$ show apache/skywalking
Apache SkyWalking is an open-source APM (Application Performance Monitoring) system designed for microservices, cloud-native, and container-based architectures. It provides distributed tracing, metrics aggregation, log analysis, and profiling capabilities (including eBPF, async-profiler, and JFR). The system features a modular, plugin-based architecture where receivers ingest telemetry data, analyzers process it, storage plugins persist it, and query services expose it via GraphQL, PromQL, LogQL, and TraceQL interfaces. Alarm mechanisms detect anomalies and trigger notifications through multiple channels. The codebase demonstrates strong engineering practices with clear separation of concerns, extensive use of interfaces and dependency injection, and reusable patterns like the module system, storage abstraction, and analysis listener framework.
The LLM identified 5 files worth highlighting for reuse:
Defines the core plugin interface for SkyWalking's modular architecture. Enables dynamic loading of receivers, storage, analyzers, and other components. Demonstrates a clean, extensible plugin pattern where each module declares its services and dependencies. Highly reusable in any Java application requiring plugin-based extensibility.
Abstract data access object interface that defines common storage operations (prepare, insert, delete, etc.) for different data types (traces, metrics, logs). Combined with StorageBuilder and Convert2Entity patterns, it allows switching storage backends (Elasticsearch, BanyanDB, JDBC) by implementing this interface. Shows excellent separation of concerns and is easily extractable for storage abstraction in other projects.
Interface for extending trace analysis with custom logic. Used by the core trace analyzer to notify listeners of span and segment events. Includes specialized listeners (EntryAnalysisListener, ExitAnalysisListener, etc.) and strategy implementations (FromEntrySpan, FromFirstSpan). Demonstrates a powerful observer pattern for adding analysis capabilities without modifying core logic, reusable in any trace processing system.
Processes incoming metric samples with windowing and sampling strategies. Uses SampleBuilder to create metric samples and supports concurrent updates. Shows efficient in-memory aggregation techniques for high-throughput metric streams. The design is self-contained and could be adapted for any metric stream processing scenario requiring windowed aggregations.
Central service for evaluating alarm rules and triggering notifications. Works with AlarmRulesWatcherService to dynamically update rules without restart. Demonstrates a well-structured alarming system that separates rule evaluation from notification handling, supports multiple notification channels, and handles rule updates safely. Reusable as an alerting engine in monitoring systems.