GCS Data Lake: Onboarding via API

This feature requires StarTree release 0.16.0 or later, and must be enabled on demand — contact StarTree support to activate it.

This page shows how to onboard a GCS Data Lake External Table with the StarTree controller REST APIs instead of the Data Portal UI. Use it for automation, infrastructure-as-code, or onboarding many tables at once. GCS Data Lake uses Google Cloud Storage accessed through its S3-compatible (“interop”) endpoint (storage.googleapis.com). The catalog type is gcs-interop and it reuses the same S3 wiring as S3 Data Lake — only the endpoint, addressing style, and credential source differ.

How it works

Onboarding is four calls. Each one feeds the next:

Validate & browse   POST /connections/browse   →  validate; list files under the GCS prefix
Preview             POST /tables/preview       →  infer schema + enriched table config
Create schema       POST /schemas              →  register the schema
Create table        POST /tables               →  register the External Table

The preview call (step 2) is the core step: it samples the source, infers a Pinot schema, and returns a ready-to-use table config. Steps 3 and 4 just persist its output. Once the table exists, the controller’s watcher runs the first sync and re-syncs on schedule — no manual trigger. Track progress with the observability endpoints.

All paths are relative to your controller base URL. The examples assume export CONTROLLER=https://<your-controller>. On StarTree Cloud, the controller is reached through the data-plane proxy, so use export CONTROLLER=https://<data-plane-host>/api/pinot.If your controller requires authentication, add an Authorization header to every request — e.g. -H "Authorization: Bearer <token>". The examples below omit it for brevity.

Prerequisites

StarTree release with GCS External Table support enabled, and tiered storage configured on the cluster with a GCS_INTEROPERABLE tier backend. Contact StarTree support if unsure.
Network access to the controller REST endpoint, plus an Authorization token if your cluster requires auth.
A GCS bucket with Parquet files and a GCS HMAC key (access ID + secret) with storage.objects.get and storage.objects.list on the bucket. Generate HMAC keys in the Google Cloud Console → Cloud Storage → Settings → Interoperability.
The GCS bucket name, key prefix, and the project’s region (used as a placeholder — GCS ignores it but the field is required).

Limitations

HMAC keys are the only credential type — there’s no IAM-role or workload-identity option for GCS Data Lake today.
disable.integrity.protections must be true for every GCS connection — GCS’s checksum behavior differs from AWS S3’s, and the AWS SDK mis-validates it otherwise. See the Note below.
No schema evolution — same limitation as the S3 Data Lake source; a source-side column rename requires a manual schema change.

Authentication

GCS Data Lake authenticates using HMAC keys (Google’s S3-interop credentials) — there’s no assumed-role equivalent since this path goes through the AWS SDK’s S3-compatible client, not native GCP IAM. Choose one of two methods.

disable.integrity.protections must be set to true for all GCS connections, regardless of method. GCS rejects the AWS SDK’s default request checksums on writes and returns whole-object checksums on range GETs that the SDK mis-validates. Setting this flag relaxes both to WHEN_REQUIRED, which is required for reads and writes to succeed.

Inline HMAC keys

Quick tests, or when Secret Manager is not available. Generate HMAC keys in the Google Cloud Console → Cloud Storage → Settings → Interoperability.

{
  "catalog.gcs-interop.bucketName": "<gcs-bucket>",
  "catalog.gcs-interop.prefix": "<prefix>",
  "catalog.gcs-interop.region": "auto",
  "catalog.gcs-interop.endpoint": "https://storage.googleapis.com",
  "catalog.gcs-interop.accessKey": "<hmac-access-id>",
  "catalog.gcs-interop.secretKey": "<hmac-secret>",
  "catalog.gcs-interop.disable.integrity.protections": "true"
}

GCP Secret Manager (recommended for production)

accessKey/secretKey are treated as secret names rather than literal values, and resolved from GCP Secret Manager at read time — no HMAC material stored in the table config.

{
  "catalog.gcs-interop.bucketName": "<gcs-bucket>",
  "catalog.gcs-interop.prefix": "<prefix>",
  "catalog.gcs-interop.region": "auto",
  "catalog.gcs-interop.endpoint": "https://storage.googleapis.com",
  "catalog.gcs-interop.accessKey": "<access-id-secret-name>",
  "catalog.gcs-interop.secretKey": "<secret-key-secret-name>",
  "catalog.gcs-interop.keyType": "SECRET",
  "catalog.gcs-interop.secretmanagertype": "GCS",
  "catalog.gcs-interop.gcpprojectid": "<gcp-project-id>",
  "catalog.gcs-interop.gcpkeypath": "<path-to-sa-key.json>",
  "catalog.gcs-interop.disable.integrity.protections": "true"
}

The service account at gcpkeypath needs Secret Manager read access to the two named secrets.

