omni-query

作者: exploreomni

使用REST API对Omni Analytics的语义层运行查询、解释结果,并串联查询以进行多步骤分析。每当需要时使用此技能……

npx skills add https://github.com/exploreomni/omni-claude-skills --skill omni-query

Omni Query

Run queries against Omni's semantic layer via the REST API. Omni translates field selections into optimized SQL — you specify what you want (dimensions, measures, filters), not how to get it.

Tip: Use omni-model-explorer first if you don't know the available topics and fields.

Prerequisites

export OMNI_BASE_URL="https://yourorg.omniapp.co"
export OMNI_API_KEY="your-api-key"

You also need a model ID and knowledge of available topics and fields.

API Discovery

When unsure whether an endpoint or parameter exists, fetch the OpenAPI spec:

curl -L "$OMNI_BASE_URL/openapi.json" \
  -H "Authorization: Bearer $OMNI_API_KEY"

Use this to verify endpoints, available parameters, and request/response schemas before making calls.

Running a Query

Basic Query

curl -L -X POST "$OMNI_BASE_URL/api/v1/query/run" \
  -H "Authorization: Bearer $OMNI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "modelId": "your-model-id",
      "table": "order_items",
      "fields": [
        "order_items.created_at[month]",
        "order_items.total_revenue"
      ],
      "limit": 100,
      "join_paths_from_topic_name": "order_items"
    }
  }'

Query Parameters

ParameterRequiredDescription
modelIdYesUUID of the Omni model
tableYesBase view name (the FROM clause)
fieldsYesArray of view.field_name references
join_paths_from_topic_nameRecommendedTopic for join resolution
limitNoRow limit (default 1000, max 50000, null for unlimited)
sortsNoArray of sort objects
filtersNoFilter object
pivotsNoArray of field names to pivot on

Field Naming

Fields use view_name.field_name. Date fields support timeframe brackets:

users.created_at[date]      — Daily
users.created_at[week]      — Weekly
users.created_at[month]     — Monthly
users.created_at[quarter]   — Quarterly
users.created_at[year]      — Yearly

Sorts

"sorts": [
  { "column_name": "order_items.total_revenue", "sort_descending": true }
]

Filters

"filters": {
  "order_items.created_at": "last 90 days",
  "order_items.status": "complete",
  "users.state": "California,New York"
}

Expressions: "last 90 days", "this quarter", "2024-01-01 to 2024-12-31", "not California", "null", "not null", ">100", "between 10 and 100", "contains sales", "starts with A". See references/filter-expressions.md for the complete expression syntax reference.

Pivots

{
  "query": {
    "fields": ["order_items.created_at[month]", "order_items.status", "order_items.count"],
    "pivots": ["order_items.status"],
    "join_paths_from_topic_name": "order_items"
  }
}

Handling Results

Default response: base64-encoded Apache Arrow table. Arrow results are binary — you cannot parse individual row data from the raw response. To verify a query returned data, check summary.row_count in the response.

For human-readable results, request CSV instead:

{ "query": { ... }, "resultType": "csv" }

Decoding Arrow Results

import base64, pyarrow as pa
arrow_bytes = base64.b64decode(response["data"])
reader = pa.ipc.open_stream(arrow_bytes)
df = reader.read_all().to_pandas()

Long-Running Queries

If the response includes remaining_job_ids, poll until complete:

curl -L -X POST "$OMNI_BASE_URL/api/v1/query/wait" \
  -H "Authorization: Bearer $OMNI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "jobIds": ["job-id-1", "job-id-2"] }'

Running Queries from Dashboards

Extract and re-run queries powering existing dashboards:

# Get all queries from a dashboard
curl -L "$OMNI_BASE_URL/api/v1/documents/{dashboardId}/queries" \
  -H "Authorization: Bearer $OMNI_API_KEY"

# Run as a specific user
{ "query": { ... }, "userId": "user-uuid-here" }

# Cache policy (valid values: Standard, SkipRequery, SkipCache)
{ "query": { ... }, "cache": "SkipCache" }

Multi-Step Analysis Pattern

For complex analysis, chain queries:

  1. Broad query — understand the shape of the data
  2. Inspect results — identify interesting segments or patterns
  3. Focused follow-ups — filter based on findings
  4. Synthesize — combine results into a narrative

Common Query Patterns

Time Series: fields + date dimension + ascending sort + date filter

Top N: fields + metric + descending sort + limit

Aggregation with Breakdown: multiple dimensions + multiple measures + descending sort by key metric

Known Bugs

  • IS_NOT_NULL filter generates IS NULL (reported Omni bug) — workaround: invert the filter logic or use the base view to apply the filter differently.
  • Boolean filters may be silently dropped when a pivots array is present — if boolean filters aren't applying, remove the pivot and test again.

Linking to Results

Queries are ephemeral — there is no persistent URL for a query result. To give the user a shareable link:

  • For existing dashboards: {OMNI_BASE_URL}/dashboards/{identifier} (the identifier comes from the document API response)
  • For new analysis: Create a document via omni-content-builder with the query as a queryPresentation, then share {OMNI_BASE_URL}/dashboards/{identifier}

Docs Reference

Related Skills

  • omni-model-explorer — discover fields and topics before querying
  • omni-content-explorer — find dashboards whose queries you can extract
  • omni-content-builder — turn query results into dashboards

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