elastic/apm-server
Apm Server
The server acts as a centralized ingestion engine designed to collect, normalize, and index distributed telemetry data. It functions as a backend processor that receives performance metrics, traces, and error logs from application agents, transforming them into structured documents for storage and analysis within search and analytics platforms.
The system distinguishes itself through a high-throughput ingestion pipeline that utilizes asynchronous event processing and backpressure-aware flow control to maintain stability during traffic spikes. It employs modular, plugin-based transformation stages to sanitize and enrich incoming payloads, ensuring that heterogeneous data is mapped into a unified schema before being committed to the backend via optimized bulk indexing.
Beyond core ingestion, the project provides comprehensive observability capabilities, including distributed request tracing, service dependency mapping, and application error tracking. It supports specialized monitoring for mobile instrumentation and language model applications, while offering diagnostic tools for performance anomaly correlation and system-level metrics collection.
The repository includes a suite of utilities for local development orchestration, container synchronization, and automated testing of deployment upgrade paths.
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.
