After a decade of successful Munin deployment across 100+ nodes, I've hit the infrastructure monitoring wall that many growing operations face. The classic pull-based architecture shows its limitations when:

• Node count exceeds 100 servers
• Client-side processing creates timeouts during peak loads
• Plugin overhead becomes unmanageable

The ideal replacement should handle:

# Pseudocode for monitoring system requirements
class MonitoringRequirements:
    def __init__(self):
        self.push_based = True
        self.horizontal_scaling = True
        self.metric_cardinality = "high"
        self.storage_backend = "time-series_optimized"
        self.query_language = "promQL/similar"

Prometheus + Grafana emerges as the strongest candidate for these technical requirements:

# Sample prometheus.yml configuration for high-volume scraping
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'node'
    scrape_interval: 30s
    static_configs:
      - targets: ['node-exporter1:9100', 'node-exporter2:9100']
    metric_relabel_configs:
      - source_labels: [__name__]
        regex: '(node_memory_.*|node_cpu_.*)'
        action: keep

Transition phases should include:

1. Parallel run period (2-4 weeks)
2. Metric mapping between systems
3. Gradual plugin replacement

Example metric conversion:

# Munin plugin output vs Prometheus exporter
Munin Format:
memory.value 3848932

Prometheus Format:
# HELP node_memory_used_bytes Memory used in bytes
# TYPE node_memory_used_bytes gauge
node_memory_used_bytes 3848932

For handling 100+ nodes efficiently:

// Node.js collector example with batching
const { collectDefaultMetrics, Registry } = require('prom-client');
const registry = new Registry();

collectDefaultMetrics({
  timeout: 10000,
  register: registry,
  prefix: 'node_'
});

// Batch metrics push every 30s
setInterval(() => {
  const metrics = await registry.metrics();
  pushToGateway(metrics);
}, 30000);

After a decade of reliable service, Munin's centralized polling architecture shows its limitations when handling 100+ nodes under load. The classic 5-minute cron-based collection becomes problematic when:

# Typical munin-update behavior under load
/usr/bin/munin-update --debug 2>&1 | logger -t munin-update
# Timeout issues manifest as:
ERROR: Node server42.example.com timed out.
WARNING: Processing took 317 seconds (max 300)

For production environments with 100+ servers, we need:

• Push-based metrics collection (not pull)
• Distributed processing architecture
• Efficient storage backends
• Horizontal scaling capabilities

1. Prometheus + Grafana

# Sample node_exporter systemd unit (push alternative)
[Unit]
Description=Prometheus Node Exporter
After=network.target

[Service]
User=node_exporter
ExecStart=/usr/local/bin/node_exporter \
  --web.listen-address=":9100" \
  --collector.systemd \
  --collector.filesystem.ignored-mount-points="^/(sys|proc|dev|run)($|/)"

[Install]
WantedBy=multi-user.target

2. InfluxDB + Telegraf

The TICK stack handles high cardinality data better than RRD:

# telegraf.conf snippet for system metrics
[[inputs.cpu]]
  percpu = true
  totalcpu = true

[[inputs.disk]]
  ignore_fs = ["tmpfs", "devtmpfs"]

[[inputs.system]]
  # No interval needed - telegraf handles batching

3. VictoriaMetrics

For Munin-like simplicity with modern scaling:

# victoria-metrics-prod deployment example
docker run -d --name vminsert \
  -p 8480:8480 \
  victoriametrics/vminsert \
  -storageNode=vmstorage:8400

docker run -d --name vmstorage \
  -v /data/victoria-data:/storage \
  victoriametrics/vmstorage \
  -retentionPeriod=12months

Transition gradually using these approaches:

# Parallel collection during transition
#!/bin/bash
# Collect legacy munin data
/usr/share/munin/munin-update
# Send same metrics to new system
node_exporter --web.listen-address=":9101" \
  --collector.textfile.directory="/var/lib/munin-node/plugin-state/"

Key metrics to preserve from Munin setup:

• Disk growth trends (per partition)
• System load vs CPU cores ratio
• Memory usage patterns (not just %)
• Network throughput saturation

# PromQL equivalent of munin load average
avg(node_load1{instance=~"web.*"} / on(instance) count(node_cpu_seconds_total{mode="system"}) by(instance))