# What Is Observability? Key Components and Best Practices
URL:: https://www.honeycomb.io/blog/what-is-observability-key-components-best-practices
Author:: Emil Protalinski
## Highlights
> • **Logs provide a textual narrative**, helping you understand the "what" and "why" of events and issues.
> • **Metrics offer quantitative data on system performance** and resource utilization, helping you gain insights into the "how much" and "when" aspects.
> • **Traces let you visualize the entire journey of a request or transaction**, revealing the "flow" and "where" latency occurs. ([View Highlight](https://read.readwise.io/read/01hg8mzd97yz834q1s93j44mvm))
> **Logs are valuable for debugging issues, diagnosing errors, and auditing system activity.** They provide a textual narrative of system events, making it easier to understand the sequence of actions leading up to a problem. ([View Highlight](https://read.readwise.io/read/01hg8mzpey0wmtgcyfbhspe0xe))
> **[Metrics](https://www.honeycomb.io/metrics) are quantitative measurements or numerical values that represent specific aspects of a system's performance, resource utilization, or behavior.** Metrics are typically collected at regular intervals and can be split into two groups: infrastructure metrics and application metrics. ([View Highlight](https://read.readwise.io/read/01hg8mzx13w2c4wkv2j1q8apqh))
> [Distributed traces](https://www.honeycomb.io/distributed-tracing/), also known as just [traces](https://docs.honeycomb.io/working-with-your-data/tracing/), capture a chronological record of the events and processing steps that occur with each end-to-end transaction or request as they move through various components, services, and nodes of a distributed system. Each trace records the timing and context of individual operations, enabling a visualization of the entire flow. **By providing a detailed view of how requests propagate through microservices, traces are critical for understanding the end-to-end performance of distributed systems**, identifying bottlenecks, and diagnosing latency issues. ([View Highlight](https://read.readwise.io/read/01hg8n0a4x0ebbehxpg5zfxjj5))
> Monitoring is the collection of predefined metrics. Monitoring tracks and measures specific aspects of a software system's performance and availability. **Its primary goal is to provide alerts and notifications when predefined thresholds or conditions are met, signaling potential issues.** Monitoring is suitable for quickly identifying critical issues, such as server downtime, high CPU utilization, or low disk space. It is more reactive in nature and excels at providing early warnings for well-defined problems.
> **Observability’s primary purpose is to facilitate proactive issue detection and resolution. It emphasizes real-time or near-real-time data collection and analysis**, enabling teams to monitor the system's current state and detect issues as they occur. Observability is useful for diagnosing complex issues in distributed systems, optimizing system performance, understanding user behavior, and maintaining system reliability in dynamic and cloud-native environments.
> Monitoring and observability serve different purposes and can be applied at different stages of the software development and operations lifecycle. **Monitoring focuses on predefined metrics and alerts, while observability provides a comprehensive view of system behavior.** Imagine attending a dinner with friends: monitoring keeps track of how many dishes to order, and observability ensures the dinner is a success no matter what happens. ([View Highlight](https://read.readwise.io/read/01hg8n11r5qm2sab4st6yhjfes))
> he challenges in observability
> While observability can be a powerful practice, it also comes with challenges that companies and teams must address:
> • **Data volume, noise, and costs:** Vast amounts of data that is not equally valuable can be overwhelming to manage, evaluate, and analyze. [Sampling](https://www.honeycomb.io/blog/tuning-refinery-dynamic-sampling) can be useful in lessening the time and financial burdens of telemetry.
> • **Data variety:** Combining and correlating data from logs, metrics, and traces can be complex, especially when different components use different data types, formats, structures, or standards. Frameworks like [OpenTelemetry](https://www.honeycomb.io/getting-started/getting-started-with-opentelemetry) can alleviate this pain point.
> • **Real-time processing:** Achieving low-latency data processing of observability data at scale can be technically difficult and resource-intensive.
> • **Data privacy and security:** Protecting observability data, which may contain sensitive information such as user data or access logs, requires investment and planning.
> • **Distributed systems complexity:** Ensuring consistent observability practices across multiple services can be complex and difficult to manage.
> • **Instrumentation overhead:** Adding observability instrumentation to applications can introduce overhead, impacting performance.
> • **Skills and training:** Effectively using observability tools and interpreting data may require training to obtain skills and harness the full potential of observability. This is true of some tools—however, we at Honeycomb understand this challenge and frequently add features to make observability accessible to everyone. Our [Query Assistant](https://www.honeycomb.io/blog/introducing-query-assistant), for example, allows engineers to query their systems in plain English.
> • **Cultural shift:** Adopting observability may require overcoming resistance to changing towards data-driven decision-making and collaboration across teams.
> • **Data retention policies:** Determining how long to retain observability data for analysis and compliance purposes may require a legal investment. ([View Highlight](https://read.readwise.io/read/01hg8n1vx5kddaq6c9ecxaj9fg))
---
Title: What Is Observability? Key Components and Best Practices
Author: Emil Protalinski
Tags: readwise, articles
date: 2024-01-30
---
# What Is Observability? Key Components and Best Practices
URL:: https://www.honeycomb.io/blog/what-is-observability-key-components-best-practices
Author:: Emil Protalinski
## AI-Generated Summary
In this article, we will demystify observability—a concept that has become indispensable in modern software development and operations.
