Observability vs Monitoring

Ensuring the performance, reliability, and availability of complex systems is crucial in software development and operations. To achieve this, two critical practices come into play: observability and monitoring. While these terms are often used interchangeably, they represent distinct approaches to understanding and managing system behavior. In this blog post, we will delve into observability and monitoring, exploring their differences, benefits, and how they work together to provide valuable insights into system performance.

Observability is a concept that originated in control theory but has now found its way into the realm of software systems. It refers to the ability to understand what is happening inside a system based on its external outputs. In other words, it is the degree to which we can measure the internal state of a system by analyzing its external behavior.

Highlights: Observability vs Monitoring

• The Role of Monitoring

To understand the difference between observability vs. monitoring, we need first to discuss the role of monitoring. Monitoring is the evaluation to help identify the most practical and efficient use of resources. So the big question I put to you is what to monitor. This is the first step to preparing a monitoring strategy.

There are a couple of questions you can ask yourself to understand fully if monitoring is enough or if you need to move to an observability platform. Firstly, you should consider what you should be monitoring, why you should be monitoring, and how to monitor it.? 

• Options: Open source or commercial

Knowing this lets you move into the different tools and platforms available. Some of these tools will be open source, and others commercial. When evaluating these tools, one word of caution: does each tool work in a silo, or can it be used across technical domains? Silos are breaking agility in every form of technology.

For pre-information, you may find the following posts helpful:

1. Microservices Observability
2. Auto Scaling Observability
3. Network Visibility
4. WAN Monitoring
5. Distributed Systems Observability
6. Prometheus Monitoring
7. Correlate Disparate Data Points
8. Segment Routing

• A key point: Video on Observability vs. Monitoring

In the following video, We will start by discussing how our approach to monitoring needs to adapt to the current megatrends, such as the rise of microservices. Failures are unknown and unpredictable. Therefore a pre-defined monitoring dashboard will have difficulty keeping up with the rate of change and unknown failure modes. For this, we should look to have the practice of observability for software and monitoring for infrastructure.

Back to Basics with Observability vs Monitoring

Monitoring and distributed systems

By utilizing distributed architectures, the cloud native ecosystem allows organizations to build scalable, resilient, and novel software architectures. But the ever-changing nature of distributed systems means that previous approaches to monitoring can no longer keep up. The introduction of containers made the cloud flexible and empowered distributed systems.

Nevertheless, the ever-changing nature of these systems can cause them to fail in many ways. Distributed systems are inherently complex, and, as systems theorist Richard Cook notes, “Complex systems are intrinsically hazardous systems.”

Cloud-native systems require a new approach to monitoring, one that is open-source compatible, scalable, reliable, and able to control massive data growth. But cloud-native monitoring can’t exist in a vacuum: it needs to be part of a broader observability strategy.

Key Features of Observability:

1. High-dimensional data collection: Observability involves collecting a wide variety of data from different system layers, including metrics, logs, traces, and events. This comprehensive data collection provides a holistic view of the system’s behavior.

2. Distributed tracing: Observability allows tracing requests as they flow through a distributed system, enabling engineers to understand the entire path and identify performance bottlenecks or errors.

3. Contextual understanding: Observability emphasizes capturing contextual information alongside the data, enabling teams to correlate events and understand the impact of changes or incidents.

Benefits of Observability:

1. Faster troubleshooting: By providing detailed insights into system behavior, observability helps teams quickly identify and resolve issues, minimizing downtime and improving system reliability.

2. Proactive monitoring: Observability allows teams to detect potential problems before they become critical issues, enabling proactive measures to prevent service disruptions.

3. Improved collaboration: With observability, different teams, such as developers, operations, and support, can have a shared understanding of the system’s behavior, leading to improved collaboration and faster incident response.

Monitoring:

On the other hand, monitoring focuses on collecting and analyzing metrics to assess the health and performance of a system. It involves setting up predefined thresholds or rules and generating alerts based on specific conditions.

Key Features of Monitoring:

1. Metric-driven analysis: Monitoring relies on predefined metrics collected and analyzed to measure system performance, such as CPU usage, memory consumption, response time, or error rates.

2. Alerting and notifications: Monitoring systems generate alerts and notifications when predefined thresholds or rules are violated, enabling teams to take immediate action.

3. Historical analysis: Monitoring systems provide historical data, allowing teams to analyze trends, identify patterns, and make informed decisions based on past performance.

