The monitoring and observability of current complex software solutions can be even more intricate than the solutions themselves. Not only do monitoring and observability software or service providers need to understand these complexities, but they also need to provide clear and efficient insights.
One aspect of providing a view into the monitored system is through metrics. In this article, we will group them into two intersecting types to enhance understanding of monitoring/observability concepts and make the use of monitoring and observability tools easier. We will also discuss the challenges and limitations of particular data groups.
Definition of monitoring
Monitoring, also known as observability, refers to the process of collecting and analyzing data in order to gain insights into the functioning, performance, and health of a system or application. It involves systematically observing and tracking various metrics, events, and behaviors to understand the system's behavior and detect any anomalies or issues.
What is monitoring/observability data?
Monitoring/observability data refers to sets of values that are collected and analyzed by a monitoring tool to provide a visual representation of a system's performance and health. These values are often presented through user interface forms such as charts, tables, and graphs, allowing users to easily interpret and derive insights from the data.
Learn more about aour approach to end-to-end monitoring services.
Types of monitoring/observability data
Data modality division
Monitoring/observability data can be collected and represented in different modes. Based on these modes, we can define the following categories:
Metrics
These are numerical values representing system properties or characteristics at a specific time. Metrics measure workload, performance, availability, reliability, and other aspects of a system. Examples include requests per second, response time, error rate, and CPU utilization.
Logs
Logs are records of events occurring within or between systems, providing detailed information on system behavior, actions, errors, and contextual information like user identities and IP addresses.
Distributed traces
These are collections of correlated events representing end-to-end request execution across distributed systems. Distributed traces visualize and analyze latency, performance, and dependencies, revealing details such as request travel time and involved services.
Each category has its advantages and limitations. Metrics offer high-level summaries and trends but may lack event details. Logs provide rich and granular information but can be noisy and challenging to analyze. Distributed traces provide comprehensive views but require additional instrumentation and processing overhead.
Application domain division
This division highlights that each data type represents a specific domain or area of monitoring and observability. It distinguishes different aspects of monitoring data based on the application or focus of monitoring activities. Each type presents its own set of advantages and disadvantages. Here are the various categories with their pros and cons:
Performance monitoring data
Focuses on measuring and assessing system, application, or process performance, including metrics like response time, throughput, error rates, resource utilization, and system availability. Performance monitoring data provides valuable insights into system efficiency and resource usage, allowing proactive optimization and issue resolution. However, it may lack visibility into specific application behaviors.
Network monitoring data
Involves tracking and analyzing network activities and performance, encompassing metrics like bandwidth usage, network latency, packet loss, traffic patterns, and device status. Network telemetry monitoring data can prove beneficial in identifying bottlenecks and optimizing network performance, yet it may not capture the intricacies at the application level.
Security monitoring data
Collected to identify and respond to potential security threats and breaches, this includes log files, IDS alerts, firewall logs, and authentication records. Security monitoring data plays a crucial role in threat detection and response but demands extensive analysis due to the large volumes of data involved.
Environmental monitoring data
Focuses on monitoring environmental conditions, such as temperature, humidity, air quality, noise levels, and other factors impacting the environment. This type of data ensures optimal operating conditions, although its relevance may be limited in certain domains.
Application monitoring data
Involves tracking and analyzing specific software application performance and behavior, covering metrics like response time, transaction success rate, resource usage, and user interactions. Application monitoring data offers detailed insights into software performance but necessitates instrumentation and may not encompass the underlying infrastructure.
Website analytics data
Provides insights into website performance and usage, including metrics like page views, unique visitors, bounce rate, conversion rate, user demographics, and behavior. Website analytics data provides metrics on user engagement but might not provide a deep understanding of the system as a whole.
Infrastructure monitoring data
Tracks and analyzes the health and performance of infrastructure components like servers, databases, storage systems, and hardware or virtual resources. Metrics include CPU usage, memory utilization, disk space, and network connectivity. Infrastructure monitoring data is vital for assessing the health of infrastructure components and diagnosing infrastructure problems, yet it may not capture application-specific insights.
Energy monitoring data
Focuses on measuring and analyzing energy consumption patterns and efficiency, encompassing electricity usage, device or system energy usage, and energy-saving initiatives. Energy monitoring data facilitates energy optimization, although its applicability may vary across different domains.
Social media monitoring data
Involves tracking and analyzing social media platforms for mentions, sentiment analysis, brand reputation, and engagement metrics. Social media monitoring data assists in evaluating brand reputation but may not directly correlate with application performance.
User experience (UX) data
Measures user interactions with a website or application, including page load time, bounce rate, conversion rate, and satisfaction score. These metrics help identify usability issues, optimize user journeys, and improve customer retention.
In the realm of application domain division, each type of observability data presents its own set of advantages and disadvantages. Selecting the most appropriate types of observability data depends on specific requirements and objectives. While challenges may arise in terms of implementation, data interpretation, and privacy considerations, the benefits of monitoring the user experience outweigh these challenges by enabling superior UX and yielding meaningful outcomes.
You can learn more about data monitoring in our previous article, where we explain the importance of monitoring from data engineering perspective.
Summary
We have discussed two types of monitoring/observability data divisions, but it is possible to define additional categorizations. Each data category and metric within it has its own advantages and disadvantages, providing specific dimensions of information about the monitored system.
To achieve comprehensive observability of any system, it is essential to collect and utilize multiple types of monitoring data. By combining diverse data types, a robust monitoring tool can offer more accurate and comprehensive insights into the performance and health of a system.
