Distributed Tracing Platform at Kochi city

End-to-End Trace Visualization: Provides a visual representation of the entire path a request takes through various services and components in the distributed system, allowing users to understand the flow of requests and identify bottlenecks.

Trace Context Propagation: Facilitates the propagation of trace context across different services and components, ensuring that requests can be correlated and traced across the entire system.

Distributed Contextual Logging: Allows for the aggregation and correlation of logs generated by different services involved in processing a request, providing additional context for troubleshooting and debugging.

Trace Sampling: Enables the sampling of trace data to manage the volume of data collected while still providing representative insights into system behavior and performance.

Service Dependency Mapping: Automatically discovers and maps the dependencies between different services and components in the distributed system, helping users understand the architecture and relationships between various elements.

Performance Monitoring and Metrics: Collects and displays performance metrics such as latency, throughput, and error rates for individual services and the system as a whole, allowing users to monitor and optimize system performance.

Error Detection and Alerting: Detects errors and anomalies in the system, such as failed requests or abnormal behavior, and provides alerts to notify users of potential issues that require attention.

Distributed Tracing Standards Support: Supports industry-standard distributed tracing protocols and formats such as OpenTelemetry, Jaeger, and Zipkin, ensuring compatibility and interoperability with other tracing tools and systems.

Instrumentation: Applications and services need to be instrumented to emit trace data. This usually involves adding code to each service to start and propagate a unique trace identifier as requests are processed.

Trace Context Propagation: The platform should provide mechanisms for propagating trace context across different services, often through HTTP headers or messaging protocols.

Data Collection: The platform collects trace data generated by instrumented services. This data typically includes information about the timing and duration of each request, as well as metadata such as service names, endpoints, and any contextual information.

Storage: The trace data needs to be stored in a scalable and efficient manner. This might involve distributed databases, data lakes, or other storage solutions capable of handling large volumes of trace data.

Analysis and Visualization: The platform should provide tools for analyzing and visualizing trace data. This might include dashboards, search capabilities, and visualization tools to help identify performance bottlenecks, error patterns, and other issues.

Alerting and Monitoring: Distributed tracing platforms often include alerting mechanisms to notify operators or developers about performance degradation, errors, or other issues detected in the system.

Integration: Integration with other monitoring and observability tools is essential. This might include integrations with logging systems, metrics platforms, APM (Application Performance Monitoring) tools, and more.

Customizable Dashboards and Reports: Provides customizable dashboards and reporting capabilities, allowing users to create tailored views of trace data and insights specific to their needs and use cases.