Benefits of Monitoring:

1. Performance optimization: Monitoring helps identify performance bottlenecks and inefficiencies within a system, enabling teams to optimize resources and improve overall system performance.

2. Capacity planning: By monitoring resource utilization and workload patterns, teams can accurately plan for future growth, ensuring sufficient resources are available to meet demand.

3. Compliance and SLA enforcement: Monitoring systems help organizations meet compliance requirements and enforce service level agreements (SLAs) by tracking and reporting on key metrics.

Observability and Monitoring: A Unified Approach:

While observability and monitoring differ in their approaches and focus, they are not mutually exclusive. When used together, they complement each other and provide a more comprehensive understanding of system behavior.

Observability enables teams to gain deep insights into system behavior, understand complex interactions, and troubleshoot issues effectively. Conversely, monitoring provides a systematic approach to tracking predefined metrics, generating alerts, and ensuring the system meets performance requirements.

Organizations can create a robust system monitoring and management strategy by combining observability and monitoring. This integrated approach empowers teams to quickly detect, diagnose, and resolve issues, improving system reliability, performance, and customer satisfaction.

The Starting Point: Observability vs Monitoring

You need to measure and gather the correct event information in your environment, which will be done with several tools. This will let you know what is affecting your application performance and infrastructure. As a good starting point, there are four golden signals for Latency, saturation, traffic, and errors. These are Google’s Four Golden Signals. The four most important metrics to keep track of are: 

1. 1. 1. Latency: How long it takes to serve a request
      2. Traffic: The number of requests being made.
      3. Errors: The rate of failing requests. 
      4. Saturation: How utilized the service is.

So now we have some guidance on what to monitor and let us apply this to Kubernetes to, for example, let’s say, a frontend web service that is part of a tiered application, we would be looking at the following:

1. 1. 1. How many requests is the front end processing at a particular point in time,
      2. How many 500 errors are users of the service received, and 
      3. Does the request overutilize the service?

So we already know that monitoring is a form of evaluation to help identify the most practical and efficient use of resources. With monitoring, we observe and check the progress or quality of something over time. So within this, we have metrics, logs, and alerts. Each has a different role and purpose.

Monitoring: The role of metrics

Metrics are related to some entity and allow you to view how many resources you consume. The metric data consists of numeric values instead of unstructured text, such as documents and web pages. Metric data is typically also time series, where values or measures are recorded over some time. 

An example of such metrics would be available bandwidth and latency. It is essential to understand baseline values. Without a baseline, you will not know if something is happening outside the norm.

What are the average baseline values for bandwidth and latency metrics? Are there any fluctuations in these metrics? How do these values rise and fall during normal operations and peak usage? And this may change over the different days in the week and months.

If, during normal operations, you notice a rise in these values. This would be deemed abnormal and should act as a trigger that something could be wrong and needs to be investigated. Remember that these values should not be gathered as a once-off but can be gathered over time to understand your application and its underlying infrastructure better.

• A key point: Video on Prometheus Metric Types

In this video tutorial, we are going through the basics of how monitoring systems work, particularly the role of Prometheus and its pull approach, along with the different metrics that Prometheus can scrap.

Monitoring: The role of logs

Logging is an essential part of troubleshooting application and infrastructure performance. Logs give you additional information about the events. This is important for troubleshooting or discovering the root cause of the events. Logs will have a lot more detail than metrics. So you will need some way to parse the logs or use a log shipper.

A typical log shipper will take these logs from the standard out in a Docker container and ship them to a backend for processing.

FluentD or Logstash has its pros and cons and can be used here to the group and sent to a backend database that could be the ELK stack ( Elastic Search). Using this approach, you can add different things to logs before sending them to the backend. For example, you can add GEO IP information. And this will add richer information to the logs that can help you troubleshoot.

Monitoring: The role of alerting

Then we have the alerting, and it would be best to balance how you monitor and what you alert on. So, we know that alerting is not always perfect, and getting the right alerting strategy in place will take time. It’s not a simple day-one installation and requires much effort and cross-team collaboration.

You know that alerting on too much will cause alert fatigue. And we are all too familiar with the problems alert fatigue can bring and tensions to departments.

To minimize this, consider Service Level Objective (SLO) for alerts. SLOs are measurable characteristics such as availability, throughput, frequency, and response times. Service Level Objectives are the foundation for a reliability stack. Also, it would be best if you also considered alert thresholds. If these are too short, you will get a lot of false positives on your alerts. 

