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Operate & Observe

Operations

This section covers running AetheriusDB day to day: how to read what it is doing, how it behaves when a machine runs out of headroom, how to reproduce failures deterministically, and how it uses accelerators when they are available.

Operating a database is mostly about the bad days, and these features are built for them. Every error is a stable, typed code with a remediation path — not an opaque string — so your tooling can act on it. Metrics are just system views you query in SQL. And when a machine runs out of headroom, the engine fails the one expensive query rather than taking the whole database down with it.

  • Typed, actionable errors — a stable code and a fix, wired straight into your alerting.
  • Metrics as SQL — observe throughput, lag, and planner decisions by querying system views; no separate metrics endpoint to scrape and reconcile.
  • Graceful under pressure — expensive queries are rejected before they consume capacity, so one heavy query can’t starve everyone else’s latency.
  • Reproducible failures — deterministic simulation replays a rare race bit-for-bit from a single seed, so “it only happens in prod” becomes debuggable.
  • Observability & Error Taxonomy — every error is a stable typed code with a remediation path, and metrics are queryable as system views. Start here to wire errors and telemetry into your tooling.
  • Hardware Limits & Graceful Exhaustion — how the engine rejects expensive queries before they consume capacity, and fails a single query rather than the whole database when limits are reached.
  • Deterministic Simulation Testing — run a cluster under simulated time and network so a single seed reproduces a rare race bit-for-bit.
  • Heterogeneous GPU Dispatch — how tensor-heavy and large-aggregation queries can offload to a GPU when one is present, decided per query.

Each feature page carries an availability badge (Stable / Opt-in / Beta / Roadmap) so you always know what is on by default versus what is gated or aspirational.