Load Quickwit data to DuckDB

Quickwit is a high-performance search engine for log management and observability that provides a REST API for indexing, searching, and cluster management. Everything needed to build a working Quickwit → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Quickwit to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Quickwit to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Quickwit API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Quickwit API at a glance

These values come from the Quickwit API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Quickwit API?

Quickwit does not provide built-in authentication for its REST API. Users are typically expected to secure access via network/firewall configurations or by placing the service behind a reverse proxy that handles authentication headers.

1. Get your credentials

Quickwit does not natively manage API keys or user credentials at the application level. To secure your Quickwit REST API, you must deploy the Quickwit node behind a reverse proxy (such as Nginx, Traefik, or an AWS Application Load Balancer). Follow these steps: 1. Deploy your Quickwit instance in a private network or behind a reverse proxy. 2. Configure the proxy to enforce authentication (e.g., Basic Authentication, Bearer token, or mTLS). 3. Generate your credentials (username/password or token) within the proxy's configuration management system. 4. Use these proxy-managed credentials when configuring your dlt source to include them in the HTTP headers.

2. Add them to .dlt/secrets.toml

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[sources.quickwit_source]
api_base = "https://your-proxy-domain.com/api/v1"
auth_username = "your_username"
auth_password = "your_password"
# If using a Bearer token:
# auth_token = "Bearer your_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Quickwit data can I load into DuckDB?

These are the Quickwit endpoints dlt can load into DuckDB:

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How do I load only new Quickwit records?

Quickwit exposes start_offset on api/v1/{index}/search, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "search", "endpoint": {
    "path": "api/v1/{index}/search",
    "data_selector": "hits",
    "incremental": {"cursor_path": "start_offset", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Quickwit pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /indexes and /{index_id}/search from the Quickwit API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def quickwit_source(api_base=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<quickwit_node_host>:<port>/api/v1",
            "auth": {"type": "api_key", "api_key": api_base},
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "api/v1/{index}/search", "data_selector": "hits"}},
            {"name": "indexes", "endpoint": {"path": "api/v1/indexes"}}
        ],
    }
    yield from rest_api_resources(config)


def load_quickwit_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="quickwit_pipeline",
        destination="duckdb",
        dataset_name="quickwit_data",
    )
    load_info = pipeline.run(quickwit_source())
    print(load_info)


if __name__ == "__main__":
    load_quickwit_to_duckdb()

Run it with python quickwit_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Quickwit data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("quickwit_pipeline").dataset()
df = data.search.df()
print(df.head())

SQL:

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SELECT * FROM quickwit_data.search LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Quickwit to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Quickwit loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Quickwit data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack