From 2b0753c0ce31022feb017c90fbf7288dd69f4a07 Mon Sep 17 00:00:00 2001 From: ngwlf <88664532+ngwlf@users.noreply.github.com> Date: Mon, 16 Dec 2024 15:34:18 +0100 Subject: [PATCH 1/4] Update native_client_attestation_oidc_and_oauth.puml with OIDC app2app flow added more detail to the OIDC flow --- ...ive_client_attestation_oidc_and_oauth.puml | 26 ++++++++++++------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/src/plantuml/tmp/native_client_attestation_oidc_and_oauth.puml b/src/plantuml/tmp/native_client_attestation_oidc_and_oauth.puml index 7645c36..a3418b8 100644 --- a/src/plantuml/tmp/native_client_attestation_oidc_and_oauth.puml +++ b/src/plantuml/tmp/native_client_attestation_oidc_and_oauth.puml @@ -24,6 +24,7 @@ box "Mobile Device" #GhostWhite participant MUA as "Mail\nUser Agent" participant Client as "Client" participant AndroidTEE as "Android TEE" + participant Authenticator as "Authenticator" end box box "Anbieter" #TECHNOLOGY @@ -120,21 +121,28 @@ deactivate AuthS Client -> UserAgent: Navigate to Request URI activate UserAgent UserAgent -> AuthS: Authorization Request (with Request URI) + activate AuthS - AuthS -> IDP: PAR Request (OpenID Connect), redirect_uri, client_id_idpsek +group OIDC user authentication with confidential client + AuthS -> IDP: PAR Request (OpenID Connect), redirect_uri, client_id_idpsek\nsee https://gemspec.gematik.de/docs/gemSpec/gemSpec_IDP_Sek/latest/#7.1.2 activate IDP note right: AS A acts as Relying Party\n for the IDP\n(client_id_idpsek) - IDP --> AuthS: PAR Response, request_uri, expires_in - AuthS --> UserAgent: Redirect to IDP, request_uri - UserAgent -> IDP: Navigate to request_uri - IDP --> UserAgent: Authentication Prompt, consent - UserAgent -> IDP: User Credentials, consent - IDP --> UserAgent: Redirect to AuthS, auth_code, redirect_uri - UserAgent -> AuthS: Redirect to AuthS, auth_code, redirect_uri + IDP --> AuthS: URI-PAR Response, request_uri, expires_in + AuthS --> Client: Redirect URI-PAR to IDP, request_uri + Client --> Authenticator: Redirect URI-PAR to IDP, request_uri +activate Authenticator + Authenticator -> IDP: Navigate to URI-PAR, request_uri + IDP --> Authenticator: Authentication Prompt, consent + Authenticator -> IDP: User Credentials, consent + IDP --> Authenticator: Redirect to AuthS, auth_code, redirect_uri + Authenticator -> Client: Redirect to AuthS, auth_code, redirect_uri +deactivate Authenticator + Client-> AuthS: Redirect to AuthS, auth_code, redirect_uri AuthS -> IDP: Token Request (Authorization Code Grant), auth_code IDP -> IDP: Validate\nAuthorization\nCode IDP --> AuthS: Authentication Response (ID Token) deactivate IDP +end AuthS -> AuthS: Validate\nID Token AuthS --> UserAgent: Authorization Code UserAgent -> Client: Redirect with Authorization Code @@ -167,4 +175,4 @@ PEP --> Client: Resource Data deactivate PEP deactivate RS -@enduml \ No newline at end of file +@enduml From 640c271920cefc704c40d1e539a860eb30ec10fb Mon Sep 17 00:00:00 2001 From: gem-cp Date: Tue, 17 Dec 2024 13:59:30 +0100 Subject: [PATCH 2/4] No code changes madenew OpenTelemetry.md --- docs/tmp/OpenTelemetry.md | 494 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 494 insertions(+) create mode 100644 docs/tmp/OpenTelemetry.md diff --git a/docs/tmp/OpenTelemetry.md b/docs/tmp/OpenTelemetry.md new file mode 100644 index 0000000..af62423 --- /dev/null +++ b/docs/tmp/OpenTelemetry.md @@ -0,0 +1,494 @@ +# Open Telemetry + +Siehe https://opentelemetry.io/ + +Das OpenTelemetry Protocol (OTLP) ist ein **allgemeines, herstellerunabhängiges Telemetrie-Datenübertragungsprotokoll**. Es wurde entwickelt, um die Art und Weise zu standardisieren, wie Telemetriedaten (wie Traces, Metriken und Logs) von Anwendungen und Infrastrukturkomponenten zu Backend-Systemen für Analyse und Beobachtung gesendet werden. + +Hier sind die Hauptfunktionen von OTLP: + +* **Standardisierung der Datenübertragung:** OTLP definiert ein einheitliches Datenmodell