elastic/apm-server

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.

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