The ability to measure the internal states of a system by examining its outputs is called Observability. A system becomes 'observable' when it is possible to estimate the current state using only information from outputs, namely sensor data. You can use the data from Observability to identify and troubleshoot problems, optimize performance, and improve security.
In the next few sections, we'll take a closer look at the three pillars of Observability: Metrics, Logs, and Traces.
What is the difference between Observability & Monitoring?
‘Observability wouldn’t be possible without monitoring.’
Monitoring is another term that closely relates to observability. The major difference between Monitoring and Observability is that the latter refers to the ability to gain insights into the internal workings of a system, while the former refers to the act of collecting data on system performance and behavior.
In addition to that, Monitoring doesn't really think about the end goal. It focuses on predefined metrics and thresholds to detect deviations from expected behavior. Observability aims to provide a deep understanding of system behavior, allowing exploration and discovery of unexpected issues.
In terms of perspective & mindset, Monitoring adopts a "top-down" approach with predefined alerts based on known criteria. Whereas, Observability takes a "bottom-up" approach, encouraging open-ended exploration and adaptability to changing requirements.
Observability
Monitoring
Tells you why a system is at fault.
Notifies that you have a system at fault.
Acts as a knowledge base to define what needs monitoring.
Focuses only on monitoring systems and detecting faults across them.
Focuses on giving context to data.
Data collection focused.
Give a more complete assessment of the overall environment.
Keeping track of monitoring KPIs.
Observability is a traversable map.
Monitoring is a single plane.
It gives you complete information.
It gives you limited information.
Observability creates the potential to monitor different events.
Monitoring is the process of using Observability.
Monitoring detects anomalies and alerts you to potential problems. However, Observability not only detects issues but also helps you to understand their root causes and underlying dynamics.
Observability, built on the Three Pillars (Metrics, Logs, Traces), revolves around the core concept of "Events." Events are the fundamental units of monitoring and telemetry, each time stamped and quantifiable. What distinguishes events is their context, especially in user interactions. For example, when a user clicks "Pay Now" on an eCommerce site, this action is an event, expected within seconds.
In monitoring tools, "Significant Events" are key. They trigger:
Automated Alerts: Notifying SREs or operations teams.
Imagine a server's disk nearing 99% capacity; it's significant, but understanding which applications and users cause this is vital for effective action.
Metrics
Metrics serve as numeric indicators, offering insights into a system's health. While some metrics like CPU, memory, and disk usage are obvious system health indicators, numerous other critical metrics can uncover underlying issues. For instance, a gradual increase in OS handles can lead to a system slowdown, eventually necessitating a reboot for accessibility. Similar valuable metrics exist throughout the various layers of the modern IT infrastructure.
Careful consideration is crucial when determining which metrics to continuously collect and how to analyze them effectively. This is where domain expertise plays a pivotal role. While most monitoring tools can detect evident issues, the best ones go further by providing insights into detecting and alerting complex problems. It's also essential to identify the subset of metrics that serve as proactive indicators of impending system problems. For instance, an OS handle leak rarely occurs abruptly.
Tracking the gradual increase in the number of handles in use over time makes it possible to predict when the system might become unresponsive, allowing for proactive intervention.
Advantages of Metrics
Challenges of Metrics
Quantitative and intuitive for setting alert thresholds
Lightweight and cost-effective for storage
Excellent for tracking trends and system changes
Provides real-time component state data
Constant overhead cost; not affected by data surges
Limited insight into the "why" behind issues
Lack context of individual interactions or events
Risk of data loss in case of collection/storage failure
Fixed interval collection may miss critical details
Excessive sampling can impact performance and costs
Logs frequently contain intricate details about how an application processes requests. Unusual occurrences, such as exceptions, within these logs can signal potential issues within the application. It's a vital aspect of any observability solution to monitor these errors and exceptions in logs. Parsing logs can also reveal valuable insights into the application's performance.
Logs often hold insights that may remain elusive when using APIs (Application Programming Interfaces) or querying application databases. Many Independent Software Vendors (ISVs) don't offer alternative methods to access the data available in logs. Therefore, an effective observability solution should enable log analysis and facilitate the capture of log data and its correlation with metric and trace data.
