Why this tableau-review pattern gets slow in production: a source-level explanation
Most tableau-review articles stop at "how to use it" and never cover "when not to use it". This is an attempt at the second half.
What genuinely surprised me was the tail. The average looked great while P99 jumped by an order of magnitude past some threshold. The cause was not tableau-review itself but our upstream connection reuse — the load test traffic was too clean and hid the long-tail requests.
# Load test: do not jump straight to the max concurrency. # Ramp it up, otherwise you miss the knee. for c in 50 100 200 400 800; do wrk -t8 -c$c -d60s --latency http://127.0.0.1:8080/api/feed sleep 20 done
One last trap: in container environments remember to adjust the memory-related parameters in step. Otherwise the host limit and the process expectation disagree, and the symptom is intermittent, unreproducible failure.
On trade-offs, my view is this: if nobody on the team owns this area long-term, do not introduce a second mechanism. With two coexistence you first have to work out which one is even in play when things break, and that costs far more than the performance you saved.
The first thing was to collapse the variables. We were changing config and upgrading the version at the same time, and afterwards nobody could say which change caused what. We rolled back to moving one variable at a time, re-ran three times, and only then did the curve settle. Tedious, but not skippable.