## Highlights
> • **Logs provide a textual narrative**, helping you understand the "what" and "why" of events and issues.
> • **Metrics offer quantitative data on system performance** and resource utilization, helping you gain insights into the "how much" and "when" aspects.
> • **Traces let you visualize the entire journey of a request or transaction**, revealing the "flow" and "where" latency occurs. ([View Highlight](https://read.readwise.io/read/01hg8mzd97yz834q1s93j44mvm))
> **Logs are valuable for debugging issues, diagnosing errors, and auditing system activity.** They provide a textual narrative of system events, making it easier to understand the sequence of actions leading up to a problem. ([View Highlight](https://read.readwise.io/read/01hg8mzpey0wmtgcyfbhspe0xe))
> **[Metrics](https://www.honeycomb.io/metrics) are quantitative measurements or numerical values that represent specific aspects of a system's performance, resource utilization, or behavior.** Metrics are typically collected at regular intervals and can be split into two groups: infrastructure metrics and application metrics. ([View Highlight](https://read.readwise.io/read/01hg8mzx13w2c4wkv2j1q8apqh))
> [Distributed traces](https://www.honeycomb.io/distributed-tracing/), also known as just [traces](https://docs.honeycomb.io/working-with-your-data/tracing/), capture a chronological record of the events and processing steps that occur with each end-to-end transaction or request as they move through various components, services, and nodes of a distributed system. Each trace records the timing and context of individual operations, enabling a visualization of the entire flow. **By providing a detailed view of how requests propagate through microservices, traces are critical for understanding the end-to-end performance of distributed systems**, identifying bottlenecks, and diagnosing latency issues. ([View Highlight](https://read.readwise.io/read/01hg8n0a4x0ebbehxpg5zfxjj5))
> Monitoring is the collection of predefined metrics. Monitoring tracks and measures specific aspects of a software system's performance and availability. **Its primary goal is to provide alerts and notifications when predefined thresholds or conditions are met, signaling potential issues.** Monitoring is suitable for quickly identifying critical issues, such as server downtime, high CPU utilization, or low disk space. It is more reactive in nature and excels at providing early warnings for well-defined problems.
> **Observability’s primary purpose is to facilitate proactive issue detection and resolution. It emphasizes real-time or near-real-time data collection and analysis**, enabling teams to monitor the system's current state and detect issues as they occur. Observability is useful for diagnosing complex issues in distributed systems, optimizing system performance, understanding user behavior, and maintaining system reliability in dynamic and cloud-native environments.
> Monitoring and observability serve different purposes and can be applied at different stages of the software development and operations lifecycle. **Monitoring focuses on predefined metrics and alerts, while observability provides a comprehensive view of system behavior.** Imagine attending a dinner with friends: monitoring keeps track of how many dishes to order, and observability ensures the dinner is a success no matter what happens. ([View Highlight](https://read.readwise.io/read/01hg8n11r5qm2sab4st6yhjfes))
> he challenges in observability
> While observability can be a powerful practice, it also comes with challenges that companies and teams must address:
> • **Data volume, noise, and costs:** Vast amounts of data that is not equally valuable can be overwhelming to manage, evaluate, and analyze. [Sampling](https://www.honeycomb.io/blog/tuning-refinery-dynamic-sampling) can be useful in lessening the time and financial burdens of telemetry.
> • **Data variety:** Combining and correlating data from logs, metrics, and traces can be complex, especially when different components use different data types, formats, structures, or standards. Frameworks like [OpenTelemetry](https://www.honeycomb.io/getting-started/getting-started-with-opentelemetry) can alleviate this pain point.
> • **Real-time processing:** Achieving low-latency data processing of observability data at scale can be technically difficult and resource-intensive.
> • **Data privacy and security:** Protecting observability data, which may contain sensitive information such as user data or access logs, requires investment and planning.
> • **Distributed systems complexity:** Ensuring consistent observability practices across multiple services can be complex and difficult to manage.
> • **Instrumentation overhead:** Adding observability instrumentation to applications can introduce overhead, impacting performance.
> • **Skills and training:** Effectively using observability tools and interpreting data may require training to obtain skills and harness the full potential of observability. This is true of some tools—however, we at Honeycomb understand this challenge and frequently add features to make observability accessible to everyone. Our [Query Assistant](https://www.honeycomb.io/blog/introducing-query-assistant), for example, allows engineers to query their systems in plain English.
> • **Cultural shift:** Adopting observability may require overcoming resistance to changing towards data-driven decision-making and collaboration across teams.
> • **Data retention policies:** Determining how long to retain observability data for analysis and compliance purposes may require a legal investment. ([View Highlight](https://read.readwise.io/read/01hg8n1vx5kddaq6c9ecxaj9fg))