disk-wear

name	disk-wear
description	Analyze disk write patterns on the PVE host to assess wear and identify top writers by VM, k8s app, and PVC. Use when: (1) User asks about disk wear, disk writes, or storage health, (2) User says "what's wearing the disk", "disk analysis", "I/O analysis", (3) User wants to check write rates by VM, k8s namespace, or PVC, (4) Periodic quarterly disk health review. Combines PVE host I/O stats (SSH), Prometheus metrics (PromQL), and k8s PVC-to-pod mapping for a full breakdown.
author	Claude Code
version	1.0.0
date	"2026-04-17T00:00:00.000Z"

Disk Wear Analysis

Infrastructure

Resource	Address	Notes
PVE host	root@192.168.1.127 (SSH)	Dell R730, PERC H730 RAID
Prometheus	prometheus-server.monitoring.svc:80	Query via alertmanager pod (wget)
SSD	Slot 4, Samsung 850 EVO 1TB	Rated 150 TBW
HDD sdc	RAID1 (2x 11.7TB SAS 7200RPM)	Main data disk, enterprise rated ~550 TB/yr
HDD sda	1.2TB SAS 10K RPM	Backup only

Step 1: Physical Disk Overview + SSD Health

ssh root@192.168.1.127 'echo "=== UPTIME ===" && uptime && echo "" && \
echo "=== PHYSICAL DISK CUMULATIVE (since boot) ===" && iostat -d -k sda sdb sdc 2>/dev/null && echo "" && \
echo "=== SSD SMART (Samsung 850 EVO, slot 4) ===" && \
smartctl -d sat+megaraid,4 -A /dev/sda 2>/dev/null | grep -iE "power_on|reallocat|written|wear|pending|uncorrect"'

Interpret SSD health:

• Wear_Leveling_Count: 100 = new, 0 = dead. Calculate (100 - value)% wear used.
• Total_LBAs_Written: multiply by 512 bytes for total TB written. Compare against 150 TBW rating.
• Estimate remaining life: (150 TBW - current TBW) / annual write rate.

Step 2: Real-Time Snapshot (30 seconds)

SSH to PVE host and take two reads of block device stats 30 seconds apart. This gives instantaneous write rates independent of Prometheus scrape intervals.

ssh root@192.168.1.127 'bash -s' << 'SCRIPT'
echo "=== 30-SECOND SNAPSHOT ($(date)) ==="
declare -A snap1
for dm in /sys/block/dm-*; do
  name=$(basename $dm)
  snap1[$name]=$(cat $dm/stat 2>/dev/null | awk '{print $7}')
done
for d in sda sdb sdc; do
  snap1[$d]=$(cat /sys/block/$d/stat 2>/dev/null | awk '{print $7}')
done

sleep 30

printf "%-12s %10s %10s  %s\n" "DEVICE" "kB/s" "GB/day" "NAME"
echo "-------------------------------------------------------------------"
results=""
for dm in /sys/block/dm-*; do
  name=$(basename $dm)
  s2=$(cat $dm/stat 2>/dev/null | awk '{print $7}')
  s1=${snap1[$name]:-0}
  diff=$((s2 - s1))
  if [ "$diff" -gt 100 ]; then
    kbps=$((diff / 2 / 30))
    gbday=$(echo "scale=1; $kbps * 86400 / 1048576" | bc)
    lvm=$(dmsetup info --columns --noheadings -o name /dev/$name 2>/dev/null)
    results="$results\n$name $kbps $gbday $lvm"
  fi
done
for d in sda sdb sdc; do
  s2=$(cat /sys/block/$d/stat 2>/dev/null | awk '{print $7}')
  s1=${snap1[$d]:-0}
  diff=$((s2 - s1))
  kbps=$((diff / 2 / 30))
  gbday=$(echo "scale=1; $kbps * 86400 / 1048576" | bc)
  results="$results\n$d $kbps $gbday (physical)"
done
echo -e "$results" | sort -k2 -rn | head -30 | while read dev kbps gbday name; do
  printf "%-12s %8s kB/s %8s GB/day  %s\n" "$dev" "$kbps" "$gbday" "$name"
done
SCRIPT

Step 3: Prometheus — Per-App Write Attribution

Query Prometheus from inside the cluster (alertmanager pod has wget).

