Distributed Tracing

Traces represent the entire journey of a request through your application. Spans, on the other hand, represent individual operations within the request, such as database queries, HTTP requests, or function calls. Spans are organized hierarchically to represent the flow of execution within your application.

Distributed tracing offers several benefits for understanding and troubleshooting complex systems in distributed environments:

• Holistic view of transactions across multiple services and components involved in a request
• Captures detailed timing information for each span or operation within a transaction
• Pinpoint slow database queries, inefficient API calls, or high latency network connections
• Trace the flow of requests and identifying the exact service or component responsible for the issue and redecue MTTR
• Automatically maps out service dependencies by capturing the interactions between different services
• Propagate contextual information such as request IDs, headers, and metadata across service boundaries

Atatus ensures minimal overhead with Distributed Tracing instrumentation through techniques like intelligent sampling to reduce data volume, efficient data collection methods to minimize resource usage, and asynchronous processing for batching data transmission, all aimed at minimizing impact on application performance while still providing comprehensive tracing capabilities.

Distributed tracing provides end-to-end visibility into requests as they traverse through various microservices. It captures detailed information about each transaction, including latency, errors, and dependencies. By analyzing distributed traces, enterprises can pinpoint performance bottlenecks, understand the flow of requests across services, and optimize resource utilization to improve overall system performance.

Yes, Atatus allows you to customize traces and spans according to your specific requirements. You can add custom tags, metadata, and annotations to traces and spans to provide additional context and insights into your application's behavior.

Atatus uses various propagation mechanisms, such as HTTP headers (e.g., Trace Context headers), to propagate trace and span context between different services. This ensures that the entire trace is correlated and aggregated correctly, even as requests traverse multiple services.

Atatus uses standard context propagation mechanisms like HTTP headers (e.g., Trace Context headers) or messaging protocols (e.g., AMQP, Kafka) to propagate trace and span context between services.

When a request enters a service, it extracts trace context from incoming requests and injects it into outgoing requests to ensure continuity of the trace across service boundaries. This allows Atatus to correlate and aggregate data from multiple services into a single trace.

Yes, Atatus allows you to correlate distributed traces with logs and metrics collected from your applications and infrastructure. By tagging logs and metrics with trace identifiers or other contextual information, you can easily navigate between different data sources to troubleshoot and diagnose issues across your distributed system.

Atatus supports a wide range of products across various domains to help organizations monitor and optimize their systems and applications.

• Application Performance Monitoring
• Real User Monitoring
• Synthetic Monitoring
• Logs Monitoring
• Infrastructure Monitoring
• API Analytics
• Database Monitoring
• Kubernetes Monitoring