System Overview

DeepTrace is a sophisticated distributed tracing framework designed for modern microservices architectures. This document provides a comprehensive overview of the system architecture, core components, and design principles.

Architecture Philosophy

DeepTrace is built on several key architectural principles:

1. Non-Intrusive Design

• Zero Code Changes: Applications require no modification
• eBPF-Based: Leverages kernel-level instrumentation
• Transparent Operation: Minimal impact on application behavior

2. Scalable Architecture

• Distributed Components: Agent-server architecture for scalability
• Horizontal Scaling: Components scale independently
• Efficient Data Flow: Optimized for high-throughput environments

3. Intelligent Correlation

• Transaction Semantics: Uses application-level transaction logic
• Multi-Dimensional Analysis: Combines temporal and semantic correlation
• Adaptive Algorithms: Adjusts to different application patterns

High-Level Architecture

graph TB
    subgraph "Microservices Cluster"
        subgraph "Host 1"
            APP1[Service A]
            APP2[Service B]
            AGENT1[DeepTrace Agent]
            APP1 -.-> AGENT1
            APP2 -.-> AGENT1
        end
        
        subgraph "Host 2"
            APP3[Service C]
            APP4[Service D]
            AGENT2[DeepTrace Agent]
            APP3 -.-> AGENT2
            APP4 -.-> AGENT2
        end
        
        subgraph "Host N"
            APPN[Service N]
            AGENTN[DeepTrace Agent]
            APPN -.-> AGENTN
        end
    end
    
    subgraph "DeepTrace Infrastructure"
        subgraph "Server Cluster"
            SERVER[DeepTrace Server]
            ASSEMBLER[Trace Assembler]
            API[Query API]
        end
        
        subgraph "Storage Layer"
            ES[(Elasticsearch)]
            CACHE[(Redis Cache)]
        end
        
        subgraph "Interface Layer"
            WEB[Web Dashboard]
            CLI[CLI Tools]
        end
    end
    
    AGENT1 --> ES
    AGENT2 --> ES
    AGENTN --> ES
    
    ES --> SERVER
    SERVER --> ASSEMBLER
    ASSEMBLER --> ES
    
    SERVER --> API
    API --> WEB
    API --> CLI
    
    ES --> WEB
    CACHE --> API

Core Components

1. DeepTrace Agent

The agent is deployed on each host and is responsible for:

Data Collection

• eBPF Programs: Kernel-level network monitoring
• System Call Interception: Captures network I/O operations
• Protocol Parsing: Extracts application-layer information
• Metadata Extraction: Collects timing and context information

Local Processing

• Span Construction: Builds individual request/response spans
• Span Correlation: Correlates related spans using transaction semantics
• Data Compression: Reduces transmission overhead
• Local Buffering: Handles temporary network issues
• Process Filtering: Monitors only relevant applications

Communication

• Direct Storage: Sends constructed spans directly to Elasticsearch
• Batch Processing: Efficient bulk data transmission to storage
• Connection Management: Maintains Elasticsearch connection health
• Configuration Management: Receives configuration from management interface

2. DeepTrace Server

The server provides centralized processing and management:

Data Management

• Data Retrieval: Pulls correlated spans from Elasticsearch for assembly
• Validation: Ensures data integrity and completeness during retrieval
• Query Optimization: Efficiently queries spans for trace assembly
• Batch Processing: Processes spans in optimized batches

Trace Assembly

• Graph Construction: Builds trace dependency graphs from correlated spans
• Path Analysis: Identifies complete request paths
• Optimization: Removes redundant or incorrect trace connections
• Validation: Ensures trace completeness and accuracy

3. Storage Layer

Elasticsearch Cluster

• Primary Storage: Stores all span and trace data
• Full-Text Search: Enables complex queries
• Time-Series Optimization: Efficient time-based queries
• Scalable Storage: Handles large data volumes

Redis Cache

• Query Acceleration: Caches frequent queries
• Session Management: Handles user sessions
• Real-Time Data: Stores live monitoring data
• Configuration Cache: Caches system configuration

4. Interface Layer

Web Dashboard

• Trace Visualization: Interactive trace exploration
• Service Maps: Dependency visualization
• Performance Metrics: Real-time performance monitoring
• Alert Management: Configurable alerting system

CLI Tools

• System Management: Command-line administration
• Batch Operations: Bulk data processing
• Automation: Scriptable operations
• Debugging: Diagnostic and troubleshooting tools

Data Flow Architecture

1. Span Collection and Correlation Flow

sequenceDiagram
    participant App as Application
    participant eBPF as eBPF Program
    participant Agent as DeepTrace Agent
    participant ES as Elasticsearch
    
    App->>eBPF: Network System Call
    eBPF->>eBPF: Extract Metadata
    eBPF->>Agent: Send Raw Data
    Agent->>Agent: Construct Span
    Agent->>Agent: Correlate Spans
    Agent->>Agent: Process & Buffer
    Agent->>ES: Store Correlated Spans

2. Trace Assembly Flow

sequenceDiagram
    participant Server as DeepTrace Server
    participant ES as Elasticsearch
    participant Assembler as Trace Assembler
    
    Server->>ES: Query Correlated Spans
    ES->>Server: Return Correlated Span Data
    Server->>Assembler: Process Correlated Spans
    Assembler->>Assembler: Build Complete Traces
    Assembler->>ES: Store Assembled Traces

