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What does logging, monitoring & observability mean in the world of devops?

Typical Log & Monitoring Dashboard

The beginning of wisdom is the definition of terms.

  1. Real-time search and analysis
  2. Integration with other DevOps tools and platforms
  3. The ability to set up alerts and notifications based on specific log data patterns.
  1. Entry time
  2. Exit time and
  3. Purpose of visit and location.
Example of a Log file
  • System resource usage
  • Application performance
  • Error rates.
Monitoring Dashboard

Logging — Historical analysis, and auditing/record keeping for compliance reasons, whereas

Monitoring — More real-time visibility and proactive alerting to prevent issues which can lead to outages in future.

  • The appearance of a subject,
  • Clothes/accessories that they are wearing,
  • Their walk/gait and other mannerisms
More monitoring data, more insights!

Observability is Logging +Monitoring+ Tracing + Profiling i.e., the superset of all the above concepts

“Observability is the foundation for building reliable, scalable software. Without it, you’re flying blind and hoping for the best.” ~ Torkel Ödegaard

  1. Complexity — Traditional approaches to logging and monitoring can be complex and resource-intensive, requiring the use of multiple tools and technologies to collect and analyze data from different systems and applications. This can make it difficult for teams to gain a comprehensive view of their systems and can lead to inefficiencies in their workflows and processes.
  2. Limited visibility — Traditional approaches to logging and monitoring often provide limited visibility into the behavior and performance of systems and applications. This can make it difficult for teams to find and resolve issues quickly and can lead to longer mean time to resolution (MTTR) for issues.
  3. Silos — Traditional approaches to logging and monitoring can create silos between development and operations teams, as these teams may use different tools and technologies to collect and analyze data. This can make it difficult for teams to collaborate and can hinder the flow of information between teams.
  • Centralized logging — involves collecting log data from various sources and storing it in a central location for easier analysis and visualization.
  • Continuous monitoring — involves using automated tools and processes to continuously monitor the performance and availability of applications and infrastructure.
  • Performance monitoring — involves monitoring key performance indicators (KPIs) to ensure that applications and systems are meeting their performance targets. For example - monitoring things like response times, CPU utilization, and memory usage.
  • Event correlation — involves analyzing log data and other monitoring to identify patterns and relationships between different events and issues. This can help DevOps teams identify the root cause of problems and take more targeted actions to fix them.
  • Automated incident response — This involves setting up automated processes to respond to specific events or issues identified through monitoring and logging. This can include automatically restarting failed services, scaling up infrastructure to meet increased demand, or sending notifications to the appropriate team members.



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Omkar Kadam

I’m a DevOps Engineer by profession, who likes to solve complex engineering problems with unconventional/out-of-the-box/ Innovative solutions.