Today, many organizations use a mosaic of tools to monitor their technology ecosystem. This requires a lot of manual effort, to get fragmented views of your information technology systems and business in general. But fragmented views, in complex and distributed systems, can incompletely place the focus on system elements, leading to erroneous decision making that later negatively impacts the business.
This is where the concept of observability comes in. It provides greater control over complex systems. In this article we will share what the concept of observability means, why it is important, and how it can positively impact e-commerce businesses.
Let's get started!
What is observability?
In computing, the ability to measure the internal states of a system by examining its outputs is known as observability. We say that a system is considered "observable" if the current state can be estimated using only the information from the results, that is, the sensor data.
It may seem like the word observability is new to the industry, but it is not. Actually, the term originated several decades ago with control theory, which has to do with describing and understanding self-regulation systems.
The truth is that nowadays, the concept of observability has been applied more and more to describe the improvement of the performance of distributed information technology systems. It is in this context, where observability uses three types of telemetry data: metrics, logs, and traces. Which we will see later in this article.
Beyond the term, observability is a management strategy. Focused on keeping the most relevant, important, and core issues at or near the top of an operations process flow. It is also used to describe software processes that facilitate the separation of critical information from routine information. It can also refer to the extraction and processing of critical information in the higher-level architecture of operations systems.
But... Why is observability important for companies?
In recent years, enterprises have been rapidly adopting cloud-native infrastructure servicessuch as AWS in the form of microservices, serverless, and container technologies.
Against this backdrop, tracking an event back to its source in distributed systems requires thousands of processes running in the cloud, on-premises, or both. Conventional monitoring techniques and tools struggle to track the many communication paths and interdependencies in these distributed architectures.
By focusing on the states of a system rather than the state of system elements, observability provides better insight into the functionality and ability of the system to accomplish its mission. It also provides an optimal user and customer experience.
Observability is proactive when necessary. This means that it includes techniques to add visibility to areas where it might be lacking. Furthermore, it is reactive in the sense that it prioritizes existing critical data.
An observability-based management strategy can also link raw data to more useful "health of IT" measures, such as key performance indicators (KPIs), which are effectively a sum of conditions to represent broad experience and satisfaction of the user.
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The differences between monitoring and observability
The concepts of monitoring and observability are related, but the relationship is complex. The following are some of the main differences:
- Monitoring tools collect information passively. Most of which turns out to be negligible. This can drown the operations team and even AI tools in data. Observability actively collects data to focus on what is relevant, such as the factors that drive operational decisions and actions.
- Monitoring tends to collect information from available sources, such as management information bases, application programming interfaces (APIs), and logs. While observability will also use these sources, it will often add new specific information access points to collect essential information.
- On the other hand, monitoring focuses on infrastructure, where observability equally focuses on applications. That means that observability will often include a focus on workflows, while monitoring focuses on point observations.
- The data available through monitoring is often the only expected result. Observability assumes that data sources will contribute to an analytical process that will then optimally represent the state of an application or system.
In the following video from @ReliabilityEngineering we find a good overview on observability and monitoring.
The three pillars of observability
As we mentioned a moment ago, the main source data types for observability, also called the three pillars of observability are: logs, metrics, and traces. Let's see what they are about:
1. Logs.
Event records, usually in textual or human-readable format, are known as logs. They are almost always generated by infrastructure elements, including both network devices and servers. They can also be generated by platform software, including operating systems and middleware. Some apps will record what the developer believes to be critical information.
Registration information tends to be historical or retrospective. And it is often used to set the context in operations management. However, there are logs that represent collections of events or telemetry data, and detailed information may be available in real time.
2. Metrics.
This type of operational data is typically accessed in real time through an API using a pull or poll strategy, or as a generated event or telemetry, such as a push or notification. Because they are event-based, most fault management tasks are metric-based.
3. Traces.
These are records of information paths or workflows designed to follow a unit of work, such as a transaction, through the sequence of processes that application logic indicates it should follow.
Because the direction of work is typically a function of the logic of individual components or of steering tools such as service buses or meshes, a trace is an indirect way of evaluating an application's logic. Some trace data may be available from workflow processes, such as service buses or cloud-native microservices and utility networks.
However, it may be necessary to incorporate tracking tools into the software development process to gain complete visibility.
Integrating the three pillars of observability
Working with these data classes does not guarantee observability. Especially if you work with them independently or if you use different tools for each function.
