Payload Autodl JSON

To define a JSON schema for payload analysis data ingestion, you use a standard machine-readable structure that enforces string-typed fields for all row values, ensuring empty values are stored as empty strings for consistent database storage.

The autodl JSON schema for failed blocking jobs includes payload-level fields like payload_tag, version, stream, architecture, and phase, alongside job-level details, candidate-level data, and revert_pr fields with strict 0-value defaults.

The payload analysis workflow handles empty values by storing them as empty strings, enforces string-typed fields for all row values, and applies strict 0-value defaults for specific fields to maintain data consistency for downstream analytics.

You need to generate a payload-analysis-autodl JSON file during the analyze-payload workflow when processing failed blocking jobs and their candidate PRs, producing a machine-readable output for downstream analytics and storage systems.

Yes, the autodl JSON schema is specifically designed to enable consistent ingestion of per-job results into a database, documenting candidate-level data and revert_pr fields across failed blocking jobs and their candidate PRs.

All row values are string-typed in the payload autodl JSON schema to standardize the flat denormalized table structure, ensuring consistent data ingestion into downstream storage systems and preventing type mismatch errors.