<h2 id="what-is-observability">What is Observability?</h2>
Observability is the ability to monitor, measure and understand the internal states of a complex system or application by examining its external outputs, such as metrics, logs and performance metrics. 
These outputs of massive real-time and collected data provide proactive insights, automated analytics, and actionable intelligence together with the application of cross-domain data correlation and machine learning.
A system is considered “observable” if the current state can be estimated by only using its outputs. The more granular the external outputs, the better the system’s observability, and the quicker and more accurately you can identify the root cause of a problem and resolve it. 
In modern, distributed software systems and cloud computing, Observability plays an increasingly important role in ensuring the reliability, performance, and security of applications and infrastructure.
Background
The term &quot;observability&quot; originated from control theory in engineering, which is concerned with describing, understanding and automating a dynamic system to maintain it at a desired level. Examples of these include automatically controlling the flow of water through pipes and the speed of a vehicle over inclines and declines, based on feedback from the system. 
<h2 id="how-does-observability-work">How does observability work?</h2>
In IT and cloud computing, observability relies on software tools and practices designed to collect, aggregate and analyze as much data as possible from various system components, to provide comprehensive views into the internal states of a system at the most critical point: when data is sent to another system for processing and usage.
These components include hardware and network infrastructure, applications, serverless services, middleware, and databases.
<h3 id="3-pillars-of-observability">3 Pillars of observability</h3>
To understand how observability works, it is crucial to examine the three pillars of observability, referring to the three types of telemetry data upon which observability is built on. 
<ul>
<li>Logs
</li>
<li>Metrics
</li>
<li>Traces
</li>
</ul>
When tied together by an observability solution, the three pillars are used to provide deep visibility into distributed systems and allow teams to get to the root cause of a multitude of issues. This helps to effectively monitor, troubleshoot and debug applications and networks and improve the system’s performance. The goal is to meet customer experience expectations, service level agreements (SLAs) and other business requirements.
<h3 id="logs">Logs</h3>
Logs are detailed, immutable text records of every event that happens at a particular time, including a timestamp that tells when it occurred and a payload that provides context. They come from every piece of software, user action, and network activity. 
Developers can use these logs to &#39;play back&#39; for troubleshooting and debugging purposes. Along with this detailed information, logs contain valuable metadata and additional data, which makes them easier to query. 
As a proven way of obtaining valuable information regarding system health, logs are typically the first place you look when something goes wrong in a system. 
Observability solutions centralize log and event data together with other performance insights, saving teams the chore of digging into logs and granting them visibility across the entire enterprise. Observability solutions can also catalog logs for future analysis or have them invoke specific alert tasks for predetermined events. 
This greatly increases response times, allowing teams to develop proactive solutions to solve and prevent the same issues from recurring.
<h3 id="metrics">Metrics</h3>
A metric is a numeric value measured over an interval of time including specific attributes such as timestamp, name, KPIs and value. 
They are designed to be calculated, aggregated, or averaged. Unlike logs, metrics are structured by default, which makes it easier to query and optimize for storage, giving you the ability to retain them for longer periods.
An example of metrics is the measuring of how much memory or CPU capacity an application uses over a five-minute span, or how much latency an application experiences during a spike in usage. 
Businesses now apply metrics to almost everything they do, using them to define success and spot trends at the onset to help determine the best course of action.
While metrics are able to report on trends or anomalies over time, they often provide limited insights when something is broken. Observability solutions utilize metrics to measure precise system performance values and create responses. By building metrics from system data points regarding items such as service-level indicators, latency, and downtime values, observability solutions can present organizations with actionable visualizations of overall or specific system performance to help stay a step ahead of emerging problem spots or performance bottlenecks.
<h3 id="traces">Traces</h3>
A trace represents the end-to-end journey of a user request through a distributed system. As a request moves through the host system, every operation performed on it — called a “span” — is encoded with important data relating to the microservice performing that operation. This request can start from the UI or mobile app through the entire distributed architecture and back to the user.
In a nutshell, the trace records every operation made to fulfill the request, the chain of calls from one touchpoint to another, the times of the calls, and the latency between each hop.
By viewing traces, each of which includes one or more spans, you can track its course through a distributed system and identify the cause of a bottleneck or breakdown. 
Observability solutions centralize the task of tracing issues to determine the root cause, which is otherwise a frustrating task for distributed networks when done manually, and now even more complicated with the inclusion of cloud networks, edge servers and the IoT. Through distributed tracing (or distributed request tracing), observability solutions can reach across the enterprise to give domain- and system-agnostic visibility into system functions.