Rather, you'll achieve a successful approach to observability by integrating your logs, metrics, and traces into a single solution. When you do this, you not only understand when problems occur, but you can immediately shift focus to understanding why those problems occur.
All three pillars are vital to observability, but each has unique limitations that must be taken into account. For example, metrics are difficult to label and order, and can be difficult to use for troubleshooting; records can be difficult to classify and aggregate to draw meaningful conclusions or relationships; traces can produce huge amounts of unnecessary data.
Thus, observability practitioners may still encounter limitations in gathering real information, finding too many places to look for problems, or having difficulty digging deeper, and consequently, in translating issues into actionable problems.
At Orienteed we believe that it may be more effective to use the three pillars of observability through a goal-oriented lens. You can set business goals, such as service level goals, and then set observability goals that align with those goals. For example, if your company is concerned about latency or throughput, set appropriate latency or throughput goals, and then use the three pillars to approach observability with those goals in mind.
What are the benefits of observability?
We can say that the main benefit of observability is the improvement of the user experience, which is produced by focusing operations tasks on problems that threaten that experience. Proper application of observability as a management strategy can improve application availability and performance.
Observability practices will also typically reduce operating costs by speeding up handling of adverse conditions. This happens by reducing the amount of irrelevant or redundant information and prioritizing notification of critical events. These improvements are most noticeable in larger business operations where large operations teams are required.
In addition, observability practices provide useful information in reliability and performance management. And even in infrastructure design and tool selection. This is because a focus on the truly critical information helps identify vulnerabilities that can be fixed by changing configurations, application design, and resource levels.
Implementation of an observability plan
Observability starts with a plan, then moves to an architecture, and finally to an observability platform. It is advisable to follow this approach or there will be an increased risk of challenges and complications.
An observability plan can begin by identifying the specific benefits desired. Then linking each to a description of the data that would be needed to achieve it. While it is important that this link consider available monitoring and telemetry data, it is equally vital to identify relevant information that is not currently collected, or that is extracted in a system that does not contribute its data to observability analysis.
The observability architecture is a schematic representation of the relationship between the source data and the presentation of the data to operations staff, Artificial Intelligence and machine learning systems, etc. All data sources should be identified, along with the information each source is expected to provide. Above the data sources, the diagram should identify the tools that collect and present the information, the tool options for data analysis and filtering, and the tool options for data presentation.
The final step in implementing an observability plan is a specific toolset or observability platform. The difference between the two can be subtle:
- Specific tools: Possess monitoring features that can be used to support observability, but rely on a human operator or a separate software layer to support collective analysis. A toolset approach will typically require considerable customization, but will accommodate existing software and data sources.
- An observability platform: It is an integrated software application that collects information, performs analysis including KPI derivations, and presents actionable results to trading users. A platform may still require customization to accommodate all available data sources, and may also restrict the way data is integrated.
Remember that the value of observability depends on following a plan, with at least those three implementation steps in an organized way. Omitting or skimping on it, will put the concept at risk, and the investment in it.
Observability for e-commerce
If you are looking to implement an observability plan for your e-commerce business or need to improve the current practices of the responsible team, these may be the benefits of doing so:
- Create and retain satisfied customers. By improving the observability of your environments, you'll be able to better understand where the friction lies and fix unexpected issues on the path to purchase before they impact business outcomes.
- Detect fraudulent activities to protect customers. Know immediately when suspicious activity increases in user behavior, but also know what to do next. Confidently establish configurable risk protocols, protecting the business and your customers.
- Deliver first-rate customer experiences. Monitor ad performance, customer wait times, inventory turnover, and more to ensure enjoyable customer interactions. Use real-time consumer insights to continually adjust and make improvements.
- Preserve revenue and manage costs. Whether it's an increase in ad spend, customer acquisition costs, or a drop in average order value, you'll be able to discover and correct the key factors to get revenue back on track. From losing a few dollars to losing thousands, by identifying and alleviating gaps no matter the size.
Now is your turn!
Now that you know what observability is, its benefits and importance, we are sure that you will be able to make a better decision on the strategy and plan to follow.
Pursuing observability for your eCommerce ecosystem is a good start, but it's also true that ensuring observability can pose significant business challenges.
Observability must ingest and sort through huge amounts of data and then perform analysis to provide clear and actionable results. But the sheer volume of raw data, especially from multiple sources, makes analysis difficult, and the resulting output is of little value if it doesn't actually tell the business everything it wants to know.