Advantages of Logs
Challenges of Logs
Easy to generate, typically timestamp + plain text
Often require minimal integration by developers
Most platforms offer standardized logging frameworks
Human-readable, making them accessible
Provide granular insights for retrospective analysis
Can generate large data volumes, leading to costs
Impact on application performance, especially without asynchronous logging
Retrospective use, not proactive
Persistence challenges in modern architectures
Risk of log loss in containers and auto-scaling environments
Do you know the tool combination for centralized logging, log analysis, and real-time data visualization? Read our blog on ELK Stack for introduction.
Traces
Tracing is a relatively recent development, especially suited to the complex nature of contemporary applications. It works by collecting information from different parts of the application and putting it together to show how a request moves through the system.
A trace represented as spans: span A is the root span, span B is a child of span A. Source.
The primary advantage of tracing lies in its ability to deconstruct end-to-end latency and attribute it to specific tiers or components. While it can't tell you exactly why there's a problem, it's great for figuring out where to look.
Advantages of Traces
Challenges of Traces
Ideal for pinpointing issues within a service
Offers end-to-end visibility across multiple services
Identifies performance bottlenecks effectively
Aids debugging by recording request/response flows
Provides contextual insights into system behavior
Limited ability to reveal long-term trends
Complex systems may yield diverse trace paths
Doesn't explain the cause of slow or failing spans (steps)
Adds overhead, potentially impacting system performance
Integrating tracing used to be difficult, but with service meshes, it's now effortless. Service meshes handle tracing and stats collection at the proxy level, providing seamless observability across the entire mesh without requiring extra instrumentation from applications within it.
Each above discussed component has its pros & cons even though one might want to use them all. 🧑💻
Observability tools gather and analyze data related to user experience, infrastructure, and network telemetry to proactively address potential issues, preventing any negative impact on critical business key performance indicators (KPIs).
Some popular observability tooling options include:
Prometheus: A leading open-source monitoring and alerting toolkit known for its scalability and support for multi-dimensional data collection.
Grafana: A visualization and dashboarding platform often used with Prometheus, providing rich insights into system performance.
Jaeger: An open-source distributed tracing system for monitoring and troubleshooting microservices-based architectures.
Elasticsearch: A search and analytics engine that, when paired with Kibana and Beats, forms the ELK Stack for log management and analysis.
Honeycomb: An event-driven observability tool that offers real-time insights into application behavior and performance.
Datadog: A cloud-based observability platform that integrates logs, metrics, and traces, providing end-to-end visibility.
New Relic: Offers application performance monitoring (APM) and infrastructure monitoring solutions to track and optimize application performance.
Sysdig: Focused on container monitoring and security, Sysdig provides deep visibility into containerized applications.
Zipkin: An open-source distributed tracing system for monitoring request flows and identifying latency bottlenecks.
Squadcast: An incident management platform that integrates with various observability tools, streamlining incident response and resolution.
Conclusion
Logs, metrics, and traces are essential Observability pillars that work together to provide a complete view of distributed systems. Incorporating them strategically, such as placing counters and logs at entry and exit points and using traces at decision junctures, enables effective debugging. Correlating these signals enhances our ability to navigate metrics, inspect request flows, and troubleshoot complex issues in distributed systems.
Observability and Incident Management are also closely related domains. By combining both, you can create a more efficient and effective way to respond to incidents.
In essence, Squadcast can help you to minimize the impact of incidents on your business and improve the overall reliability of your systems. Start your free trial of Squadcast incident platform today, which seamlessly integrates with a wide range of observability tools including Honeycomb, Datadog, New Relic, Prometheus, and Grafana. In addition to these integrations, Squadcast also has a public API that you can use to integrate with other tools. This means that you can integrate Squadcast with any observability tool that has an API. Here’s where you can book a Demo today.
Squadcast is a Reliability Workflow platform that integrates On-Call alerting and Incident Management along with SRE workflows in one offering. Designed for a zero-friction setup, ease of use and clean UI, it helps developers, SREs and On-Call teams proactively respond to outages and create a culture of learning and continuous improvement.
What you should do now
Schedule a demo with Squadcast to learn about the platform, answer your questions, and evaluate if Squadcast is the right fit for you.
Curious about how Squadcast can assist you in implementing SRE best practices? Discover the platform's capabilities through our Interactive Demo.
