Live Elasticsearch Online Training starting on March 2! See all classes


Observability vs. Monitoring


What Is Meant by Monitoring?

Monitoring refers to gathering and analyzing data from applications and infrastructure to track performance to identify, mitigate, and resolve issues. This is established by tracking metrics that provide insight and help guide business decisions — for example, monitoring the availability and responsiveness of the service by measuring network metrics like connectivity and latency. The beauty and benefits of monitoring lie primarily in analyzing long-term trends and alerting. It shows you how your applications are functioning, how they are growing and how they are used over time.

What Is Meant by Observability?

Observability is the ability to understand and get visibility into the internal state of your system and infrastructure through external data — logs, metrics, events, and traces — generated by the system. Observability provides context and a deep understanding of interdependencies between the different applications across your IT environment. Thus, a more observable system can better identify issues and their root causes faster, proactively, and more accurately.

Observability, in a nutshell, helps us understand the what, how, and why of issues based output of the system.

Read more about observability.

Observability vs. Monitoring: What Is the Difference?

When it comes to observability vs. monitoring, the line between them is often blurry for software development, DevOps, and IT teams. However, while they do have some similarities, observability and monitoring are pretty different and approach the problem differently.

Fundamentally, monitoring is reactive, and observability is proactive.

Monitoring uses dashboards to capture and display predetermined data that helps IT teams detect potential problems and long-term performance trends. However, while it notifies DevOps teams of operational issues using alerts, it doesn’t pinpoint the individual component or underlying reason behind the issue, especially in a highly complex distributed system.

On the other hand, observability provides insights and comprehensively assesses the entire IT environment using data gathered from each internal system. This granular and contextual insight it provides can help teams understand, identify, and troubleshoot the root cause of issues across the IT infrastructure. Thus, it acts as a knowledge base for engineers to define what they want to monitor and how to improve performance.

To sum up the difference between observability vs. monitoring, monitoring tells you what is wrong, while observability answers the how and why of the wrong. Hence, it is best to see both as complementary strategies to provide robust insight into your IT infrastructure because monitoring alone notifies you of what is broken but not why.

What Are the Similarities?

Fundamentally, observability and monitoring use the same type of telemetry data. These are additionally referred to as the “three pillars of observability,” which are:

  • Logs are application and system records of events in your software. It also provides context and details on how an issue impacted the system.
  • Metrics give a numerical assessment of the system’s performance and resource utilization. This assessment can show the difference between past and current values (delta metrics) within a specific period (gauge metrics) or changes over time (cumulative metrics).
  • Traces show how operations move throughout distributed cloud environments. Thus, it contextualizes troubleshooting to specific user actions or service relationships.

The similarity between observability and monitoring lie in their end goal, which is to get insight into the infrastructure of your complex distributed systems and deliver a great user experience.

The Relationship Between Observability and Monitoring

Monitoring notifies teams of issues to address issues before they escalate using telemetry data and alerts. However, to prevent recurrences of the same issue, in-depth analysis and pinpointing of the underlying cause of the issue are needed. This can only be provided by observability.

Let’s paint a scenario.

Memory, CPU utilization rates, and cache hit ratio are often tracked when monitoring a database. Thus, alerts are usually set on them to ensure they don’t exceed the threshold. An alert stating that the CPU utilization has increased or that the cache hit ratio has decreased could be a sign of a poorly written or optimization query. However, an observability tool would need to be utilized for you to be sure.

Monitoring complex distributed applications are much more than this conventional database performance use case. It is also much harder to debug. Thus, the bottom line of the observability vs. monitoring dilemma is that pairing the two approaches is essential to address issues more efficiently and promptly. By doing this, teams will be able to know the occurrence, understand the context, and provide answers to the what, how, and why of today’s distributed multi-cloud microservice architectures with multiple dependencies and shifting complexities.

Observability or Monitoring: Which One Is Better?

While they share similarities and differences, it isn’t a monitoring vs. observability situation. You should instead think of them as complementary strategies that help you efficiently achieve your goal — understanding your system better.

