CONCEPT Cited by 1 source

Unused / reclaimable disk buffer¶

Definition¶

Unused / reclaimable disk buffer is the deliberate reservation of a slice of a storage device that is never allocated for primary data — partially held unused and partially held used-but-reclaimable (for caching or other reclaimable purposes) — so that the operator can reclaim the buffer on demand when the storage subsystem is under stress.

Canonical statement (Source: sources/2025-06-20-redpanda-behind-the-scenes-redpanda-clouds-response-to-the-gcp-outage):

"Additionally, as a reliability measure, we leave disk space unused and used-but-reclaimable (for caching), which we can reclaim if the situation warrants it. This outage was not that situation."

Why the buffer exists¶

The buffer is a form of static stability — overprovisioning so that a failure mode can be absorbed without a runtime dependency on a provisioning service or operator intervention. In the tiered-storage-as- fallback architecture, the buffer specifically absorbs:

Flush-backlog growth during object-storage outages, where the tiered-storage layer can't accept async writes.
Transient segment accumulation before compaction / retention GC catches up.
Replication catch-up when a replica comes back online and needs to stream missed data.
Capacity planning error margins — imperfect forecasting always underestimates some workloads.

Without the buffer, any of these events eventually fills the disk and blocks the write path. With the buffer, the system has a well-understood budget before write-path impact.

The two components¶

Unused reserve — disk space that is never written to under steady state. Pure overprovisioning. Simple: always available to reclaim.
Used-but-reclaimable reserve — disk space used for caching, such that it can be freed by dropping the cache contents. Provides steady-state utility (cache hit rates) but is logically part of the reserve.

The distinction matters because steady-state operators see a higher disk-utilisation number (the used-but-reclaimable space appears used) — but the reliability guarantee is the same: both halves can be released under duress.

Integration with tiered storage¶

In a Redpanda-style streaming broker:

Primary data on NVMe flushes to object storage asynchronously.
If object storage has elevated error rates, the flush backlog grows — primary data accumulates on NVMe.
The reserve absorbs the backlog for some time window without saturation.
If the window is too long, the reserve runs out and write-path backpressure kicks in.

Per the Redpanda post: the 2025-06-12 GCP outage did not reach the saturation threshold — confirmed by absence of "high disk utilization alerts, which we typically receive when the tiered storage subsystem has been experiencing issues for an extended period (days)." The reserve absorbed an hour-scale event; the saturation alarm is calibrated to days.

Reclaim-on-demand mechanism¶

The reserve is reclaimable under operator or automated control:

Cache reclaim — drop in-memory + on-disk cache contents, free the space.
Compaction acceleration — invoke GC / retention cleanup to drop older segments early.
Retention shortening — temporarily reduce retention for non-critical topics.
Tier promotion — invoke immediate flush of oldest local segments to tiered storage (if the tier is healthy) to free local NVMe.

The post does not disclose which of these mechanisms Redpanda uses or the trigger threshold.

concepts/static-stability — the AWS-canon reliability principle the disk buffer instantiates at the storage- capacity axis.
concepts/tiered-storage-as-primary-fallback — the architecture that makes the buffer the load-bearing element for write-path availability.
concepts/graceful-degradation — if the buffer runs out, the system should back-pressure producers rather than crash — the degradation-mode response.
concepts/memory-overcommit-risk — the anti-pattern counterpart: capacity planning that relies on overcommit rather than reserve.

Sizing trade-offs¶

Larger reserve: - More absorption capacity for object-store outages. - Higher cost per GB of useful capacity. - Harder to convince finance teams to pay for.

Smaller reserve: - Lower steady-state cost. - Shorter absorption window for outages. - Higher risk of write-path impact during long outages.

The right sizing depends on: - Object-store provider SLO — longer-outage providers need larger reserves. - Workload write rate — high write rate fills reserves faster. - Async flush rate — healthy systems' steady-state flush speed. - Incident response SLO — how quickly can operators trigger reclaim / add capacity.

Redpanda does not disclose its reserve sizing policy.

Caveats¶

Sizing policy not disclosed. The Redpanda post states the property but not the percentage.
Reclaim automation vs manual. The post says "we can reclaim if the situation warrants it" — passive voice; unclear if reclaim is automated or requires operator action.
Caching reserve has UX cost on reclaim. Dropping the cache to free space during an outage will cause a period of elevated cache misses; this is a trade made deliberately but it's a trade.
Not a substitute for object-store fault-tolerance. A long enough object-store outage exceeds any finite reserve. The buffer buys time, not infinite protection.
Different from file-system reserved blocks. ext4's 5% root reserve is a similar idea at OS level, but the Redpanda reserve is at the application layer — invisible to the kernel.
The buffer is NVMe-local. Cross-broker replication is a separate reliability axis; this concept is about single-broker storage capacity.

Seen in¶

sources/2025-06-20-redpanda-behind-the-scenes-redpanda-clouds-response-to-the-gcp-outage — canonical statement of unused + used-but-reclaimable disk as a deliberate reliability measure, load-bearing during the 2025-06-12 GCP outage for absorbing tiered-storage flush backlog without write-path impact.