> ## Documentation Index > Fetch the complete documentation index at: https://docs.startree.ai/llms.txt > Use this file to discover all available pages before exploring further. # Nessie: Onboarding via API > Onboard a Nessie-cataloged Iceberg table as a StarTree External Table via the generic Iceberg REST adapter, addressing a branch or ref as the catalog prefix. 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 **Nessie** External Table with the StarTree controller REST APIs. There is no Data Portal wizard for Nessie yet — this API flow is the only way to onboard one today. Nessie tables are accessed over the same **Iceberg REST protocol** used for [AWS Glue](../glue/onboarding-api), [Amazon S3 Tables](../s3tables/onboarding-api), and [Unity Catalog](../unity/onboarding-api) — but there's no dedicated `serviceType` for Nessie. Use the generic spec-compliant adapter, `catalogType=iceberg-rest`, `serviceType=rest`, and address a specific branch/ref with `catalog.iceberg-rest.prefix` (Nessie's version of a multi-catalog prefix). Data files must be **Parquet**. > Using **AWS Glue**, **Amazon S3 Tables**, or **Unity Catalog** instead? See their respective onboarding pages linked above. ## How it works Onboarding is four calls. Each one feeds the next: ```text theme={null} 1. Validate & browse POST /connections/browse → validate; list namespaces/tables 2. Preview POST /tables/preview → infer schema + enriched table config 3. Create schema POST /schemas → register the schema 4. 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](#monitor-onboarding). All paths are relative to your controller base URL. The examples assume `export CONTROLLER=https://`. On StarTree Cloud, the controller is reached through the data-plane proxy, so use `export CONTROLLER=https:///api/pinot`. If your controller requires authentication, add an `Authorization` header to every request — e.g. `-H "Authorization: Bearer "`. The examples below omit it for brevity. ## Prerequisites * StarTree 0.16.0 or later with the External Table Beta feature enabled, and **tiered storage configured** on the cluster. Contact StarTree support if unsure. * Network access to the controller REST endpoint, plus an `Authorization` token if your cluster requires auth. * A running Nessie server (self-hosted or managed) with its Iceberg REST API reachable — this is enabled by default on the official Nessie server/Docker image, served under `/iceberg`. * The **branch/ref** to query (defaults to Nessie's server-side default, usually `main`, if you don't set one explicitly) and the namespace/table to onboard. * If your Nessie deployment sits behind auth (an OAuth2/OIDC proxy, or a static bearer token), have the credentials ready — see [Authentication](#authentication). * Read access to the underlying object storage. This connector supports **S3-compatible storage** for the data files. ## Limitations * **No dedicated `serviceType` for Nessie** — it's reached through the generic spec-compliant `rest` adapter, so there's no Nessie-specific request validation; a misconfigured branch/ref `prefix` surfaces as a generic REST error, not a Nessie-specific one. * **No credential vending.** Unlike Unity Catalog, Nessie doesn't automatically vend short-lived storage credentials — configure static keys, an assumed role, or the cluster's node role explicitly. ## Authentication An External Table authenticates in two places, configured independently: * **Catalog (REST)** — keys under `catalog.iceberg-rest.auth.rest.*`. Choose one of three methods. * **Storage (S3 data files)** — keys under `catalog.iceberg-rest.auth.storage.*`. Choose one of three methods — same mechanism used for [Glue](../glue/onboarding-api#storage-authentication-choose-one) and [S3 Tables](../s3tables/onboarding-api#storage-authentication-choose-one). ### Catalog (REST) authentication: choose one #### No authentication (default for self-hosted Nessie) Most self-hosted Nessie deployments (e.g. the quickstart Docker image) run without REST auth. Omit `auth.rest.*` entirely. ```json theme={null} {} ``` #### Bearer token `authType` doesn't need to be set explicitly — it's auto-detected as token auth when `.token` is present. ```json theme={null} { "catalog.iceberg-rest.auth.rest.token": "" } ``` #### OAuth2 client-credentials Use this if your Nessie deployment sits behind an OAuth2/OIDC proxy (e.g. Keycloak) that requires a client-credentials grant. `authType` is optional; it's auto-detected as `oauth2` once the three required keys below are all present. ```json theme={null} { "catalog.iceberg-rest.auth.rest.authType": "oauth2", "catalog.iceberg-rest.auth.rest.oauthTokenUri": "", "catalog.iceberg-rest.auth.rest.oauthClientId": "", "catalog.iceberg-rest.auth.rest.oauthClientSecret": "", "catalog.iceberg-rest.auth.rest.oauthScopes": "" } ``` ### Storage authentication: choose one #### Assumed IAM role (recommended for production) No static secrets in the table config — every S3 read assumes `roleArn` via STS. ```json theme={null} { "catalog.iceberg-rest.auth.storage.roleArn": "arn:aws:iam::123456789012:role/pinot-external-table-reader", "catalog.iceberg-rest.auth.storage.externalId": "", "catalog.iceberg-rest.auth.storage.region": "us-west-1" } ``` #### Cluster node role Omit every `auth.storage.