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JamesOsgoodJamesOsgood
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Support multiple return targets per query.
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‎README.md

Lines changed: 16 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@ Allows MongoDB to be used as a data source for Grafana by providing a proxy to c
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Create a new data source of type MongoDB as shown below. The MongoDB details are :
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* **MongoDB URL** - `mongodb://rpiread:rpiread@rpi-sensorreadings-shard-00-00-fgscn.mongodb.net:27017,rpi-sensorreadings-shard-00-01-fgscn.mongodb.net:27017,rpi-sensorreadings-shard-00-02-fgscn.mongodb.net:27017/test?ssl=true&replicaSet=RPI-SensorReadings-shard-0&authSource=admin`
28+
* **MongoDB URL** - `mongodb://rpiread:rpiread@rpi-sensor-data-shard-00-00-ifxxs.mongodb.net:27017,rpi-sensor-data-shard-00-01-ifxxs.mongodb.net:27017,rpi-sensor-data-shard-00-02-ifxxs.mongodb.net:27017/test?ssl=true&replicaSet=rpi-sensor-data-shard-0&authSource=admin`
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* **MongoDB Database** - `rpi`
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<img src="src/img/sample_datasource.png" alt="Sample Data Source" style="width: 500px;"/>
@@ -84,6 +84,21 @@ Note that ```_id``` field of the bucketAuto output contains the start and end of
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The dashboard in `examples\RPI MongoDB Bucket - Atlas.json` shows this.
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#### Example 3 - Using a Tabel Panel
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Table panels are now supported with queries of the form
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```javascript
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db.sensor_value.aggregate(
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[
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{ "$match" : { "ts" : { "$gte" : "$from", "$lt" : "$to" }}},
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{ "$group": { "_id": { "sensor_name" : "$sensor_name", "sensor_type" : "$sensor_type" }, "cnt" : { "$sum" : 1 }, "ts" : { "$max" : "$ts" } } },
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{ "$project": { "name" : { "$concat" : ["$_id.sensor_name",":","$_id.sensor_type" ]}, "value" : "$cnt", "ts" : 1, "_id" : 0} }
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])
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```
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The dashboard in `examples\Sensor Values Count - Atlas.json` shows this.
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## Running the proxy as a service on a Mac
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* Install [forever-mac](https://www.npmjs.com/package/forever-mac)

‎dist/README.md

Lines changed: 16 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@ Allows MongoDB to be used as a data source for Grafana by providing a proxy to c
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Create a new data source of type MongoDB as shown below. The MongoDB details are :
2727

28-
* **MongoDB URL** - `mongodb://rpiread:rpiread@rpi-sensorreadings-shard-00-00-fgscn.mongodb.net:27017,rpi-sensorreadings-shard-00-01-fgscn.mongodb.net:27017,rpi-sensorreadings-shard-00-02-fgscn.mongodb.net:27017/test?ssl=true&replicaSet=RPI-SensorReadings-shard-0&authSource=admin`
28+
* **MongoDB URL** - `mongodb://rpiread:rpiread@rpi-sensor-data-shard-00-00-ifxxs.mongodb.net:27017,rpi-sensor-data-shard-00-01-ifxxs.mongodb.net:27017,rpi-sensor-data-shard-00-02-ifxxs.mongodb.net:27017/test?ssl=true&replicaSet=rpi-sensor-data-shard-0&authSource=admin`
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* **MongoDB Database** - `rpi`
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3131
<img src="src/img/sample_datasource.png" alt="Sample Data Source" style="width: 500px;"/>
@@ -84,6 +84,21 @@ Note that ```_id``` field of the bucketAuto output contains the start and end of
8484

8585
The dashboard in `examples\RPI MongoDB Bucket - Atlas.json` shows this.
8686

87+
#### Example 3 - Using a Tabel Panel
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Table panels are now supported with queries of the form
90+
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```javascript
92+
db.sensor_value.aggregate(
93+
[
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{ "$match" : { "ts" : { "$gte" : "$from", "$lt" : "$to" }}},
95+
{ "$group": { "_id": { "sensor_name" : "$sensor_name", "sensor_type" : "$sensor_type" }, "cnt" : { "$sum" : 1 }, "ts" : { "$max" : "$ts" } } },
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{ "$project": { "name" : { "$concat" : ["$_id.sensor_name",":","$_id.sensor_type" ]}, "value" : "$cnt", "ts" : 1, "_id" : 0} }
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])
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```
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The dashboard in `examples\Sensor Values Count - Atlas.json` shows this.
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## Running the proxy as a service on a Mac
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89104
* Install [forever-mac](https://www.npmjs.com/package/forever-mac)

