InfluxDB Cloud connector

Use this as an implementation starting point: adapt the worked example to the requested dataset and the source documentation. See Custom API for the shared SDK pattern and document-upload workflow.

What this connector is forCopy Anchor Link

Time-series data for sensor measurements, metrics, and telemetry analysis.

Use this guide to choose the resources and row meanings for a Vortexify source integration. Connection ownership belongs to a connector; each dataset gets its own sync, configuration, and extraction behavior.

Data and tables you can pullCopy Anchor Link

The following table families are a starting point for source selection. They describe possible datasets to implement using the vendor reference, not a guarantee that the example implements every table. Actual availability depends on the product edition, deployment, account permissions, and API/schema version.

Relationships and extraction considerationsCopy Anchor Link

These are proposed dataset names, not a universal schema guaranteed by the vendor. Preserve source object IDs and relationship references, and split nested child collections into separate tables when appropriate. Choose the concrete endpoint/report and row meaning from the vendor reference before implementing a sync.

Connecting to InfluxDB CloudCopy Anchor Link

Identify the product generation first. InfluxDB Cloud TSM and InfluxDB 3 use different query languages/interfaces. Use a scoped token, correct region/host, and explicit time bounds.

This worked read selects InfluxDB 2 / Cloud TSM: supply url, token, org, and a reviewed time-bounded flux_query. InfluxDB 3 uses a different driver/query contract.

requirements.inCopy Anchor Link

Declare these packages in the resource's requirements.in. Use the published vortexify package for the SDK and the source-specific driver/client shown below. Choose compatible dependency versions for your runtime; the connector and sync reference explains where each file belongs.

vortexify==0.1.1
pandas>=2,<3
influxdb-client

Use the source SDK and its supported runtime. Close source clients and keep credentials in platform secret configuration.

Worked example: InfluxDB 2 Flux query batchesCopy Anchor Link

This source-read pattern shows how to shape the selected resource into DataFrames. Use it inside the dataset sync, retaining shared connection ownership in the connector. Its scope is the named resource and filters; adapt the projection, batching, and preview to the requested table.

Source readCopy Anchor Link

from influxdb_client import InfluxDBClient
import pandas as pd


def read_batches(config):
    with InfluxDBClient(
        url=config["url"], token=config["token"], org=config["org"]
    ) as client:
        batch = []
        for record in client.query_api().query_stream(config["flux_query"]):
            batch.append(record.values)
            if len(batch) == 1000:
                yield pd.DataFrame(batch)
                batch = []
        if batch:
            yield pd.DataFrame(batch)

What to adaptCopy Anchor Link

This explicitly targets InfluxDB Cloud TSM / InfluxDB 2 Flux, not InfluxDB 3. Supply a reviewed Flux query with range(start: ..., stop: ...) and measurement/field filters. The record stream contains long-form field values unless the query pivots them. InfluxDB 3 requires its own SQL/InfluxQL client and contract.

Use the custom API SDK starting point to connect the read operation to Connector and Sync. For a source with its own driver, retain that client in Connector.client() and replace the HTTP request with the driver read. An extract(*, avg_row_size_bytes=None) implementation yields bounded DataFrames. An independent preview() uses a fresh connector and a small read; it must not call extraction or advance real state.

Choose explicit output columns and stable IDs for the requested table. The examples use json_normalize where useful to illustrate row shaping; nested arrays still need deliberate child tables and a stable declared output schema. A first-page sample is useful for development, while a full sync must follow all relevant continuation/parent-resource paths. Design incremental watermarks, update/deletion behavior, and rate-limit handling for the actual source before enabling incremental mode.

The purpose is to give a coding agent a concrete pattern to adapt, including for similar sources. These snippets are examples rather than a claim of complete vendor coverage. Check the source operation reference and representative responses before running with a customer's account; live vendor execution has not been verified here.

Sources and further readingCopy Anchor Link

• Vendor connection and resource reference — The interface/access prerequisites underlying this connection path; follow the selected resource's documentation for fields, queries, and pagination.
• Vortexify connectors and syncs — Connector ownership, schemas, preview isolation, extraction, and checkpoint contracts.
• Vortexify CLI workflow — Registering resources, committing source, and providing credential configuration.