--- url: /v6.2/en-us/cli-reference-data.md --- # Data & Execution Commands This page covers data onboarding, modeling, metrics, execution, and AI-related capabilities. It aligns most closely with the `hbi-data`, `hbi-data-modeling`, `hbi-pipeline`, `hbi-notebook`, `hbi-data-alert`, and `hbi-data-agent` skills. Each command family includes a minimal runnable example and representative output so you can start with a concrete command immediately. > \[!TIP] HQL Expressions > Commands on this page — including `data-model query`, `metric`, and `measure` — make extensive use of [HQL (Hengshi Query Language)](https://api.hengshi.com/function_reference.html) expressions for aggregations, calculated fields, and query conditions. HQL supports aggregation functions (SUM, AVG, COUNT), time functions (YEAR, MONTH, PERIODCOMPARE), and conditional logic. See the full [HQL Function Reference](https://api.hengshi.com/function_reference.html). ## `connection` **Purpose**: manage data connections, browse schemas/tables, and test connectivity. **Common subcommands**: * connection lifecycle: `types` `list` `show` `test` `create` `delete` `update` * internal auth rules: `auth-rules` `auth-rule-get` `auth-rule-create` `auth-rule-update` `auth-rule-delete` * inheritance control: `auth-inherit-get` `auth-inherit-update` * browsing and query: `browse` `query` **Minimal runnable example**: ```bash hbi connection list --output json ``` **Representative output**: ```json [ { "id": 39774, "title": "analytics-db", "accessCount": 273, "options": { "type": "postgresql", "maxConnNum": 10, "metaConfig": { "dbMajorVersion": 13, "dbMinorVersion": 11, "dbProductName": "PostgreSQL" } }, "createdAt": "2022-10-27 13:54:24", "visible": true } ] ``` **Common next steps**: * Show connection details: `hbi connection show 39774 --output json` * Test connectivity: `hbi connection test 39774` * Browse schemas/tables: `hbi connection browse 39774 --output json` **Browse by schema/path**: ```bash # List schema/path levels only hbi connection browse 39774 --schemas --output json # List every table under the exact public schema hbi connection browse 39774 --schema public --output json # Repeat --schema for a multi-level path; --query filters only within the selected subtree hbi connection browse 39774 --schema hive --schema default --query orders --output yaml ``` Each `--schema` supplies one path segment. Repeat it to preserve segment order. Do not join a multi-level path as `catalog.schema`, because segment names can contain dots. Combining `--schema` with `--query` uses AND semantics. Without `--schema`, `--query` keeps its existing fuzzy name filter. ## `dataset` **Purpose**: manage datasets, fields, granularities, previews, and import/export. **Common subcommands**: * lifecycle: `list` `show` `update` `replace` `delete` `duplicate` `import` * preview and state: `preview` `status` `schedule` `lineage` `export` * knowledge enhancement: `knowledge` `example` * creation paths: `create` `create-from-file` `upload` `create-custom-sql` `create-api` `create-reference` * transformation flows: `create-union` `create-fusion` `create-aggregate` `create-pivot` `create-unpivot` * fields and expressions: `fields` `expression-rewrite` `column-create` `column-update` `column-delete` * field supplements: `column-values` `column-geo` `column-geo-role` `column-geo-role-reset` * granularities: `granularity-list` `granularity-create` `granularity-update` `granularity-delete` **Minimal runnable example**: ```bash hbi dataset list --app 4812 --output json ``` **Representative output**: ```json [ { "id": 3, "title": "Sales Order Detail", "appId": 4812, "connectionId": 39774, "createdAt": "2025-01-10 09:00:00", "updatedAt": "2026-05-20 16:30:00", "status": "ready" } ] ``` **Common next steps**: * Preview rows: `hbi dataset preview --app 4812 --dataset 3 --limit 10 --output json` * Show fields: `hbi dataset fields --app 4812 --dataset 3 --output json` * Show dataset status: `hbi dataset status --app 4812 --dataset 3 --output json` ## `data-model` **Purpose**: manage data models and joins. **Common subcommands**: * `list` `show` `preview` `query` * `lineage` `tree` * `suggest-joins` * `join-add` `join-list` `join-delete` **Minimal runnable example**: ```bash hbi data-model query --app 4812 --dataset 3 "SUM({amount})" --output json ``` **Representative output**: ```json { "result": [ { "SUM({amount})": 1234567.89 } ], "executionTime": 234 } ``` **Common next steps**: * Group by dimension: `hbi data-model query --app 4812 --dataset 3 "SUM({amount})" --by "group({region})" --output json` * Show the model tree: `hbi data-model tree --app 4812 --dataset 3 --output json` * Ask for suggested joins: `hbi data-model suggest-joins --app 4812 --dataset 3 --output json` ## `metric` **Purpose**: manage atomic metrics. **Common subcommands**: * `create` `delete` `update` * `list` `show` * `query` **Minimal runnable example**: ```bash hbi metric list --app 4812 --dataset 3 --output json ``` **Representative output**: ```json [ { "id": 101, "name": "Total Sales", "expression": "SUM({amount})", "datasetId": 3, "createdAt": "2025-02-10 10:00:00" } ] ``` **Common next steps**: * Show metric details: `hbi metric show --app 4812 --dataset 3 c1 --output json` * Query metric data: `hbi metric query --app 4812 --dataset 3 c1 --output json` ## `measure` **Purpose**: manage business measures. **Common subcommands**: * `create` `delete` `update` * `list` `show` * `query` **Minimal runnable example**: ```bash hbi measure list --app 4812 --dataset 3 --output json ``` The final argument to `measure show` can be a measure