Scanner Benchmark Runbook

This runbook describes how to collect reproducible evidence for scanner pressure, scanner progress, and scanner runtime tuning. It is intended for maintainers and operators validating scanner changes in an isolated test deployment.

Use this runbook together with Scanner Runtime Controls. The runtime controls document explains each field and configuration key; this document explains how to run a comparable before/after observation and decide whether the scanner is healthier after tuning.

Scope

This runbook verifies that scanner pacing and cycle controls reduce background pressure while preserving maintenance progress. It is useful for:

mostly idle single-node, single-disk deployments with many small objects;
single-node multi-disk or erasure-set deployments where scanner work is spread across disks and sets;
distributed clusters where scanner pressure is mixed with lifecycle, replication, heal, or bitrot queues;
lifecycle expiry or transition backlog investigations;
bucket replication repair backlog investigations;
scanner-originated heal or bitrot admission investigations;
pull request evidence when scanner behavior, scanner status, or scanner controls change.

This runbook does not prove full MinIO parity, site-replication correctness, or replaced-disk heal correctness. Those flows need dedicated distributed tests because their failure modes are not limited to scanner pacing.

Safety

Run the workload only in a disposable test environment. The commands below can create many buckets and objects and can overwrite runtime scanner settings.

Record the current scanner and heal configuration before changing anything:

bash

mkdir -p artifacts
mc admin config get ALIAS scanner > artifacts/scanner-config.before.txt
mc admin config get ALIAS heal > artifacts/heal-config.before.txt

Replace ALIAS, endpoint, and credentials with values for the test deployment. Do not paste production credentials into saved artifacts.

Required Tools

mc or a compatible admin client for config changes.
awscurl or another SigV4-capable HTTP client for /v3/scanner/status.
jq for status extraction.
pidstat, mpstat, iostat, top, or equivalent host telemetry.
warp, s3bench, or the repository object benchmark scripts for workload generation.

Test Matrix

At minimum, collect two runs on the same RustFS commit and the same workload:

Run	Purpose	Example scanner settings
Baseline	Observe current behavior without additional pacing changes.	Existing config.
Pacing override	Measure whether cooperative scanner sleeps reduce pressure.	`scanner.delay="30"` and `scanner.max_wait="15"`.

Add bounded-cycle runs when a single scan cycle is too long:

Run	Purpose	Example scanner settings
Duration budget	Bound wall-clock time per cycle.	`scanner.cycle_max_duration="1800"`.
Object budget	Bound objects processed per cycle.	`scanner.cycle_max_objects="1000000"`.
Directory budget	Bound directories entered per cycle.	`scanner.cycle_max_directories="100000"`.

When comparing runs, keep hardware, RustFS commit, object count, object size, bucket count, scanner-enabled state, and foreground workload constant.

Deployment Matrix

Use the smallest deployment that reproduces the symptom, but do not treat single-node validation as the whole scanner test surface. Scanner changes that touch queues, admission, or cross-node maintenance should include a distributed run when practical.

Deployment	What it validates	Minimum evidence
Single-node, single-disk	Small-object scanner pressure, pacing, cycle interval, and basic progress.	Scanner status time series plus host CPU and disk telemetry.
Single-node, multi-disk or erasure set	Set and disk scan concurrency, cycle budgets, checkpoint movement, usage cache persistence, and active path age.	Scanner status time series, per-disk host telemetry, and before/after data usage freshness.
Distributed cluster	Lifecycle transition queues, bucket replication repair admission, scanner-originated heal and bitrot admission, and queue/backlog pressure under cross-node work.	Scanner status time series from the cluster, host telemetry from each node, and subsystem-specific queued/skipped/missed counters.

The single-node, single-disk run is the baseline because it is cheap and repeatable. It is not sufficient for PRs that claim to improve distributed queue behavior, replication repair, or heal/bitrot admission.

Workload Shape

For the baseline small-object scanner-pressure run, use a workload that creates many small objects and then leaves the service mostly idle while the scanner walks the namespace. A useful minimum shape is:

one RustFS node;
one data disk;
several buckets;
at least 100,000 small objects total;
scanner enabled;
no sustained foreground workload during the observation window.

