Monitoring System Selection Overview

This document provides a categorized overview of commonly encountered monitoring systems, outlining which monitoring systems are suitable for specific scenarios.

uptime-kuma

Official website: https://github.com/louislam/uptime-kuma

A simple online monitoring system written in Node.js with agentless deployment.

Advantages:

• Low resource consumption, can be deployed without a database
• Can perform basic website uptime monitoring for HTTP, HTTPS, TCP
• Supports group management, can be categorized by business

Disadvantages:

• Simple, cannot monitor complex performance scenarios

Applicable scenarios:

• Availability monitoring for small websites or services
• Simple monitoring needs for personal projects or small teams
• Lightweight monitoring solutions requiring rapid deployment

Nightingale Monitoring

Official website: https://flashcat.cloud/product/nightingale/

An open-source and commercial monitoring system written in Go, with the core being a monitoring engine (responsible for extracting data, generating alert information, and managing business topology), which provides good abstraction and independence.

Advantages:

• Supports data acquisition from multiple data sources: Prometheus-style data sources, ES data sources, databases as data sources
• Supports customized alerting components, allowing implementation of your own alerting platform or using complementary commercial software https://flashcat.cloud/product/flashduty/
• Good component abstraction, core function is data extraction and alert generation. The platform is not designed as an all-in-one solution
• Supports acting as a data gateway, accepting push data and forwarding to Prometheus series databases, which is convenient
• Comes with a versatile agent that can serve as a monitoring data collection endpoint for multiple functions

Disadvantages:

• Slightly complex to get started
• Some features are missing, only available in commercial version. Especially: alert rules in the open-source version are relatively simple, can only perform simple data selection and alert when found, lacks advanced expressions

Applicable scenarios:

• Monitoring environments requiring integration of multiple data sources
• Teams with customization needs for alerting components
• Medium to large enterprise monitoring systems requiring flexible architecture

Zabbix

Official website: https://www.zabbix.com/

An enterprise-level distributed monitoring system written in C, supporting large-scale deployment and distributed monitoring.

Advantages:

• Comprehensive functionality, supports various monitoring scenarios (network devices, servers, applications, etc.)
• Supports auto-discovery and auto-registration, can automatically add monitoring targets
• Rich visualization features, supports custom dashboards and reports
• Active community with many ready-made monitoring templates
• Supports distributed deployment, can handle large-scale monitoring scenarios
• Complete alerting mechanism, supports multiple notification methods
• Supports SNMP, IPMI, JMX and other protocols

Disadvantages:

• Deployment and configuration are relatively complex, steep learning curve
• May be too heavyweight for small projects
• Database performance may become a bottleneck in large-scale monitoring
• Alert rule configuration is relatively complex and not flexible enough

Applicable scenarios:

• Large and medium enterprise-level monitoring
• Complex environments requiring monitoring of various types of devices and systems
• Scenarios with high requirements for monitoring system functionality completeness

Prometheus + Alert Manager

Official website: https://prometheus.io/

A cloud-native monitoring system and time-series database written in Go, designed for microservices and containerized environments.

Advantages:

• Multi-dimensional data model using labels to identify time series, flexible querying
• Powerful query language PromQL for complex data analysis
• No dependency on distributed storage, single server nodes are autonomous
• Pulls time series data via HTTP protocol
• Discovers targets through service discovery or static configuration
• Supports pushing data to Prometheus gateway
• Supports hierarchical federation for scaling
• Active community with many Exporters available
• Good integration with cloud-native technologies like Kubernetes
• Alertmanager provides flexible alert management functionality

Disadvantages:

• For non-cloud-native environments, requires additional Exporter configuration
• Not suitable for long-term storage of large amounts of historical data by default
• Steep learning curve, especially PromQL query language
• For complex monitoring topologies, requires considerable configuration work

Applicable scenarios:

• Cloud-native and microservice architecture environments
• Containerized (Docker, Kubernetes) deployed applications
• Scenarios requiring flexible querying and analysis of monitoring data
• Environments with high scalability requirements for monitoring systems

Grafana

Official website: https://grafana.com/

An open-source monitoring visualization platform focused on metrics analysis and visual presentation.

Advantages:

• Powerful visualization capabilities, supports rich chart types and dashboard configurations
• Supports integration with multiple data sources like Prometheus, InfluxDB, Elasticsearch, etc.
• Built-in alerting functionality, supports multiple notification channels
• Active plugin ecosystem with good extensibility
• Easy to deploy, supports Docker, Kubernetes and other deployment methods
• Provides enterprise edition with more advanced features (like RBAC permission management)

Disadvantages:

• Does not store data itself, needs to integrate with other data sources
• Complex data processing and analysis scenarios may require combining with other tools
• Performance requirements for large-scale deployments

Datadog

Official website: https://www.datadoghq.com/

A SaaS-based all-in-one monitoring platform covering infrastructure, application performance, and log monitoring.

Advantages:

• High integration, provides comprehensive monitoring solutions
• SaaS deployment model, no need to maintain monitoring infrastructure
• Supports wide range of cloud platforms and application technology stacks
• Powerful data analysis and visualization capabilities
• Excellent user experience with modern interface design
• Comprehensive APM functionality, supports distributed tracing
• Complete collaboration and alerting mechanisms

Disadvantages:

• Commercial product, requires payment for use
• For teams with self-built infrastructure, costs may be high
• Data stored on third-party platform, need to consider data security issues

Elastic Stack (ELK)

Official website: https://www.elastic.co/observability

An open-source log and metrics analysis platform composed of Elasticsearch, Logstash, Kibana and other components.

Advantages:

• Outstanding performance in log analysis
• Supports metrics monitoring and application performance monitoring (APM)
• Open-source solution with active community
• Supports large-scale data processing and analysis
• Rich visualization features
• Diverse data collection methods
• AI-driven analysis capabilities

Disadvantages:

• High deployment and maintenance complexity
• High hardware resource requirements
• Slightly insufficient in metrics monitoring compared to professional monitoring systems
• Relatively steep learning curve

Sentry

Official website: https://sentry.io/

A platform focused on error tracking and application performance monitoring.

Advantages:

• Specialized in error monitoring, provides detailed error context information
• Supports multiple programming languages and frameworks
• Has application performance monitoring (APM) capabilities
• Supports session replay for issue reproduction and debugging
• Easy to integrate, provides multiple SDKs
• Can integrate with development tools like Jira, Slack

Disadvantages:

• Mainly focuses on error monitoring, other monitoring features are relatively limited
• Commercial version has more features, free version has certain limitations
• Limited support for infrastructure monitoring