When it comes to server monitoring, professionals typically leverage these industry-standard solutions:

// Example: Basic Prometheus config snippet
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'node'
    static_configs:
      - targets: ['localhost:9100']

Strengths:

• Multi-dimensional data model with time series
• Powerful query language (PromQL)
• Excellent Kubernetes integration

Weaknesses:

• Requires additional components for full observability
• No long-term storage by default

Typically paired with Prometheus or other data sources:

// Sample dashboard JSON configuration
{
  "panels": [{
    "title": "CPU Usage",
    "type": "graph",
    "datasource": "Prometheus",
    "targets": [{
      "expr": "100 - (avg by(instance)(rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)"
    }]
  }]
}

Still widely used in legacy environments:

# Example Nagios check command
define command {
    command_name    check_http
    command_line    /usr/lib/nagios/plugins/check_http -H $HOSTADDRESS$ -p $ARG1$
}

Particularly strong in:

• Autodiscovery capabilities
• Built-in visualization
• Distributed monitoring

For containerized environments:

• Datadog: All-in-one SaaS solution
• New Relic: APM-focused monitoring
• Sysdig: Container-native visibility

# Docker monitoring with cAdvisor
docker run \
  --volume=/:/rootfs:ro \
  --volume=/var/run:/var/run:ro \
  --volume=/sys:/sys:ro \
  --volume=/var/lib/docker/:/var/lib/docker:ro \
  --publish=8080:8080 \
  --detach=true \
  --name=cadvisor \
  google/cadvisor:latest

Consider these factors:

1. Infrastructure complexity
2. Team expertise
3. Budget constraints
4. Integration requirements

For most modern cloud-native stacks, a combination of Prometheus (metrics), Grafana (visualization), and ELK (logs) provides comprehensive coverage.

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In modern DevOps environments, server monitoring is crucial for maintaining system health, performance, and security. The right tools can help detect issues before they escalate, optimize resource usage, and ensure high availability.

Here are some of the most widely-used server monitoring tools in production environments:

Prometheus + Grafana

Strengths:

• Open-source and highly scalable
• Powerful query language (PromQL)
• Excellent visualization through Grafana

Weaknesses:

• Requires more setup than SaaS solutions
• Not ideal for short-term, ephemeral monitoring

# Sample Prometheus config for node monitoring
scrape_configs:
  - job_name: 'node'
    static_configs:
      - targets: ['localhost:9100']

Datadog

Strengths:

• Comprehensive SaaS solution
• Excellent APM and log management
• Hundreds of integrations

Weaknesses:

• Can become expensive at scale
• Less control than self-hosted solutions

New Relic

Strengths:

• Excellent application performance monitoring
• User-friendly interface
• Good for full-stack observability

Weaknesses:

• Pricing can be opaque
• Some features require premium plans

Here's a simple Python script to check server health metrics:

import psutil
import time

def monitor_system():
    while True:
        cpu = psutil.cpu_percent(interval=1)
        mem = psutil.virtual_memory().percent
        disk = psutil.disk_usage('/').percent
        
        print(f"CPU: {cpu}% | Memory: {mem}% | Disk: {disk}%")
        time.sleep(5)

if __name__ == "__main__":
    monitor_system()

Consider these factors when selecting a monitoring solution:

• Team size and expertise
• Budget constraints
• Required monitoring depth
• Integration needs with existing tools

For production systems, consider implementing:

• Distributed tracing
• Log aggregation
• Anomaly detection
• Synthetic monitoring

For more comprehensive comparisons, check the Wikipedia comparison of network monitoring systems.