‎dist/server/mongodb-proxy.js

Lines changed: 23 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -90,9 +90,13 @@ function queryFinished(requestId, queryId, results, res, next)
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output = []
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for ( var i = 0; i < queryStatus.length; i++)
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{
93-
if ( queryStatus[i].results.datapoints.length > 0)
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var queryResults = queryStatus[i].results
94+
var keys = Object.keys(queryResults)
95+
for (var i = i; i < keys.length; i++)
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{
95-
output.push(queryStatus[i].results)
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var tg = keys[i]
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console.log(tg)
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output.push(queryResults[tg])
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}
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}
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res.json(output);
@@ -285,18 +289,28 @@ function runAggregateQuery( requestId, queryId, body, queryArgs, res, next )
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{
286290
try
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{
288-
datapoints = []
292+
var results = {}
289293
for ( var i = 0; i < docs.length; i++)
290294
{
291295
var doc = docs[i]
292-
tg = doc.name
293-
datapoints.push([doc['value'], doc['ts'].getTime()])
296+
var tg = doc.name
297+
var dp = null
298+
if (tg in results)
299+
{
300+
dp = results[tg]
301+
}
302+
else
303+
{
304+
dp = { 'target' : tg, 'datapoints' : [] }
305+
results[tg] = dp
306+
}
307+
308+
results[tg].datapoints.push([doc['value'], doc['ts'].getTime()])
294309
}
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296311
client.close();
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var elapsedTimeMs = stopwatch.stop()
298-
var results = { 'target' : tg, 'datapoints' : datapoints }
299-
logTiming(body, elapsedTimeMs, datapoints)
313+
logTiming(body, elapsedTimeMs)
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// Mark query as finished - will send back results when all queries finished
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queryFinished(requestId, queryId, results, res, next)
302316
}
@@ -376,14 +390,14 @@ function logQuery(query, type)
376390
}
377391
}
378392

379-
function logTiming(body, elapsedTimeMs, datapoints)
393+
function logTiming(body, elapsedTimeMs)
380394
{
381395
if (serverConfig.logTimings)
382396
{
383397
var range = new Date(body.range.to) - new Date(body.range.from)
384398
var diff = moment.duration(range)
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386-
console.log("Request: " + intervalCount(diff, body.interval, body.intervalMs) + " - Returned " + datapoints.length + " data points in " + elapsedTimeMs.toFixed(2) + "ms")
400+
console.log("Request: " + intervalCount(diff, body.interval, body.intervalMs) + " - Returned in " + elapsedTimeMs.toFixed(2) + "ms")
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}
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}
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‎examples/Sensor Value Counts - Atlas.json

