function displayMetrics (metrics) {
const flatten = require('lodash.flatten')
const mean = require('lodash.mean')
const pad = require('lodash.pad')
const round = require('lodash.round')
const sum = require('lodash.sum')
const values = require('lodash.values')
function rpmSparkline () {
if (process.platform === 'win32') return
let sparkline = require('sparkline')
let points = []
values(metrics.routerStatus.data).forEach((cur) => {
for (let i = 0; i < cur.length; i++) {
let j = Math.floor(i / 3)
points[j] = (points[j] || 0) + cur[i]
}
})
points.pop()
return dim(sparkline(points)) + ' last 24 hours rpm'
}
let ms = ''
let rpm = ''
if (metrics.routerLatency && !empty(metrics.routerLatency.data)) {
ms = pad(`${round(mean(metrics.routerLatency.data.latency_p50))} ms`, 6)
}
if (metrics.routerStatus && !empty(metrics.routerStatus.data)) {
rpm = `${round(sum(flatten(values(metrics.routerStatus.data))) / 24 / 60)} rpm ${rpmSparkline()}`
}
if (rpm || ms) cli.log(` ${label('Metrics:')} ${ms}${rpm}`)
}
transactionResultBuckets.map(({ key, timeseries }) => {
const tpmValues = timeseries.buckets
.slice(1, -1)
.map(bucket => round(bucket.doc_count * (60 / bucketSize), 1));
return {
key,
avg: mean(tpmValues),
values: tpmValues
};
}),