Distributed Tracing

Collects and aggregates trace data from various endpoints to provide a comprehensive view of request flows from frontend devices to backend services and databases, including detailed timing information, service dependencies, and contextual logs.

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End-to-End Trace Visibility

Microservices End-to-End Visibility

Monitor and analyze service requests as they traverse through distributed systems, visualizing the complete path of a request, including its interactions with different services, databases, and external dependencies. Actively track each request's child spans within microservices architecture, using unique trace IDs to correlate related spans and gather comprehensive trace data for each request.

Traces and Spans

Root Cause Analysis with Trace Data

Trace each transaction actively across distributed architecture in real-time, capturing detailed metadata like timing, dependencies, and service interactions. By correlating trace data with error occurrences, developers can precisely reconstruct the sequence of events that led to an error, enabling root cause analysis with unprecedented accuracy while monitoring response time and error rates.

Service Dependency Mapping

Service Dependency Mapping

Map out closely related microservice relationships automatically, visualize dependencies graphically, and analyze the potential impact of changes or breakdowns in one service on other dependent services. Identify critical service paths within CI/CD pipelines to ensure secure and regular deployment of modifications.

Distributed Tracing Logs

Correlate Logs with Traces

Integrate granular event data captured in logs with high-level request paths depicted in traces, enabling them to efficiently troubleshoot and debug issues, identify root causes across service boundaries, and optimize system performance through comprehensive visibility and contextual insights.

FAQs on Distributed Tracing

What is Distributed Tracing?

Distributed Tracing is a method for monitoring and profiling applications, especially those built using a microservices architecture. It allows you to track a request as it travels through various services in your application, providing insights into the performance and behavior of each service involved.

What are traces and spans in 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.

What are the benefits of Distributed Tracing?

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

  1. Holistic view of transactions across multiple services and components involved in a request
  2. Captures detailed timing information for each span or operation within a transaction
  3. Pinpoint slow database queries, inefficient API calls, or high latency network connections
  4. Trace the flow of requests and identifying the exact service or component responsible for the issue and redecue MTTR
  5. Automatically maps out service dependencies by capturing the interactions between different services
  6. Propagate contextual information such as request IDs, headers, and metadata across service boundaries
How does Atatus ensure minimal overhead with Distributed Tracing instrumentation?

Atatu 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.

How does distributed tracing help in identifying performance bottlenecks in complex microservices architectures?

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.

Can I customize traces and spans in Atatus?

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.

How does Atatus handle distributed context propagation?

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.

How does Atatus handle context propagation between different services in a distributed environment?

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.

Can I correlate distributed traces with logs and metrics in Atatus?

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.

What products does Atatus provide?

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

  1. Application Performance Monitoring
  2. Real User Monitoring
  3. Synthetic Monitoring
  4. Logs Monitoring
  5. Infrastructure Monitoring
  6. API Analytics
  7. Database Monitoring
  8. Kubernetes Monitoring

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