DeepTrace Documentation

Welcome to the comprehensive documentation for DeepTrace - a cutting-edge, non-intrusive distributed tracing framework designed specifically for microservices architectures.

What is DeepTrace?

DeepTrace is a revolutionary distributed tracing framework that enables accurate end-to-end observation of request execution paths in microservices environments without requiring any code instrumentation. By leveraging advanced eBPF technology and intelligent transaction semantics, DeepTrace achieves over 95% tracing accuracy even under high concurrency scenarios.

Key Innovations

🚀 Non-Intrusive Design
No code changes required - DeepTrace works out of the box with your existing applications

🔍 Protocol-Aware Intelligence
Supports 20+ application protocols (HTTP, gRPC, Redis, MongoDB, etc.) with intelligent parsing

🧠 Transaction-Based Correlation
Uses dual-phase transaction inference with API affinity and persistent field similarity

⚡ High Performance
94% reduction in transmission overhead compared to traditional tracing frameworks

Architecture Overview

DeepTrace consists of two main components:

Agent: Deployed on each host, responsible for non-intrusive request collection and span correlation through eBPF
Server: Runs in Kubernetes clusters, handles trace assembly from correlated spans and provides query services

graph TB
    subgraph "Host 1"
        A1[Application 1]
        A2[Application 2]
        AG1[DeepTrace Agent]
        A1 --> AG1
        A2 --> AG1
    end
    
    subgraph "Host 2"
        A3[Application 3]
        A4[Application 4]
        AG2[DeepTrace Agent]
        A3 --> AG2
        A4 --> AG2
    end
    
    subgraph "DeepTrace Server Cluster"
        S[DeepTrace Server]
        DB[(Elasticsearch)]
        WEB[Web Interface]
        DB --> S
        S --> WEB
    end
    
    AG1 --> DB
    AG2 --> DB

Core Features

1. Protocol-Aware Span Construction

eBPF-based packet capture for non-intrusive monitoring
Protocol templates for accurate parsing of 20+ protocols
Smart request boundary detection using length-field jumps and full parsing
Efficient span creation with critical metadata extraction

2. Transaction-Based Span Correlation

Nested API affinity: Traffic intensity correlations using Pearson coefficients
Persistent field similarity: TF-IDF-weighted cosine similarity for transaction field isolation
Entropy-weighted adaptive scoring: Intelligent fusion of transaction semantics and causality metrics
15% reduction in misattributions compared to traditional delay/FIFO methods