VictoriaMetrics als zusätzliche Datenbank-Option für Messwerte ergänzt (STORE/DBASE=victoria)

Dritte, zu mongo/influx exklusive Auswahl für die laufenden Messwerte. Schreibpfad
nutzt das bestehende Influx-Line-Protocol unverändert (common/victoria_post.js);
Lesepfad (sensorapi/databases/victoria.js + victoria2json.js) holt Rohdaten per
VictoriaMetrics' /api/v1/export und bucketet/aggregiert stundenweise clientseitig,
nach Mongo-Konvention (Stunden-Start als Label, kein Zeit-Shift nötig wie bei Influx).
Scope bewusst auf die schon heute per DBASE umschaltbaren Funktionen begrenzt
(getActData/getNoiseAVGData) - getAvgData/getLongAvg/getGeigerData bleiben wie bisher.

Docker-Compose um victoriametrics-Service ergänzt (Retention explizit auf 100y
gesetzt, da VictoriaMetrics sonst nach 1 Monat Daten löscht).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

sensorapi/utilities/victoria2json.js

@@ -0,0 +1,97 @@
+// Parse VictoriaMetrics' /api/v1/export ndjson responses into the same row
+// shapes influx.js builds via csv2Json() / pivot(), for the noise-specific
+// fields (measurement "noise", separator "_", i.e. metric names noise_LAeq,
+// noise_LA_min, noise_LA_max, noise_E10tel_eq).
+
+import { DateTime } from 'luxon'
+
+const parseExportLines = (ndjsonBody) => {
+    return String(ndjsonBody)
+        .split('\n')
+        .filter((line) => line.trim() !== '')
+        .map((line) => {
+            try {
+                return JSON.parse(line)
+            } catch (e) {
+                return null
+            }
+        })
+        .filter((series) => series !== null)
+}
+
+const fieldName = (metricName) => metricName.replace(/^noise_/, '')
+
+// Merges the per-field series (one ndjson line per field) back into rows of
+// {datetime, LAeq, LA_min, LA_max, E10tel_eq}, matched by raw timestamp -
+// mirrors influx's pivot(rowKey:["_time"], columnKey:["_field"]).
+export const exportToRows = (ndjsonBody, sort) => {
+    const rowsByTime = new Map()
+    for (const series of parseExportLines(ndjsonBody)) {
+        const field = fieldName(series.metric.__name__)
+        const { values, timestamps } = series
+        for (let i = 0; i < timestamps.length; i++) {
+            const ts = timestamps[i]
+            let row = rowsByTime.get(ts)
+            if (!row) {
+                row = { datetime: DateTime.fromMillis(ts).toUTC().toISO() }
+                rowsByTime.set(ts, row)
+            }
+            row[field] = values[i]
+        }
+    }
+    const rows = Array.from(rowsByTime.values())
+    rows.sort((a, b) => (a.datetime < b.datetime ? -1 : a.datetime > b.datetime ? 1 : 0))
+    if (sort === -1) {
+        rows.reverse()
+    }
+    return rows
+}
+
+// Buckets raw E10tel_eq/LA_max samples by hour (truncated to the start of the
+// hour of each sample, same convention as sensorapi/databases/mongo.js's
+// $dateToString hour-grouping) and computes n_AVG/n_sum/count/peakcount -
+// equivalent to influx.js's aggregateWindow()/reduce() Flux pipeline.
+// Bucketing by hour-start (not hour-end, as Influx does) means no extra
+// 1-hour shift is needed downstream, unlike the DBASE === 'influx' branch.
+export const bucketNoiseAVG = (ndjsonBody, peak, long) => {
+    const buckets = new Map()
+    for (const series of parseExportLines(ndjsonBody)) {
+        const field = fieldName(series.metric.__name__)
+        if (field !== 'E10tel_eq' && field !== 'LA_max') {
+            continue
+        }
+        const { values, timestamps } = series
+        for (let i = 0; i < timestamps.length; i++) {
+            const key = DateTime.fromMillis(timestamps[i]).toUTC().startOf('hour').toISO()
+            let b = buckets.get(key)
+            if (!b) {
+                b = { sum: 0, count: 0, peakcount: 0 }
+                buckets.set(key, b)
+            }
+            if (field === 'E10tel_eq') {
+                b.sum += values[i]
+                b.count += 1
+            } else {
+                if (values[i] >= peak) {
+                    b.peakcount += 1
+                }
+            }
+        }
+    }
+    const rows = Array.from(buckets.entries())
+        .filter(([, b]) => b.count > 0)          // matches Influx's inner join: hours without E10tel_eq samples are dropped
+        .map(([datetime, b]) => {
+            let row = {
+                datetime,
+                n_AVG: 10 * Math.log10(b.sum / b.count),
+                peakcount: b.peakcount,
+            }
+            if (long) {
+                row.count = b.count
+                row.n_sum = b.sum
+            }
+            return row
+        })
+    rows.sort((a, b) => (a.datetime < b.datetime ? -1 : a.datetime > b.datetime ? 1 : 0))
+    return rows
+}