DeepFlow: Observability for Cloud & AI Applications
DeepFlow is an open-source project designed to provide deep observability for complex cloud-native and AI applications. With a focus on Zero Code implementation, DeepFlow uses eBPF technology to collect essential data like metrics, distributed tracing, request logs, and function profiling. Its smart integration with SmartEncoding ensures comprehensive and efficient access to all observability data, making it a go-to solution for DevOps and SRE teams.
Key Features
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Universal Map for Any Service: DeepFlow offers a universal map using eBPF, enabling Zero Code data collection across various services, from application to infrastructure. It supports Wasm plugins for private protocols and calculates performance signals for a full-stack overview, identifying bottlenecks effortlessly.
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Distributed Tracing for Any Request: With DeepFlow, distributed tracing requires no instrumentation, supporting applications and infrastructures in all languages. This feature includes gateways, databases, message queues, and more, ensuring comprehensive data collection without missing any blind spots.
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Continuous Profiling for Any Function: Profiling data is collected with minimal overhead, under 1% of system resources. DeepFlow visualizes function call stacks, helping locate performance issues across various types from business functions to kernel functions, seamlessly connecting them to trace data.
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Seamless Integration with Popular Stacks: DeepFlow can integrate with systems like Prometheus, OpenTelemetry, SkyWalking, and Pyroscope. It provides SQL, PromQL, and OLTP APIs, functioning as a data source in observability stacks, thus breaking down data silos.
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Performance with 10x Efficiency of ClickHouse: Through SmartEncoding, DeepFlow optimizes storage, reducing overhead significantly compared to traditional methods by injecting meta tags, allowing limitless dimension and cardinality exploration akin to BigTable.
Editions of DeepFlow
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DeepFlow Community: The community edition offers core functionality, free for developers to explore and deploy. It includes a comprehensive deployment guide and an interactive demo for firsthand experience.
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DeepFlow Enterprise: Tailored for organizational use, solving team collaboration challenges with advanced features suitable for enterprise environments.
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DeepFlow Cloud: Currently in beta, this SaaS edition offers effortless cloud access to DeepFlow's suite of tools.
Software Architecture
The DeepFlow Community Edition consists of two main components: Agent and Server. The Agent collects data on K8s nodes, legacy hosts, and cloud hosts, while the Server manages the data and provides query services. This architecture ensures efficient collection and management of observability data.
Future Plans and Contact
DeepFlow's roadmap includes exciting features, inviting contributions from the community. For more interactions, DeepFlow can be reached through Discord, Twitter, and WeChat. The project acknowledges technologies like eBPF and OpenTelemetry for their foundational support.
Designed to simplify the process of gaining observability into complex systems, DeepFlow is a significant player in the cloud-native landscape, offering innovative solutions for modern development and operations teams.