deepflowio/deepflow

Features

• eBPF-Based Collection - Implements an observability platform that uses eBPF for automatic kernel-level telemetry collection without application instrumentation.
• Unified Observability Data Models - Unifies metrics, logs, and traces into a single data model queryable via SQL and PromQL.
• Continuous Profilers - Continuously samples on-CPU and off-CPU call stacks to generate flame graphs for production performance analysis.
• eBPF-Based Application Profilers - Continuously samples stack traces via eBPF to identify on-CPU and off-CPU performance bottlenecks in compiled-language applications.
• eBPF Tooling - Leverages eBPF tooling to gather system and network performance metrics without modifying application source code.
• Observability Platforms - Provides a full observability platform using eBPF to collect metrics, logs, and traces without manual instrumentation.
• Network Protocol Parsing - Parses binary network protocol streams to extract application-layer metadata and reconstruct distributed request chains.
• Architecture Dependency Mapping - Provides automated visualization of connections and relationships between infrastructure components via network flow data.
• Distributed Request Tracking - Tracks the full lifecycle of requests across gateways, databases, and network interfaces to remove observability blind spots.
• Application Layer Protocol Dissectors - Reconstructs and decodes application-layer protocols from network traffic using eBPF to extract deep performance insights.
• Distributed Tracing - Maps request chains across services and threads using eBPF and protocol extraction to identify bottlenecks.
• Application Performance Profiling - Collects CPU and memory flame graphs in production to identify processing bottlenecks with minimal overhead.
• eBPF Profilers - Leverages eBPF for continuous kernel-level analysis and performance profiling of production processes with minimal overhead.
• Service Dependency Mapping - Automatically discovers and visualizes communication paths and interactions between application services and infrastructure.
• Telemetry Data Stores - Implements a unified backend for storing and querying industry-standard observability data using SQL and PromQL.
• High-Cardinality Metric Metadata - Applies encoding techniques to high-cardinality metric metadata to maintain query performance and reduce storage costs.
• High-Cardinality Optimizations - Reduces storage overhead for high-cardinality telemetry data by compressing and indexing common resource tags.
• Data Storage Optimizers - Optimizes the formatting of metadata tags to minimize storage overhead and support large telemetry datasets.
• Unified Telemetry Backends - Stores diverse telemetry signals in a single repository queryable via both SQL and PromQL interfaces.
• Observability Signal Unifications - Standardizes tags across metrics, logs, and traces to ensure consistent visibility across monitoring stacks.
• Topology Visualizations - Maps service interactions and infrastructure components through automated discovery to visualize and resolve performance bottlenecks.
• Cloud Resource Metadata Detection - Automatically discovers and attaches cloud infrastructure attributes to telemetry to provide operational context.
• Cloud Resource Monitoring - Provides comprehensive monitoring of cloud infrastructure by enriching telemetry with resource metadata.
• Flame Graphs - Generates call stack flame graphs for CPU, GPU, and memory usage to locate bottlenecks across business and kernel functions.
• Observability and Profiling - Cloud-native observability for distributed applications.

Istoric stele