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@@ -0,0 +1,182 @@
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{
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"__inputs": [
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{
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"name": "DS_RPI_- ATLAS",
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"label": "RPI - Atlas",
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"description": "",
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"type": "datasource",
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"pluginId": "grafana-mongodb-datasource",
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"pluginName": "MongoDB"
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}
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],
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"__requires": [
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{
14+
"type": "grafana",
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"id": "grafana",
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"name": "Grafana",
17+
"version": "5.1.3"
18+
},
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{
20+
"type": "datasource",
21+
"id": "grafana-mongodb-datasource",
22+
"name": "MongoDB",
23+
"version": "1.3.5"
24+
},
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{
26+
"type": "panel",
27+
"id": "table",
28+
"name": "Table",
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"version": "5.0.0"
30+
}
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],
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"annotations": {
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"list": [
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{
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"builtIn": 1,
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"datasource": "-- Grafana --",
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"enable": true,
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"hide": true,
39+
"iconColor": "rgba(0, 211, 255, 1)",
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"name": "Annotations & Alerts",
41+
"type": "dashboard"
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}
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]
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},
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"editable": true,
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"gnetId": null,
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"graphTooltip": 0,
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"id": null,
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"links": [],
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"panels": [
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{
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"columns": [],
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"datasource": "${DS_RPI_- ATLAS}",
54+
"fontSize": "100%",
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"gridPos": {
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"h": 13,
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"w": 18,
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"x": 0,
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"y": 0
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},
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"id": 2,
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"links": [],
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"pageSize": null,
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"scroll": true,
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"showHeader": true,
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"sort": {
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"col": 0,
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"desc": true
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},
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"styles": [
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{
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"alias": "",
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"colorMode": null,
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"colors": [
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"rgba(245, 54, 54, 0.9)",
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"rgba(237, 129, 40, 0.89)",
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"rgba(50, 172, 45, 0.97)"
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],
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"dateFormat": "YYYY-MM-DD HH:mm:ss",
80+
"decimals": 0,
81+
"mappingType": 1,
82+
"pattern": "Value",
83+
"thresholds": [],
84+
"type": "number",
85+
"unit": "none"
86+
},
87+
{
88+
"alias": "",
89+
"colorMode": null,
90+
"colors": [
91+
"rgba(245, 54, 54, 0.9)",
92+
"rgba(237, 129, 40, 0.89)",
93+
"rgba(50, 172, 45, 0.97)"
94+
],
95+
"dateFormat": "YYYY-MM-DD HH:mm:ss",
96+
"decimals": 2,
97+
"mappingType": 1,
98+
"pattern": "Time",
99+
"thresholds": [],
100+
"type": "date",
101+
"unit": "short"
102+
},
103+
{
104+
"alias": "",
105+
"colorMode": null,
106+
"colors": [
107+
"rgba(245, 54, 54, 0.9)",
108+
"rgba(237, 129, 40, 0.89)",
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"rgba(50, 172, 45, 0.97)"
110+
],
111+
"decimals": 2,
112+
"pattern": "/.*/",
113+
"thresholds": [],
114+
"type": "number",
115+
"unit": "short"
116+
}
117+
],
118+
"targets": [
119+
{
120+
"groupByAliases": [],
121+
"groupByColumns": [],
122+
"metricAggs": [
123+
{
124+
"column": "value",
125+
"type": "avg"
126+
}
127+
],
128+
"rawQuery": true,
129+
"refId": "A",
130+
"resultFormat": "time_series",
131+
"target": "db.sensor_value.aggregate(\n[\n { \"$match\" : { \"ts\" : { \"$gte\" : \"$from\", \"$lt\" : \"$to\" }}},\n { \"$group\": { \"_id\": { \"sensor_name\" : \"$sensor_name\", \"sensor_type\" : \"$sensor_type\" }, \"cnt\" : { \"$sum\" : 1 }, \"ts\" : { \"$max\" : \"$ts\" } } }, \n { \"$project\": { \"name\" : { \"$concat\" : [\"$_id.sensor_name\",\":\",\"$_id.sensor_type\" ]}, \"value\" : \"$cnt\", \"ts\" : 1, \"_id\" : 0} } \n])",
132+
"timeInterval": "auto_gf",
133+
"type": "timeserie",
134+
"whereClauses": []
135+
}
136+
],
137+
"title": "Counts",
138+
"transform": "timeseries_to_rows",
139+
"type": "table"
140+
}
141+
],
142+
"refresh": "5s",
143+
"schemaVersion": 16,
144+
"style": "dark",
145+
"tags": [],
146+
"templating": {
147+
"list": []
148+
},
149+
"time": {
150+
"from": "now/d",
151+
"to": "now"
152+
},
153+
"timepicker": {
154+
"refresh_intervals": [
155+
"5s",
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"10s",
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"30s",
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"1m",
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"5m",
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"15m",
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"30m",
162+
"1h",
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"2h",
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"1d"
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],
166+
"time_options": [
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"5m",
168+
"15m",
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"1h",
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"6h",
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"12h",
172+
"24h",
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"2d",
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"7d",
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"30d"
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]
177+
},
178+
"timezone": "",
179+
"title": "Sensor Value Counts",
180+
"uid": "9A9EQ3Omk",
181+
"version": 4
182+
}

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