLI 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 hdfs_cli_pipeline.py

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

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Pipeline hdfs_cli_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hdfs_cli_data
The duckdb destination used duckdb:/hdfs_cli.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 hdfs_cli_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 list and status from the HdfsCLI 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 hdfs_cli_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http[s]://<NAMENODE_HOST>:<HTTP_PORT>/webhdfs/v1",
            "auth": {
                "type": "api_key",
                "token": token,
            },
        },
        "resources": [
            {"name": "list", "endpoint": {"path": "webhdfs/v1/{path}?op=LISTSTATUS", "data_selector": "FileStatuses.FileStatus"}},
            {"name": "status", "endpoint": {"path": "webhdfs/v1/{path}?op=GETFILESTATUS", "data_selector": "FileStatus"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hdfs_cli_pipeline",
        destination="duckdb",
        dataset_name="hdfs_cli_data",
    )
    load_info = pipeline.run(hdfs_cli_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("hdfs_cli_pipeline").dataset()
sessions_df = data.list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM hdfs_cli_data.list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("hdfs_cli_pipeline").dataset()
data.list.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 HdfsCLI 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 security is enabled and you receive 401 Unauthorized or a RemoteException with SecurityException, verify whether Kerberos SPNEGO or a delegation token is required. Ensure Kerberos tickets are valid (kinit) or that the token is correctly passed via the delegation query parameter.

Permission denied / AccessControlException

HTTP 403 responses containing RemoteException with AccessControlException indicate insufficient HDFS permissions. Check ACLs, file ownership, and the effective user (user.name or Kerberos principal).

File not found

HTTP 404 with RemoteException FileNotFoundException means the requested path does not exist. Confirm the path syntax and that the client’s root configuration is correct.

Delegation token expiry and renewal

Delegation tokens expire. Use the WebHDFS RENEWDELEGATIONTOKEN operation or request a fresh token via GETDELEGATIONTOKEN before expiry; otherwise requests will fail with 401/403.

NameNode / network errors

Connection timeouts or DNS failures indicate mis‑configured host/port or that WebHDFS is not enabled (dfs.webhdfs.enabled). Verify the base URL and network accessibility.

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