3. Query Flow

sequenceDiagram
    participant User as User
    participant Web as Web Dashboard
    participant API as Query API
    participant Cache as Redis Cache
    participant ES as Elasticsearch
    
    User->>Web: Submit Query
    Web->>API: API Request
    API->>Cache: Check Cache
    alt Cache Hit
        Cache->>API: Return Cached Data
    else Cache Miss
        API->>ES: Execute Query
        ES->>API: Return Results
        API->>Cache: Cache Results
    end
    API->>Web: Return Data
    Web->>User: Display Results

Deployment Architectures

1. Single Host Deployment

Use Cases: Development, testing, small-scale deployments

graph TB
    subgraph "Single Host"
        APPS[Applications]
        AGENT[Agent]
        SERVER[Server]
        ES[Elasticsearch]
        WEB[Web UI]
        
        APPS --> AGENT
        AGENT --> ES
        ES --> SERVER
        SERVER --> ES
        ES --> WEB
    end

Characteristics:

• Simplified deployment and management
• Lower resource requirements
• Limited scalability
• Suitable for evaluation and development

2. Distributed Deployment

Use Cases: Production environments, large-scale systems

graph TB
    subgraph "Application Hosts"
        HOST1[Host 1<br/>Apps + Agent]
        HOST2[Host 2<br/>Apps + Agent]
        HOSTN[Host N<br/>Apps + Agent]
    end
    
    subgraph "DeepTrace Cluster"
        LB[Load Balancer]
        SERVER1[Server 1]
        SERVER2[Server 2]
        SERVERN[Server N]
    end
    
    subgraph "Storage Cluster"
        ES1[(ES Node 1)]
        ES2[(ES Node 2)]
        ESN[(ES Node N)]
    end
    
    HOST1 --> ES1
    HOST2 --> ES2
    HOSTN --> ESN
    
    ES1 --> LB
    ES2 --> LB
    ESN --> LB
    
    LB --> SERVER1
    LB --> SERVER2
    LB --> SERVERN
    
    SERVER1 --> ES1
    SERVER2 --> ES2
    SERVERN --> ESN

Characteristics:

• High availability and fault tolerance
• Horizontal scalability
• Load distribution
• Production-ready architecture

3. Kubernetes Deployment

Use Cases: Container orchestration environments

graph TB
    subgraph "Kubernetes Cluster"
        subgraph "Application Namespace"
            PODS[Application Pods]
            AGENTS[Agent DaemonSet]
        end
        
        subgraph "DeepTrace Namespace"
            SERVERS[Server Deployment]
            CONFIG[ConfigMaps]
            SECRETS[Secrets]
        end
        
        subgraph "Storage Namespace"
            ES_CLUSTER[Elasticsearch StatefulSet]
            PV[Persistent Volumes]
        end
    end
    
    PODS -.-> AGENTS
    AGENTS --> ES_CLUSTER
    ES_CLUSTER --> SERVERS
    SERVERS --> ES_CLUSTER
    CONFIG --> SERVERS
    SECRETS --> SERVERS
    ES_CLUSTER --> PV

Characteristics:

• Native Kubernetes integration
• Automatic scaling and healing
• Resource management
• Service discovery integration

Scalability Considerations

1. Agent Scalability

Horizontal Scaling

• Per-Host Deployment: One agent per host
• Process Isolation: Independent agent processes
• Resource Limits: Configurable resource constraints
• Load Distribution: Automatic workload balancing

Vertical Scaling

• Multi-Threading: Parallel span processing
• Memory Management: Efficient memory utilization
• CPU Optimization: Optimized eBPF programs
• I/O Efficiency: Batched network operations

2. Server Scalability

Horizontal Scaling

• Stateless Design: Servers can be added/removed dynamically
• Load Balancing: Distribute agent connections
• Partition Tolerance: Handle network partitions gracefully
• Auto-Scaling: Kubernetes-based automatic scaling

Vertical Scaling

• Parallel Processing: Multi-threaded correlation algorithms
• Memory Optimization: Efficient data structures
• CPU Utilization: Optimized algorithms
• Storage Optimization: Efficient Elasticsearch usage

3. Storage Scalability

Data Partitioning

• Time-Based Sharding: Partition by time periods
• Service-Based Sharding: Partition by service
• Hash-Based Sharding: Distribute by hash functions
• Hybrid Approaches: Combine multiple strategies

Performance Optimization

• Index Optimization: Efficient query indexes
• Compression: Data compression strategies
• Caching: Multi-level caching
• Archival: Automated data lifecycle management

Security Architecture

1. Data Protection

Encryption

• In-Transit: TLS encryption for all communications
• At-Rest: Elasticsearch encryption
• Key Management: Secure key rotation
• Certificate Management: Automated certificate lifecycle

Access Control

• Authentication: Multi-factor authentication
• Authorization: Role-based access control
• API Security: Secure API endpoints
• Audit Logging: Comprehensive audit trails

2. Network Security

Network Isolation

• VPC/VNET: Private network deployment
• Firewall Rules: Restrictive network policies
• Service Mesh: Encrypted service communication
• Network Monitoring: Traffic analysis and monitoring

Endpoint Security

• Agent Security: Secure agent deployment
• Server Hardening: Security-hardened servers
• Container Security: Secure container images
• Vulnerability Management: Regular security updates

Performance Characteristics

1. Throughput Metrics

2. Latency Metrics

This architectural overview provides the foundation for understanding DeepTrace's design and implementation. The modular, scalable architecture enables deployment across a wide range of environments while maintaining high performance and reliability.