`aidp-rest-generic` — Generic REST via AIDP `aidataplatform` (`type=GENERIC_REST`)

aidp-rest-generic — Generic REST via AIDP aidataplatform (type=GENERIC_REST)

Read from arbitrary REST APIs as a Spark DataFrame. The connector requires a server-published manifest (a small JSON describing each API endpoint, parameters, and response schema) so it knows how to parse responses without a custom integration.

When to use

• Any REST endpoint that exposes a manifest URL (custom enterprise APIs commonly do).
• Mentioned: "Generic REST", "manifest URL", "REST connector".

When NOT to use

• For Fusion ERP/HCM/SCM REST → aidp-fusion-rest. Different shape (no manifest; ≤499/page paging).
• For Fusion BICC bulk extracts → aidp-fusion-bicc.
• For EPM Cloud Planning → aidp-epm-cloud.
• For Essbase → aidp-essbase.

Read

import os
from oracle_ai_data_platform_connectors.aidataplatform import (
    AIDP_FORMAT, aidataplatform_options,
)

opts = aidataplatform_options(
    type="GENERIC_REST",
    user=os.environ["REST_USER"],
    password=os.environ["REST_PASSWORD"],
    schema=os.environ.get("REST_SCHEMA", "default"),
    extra={
        "base.url":     os.environ["REST_BASE_URL"],   # e.g. http://api.internal/v1
        "manifest.url": os.environ["REST_MANIFEST_URL"], # e.g. http://api.internal/v1/manifest
        "auth.type":    "basic",
        "api":          os.environ["REST_API"],         # e.g. "getOrdersByOrderID"
        # Any number of derived.property.<name> values feed into the API call:
        "derived.property.orderNo": os.environ.get("REST_ORDER_NO", "12345"),
    },
)
df = spark.read.format(AIDP_FORMAT).options(**opts).load()
df.show(5)

Manifest contract

The manifest describes:

• apis — the named API operations (e.g. getOrdersByOrderID)
• parameters — what the connector should send (path/query/body)
• responseSchema — the Spark schema the connector should infer

If you don't have a manifest URL, this connector won't work — fall back to the requests-based pattern in aidp-fusion-rest and adapt for your API.

Multiple derived properties

Pass each as a separate extra={} key:

extra={
    "base.url":     "...",
    "manifest.url": "...",
    "auth.type":    "basic",
    "api":          "searchOrders",
    "derived.property.fromDate":  "2025-01-01",
    "derived.property.toDate":    "2025-12-31",
    "derived.property.status":    "OPEN",
}

Manifest from a workspace / volume path (manifest.path)

If the manifest is a static file you've uploaded to your AIDP workspace or a Volume — instead of being served over HTTP — use manifest.path instead of manifest.url. Same shape, different source. Useful when the manifest is hand-authored or version-pinned alongside your notebook.

opts = aidataplatform_options(
    type="GENERIC_REST",
    user=os.environ["REST_USER"],
    password=os.environ["REST_PASSWORD"],
    schema="default",
    extra={
        "base.url":      os.environ["REST_BASE_URL"],
        "manifest.path": "/Volumes/myvol/manifests/orders_api.json",
        "auth.type":     "basic",
        "api":           "searchOrders",
        "derived.property.status": "OPEN",
    },
)
df = spark.read.format(AIDP_FORMAT).options(**opts).load()

The path can be:

• /Volumes/<catalog>/<schema>/<volume>/path/to/manifest.json (AIDP Volume)
• /Workspace/Shared/.../manifest.json (workspace file — works but FUSE-flaky)

Volume paths are the preferred location.

Gotchas

• auth.type=basic only. If the API uses OAuth / API key headers / mTLS, this connector won't help — use the Python requests path.
• Manifest must be reachable from the AIDP cluster's VCN. Egress restrictions apply.
• Schema schema option is the AIDP/Spark logical schema for the resulting DataFrame, not a server-side one. Use default if unsure.
• Paging is handled by the connector based on the manifest. If the manifest declares maxPageSize, the connector batches automatically.

References

• Helper: scripts/oracle_ai_data_platform_connectors/aidataplatform.py
• Official sample: oracle-samples/oracle-aidp-samples → data-engineering/ingestion/Read_Only_Ingestion_Connectors.ipynb