The ability to measure the internal states of a system by examining its outputs is called Observability. A system becomes 'observable' when it is possible to estimate the current state using only information from outputs, namely sensor data. You can use the data from Observability to identify and troubleshoot problems, optimize performance, and improve security.
In the next few sections, we'll take a closer look at the three pillars of Observability: Metrics, Logs, and Traces.
What is the difference between Observability & Monitoring?
‘Observability wouldn’t be possible without monitoring.’
Monitoring is another term that closely relates to observability. The major difference between Monitoring and Observability is that the latter refers to the ability to gain insights into the internal workings of a system, while the former refers to the act of collecting data on system performance and behavior.
In addition to that, Monitoring doesn't really think about the end goal. It focuses on predefined metrics and thresholds to detect deviations from expected behavior. Observability aims to provide a deep understanding of system behavior, allowing exploration and discovery of unexpected issues.
In terms of perspective & mindset, Monitoring adopts a "top-down" approach with predefined alerts based on known criteria. Whereas, Observability takes a "bottom-up" approach, encouraging open-ended exploration and adaptability to changing requirements.
Observability
Monitoring
Tells you why a system is at fault.
Notifies that you have a system at fault.
Acts as a knowledge base to define what needs monitoring.
Focuses only on monitoring systems and detecting faults across them.
Focuses on giving context to data.
Data collection focused.
Give a more complete assessment of the overall environment.
Keeping track of monitoring KPIs.
Observability is a traversable map.
Monitoring is a single plane.
It gives you complete information.
It gives you limited information.
Observability creates the potential to monitor different events.
Monitoring is the process of using Observability.
Monitoring detects anomalies and alerts you to potential problems. However, Observability not only detects issues but also helps you to understand their root causes and underlying dynamics.
Observability, built on the Three Pillars (Metrics, Logs, Traces), revolves around the core concept of "Events." Events are the fundamental units of monitoring and telemetry, each time stamped and quantifiable. What distinguishes events is their context, especially in user interactions. For example, when a user clicks "Pay Now" on an eCommerce site, this action is an event, expected within seconds.
In monitoring tools, "Significant Events" are key. They trigger:
Automated Alerts: Notifying SREs or operations teams.
Imagine a server's disk nearing 99% capacity; it's significant, but understanding which applications and users cause this is vital for effective action.
Metrics
Metrics serve as numeric indicators, offering insights into a system's health. While some metrics like CPU, memory, and disk usage are obvious system health indicators, numerous other critical metrics can uncover underlying issues. For instance, a gradual increase in OS handles can lead to a system slowdown, eventually necessitating a reboot for accessibility. Similar valuable metrics exist throughout the various layers of the modern IT infrastructure.
Careful consideration is crucial when determining which metrics to continuously collect and how to analyze them effectively. This is where domain expertise plays a pivotal role. While most monitoring tools can detect evident issues, the best ones go further by providing insights into detecting and alerting complex problems. It's also essential to identify the subset of metrics that serve as proactive indicators of impending system problems. For instance, an OS handle leak rarely occurs abruptly.
Tracking the gradual increase in the number of handles in use over time makes it possible to predict when the system might become unresponsive, allowing for proactive intervention.
Advantages of Metrics
Challenges of Metrics
Quantitative and intuitive for setting alert thresholds
Lightweight and cost-effective for storage
Excellent for tracking trends and system changes
Provides real-time component state data
Constant overhead cost; not affected by data surges
Limited insight into the "why" behind issues
Lack context of individual interactions or events
Risk of data loss in case of collection/storage failure
Fixed interval collection may miss critical details
Excessive sampling can impact performance and costs
Logs frequently contain intricate details about how an application processes requests. Unusual occurrences, such as exceptions, within these logs can signal potential issues within the application. It's a vital aspect of any observability solution to monitor these errors and exceptions in logs. Parsing logs can also reveal valuable insights into the application's performance.
Logs often hold insights that may remain elusive when using APIs (Application Programming Interfaces) or querying application databases. Many Independent Software Vendors (ISVs) don't offer alternative methods to access the data available in logs. Therefore, an effective observability solution should enable log analysis and facilitate the capture of log data and its correlation with metric and trace data.