*` credential key — only `region` is required. Credential resolution falls back to the AWS SDK default chain (the node's instance profile / IRSA role), which must already have S3 access. ```json theme={null} { "catalog.iceberg-rest.auth.storage.region": "us-west-1" } ``` #### Static access keys Quick tests, or when role-based access isn't available. Accepts either `accessKeyId`/`secretAccessKey` or the shorter `accessKey`/`secretKey` aliases. ```json theme={null} { "catalog.iceberg-rest.auth.storage.accessKeyId": "", "catalog.iceberg-rest.auth.storage.secretAccessKey": "", "catalog.iceberg-rest.auth.storage.region": "us-west-1" } ``` ### Selecting a branch or ref `catalog.iceberg-rest.prefix` pins every request to a specific Nessie branch, tag, or ref (e.g. `main`, a feature branch, or a commit hash) — this is the same operator-supplied "prefix" mechanism generic multi-catalog Iceberg REST servers use, applied to Nessie's branch model. Leave it unset to use the server's default branch. ```json theme={null} { "catalog.iceberg-rest.prefix": "main" } ``` *** ## 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** — this both validates the connection and lists namespaces. * Set `path` to a namespace name to list the **tables** inside it. ### Request | Field | Type | Required | Description | | ------------------- | ------ | -------- | ------------------------------------------------------------------------------------ | | `connection.type` | string | Yes | Use `CATALOG` for all External Table sources. | | `connection.params` | object | Yes | Catalog-specific connection settings (see example below). | | `path` | string | No | Where to browse. `""` or omitted = root. A namespace name = that namespace's tables. | ```bash theme={null} curl -X POST "$CONTROLLER/connections/browse" \ -H "Content-Type: application/json" \ -d '{ "connection": { "type": "CATALOG", "params": { "catalogType": "iceberg-rest", "catalog.iceberg-rest.serviceType": "rest", "catalog.iceberg-rest.restUri": "http://:19120/iceberg", "catalog.iceberg-rest.prefix": "main", "catalog.iceberg-rest.auth.storage.region": "us-west-1" } }, "path": "" }' ``` | Key | Value | Description | | ---------------------------------- | ------------------------------------ | -------------------------------------------------------------------------------------- | | `catalog.iceberg-rest.restUri` | `http://:19120/iceberg` | The Nessie server's Iceberg REST endpoint. | | `catalog.iceberg-rest.serviceType` | `rest` | Selects the generic spec-compliant adapter — there's no Nessie-specific `serviceType`. | | `catalog.iceberg-rest.prefix` | `main` (or any branch/ref) | Pins requests to a Nessie branch, tag, or ref. | ### Response ```json theme={null} { "items": [ { "name": "transportation", "type": "NAMESPACE" }, { "name": "nyc_taxi_trips", "type": "TABLE" } ] } ``` | `items[].type` | Meaning | | -------------- | ----------------------------------------------------------- | | `NAMESPACE` | A namespace you can drill into — pass its `name` as `path`. | | `TABLE` | A selectable table. | *** ## Step 2: Preview the schema **`POST /tables/preview`** Samples the source, infers a Pinot schema, and returns an enriched table config (S3 tier, raw field configs, time column) 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 Iceberg metadata. **Set this to `true` for External Tables.** | | `schemaInferenceEnabled` | boolean | `false` | Infer the schema from sampled rows. Fallback when no embedded schema exists. | Resolution order: explicit `schema` → embedded schema → inferred schema. **`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.) ```bash theme={null} curl -X POST "$CONTROLLER/tables/preview" \ -H "Content-Type: application/json" \ -d '{ "tableConfig": { "tableName": "nyc_taxi_trips_OFFLINE", "tableType": "OFFLINE", "task": { "taskTypeConfigsMap": { "ExternalTableSyncTask": { "catalogType": "iceberg-rest", "executor": "controller", "inputFormat": "parquet", "catalog.iceberg-rest.serviceType": "rest", "catalog.iceberg-rest.restUri": "http://:19120/iceberg", "catalog.iceberg-rest.prefix": "main", "catalog.iceberg-rest.table.namespace": "transportation", "catalog.iceberg-rest.table.tableName": "nyc_taxi_trips", "catalog.iceberg-rest.auth.storage.region": "us-west-1" } } } }, "config": { "inference": { "embeddedSchemaEnabled": true } } }' ``` **Setting the namespace and table.