For a distributed backlog run, use the same structure but add the relevant subsystem condition, such as lifecycle rules, a bucket replication target, or a configured heal/bitrot scenario. Keep the status and host telemetry cadence the same so the result remains comparable with the baseline run.

The repository object benchmark script can generate object traffic if warp or s3bench is installed:

bash

scripts/run_object_batch_bench.sh \
  --tool warp \
  --endpoint http://127.0.0.1:9000 \
  --access-key "$RUSTFS_ACCESS_KEY" \
  --secret-key "$RUSTFS_SECRET_KEY" \
  --bucket scanner-bench \
  --auto-new-bucket \
  --concurrency 64 \
  --duration 10m \
  --sizes 1KiB,4KiB,16KiB \
  --warp-mode put \
  --out-dir artifacts/object-load

If the object generator cannot create enough objects in one run, repeat the same command with new buckets or prefixes and record the final object count.

Status Collection

Capture scanner status before the workload, after the workload finishes, and throughout the idle observation window.

The repository includes a scanner validation harness for repeatable collection:

bash

export RUSTFS_ACCESS_KEY="<admin-access-key>"
export RUSTFS_SECRET_KEY="<admin-secret-key>"

scripts/run_scanner_validation_harness.sh \
  --alias ALIAS \
  --endpoint http://127.0.0.1:9000 \
  --deployment single-disk \
  --workload-label small-object-idle \
  --samples 30 \
  --interval-secs 60 \
  --out-dir artifacts/scanner-validation

The harness writes scanner/heal config snapshots, scanner status samples, background heal status samples, host telemetry when available, run metadata, scanner-summary.csv, and scanner-validation-report.md.

Use --metrics-endpoints when the validation needs per-node distributed evidence. The value is a comma-separated list of RustFS endpoints:

bash

scripts/run_scanner_validation_harness.sh \
  --alias ALIAS \
  --endpoint http://node-a:9000 \
  --deployment distributed \
  --workload-label lifecycle-replication-heal-backlog \
  --metrics-endpoints http://node-a:9000,http://node-b:9000,http://node-c:9000,http://node-d:9000 \
  --samples 30 \
  --interval-secs 60 \
  --out-dir artifacts/scanner-validation-distributed

Each sample stores /v3/scanner/status, one /v3/background-heal/status response per endpoint listed in --metrics-endpoints, and one by-host admin metrics response per listed endpoint. When --metrics-endpoints is omitted, the harness captures background-heal status only from --endpoint.

For bucket metrics freshness validation, use the same harness around a post-start bucket creation workload:

Start RustFS from an empty data path.
Start the harness before creating buckets.
Create a bucket, upload objects, and keep the harness running until at least one usage save is observed.
Compare scanner-summary.csv with /rustfs/admin/v3/metrics?types=1&n=1 bucket metrics.

This covers the issue 3496 timing where scanner startup sees no buckets, the bucket is created after startup, and the first metrics collection must not confuse cold usage cache with real zero usage. The expected evidence is that dirty usage is marked, life_time_scan_cycle or life_time_scan_bucket_drive advances, life_time_scan_object advances for object workloads, and life_time_save_usage plus usage_last_save_result=success appear before accepting non-zero bucket usage metrics as fresh.

For distributed runs, capture scanner admin metrics from every node with by-host=true. The metrics endpoint reports the node that handles the request; by-host=true preserves that node's host view but does not collect peer nodes. These per-node artifacts include active path age, checkpoint state, pacing pressure, source work, and queued/skipped/missed downstream admission counters. The validation harness can collect these artifacts automatically with --metrics-endpoints; the manual loop below is useful when adding extra nodes or collecting ad hoc snapshots outside the harness window.

bash

for endpoint in http://node-a:9000 http://node-b:9000 http://node-c:9000; do
  node="${endpoint#http://}"
  node="${node%%:*}"
  awscurl \
    --service s3 \
    --region us-east-1 \
    --access_key "$RUSTFS_ACCESS_KEY" \
    --secret_key "$RUSTFS_SECRET_KEY" \
    --request GET \
    "${endpoint}/rustfs/admin/v3/metrics?types=1&by-host=true&n=1" \
    > "artifacts/scanner-metrics.${node}.$(date -u +%Y%m%dT%H%M%SZ).ndjson"
done