field name such as `m1` or a display label such as `Monthly Paid Sales`. For a label, the CLI requires an exact match within the selected dataset. If the label matches more than one measure, the command lists the candidate field names so you can retry with one of them. `measure update` and `measure delete` continue to accept field names only. ```bash hbi measure show --app 4812 --dataset 3 "Monthly Paid Sales" --output json ``` When creating or updating a business measure, you can also define its filters and time axis. An update can additionally replace or clear its analysis dimensions: * `-w, --where `: an HQL filter expression; repeat the option to combine multiple expressions with `AND`. * `-D, --dimension `: replace the analysis dimensions. Repeat the option or use a comma-separated value. Field names take precedence; labels use exact matching. * `--clear-dimensions`: clear all analysis dimensions. This option conflicts with `--dimension`. * `--time-dimension `: the field used as the time axis. * `--time-granularity `: the time granularity. Supported values are `year`, `qoy`, `quarter`, `moy`, `month`, `week`, `dom`, `dow`, and `day`. `--time-dimension` and `--time-granularity` must be provided together. When `measure update` explicitly sets filters or a time axis, the measure uses that custom analysis context instead of its existing dataset granularity configuration. ```bash hbi measure create --app 4812 --dataset 3 \ --label "Monthly Paid Sales" \ --expression "SUM({amount})" \ --where "{status} = 'paid'" \ --time-dimension order_date \ --time-granularity month hbi measure update --app 4812 --dataset 3 m1 \ --dimension region \ --dimension "Product Category,Order Date" \ --where "{amount} > 0" \ --time-dimension order_date \ --time-granularity day hbi measure update --app 4812 --dataset 3 m1 --clear-dimensions ``` **Representative output**: ```json [ { "id": 201, "name": "Monthly Sales Growth Rate", "expression": "PERCENTCHANGE(SUM({amount}), month({date}))", "datasetId": 3, "createdAt": "2025-03-15 11:00:00" } ] ``` **Common next steps**: * Show measure details: `hbi measure show --app 4812 --dataset 3 m1 --output json` * Search measures: `hbi measure query --app 4812 --query "sales" --output json` ## `pipeline` **Purpose**: manage pipeline flows and nodes. **Common subcommands**: * pipeline lifecycle: `list` `show` `create` `update` `delete` `duplicate` * runtime state: `status` `errors` `schedule` * node read/write entrypoints: `node` `edit` **Minimal runnable example**: ```bash hbi pipeline list --output json ``` **Representative output**: ```json [ { "id": 450, "title": "Daily Aggregation", "createdAt": "2025-04-01 08:00:00", "updatedAt": "2026-05-25 10:00:00", "status": "success" } ] ``` **Common next steps**: * Show pipeline details: `hbi pipeline show 450 --output json` * Show runtime status: `hbi pipeline status 450 --output json` * Edit nodes: `hbi pipeline edit add 450 --help` `edit` is the most common deeper entrypoint, with: * `add` * `remove` * `connect` ## `notebook` **Purpose**: manage notebooks, paragraphs, and execution-related resources. **Common subcommands**: * notebook lifecycle: `list` `show` `create` `update` `delete` * paragraphs: `paragraphs` `add-paragraph` `update-paragraph` `execute` `delete-paragraph` * connections: `connections` `add-connection` `authorize-connection` `revoke-connection` `remove-connection` * other: `schedule` `languages` **Minimal runnable example**: ```bash hbi notebook list --output json ``` **Representative output**: ```json [ { "id": 350, "title": "Data Analysis Notes", "createdAt": "2025-05-10 14:00:00", "updatedAt": "2026-06-01 09:30:00" } ] ``` **Common next steps**: * Show notebook details: `hbi notebook show 350 --output json` * List paragraphs: `hbi notebook paragraphs 350 --output json` * Execute one paragraph: `hbi notebook execute 350 para_123` ## `data-alert` **Purpose**: manage data alerts. **Common subcommands**: * `list` `show` * `create` `update` `delete` * `enable` `disable` * `validate` **Minimal runnable example**: ```bash hbi data-alert list 4812 --output json ``` **Representative output**: ```json [ { "id": 520, "title": "Daily Sales Alert", "enabled": true, "createdAt": "2025-06-01 10:00:00" } ] ``` **Common next steps**: * Show alert details: `hbi data-alert show 4812 520 --output json` * Enable the alert: `hbi data-alert enable 4812 520` * Disable the alert: `hbi data-alert disable 4812 520 --dry-run` ## `data-agent` **Purpose**: manage Data Agent / ChatBI backend configuration. **Common subcommands**: * `config` * `prompt` * `vector` **Minimal runnable example**: ```bash hbi data-agent config get --output json ``` **Representative output**: ```json { "enabled": true, "model": "gpt-4", "basePrompt": "You are a data analysis assistant", "updatedAt": "2026-05-30 16:00:00" } ``` **Common next steps**: * List prompts: `hbi data-agent prompt list --output json` * Show vector status: `hbi data-agent vector status --output json` ## Where to drill down next | Scenario | Start with | | --- | --- | | data connections, schema browsing, and connectivity checks | `connection` | | dataset lifecycle, preview, fields, and granularities | `dataset` | | joins and model queries | `data-model` | | pipeline resources | `pipeline` | | node add/remove/connect operations | `pipeline edit` | | notebooks, paragraphs, and connections | `notebook` | | alert definitions and enable/disable actions | `data-alert` | | Data Agent config, prompts, and vector state | `data-agent` | When you need exact flags and examples, use that command tree's `--help` in the terminal.