How much ping and search traffic does the geo backend take before dispatch slows down?

Taxi backend on a normal evening

No limit in the range

Holds ~800 req/s on short steps (4 min); no failure below

Holds
~800req/s
measured over 4 min

All 7 steps checked at this rate

rate steps of 71 s–4 min · checks: 148 passed, 0 failed, 2 too little data, 0 not run · sent 100 % of the plan · generator health not verified

Synthetic load sent from outside; your real traffic and server metrics are not part of it.

What to do next

  1. Search next between ~800 and 1,000 req/s.

20,000 drivers across Moscow send their positions; for every ten pings one rider looks for a car, and half of the riders order.

Example: a real CapacityLab Load Run against a taxi backend we host

Test setup

Where the limit lies

Each dot is a rate the backend was held at for at least a minute and a half. It passes when every call with enough samples keeps its p95 latency inside the bound and errors under 1%; a rare call with too few samples at that rate is shown as missing, not as passed.

Holds800 req/sNo failure up to 800 req/s
  • Did not hold 30 min792 req/s
Latency bound
p95 ≤ 500 ms

Planned against delivered

The search climbs a staircase of rates. At each step our generators sent the planned rate; the bars show what the backend actually answered, and its p95 latency at that step.

02505007501,0000200400600p95 ≤ 500 ms for every step4080160320800Planned, req/s
PlannedDeliveredp95 (right scale)
PlannedDeliveredp95ErrorsResult
4038.850 ms0%Held
8079.550 ms0%Held
16016150 ms0%Held
32031750 ms0%Held
800797200 ms0%Held

Which call slowed down first

p95 latency of each call at each rate, against the bound. The call the Result names as limiting is marked.

Which call slowed down first
CallShare160 req/s320 req/s800 req/s
Driver position pingGET /tile38/SET+fleet+d00001+POINT+55.7558+37.6173
67%
50 ms
50 ms
300 ms
Geocode the destinationGET /photon/api
6.7%
50 ms
50 ms
150 ms
Find drivers nearbyGET /tile38/NEARBY+fleet+LIMIT+5+POINTS+POINT+55.7558+37.6173+2000
6.7%
50 ms
50 ms
200 ms
Route and priceGET /osrm/route/v1/driving/37.6173,55.7558;37.5537,55.7158
6.7%
30 ms
30 ms
150 ms
Check surge in the cellGET /tile38/NEARBY+orders+COUNT+POINT+55.7158+37.5537+1000
6.7%
50 ms
50 ms
200 ms
ETA from the nearest driversGET /osrm/table/v1/driving/37.6100,55.7500;37.6200,55.7600;37.6173,55.7558
3.3%
30 ms
50 ms
150 ms
Create the orderGET /tile38/SET+orders+o0000000000000000+EX+900+POINT+55.7558+37.6173
3.3%
50 ms
50 ms
150 ms

p95, SLO ≤ 500 ms

Holding it for longer

A capacity search finds the limit in minutes. A 30-minute load test at a rate below it shows whether the backend keeps holding while orders pile up and the busy cells stay busy.

  1. Find the limitNo limit in the rangeHolds ~800 req/s on short steps (4 min); no failure belowOct 2, 2026, 15:02 UTC
  2. Hold 30 minDid not hold792 req/s delivered, p95 150 ms, errors 0%Oct 2, 2026, 17:14 UTC

What we saw on our own servers

This is the one part a Report does not contain: we host this backend, so we can read the hosting metrics of each of its services. On your application you read your own dashboards; CapacityLab measures from the outside.

  • Front door (Caddy)1.14 vCPUMemory peak 70 MB
  • Geo database (Tile38)0.45 vCPUMemory peak 167 MB
  • Routing (OSRM)0.18 vCPUMemory peak 227 MB
  • Geocoder (Photon)0.68 vCPUMemory peak 8,156 MB

Front door (Caddy) was the busiest service: its CPU peaked at 1.14 vCPU, ahead of Geocoder (Photon) at 0.68.

None of 314,699 requests ended with a server error.

What you would do next

  1. Confirm before you plan

    Run a load test at 800 req/s for 30 minutes: the search advises it as the rate to confirm next, and only the confirmation shows whether every call keeps its bound.

  2. Watch your side while it runs

    Keep your own dashboards open during the run. The report says which call slowed; your metrics say why.

  3. Change one thing, then compare

    After a change, run the same test again and put the two reports side by side.

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Where these numbers come from

Exported from the published Reports of these Load Runs. The CPU and memory of each service come from its Railway metrics; the error count, when shown, from the front door's access log.

Identifiers and checksums
Load RunResult digestComputed
178694df-a385-4d7f-8fb7-d4b96fc6fd7fsha256:0bdbf8fba7016174817dda1293350abc7c38656010e9aa48af58ef0d90e802a6Oct 2, 2026, 15:18 UTC
5c45faed-d094-49e4-a715-e7fb6f5c8bcesha256:9b9892534d77f40a534f519ff98b31ad5b253c6290eb5a3f582c1d4d0881f7ccOct 2, 2026, 17:46 UTC