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Find the Hotspot, Then Cut the Dynos

Written by Claude Fable 5.1 on 2026-09-01, last edited 2026-09-03 · History · Prompts

The Problem

In January 2026 we profiled the Hello Weather API and concluded it was already efficient. A request cost about 8ms of CPU against a mock data source. Most of that was JSON serialization, and it grew with the size of the response. The write-up said the remaining options all had trade-offs, and the cheapest was swapping the JSON library. Meanwhile production was running 40 Standard-2X web dynos on Heroku. It had been 4 before the current version of the app shipped in late 2025 with sun and moon calculations, a larger JSON payload, scores, and chart attributes. Six days later a cost analysis compared that fleet against moving to another cloud and found that code changes would pay back ten times faster. So the plan was to trim the web view path, where a 10ms request could lose 7.

The profile had timed the wrong output. The server builds several output shapes from one weather object, and the January numbers came from a shape most clients don’t ask for. About three quarters of production requests want the format the current iOS app reads. That format carries the derived attributes (values computed from the raw forecast, like pressure trends and scores) that the app shows. Nobody had timed that path on its own. On February 12 a local run put it at about 126ms per request. Roughly 70ms of that was in the current-conditions derived attributes and another 19ms in scores. Astronomy, which the January plan had ranked as the top optimization, was 3.6ms.

The server runs on Falcon with fibers, so waiting on vendors overlaps and a dyno runs out of CPU before anything else (see Falcon and Ruby Async). At 126ms per request on the dominant path, the one three quarters of traffic takes, that serialization was most of the fleet. We only found it by benchmarking that path.

The Solution

Four steps, each with a measurement in front of it:

Benchmark the path traffic actually takes

Production metrics tell you how long a request took, not which Ruby method took it. They also include time waiting on vendors, which no server change can move. So the probe that found the hotspot runs the full request path in a rails runner process against a mock source, and every millisecond is our Ruby. It stubs out the hit-tracking write, counts allocations alongside wall time, and starts the garbage collector on a schedule so its pauses land the same way in every run. Trimmed to the measuring loop:

# scripts/benchmark/weather_request_probe.rb (trimmed)
iters  = ENV.fetch("ITERS", "220").to_i
warmup = ENV.fetch("WARMUP", "45").to_i
params = { source: "mock", output: ENV.fetch("OUTPUT", "app"), lat: 41.87, lon: -87.62 }

samples_ms = []
allocs = []

(warmup + iters).times do |i|
  GC.start if (i % 25).zero?
  before_alloc = GC.stat(:total_allocated_objects)
  t0 = Process.clock_gettime(Process::CLOCK_MONOTONIC)

  Weather.new(params).to_json

  elapsed_ms = (Process.clock_gettime(Process::CLOCK_MONOTONIC) - t0) * 1000.0
  next if i < warmup
  samples_ms << elapsed_ms
  allocs << (GC.stat(:total_allocated_objects) - before_alloc)
end

sorted = samples_ms.sort
puts JSON.pretty_generate(
  mean_ms: (samples_ms.sum / samples_ms.length).round(3),
  p95_ms: sorted[(sorted.length * 0.95).floor].round(3),
  allocs_per_req: (allocs.sum.to_f / allocs.length).round(1),
)

A second probe answers the next question: which method? It wraps the same call in a TracePoint on :call and :return, filtered to the three files that hold derived attributes, scores, and chart values. Then it ranks methods by total time:

# scripts/benchmark/derived_hotspots_probe.rb (trimmed)
target_suffixes = %w[derived_attributes.rb derived_scores.rb derived_chart_attributes.rb]
stats = Hash.new { |h, k| h[k] = { calls: 0, total_s: 0.0 } }
stack = []

trace = TracePoint.new(:call, :return) do |tp|
  next unless target_suffixes.any? { |suffix| tp.path.end_with?(suffix) }

  if tp.event == :call
    stack << ["#{File.basename(tp.path)}##{tp.method_id}", Process.clock_gettime(Process::CLOCK_MONOTONIC)]
  elsif (top = stack.pop)
    key, t0 = top
    stats[key][:calls] += 1
    stats[key][:total_s] += Process.clock_gettime(Process::CLOCK_MONOTONIC) - t0
  end
end

warmup.times { work.call }
trace.enable
n.times { work.call }
trace.disable

ranked = stats.map { |key, v| { method: key, calls: v[:calls], total_ms: (v[:total_s] * 1000).round(3) } }
puts JSON.pretty_generate(ranked.sort_by { |row| -row[:total_ms] }.first(40))

The call count column pointed at the fix. Methods like the pressure trend ran several times per object per request: once for the serializer and again for each sibling attribute built from them. No single method was slow. The same method was just running again for every reader.

These scripts lived in /tmp while we did the work. On February 24, after the results were in, we moved them into the repo with rake wrappers and a JSON file per run so two runs can be diffed. The limit is the file filter. It’s a guess about where the hotspot lives, and a hotspot outside those three files wouldn’t show up.

Memoize the accessor everything reads through

The weather objects keep their attributes behind a lazy accessor. A value can be stored directly or as a proc, and a proc runs when the attribute is first read. That keeps building the object cheap when a caller only wants a few fields. The alternative, computing every attribute up front, would charge every request for fields it never reads. But the lazy accessor didn’t remember the answer. Every read called the proc again. The serializer reads a trend attribute directly, then the attribute that names it, then the one that phrases it, and each of those reads the trend again.

