docker / go-metrics

Metric Types (Timer, Gauge, Counter)

This outline provides a comprehensive overview of the different metric types available in the go-metrics library, including Timer, Gauge, and Counter. It explores their functionalities, use cases, and examples to guide developers in effectively utilizing each metric type.

Timer

A Timer metric is designed to measure the duration of events. This type of metric is valuable for understanding the timing of operations and identifying performance bottlenecks.

Use Cases

  • Measuring response times: Track the time it takes for a service to respond to requests, helping identify slow responses and potential issues.
  • Analyzing task execution times: Measure the duration of specific tasks within an application, providing insights into their efficiency and identifying areas for optimization.
  • Evaluating system latency: Gauge the latency of various components or operations within a system, highlighting potential delays and bottlenecks.

Options

  • Time (time.Duration): Returns the total time measured by the timer in nanoseconds.
  • Count (uint64): Provides the number of events that have been timed.
  • Mean (float64): Calculates the average duration of all events measured by the timer.
  • StdDev (float64): Returns the standard deviation of event durations.
  • Min (time.Duration): Reports the minimum duration of events measured by the timer.
  • Max (time.Duration): Identifies the maximum duration of events measured by the timer.
  • Percentiles: Calculates percentiles of event durations (e.g., 50th, 90th, 95th) for understanding the distribution of timing.

Examples

// Create a new Timer metric
          timer := NewTimer()
          
          // Start timing an event
          timer.Start()
          
          // Perform the operation
          // ...
          
          // Stop timing the event
          timer.Stop()
          
          // Retrieve the timer metrics
          duration := timer.Time()
          count := timer.Count()
          mean := timer.Mean()

Gauge

A Gauge metric represents a value that can change over time. It is used to track the current state of a system or component.

Use Cases

  • Monitoring resource utilization: Track CPU, memory, disk usage, or other resource consumption to identify potential resource constraints or inefficiencies.
  • Tracking system health: Monitor key system metrics like queue lengths, connection counts, or error rates to gain insights into the overall health and stability of the system.
  • Observing application state: Monitor variables or counters within an application to understand its internal state and behavior.

Options

  • Value (float64): Returns the current value of the gauge.

Examples

// Create a new Gauge metric
          gauge := NewGauge()
          
          // Set the gauge value
          gauge.Update(10)
          
          // Retrieve the current gauge value
          value := gauge.Value()

Counter

A Counter metric is designed to count events or occurrences. It provides a simple way to track the number of times a specific event has happened.

Use Cases

  • Counting requests: Track the number of incoming requests to a service or API endpoint for monitoring load and performance.
  • Tracking errors: Count the number of errors occurring within an application to identify potential issues and areas for improvement.
  • Monitoring events: Track the occurrences of specific events within a system, such as user logins, file downloads, or system restarts.

Options

  • Count (uint64): Returns the current count of events.
  • Inc (int64): Increment the counter by a specified amount.
  • Dec (int64): Decrement the counter by a specified amount.

Examples

// Create a new Counter metric
          counter := NewCounter()
          
          // Increment the counter
          counter.Inc(1)
          
          // Retrieve the current count
          count := counter.Count()

These examples illustrate the basic usage of Timer, Gauge, and Counter metrics. Each type provides a unique perspective on system performance and behavior, enabling developers to effectively monitor and analyze different aspects of their applications. Refer to the go-metrics library documentation for further details and advanced usage options.