elastic/apm-server
Apm Server
Le serveur agit comme un moteur d'ingestion centralisé conçu pour collecter, normaliser et indexer les données de télémétrie distribuées. Il fonctionne comme un processeur backend qui reçoit des métriques de performance, des traces et des journaux d'erreurs provenant d'agents applicatifs, les transformant en documents structurés pour le stockage et l'analyse au sein de plateformes de recherche et d'analyse.
Le système se distingue par un pipeline d'ingestion à haut débit qui utilise le traitement d'événements asynchrone et le contrôle de flux conscient de la contre-pression pour maintenir la stabilité lors des pics de trafic. Il emploie des étapes de transformation modulaires basées sur des plugins pour assainir et enrichir les charges utiles entrantes, garantissant que les données hétérogènes sont mappées dans un schéma unifié avant d'être validées dans le backend via une indexation en masse optimisée.
Au-delà de l'ingestion de base, le projet fournit des capacités d'observabilité complètes, incluant le traçage de requêtes distribuées, la cartographie des dépendances de service et le suivi des erreurs applicatives. Il prend en charge une surveillance spécialisée pour l'instrumentation mobile et les applications de modèles de langage, tout en offrant des outils de diagnostic pour la corrélation des anomalies de performance et la collecte de métriques au niveau du système.
Le dépôt inclut une suite d'utilitaires pour l'orchestration du développement local, la synchronisation des conteneurs et les tests automatisés des chemins de mise à niveau du déploiement.
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.