und Kodierungsformat für die Übertragung von Traces, Metriken und Logs. Dies beseitigt die Notwendigkeit, verschiedene proprietäre Protokolle zu verwenden und vereinfacht die Integration von Telemetriedaten in verschiedene Backend-Systeme. +* **Herstellerunabhängigkeit:** OTLP ist nicht an einen bestimmten Anbieter oder ein bestimmtes Produkt gebunden. Es kann mit verschiedenen Backend-Systemen verwendet werden, die OTLP unterstützen, wie z.B. Jaeger, Zipkin, Prometheus, Grafana und viele kommerzielle Observability-Plattformen. +* **Effizienz und Skalierbarkeit:** OTLP verwendet gRPC und Protocol Buffers für eine effiziente und performante Datenübertragung. Es ist für hohe Durchsatzraten und große Datenmengen ausgelegt und kann horizontal skaliert werden. +* **Einfache Integration:** OTLP wird von den OpenTelemetry SDKs in verschiedenen Programmiersprachen unterstützt. Dies erleichtert die Instrumentierung von Anwendungen und die Integration von OTLP in bestehende Systeme. +* **Transport Agnostisch:** OTLP selbst definiert nur das Datenmodell und die Kodierung. Es kann über verschiedene Transportprotokolle wie gRPC, HTTP/1.1 (in Zukunft HTTP/2 und HTTP/3) übertragen werden. + +**Zusammenfassend lässt sich sagen, dass OTLP Folgendes macht:** + +1. **Definiert ein standardisiertes Datenmodell** für die Darstellung von Traces, Metriken und Logs. +2. **Ermöglicht die Übertragung dieser Daten** von Anwendungen und Infrastrukturkomponenten zu Backend-Systemen. +3. **Vereinfacht die Integration** mit verschiedenen Observability-Tools und -Plattformen. +4. **Sorgt für eine effiziente und skalierbare Datenübertragung.** + +**Vorteile von OTLP:** + +* **Verbesserte Interoperabilität:** Verschiedene Tools und Systeme können nahtlos miteinander kommunizieren. +* **Geringere Komplexität:** Entwickler müssen sich nicht mit verschiedenen proprietären Protokollen auseinandersetzen. +* **Zukunftssicherheit:** OTLP ist ein offener Standard, der von einer großen Community unterstützt wird. +* **Flexibilität:** Unternehmen können die besten Tools für ihre Bedürfnisse wählen, ohne an einen bestimmten Anbieter gebunden zu sein. + +**Kurz gesagt, OTLP ist ein wichtiger Bestandteil des OpenTelemetry-Ökosystems und spielt eine entscheidende Rolle bei der Standardisierung und Vereinfachung der Übertragung von Telemetriedaten für die Observability.** + +OpenTelemetry kann den Versand von mehreren JSON-Objekten zusammenfassen und in einem bestimmten Intervall, wie z.B. alle 5 Minuten, versenden. Dies wird durch **Batching** und **Scheduling** erreicht. + +Hier ist, wie es funktioniert: + +**1. Batching (Zusammenfassung):** + +* OpenTelemetry SDKs bieten in der Regel **Batch Processors** (z.B. `BatchSpanProcessor` für Traces, `BatchLogRecordProcessor` für Logs). +* Diese Processors sammeln Telemetriedaten (Spans, Log Records, etc.) im Speicher, anstatt sie sofort zu versenden. +* Sie konfigurieren den Batch Processor mit Parametern wie: + * **`max_queue_size`:** Die maximale Anzahl von Telemetriedaten, die im Speicher gehalten werden können. + * **`scheduled_delay_millis`:** Das Intervall (in Millisekunden), nach dem die gesammelten Daten versendet werden (z.B. 300000 für 5 Minuten). + * **`export_timeout_millis`:** Die maximale Zeit, die für den Export eines Batches gewartet wird. + * **`max_export_batch_size`:** Die maximale Anzahl von Telemetriedaten, die in einem einzelnen Exportvorgang gesendet werden. Wenn der Batch größer ist, wird er in mehrere Exporte aufgeteilt. + +**2. Scheduling (Zeitgesteuerter Versand):** + +* Der Batch Processor verwendet einen internen Timer, um den Versand der gesammelten Daten in regelmäßigen Abständen auszulösen. +* Basierend auf dem konfigurierten `scheduled_delay_millis` (z.B. 5 Minuten) wird der Batch Processor die Daten an den Exporter weiterleiten. + +**3. Exporter (Versand):** + +* Der Exporter ist verantwortlich für die eigentliche Übertragung der Daten an das