InfluxDB Client Python API Docs | dltHub

InfluxDB Client is a Python client library for interacting with the InfluxDB HTTP REST API (management, query and write APIs) for InfluxDB v2 (and compatibility for 1.8+). The REST API base URL is https://<INFLUXDB_HOST>:8086 and all requests require a token passed in Authorization header (Bearer/Token).

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 InfluxDB Client data in under 10 minutes.

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What data can I load from InfluxDB Client?

Here are some of the endpoints you can load from InfluxDB Client:

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How do I authenticate with the InfluxDB Client API?

InfluxDB v2 uses an API token for auth. Send Authorization: Token <your_token> in request headers (client libraries accept token parameter). The Python client accepts token and org when instantiating InfluxDBClient.

1. Get your credentials

1. Log in to InfluxDB Cloud or your InfluxDB OSS web UI. 2) Open Settings -> Tokens (or Organization -> Tokens). 3) Click Generate Token (choose read/write or read-only scopes). 4) Copy the generated token and store securely.

2. Add them to .dlt/secrets.toml

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[sources.influxdb_client_source]
# put in [sources.influxdb_source]
url = "https://your-influx-host:8086"
token = "your_token_here"
org = "your_org_name"

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 InfluxDB Client 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 influxdb_client_pipeline.py

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

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Pipeline influxdb_client_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset influxdb_client_data
The duckdb destination used duckdb:/influxdb_client.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 influxdb_client_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 buckets and users from the InfluxDB Client 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 influxdb_client_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<INFLUXDB_HOST>:8086",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "buckets", "endpoint": {"path": "api/v2/buckets", "data_selector": "buckets"}},
            {"name": "users", "endpoint": {"path": "api/v2/users", "data_selector": "users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="influxdb_client_pipeline",
        destination="duckdb",
        dataset_name="influxdb_client_data",
    )
    load_info = pipeline.run(influxdb_client_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("influxdb_client_pipeline").dataset()
sessions_df = data.buckets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM influxdb_client_data.buckets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("influxdb_client_pipeline").dataset()
data.buckets.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 InfluxDB Client 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 Authorization header is missing or token invalid the API returns 401/403. Verify token value and scope (read vs write) and that Authorization header is formatted as: Authorization: Token .

Pagination and listing

Most list endpoints (GET /api/v2/buckets, /api/v2/orgs, /api/v2/users, /api/v2/authorizations, /api/v2/labels) support pagination via query parameters offset, limit and an alternative cursor parameter after (seek by resource id). Use limit and subsequent calls with after or offset to walk pages.

Query behavior and chunked responses

Flux query endpoint can return chunked responses when requested; the Python client exposes query_api.query and query_csv and can return generator/iterator or FluxTable objects. When serialized to JSON query results are represented as a top-level array of records (each record an object with keys like _time, _value, _field, _measurement).

Rate limits and server errors

Cloud and reverse proxies may return 429 for rate limiting. Server errors (5xx) map to InfluxDBServerError in python client; client raises InfluxDBClientError for unexpected 4xx/other non-200 statuses.

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