Step 1: Validate and browse the connection

POST /connections/browse There is no separate “validate” endpoint. Browsing the catalog is the validation step: a 200 with an items list (even an empty one) confirms your credentials and connectivity.

Set path to "" to browse the root prefix — this both validates the connection and lists files/directories.

Request

Field	Type	Required	Description
`connection.type`	string	Yes	Use `CATALOG` for all External Table sources.
`connection.params`	object	Yes	GCS connection settings (see examples below).
`path`	string	No	Where to browse. `""` or omitted = root prefix.

Inline HMAC
GCP Secret Manager

curl -X POST "$CONTROLLER/connections/browse" \
  -H "Content-Type: application/json" \
  -d '{
    "connection": {
      "type": "CATALOG",
      "params": {
        "catalogType": "gcs-interop",
        "catalog.gcs-interop.bucketName": "<gcs-bucket>",
        "catalog.gcs-interop.prefix": "<prefix>",
        "catalog.gcs-interop.region": "auto",
        "catalog.gcs-interop.endpoint": "https://storage.googleapis.com",
        "catalog.gcs-interop.accessKey": "<hmac-access-id>",
        "catalog.gcs-interop.secretKey": "<hmac-secret>",
        "catalog.gcs-interop.disable.integrity.protections": "true"
      }
    },
    "path": ""
  }'

curl -X POST "$CONTROLLER/connections/browse" \
  -H "Content-Type: application/json" \
  -d '{
    "connection": {
      "type": "CATALOG",
      "params": {
        "catalogType": "gcs-interop",
        "catalog.gcs-interop.bucketName": "<gcs-bucket>",
        "catalog.gcs-interop.prefix": "<prefix>",
        "catalog.gcs-interop.region": "auto",
        "catalog.gcs-interop.endpoint": "https://storage.googleapis.com",
        "catalog.gcs-interop.accessKey": "<access-id-secret-name>",
        "catalog.gcs-interop.secretKey": "<secret-key-secret-name>",
        "catalog.gcs-interop.keyType": "SECRET",
        "catalog.gcs-interop.secretmanagertype": "GCS",
        "catalog.gcs-interop.gcpprojectid": "<gcp-project-id>",
        "catalog.gcs-interop.gcpkeypath": "<path-to-sa-key.json>",
        "catalog.gcs-interop.disable.integrity.protections": "true"
      }
    },
    "path": ""
  }'

Response

{
  "items": [
    { "name": "2024/01/", "type": "DIR" },
    { "name": "data.parquet", "type": "FILE" }
  ]
}

`items[].type`	Meaning
`DIR`	A directory you can drill into — pass its `name` as `path`.
`FILE`	A Parquet file under the prefix.

Step 2: Preview the schema

POST /tables/preview Samples the Parquet files, infers a Pinot schema, and returns an enriched table config plus sample rows. Review it, tweak if needed, then carry the schema and config forward to steps 3 and 4.

The request and response share the same JSON shape. You send a tableConfig describing the source; the response fills in schema, the enriched tableConfigs.offline, sampled rows, and a summary.

Request

Top-level fields:

Field	Type	Required	Description
`tableConfig`	object	Yes	A single OFFLINE table config whose `ExternalTableSyncTask` block describes the source.
`config`	object	No	Sampling and inference controls (see below). Defaults are applied if omitted.
`schema`	object	No	An explicit schema to use instead of inferring one.

config.inference — how the schema is derived:

Field	Type	Default	Description
`embeddedSchemaEnabled`	boolean	`false`	Read the schema embedded in the Parquet metadata. Set this to `true` for External Tables.
`schemaInferenceEnabled`	boolean	`false`	Infer the schema from sampled rows. Fallback when no embedded schema exists.

config.sampling — how rows are sampled:

Field	Type	Default	Description
`min`	integer	`1`	Minimum records to sample.
`max`	integer	`100`	Maximum records to sample.

To list source files without sampling any data, set config.previewFiles.previewFilesOnly = true. The response returns matching file URIs in sourceFiles and skips schema inference. (OFFLINE only.)