Backend. +* OpenTelemetry bietet verschiedene Exporter, einschließlich eines OTLP Exporters, der die Daten im OTLP-Format (über gRPC oder HTTP) versenden kann. +* Es gibt auch Exporter, die Daten in anderen Formaten, wie z.B. JSON, versenden können. In diesem Fall würde der Exporter die gesammelten Telemetriedaten in eine Reihe von JSON-Objekten konvertieren und diese dann als einen Batch senden. + +**Beispiel (vereinfachtes Konzept mit `BatchSpanProcessor` für Traces):** + +```python +from opentelemetry import trace +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import BatchSpanProcessor +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter # Beispielhaft OTLP + +# Konfiguration des Tracer Providers +trace.set_tracer_provider(TracerProvider()) + +# Konfiguration des OTLP Exporters +otlp_exporter = OTLPSpanExporter(endpoint="") + +# Konfiguration des BatchSpanProcessors (5 Minuten Intervall) +span_processor = BatchSpanProcessor(otlp_exporter, scheduled_delay_millis=300000, max_queue_size=2048, max_export_batch_size=512) + +# Hinzufügen des Processors zum Tracer Provider +trace.get_tracer_provider().add_span_processor(span_processor) + +# Jetzt werden alle erzeugten Spans vom BatchSpanProcessor gesammelt und alle 5 Minuten an den OTLP Exporter gesendet. +``` + +**Zusammenfassung:** + +OpenTelemetry bietet durch die Kombination von Batch Processors und Exportern die Flexibilität, Telemetriedaten zu sammeln, zu bündeln und in einem gewünschten Intervall zu versenden. Obwohl OTLP selbst nicht direkt JSON verwendet, kann der Exporter die Daten in JSON konvertieren, bevor sie gesendet werden, falls das Backend dies erfordert. Die Konfiguration des Batching-Verhaltens ermöglicht es Ihnen, den Versand an Ihre spezifischen Anforderungen und die Leistungsfähigkeit Ihres Backends anzupassen. + +**Wichtig:** + +* Die genaue Implementierung und die verfügbaren Konfigurationsoptionen können je nach verwendeter OpenTelemetry-Sprach-SDK variieren. +* Die Wahl des richtigen Batching-Intervalls und der Batch-Größe hängt von verschiedenen Faktoren ab, wie z.B. dem Volumen der Telemetriedaten, der Netzwerklatenz und der Kapazität des Backends. Es ist wichtig, diese Parameter sorgfältig zu testen und zu optimieren, um eine optimale Leistung zu gewährleisten. + +Here's an example of how you might represent performance data for several HTTP requests and responses using JSON, keeping in mind that OpenTelemetry primarily uses OTLP and not JSON for transmission. This example shows how span data from HTTP requests could be *represented* in JSON after being exported and transformed, perhaps for a system that doesn't support OTLP directly. + +**Example JSON Structure for Multiple HTTP Requests (Inspired by OpenTelemetry Spans):** + +```json +[ + { + "traceId": "4bf92f3577b34da6a3ce929d0e0e4736", + "spanId": "00f067aa0ba902b7", + "parentSpanId": null, + "name": "HTTP GET /api/users", + "kind": "SERVER", + "startTimeUnixNano": "1678886400000000000", + "endTimeUnixNano": "1678886400150000000", + "attributes": { + "http.method": "GET", + "http.url": "https://api.example.com/api/users", + "http.target": "/api/users", + "http.host": "api.example.com", + "http.scheme": "https", + "http.status_code": 200, + "http.response_content_length": "1234", + "net.peer.ip": "192.168.1.10", + "net.peer.port": "443" + }, + "status": { + "code": "OK" + } + }, + { + "traceId": "4bf92f3577b34da6a3ce929d0e0e4736", + "spanId": "74755584d576b4d9", + "parentSpanId": "00f067aa0ba902b7", + "name": "HTTP GET /api/users/123", + "kind": "CLIENT", + "startTimeUnixNano": "1678886400050000000", + "endTimeUnixNano": "1678886400100000000", + "attributes": { + "http.method": "GET", + "http.url": "https://internal-api/api/users/123", + "http.target": "/api/users/123", + "http.host": "internal-api", + "http.scheme": "https", + "http.status_code": 