Part 1: Building a Production-Grade Traffic Capture and Replay System

Traffic capture can help teams reproduce real production conditions for testing and capacity planning, addressing gaps in conventional observability platforms that commonly sample data and omit request and response payloads. The first installment of a three-part series on capture, transformation, and replay recommends establishing performance budgets, such as minimal added latency and CPU use, before selecting a capture approach. Options include reverse, forward, and transparent proxies for HTTP/S traffic; tcpdump and Wireshark for packet-level collection and analysis; application logging; and Linux eBPF tools for kernel- or process-level visibility. Key technical challenges include safely capturing inbound and outbound traffic, decrypting TLS traffic, supporting varied protocols such as databases, Kafka, gRPC, and GraphQL, handling compression, and storing large volumes of captures without overwhelming indexing systems. The discussion also covers deployment considerations across VMs, containers, Kubernetes, and serverless environments, along with scalable architectures using collectors, queues, autoscaling, sampling, sharding, edge processing, and cloud storage. Effective systems require filtering and rotating capture files, measuring overhead under load, using portable data formats, and validating capacity with representative non-production traffic before deployment.

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