hyperdxio/hyperdx

Features

• Centralized Logging Systems - Aggregates logs from cloud environments, containers, and third-party shippers into one searchable place.
• OpenTelemetry Ingestion - Provides a vendor-neutral ingestion system for collecting logs and traces using the OpenTelemetry Protocol.
• Telemetry Collection and Aggregation - Functions as a unified platform for collecting, aggregating, and visualizing logs, metrics, and traces across distributed environments.
• Schema-Less - Processes and indexes structured JSON logs dynamically without requiring predefined database schemas.
• LLM Observability - Provides monitoring and tracing tools specifically designed to track performance and debug large language model applications.
• LLM Tracing Systems - Captures and sends traces from language model workflows to a centralized platform for debugging.
• Structured Log Parsing - Implements a schema-less processing system to dynamically parse and index structured JSON logs.
• Full Text Search - Provides high-performance full-text search for locating specific logs and spans across large telemetry datasets.
• Log Tenant Isolation - Ensures security and privacy by separating telemetry data into distinct logical partitions per account.
• Telemetry Query Languages - Allows users to filter and analyze telemetry data using standard SQL expressions and where clauses.
• Self-Hosted Deployment Platforms - Supports installation on private infrastructure to provide full control over sensitive telemetry data.
• Telemetry Collectors - Employs a collector layer to route observability data to a remote backend, minimizing application latency.
• Identity and Access Management - Implements a secure identity and access management system supporting SAML and single sign-on.
• Telemetry Correlation Workflows - Collects and correlates logs, metrics, and traces from frontend and backend systems for end-to-end observability.
• CloudWatch Log Exporting - Implements a mechanism to stream logs from AWS CloudWatch into the observability platform.
• Log Routing - Provides dedicated filter configurations to route events from Fluent Bit processors to the platform.
• Application Logging - Provides mechanisms to send application-level logs to a central store for diagnosing system behavior.
• Distributed Tracing Systems - Implements a system for tracking requests across microservices and frontend applications to correlate logs and spans.
• End-to-End Message Tracing - Tracks requests from the frontend client through all backend services to provide a complete execution path.
• Framework Instrumentation - Offers pre-built integrations for automatically capturing telemetry from common web frameworks and database drivers.
• Frontend and Backend Observability - Instruments JavaScript runtimes in browsers to capture network requests and exceptions for correlation with backend telemetry.
• LLM Execution Tracing - Captures full LLM execution context, including prompts and tool calls, for detailed request tracing.
• Log Ingestion - Provides mechanisms for collecting and importing log data from AWS SNS for centralized analysis.
• Log Streaming - Provides log drain integration to stream application logs from Heroku cloud environments in real-time.
• Container Log Streamers - Integrates with container runtimes to forward logs directly to centralized storage using native drivers.
• Logging and Telemetry - Acts as a centralized store for ingesting, searching, and analyzing structured and unstructured logs.
• Cloud Log Shippers - Ships a specialized shipper to route application logs from Fly.io environments to the platform.
• System Metrics Collection - Gathers host-level and application performance metrics for centralized system analysis.
• Observability Platforms - Provides a centralized platform for collecting, indexing, and visualizing logs, metrics, and traces using OpenTelemetry standards.
• Distributed Tracing - Tracks requests across multiple services and serverless functions using OpenTelemetry standards to visualize execution paths.
• Log Forwarders - Implements log aggregator configurations that forward events from Fluentd for indexing and analysis.
• Observability Platform Log Exporting - Provides a log drain integration to route server logs from Vercel cloud deployments to the platform.
• Kubernetes Monitors - Collects logs, metrics, and events specifically from Kubernetes pods, nodes, and containers.
• OpenTelemetry Standard Integrations - Captures logs, API requests, and database queries using vendor-neutral standards to avoid lock-in.
• Self-Hosted Infrastructure Platforms - Provides a private observability stack deployed via Docker to maintain full control over system data.
• Telemetry Agents - Uses a dedicated collector layer to buffer and forward telemetry data to minimize application latency.
• Telemetry Correlation - Links logs and traces via shared identifiers to allow seamless jumping between related events.
