Types of Metrics Explained
Understand the fundamental metric types: gauges, counters, histograms, and summaries. Learn when and how to apply each type for effective monitoring.
Types of Metrics Explained is a free System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What are Metric Types?
Welcome to "Types of Metrics Explained"! In observability, metrics are crucial for understanding your system's health and performance.
But not all numbers are the same! Categorizing metrics helps us collect, store, and analyze them effectively.
We'll explore the four fundamental types: Gauges, Counters, Histograms, and Summaries.
Gauges: Snapshot of Now
A Gauge represents a single numerical value that can go up and down over time. Think of it like a car's speedometer or a thermometer.
Gauges are perfect for capturing the current state of a system at a specific moment.
- Use for: Current CPU usage, memory consumption, queue size, temperature.
- Nature: Point-in-time value.
Gauge Code Example
Here's a simple Java example simulating a gauge tracking current CPU utilization. Notice how its value can change freely.
public class Main {
public static void main(String[] args) {
double cpuUsage = 0.5; // 50% CPU
System.out.println("Current CPU Usage: " + cpuUsage);
// Later, CPU usage might change
cpuUsage = 0.8; // 80% CPU
System.out.println("Updated CPU Usage: " + cpuUsage);
cpuUsage = 0.3; // 30% CPU
System.out.println("Further Updated CPU Usage: " + cpuUsage);
}
}Counters: Always Increasing
A Counter is a cumulative metric that only ever increases. It represents a total count of something that has occurred since the system started.
It can reset to zero only when the monitored system restarts.
- Use for: Total requests served, errors encountered, bytes sent, login attempts.
- Nature: Monotonically increasing total.
Counter Code Example
This Java example demonstrates a counter for total requests. Each 'request' simply increments the counter.
public class Main {
private static long totalRequests = 0;
public static void handleRequest() {
totalRequests++;
System.out.println("Requests handled: " + totalRequests);
}
public static void main(String[] args) {
System.out.println("Initial requests: " + totalRequests);
handleRequest(); // First request
handleRequest(); // Second request
// ... more requests later
handleRequest(); // Third request
}
}Histograms: Value Distributions
Histograms sample observations (like request durations or response sizes) and count them in configurable buckets.
They give you insight into the distribution of values, not just the average. This is vital for understanding latency and performance.
- Benefit: You can calculate percentiles (e.g., 99th percentile latency) on the server side.
- Use for: Request latency, response sizes, data transfer rates.
Summaries: Pre-calculated Percentiles
Summaries are similar to histograms but often pre-calculate configurable quantiles (like p99, p95, p50) on the client side.
Instead of sending raw data, the client library sends pre-computed statistics (sum, count, and quantiles) to the monitoring system.
- Benefit: Less data sent over the network, but less flexible for custom percentile calculations later.
- Use for: Latency measurements where specific percentiles are known to be needed.
Histograms vs. Summaries
Both Histograms and Summaries track distributions, but they differ in where calculations happen:
- Histograms: Send raw data (counts in buckets). Percentiles are calculated on the server. More flexible for ad-hoc analysis.
- Summaries: Calculate percentiles on the client and send pre-computed results. More resource-efficient if you know exactly which percentiles you need.
For most modern systems, Histograms are generally preferred due to their flexibility.
Picking the Best Metric Type
Choosing the right metric type is key for effective monitoring:
- Gauges: For current values that can go up/down (e.g., disk usage, active users).
- Counters: For cumulative totals that only increase (e.g., total errors, processed items).
- Histograms: For distributions of values where you need server-side percentile calculation and flexibility (e.g., request durations).
- Summaries: For distributions where client-side pre-calculated percentiles are sufficient and network efficiency is critical (less common than histograms now).
Metric Type Challenge
Your application processes user orders. You want to track the total number of orders placed since the application started, and also the current number of items in the processing queue.
Key Takeaways on Metrics
Great job! You've now learned about the four fundamental metric types:
- Gauges: For current, fluctuating values.
- Counters: For cumulative, ever-increasing totals.
- Histograms: For understanding the distribution of values and calculating percentiles server-side.
- Summaries: For pre-calculated percentiles client-side.
Understanding these types helps you choose the right tool for the right job, leading to more insightful monitoring and faster debugging!
Frequently asked questions
Is the “Types of Metrics Explained” lesson free?
Yes — the full text of “Types of Metrics Explained” is free to read here on the web, and the System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) course, upgrade to CoddyKit PRO.
What will I learn in “Types of Metrics Explained”?
Understand the fundamental metric types: gauges, counters, histograms, and summaries. Learn when and how to apply each type for effective monitoring. You practise System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)?
No prior experience is required. System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Types of Metrics Explained” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) lesson?
Yes. Every System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.