Advantages of Logs
Challenges of Logs
Easy to generate, typically timestamp + plain text
Often require minimal integration by developers
Most platforms offer standardized logging frameworks
Human-readable, making them accessible
Provide granular insights for retrospective analysis
Can generate large data volumes, leading to costs
Impact on application performance, especially without asynchronous logging
Retrospective use, not proactive
Persistence challenges in modern architectures
Risk of log loss in containers and auto-scaling environments
Do you know the tool combination for centralized logging, log analysis, and real-time data visualization? Read our blog on ELK Stack for introduction.
Traces
Tracing is a relatively recent development, especially suited to the complex nature of contemporary applications. It works by collecting information from different parts of the application and putting it together to show how a request moves through the system.
A trace represented as spans: span A is the root span, span B is a child of span A. Source.
The primary advantage of tracing lies in its ability to deconstruct end-to-end latency and attribute it to specific tiers or components. While it can't tell you exactly why there's a problem, it's great for figuring out where to look.
Advantages of Traces
Challenges of Traces
Ideal for pinpointing issues within a service
Offers end-to-end visibility across multiple services
Identifies performance bottlenecks effectively
Aids debugging by recording request/response flows
Provides contextual insights into system behavior
Limited ability to reveal long-term trends
Complex systems may yield diverse trace paths
Doesn't explain the cause of slow or failing spans (steps)
Adds overhead, potentially impacting system performance
Integrating tracing used to be difficult, but with service meshes, it's now effortless. Service meshes handle tracing and stats collection at the proxy level, providing seamless observability across the entire mesh without requiring extra instrumentation from applications within it.
Each above discussed component has its pros & cons even though one might want to use them all. 🧑💻
Observability tools gather and analyze data related to user experience, infrastructure, and network telemetry to proactively address potential issues, preventing any negative impact on critical business key performance indicators (KPIs).
Some popular observability tooling options include:
Prometheus: A leading open-source monitoring and alerting toolkit known for its scalability and support for multi-dimensional data collection.
Grafana: A visualization and dashboarding platform often used with Prometheus, providing rich insights into system performance.
Jaeger: An open-source distributed tracing system for monitoring and troubleshooting microservices-based architectures.
Elasticsearch: A search and analytics engine that, when paired with Kibana and Beats, forms the ELK Stack for log management and analysis.
Honeycomb: An event-driven observability tool that offers real-time insights into application behavior and performance.
Datadog: A cloud-based observability platform that integrates logs, metrics, and traces, providing end-to-end visibility.
New Relic: Offers application performance monitoring (APM) and infrastructure monitoring solutions to track and optimize application performance.
Sysdig: Focused on container monitoring and security, Sysdig provides deep visibility into containerized applications.
Zipkin: An open-source distributed tracing system for monitoring request flows and identifying latency bottlenecks.
Squadcast: An incident management platform that integrates with various observability tools, streamlining incident response and resolution.
Conclusion
Logs, metrics, and traces are essential Observability pillars that work together to provide a complete view of distributed systems. Incorporating them strategically, such as placing counters and logs at entry and exit points and using traces at decision junctures, enables effective debugging. Correlating these signals enhances our ability to navigate metrics, inspect request flows, and troubleshoot complex issues in distributed systems.
Observability and Incident Management are also closely related domains. By combining both, you can create a more efficient and effective way to respond to incidents.
In essence, Squadcast can help you to minimize the impact of incidents on your business and improve the overall reliability of your systems. Start your free trial of Squadcast incident platform today, which seamlessly integrates with a wide range of observability tools including Honeycomb, Datadog, New Relic, Prometheus, and Grafana. In addition to these integrations, Squadcast also has a public API that you can use to integrate with other tools. This means that you can integrate Squadcast with any observability tool that has an API. Here’s where you can book a Demo today.
Squadcast is a Reliability Workflow platform that integrates On-Call alerting and Incident Management along with SRE workflows in one offering. Designed for a zero-friction setup, ease of use and clean UI, it helps developers, SREs and On-Call teams proactively respond to outages and create a culture of learning and continuous improvement.
What you should do now
Schedule a demo with Squadcast to learn about the platform, answer your questions, and evaluate if Squadcast is the right fit for you.
Curious about how Squadcast can assist you in implementing SRE best practices? Discover the platform's capabilities through our Interactive Demo.