200, + "http.response_content_length": "256", + "net.peer.ip": "10.0.0.5", + "net.peer.port": "8080" + }, + "status": { + "code": "OK" + } + }, + { + "traceId": "8a3c60f7d4dff4d6b2f9f8e7d8d7c8f7", + "spanId": "245fa4b9655567cd", + "parentSpanId": null, + "name": "HTTP POST /api/orders", + "kind": "SERVER", + "startTimeUnixNano": "1678886401000000000", + "endTimeUnixNano": "1678886401500000000", + "attributes": { + "http.method": "POST", + "http.url": "https://api.example.com/api/orders", + "http.target": "/api/orders", + "http.host": "api.example.com", + "http.scheme": "https", + "http.status_code": 201, + "http.request_content_length": "567", + "net.peer.ip": "192.168.1.20", + "net.peer.port": "443" + }, + "status": { + "code": "OK" + } + }, + { + "traceId": "8a3c60f7d4dff4d6b2f9f8e7d8d7c8f7", + "spanId": "195ee4b965556711", + "parentSpanId": "245fa4b9655567cd", + "name": "database.query", + "kind": "CLIENT", + "startTimeUnixNano": "1678886401100000000", + "endTimeUnixNano": "1678886401400000000", + "attributes": { + "db.system": "postgresql", + "db.statement": "INSERT INTO orders (user_id, product_id) VALUES ($1, $2)", + "net.peer.ip": "10.0.0.10", + "net.peer.port": "5432" + }, + "status": { + "code": "OK" + } + }, + { + "traceId": "f4a7b8c9d0e1f23456789abcdef01234", + "spanId": "c3d4e5f6a7b89012", + "parentSpanId": null, + "name": "HTTP GET /api/products/99", + "kind": "SERVER", + "startTimeUnixNano": "1678886402000000000", + "endTimeUnixNano": "1678886402200000000", + "attributes": { + "http.method": "GET", + "http.url": "https://api.example.com/api/products/99", + "http.target": "/api/products/99", + "http.host": "api.example.com", + "http.scheme": "https", + "http.status_code": 404, + "http.response_content_length": "42", + "net.peer.ip": "192.168.1.30", + "net.peer.port": "443" + }, + "status": { + "code": "ERROR", + "message": "Not Found" + } + } +] +``` + +**Explanation of the Fields:** + +* **`traceId`:** A unique identifier for the entire request trace (all related spans). +* **`spanId`:** A unique identifier for a specific operation within the trace (a single span). +* **`parentSpanId`:** The `spanId` of the span that caused this span to be created. `null` indicates a root span. +* **`name`:** A human-readable name for the operation (e.g., "HTTP GET /api/users"). +* **`kind`:** Indicates the type of span: + * `SERVER`: Represents the server side of an RPC or a process starting a trace. + * `CLIENT`: Represents the client side of an RPC. + * `INTERNAL`: Indicates an internal operation within an application. + * `PRODUCER`: Represents a producer in an asynchronous messaging scenario. + * `CONSUMER`: Represents a consumer in an asynchronous messaging scenario. +* **`startTimeUnixNano`:** The start time of the span in nanoseconds since the Unix epoch (UTC). +* **`endTimeUnixNano`:** The end time of the span in nanoseconds since the Unix epoch (UTC). +* **`attributes`:** Key-value pairs providing more context about the operation. These follow OpenTelemetry's semantic conventions (e.g., `http.method`, `http.status_code`, `net.peer.ip`). + * **`http.method`:** The HTTP method (GET, POST, etc.). + * **`http.url`:** The full URL of the request. + * **`http.target`:** The path and query string of the request. + * **`http.host`:** The hostname from the URL. + * **`http.scheme`:** The URL scheme (http or https). + * **`http.status_code`:** The HTTP status code of the response. + * **`http.response_content_length`:** The size of the response body in bytes. + * **`http.request_content_length`:** The size of the request body in bytes. + * **`net.peer.ip`:** The IP address of the client or server. + * **`net.peer.port`:** The port number of the client or server. + * **`db.system`:** Type of the database system (e.g. postgresql, mysql) + * **`db.statement`:** SQL query for database operations. +* **`status`:** Information about the outcome of the operation. + * **`code`:** `OK`, `ERROR`, or `UNSET`. + * **`message`:** An error message (if applicable). + +**Important Considerations:** + +* **OTLP is