Inline HMAC
GCP Secret Manager

curl -X POST "$CONTROLLER/tables/preview" \
  -H "Content-Type: application/json" \
  -d '{
    "tableConfig": {
      "tableName": "my_gcs_table_OFFLINE",
      "tableType": "OFFLINE",
      "task": {
        "taskTypeConfigsMap": {
          "ExternalTableSyncTask": {
            "catalogType": "gcs-interop",
            "executor": "controller",
            "inputFormat": "parquet",
            "catalog.gcs-interop.bucketName": "<gcs-bucket>",
            "catalog.gcs-interop.prefix": "<prefix>",
            "catalog.gcs-interop.region": "auto",
            "catalog.gcs-interop.endpoint": "https://storage.googleapis.com",
            "catalog.gcs-interop.accessKey": "<hmac-access-id>",
            "catalog.gcs-interop.secretKey": "<hmac-secret>",
            "catalog.gcs-interop.table.namespace": "default",
            "catalog.gcs-interop.table.tableName": "my_gcs_table",
            "catalog.gcs-interop.disable.integrity.protections": "true"
          }
        }
      }
    },
    "config": {
      "inference": { "embeddedSchemaEnabled": true },
      "sampling":  { "min": 1, "max": 100 }
    }
  }'

curl -X POST "$CONTROLLER/tables/preview" \
  -H "Content-Type: application/json" \
  -d '{
    "tableConfig": {
      "tableName": "my_gcs_table_OFFLINE",
      "tableType": "OFFLINE",
      "task": {
        "taskTypeConfigsMap": {
          "ExternalTableSyncTask": {
            "catalogType": "gcs-interop",
            "executor": "controller",
            "inputFormat": "parquet",
            "catalog.gcs-interop.bucketName": "<gcs-bucket>",
            "catalog.gcs-interop.prefix": "<prefix>",
            "catalog.gcs-interop.region": "auto",
            "catalog.gcs-interop.endpoint": "https://storage.googleapis.com",
            "catalog.gcs-interop.accessKey": "<access-id-secret-name>",
            "catalog.gcs-interop.secretKey": "<secret-key-secret-name>",
            "catalog.gcs-interop.keyType": "SECRET",
            "catalog.gcs-interop.secretmanagertype": "GCS",
            "catalog.gcs-interop.gcpprojectid": "<gcp-project-id>",
            "catalog.gcs-interop.gcpkeypath": "<path-to-sa-key.json>",
            "catalog.gcs-interop.table.namespace": "default",
            "catalog.gcs-interop.table.tableName": "my_gcs_table",
            "catalog.gcs-interop.disable.integrity.protections": "true"
          }
        }
      }
    },
    "config": {
      "inference": { "embeddedSchemaEnabled": true },
      "sampling":  { "min": 1, "max": 100 }
    }
  }'

Response

Field	Type	Description
`schema`	object	The resolved Pinot schema. Use this in step 3.
`tableConfigs.offline`	object	The enriched OFFLINE table config, ready to persist. Use this in step 4. Secrets are masked as `*****`.
`rows`	array	Sample records after schema + ingestion transforms.
`sourceRows`	array	Raw records before transformation.
`summary`	object	Run summary: `nSourceRows`, `nRows`, `nColumns`, and `summary.batch.nMatchingFiles` (files found).

Step 3: Create the schema

POST /schemas Send the schema object from the preview response. Rename its schemaName to match your table.

curl -X POST "$CONTROLLER/schemas" \
  -H "Content-Type: application/json" \
  -d @schema.json

{ "status": "my_gcs_table successfully added" }

Step 4: Create the table

POST /tables Send the enriched tableConfigs.offline object from the preview response. The ExternalTableSyncTask block marks the table as external, and the tierConfigs array pins it to the GCS_INTEROPERABLE tier.

curl -X POST "$CONTROLLER/tables" \
  -H "Content-Type: application/json" \
  -d @tableConfig.json

{ "status": "Table my_gcs_table_OFFLINE successfully added" }

The controller’s External Table watcher then discovers the table, runs the first sync, and re-syncs at the schedule (cron) interval. There is no separate start call.

The tableConfigs.offline returned by /tables/preview includes a tierConfigs block pre-configured for GCS_INTEROPERABLE. Verify that the tier backend credentials match the source catalog credentials (they can differ if you use separate HMAC keys or Secret Manager secrets for the deep store).

