Distributed Tracing
7 articles from official documentation
Maximize Your Trace Budget with Volumetric Sampling in Grafana Cloud
Struggling with trace budget limitations? Volumetric sampling in Grafana Cloud dynamically optimizes your sampling strategy, ensuring you capture the most relevant traces. This approach categorizes traces and adjusts sample rates intelligently, freeing you from the hassle of manual policies.
- →Specify a percentage target to let the volumetric policy manage trace sampling automatically.
- →Understand that the policy categorizes traces into buckets based on attributes, tracking frequency and sample rates.
Tempo 3.0: Unlocking Scalable Observability with TraceQL Metrics
Tempo 3.0 redefines observability with a Kafka-compatible architecture that enhances scalability and lowers total cost of ownership. Dive into how TraceQL metrics allow you to query performance data directly from traces, making troubleshooting a breeze.
- →Leverage the Kafka-compatible architecture for scalable trace ingestion and querying.
- →Utilize TraceQL metrics to query performance data directly from trace data.
Mastering Jaeger Tracing: Deployment Insights for Production
Jaeger tracing is crucial for observability in microservices, helping you pinpoint performance bottlenecks. Learn how to deploy Jaeger effectively, including the importance of clock skew adjustment and storage configurations.
- →Configure `--query.max-clock-skew-adjustment` to manage clock drift effectively.
- →Use `SPAN_STORAGE_TYPE` to select the appropriate storage backend for your needs.
Decoding Jaeger's Architecture: Tracing Data Model Insights
Jaeger is essential for observability in microservices, providing deep insights into system performance. Understanding its tracing data model, including spans and tags, is crucial for effective debugging and monitoring. Dive into how Jaeger structures tracing data to optimize your observability strategy.
- →Understand spans as logical units of work with operation names and durations.
- →Utilize tags for attributes, but remember that nested tags are not supported.
Mastering OTLP Exporter Configuration for Tracing
Get your tracing data flowing smoothly with OTLP exporter configuration. Learn how to set up endpoints for traces, metrics, and logs with specific environment variables. This article dives into the details that can make or break your observability strategy.
- →Set the base endpoint using `OTEL_EXPORTER_OTLP_ENDPOINT` for all signal types.
- →Use specific endpoints for traces, metrics, logs, and profiles to optimize data flow.
Mastering Context Propagation for Effective Tracing
Context propagation is crucial for tracing in microservices, ensuring that signals from one service correlate with another. By using the W3C TraceContext specification, you can effectively manage context across service boundaries.
- →Understand context as an object that correlates signals between services.
- →Use the W3C TraceContext specification for standardized context propagation.
Mastering Async Workflow Observability in Dapr
Async workflows can be a black box, making observability a challenge. By leveraging W3C trace context, Dapr enhances the visibility of your workflows, allowing you to track execution across systems. Dive into how durable execution and context propagation can transform your monitoring strategy.
- →Enable durabletask-go to track workflow orchestrations effectively.
- →Serialize W3C context into workflow activity messages for better traceability.
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