3a. Top PVC Writers (1h rate)

kubectl exec -n monitoring prometheus-alertmanager-0 -- wget -qO- 'http://prometheus-server/api/v1/query' \
  --post-data='query=topk(20,rate(node_disk_written_bytes_total{instance=~"pve.*"}[1h])*on(device)group_left(lv_name,vg_name)node_disk_device_mapper_info{instance=~"pve.*",lv_name=~"vm-9999-pvc-.*"})' \
  2>/dev/null | python3 -c "
import json,sys
d=json.load(sys.stdin)
for r in d['data']['result']:
    m = r['metric']
    val = float(r['value'][1])
    gb_day = val * 86400 / 1073741824
    if gb_day > 0.05:
        lv = m.get('lv_name','?').replace('vm-9999-','')
        print(f'{gb_day:8.1f} GB/day  {lv}')
"

Then enrich PVC UUIDs with names:

kubectl get pv -o custom-columns=NAME:.metadata.name,PVC:.spec.claimRef.name,NS:.spec.claimRef.namespace | grep "pvc-<UUID>"

3b. Top VM Writers (1h rate)

kubectl exec -n monitoring prometheus-alertmanager-0 -- wget -qO- 'http://prometheus-server/api/v1/query' \
  --post-data='query=topk(10,rate(node_disk_written_bytes_total{instance=~"pve.*"}[1h])*on(device)group_left(lv_name,vg_name)node_disk_device_mapper_info{instance=~"pve.*",lv_name!~"vm-9999-.*|root|swap|data.*|nfs.*|backup.*|ssd.*"})' \
  2>/dev/null | python3 -c "
import json,sys
d=json.load(sys.stdin)
for r in d['data']['result']:
    m = r['metric']
    val = float(r['value'][1])
    gb_day = val * 86400 / 1073741824
    print(f'{gb_day:8.1f} GB/day  {m.get(\"lv_name\",\"?\")}')
"

Enrich VM IDs with names:

ssh root@192.168.1.127 'qm list' 2>/dev/null

3c. Aggregate PVC Writes by K8s Namespace

After collecting the top PVC writers from 3a, map each PVC UUID to its namespace using kubectl get pv, then sum by namespace. Present as a table:

Namespace	GB/day	Top PVC
dbaas	...	mysql-standalone, pg-cluster
monitoring	...	prometheus-data

3d. Historical Trend (7-day total)

kubectl exec -n monitoring prometheus-alertmanager-0 -- wget -qO- 'http://prometheus-server/api/v1/query' \
  --post-data='query=topk(10,increase(node_disk_written_bytes_total{instance=~"pve.*",device=~"sda|sdb|sdc"}[7d]))' \
  2>/dev/null | python3 -c "
import json,sys
d=json.load(sys.stdin)
for r in d['data']['result']:
    m = r['metric']
    val = float(r['value'][1])
    tb = val / 1099511627776
    print(f'{tb:8.2f} TB/7d  device={m.get(\"device\",\"?\")}')
"

Step 4: Interpretation

Baselines

Metric	Healthy	Warning	Critical
sdc (HDD RAID1) annualized	<200 TB/yr	200-400 TB/yr	>400 TB/yr
sdb (SSD) wear used	<50%	50-80%	>80%
Single PVC write rate	<20 GB/day	20-50 GB/day	>50 GB/day
Single VM write rate	<50 GB/day	50-100 GB/day	>100 GB/day
NFS volume total	<20 GB/day	20-50 GB/day	>50 GB/day

Known Write Sources (expected baseline, April 2026)

Source	Expected GB/day	Notes
MySQL standalone	5-10	uptimekuma heartbeats + phpipam. skip-log-bin, no GR
PostgreSQL cluster	5-15	Technitium DNS query logs (90-day retention) + app DBs
k8s-master etcd	30-50	etcd WAL + snapshot compaction
k8s-node VMs	10-30 each	containerd layers, kubelet journals, ephemeral storage
Prometheus	3-5	TSDB compaction
home-assistant	10-15	Recorder database (SQLite/MariaDB)
NFS volume	5-10	Minimal after TrueNAS deprecation

Red Flags (investigate immediately)

• Any single PVC >50 GB/day
• MySQL log_bin = ON (should be OFF — skip-log-bin in standalone config)
• Technitium MySQL or SQLite query log plugins re-installed (should be uninstalled)
• NFS writes >30 GB/day (media ingestion or backup churn)
• SSD wear >80% or projected life <2 years
• k8s node VM writes >100 GB/day (something writing heavily to ephemeral storage)

Step 5: Report Format

Present findings as three tables:

1. Physical Disks

Disk	Type	7d Total	Rate GB/day	Annualized	Status

2. Top Writers (VMs + PVCs combined, sorted by rate)

Rank	Name	Type	GB/day	Status	Notes

3. By K8s Namespace

Namespace	PVC Writes GB/day	Top Contributor

End with:

• Annualized wear projections
• Comparison with previous run (if user provides one)
• Action items for any WARNING/CRITICAL findings