The fix replaces the proc with its result on the first read. Here’s the accessor after the change, in full:

module LazyAttrAccessor
  extend ActiveSupport::Concern

  class_methods do
    def lazy_attr_accessor(names)
      names.each do |name|
        attr_writer(name)

        define_method(name) do
          val = instance_variable_get("@#{name}")
          if val.respond_to?(:call)
            result = val.call
            if result.respond_to?(:call)
              raise ArgumentError, "lazy_attr_accessor does not support nested callables for :#{name}"
            end
            instance_variable_set("@#{name}", result)
            result
          else
            val
          end
        end
      end
    end
  end
end

The nested-callable check is the one line that isn’t about speed. Before the change, a proc that returned a proc got called once, and the inner proc went back to the caller. After the change, the inner proc would be stored as the memoized value and called on the next read, with no warning. Raising makes the rule plain: a callable runs once, and its result is the value. Nil and false get memoized too, because the check asks whether the stored object responds to call, not whether it’s truthy.

Before merging, we checked the change against the fiber model, because memoizing on an object under a concurrent server is how you get stale reads. Three facts made it safe. Every request builds its own weather object, so nothing is shared between requests. The vendor fetches run as sibling fibers under a barrier that waits for all of them before serialization starts, so by the time a lazy attribute is read, the request is back in plain sequential code. And the procs are pure computation over data already fetched. We grepped them for HTTP and fetch calls to confirm that, so a read never hands control to the scheduler partway through. If two fibers ever did read one object, the worst case is the same value computed twice.

On February 19 the change took the dominant path from 132ms to 16ms per request locally, an 88% cut. A stricter rerun at 360 iterations, comparing commits five days apart, put the before and after at 131.51ms and 16.34ms mean. One test broke. It built a weather object once and checked the icon under two frozen clock times, and the second check now saw the first answer. The fix was to build a fresh object for each time, which is what a real request does.

Take the smaller wins, then stop

With the big cost gone, the same probe ranked what was left. Each candidate had to beat a baseline run interleaved with it, so drift on the machine didn’t count as a win. Three shipped over the next five days. The web view adapter used to serialize to a hash and then wrap it. Replacing that with direct delegation cut the web view path 20%, from 11.29ms to 9.04ms, though web view paths were a small share of traffic by then. A process-wide cache for translation keys with no interpolation took another 8.9% off the dominant path, 18.77ms to 17.11ms, and saved 4,800 objects per request:

I18N_KEY_CACHE = Concurrent::Map.new

def t(key, **options)
  locale = units.language.to_sym
  return I18n.t(key, locale: locale, **options) unless options.empty?

  locale_cache = I18N_KEY_CACHE.compute_if_absent(locale) { Concurrent::Map.new }
  locale_cache.compute_if_absent(key) { I18n.t(key, locale: locale).freeze }
end

Keys with interpolation go straight to the translation library, because a correct cache key would have to include the options, and a follow-up probe found that caching those too made the path 3% slower. The third change moved the timezone and the “now” cutoff out of the hourly and daily loops, so they’re computed once per block.

We stopped because of a report on February 24, not because we ran out of ideas. Memoizing the trend methods one by one measured at plus 0.19% against the baseline, inside noise. Aggregating the score traversals measured at plus 1.76%. Encoding a prebuilt hash took 0.06ms of a 17ms request, so swapping the JSON library couldn’t matter. The largest lever left was translation, and stubbing it out entirely put its ceiling at about 3%. The report closed the track and wrote down when to reopen it, tied to pressure in production. One more memoization did land in April. It measured 3.6%, and we justified it by the trace showing the repeated calls, not by the request mean.

Walk the dynos down under guardrails

The memoization deployed on February 19. On February 23 the Heroku dashboard still showed 40 web dynos at a one-minute load average of 0.04. The count was the cost, and the question was how far it could drop before latency or errors moved. We rejected one big cut on a hunch, because the traffic has a shape. A silent push wakes thousands of devices at the top and bottom of every hour, so a fleet that looks fine at 9:34 can fall over at the next spike (see Heroku Capacity for the tooling that grew out of this).

Each step ran the same loop. Scale down, wait three minutes for the new fleet to settle, pull the router and runtime logs several times twenty seconds apart, drop duplicate request ids, and summarize. The summary had p50, p95, and p99 service time by path, status counts, one-minute load, memory, and swap. A step held only if every limit held, and we wrote the limits down before the first cut:

The sequence was 40 to 28 to 20 to 18 to 14 to 10 to 8, all on the evening of February 23 into the early hours of February 24 UTC. We checked the last step against a top-of-hour spike. Two moments broke the rhythm. At 20 dynos a short sample showed two 504s, a 0.27% error rate against the 0.2% line. Both were widget requests to one vendor that took two seconds, so a vendor timeout and not a saturated dyno, and a second, larger sample came back clean. At 10 dynos the first sample had a 502 and a 504. The router codes said one was a client disconnect and the other a vendor failure, and again we took a larger sample before the next cut. The rule was that a missed limit with a known outside cause earns a bigger sample, not a pass.

Two days later another capture showed steady headroom, and the fleet went to 6, where it has stayed. In May we tried 5 and rolled it back. A full spike capture passed the hard limits but left too little margin on p95 latency.

Results

Lessons Learned