Monitoring is not enough.

Even with all of these in place, monitoring is not enough. Due to the sheer complexity of today’s landscape, you need to consider and think differently about the tools you use and how you use the intelligence and data you receive from them to resolve issues before they become incidents.  That monitoring by itself is not enough.

The tool used to monitor is just a tool that probably does not cross technical domains, and different groups of users will administer each tool without a holistic view. The tools alone can take you only half the way through the journey.  Also, what needs to be addressed is the culture and the traditional way of working in silos. A siloed environment can affect the monitoring strategy you want to implement. Here you can look into an Observability platform.

Observability vs Monitoring

So when it comes to observability vs monitoring, we know that monitoring can detect problems and tell you if a system is down, and when your system is UP, Monitoring doesn’t care. Monitoring only cares when there is a problem. The problem has to happen before monitoring takes action. It’s very reactive. So if everything is working, monitoring doesn’t care.

On the other hand, we have an Observability platform, a more proactive practice. It’s about what and how your system and services are doing. Observability lets you improve your insight into how complex systems work and let’s quickly get to the root cause of any problem, known and unknown.

Observability is best suited for interrogating systems to explicitly discover the source of any problem, along any dimension or combination of dimensions, without first predicting. This is a proactive approach.

The pillars of observability

This is achieved by combining logs, metrics, and traces. So we need data collection, storage, and analysis across these domains. While also being able to perform alerting on what matters most. Let’s say you want to draw correlations between units like TCP/IP packets and HTTP errors experienced by your app.

The Observability platform pulls the context from different sources of information like logs, metrics, events, and traces into one central context. Distributed tracing adds a lot of value here.

Also, when everything is placed into one context, you can quickly switch between the necessary views to troubleshoot the root cause. An excellent key component of any observability system is the ability to view these telemetry sources with one single pane of glass. 

Known and Unknown / Observability Unknown and Unknown

Monitoring automatically reports whether known failure conditions are occurring or are about to occur. In other words, they are optimized for reporting on unknown conditions about known failure modes. This is referred to as known unknowns. In contrast, Observability is centered around discovering if and why previously unknown failure modes may be occurring: in other words, to discover unknown unknowns.

The monitoring-based approach of metrics and dashboards is an investigative practice that leads with the experience and intuition of humans to detect and make sense of system issues. This is okay for a simple legacy system that fails in predictable ways, but the instinctual technique falls short for modern systems that fail in unpredictable ways.

With modern applications, the complexity and scale of their underlying systems quickly make that approach unattainable, and we can’t rely on hunches. Observability tools differ from traditional monitoring tools because they enable engineers to investigate any system, no matter how complex. You don’t need to react to a hunch or have intimate system knowledge to generate a hunch.

Monitoring vs Observability: Working together?

Monitoring best helps engineers understand infrastructure concerns. While Observability best helps engineers understand software concerns. So Observability and Monitoring can work together. First, the infrastructure does not change too often, and when it fails, it will fail more predictably. So we can use monitoring here.

This is compared to software system states that change daily and are unpredictable. Observability fits this purpose. The conditions that affect infrastructure health change infrequently, relatively easier to predict. We have several well-established practices to predict, such as capacity planning and the ability to remediate automatically (e.g., as auto-scaling in a Kubernetes environment. All of which can be used to tackle these types of known issues. 

Monitoring and infrastructure problems

Due to its relatively predictable and slowly changing nature, the aggregated metrics approach monitors and alerts perfectly for infrastructure problems. So here, a metric-based system works well. Metrics-based systems and their associated alerts help you see when capacity limits or known error conditions of underlying systems are being reached.

Now we need to look at monitoring the Software. Now we need access to high-cardinality fields. This may include the user id or a shopping cart id. Code that is well-instrumented for Observability allows you to answer complex questions that are easy to miss when examining aggregate performance.

Conclusion:

Observability and monitoring are essential practices in modern software development and operations. While observability focuses on understanding system behavior through comprehensive data collection and analysis, monitoring uses predefined metrics to assess performance and generate alerts. By leveraging both approaches, organizations can gain a holistic view of their systems, enabling proactive measures, faster troubleshooting, and optimal performance. Embracing observability and monitoring as complementary practices can pave the way for more reliable, scalable, and efficient systems in the digital era.