API Error Taxonomy for File Pipelines

Define stable, actionable error classes for upload and processing APIs.

code security document image video audio archive

Why Error Taxonomy Matters

Without a stable taxonomy, clients and support teams cannot distinguish user mistakes from platform defects. A typed error model improves retry logic, telemetry quality, and incident triage.

Recommended Classes

  • input_invalid: malformed or policy-violating user input.
  • type_mismatch: extension/signature/parser disagreement.
  • processing_failed: decode/transcode/extraction failure.
  • resource_limited: quota, timeout, or concurrency budget exceeded.
  • internal_error: unexpected service fault.

Operational Rules

Each class should map to deterministic status codes, retry policy, and user-facing message patterns. Instrument metrics by class and reason code so regressions are measurable.

Recommended Tools

MIME Inspector

Compare extension and signature hints to detect type mismatches.

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Batch MIME Classifier

Classify many files at once and highlight mismatch risks.

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Checksum Generator & Verifier

Compute SHA256 and verify file integrity against expected hashes.

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