Preferred:** This is a JSON representation *for illustrative purposes*. OpenTelemetry uses OTLP, which is more efficient. +* **Semantic Conventions:** The `attributes` use OpenTelemetry's semantic conventions. You can find a comprehensive list of these conventions in the OpenTelemetry specification. These conventions provide a common vocabulary for describing various types of operations. +* **Custom Attributes:** You can add your own custom attributes to spans to capture application-specific data. +* **Tools for Transformation:** If you need to transform OTLP data to JSON, you can use tools like the OpenTelemetry Collector, which can export data in various formats, or you can write custom code to perform the transformation after receiving the data from an OTLP exporter. + +This comprehensive example helps you understand how you can represent OpenTelemetry performance data in JSON format, even though it's not the standard way OpenTelemetry transmits data. Remember to use OTLP if you can. Use this JSON representation for compatibility with systems that need it. + +# OpenTelemetry in Kubernetes + +Using OpenTelemetry in Kubernetes involves instrumenting your applications to generate telemetry data (traces, metrics, and logs), deploying the OpenTelemetry Collector to gather and process that data, and exporting it to backend systems for analysis. Here's a comprehensive guide on how to do this: + +**1. Instrumentation** + +* **Choose Libraries:** Select the appropriate OpenTelemetry SDK for the programming language(s) used in your microservices (e.g., Java, Python, Go, Node.js, .NET). +* **Add Dependencies:** Include the necessary OpenTelemetry libraries in your application's dependencies (e.g., using Maven, Gradle, pip, npm). +* **Initialize the SDK:** + * Set up a `TracerProvider` to create tracers. + * Set up an `Exporter` to define where to send the telemetry data (e.g., to the OpenTelemetry Collector). + * Set up a `BatchSpanProcessor` to batch spans before sending them. This is essential for performance. + * Configure resource attributes to identify your service in the cluster (e.g., service name, namespace, pod name). You can often automatically get Kubernetes attributes using the `opentelemetry-resource-detector-kubernetes` library. +* **Instrument Code:** + * Create spans to represent operations within your code. You'll want to create spans for incoming/outgoing requests, database calls, and other significant operations. + * Use OpenTelemetry's automatic instrumentation libraries if available for your language and frameworks. These libraries automatically create spans for common operations (e.g., HTTP requests, database queries) without requiring manual instrumentation. + * Add attributes to spans to provide more context (e.g., HTTP status code, error messages). + * Create metrics (counters, gauges, histograms) to track quantitative data (e.g., request count, latency, error rate). + * Propagate context (trace ID, span ID) across service boundaries by extracting context from incoming requests and injecting it into outgoing requests. This allows you to stitch together traces across multiple services. + +**Example (Python with Flask and OTLP exporter):** + +```python +from opentelemetry import trace +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import BatchSpanProcessor +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter +from opentelemetry.instrumentation.flask import FlaskInstrumentor +from opentelemetry.instrumentation.requests import RequestsInstrumentor +from opentelemetry.sdk.resources import Resource, get_aggregated_resources + +from opentelemetry_resourcedetector_kubernetes import KubernetesResourceDetector + +from flask import Flask, request + +# --- OpenTelemetry Configuration --- +# Detect Kubernetes specific attributes (pod name, namespace etc.) +resource = get_aggregated_resources([ + KubernetesResourceDetector(), +]) + +# Or manually set resource attributes: +# resource = Resource.create(attributes={ +# "service.name": "my-flask-app", +# "service.namespace": "my-namespace", +# "k8s.pod.name": os.environ.get("HOSTNAME"), +# }) + +# Set up the tracer provider +trace.set_tracer_provider(TracerProvider(resource=resource)) + +# Configure the OTLP exporter to send data to the OpenTelemetry Collector +otlp_exporter = OTLPSpanExporter(endpoint="opentelemetry-collector.monitoring.svc.cluster.local:4317", insecure=True) # Replace with your collector address + +# Use batch span processor to send spans in batches +span_processor = BatchSpanProcessor(otlp_exporter) +trace.get_tracer_provider().add_span_processor(span_processor) + +# --- Flask App --- +app = Flask(__name__) + +# Automatically instrument Flask and Requests +FlaskInstrumentor().instrument_app(app) +RequestsInstrumentor().instrument() + +tracer = trace.get_tracer("my-flask-app-tracer") + +@app.route("/") +def hello(): + with tracer.start_as_current_span("handle-root-request") as span: + span.set_attribute("http.method", request.method) + span.set_attribute("http.url", request.url) + # ... your application logic ... + return "Hello from Flask!" + +if __name__ == "__main__": + app.run(debug=True, host="0.0.0.0") +``` + +**2. OpenTelemetry Collector Deployment** + +The OpenTelemetry Collector is a crucial component that acts as a vendor-agnostic intermediary for receiving, processing, and exporting telemetry data. You'll deploy it within your Kubernetes cluster. + +* **Deployment Modes:** + * **Agent:** Deploy the Collector as a DaemonSet to run an instance on each node. This is suitable for collecting node-level metrics and logs and for forwarding telemetry data from applications running on the same node. + * **Sidecar:** Deploy a Collector instance alongside each application pod. Useful for application-specific processing or when strict network isolation is required. + * **Gateway:** Deploy the Collector as a Deployment (with multiple replicas for high availability). This is the most common mode and acts as a central point for receiving data from agents, sidecars, or directly from applications. It can also perform more complex processing and filtering before sending data to backends. + +* **Configuration (config.yaml):** + * **Receivers:** Define how the Collector will receive data. Common receivers include: + * `otlp`: Receives data in the OpenTelemetry Protocol format (gRPC or HTTP). + * `jaeger`: Receives data in Jaeger format. + * `zipkin`: Receives data in Zipkin format. + * `hostmetrics`: (For agents) Scrapes host-level metrics. + * `kubeletstats`: (For agents) Scrapes container metrics from the Kubelet. + * `kubernetes_cluster`: Receives cluster-level metrics. + * **Processors:** (Optional) Define how to process the data. Common processors include: + * `batch`: Batches data before sending it to exporters, improving performance. + * `memory_limiter`: Prevents the Collector from consuming too much memory. + * `attributes`: Adds, modifies, or deletes attributes. + * `resource`: Adds, modifies, or deletes resource attributes. + * `filter`: Filters spans, metrics or logs based on certain criteria. + * **Exporters:** Define where to send the processed data. Common exporters include: + * `otlp`: Sends data to another OTLP endpoint (e.g., another Collector, an observability backend). + * `jaeger`: Sends data to a Jaeger backend. + * `zipkin`: Sends data to a Zipkin backend. + * `prometheus`: Exposes metrics in Prometheus format. + * `logging`: Logs the data to the console (useful for debugging). + * **Service:** Defines the pipelines that connect receivers, processors, and exporters. + +**Example `config.yaml` (Gateway Deployment):** + +```yaml +receivers: + otlp: + protocols: + grpc: + http: + +processors: + batch: + timeout: 10s + memory_limiter: + check_interval: 1s + limit_mib: 2048 + resource: + attributes: + - key: environment + value: production + action: upsert + +exporters: + otlp: # Example: sending to another OTLP endpoint like Honeycomb, Lightstep + endpoint: "api.honeycomb.io:443" + headers: + "x-honeycomb-team": "YOUR_HONEYCOMB_API_KEY" + logging: + loglevel: debug + +service: + pipelines: + traces: + receivers: [otlp] + processors: [batch, memory_limiter] + exporters: [otlp, logging] + metrics: + receivers: [otlp] + processors: [batch, memory_limiter, resource] + exporters: [otlp] +``` + +* **Deployment (YAML):** + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: opentelemetry-collector + namespace: monitoring # example namespace +spec: + replicas: 2 + selector: + matchLabels: + app: opentelemetry-collector + template: + metadata: + labels: + app: opentelemetry-collector + spec: + containers: + - name: otel-collector + image: otel/opentelemetry-collector-contrib:latest # Or a specific version + command: + - "/otelcol-contrib" + - "--config=/conf/config.yaml" + args: + - --log-level=DEBUG + ports: + - containerPort: 4317 # OTLP gRPC + - containerPort: 4318 # OTLP HTTP + - containerPort: 8888 # Metrics + - containerPort: 55679 # zPages (for debugging) + volumeMounts: + - name: otel-collector-config-vol + mountPath: /conf + volumes: + - name: otel-collector-config-vol + configMap: + name: otel-collector-config +--- +apiVersion: v1 +kind: ConfigMap +metadata: + name: otel-collector-config + namespace: monitoring +data: + config.yaml: | + # --- Paste your config.yaml content here --- +--- +apiVersion: v1 +kind: Service +metadata: + name: opentelemetry-collector + namespace: monitoring +spec: + selector: + app: opentelemetry-collector + ports: + - name: otlp-grpc + port: 4317 + targetPort: 4317 + - name: otlp-http + port: 4318 + targetPort: 4318 + - name: metrics + port: 8888 + targetPort: 8888 +``` + +**3. Backend Systems** + +* **Choose Backends:** Select the observability backend(s) where you want to store and analyze your telemetry data. Popular options include: + * **Open Source:** Jaeger, Zipkin (for traces), Prometheus, Grafana (for metrics and visualizations), Elasticsearch, Fluentd, Kibana (for logs). + * **Commercial:** Honeycomb, Lightstep, Datadog, New Relic, Dynatrace, AWS X-Ray. +* **Configure Exporters:** Configure the appropriate exporters in your OpenTelemetry Collector's `config.yaml` to send data to your chosen backends. + +**4. Deployment and Verification** + +1. **Deploy the Collector:** Apply the Collector's deployment YAML to your Kubernetes cluster using `kubectl apply -f collector.yaml`. +2. **Deploy Applications:** Deploy your instrumented applications to Kubernetes. +3. **Verify:** + * Check the Collector logs to ensure it's receiving data and not reporting errors. Use `kubectl logs -f deployment/opentelemetry-collector -n monitoring`. + * Use the observability backend's UI to view your traces, metrics, and logs. You should be able to see data flowing in from your applications. + +**5. Advanced Considerations** + +* **Auto-Instrumentation with Operator:** The OpenTelemetry Operator for Kubernetes can simplify instrumentation by automatically injecting the necessary SDKs and configurations into your application pods. +* **Sampling:** To reduce the volume of data sent to backends, especially for high-traffic services, configure sampling in your application's tracer or in the Collector's processors. +* **Security:** + * Use TLS for communication between your applications, the Collector, and backends. + * Secure the Collector's endpoints (e.g., using network policies). +* **Resource Management:** Monitor the Collector's resource usage (CPU, memory) and adjust resource limits and requests as needed. +* **High Availability:** Deploy the Collector in Gateway mode with multiple replicas for high availability and fault tolerance. +* **Logs:** While OpenTelemetry is primarily focused on traces and metrics, you can also use the Collector to receive and process logs. You can use a sidecar container running Fluent Bit or Fluentd to forward logs to the OpenTelemetry