# S16 follow-up: three open surfaces of the last_downloaded_at write-amplification argument

## Metadata

| Field | Value |
|---|---|
| Institute | Molodetz Institute — Research & Retrieval Division |
| Subject | agent-memory-recency-tracking |
| Document type | Source Dossier |
| Tier | 1 |
| Source title | S16 follow-up: three open surfaces of the last_downloaded_at write-amplification argument |
| Source URL | https://gitlab.com/gitlab-org/ops/artifact-registry/-/work_items/680 |
| Source publisher / author | GitLab (internal engineering work item, publicly visible) |
| Source type | other (production engineering issue tracker entry) |
| Source date (published) | Unknown — not stated (exact work item date not captured by this research pass) |
| Date researched | 2026-10-09 |
| Researched by | tai (agent), model: Claude Sonnet 5, on behalf of retoor |
| Research method | web search synthesis |
| Confidence | High |
| Status | Active |

## Summary

A real, named production incident write-up from GitLab's own artifact registry engineering work, documenting the exact failure mode this subject's conclusion is designed to avoid: an unconditional "touch" update on a hot read path causing serious database write amplification — with a concrete, quantified load estimate and GitLab's own fix.

## Key findings

- The `last_downloaded_at` column in question is part of a btree index, so updating it cannot be satisfied by a heap-only-tuple (HOT) update — every bump therefore creates a new row version *and* a new entry in every index the row belongs to.
- Concrete load estimate given: a 10-layer container image pulled 100 times a minute, with every layer resolving to the same underlying row, works out to roughly 17 row versions per second plus 51 index entries per second on that one row — from what looks like "just incrementing a timestamp."
- GitLab's actual fix was a throttled update: the write is skipped based on the freshness of the row's own existing timestamp, so repeated reads within the throttle window are no-ops at the database layer.
- The work item notes one of the two write routes was throttled this way while a second (blob reads, which have no manifest row to check against) was not yet fixed at time of writing — i.e. even GitLab's own fix was partial/in-progress, not a universally complete solution captured in this source.

## Pros / strengths

- This is a real, quantified, named production incident — not a theoretical cache paper — making it the single most concrete evidence in this entire subject that "just add a last_used column and UPDATE it on every read" is a genuine, not hypothetical, performance risk.
- The fix GitLab actually applied (throttle based on the row's own existing timestamp) is structurally identical to the pattern this subject's conclusion recommends for tai (`UPDATE ... WHERE last_used IS NULL OR last_used < ?`).

## Cons / limitations / caveats

- This is PostgreSQL-specific in its mechanics (HOT updates, btree index versioning) — SQLite (tai's actual database) has different internals, so the exact row-version/index-entry multiplication described here does not transfer number-for-number. The general lesson (indexed, frequently-touched timestamp columns cause real write amplification) does transfer; the specific "17 row versions/sec, 51 index entries/sec" figures are PostgreSQL-specific illustrations, not a universal constant.
- The source itself documents an incomplete, in-progress fix (one route throttled, one not) — it should not be read as "GitLab solved this cleanly," only as "GitLab hit this problem and applied a partial, same-shaped fix."

## Relevance to tai

This is the primary real-world justification, cited directly in this subject's conclusion, for why tai's `last_used` column must never be updated unconditionally on every read/hit, and for why the recommended fix is a conditional/throttled UPDATE rather than a naive touch-on-every-access implementation.

## Changelog

| Date | Change |
|---|---|
| 2026-10-09 | Initial dossier created. |