The first sync runs on the watcher’s next tick. To kick it off immediately:

curl -X POST "$CONTROLLER/tasks/schedule?taskType=ExternalTableSyncTask&tableName=my_gcs_table_OFFLINE"

Quickstart: onboard a table end-to-end

The whole flow as one script (inline HMAC mode). Fill in the variables at the top, run the script, and the first sync starts automatically.

#!/usr/bin/env bash
set -euo pipefail

CONTROLLER="https://<your-controller>"
AUTH="Authorization: Bearer <token>"
BUCKET="<gcs-bucket>"
PREFIX="<prefix>"
REGION="<region>"       # Required field; GCS ignores the value
HMAC_KEY="<hmac-access-id>"
HMAC_SECRET="<hmac-secret>"
TABLE="my_gcs_table"

# 1. Validate & browse — confirms credentials and lists files.
curl -sf -X POST "$CONTROLLER/connections/browse" \
  -H "$AUTH" -H 'Content-Type: application/json' \
  --data-raw "{
    \"connection\": {
      \"type\": \"CATALOG\",
      \"params\": {
        \"catalogType\": \"gcs-interop\",
        \"catalog.gcs-interop.bucketName\": \"$BUCKET\",
        \"catalog.gcs-interop.prefix\": \"$PREFIX\",
        \"catalog.gcs-interop.region\": \"auto\",
        \"catalog.gcs-interop.endpoint\": \"https://storage.googleapis.com\",
        \"catalog.gcs-interop.accessKey\": \"$HMAC_KEY\",
        \"catalog.gcs-interop.secretKey\": \"$HMAC_SECRET\",
        \"catalog.gcs-interop.disable.integrity.protections\": \"true\"
      }
    },
    \"path\": \"\"
  }" | jq .

# 2. Preview — infer schema + enriched table config.
curl -sf -X POST "$CONTROLLER/tables/preview" \
  -H "$AUTH" -H 'Content-Type: application/json' \
  --data-raw "{
    \"tableConfig\": {
      \"tableName\": \"${TABLE}_OFFLINE\",
      \"tableType\": \"OFFLINE\",
      \"task\": {
        \"taskTypeConfigsMap\": {
          \"ExternalTableSyncTask\": {
            \"catalogType\": \"gcs-interop\",
            \"executor\": \"controller\",
            \"inputFormat\": \"parquet\",
            \"catalog.gcs-interop.bucketName\": \"$BUCKET\",
            \"catalog.gcs-interop.prefix\": \"$PREFIX\",
            \"catalog.gcs-interop.region\": \"auto\",
            \"catalog.gcs-interop.endpoint\": \"https://storage.googleapis.com\",
            \"catalog.gcs-interop.accessKey\": \"$HMAC_KEY\",
            \"catalog.gcs-interop.secretKey\": \"$HMAC_SECRET\",
            \"catalog.gcs-interop.table.namespace\": \"default\",
            \"catalog.gcs-interop.table.tableName\": \"$TABLE\",
            \"catalog.gcs-interop.disable.integrity.protections\": \"true\"
          }
        }
      }
    },
    \"config\": {
      \"inference\": { \"embeddedSchemaEnabled\": true },
      \"sampling\":  { \"min\": 1, \"max\": 100 }
    }
  }" > preview.json

# 3. Extract schema and table config from preview response.
jq --arg t "$TABLE" '.schema | .schemaName = $t' preview.json > schema.json
jq '.tableConfigs.offline'                        preview.json > tableConfig.json

# 4. Create the schema.
curl -sf -X POST "$CONTROLLER/schemas" \
  -H "$AUTH" -H 'Content-Type: application/json' -d @schema.json

# 5. Create the table — watcher starts the first sync automatically.
curl -sf -X POST "$CONTROLLER/tables" \
  -H "$AUTH" -H 'Content-Type: application/json' -d @tableConfig.json

# 6. (optional) Poll until sync completes.
curl -sf "$CONTROLLER/tables/${TABLE}_OFFLINE/externalTable/status" -H "$AUTH" \
  | jq -e '.status == "COMPLETED" and .segmentsUploaded == .filesDiscovered'

Once step 6 exits 0, verify it’s queryable.

Monitor onboarding

Three read-only endpoints report ingestion progress — run status, ingestion checkpoint, and source file count — and require executor=controller (set automatically). See Observability for full request and response details.

