What are Metrics in Observability: Definition, Importance, Challenges, Best Practices

Observability enables developers to understand their software systems on a deeper level. It allows teams to quickly detect issues, analyze their root causes, and take necessary action to improve system performance and reliability. These features reduce downtime and enhance user experience, optimizing business operations as a result.

Logs, Metrics, and Traces, also referred to as the three pillars of observability, are crucial tools to help you reason about the internal state of your complex environments. To fully understand how observability works, it's imperative to understand each of these pillars in depth, including what they are and how they function. This article will cover everything you need to know about metrics in the context of observability, from what the most popular metric types are to best practices for implementing metric use in your troubleshooting process.

Key Takeaways

• Metrics refer to key performance indicators (KPIs) in observability. With this data, teams can understand an application's real-time condition or performance.
• The three most common metric types are system, application, and business metrics, each of which are valuable in different analysis contexts.
• Metrics are crucial aspects for monitoring and analysis. Thus, you cannot achieve observability in a system without taking metrics into account.
• Handling and collecting metrics can come with challenges, including data overload, metric inaccuracy/inconsistency, alert fatigue, and scalability.
• Using metrics to achieve observability involves implementing the best practices. It's crucial to use the right tools and to find the appropriate and necessary metrics for your particular use case.

What Are Metrics and Why Are They Important?

Metrics, in the context of observability, are environment key performance indicators. They refer to time-series data (any data point recorded at set increments), measurement units, and data collection frequencies, all of which come from specific components within a system. Naturally, the collected metric types depend heavily on the underlying system's requirements and configuration.

Types of Metrics

Here are some examples of common metric types:

• CPU Usage
• Memory Utilization
• Error Rates
• Network Latency
• Response Time
• User Engagement

Real-time metric monitoring provides key insights into the high-level behavior of the components within your environment, enabling effective regulation and enhancing system visibility.

Broadly speaking, here's how metric types can be broken up:

No two metrics are created equal; each has it's own unique purpose and significance. You’ll need multiple metric types to fully assess, compare, and track your system performance.

System Metrics

System metrics are KPIs that quantify your system performance. Monitoring system metrics helps ensure that your server has enough available resources, by examining those that are reaching their configured limit. System metrics are infrastructure-level metrics, which include CPU usage, memory usage, disk I/O, and network traffic, to name a few.

Application Metrics

Application metrics are KPIs which quantify application performance. There are a number of metrics which fall under this category, but four key metrics in particular – called the "Golden Signals" – are the most popular ones, providing the building blocks for a robust application monitoring strategy. The Golden Signals are listed below:

• Response Time: Also known as latency, these metrics measure the time it takes for a service to respond to a request.
• Request Rate: Measures the total number of requests per second.
• Error Rate: These are request failure rates. Errors usually happen because of network and infrastructure variables.
• Throughput: Aka saturation, it represents the percent of resources your application puts into the system.

Business Metrics

Business metrics are KPIs which quantify business performance. They help to answer questions like "how many errors does this API generate?" or "how many API requests occur daily across ". They give insights into what needs optimization and what is functioning well, by succinctly quantifying environment component behavior. Business metrics are often fed into business intelligence tools to drive better business outcomes.

Here are some example business metrics:

• Transaction volume: Assists in determining the scale of your operations. Identifying patterns over time helps find peak usage periods and optimize server capacity.
• Revenue per hour: This represents the revenue earned each working hour. It helps gauge your operational efficiency and productivity.
• User engagement metrics: Includes data like page views, session duration, and user feedback to represent user engagement. These metrics help determine what is and isn't working with your user-base.

Importance of Metrics in Observability

Metrics are crucial factors that are required to achieve fully functional observability in software, systems, and infrastructures. They play a part in creating an environment where you can easily comprehend how applications are performing internally, and develop reasoning as to why certain pieces aren't functioning as expected. This is incredibly useful, as it leads to catching problems early on in the troubleshooting cycle before it becomes a significant issue down the road.

Here are some examples which demonstrate how metrics can aid in your observability efforts:

Monitoring Performance

Metrics are a requirement for enabling real-time monitoring of an application or system. They're the key to avoiding downtime and other potential problems. There are a number of key metrics which are vital for understanding system performance (which we will touch on later on).

Some performance examples which can be monitored include:

• End-user experience
• Resource utilization
• System reliability
• Responsiveness of software application

Here are some of the benefits offered by metrics in performance monitoring:

• Provide visibility into various aspects of a system/application
• Help track service level agreements to ensure systems/applications achieve predetermined performance standards
• Ensure the application's performance is optimal
• Help prevent or reduce application downtime

Identifying Issues and Bottlenecks

Metrics can also be used to identify underlying issues, to swiftly address any system issues or bottlenecks encountered. There are a number of ways one can use metrics in this fashion, including analyzing correlations and patterns in metrics, to hone in on where in your environment the issue is coming from.

