Synthetic traffic generators are useful for baseline performance metrics, but they consistently fail to replicate the chaos, header edge cases, and payload diversity of actual production users. TrafficMirror solves this by asynchronously capturing live inbound HTTP requests and fanning them out to target staging or shadow environments.
Asynchronous Shadow Replay Architecture
TrafficMirror hooks directly into your ASGI or WSGI pipeline using zero-copy memory buffers. It duplicates incoming HTTP requests, strips sensitive headers (like auth tokens or PII) via configurable sanitization rules, and dispatches payload copies through a high-throughput async queue.
from traffic_mirror import MirrorMiddleware, SanitizerRule
# Define sanitization to strip PII and sensitive headers
sanitizer = SanitizerRule(
strip_headers=["Authorization", "Cookie", "X-Api-Key"],
redact_json_paths=["$.payment_method.card_number", "$.user.email"]
)
# Attach TrafficMirror middleware to your ASGI app
app.add_middleware(
MirrorMiddleware,
target_url="https://staging-shadow.internal.net/api/v1",
sample_rate=0.25, # Mirror 25% of live production traffic
sanitizer=sanitizer,
timeout_ms=150, # Non-blocking async drop after threshold
)Zero-Impact Latency Guarantees
A primary risk with traffic cloning is adding overhead to the critical path. TrafficMirror operates completely out-of-band. The mirroring task is pushed to dedicated worker threads that execute I/O asynchronously. If the staging endpoint experiences latency or drops connections, the main production request loop remains completely untouched.
Catching Regressions Before Release
By replaying actual user workloads against pre-production microservices, you expose unhandled exception paths, test database lock contention under real query patterns, and measure memory footprints against actual payload spikes long before code reaches live deployment.