分散トレーシングの仕組み
サービス境界を越えたコンテキスト伝播など、分散トレーシングの仕組みを学びます。複数のマイクロサービスを通過するリクエストを追跡する方法も確認します。
「分散トレーシングの仕組み」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2/4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 is Context Propagation?
Imagine a request traveling through many services. How do we know it's all part of the same original operation? This is where context propagation comes in.
It's the mechanism that ensures unique identifiers (like a Trace ID) and other relevant information follow a request as it moves between different services or components.
Without it, each service would start a "new" trace, making it impossible to see the full end-to-end journey.
What is Trace Context?
The "context" being propagated isn't just a single ID. It's a small bundle of information called the trace context.
- Trace ID: The unique identifier for the entire request journey.
- Span ID: The ID of the current operation within the trace.
- Parent Span ID: The ID of the operation that called the current one.
- Trace Flags: Information like whether the trace is sampled (should be recorded).
This context is crucial for linking operations together.
Context in HTTP Headers
When services communicate over HTTP, the trace context is typically propagated using special HTTP headers.
The calling service injects the context into the outgoing request's headers. The receiving service then extracts this context from the incoming request's headers.
Common header formats include W3C Trace Context (traceparent, tracestate) and B3 Propagation headers.
Tracing a Service Call
Let's trace a simple request:
- User makes a request to Service A.
- Service A starts a new trace and span.
- Service A calls Service B, injecting its current trace context into the HTTP headers.
- Service B receives the request, extracts the context, and creates a new span linked to Service A's span.
- Service B may then call Service C, propagating the context further.
This chain allows us to see the full path.
Injecting Context into Requests
Imagine we have a TraceContext object. Before making an HTTP call, we'd inject its details into the request headers. This example simulates adding a traceparent header.
Try running this example:
public class ClientService {
public static void main(String[] args) {
String traceId = "a1b2c3d4e5f6g7h8";
String spanId = "i9j0k1l2m3n4o5p6";
String traceparentHeader = String.format("00-%s-%s-01", traceId, spanId);
System.out.println("--- Client Service ---");
System.out.println("Preparing outgoing request.");
System.out.println("Injecting trace context into header:");
System.out.println(" traceparent: " + traceparentHeader);
System.out.println("Making call to Service B...");
}
}Extracting Context from Requests
When Service B receives the request, it looks for these special headers. It then extracts the trace context to understand its place in the overall operation.
This example simulates extracting the traceparent header.
Try running this example:
public class ServerService {
public static void main(String[] args) {
// Simulate an incoming request header
String incomingTraceparent = "00-a1b2c3d4e5f6g7h8-i9j0k1l2m3n4o5p6-01";
System.out.println("--- Server Service ---");
System.out.println("Received incoming request.");
System.out.println("Extracting trace context from header:");
System.out.println(" traceparent: " + incomingTraceparent);
// Parse the header (simplified)
String[] parts = incomingTraceparent.split("-");
if (parts.length == 4) {
System.out.println(" Extracted Trace ID: " + parts[1]);
System.out.println(" Extracted Parent Span ID: " + parts[2]);
} else {
System.out.println(" Could not parse traceparent header.");
}
}
}Automated Instrumentation
Manually injecting and extracting context for every call would be tedious and error-prone. This is where instrumentation libraries come in.
These libraries, often part of an observability framework like OpenTelemetry, automatically:
- Generate new trace and span IDs.
- Inject context into outgoing requests (e.g., HTTP clients).
- Extract context from incoming requests (e.g., HTTP servers).
- Create new child spans linked to the parent.
They handle the heavy lifting for you!
Beyond HTTP: Other Protocols
While HTTP headers are common, context propagation isn't limited to them. Tracing needs to work across various communication methods:
- Message Queues: Context can be added as metadata to messages (e.g., Kafka headers, RabbitMQ properties).
- gRPC: Context is propagated via gRPC metadata.
- Databases: Sometimes, context can be passed within a database transaction or even as comments in queries for advanced scenarios.
The principle remains the same: pass the trace context along.
Full Request Journey
With proper context propagation, a distributed tracing system can reconstruct the entire journey of a request.
This allows you to visualize:
- Which services were involved.
- The order of operations.
- How long each service took.
- Where errors occurred.
This end-to-end visibility is invaluable for debugging and performance optimization in complex microservice architectures.
Propagating the Context
You're building a microservice application. Service A calls Service B, and you want to ensure the trace context is correctly passed between them to link their operations.
Recap: How Tracing Works
In this lesson, we explored the core mechanism behind distributed tracing: context propagation.
- Trace context (IDs, flags) is passed between services.
- HTTP headers are a common way to propagate context.
- Instrumentation libraries automate the injection and extraction of context.
- This allows for end-to-end visibility of requests across distributed systems.
Understanding this process is key to leveraging distributed tracing effectively!
よくある質問
「分散トレーシングの仕組み」レッスンは無料ですか？
はい。「分散トレーシングの仕組み」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。
「分散トレーシングの仕組み」で何を学びますか？
サービス境界を越えたコンテキスト伝播など、分散トレーシングの仕組みを学びます。複数のマイクロサービスを通過するリクエストを追跡する方法も確認します。 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2/4です。
「分散トレーシングの仕組み」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。