# DeepFlow — eBPF Observability for Cloud & AI

> DeepFlow offers zero-code eBPF observability for Kubernetes/VMs—flows, metrics, traces, profiling—with OpenTelemetry support and a Docker Compose deploy.

## Install

Save as a script file and run:

## Quick Use

**Standalone (docker-compose package in repo):**
```bash
unset DOCKER_HOST_IP
DOCKER_HOST_IP="10.1.2.3"  # set to the machine IP you deploy on

wget https://deepflow-ce.oss-cn-beijing.aliyuncs.com/pkg/docker-compose/stable/linux/deepflow-docker-compose.tar
tar -zxf deepflow-docker-compose.tar
sed -i "s|FIX_ME_ALLINONE_HOST_IP|$DOCKER_HOST_IP|g" deepflow-docker-compose/docker-compose.yaml
docker-compose -f deepflow-docker-compose/docker-compose.yaml up -d
```

## Intro

DeepFlow offers zero-code eBPF observability for Kubernetes/VMs—flows, metrics, traces, profiling—with OpenTelemetry support and a Docker Compose deploy.

- **Best for:** Kubernetes and VM observability with minimal instrumentation
- **Works with:** Linux + eBPF; Kubernetes/VMs; OpenTelemetry instrumentation
- **Setup time:** 20–60 minutes

## Practical Notes

- GitHub: 4,074 stars · 453 forks; pushed 2026-05-12 (verified via GitHub API).
- Repo includes `manifests/deepflow-docker-compose/docker-compose.yaml` for all-in-one deployment.
- Topics include `opentelemetry`, `kubernetes`, and `llm`, signaling focus on modern cloud + AI workloads.

## Main

Use DeepFlow as a **“no-regrets baseline”** before you add app-level tracing everywhere:

- Start with the all-in-one Compose deployment to validate data flow end-to-end.
- Connect one Kubernetes cluster or one VM pool first; confirm you can see service maps / flows and correlate spikes.
- Only then add targeted OpenTelemetry instrumentation for the top 1–3 critical services.

For production, plan capacity around your traffic volume (agents can help estimate storage/retention from your p95 throughput) and keep a clear retention policy for traces vs metrics vs logs.

### FAQ

**Q: Is DeepFlow only for Kubernetes?**
A: No—README and repo structure mention both Kubernetes and host/VM deployments.

**Q: Do I still need OpenTelemetry?**
A: Often yes for deep app semantics, but DeepFlow can give you useful coverage with minimal instrumentation.

**Q: What should I verify first?**
A: That the all-in-one deployment is stable and you can correlate a known incident spike across flows/metrics/traces.

## Source & Thanks

> Source: https://github.com/deepflowio/deepflow
> License: Apache-2.0
> GitHub stars: 4,074 · forks: 453

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## 快速使用

**Standalone（仓库提供的 docker-compose 包）：**
```bash
unset DOCKER_HOST_IP
DOCKER_HOST_IP="10.1.2.3"  # 改成部署机器的 IP

wget https://deepflow-ce.oss-cn-beijing.aliyuncs.com/pkg/docker-compose/stable/linux/deepflow-docker-compose.tar
tar -zxf deepflow-docker-compose.tar
sed -i "s|FIX_ME_ALLINONE_HOST_IP|$DOCKER_HOST_IP|g" deepflow-docker-compose/docker-compose.yaml
docker-compose -f deepflow-docker-compose/docker-compose.yaml up -d
```

## 简介

DeepFlow 提供零侵入的 eBPF 可观测性：覆盖 K8s/主机的流量、指标、分布式追踪与性能剖析，兼容 OpenTelemetry，并提供一体化 Docker Compose 部署与多种安装方式。

- **适合谁：** K8s/主机全链路观测，尽量少改业务代码
- **可搭配：** Linux + eBPF；Kubernetes/主机；可接 OpenTelemetry
- **准备时间：** 20–60 分钟

## 实战建议

- GitHub：4,074 stars · 453 forks；最近更新 2026-05-12（GitHub API 验证）。
- 仓库包含 `manifests/deepflow-docker-compose/docker-compose.yaml`，可一体化部署。
- topics 覆盖 `opentelemetry` / `kubernetes` / `llm`，适配云原生与 AI 业务的观测需求。

## 主要内容

把 DeepFlow 当作 **“先铺底、再精修”** 的观测基座：

- 先用 all-in-one Compose 跑通数据链路，确认采集/存储/查询完整可用。
- 先接入一个集群或一组主机，验证能看到服务拓扑与流量路径，并能定位性能尖刺。
- 再对最关键的 1–3 个服务做 OpenTelemetry 精细化埋点，避免一上来全量改代码。

上生产时，按流量与保留周期规划容量；对 traces / metrics / logs 设定不同 retention，减少成本与合规风险。

### FAQ

**DeepFlow 只能用于 Kubernetes 吗？**
答：不是。README 与仓库结构同时覆盖 K8s 与主机/VM 的部署场景。

**还需要 OpenTelemetry 吗？**
答：多数情况下需要做关键服务的语义埋点；但 DeepFlow 可先提供“零侵入”的基础覆盖。

**第一步该验证什么？**
答：先确认一体化部署稳定，并能在一次已知问题中把流量/指标/追踪关联起来。

## 来源与感谢

> Source: https://github.com/deepflowio/deepflow
> License: Apache-2.0
> GitHub stars: 4,074 · forks: 453


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Source: https://tokrepo.com/en/workflows/deepflow-ebpf-observability-for-cloud-ai
Author: Script Depot