メトリクスの種類
ゲージ、カウンター、ヒストグラム、サマリーという基本的なメトリクスの種類を理解します。効果的なモニタリングのために、それぞれをいつ、どのように使うかを学びます。
「メトリクスの種類」はCoddyKit上の無料System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)レッスンです。 これはレッスン1/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはSystem Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)コースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
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!
よくある質問
「メトリクスの種類」レッスンは無料ですか？
はい。「メトリクスの種類」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)コースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)コースには全4レッスンが含まれています。
「メトリクスの種類」で何を学びますか？
ゲージ、カウンター、ヒストグラム、サマリーという基本的なメトリクスの種類を理解します。効果的なモニタリングのために、それぞれをいつ、どのように使うかを学びます。 ブラウザで直接実行するハンズオンコードでSystem Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)を演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)を始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのSystem Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)は初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン1/4です。
「メトリクスの種類」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このSystem Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)レッスンでコードを書いて実行できますか？
はい。すべてのSystem Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)レッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。