edge-detection

Mapping integration edges in microservices architecture requires analyzing code dependencies to detect external services like databases, queues, and APIs. This process identifies native integration patterns and generates specific nodes for each detected resource, creating an enriched knowledge graph of your system.

Code dependency analysis for external service detection scans your codebase and dependency manifests to identify integration points. It recognizes common patterns like HTTP calls and database connections, creating specific nodes for detected cloud services, SDKs, and APIs rather than generic placeholders.

Visualizing microservices dependencies automatically involves detecting integration edges by analyzing code. This approach discovers REST API calls, database connections, and message queue interactions, outputting an enriched knowledge graph JSON that maps how your services communicate.

A knowledge graph JSON is required as input to analyze code dependencies and integration points. The analysis uses this existing graph as a base, scanning your codebase to detect native integration patterns and outputting an enriched graph with specific nodes for external services.

Code analysis detects specific cloud services and SDKs by recognizing native integration patterns and dependencies. It identifies external resources like S3 buckets or message queues, generating specific nodes for each detected service in the enriched knowledge graph output rather than generic connection placeholders.

Integration patterns identified through code dependency analysis include HTTP calls, database connections, message queues, cloud services, and SDKs. The analysis detects these native patterns by scanning code and dependency manifests, creating specific nodes for each external resource in the knowledge graph.