Pro Tip

You can leverage third party tools like Edge Delta for more robust anomaly detection. Edge Delta spots anomalies by analyzing metric and log data, and leverages AI for faster troubleshooting. It additionally automatically correlates logs to anomalies during the alerting process, providing you with the ability to be more proactive with incident response.

Metrics can help provide insights into these common performance concerns:

• Development time
• Resource constraints
• Underutilized caches

Metrics also help with the early detection and resolution of these issues, before they heavily impact production. Proper troubleshooting and remediation are crucial before they escalate. Not only do metrics serve as valuable diagnostic tools for troubleshooting and root cause analysis, metrics like high request counts and CPU usage deviations can highlight potential future issues, enabling proactive resolution approaches.

Capacity Planning

Capacity planning is, in essence, identifying how much production capacity you need to meet customer demand. It is essential for properly budgeting and scaling, to identify optimal levels of operations.

Metrics assist with capacity planning by giving insights into resource utilization trends. You can ensure that sufficient capacity is available to handle the anticipated loads.

Here are some of the metrics crucial for capacity planning:

• Throughput
• Response time
• Error rates
• Availability
• Utilization
• Efficiency

Here’s how metrics help with capacity planning:

• Improves application or system availability
• Lowers services to clients
• Helps minimize application or system downtime
• Predicts future resource requirements to make informed decisions that align with your capacity planning needs

Improving Reliability

Systems have pain points that present potential threats. Fortunately, these vulnerabilities can be handled and remain reliable with the help of metrics.

Metrics improve system reliability by providing data that supports proactive maintenance and optimization. Here are some of these metrics:

• Maintenance overtime
• On-time work order completion rate
• First-time fix rate
• Planned maintenance percentage

Here are ways that metrics help improve reliability:

• Empower you to gauge the likelihood of failures
• Enables estimation of repair times
• Plans replacements for a better system or application reliability

Key Metrics to Monitor

Key metrics provide a comprehensive view of your company's health. They assess your business’ various facets like marketing, operations, finance, and customer service.

Here are the key metrics you should be familiar with:

Identifying which metrics are essential for your business success depends on your industry, strategic priorities, business model, and more. However, these metrics are consistently useful for organizations working to optimize their systems.

CPU and Memory Usage

CPU and Memory are key measures of how a computer is handling load. High CPU and Memory Usage levels indicate the current task or set of tasks is extremely heavy, which may be the result of errors or high usage rates depending on scenario. System monitoring tools are key to ensuring these values stay within a reasonable range.

Disk I/O and Network Traffic

Disk I/O measures the read and write operations on storage devices, which is a good proxy for understanding how your system handles data transfers. Meanwhile, network traffic monitors the speed and volume of data as it moves across computer networks, an important metric to keep in mind when working to ensure efficient data transfer.

Response Time and Error Rates

Response time is a metric that directly impacts user experience and satisfaction. It measures how long a system responds to a request. Error rates are the frequency of errors in your application. Measuring it lets you pinpoint weak areas so you can take action. Critical errors may lead to system crashes, so this metric is crucial.

Custom Metrics

Custom metrics let you enjoy accuracy in measuring specific data. Thus, creating the appropriate custom metrics is crucial in accurate metrics collection. These metrics should cater specifically to your application or business needs.

In Google Analytics, for example, custom metrics can be the following:

• Video View Count
• Form Submission Count
• Total Discounted Amount of Purchases

To get these metrics, you'll have to configure them in your Google Analytics dashboard.

Challenges in Monitoring Metrics

Handling metrics poses several challenges, including finding the relevant ones for you. For instance, it can be challenging to identify metrics needed for specific analyses of certain components or workflows. Improper metric usage causes a lack of proper visibility, and in the worst of cases can lead to significant downtime or wasted resources.

Metrics monitoring is a proactive process which requires a strategic approach. Besides defining the right metrics, other challenges may arise. Here are some examples:

Data Overload

Having an excessive number of metrics can skew your analysis. While generating many metrics may seem appealing, it becomes an obstacle which prevents effective management.

Challenge: Managing and processing large volumes of data from various sources can be taxing. This situation increases the complexity of analysis and decision-making.

Solution:

1. Implement big data aggregation. This solution involves pulling and organizing massive raw data into a more consumable and comprehensive medium: For example, taking the average of data values within a large collection.
2. Create metric collection filters. With filters, you can prevent unnecessary data from being added for analysis. As a result, it reduces the size of a data set, making it more manageable. For instance, you may only need impressions from Mondays in a specific region in the US during your analysis, so filtering out metrics from other days and regions removes data noise from the analysis process.

Metric Accuracy and Consistency

Challenge: Collecting accurate metrics consistently can be challenging due to the following:

• Incomplete Datasets
• Data Manipulation
• Technological Limitations

Solution:

1. Implement best practices for consistent and error-free metrics. For instance, perform data quality post-mortems and establish consistent data collection procedures.
2. Evaluate time synchronization. Evaluate the stability and consistency of time synchronization over a chosen period.
3. Perform calibration processes. Perform calibration by comparing the readings of a measuring instrument to a known reference. This solution helps determine its accuracy, and you can adjust when needed.