<h3 id="the-3-pillars-used-by-observability-solutions">The 3 Pillars used by observability solutions</h3>
An observability solution provides a single, unified console to integrate and correlate the three types of telemetry data in real-time. This provides separate teams (such as DevOps, site reliability engineering (SREs) teams and IT staff ) complete and contextual information to fully understand events which could indicate, be the cause of or solution to application performance issues. These teams understand when the problem occurs, and, more importantly, why they are occurring. Such is the value of using an observability solution.
Furthermore, it is able to quickly pinpoint failures across applications, networks and systems, continuously monitor affected systems until resolution is achieved, which is crucial in IT operations&#39; ability to ensure uninterrupted service delivery while the end-user experience remains unaffected by any errors.
<h2 id="why-do-we-need-observability">Why do we need observability?</h2>
Observability has become increasingly important in software development because it gives you greater control over complex systems.
In pursuing digital transformation, organizations of all sizes have modernized applications, adopted microservices and started to rely on distributed system architecture. They are also rapidly adopting modern development practices, such as agile development, continuous integration and continuous deployment (CI/CD), DevOps, multiple programming languages.
This all leads to possessing complex, diverse and distributed IT environments across different clouds, systems, applications and database infrastructures. 
<h3 id="challenges-addressed-by-observability">Challenges addressed by observability</h3>
This brings up the next issue - how will they manage it all? The adoption of current technologies brings about specific problems which cannot be addressed by the simpler monitoring systems of the past, such as:
Distributed systems
With distributed systems composed of a far higher number of interconnected parts, the number and types of failures that can occur is higher too. New types of failures are also increasingly likely to appear due to the constant updates that distributed systems receive.
Additionally, problems within a distributed environment are also significantly more difficult to understand, as distributed environments produce more &quot;unknown unknowns&quot; than simpler ones. Monitoring fails to fully address problems in complex environments as they can only monitor &quot;known unknowns&quot;. 
Observability is more suitable for the unpredictability of distributed systems. It allows you to ask questions about your system&#39;s behavior as issues arise, such as “Why is X not functioning?” or “What is increasing latency right now?” and so on.
Cloud environments and cloud-native infrastructure
As a large component of distributed systems, hybrid cloud, multicloud strategies and cloud-native infrastructure are being increasingly adopted by enterprises, in the form of microservices, serverless functions and container technologies. Thousands of processes running on the cloud, on-premises or both, are required to trace an origin in distributed systems now further complicated by cloud infrastructure. 
Observability tools are capable of tracking the many communication pathways and interdependencies in these distributed architectures, which would be a challenge for conventional IT monitoring techniques. Observability tools can also provide a view of the entire IT infrastructure, regardless of where applications and services are deployed, and the vast amount of data which is increasingly generated. 
Edge devices
The growth in <a href="https://www.hicloud.sg/products/rebranded/iot">Internet of Things (IoT)</a> devices has led to new challenges in monitoring and managing these environments. They require real-time insights and fast response times, which may involve creating lightweight agents for data collection, using edge-friendly data formats and protocols, and incorporating decentralized data processing and analysis techniques, still with robust security and privacy features in place.
Observability tools extend the capability of previous monitoring systems and provide teams greater visibility and insights into their full IT stack to quickly identify the root causes of issues, leading to enhanced analysis and troubleshooting. This allows organizations to be proactive in creating forecasts and predictions about their applications and business.
<h2 id="observability-vs-monitoring-whats-the-difference">Observability vs Monitoring: What&#39;s the difference?</h2>
A common question regarding observability is the difference between it and monitoring. The key difference between observability and monitoring is: monitoring is reactionary, and observability is based on proactive response.
Essentially, monitoring can only act after being told what to monitor, and requires you to know what’s important to monitor in advance. Observability lets you determine what is important by watching how the system performs over time and asking relevant questions about it.
<h3 id="monitoring">Monitoring</h3>
Monitoring is the act of observing a system&#39;s performance over time, by collecting and analyzing system data. Monitoring tools help track errors, identify issues, and send alerts and notifications. Monitoring helps teams understand and make inferences about the current state of infrastructure and applications, such as load time affecting user experience.
<h3 id="observability">Observability</h3>
Observability extends beyond monitoring and helps accelerate problem resolution by providing actionable insights. An observability strategy will dig deeper into occurrences to reveal the “why” (root cause) happening behind the scenes. These actionable insights are built to be highly accurate based on holistic data performance.
