{"id":10059,"count":839,"description":"Keeping track of metrics requires teams to know what metrics to follow. Conversely, observability helps teams determine what metrics are essential by monitoring system performance, asking pertinent questions and noting relevant information. Thus, observability identifies areas for monitoring.\r\n\r\nMany organizations adopt <\/span>cloud-native technologies<\/span><\/a> through <\/span>microservices<\/span><\/a>, <\/span>containers<\/span><\/a>, or <\/span>serverless<\/span><\/a> solutions. Tracing an event to its source with these distributed technologies became increasingly difficult.<\/span>\r\n\r\nIn the past, <\/span>monitoring tools<\/span><\/a> 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.<\/span>\r\n

What is Observability?<\/b><\/h2>\r\nObservability 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.<\/span>\r\n

The Difference between Observability and Monitoring<\/b><\/h2>\r\nMany DevOps teams refer to <\/span>monitoring and observability<\/span><\/a> 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.<\/span>\r\n\r\nMonitoring<\/span><\/a> 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.<\/span>\r\n

Benefits of Data Observability<\/b><\/h2>\r\nObservability gives development teams real-time visibility into their distributed system, allowing them to optimize the debugging process when there\u2019s an error in the code. That is achieved by tracking the system and providing relevant data to make decisions swiftly.<\/span>\r\n\r\nMonitoring and Observability<\/b><\/a> \u2014 What\u2019s the Difference and Why Does It Matter? Let's Find Out.<\/b>\r\n\r\nAsides from identifying valuable metrics, here are some other functions of observability:<\/span>\r\n\r\nBetter alerting<\/b>. <\/span>Observability platforms<\/span><\/a> 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.<\/span>\r\n\r\nConsistent workflow<\/b>. 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.<\/span>\r\n\r\nTime-saving<\/b>. Effective <\/span>observability software<\/span><\/a> 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.<\/span>\r\n\r\nAccelerated developer velocity<\/b>. 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.<\/span>\r\n

What to Consider When Choosing Observability Tools<\/b><\/h2>\r\nThere are many <\/span>observability tools<\/span><\/a> 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:<\/span>\r\n\r\nIntegration with modern tools<\/b>. 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.<\/span>\r\n\r\nEase of use<\/b>. 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.<\/span>\r\n\r\nProvision of real-time data<\/b>. 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.<\/span>\r\n\r\nAdoption of <\/b>machine learning<\/b><\/a>. Observability software should adopt a machine learning model and automate processes and data curation. This enables detection and makes response to anomalies fast.<\/span>\r\n\r\nAccordance with business value<\/b>. All technology used by your organization should align with your business purpose. Observability tools should identify and evaluate data \u2014 such as system stability and deployment speed \u2014 that improve your business.<\/span>\r\n

Difference between Observability and Visibility<\/b><\/h2>\r\nAlthough observability and visibility have many similarities, they are two different concepts in development and operations:<\/span>\r\n\r\nVisibility 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.<\/span>\r\n\r\nObservability 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.<\/span>\r\n

Three Pillars of Observability<\/b><\/h2>\r\nThree primary data classes are used in observability, often referred to as the pillars of observability. These three pillars are logs, traces, and metrics.<\/span>\r\n\r\nLogs<\/b>: 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\u2019s 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.<\/span>\r\n\r\nLog 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.<\/span>\r\n\r\nMetrics<\/b>: 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\u2019s 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.<\/span>\r\n\r\nMany teams prefer metrics because one can match them across other system components and get a clear picture of performance and system behavior.<\/span>\r\n\r\nTraces<\/b>: A trace describes the full journey as it moves along a distributed system. As requests pass through the system, each action performed on it \u2014 referred to as a span \u2014 is filled with data concerning the action performed by the microservice.<\/span>\r\n\r\nTracing 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.<\/span>\r\n

Learn More About Observability at The New Stack<\/b><\/h2>\r\nFor The New Stack\u2019s coverage of the observability space, we look at how pre-existing monitoring technologies such as <\/span>New Relic<\/span><\/a> and <\/span>Dynatrace<\/span><\/a> are optimized to support this new environment. We also examine the technologies from companies formed specifically to deal with observability and monitoring, such as <\/span>Honeycomb.io<\/span><\/a> and <\/span>SignalFx<\/span><\/a>.<\/span>\r\n\r\nDiscover more about developments in observability and monitoring.<\/b>","link":"https:\/\/thenewstack.io\/observability\/","name":"Observability","slug":"observability","taxonomy":"category","parent":0,"meta":[],"acf":[],"yoast_head":"\nObservability Overview, News and Trends | The New Stack<\/title>\n<meta name=\"description\" content=\"Observability tools measure the internal status of cloud-native architectures. 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