Alert Fatigue

Alerts provide developers with relevant and actionable information regarding the state of their systems. However, creating notifications after each new event can become incredibly overwhelming, reducing the effectiveness of your monitoring approach.

Challenge: Monitoring systems may generate an excessive number of alerts. When alerts are irrelevant, it diminishes your ability to pinpoint issues.

Solution:

1. Implement intelligent alerting. With intelligent alerts, you can allow notifications to be grouped over time into a single summary alert.
2. Set alarm schedules. For instance, you can set downtime schedules so all alerts and notifications are silenced as intended.

Pro Tip

You can leverage threshold-based alerts using tools like Edge Delta. With this approach, you can recognize issues faster for a more proactive incident response.

Scalability

Scalability is your system’s resilience under increasing workloads, and it's essential for dynamic environments. This is incredibly important for systems like Kubernetes, which orchestrates and constantly modifies the underlying architecture to adapt to system needs. Your system should be able to handle a dynamic number of workloads by either increasing or decreasing resources appropriately.

Challenge: Monitoring systems cannot adapt quickly to infrastructure size and configuration changes.

Solution:

1. Implement scalable solutions. You can implement scalable solutions to handle growth and change while delivering real-time insights and efficiency in resource utilization.
2. Develop a cloud solution for seamless integration. You can create a cloud solution designed specifically for cloud environments as they integrate seamlessly with cloud and container platforms. This enables you to maintain consistent monitoring coverage across your entire distributed infrastructure.

Best Practices for Using Metrics in Observability

There are a number of factors to consider when deciding what metrics to collect, and use, along with how best to analyze them, to derive clear and actionable insights.

Defining Clear Objectives

Collected metrics must have the capacity to properly visualize your performance patterns. They should tell you if your system performance measurably confirms your organizational goals. Clarifying your strategic vision allows you to align metrics with your goals, business or otherwise, and define your mission, purpose, and value propositions. Additionally, it is key for reassessing short and long term objectives, and for ensuring everyone within the organization is on the same page.

Choosing the Right Tools

Choosing the right metrics for performance monitoring is incredibly important. Avoid irrelevant metrics and those that may put you on the wrong track. When choosing tools for metric collection, visualization, and analysis, try to follow these tips:

• Metric values can be made up of different components. Select tools that gracefully integrate many data sources.
• Evaluate their features, benefits, and drawbacks and compare them with your goals.
• Look up their functionality, cost, and security.

Regularly Reviewing Metrics

Monitor and report your operation metrics regularly. This practice involves tools that enable the timely collection, storage, analysis, and accurate visualization of data.

Regularly reviewing and updating metrics enables checking if they remain relevant to your organization's goals and objectives. Metrics need adjustments with the changes in your strategies or factors like market conditions.

Through regularly reviewing metrics, you can also identify obsolete ones and discard them. An inventory turnover, sales trends, and ROI analysis help determine any obsolescence.

Implementing Automated Alerts

You can create custom automated alerts based on your monitored metrics. This practice helps evaluate conditions on the resource metrics at specified intervals.

While alerting systems are convenient, you must set appropriate thresholds and alert conditions for them to be effective. An ideal threshold allows your metrics milestones to be tackled rigorously. Alerts are set for proactive issue resolution before any risk is actionable.

Continuous Improvement

Specific metrics advocate for the continuous improvement of your organization. They help track progress so you understand how to continue enhancing your operations.

Some continuous improvement metrics are:

• Quality
• Cost
• Safety
• Time
• Customer Satisfaction
• Return on investment

Other than tracking progress, metrics help businesses adapt to change, identify opportunities, and encourage consistency.

You can maximize the value of metrics to your observability efforts by:

• Training and enabling your teams with observability tools
• Understanding highly specialized technologies and achieving full-stack observability
• Eliminating redundant tools to reduce data silos and save costs

Conclusion

Metrics are KPIs which provide valuable insights into the performance and health of applications and systems. Organizations can effectively monitor troubleshoot, and optimize complex systems by using metrics, creating an overall culture of observability.  

Furthermore, establishing clear objectives, selecting the right tools, reviewing and updating metrics, implementing automated alerts, and fostering a culture of continuous improvement are essential best practices for leveraging the power of metrics in observability.

Embracing these best practices ensures the stability and resilience of systems and fosters adaptability, innovation, and sustained success.

FAQs on Metrics in Observability

What are the three pillars of observability?

The pillars of observability include logs, metrics, and traces, each of which provide insights into a system’s health and functionality.

What is the difference between metrics and tracing?

Metrics show quantitative insights from your system. Meanwhile, tracing records the path of requests as they travel through your system.

What is the difference between metrics and logs in observability?

Logs are generally used for troubleshooting. Meanwhile, metrics are data used to monitor performance and detect crucial events.

Sources

• Microsoft
• Edge Delta
• StrongDM
• Google

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