Best practices for real-time streaming of eBPF SSL/TLS capture data in Kubernetes?

[aman1105-sa]

I'm working on a Kubernetes-native SSL/TLS monitoring system using eBPF (similar to your lesson 30-sslsniff).

Current Setup:

• Using eCapture (eBPF-based SSL capture tool) as a DaemonSet
• Need to stream captured HTTP data in real-time to create Kubernetes CRDs
• Facing issues with buffered stdout - data doesn't appear until process exits

Questions:

1. What's the recommended approach for real-time streaming of eBPF capture data (perf buffers, ring buffers, or something else)?
2. Have you seen any examples of integrating eBPF capture tools with Kubernetes for observability?
3. Are there alternatives to stdout for getting immediate output from eBPF programs in daemon mode?

Any guidance or pointers to relevant lessons would be appreciated!

[yunwei37]

Thanks for your detailed description! Your challenge (capturing TLS plaintext in Kubernetes with real-time visibility) goes beyond the perf vs ring buffer discussion and involves system design choices. Here’s a concise breakdown of your questions:

1. Perf buffer vs Ring buffer?

At kernel–userspace boundaries, both perf (BPF_MAP_TYPE_PERF_EVENT_ARRAY) and ring buffer (BPF_MAP_TYPE_RINGBUF) are viable:

• Perf buffer (used by existing tools like [eCapture](https://github.com/gojue/ecapture) and [30-sslsniff](https://eunomia.dev/en/tutorials/30-sslsniff/)):
  Older, per-CPU buffers causing memory overhead (#CPUs × buffer size) and possible event reordering.

• Ring buffer (recommended for new code, introduced in Linux 5.8):
  Single, shared buffer with better memory efficiency, simpler ordering, lower overhead ([Kernel.org](https://www.kernel.org/doc/Documentation/bpf/ringbuf.rst)).

Choosing a buffer type won't solve your stdout buffering issue—this is about userspace/logging design, not kernel-to-user transport.

2. Recommended integration patterns for Kubernetes

Production-grade Kubernetes eBPF tools like [Inspektor Gadget](https://github.com/inspektor-gadget/inspektor-gadget), [Tetragon](https://tetragon.io/docs/getting-started/file-events/), and [Cilium+Hubble](https://www.spectrocloud.com/blog/getting-started-with-cilium-for-kubernetes-networking-and-observability) share a common architecture:

• Control plane (CRDs): Define policies and tracing configurations, not individual events.
• Data plane: Stream events separately (gRPC/WebSocket/TCP) to handle large-scale observability efficiently.

For your scenario (HTTP/TLS capture):

• Avoid creating one CRD per event; Kubernetes API servers and etcd are unsuitable for massive event volumes.
• Instead, aggregate/filter events centrally and only elevate significant events or violations as CRDs.

（Some answer generated by AI, hop it's helpfull