{"uid":"cap_FTbKDoSDoTfpL6Br-Z6gu","slug":"transformation-throughput-exact-row-deduplication-numeric-interval-0f294a9d","name":"Transformation Throughput — Exact Row Deduplication + Numeric Interval Filter","description":"Transformation throughput — Exact row deduplication + Numeric interval filter: Exact row deduplication + Numeric interval filter for supplied transformation throughput records (processed_rows, duration_seconds). 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-043?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_L4L09ADwY42Kz034AYJpx","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Deduplicates exact rows and filters by a numeric interval on transformation throughput records (processed_rows, duration_seconds) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Deduplicate my transformation throughput records and then filter them to only keep rows where processed_rows is between 10 and 500 — here's my dataset of up to 200 rows with row_id, dataset_group, date, processed_rows, and duration_seconds fields.","exampleUseCases":[{"title":"Clean duplicate ETL pipeline logs","prompt":"I have a batch of transformation throughput logs with some duplicate entries — can you deduplicate them and then filter to only rows where processed_rows is between 50 and 10000? The dataset has row_id, dataset_group, date, processed_rows, and duration_seconds columns, up to 150 rows."},{"title":"Filter slow pipeline runs by duration","prompt":"Take my transformation records and first remove any exact duplicate rows, then give me only the entries where duration_seconds falls between 5 and 300 seconds — I want to focus on the mid-range runs, not the very fast or very slow ones."},{"title":"Audit throughput anomalies in specific range","prompt":"I need to audit my data pipeline throughput — deduplicate my 200-row dataset and then apply a numeric interval filter on the processed_rows field, keeping only records between 1000 and 50000, so I can spot anomalies in that band."}],"resultDescription":"Two analytical reports per call: one showing the result of exact row deduplication (rows with all fields identical removed), and one showing the subset of rows where the specified numeric field (processed_rows or duration_seconds) falls within the provided minimum and maximum bounds. Reports are generated locally from supplied data with no external lookups.","failureModes":["Exceeding 200 rows causes rejection","Invalid date format (not YYYY-MM-DD) causes validation error","Missing required fields (row_id, dataset_group, date, processed_rows, duration_seconds) causes schema error","minimum greater than maximum may return empty interval filter results","Numeric values outside ±1,000,000,000,000 boundary are rejected","Empty rows array causes rejection"],"whenToPreferThis":"Choose this endpoint when you need both exact row deduplication and a numeric interval filter applied to transformation throughput data (processed_rows or duration_seconds) in a single call, without needing live lookups, ML predictions, or external integrations. Ideal for data engineering pipelines that need lightweight, local analytical cleaning on batches up to 200 rows at very low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:39.232Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-exact-row-deduplication-numeric-interval-0f294a9d"}