• Telemetry Ingestion - Implements a collection system for telemetry data from container orchestrators, virtual machines, and language SDKs.
• Session Recording - Records and replays user interactions in the browser to visually reconstruct and debug the user experience.
• Telemetry Visualization - Aggregates and visualizes logs, traces, and metrics in a unified interface for cross-linked searching.
• Charts and Visualization - Graphs and aggregates telemetry events into custom charts and dashboards to monitor system health.
• Custom Event Tracking - Provides mechanisms to record specific user actions and associated metadata to monitor application milestones.
• Attribute Filtering - Provides search capabilities to isolate events using key-value pairs, numeric ranges, and property checks.
• Event Tagging - Associates identifiers like user IDs or emails with telemetry spans and logs for targeted filtering.
• Raw SQL Execution - Allows the execution of raw SQL WHERE clauses for advanced filtering of telemetry data.
• Container Metric Collectors - Provides sidecar containers to collect resource usage statistics and logs from Kubernetes orchestration platforms.
• Docker Container Deployments - Provides a pre-configured container stack that deploys the collector and server using a single Docker image.
• Log Event Clustering - Implements automated clustering to aggregate large volumes of logs into recurring patterns for faster analysis.
• Serverless Telemetry - Provides an SDK-based system that captures logs, traces, and exceptions from serverless functions.
• Sidecar Containers - Deploys companion containers to gather and forward logs without modifying the primary application process.
• React Native Integrations - Captures network requests, exceptions, and navigation events in React Native apps to correlate with backend events.
• Network Traffic Analyzers - Captures and records HTTP request headers and body payloads to debug network interactions.
• Enterprise Identity Providers - Integrates with enterprise identity providers using SAML protocols to manage user authentication at scale.
• SAML SSO Integrations - Provides authentication via SAML protocols for delegating identity management to external enterprise providers.
• Session Replays - Records user interactions and links them to backend telemetry to visually reconstruct and resolve frontend errors.
• Alert Routing - Implements a system for routing alerts to various external communication channels like chat apps and email.
• Alerting Systems - Triggers notifications when the volume of logs or spans matching a query crosses a specific threshold.
• Anomaly Detection - Detects unusual patterns in telemetry data to send proactive notifications regarding system anomalies.
• Java Integrations - Provides an automatic tracing system for Java services that captures request flows without manual code changes.
• AWS Lambda Integrations - Collects logs, metrics, and traces from AWS Lambda functions using language layers to avoid manual instrumentation.
• Error Tracking - Records uncaught exceptions and manual errors with full stack traces and original code context.
• Source Map Deobfuscators - Translates minified production stack traces back to original source code using uploaded source maps.
• Go Integrations - Implements tracing integration for Go services using standard HTTP servers and web frameworks.
• Automated Log Group Discovery - Automatically scans cloud regions for new log groups and updates forwarding settings for streaming.
• LLM Performance Monitoring - Tracks the performance and execution of language model workflows through specialized tracing and metrics dashboards.
• AI Workload Dashboards - Ships pre-configured dashboards that display metrics and traces to track the health of AI workloads.
• Performance Visualization - Provides visual dashboards for monitoring real-time performance data, service latency, and execution traces.
• Telemetry Metadata Definitions - Attaches resource tags such as pod names and namespaces to all ingested logs, metrics, and traces.
• Observability Tracing - Records execution spans and traces for serverless functions to monitor backend execution.
• Performance Trend Analysis - Provides interfaces for aggregating and analyzing performance patterns and user interactions over time.
• Visual Graphing - Includes a chart builder to graph logs, metrics, and traces for trend analysis and error grouping.
• Threshold Monitoring - Monitors searches or charts for threshold breaches and sends notifications to integrated communication channels.
• Time-Window Filtering - Features a tool to filter search results using natural language or visual histograms to define precise time windows.
• Session Replay Tools - Records user interactions in the browser and links them to backend telemetry for debugging.
• User Metadata Management - Attaches custom identity attributes and metadata to sessions for precise telemetry filtering.

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