Validate Primary Key

Overview

You send a rectangular table and 1 to 8 key columns. We report whether the key is unique and non-null, with duplicate and null counts. Diagnostics carry 0-based row indices and a SHA-256 fingerprint of the canonical key, never the key values. INTEGER 1 and NUMBER 1.0 stay distinct. This is not a database constraint and does not rewrite rows.

Printing duplicate keys into logs and tickets leaks identifiers. Fingerprints and row indices are enough to locate the row without repeating the value.

When to use it

Scope

Non-goals

• Does not create a database constraint
• Does not drop or rewrite duplicate rows
• Does not approximate uniqueness

Properties

Capabilities

Cost & billing

Flat rate per invocation

File handling

How we handle your data

Input schema

Output schema

API contract

curl -sS -X POST "$BASE/v1/execute" -H "Authorization: Bearer $KEY" -H 'Content-Type: application/json' -d '{"skillKey":"data.validate_primary_key","input":{"table":{"columns":[{"name":"id","type":"STRING"}],"rows":[["a"],["b"]]},"keyColumns":["id"]}}'

import { Squillify } from "@squillify/sdk";

const sq = new Squillify({ apiKey: process.env.SQUILLIFY_API_KEY });

const { output } = await sq.execute("data.validate_primary_key", {"table":{"rows":[["a"],["b"]],"columns":[{"type":"STRING","name":"id"}]},"keyColumns":["id"]});

Errors

Other limits

Limitations

• Does not create a database constraint
• Does not drop or rewrite duplicate rows
• Does not approximate uniqueness
• Deleting duplicates
• Fuzzy or phonetic uniqueness

How to call

POST /v1/execute (requires authentication)

{
  "skillKey": "data.validate_primary_key",
  "input": {
    "table": {
      "rows": [
        [
          "a"
        ],
        [
          "b"
        ]
      ],
      "columns": [
        {
          "type": "STRING",
          "name": "id"
        }
      ]
    },
    "keyColumns": [
      "id"
    ]
  }
}

Examples

{
  "table": {
    "rows": [
      [
        "a"
      ],
      [
        "b"
      ]
    ],
    "columns": [
      {
        "type": "STRING",
        "name": "id"
      }
    ]
  },
  "keyColumns": [
    "id"
  ]
}

{
  "duplicateCount": 0,
  "truncated": false,
  "valid": true,
  "violationCount": 0,
  "diagnostics": [],
  "nullCount": 0,
  "rowCount": 2
}

{
  "table": {
    "rows": [
      [
        "a"
      ],
      [
        "a"
      ]
    ],
    "columns": [
      {
        "type": "STRING",
        "name": "id"
      }
    ]
  },
  "keyColumns": [
    "id"
  ]
}

{
  "duplicateCount": 1,
  "truncated": false,
  "valid": false,
  "violationCount": 1,
  "diagnostics": [
    {
      "fingerprint": "b788d768c7ed152e2094e2fc155e009c32bf2195e5aded0626b209e8bdd05366",
      "code": "DUPLICATE_KEY",
      "severity": "ERROR",
      "rowIndices": [
        0,
        1
      ]
    }
  ],
  "nullCount": 0,
  "rowCount": 2
}

Recommended use cases

• Prove an extract's id is unique before a load, without logging the ids
• Distinguish INTEGER 1 from NUMBER 1.0 on a composite key

Avoid for

• Deleting duplicates
• Fuzzy or phonetic uniqueness

Related skills

Composable with

Safety & privacy

• Runs without calling any external service.
• Your payloads are never stored permanently.
• Telemetry captures metadata only — never your payload contents.
• We never train models on your payloads.
• No human reviews your payloads.

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