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-- Module implementation
local M = {
namespace = '',
finalize = function (_ --[[metrics]]) end,
}
local snapshots, snapshots_count = {}, 120
-- Gauge metrics
local gauges = {
['worker.concurrent'] = true,
['worker.rss'] = true,
}
local function merge(t, results, prefix)
for _, result in pairs(results) do
if type(result) == 'table' then
for k, v in pairs(result) do
local val = t[prefix..k]
t[prefix..k] = (val or 0) + v
end
end
end
end
local function getstats()
local t = {}
merge(t, map 'stats.list()', '')
merge(t, map 'cache.stats()', 'cache.')
merge(t, map 'worker.stats()', 'worker.')
return t
end
local function snapshot_end()
snapshots_count = false
end
-- Function to sort frequency list
local function snapshot_start()
local prev = getstats()
while snapshots_count do
local is_empty = true
-- Get current snapshot
local cur, stats_dt = getstats(), {}
for k,v in pairs(cur) do
if gauges[k] then
stats_dt[k] = v
else
stats_dt[k] = v - (prev[k] or 0)
end
is_empty = is_empty and stats_dt[k] == 0
end
prev = cur
-- Calculate upstreams and geotag them if possible
local upstreams
if http.geoip then
upstreams = stats.upstreams()
for k,v in pairs(upstreams) do
local gi
if string.find(k, '.', 1, true) then
gi = http.geoip:search_ipv4(k)
else
gi = http.geoip:search_ipv6(k)
end
if gi then
upstreams[k] = {data=v, location=gi.location, country=gi.country and gi.country.iso_code}
end
end
end
-- Aggregate per-worker metrics
local wdata = {}
for _, info in pairs(map 'worker.info()') do
if type(info) == 'table' then
wdata[tostring(info.pid)] = {
rss = info.rss,
usertime = info.usertime,
systime = info.systime,
pagefaults = info.pagefaults,
queries = info.queries
}
end
end
-- Publish stats updates periodically
if not is_empty then
local update = {time=os.time(), stats=stats_dt, upstreams=upstreams, workers=wdata}
table.insert(snapshots, update)
if #snapshots > snapshots_count then
table.remove(snapshots, 1)
end
end
worker.sleep(1)
end
end
-- Function to sort frequency list
local function stream_stats(_, ws)
-- Initially, stream history
local ok, last = true, nil
local batch = {}
for i, s in ipairs(snapshots) do
table.insert(batch, s)
if #batch == 20 or i + 1 == #snapshots then
ok = ws:send(tojson(batch))
batch = {}
end
end
-- Publish stats updates periodically
while ok do
-- Get last snapshot
local id = #snapshots - 1
if id > 0 and snapshots[id].time ~= last then
local push = tojson(snapshots[id])
last = snapshots[id].time
ok = ws:send(push)
end
worker.sleep(1)
end
end
-- Render stats in Prometheus text format
local function serve_prometheus()
-- First aggregate metrics list and print counters
local slist, render = getstats(), {}
local latency = {}
local counter = '# TYPE %s counter\n%s %f'
for k,v in pairs(slist) do
k = select(1, k:gsub('%.', '_'))
-- Aggregate histograms
local band = k:match('answer_([%d]+)ms')
if band then
table.insert(latency, {band, v})
elseif k == 'answer_slow' then
table.insert(latency, {'+Inf', v})
-- Counter as a fallback
else
local key = M.namespace .. k
table.insert(render, string.format(counter, key, key, v))
end
end
-- Fill in latency histogram
local function kweight(x) return tonumber(x) or math.huge end
table.sort(latency, function (a,b) return kweight(a[1]) < kweight(b[1]) end)
table.insert(render, string.format('# TYPE %slatency histogram', M.namespace))
local count, sum = 0.0, 0.0
for _,e in ipairs(latency) do
-- The information about the %Inf bin is lost, so we treat it
-- as a timeout (3000ms) for metrics purposes
count = count + e[2]
sum = sum + e[2] * (math.min(tonumber(e[1]), 3000.0))
table.insert(render, string.format('%slatency_bucket{le="%s"} %f', M.namespace, e[1], count))
end
table.insert(render, string.format('%slatency_count %f', M.namespace, count))
table.insert(render, string.format('%slatency_sum %f', M.namespace, sum))
-- Finalize metrics table before rendering
if type(M.finalize) == 'function' then
M.finalize(render)
end
return table.concat(render, '\n') .. '\n'
end
-- Export module interface
M.init = snapshot_start
M.deinit = snapshot_end
M.endpoints = {
['/stats'] = {'application/json', getstats, stream_stats},
['/frequent'] = {'application/json', function () return stats.frequent() end},
['/upstreams'] = {'application/json', function () return stats.upstreams() end},
['/bogus'] = {'application/json', function () return bogus_log.frequent() end},
['/metrics'] = {'text/plain; version=0.0.4', serve_prometheus},
}
return M
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