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Observability

▾ 5 MINUTE READ — CLOSE

Many organizations adopt cloud-native technologies through microservices, containers, or serverless solutions. Tracing an event to its source with these distributed technologies became increasingly difficult.

In the past, monitoring tools could not adequately track communication pathways and interdependencies in cloud computing systems. Observability tools were introduced to improve the performance of information technology (IT) systems by watching system performance.

What is Observability?

Observability is the process of monitoring and measuring the internal status of a system by evaluating its output. The output is comprised of logs, traces, and metrics. Observability aims to understand what happens across various environments, networks, and technologies, with the goal of resolving issues sooner rather than later.

The Difference between Observability and Monitoring

Many DevOps teams refer to monitoring and observability interchangeably. There are significant differences between these concepts. Monitoring allows you to watch the state of your system based on predetermined metrics and logs. Observability is derived from control theory and understands the status of your internal systems by the outputs.

Monitoring requires teams to know what metrics to follow and helps keep track of them. On the other hand, observability tells teams what metrics are essential by watching overall system performance, asking relevant questions, and noting important information. In other words, observability identifies areas that need to be monitored.

Benefits of Data Observability

Observability gives development teams real-time visibility into their distributed system, allowing them to optimize the debugging process when there’s an error in the code. That is achieved by tracking the system and providing relevant data to make decisions swiftly.

Monitoring and Observability — What’s the Difference and Why Does It Matter? Let’s Find Out.

Asides from identifying valuable metrics, here are some other functions of observability:

Better alerting. Observability platforms allow developers to identify and solve problems faster by providing insights that show what changes have occurred in the system and the issues caused by those changes. This makes debugging and troubleshooting easy for teams.

Consistent workflow. With observability, development teams can see the entire journey of each request along with contextual data from traces. This capability optimizes performance and the debugging process.

Time-saving. Effective observability software helps reduce the time spent figuring out where an issue is from, what part of the deployment process the error is in or what third-party application led to the problem. Observability saves time by readily providing necessary data.

Accelerated developer velocity. Observability performs some functions of monitoring tools and makes troubleshooting swift and effective by removing developers’ uneasy areas. This feature gives development teams time to develop innovative ideas and carry out forward-facing activities.

What to Consider When Choosing Observability Tools

There are many observability tools available in the market. The tools best suited for your organization’s needs are vital for success. Depending on your systems, here are some factors to look out for when deciding on an observability tool:

Integration with modern tools. An adequate observability tool should not only work with your current stack but also have a proven history of updates that make it compatible with new platforms.

Ease of use. Your observability architecture should be easy to learn, understand, and use. Difficult to understand tools do not get added to workflows, defeating the architecture’s purpose.

Provision of real-time data. Good observability platforms should provide information in your distributed systems via queries, dashboards, and reports so that teams can take the necessary action in time.

Adoption of machine learning. Observability software should adopt a machine learning model and automate processes and data curation. This enables detection and makes response to anomalies fast.

Accordance with business value. All technology used by your organization should align with your business purpose. Observability tools should identify and evaluate data — such as system stability and deployment speed — that improve your business.

Difference between Observability and Visibility

Although observability and visibility have many similarities, they are two different concepts in development and operations:

Visibility is the ability to monitor every stage in the development process and align it with the needs of stakeholders. In an attempt to undergo security modernization, organizations channeled multiple resources into achieving visibility. API-driven architectures enabled the aggregation of multiple logs, giving companies a clear view of systems. Visibility birthed the first generation of analytics.

Observability expands on the goals of monitoring software and provides organizations with a view of their systems, and enables correlation and inspection of data to provide insights that align with business objectives. Observability tracks systems to determine essential attributes that should be monitored.

Three Pillars of Observability

Three primary data classes are used in observability, often referred to as the pillars of observability. These three pillars are logs, traces, and metrics.

Logs: Logs are text records that a system makes of events while codes are run. A log often includes a timestamp that reflects the event’s time and a payload of details about the event itself. A log’s format could be plain, structured, or binary. Although plain text logs are the most common, structured logs that include easily queried metadata are gaining prominence.

Log files provide in-depth system details and are often the first place you look when you detect a fault. By reviewing logs, teams can easily troubleshoot codes and discover why an error occurred.

Metrics: Metrics are numerical representations of data measured over some time. These metrics usually include name, timestamp, KPIs, and labels. Metrics are useful in determining a service’s overall behavior as they are structured by default. This means that the data derived from metrics can easily be optimized and stored for longer periods.

Many teams prefer metrics because one can match them across other system components and get a clear picture of performance and system behavior.

Traces: A trace describes the full journey as it moves along a distributed system. As requests pass through the system, each action performed on it — referred to as a span — is filled with data concerning the action performed by the microservice.

Tracing is the observability technique that allows teams to see and understand the action lifecycle across all distributed system nodes. Traces provide context to the data from logs and metrics in observability as they allow you to profile systems.