Although monitoring helps one track what’s happening within the environment, observability is necessary for any contextualized analysis of your infrastructure landscape. On the other hand, the opposite is also true. Monitoring is necessary for observability and, thus, is a prerequisite. This is because you cannot observe an unmonitored system.

Both approaches are, therefore, essential to get meaningful insight into today’s complex IT systems.

How They Compare with Telemetry and APM

Observability, monitoring, APM and tracing are separate but supplemental concepts that play a role in getting actionable insights into the health of your system. However, these concepts significantly differ in what they are.

Observability vs. Telemetry

Telemetry is the automated collection of data across your diverse environments to centralized locations for analysis. By telemetry, we mean traces, metrics, and logs. This data can then be used to understand the behavior and performance of your infrastructure.

However, while telemetry offers the data, observability does something much different – it focuses on using this telemetry data to debug issues. Observability integrates the various forms of telemetry data to help you better understand how your infrastructure operates.

Monitoring vs. Telemetry

An often not talked about characterics of telemetry is visibility. Besides collecting data, telemetry provides the data that helps teams track the system’s overall health across various environments. The key difference between monitoring vs. telemetry is that monitoring uses the telemetry data collected to monitor and alert the team of any metrics that fall outside the specified threshold.

Telemetry can be thought of as the data that needs to be extracted to set real-time alerting for monitoring.

Observability vs. APM

Application performance monitoring (APM) is designed to monitor and manage issues affecting applications’ performance and availability. APM is also used to assess the user experience as an application’s workload increases.

The main difference between APM vs. observability is in the level of insight they each provide from the same collected telemetry. APM focuses on aggregating data for metrics to provide insight into the where and when of performance bottlenecks. This can also identify issues and compare current metrics to historical telemetry data. Observability, however, provides a more technical and contextual analysis that can be used for issues debugging and paint a picture of the system’s overall health.

Monitoring vs. APM

Monitoring your infrastructure offers a lot, from having a clear picture of the infrastructure landscape to setting alerts and taking action using telemetry data.

APM is a type of monitoring that focuses on end-to-end application performance. This means that the monitoring vs. APM comparison comes down to the fact that APM is limited to applications, while monitoring goes beyond and is looking at every component in our highly distributed environment. Thus traditional APM can be challenging and narrow, especially in today’s modern IT environment of cloud-native applications and microservices that produce various data types.

Observability and Monitoring Tools

To effectively and adequately manage complex distributed systems, you need an observability tool that does much more than just monitoring and can handle the complexity of a cloud environment. It can help you better understand your system, meet the ever-increasing demand, and offer automation while leaving room for manual instrumentation. This is particularly important in today’s technology space, where a business can have massive volumes of disparate, high-velocity telemetry data spread across dynamically changing multi-cloud environments with various dependencies.

Your tool of choice should be able to aggregate everything you need in one place, from telemetry data in your servers and applications to your stacks in your microservices and multi-cloud environments. It should integrate monitoring, logging, and tracing data and enrich your logs with valuable metadata and visualization of the environments. A solution with alerting, automation, machine learning and AIOps troubleshooting capabilities will ensure no telemetry goes undetected while proactively identifying and contextualizing raw data into meaningful information.

Luckily, a couple of monitoring and observability tools address these concerns and offer much more.

Observability and Monitoring with Sematext

Sematext Cloud is a full-stack observability platform that gives you end-to-end visibility into your

IT infrastructure. It features pre-configured, yet fully customizable dashboards, where you can see live streams of logs and metrics from various data source across your stack, including HAProxy, Apache Tomcat, JVM, and Kubernetes. You can then set up alerts to notify you whenever something abnormal happens and you can correlate the telemetry data for faster and better troubleshooting.

Sematext comes with service auto-discovery. Once installed, it constantly scans your environment for new data sources and starts monitoring their logs and metrics as soon as they are discovered, without any additional configuration. In other words, you can rest assured that you will always have complete observability, as Sematext automatically scales up and down with your environment.

Watch the video below for more information on Sematext Cloud or jump straight to trying the 14-day free trial!

Start free trial

See Also