# Dataset Partition Sizes — Exact Row Deduplication + Date by Category Pivot

> Dataset Partition Sizes — Exact Row Deduplication + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).

Runs two analytical reports on dataset partition size records: exact row deduplication and a date-by-category pivot of row_count or file_mb values, for up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-052?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-11
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-partition-sizes-exact-row-deduplication-date-by-category-pivot-9ab81fe1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HCVEGFFUCiWSTmgGQshRk

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability dataset-partition-sizes-exact-row-deduplication-date-by-category-pivot-9ab81fe1 -d '<json body>'
```

Example prompt: I have a list of dataset partition records with row_id, dataset_group, date, row_count, and file_mb — can you deduplicate the exact duplicate rows and then give me a pivot of row_count by date and dataset group? Here are the rows: [{"row_id":"row-1","dataset_group":"group-a","date":"2026-10-01","row_count":10,"file_mb":1},{"row_id":"row-2","dataset_group":"group-b","date":"2026-10-02","row_count":20,"file_mb":2}].

## When to prefer this

Choose this endpoint when you need both exact row deduplication and a date-by-category pivot on dataset partition size records (row_count and/or file_mb) in a single low-cost call. It is ideal for data engineers auditing partition logs, cleaning ETL output, or summarizing storage metrics across groups over time. Prefer this over general-purpose analytics tools when working with small-to-medium batches (up to 200 rows) and you want both a cleaned deduplicated dataset and a cross-tab pivot in one operation for $0.01.

## Known failure modes

- Input array is empty or exceeds 200 rows — rejected with validation error
- date field does not match YYYY-MM-DD pattern — row rejected or error returned
- row_id is missing or empty — validation failure
- field enum value is not 'row_count' or 'file_mb' — request rejected
- All rows are duplicates, resulting in empty deduplicated output
- Numeric fields (row_count, file_mb) are null for all rows, producing empty pivot
- Payment of 0.01 USDC not completed — request blocked by x402 paywall

## How this service works

Dataset partition sizes — Exact row deduplication + Date by category pivot: Exact row deduplication + Date by category pivot for supplied dataset partition sizes records (row_count, file_mb). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns two reports: (1) a deduplicated set of the supplied partition size rows with exact duplicates removed, and (2) a pivot table cross-tabulating the selected metric (row_count or file_mb) by date and dataset_group category — all computed locally with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "row_count",
     "file_mb"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "file_mb": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "row_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "file_mb": 1,
     "row_count": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "file_mb": 2,
     "row_count": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "file_mb": 3,
     "row_count": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "file_mb": 4,
     "row_count": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "row_count",
    "file_mb"
   ],
   "type": "string",
   "default": "row_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-exact-row-deduplication-date-by-category-pivot-9ab81fe1/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)