Distributed fleet
Scale & operationsScale past one box: a controller splits the load across a fleet of agents and merges their HDR histograms into one true result — accurate percentiles, not an average of averages.
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$ loadr run examples/15-distributed.yaml
# Distributed run: submit this to a controller and the load is partitioned
# across every connected agent (VUs split, arrival rates divided, percentiles
# merged centrally from HDR histograms — never averaged).
#
# loadr controller --bind 0.0.0.0:7625 &
# loadr agent --join controller-host:7625 --name agent-1 &
# loadr run --controller controller-host:6464 examples/15-distributed.yaml
name: distributed
description: 600 req/s split across the agent fleet
defaults:
http:
base_url: https://api.example.com
scenarios:
fleet_load:
executor: constant-arrival-rate
rate: 600 # total across ALL agents
duration: 15m
pre_allocated_vus: 300
max_vus: 900
flow:
- request:
name: search
url: "/search?q=stress"
checks: [ { type: status, equals: 200 } ]
# Scale this one live from the web UI's run page or the controller API.
manual_dial:
executor: externally-controlled
max_vus: 500
duration: 15m
flow:
- request: { name: home, url: / }
thresholds:
http_req_duration: [ "p(95)<400", "p(99)<900" ]
http_req_failed: [ "rate<0.01" ]View raw: examples/15-distributed.yaml
What it shows
- ▸Controller + agent fleet
- ▸HDR histogram merge
- ▸One plan, many machines