Example status request:

bash

awscurl \
  --service s3 \
  --region us-east-1 \
  --access_key "$RUSTFS_ACCESS_KEY" \
  --secret_key "$RUSTFS_SECRET_KEY" \
  --request GET \
  'http://127.0.0.1:9000/rustfs/admin/v3/scanner/status' \
  | jq . > "artifacts/scanner-status.$(date -u +%Y%m%dT%H%M%SZ).json"

For a time series, sample once per minute:

bash

mkdir -p artifacts/status
while sleep 60; do
  ts="$(date -u +%Y%m%dT%H%M%SZ)"
  awscurl \
    --service s3 \
    --region us-east-1 \
    --access_key "$RUSTFS_ACCESS_KEY" \
    --secret_key "$RUSTFS_SECRET_KEY" \
    --request GET \
    'http://127.0.0.1:9000/rustfs/admin/v3/scanner/status' \
    | jq . > "artifacts/status/scanner-status.${ts}.json"
done

Stop the loop after the planned observation window.

Host Telemetry

Collect host metrics over the same window as scanner status.

bash

pidstat -p "$(pidof rustfs)" 60 > artifacts/pidstat.txt
iostat -xz 60 > artifacts/iostat.txt
mpstat 60 > artifacts/mpstat.txt

If pidstat is not available, use top, ps, or the platform monitoring system, but keep the sampling interval and observation window in the report.

Runtime Tuning Examples

Persistent scanner config values use seconds for time fields. Use numeric strings instead of duration suffixes:

bash

mc admin config set ALIAS scanner delay="30" max_wait="15"
mc admin config set ALIAS scanner cycle="3600"
mc admin config set ALIAS scanner cycle_max_duration="1800"
mc admin config set ALIAS scanner cycle_max_objects="1000000"
mc admin config set ALIAS scanner cycle_max_directories="100000"

The canonical persistent bitrot cadence belongs to the heal subsystem:

bash

mc admin config set ALIAS heal bitrot_cycle="2592000"

Environment variables take precedence over persisted config and should be recorded separately:

bash

RUSTFS_SCANNER_DELAY=30
RUSTFS_SCANNER_MAX_WAIT_SECS=15
RUSTFS_SCANNER_CYCLE=3600
RUSTFS_SCANNER_CYCLE_MAX_DURATION_SECS=1800
RUSTFS_SCANNER_CYCLE_MAX_OBJECTS=1000000
RUSTFS_SCANNER_CYCLE_MAX_DIRECTORIES=100000
RUSTFS_SCANNER_BITROT_CYCLE_SECS=2592000

After each config change, read scanner status and confirm the effective value and source under runtime_config.

Observation Window

Use the same window for each run:

Generate or verify the object namespace.
Wait until foreground workload is idle.
Save scanner and heal config.
Save one scanner status snapshot.
Collect scanner status and host telemetry for at least 30 minutes, or for one complete scanner cycle when that is practical.
Save one final scanner status snapshot.

Longer windows are better for cycle interval comparisons. Short windows are acceptable for quick pressure checks only if the conclusion avoids changing defaults.

Fields To Compare

Compare these fields between baseline and tuned runs:

Field	Why it matters
`runtime_config..value` and `runtime_config..source`	Confirms the tested settings actually took effect.
`metrics.pacing_pressure.primary_pressure`	Shows whether pressure is from queues, budgets, pause activity, active scans, or no scanner pressure.
`metrics.pacing_pressure.last_cycle_total_pause_ratio`	Shows how much of the last cycle was cooperative scanner pause time.
`metrics.maintenance_control.primary_control`	Shows whether source-level maintenance is blocked, deferred, active, only pacing-limited, or idle.
`metrics.maintenance_control.sources`	Shows the source, state, reason, backlog, current or last-cycle missed work, and partial-cycle count for each scanner maintenance source.
`metrics.current_cycle_objects_scanned`	Confirms object scan progress during the current cycle.
`metrics.current_cycle_directories_scanned`	Confirms directory walk progress during the current cycle.
`metrics.last_cycle_result`	Confirms whether the previous cycle completed, stopped partially, or failed.
`metrics.last_cycle_partial_reason`	Shows which budget stopped a partial cycle.
`metrics.last_cycle_partial_source`	Shows which scanner work source consumed the stopping budget.
`metrics.source_work`	Shows cumulative work found, queued, skipped, missed, executed, and failed by source.
`metrics.current_cycle_source_work`	Shows which source is consuming the current scan cycle.
`metrics.last_cycle_source_work`	Shows which source consumed the previous scan cycle.
`metrics.replication_repair`	Splits scanner-discovered replication repair by source, kind, scanner role, and execution owner, including bucket object, delete-marker, version-purge, existing-object repair, and site replication boundary states.
`metrics.current_cycle_replication_repair`	Shows which replication repair kind is being discovered or admitted in the current cycle.
`metrics.last_cycle_replication_repair`	Shows which replication repair kind consumed the previous cycle.
`metrics.lifecycle_expiry.current_queued`	Shows scanner-driven expiry/delete work waiting in the expiry worker queue.
`metrics.lifecycle_expiry.current_active`	Shows scanner-driven expiry/delete work currently running in expiry workers.
`metrics.lifecycle_expiry.queue_missed`	Shows expiry/delete queue admission failures outside the scanner walk itself.
`metrics.lifecycle_expiry.scanner_missed`	Shows scanner-discovered expiry/delete work that could not be queued.
`metrics.lifecycle_transition.scanner_missed`	Shows scanner-discovered transition work that could not be queued.
`metrics.lifecycle_transition.queue_full`	Shows transition queue pressure outside the scanner walk itself.
`metrics.lifecycle_transition.compensation_pending`	Shows transition compensation still pending or running after queue pressure.
`metrics.lifecycle_transition.failed`	Shows transition worker failures, which should also surface as lifecycle source failure.
`metrics.current_cycle_usage_saves`	Shows usage cache saves produced by the active scan cycle.
`metrics.last_cycle_usage_saves`	Shows usage cache saves produced by the previous completed or partial scan cycle.
`metrics.usage_freshness.dirty_pending_buckets`	Shows whether bucket/object mutations are still waiting for usage refresh.
`metrics.usage_freshness.last_cycle_dirty_buckets`	Shows how many dirty buckets were picked up by the last cycle.
`metrics.usage_freshness.last_cycle_cleared_dirty_buckets`	Shows how many dirty bucket marks were cleared by a successful cycle.
`metrics.usage_freshness.last_usage_save_result`	Confirms whether the last usage save succeeded, failed, or was skipped.
`metrics.life_time_ops.scan_cycle`	Confirms scanner cycles actually started after the workload.
`metrics.life_time_ops.scan_bucket_drive`	Confirms bucket-drive scan work reached the storage layer.
`metrics.life_time_ops.scan_object`	Confirms object metadata scanning advanced for object workloads.
`metrics.life_time_ops.save_usage`	Confirms `DataUsageInfo` save work happened; this is the key freshness signal for bucket metrics.
`metrics.scan_checkpoint`	Confirms partial cycles preserve resume context.
`metrics.oldest_active_path_age_seconds`	Helps identify scanner paths that may be stuck.

Do not use a single CPU spike as the conclusion. Compare average and p95 CPU over the same observation window.

For heal or bitrot pressure investigations, also capture /v3/background-heal/status from every distributed endpoint and compare healOperations.queueLength, healOperations.activeTasks, healOperations.queuedBySource, healOperations.activeBySource, healOperations.queuedByPriority, and healOperations.activeByPriority. These fields distinguish scanner-submitted low-priority work from manual admin heal and auto-heal work.

scanner-summary.csv includes the heal operation totals needed for quick before/after comparison. In distributed runs, these fields are aggregated from the background-heal status snapshots captured across --metrics-endpoints.

Field	Why it matters
`heal_queue_length`	Total queued heal requests at the same timestamp as the scanner status sample.
`heal_active_tasks`	Total running heal tasks.
`heal_scanner_queued`	Scanner-submitted heal or bitrot work waiting in the queue.
`heal_admin_queued`	Manual/admin heal work waiting in the queue.
`heal_auto_heal_queued`	Auto-heal work waiting in the queue, typically from disk/set recovery paths.

scanner-summary.csv also includes usage freshness columns for quick post-start bucket metrics validation:

