#Introduction

Distributed tracing is a key module in the observability system, used for achieving end-to-end tracing and analysis of distributed systems. This module provides a complete solution from data collection, storage to visual analysis, helping developers and operations personnel to quickly locate service call anomalies, analyze performance bottlenecks, and trace the entire lifecycle behavior of requests.

By integrating with open-source technology stacks and self-developed components, this module supports end-to-end distributed tracing capabilities: applications generate tracing data through injection or SDK integration methods, which are then uniformly collected and stored in Elasticsearch, ultimately realized through a customized UI for multi-dimensional visual analysis. Users can conduct precise searches using rich conditions such as TraceID, service name, tags, and more.

#Advantages

The core advantages of tracing are as follows:

• End-to-End Tracing Capability
  Supports complete tracing restoration across services, processes, and container boundaries, accurately presenting complex call relationships in microservice architectures.

• Flexible Data Collection Methods
  Supports service mesh-based automatic tracing through Istio's sidecar proxy injection, which captures service-to-service communication data without any code changes. Additionally provides dual modes of OpenTelemetry automatic injection (no code modification) and SDK integration, compatible with mainstream language applications such as Java/Python/Go.

• High-Performance Storage Solutions
  Utilizes Elasticsearch as the storage backend, supporting the writing and fast retrieval of massive span data.

• Flexible Querying and Analysis Capabilities
  The self-developed UI integrates with the jaeger-query API, supporting flexible queries based on multi-dimensional conditions such as TraceID, service affiliation, tags, and span types, facilitating users in quickly pinpointing root causes of issues.

#Application Scenarios

The main application scenarios of tracing are as follows:

• Distributed System Fault Diagnosis
  In microservice architectures, complete tracing enable quick identification of service faults and anomalous calls, reducing fault diagnosis time.

• Performance Bottleneck Analysis
  By examining the latency between service calls, performance bottlenecks can be identified, guiding system optimization and resource adjustments.

• Service Dependency Analysis
  A time-series waterfall diagram clearly shows the call paths and dependencies between services, assisting architects in system design and improvement.

#Usage Limitations

When using tracing, the following constraints should be noted:

• Balancing Sampling Strategies and Performance
  • In high-load scenarios, the collection of tracing data may exert certain pressure on Elasticsearch's performance and storage; it is recommended to configure the sampling rate reasonably based on business conditions.