Verify it’s queryable

When status is COMPLETED and segmentsUploaded matches filesDiscovered, run a query against the broker (or the Data Portal query console) to confirm the data is live:

SELECT count(*) FROM my_gcs_table;

The count will be much larger than the preview’s summary.nSourceRows (preview only samples up to ~100 rows) — confirm it’s non-zero and plausible for your dataset. If status is COMPLETED but the count is 0, give segments a moment to load on the servers, then recheck; if it persists, see Troubleshooting.

GCS-specific config reference

All keys go under the ExternalTableSyncTask block in taskTypeConfigsMap.

Key	Required	Description
`catalog.gcs-interop.bucketName`	Yes	GCS bucket name.
`catalog.gcs-interop.prefix`	Yes	Key prefix for the Parquet data (e.g. `path/to/data/`).
`catalog.gcs-interop.region`	Yes	Region string. GCS ignores the value but the field must be present with the value “auto”.
`catalog.gcs-interop.endpoint`	Yes	Always `https://storage.googleapis.com`.
`catalog.gcs-interop.accessKey`	Yes	HMAC access ID, or GCP Secret Manager secret name if `keyType=SECRET`.
`catalog.gcs-interop.secretKey`	Yes	HMAC secret, or GCP Secret Manager secret name if `keyType=SECRET`.
`catalog.gcs-interop.disable.integrity.protections`	Yes	Must be `"true"`. Required for GCS range-GET and PUT compatibility with the AWS SDK.
`catalog.gcs-interop.keyType`	No	Set to `SECRET` to resolve `accessKey`/`secretKey` via GCP Secret Manager.
`catalog.gcs-interop.secretmanagertype`	If `keyType=SECRET`	Must be `GCS`.
`catalog.gcs-interop.gcpprojectid`	If `keyType=SECRET`	GCP project ID for Secret Manager lookups.
`catalog.gcs-interop.gcpkeypath`	If `keyType=SECRET`	Path to a service-account key JSON with Secret Manager read access.
`catalog.gcs-interop.table.namespace`	No	Logical namespace. Use `default` for a flat prefix.
`catalog.gcs-interop.table.tableName`	No	Logical table name (any value; used internally).

What’s next

Now that the table is created and data is loading, these are the highest-impact follow-up steps:

Add indexes for your query patterns. Without indexes, every query scans all remote Parquet data. Add a range index on time/numeric columns, an inverted index on low-cardinality filter columns, and a bloom filter on high-cardinality ID columns. → Indexes
Enable caching and preload. Set enable.prefetch.page.cache=true and preload.enable=true on the S3 tier so index data is served from local disk on repeated queries instead of re-fetched from S3. → Data and Index Caching
Protect large-scan queries from OOM. For tables that receive heavy aggregations or wide scans, enable the query OOM killer so a runaway query is killed instead of crashing the server. → Best Practices & Configs — Query OOM protection
Monitor ongoing syncs. Use the observability endpoints to check run status, ingestion checkpoint, and source file count after each scheduled sync. → Observability

For common questions and failures, see the FAQ and Troubleshooting.

Get Started

Ingestion

External Tables

Query Data

Manage Data

Observability

Visualize Data

Cluster Operations

Manage Security

Release Notes

Reference

GCS Data Lake: Onboarding via API

How it works

Prerequisites

Limitations

Authentication

Inline HMAC keys

GCP Secret Manager (recommended for production)

Step 1: Validate and browse the connection

Request

Response

Step 2: Preview the schema

Request

Response

Step 3: Create the schema

Step 4: Create the table

Quickstart: onboard a table end-to-end

Monitor onboarding

Verify it’s queryable

GCS-specific config reference

What’s next

​How it works

​Prerequisites

​Limitations

​Authentication

​Inline HMAC keys

​GCP Secret Manager (recommended for production)

​Step 1: Validate and browse the connection

​Request

​Response

​Step 2: Preview the schema

​Request

​Response

​Step 3: Create the schema

​Step 4: Create the table

​Quickstart: onboard a table end-to-end

​Monitor onboarding

​Verify it’s queryable

​GCS-specific config reference

​What’s next

How it works

Prerequisites

Limitations

Authentication

Inline HMAC keys

GCP Secret Manager (recommended for production)

Step 1: Validate and browse the connection

Request

Response

Step 2: Preview the schema

Request

Response

Step 3: Create the schema

Step 4: Create the table

Quickstart: onboard a table end-to-end

Monitor onboarding

Verify it’s queryable

GCS-specific config reference

What’s next