Field	Why it matters
`current_cycle_usage_saves`	Usage saves during the current cycle.
`last_cycle_usage_saves`	Usage saves from the last finished or partial cycle.
`usage_dirty_pending_buckets`	Dirty buckets still waiting for scanner refresh.
`usage_last_cycle_dirty_buckets`	Dirty buckets selected by the last cycle.
`usage_last_cycle_cleared_dirty_buckets`	Dirty bucket marks cleared by the last cycle.
`usage_last_save_result`	Last `DataUsageInfo` save result.
`usage_last_save_unix_secs`	Last `DataUsageInfo` save timestamp.
`life_time_scan_cycle`	Total scanner cycles observed by the node.
`life_time_scan_bucket_drive`	Total bucket-drive scans completed by the node.
`life_time_scan_object`	Total object scan operations observed by the node.
`life_time_save_usage`	Total usage save operations observed by the node.

Interpreting Results

A useful tuning result has all of these properties:

average or p95 scanner-related CPU and disk pressure decreases;
current_cycle_objects_scanned or current_cycle_directories_scanned continues to advance;
source_work.missed does not grow unexpectedly for lifecycle, replication, heal, or bitrot;
last_cycle_result is either success or a partial result with a clear budget reason and checkpoint;
data usage freshness remains acceptable for the tested deployment.

Treat these as failure signals:

CPU drops only because the scanner stops making progress;
primary_pressure stays at queued_scans while queues grow;
last_cycle_partial_reason repeats forever with no checkpoint movement;
lifecycle expiry queue_missed, scanner_missed, current_queued, or current_active grows during a run that was expected to reduce expiry backlog;
lifecycle transition scanner_missed, queue_full, compensation_pending, or failed grows during a run that was expected to reduce backlog;
bucket metrics show zero usage after post-start uploads while dirty usage remains pending and life_time_save_usage does not advance;
bucket_replication missed work with scanner_role=repair_admission grows while replication worker queues or target failures are also growing; treat this as downstream replication pressure, not only scanner pacing pressure;
site_replication active_resync grows and is interpreted as scanner-owned repair execution; scanner_role=boundary_signal and execution_owner=site_replication_runtime mean active site resync remains owned by the site replication runtime and admin resync path;
heal or bitrot work moves from queued to missed after a scanner pacing change.

PR Evidence Checklist

For scanner behavior PRs, include this evidence when available:

RustFS commit SHA and branch.
Deployment shape: node count, disk count, disk type, CPU count, memory, and object count.
Workload command or script and benchmark artifact path.
Scanner and heal config before and after tuning.
Observation window and sample interval.
Scanner status snapshots or time series.
Host CPU and disk telemetry.
Usage freshness fields from scanner-summary.csv when validating bucket metrics or issue 3496-style timing.
Short conclusion that separates pressure reduction from scanner progress.
scanner-validation-report.md from the harness when using the scripted collection path.

Final Parity Validation Closure

Use the final validation run to prove the scanner control plane is coherent, not to introduce new runtime behavior. A complete closure package should have at least these runs:

Run	Required evidence
Single-node, single-disk small-object idle	Scanner status series, host telemetry, `scanner-summary.csv`, and a conclusion that CPU or disk pressure is lower without scan progress stopping.
Single-node post-start bucket metrics freshness	Empty data path startup, post-start bucket creation/upload, bucket metrics snapshots, `scanner-summary.csv` usage freshness columns, and evidence that `DataUsageInfo` save work occurred before accepting bucket usage metrics.
Single-node erasure or multi-disk	Checkpoint movement, active path age, set/disk scan pressure, data usage freshness, and before/after scanner config.
Distributed lifecycle backlog	`maintenance_control`, lifecycle expiry/transition queue fields, source work missed/failed counts, and by-host admin metrics.
Distributed replication backlog	Bucket replication repair kind counters, `scanner_role`, `execution_owner`, site replication passive/active boundary counters, source work queued/skipped/missed counts, and by-host admin metrics.
Heal or bitrot pressure	Background heal `healOperations` queued/active source and priority counts, scanner source work for heal/bitrot, and by-host admin metrics.

The expected conclusion is MinIO-style scanner behavior at the operational contract level: scanner remains enabled, pacing is observable and adjustable, partial progress is explainable, maintenance work is attributed by source, and downstream lifecycle, replication, heal, and bitrot backlog can be diagnosed without guessing from CPU usage alone.

For documentation-only PRs, it is enough to verify links and formatting.