elastic/apm-server
Apm Server
Serverul acționează ca un motor centralizat de ingestie conceput pentru a colecta, normaliza și indexa datele de telemetrie distribuite. Funcționează ca un procesor backend care primește metrici de performanță, urme (traces) și jurnale de erori de la agenții aplicației, transformându-le în documente structurate pentru stocare și analiză în cadrul platformelor de căutare și analiză.
Sistemul se distinge printr-un pipeline de ingestie cu throughput ridicat care utilizează procesarea asincronă a evenimentelor și controlul fluxului conștient de backpressure pentru a menține stabilitatea în timpul vârfurilor de trafic. Utilizează etape de transformare modulare, bazate pe plugin-uri, pentru a igieniza și îmbogăți payload-urile primite, asigurându-se că datele eterogene sunt mapate într-o schemă unificată înainte de a fi comise în backend prin indexare bulk optimizată.
Dincolo de ingestia de bază, proiectul oferă capabilități cuprinzătoare de observabilitate, inclusiv urmărirea distribuită a cererilor, maparea dependențelor serviciilor și urmărirea erorilor aplicației. Suportă monitorizarea specializată pentru instrumentarea mobilă și aplicațiile cu modele de limbaj, oferind în același timp instrumente de diagnosticare pentru corelarea anomaliilor de performanță și colectarea metricilor la nivel de sistem.
Repository-ul include o suită de utilitare pentru orchestrarea dezvoltării locale, sincronizarea containerelor și testarea automată a căilor de upgrade ale implementării.
Features
- Application Performance Monitoring - Provides a centralized ingestion engine for distributed traces, metrics, and logs to monitor software service health.
- Metric Ingestion APIs - Provides high-throughput interfaces for ingesting telemetry and performance metrics from distributed application agents.
- HTTP Metric Ingestion - Receives telemetry data from distributed agents via standardized HTTP requests for high-throughput processing.
- Trace Ingestion - Normalizes and indexes telemetry payloads to enable end-to-end request lifecycle monitoring.
- System Metrics Collection - Gathers host-level and runtime metrics to provide visibility into the underlying health and resource utilization of monitored software services.
- Distributed Request Tracing - Captures and correlates performance data across services and infrastructure to provide end-to-end visibility into request lifecycles.
- Data Observability Platforms - Maps incoming monitoring events into a consistent schema for visualization and analysis within search platforms.
- Language Model Observability - Tracks performance and execution flow of large language model applications to optimize latency and debug integration issues.
- Incoming Data Transformations - Normalizes and maps incoming telemetry events into a consistent schema for efficient storage and analysis.
- Schema-Driven Data Normalizers - Standardizes heterogeneous performance events into a unified document structure for consistent searchability.
- Elasticsearch Data Shippers - Collects and transforms performance telemetry from application agents for storage and analysis in Elasticsearch.
- Bulk Indexing - Groups processed telemetry events into optimized batches to maximize write throughput to the search engine.
- Automated Issue Resolvers - Analyzes application errors and latency using automated insights to accelerate issue investigation and remediation.
- Backpressure Controllers - Regulates incoming data streams based on resource utilization to prevent system overload during high-load periods.
- Asynchronous Processing - Decouples data reception from indexing using background processing to maintain stability during traffic spikes.
- Telemetry Plugins - Uses modular plugins to filter, enrich, and sanitize incoming telemetry payloads before persistence.
- Correlated - Analyzes performance metrics and error patterns using automated logic to identify root causes and surface outliers within complex application environments.
- Mobile Performance Monitoring - Collects performance data from mobile clients and correlates it with backend service traces to provide a unified view of user experience.
- Error Tracking - Collects unhandled exceptions and groups them by stack trace to track software failure frequency.
- System Throughput Benchmarks - Measures throughput and resource consumption under varying event loads to identify potential bottlenecks and assess overall system stability.
- Service Dependency Mapping - Visualizes live interactions between services and infrastructure components to identify bottlenecks and monitor the health of distributed architectures.
- Soak Testing Executions - Generates continuous, sustained workloads over extended periods to identify stability issues and performance degradation in long-running systems.
