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First establish what each row represents
Before writing a query, determine whether the export contains invoice headers, invoice lines, credit memo lines, or a flattened mixture. A repeated row has a different meaning depending on which records were joined or exported together.
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Use the source system’s data dictionary and confirm these details with the accounting owner:
- Invoice and line identifiers, including whether invoice numbers are reused across companies or entities.
- Document type and posting status, so posted invoices can be distinguished from drafts and correction documents.
- Any cancellation or void status field, its exact values, and when the source sets or clears it.
- References linking the original invoice to a corrective credit memo or other reversal document.
- Relevant posting dates, company scope, currency, and how null or missing keys are represented.
The exact fields and relationships depend on the product and the export. Do not assume that a status value or key from one accounting system exists in another.
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Prefer documented cancellation signals
Use an explicit cancellation marker when its meaning and lifecycle are documented. Where cancellations are represented by corrective documents, identify the relationship between the original invoice and its correction, then confirm that the correction has the relevant posting status.
For example, Microsoft’s Business Central documentation describes a workflow in which canceling a posted sales invoice creates and posts a corrective sales credit memo, marks the initial invoice as canceled, and leaves a trail for inspection. The credit memo applies to the general-ledger and inventory-ledger entries created by the original invoice. The same documentation notes that paid or fully shipped cases may require a different correction path, so that workflow is not a universal rule for every invoice system.
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Think of the signals as alternatives with different strengths and failure modes:
| Signal | Best use | What to verify |
|---|---|---|
| Documented cancellation marker | Extracting likely cancellations directly | That the field’s meaning is established and the value is current; a missing or stale marker can mislead. |
| Linked corrective document | Reconciliation and audit review | That the original-to-correction relationship and posting state are documented and present in the data. |
| Duplicate or anomaly pattern | Surfacing records for investigation | That the grouped columns represent a genuine uniqueness rule; legitimate repeat transactions may match. |
Query candidates read-only
For a source with a documented status field, a query can start with a predicate like this. Replace the example names and values with the source’s actual schema and status vocabulary:
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SELECT
i.InvoiceId,
i.Status,
i.CorrectiveDocumentId,
i.PostedAt
FROM dbo.InvoiceLines AS i
WHERE i.Status IN ('Canceled', 'Cancelled');This is only a candidate-finding pattern. If the cancellation is recorded on an invoice header, query the header or join to it by the documented key. If the source records cancellations through linked credit memos, start from the documented correction relationship instead of assuming a line-level status exists.
Include enough information for a person to verify each result: invoice and line identifiers, customer or vendor and company scope, posting date, amount, document status, correction document ID, and which cancellation signal caused the row to be selected. Keep the query read-only while identifying candidates. A zero balance or net-zero amount alone is not evidence that an invoice was canceled.
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Use duplicate checks for triage, not classification
A grouping query can reveal repeated combinations when the chosen business key is supposed to be unique in that model. For example:
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SELECT InvoiceId, LineId, COUNT(*) AS Occurrences
FROM dbo.InvoiceLines
WHERE InvoiceId IS NOT NULL
AND LineId IS NOT NULL
GROUP BY InvoiceId, LineId
HAVING COUNT(*) > 1;Here, the example assumes that the invoice and line identifiers together should identify a single row. That assumption must be checked against the source schema, company scope, and export logic. Microsoft Fabric’s duplicate-row guidance distinguishes uniqueness checks from heuristic matching: records that share customer, item, and day, for instance, may represent legitimate repeat orders. A matching pattern is a lead for review, not proof of cancellation.
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Review each match against the accounting record
Reconcile candidates to the authoritative accounting application or posted ledger and corrective document. Preserve the link to the original invoice and its reversal so that the result remains explainable and auditable. When a correction document is expected but absent from the export, check whether the extract omits that document type or its relationship fields before treating the invoice as uncanceled.
Tune the query only after validating its meaning
For repeated queries, inspect the database execution plan and consider indexes that align with actual filter, join, and grouping columns. Microsoft’s SQL Server index design guide explains that unique indexes enforce uniqueness, cannot be created when duplicate key combinations already exist, and can provide useful information to the query optimizer when the uniqueness rule is valid.
Do not turn a cancellation search into a deletion or deduplication operation. Enforcing future uniqueness is a separate decision: first verify the business rule and resolve existing duplicate keys under an approved process. If ranking records with row numbers, choose an intentional ordering when precedence matters; Microsoft’s index design guide also discusses indexes aligned with key and sorting columns for query support.
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“One million lines” is not enough information to predict runtime. Performance depends on the database engine, schema, indexes, filter selectivity, and data distribution; the cited guidance does not benchmark this dataset.
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