Learn More About Observability at The New Stack

For The New Stack’s coverage of the observability space, we look at how pre-existing monitoring technologies such as New Relic and Dynatrace are optimized to support this new environment. We also examine the technologies from companies formed specifically to deal with observability and monitoring, such as Honeycomb.io and SignalFx.

Discover more about developments in observability and monitoring:

Observability: The 5-Year Retrospective

Beyond the 3 Pillars of Observability

Monitoring vs. Observability: What’s the Difference?


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Observability
Chronosphere: Metrics at the Scale of Uber
3 Mar 2020 9:35am, by Susan Hall
CI/CD / Observability / Technology / Sponsored / Contributed
What Is Observability?
28 Feb 2020 8:00am, by Katy Farmer
Kubernetes / Observability / Sponsored / Contributed
How AI and Full-Stack Observability Can Overcome Today’s Kubernetes Challenges
17 Feb 2020 10:34am, by Andreas Grabner
Cloud Services / DevOps / Observability
Chaosmesh: Chaos Engineering for Every Layer
13 Feb 2020 8:59am, by Susan Hall
Cloud Native Ecosystem / Cloud Services / Observability / Sponsored / Contributed
Why Even Bother to Move to the Cloud, Anyway?
12 Feb 2020 9:03am, by John Porcaro
DevOps Tools / Microservices / Observability / Sponsored / Contributed
Why New Relic Supports W3C’s Distributed Tracing Protocol
3 Feb 2020 2:51pm, by Jodee Varney
Data Science / Machine Learning / Observability
Devo: Faster Time to Insights from Data
31 Jan 2020 6:00am, by Susan Hall
Observability / Storage
IBM Opts for Humio’s Scalable Logging to Boost Struggling ELK Cloud Deployments
29 Jan 2020 10:58am, by Mike Melanson
Cloud Native Ecosystem / Cloud Services / Observability / Sponsored / Contributed
Bring Observability On Your Cloud Voyage — Or Go Home 
27 Jan 2020 9:47am, by Jason English
Kubernetes / Observability / Technology / Contributed
Is Your Kubernetes Cluster Healthy? Here are 5 Ways to Find Out
16 Jan 2020 8:56am, by Katie Lane
Observability / Contributed
3 New Year’s Resolutions for IT Ops in 2020
14 Jan 2020 12:59pm, by Pete Abrams
Microservices / Observability / Serverless
Solving Serverless and Tracing Is Key to Success in Observability
14 Jan 2020 12:39pm, by Lawrence E Hecht
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Observability / Software Development / Sponsored
How Performance Metrics and Distributed Tracing Will Drive User Experience
9 Jan 2020 4:00pm, by Jennifer Riggins and Alex Williams
Kubernetes / Observability / Sponsored
Kubernetes Performance Trouble Spots: Airbnb’s Take
7 Jan 2020 12:51pm, by Joab Jackson
Observability / Contributed
The 3 Myths of Observability
7 Jan 2020 9:20am, by Arijit Mukherji
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Observability / Serverless / Sponsored
How AWS Lambda Became What It Is Today
30 Dec 2019 1:07pm, by Alex Williams and B. Cameron Gain
Microservices / Observability
Q&A: Ben Sigelman on the Emergence of “Deep Systems” in Microservices
28 Dec 2019 8:00am, by Kimberley Mok
Cloud Native Ecosystem / Cloud Services / Observability / Sponsored / Contributed
How Porsche Informatik’s Cloud Migration Hinged on AI-Powered Observability
17 Dec 2019 9:48am, by Dave Anderson
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DevOps / Observability / Software Development / Sponsored
The New Stack Context: Raygun Portland Tech Leaders’ Lunch
13 Dec 2019 1:51pm, by TNS Staff
Cloud Native Ecosystem / Observability / Sponsored
Merging Logs and Metrics with Grafana Labs’ Loki 1.0 Launch
4 Dec 2019 8:58am, by Mike Melanson
DevOps Tools / Observability / Technology / Sponsored / Contributed
Live from Portland: Nike, Oracle, Microsoft and Chef on What the User Wants
2 Dec 2019 2:59pm, by Andre van den Assum
Microservices / Observability / Sponsored
Q&A: Epsagon Brings Automated Distributed Tracing to Microservices, Serverless
27 Nov 2019 2:00pm, by Mike Melanson
Observability / Sponsored / Contributed
OpenTelemetry’s Past, Present and Future Explained
19 Nov 2019 8:40pm, by Ran Ribenzaft
Cloud Native Ecosystem / Observability
Jaeger Graduates CNCF, Sees a Future Without Native Jaeger Clients
4 Nov 2019 9:02am, by Mike Melanson
Observability / Open Source
Linux Foundation Introduces a Telemetry Policy for All Projects
28 Oct 2019 11:20am, by Mike Melanson
Machine Learning / Observability
OpsRamp Takes AIOps to Hybrid Environments
14 Oct 2019 11:32am, by Susan Hall
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