Most enterprises continuously monitor their environment by watching and creating an alert system for a set of metrics across their hardware and software. When a metric value goes out of a set threshold, an alert goes off.
However, monitoring is not full observability, as it does not provide the root cause as to why a metric value goes above a set threshold. 
In a nutshell, a monitoring solution tells you when something isn’t right, yet can’t tell you why it isn’t right. Observability solutions provide the reasons as to why something has gone wrong, preventing the additional work needed to unearth the reason for the problem.
<h2 id="what-are-the-benefits-of-observability">What are the benefits of Observability?</h2>
By providing unified insights from across their IT ecosystems, observability solutions bring several benefits that allow developer workflows focused on optimizing system performance to become smoother and easier to manage. 
These benefits are:
<ul>
<li>Greater Visibility to discover and address &#39;unknown unknowns&#39;: An observability solution can provide enterprises with a centralized dashboard view across complex distributed systems to reveal issues that they don&#39;t know exist. This is one of the core advantages of observability: the ability to eliminate blind spots in IT infrastructure while bolstering incident responsiveness. Observability discovers conditions you might never know or think to look for, then identifies the root cause to accelerate their resolution.
</li>
<li>Proactive Problem Resolution: With full-stack observability, you can easily identify errors and their root causes during and after development —letting teams concentrate on fixing, then proactively implementing automated steps to remediate the issue instead of merely finding them. Catching and resolving issues early in development also helps ensure improved user experience, as these issues are prevented from ever having an impact in the first place.
</li>
<li>Optimized Performance and Cost: Observability solutions can identify areas for improvement, such as system bottlenecks and underutilized resources, allowing improved performance and more efficient resource allocation. This reduces costs by identifying and removing unnecessary resource expenses.
</li>
<li>Accelerated Development: Observability enables greater efficiency in monitoring and troubleshooting, which further streamlines the development process. This results in increased speed of delivery and more time for engineers to innovate in meeting the needs of the business and its customers.
</li>
<li>Data-driven decision-making: Observability solutions provide up-to-date information regarding system performance and behavior, enabling data-driven decision making for maximum impact and continuous improvement.
</li>
</ul>
<h2 id="how-do-i-implement-observability">How do I implement observability?</h2>
To achieve observability, you need proper tooling of your systems and apps to collect the appropriate telemetry data. You can make an observable system by building your own tools, using open source software or buying a commercial observability solution.
Regardless of whether you choose to build your own or use open source or commercial solutions, here&#39;s what you should look out for in your observability tools:
<ul>
<li>Support modern event-handling techniques across your entire system by: collecting all relevant information from across your entire system; separating valuable signals from irrelevant noise, and adding context so that teams can address it.
</li>
<li>Integrate with your current tools and support the languages and frameworks in your IT system. If the tools in your selected observability solution don&#39;t work with your current stack, your observability efforts are doomed to failure. 
</li>
<li>Provide real-time data and relevant insights via dashboards, reports and queries in real time once they occur, so teams can understand an issue, its impact and how to resolve it.
</li>
<li>Provide enough context to data for you to respond quickly to incidents that occur. This context includes the changes in your system’s performance over time, how the change relates to other changes in the system, the scope of the issue and any interdependencies of the affected service or component. 
</li>
<li>Visualize aggregated data by presenting insights into forms that are quickly and easily understood, such as dashboards, interactive summaries, graphs, graphic organizers and other visualizations.
</li>
<li>Use machine learning that automatically aggregates, correlates, prioritizes and curates data, allowing you to detect and respond to anomalies and other security incidents faster.
</li>
<li>Be user-friendly and easy to learn or use. This enables your observability solution to be seamlessly added to workflows for your enterprise to reap the benefits of observability.
</li>
</ul>
Finally, the observability solution you pick should deliver business value by tangibly improving the metrics important to your business, such as deployment speed, system stability and customer experience. 
<h2 id="conclusion">Conclusion</h2>
With up to 87% of organizations employing observability specialists (according to the latest research on the <a href="https://www.splunk.com/en_us/form/state-of-observability.html">State of Observability</a>,) observability is now more important than ever to keep systems across various IT environments functioning.
With more than 20 functions and over 300 integrations, <a href="https://www.hicloud.sg/multicloud-management-platform">Hi Cloud Observability</a> grants full visibility across your entire IT stack, providing the alerts and insights you need in easy-to-understand visualizations to optimize your applications and infrastructure. 
<a href="https://www.hicloud.sg/contact-us">Take the next step with Hi Cloud today.</a>

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