** Take them from the [browse](#step-1-validate-and-browse-the-connection) response — set `catalog.iceberg-rest.table.namespace` / `.tableName` to the `NAMESPACE` and `TABLE` you selected. **Use `executor: controller`** — it's required for the controller-watcher flow and the [observability endpoints](../observability). The input `tableConfig` is intentionally minimal; `/tables/preview` returns the complete `tableConfigs.offline` you persist in Step 4. ### 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). | Adjust the schema (time column, column names, null handling) before moving on. *** ## Step 3: Create the schema **`POST /schemas`** Send the `schema` object from the preview response. Rename its `schemaName` to match your table. ```bash theme={null} curl -X POST "$CONTROLLER/schemas" \ -H "Content-Type: application/json" \ -d @schema.json ``` ```json theme={null} { "status": "nyc_taxi_trips successfully added" } ``` *** ## Step 4: Create the table **`POST /tables`** Send the enriched `tableConfigs.offline` object from the preview response. Its `ExternalTableSyncTask` block is what marks the table as external. ```bash theme={null} curl -X POST "$CONTROLLER/tables" \ -H "Content-Type: application/json" \ -d @tableConfig.json ``` ```json theme={null} { "status": "Table nyc_taxi_trips_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 first sync runs on the watcher's next tick. To kick it off immediately instead of waiting, you can manually trigger a run: ```bash theme={null} curl -X POST "$CONTROLLER/tasks/schedule?taskType=ExternalTableSyncTask&tableName=nyc_taxi_trips_OFFLINE" ``` *** ## Quickstart: onboard a table end-to-end The whole flow as one script. Fill in the variables at the top, run the script, and the first sync starts automatically. ```bash theme={null} #!/usr/bin/env bash set -euo pipefail CONTROLLER="https://" AUTH="Authorization: Bearer " REST_URI="http://:19120/iceberg" BRANCH="main" NAMESPACE="transportation" TABLE="nyc_taxi_trips" STORAGE_REGION="us-west-1" # 1. Validate & browse root — lists Nessie namespaces on the selected branch. curl -sf -X POST "$CONTROLLER/connections/browse" \ -H "$AUTH" -H 'Content-Type: application/json' \ --data-raw "{ \"connection\": { \"type\": \"CATALOG\", \"params\": { \"catalogType\": \"iceberg-rest\", \"catalog.iceberg-rest.serviceType\": \"rest\", \"catalog.iceberg-rest.restUri\": \"$REST_URI\", \"catalog.iceberg-rest.prefix\": \"$BRANCH\" } }, \"path\": \"\" }" | jq . # 2. Preview — infer schema + enriched table config. curl -sf -X POST "$CONTROLLER/tables/preview" \ -H "$AUTH" -H 'Content-Type: application/json' \ --data-raw "{ \"tableConfig\": { \"tableName\": \"${TABLE}_OFFLINE\", \"tableType\": \"OFFLINE\", \"task\": { \"taskTypeConfigsMap\": { \"ExternalTableSyncTask\": { \"catalogType\": \"iceberg-rest\", \"executor\": \"controller\", \"inputFormat\": \"parquet\", \"catalog.iceberg-rest.serviceType\": \"rest\", \"catalog.iceberg-rest.restUri\": \"$REST_URI\", \"catalog.iceberg-rest.prefix\": \"$BRANCH\", \"catalog.iceberg-rest.table.namespace\": \"$NAMESPACE\", \"catalog.iceberg-rest.table.tableName\": \"$TABLE\", \"catalog.iceberg-rest.auth.storage.region\": \"$STORAGE_REGION\" } } } }, \"config\": { \"inference\": { \"embeddedSchemaEnabled\": true } } }" > preview.json # 3. Extract schema and table config from preview response. jq --arg t "$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' ``` If your Nessie deployment requires REST auth, add the relevant `catalog.iceberg-rest.auth.rest.*` keys from [Authentication](#authentication) to both requests above. Once step 6 exits `0`, [verify it's queryable](#verify-its-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](../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: ```sql theme={null} SELECT count(*) FROM nyc_taxi_trips; ``` 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](../troubleshooting). *** ## What's next Now that the table is created and data is loading, these are the highest-impact follow-up steps: 1. **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](../indexes) 2. **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](../data-and-index-caching) 3. **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](../best-practices-and-configs#query-oom-protection-large-scans) 4. **Monitor ongoing syncs.** Use the observability endpoints to check run status, ingestion checkpoint, and source file count after each scheduled sync. → [Observability](../observability) *** For common questions and failures, see the [FAQ](../faq) and [Troubleshooting](../troubleshooting).