Collector. +* **Custom Processors:** For more advanced use cases, write custom processors for the Collector to implement specific filtering, transformation, or enrichment logic. + +By following these steps, you can successfully deploy and use OpenTelemetry in your Kubernetes environment to gain deep insights into the performance and behavior of your microservices. Remember to tailor the configuration to your specific needs and choose the right backends for your analysis requirements. Using OpenTelemetry will significantly improve the observability of your applications and help you troubleshoot issues more effectively. 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EngineResourceServerAttestationServiceIDPFederationMasterUserUser AgentMailUser AgentClientAndroid TEEPDPAuthSPEPHTTP ProxyPDPPolicy EngineResourceServerAttestationServiceIDPFederationMasterClient Registration (with Client Attestation and Email)User Starts Registrationalt[Android Attestation][iOS Attestation]Generate Key Pairfor AttestationUsing Android TEE or iOS Secure EnclavePublic KeyRequest Attestation ChallengeAttestation ChallengeSign Challenge withAttestation KeyUsing Android SafetyNet/Play Integrity oriOS DeviceCheck/App Attest APIAttestation StatementiOS Attestation with App Attest API orDeviceCheck APIClient Registration Requestclient_instance.yamlIncludes attestation statement, public key,User Email and software statementVerify Client AttestationAS A forwards attestation datato Attestation ServiceValidate AttestationStatementAttestation Verification ResultVerify Email Confirmation JWTalt[Email Confirmation Required][Email Confirmation already done]Generate ConfirmationLink and send EmailReceive EmailClick ConfirmationLink in EmailOpenConfirmationLinkEmail ConfirmationRequestVerify EmailConfirmationRequestGenerate EmailConfirmation JWTJWT Claims:- iss: AS_A_ID- sub: Client_id- aud: (all AS)- exp: (Policy Engine decision)- iat: (now)- Email_verified: trueRequest ClientRegistration DecisionAS A sends input data to Policy Engine Afor registration requestEvaluate Policy basedon Input DataClient RegistrationDecision (Permit/Deny)Client Registration Response(client_id, Email Confirmation JWT)Request ClientRegistration DecisionAS A sends input data to Policy Engine Afor registration requestEvaluate Policy basedon Input DataClient RegistrationDecision (Permit/Deny)Client Registration Response(client_id)OAuth 2.0 Authorization Code Flow with PAR, PKCE and DPoPGenerate PKCECode VerifierGenerate PKCECode ChallengeGenerate DPoP Key PairPAR Request(client_id, redirect_uri, scope, etc., dpop_jkt)Authorization Code Request(inkl. DPoP Proof, code_challenge, code_challenge_method, redirect_uri)Validate DPoP ProofRequest URINavigate to Request URIAuthorization Request (with Request URI)PAR Request (OpenID Connect), redirect_uri, client_id_idpsekAS A acts as Relying Partyfor the IDP(client_id_idpsek)PAR Response, request_uri, expires_inRedirect to IDP, request_uriNavigate to request_uriAuthentication Prompt, consentUser Credentials, consentRedirect to AuthS, auth_code, redirect_uriRedirect to AuthS, auth_code, redirect_uriToken Request (Authorization Code Grant), auth_codeValidateAuthorizationCodeAuthentication Response (ID Token)ValidateID TokenAuthorization CodeRedirect with Authorization CodeGenerateDPoP Proof JWTToken Request (Authorization Code Grant)Enthält Authorization Code, DPoP Proof,client_id, redirect_uri, code_verifierValidateAuthorization CodeValidate DPoP ProofValidate PKCECode VerifierRequest Token Issuance DecisionAS A sends input data to Policy Engine Afor token requestEvaluate Policy basedon Input DataToken Issuance Decision (Permit/Deny)Access Token, Refresh TokenAccess Token bound toclient's DPoP public keyGenerate DPoP TokenAccess Protected Resource(with Access Token and DPoP Proof)Validate Access Tokenand DPoP ProofForward Request toResource Server AResource DataResource Data \ No newline at end of file