In today’s rapidly evolving software landscape, ensuring observability is crucial for building robust and reliable applications. One of the critical components of observability is metrics, which provide valuable insights into the performance and behavior of our systems. OpenTelemetry, an open-source observability framework, offers a standardized approach to capturing, exporting, and analyzing metrics. This blog post explores seven OpenTelemetry metrics for tracking better visibility. We’ll describe what each metric measures, how to track them, and how you can use these metrics to enhance observability in your applications.
OpenTelemetry Metrics is a set of standardized measurements and instrumentation practices for capturing and exporting quantitative data about the behavior and performance of software systems. OpenTelemetry provides a vendor-agnostic framework for collecting metrics, enabling consistent observability across distributed systems.
To use OpenTelemetry metrics, you need to understand some key concepts, which include:
You can now leverage these concepts to use OpenTelemetry metrics. The minimum steps include:
OpenTelemetry supports several metric types—including counter, gauge, and histogram—that you can use to capture and measure different aspects of your application’s behavior and performance.
Several vital metrics provide valuable insights into the performance and behavior of your applications. Here are seven essential OpenTelemetry metrics to consider:
HTTP response time is critical for understanding web application performance and the user experience. The metric measures the amount of time a request response takes, from when a client sends an HTTP request to when it receives a complete response from the server. Monitoring response time helps identify performance bottlenecks, optimize server-side processing, and ensure fast and responsive web applications.
To measure HTTP response time, you may use a histogram metric. A histogram will group the response times into different buckets based on their values. That way, you can identify typical response time ranges and outliers. You can also perform calculations and analyze response times based on percentiles. That way, you can set alerting thresholds that’ll help you detect unusual responses.
The error rate is an essential metric for understanding the stability and reliability of your application. The metric measures the frequency or rate at which errors occur during the execution of your code. Tracking error rates helps you identify potential issues, prioritize bug fixes, and ensure the robustness of your system.
Errors are discrete events that you can count individually. That’s why you can use a counter metric to track the number of errors per unit of time. A counter will give you a cumulative count of errors. The error rate is the percentage of requests that fail and a monitoring system will calculate this for you.
Throughput is an important metric that measures the rate at which a system processes a specific workload or the number of requests served within a time frame. Monitoring throughput helps you understand the system’s capacity, efficiency, and overall performance.
Use a counter metric to track throughput, where the counter gives you the cumulative count of operations or requests. A counter also calculates the throughput by dividing the total requests by elapsed time.
Network latency refers to the time it takes for data packets to travel from a source to a destination across a network. Latency is an essential metric for assessing the performance and responsiveness of network communications. Monitoring network latency helps identify potential network issues, optimize network performance, and ensure efficient data transmission.
You can use a histogram to track network latency, as it provides a distribution of latency values, allowing you to understand the spread and variability of network response time.
Monitoring and measuring database queries provides insights into the performance, efficiency and health of database operations. By tracking and analyzing database query metrics, you can identify slow queries, optimize database access patterns, and improve overall application performance.
You can use a counter metric to track the total count of queries executed. This helps you analyze the trends and patterns of query execution to identify performance bottlenecks and potentially optimize your query performance.
Using OpenTelemetry metrics to measure memory utilization allows you to track your application’s memory usage, helping you identify potential memory leaks, optimize memory allocation, and ensure efficient utilization of memory resources. By monitoring and analyzing memory utilization metrics, you can optimize the performance and stability of your application by identifying and addressing memory-related issues.
You can use an UpDownCounter to capture changes in memory usage, including increases and decreases. This will help you identify patterns and trends in memory consumption.
Monitoring CPU usage helps identify CPU-intensive tasks or processes that consume excessive resources. Measuring CPU usage allows you to optimize system performance, monitor system health, plan resource allocation, and troubleshoot performance-related issues within your application or system. CUP usage is an essential metric for understanding and managing the utilization of CPU resources effectively.
You can use a gauge metric to measure CPU usage, because a gauge captures the instantaneous value at a specific point in time. This allows you to detect spikes, variations, or abnormal patterns in CPU utilization.
By using OpenTelemetry metrics, you can ensure a consistent and standardized approach to metrics instrumentation, collection, and export across your applications. OpenTelemetry provides a flexible and extensible framework that integrates with your existing monitoring systems, supports various metric types, and enables distributed context propagation. Using OpenTelemetry metrics empowers you to gain comprehensive observability and greater insights into the performance and behavior of your systems.
This post was written by Mercy Kibet. Mercy is a full-stack developer with a knack for learning and writing about new and intriguing tech stacks.
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