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https://github.com/grafana/grafana.git
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7bb79158ed
- Takes the conditions property from the settings column of an alert from alerts table and turns into an ng alerting condition with the queries and classic condition. - Has temp API rest endpoint that will take the dashboard conditions json, translate it to SEE queries + classic condition, and execute it (only enabled in dev mode). - Changes expressions to catch query responses with a non-nil error property - Adds two new states for an NG instance result (NoData, Error) and updates evaluation to match those states - Changes the AsDataFrame (for frontend) from Bool to string to represent additional states - Fix bug in condition model to accept first Operator as empty string. - In ngalert, adds GetQueryDataRequest, which was part of execute and is still called from there. But this allows me to get the Expression request from a condition to make the "pipeline" can be built. - Update AsDataFrame for evalresult to be row based so it displays a little better for now
354 lines
7.6 KiB
JSON
354 lines
7.6 KiB
JSON
{
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"conditions": [
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{
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"evaluator": {
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"params": [
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3
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],
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"type": "gt"
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},
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"operator": {
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"type": "and"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 60,
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"valuesCSV": "0,0,2,2,1,1"
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},
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"hide": false,
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "B",
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"scenarioId": "predictable_pulse",
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"stringInput": ""
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},
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"params": [
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"B",
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"5m",
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"now"
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]
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},
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"reducer": {
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"params": [],
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"type": "avg"
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},
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"type": "query"
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},
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{
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"evaluator": {
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"params": [
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2,
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5
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],
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"type": "within_range"
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},
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"operator": {
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"type": "and"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 60,
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"valuesCSV": "0,0,2,2,1,1"
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},
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"hide": false,
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "B",
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"scenarioId": "predictable_pulse",
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"stringInput": ""
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},
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"params": [
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"B",
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"10m",
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"now-5m"
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]
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},
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"reducer": {
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"params": [],
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"type": "max"
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},
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"type": "query"
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},
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{
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"evaluator": {
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"params": [
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6
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],
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"type": "gt"
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},
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"operator": {
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"type": "or"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 60,
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"valuesCSV": "0,0,2,2,1,1"
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},
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "A",
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"scenarioId": "predictable_csv_wave",
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"stringInput": ""
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},
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"params": [
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"A",
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"5m",
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"now"
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]
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},
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"reducer": {
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"params": [],
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"type": "sum"
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},
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"type": "query"
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},
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{
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"evaluator": {
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"params": [
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7
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],
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"type": "gt"
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},
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"operator": {
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"type": "and"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 60,
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"valuesCSV": "0,0,2,2,1,1"
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},
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "A",
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"scenarioId": "predictable_csv_wave",
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"stringInput": ""
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},
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"params": [
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"A",
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"5m",
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"now"
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]
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},
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"reducer": {
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"params": [],
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"type": "last"
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},
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"type": "query"
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},
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{
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"evaluator": {
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"params": [],
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"type": "no_value"
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},
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"operator": {
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"type": "and"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 60,
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"valuesCSV": "0,0,2,2,1,1"
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},
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"hide": false,
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "C",
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"scenarioId": "no_data_points",
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"stringInput": ""
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},
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"params": [
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"C",
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"5m",
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"now"
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]
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},
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"reducer": {
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"params": [],
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"type": "diff"
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},
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"type": "query"
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},
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{
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"evaluator": {
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"params": [
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9
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],
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"type": "gt"
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},
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"operator": {
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"type": "or"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 30,
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"valuesCSV": "1,1,6,6,3,3"
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},
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"hide": false,
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "D",
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"scenarioId": "predictable_csv_wave",
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"stringInput": ""
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},
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"params": [
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"D",
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"5m",
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"now"
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]
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},
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"reducer": {
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"params": [],
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"type": "diff_abs"
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},
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"type": "query"
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},
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{
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"evaluator": {
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"params": [
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10
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],
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"type": "gt"
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},
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"operator": {
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"type": "and"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 30,
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"valuesCSV": "1,1,6,6,3,3"
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},
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"hide": false,
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "D",
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"scenarioId": "predictable_csv_wave",
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"stringInput": ""
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},
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"params": [
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"D",
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"5m",
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"now"
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]
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},
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"reducer": {
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"params": [],
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"type": "percent_diff"
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},
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"type": "query"
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},
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{
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"evaluator": {
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"params": [
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11
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],
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"type": "gt"
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},
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"operator": {
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"type": "and"
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},
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"query": {
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"datasourceId": 4,
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"model": {
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"alias": "",
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"csvWave": {
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"timeStep": 30,
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"valuesCSV": "1,1,6,6,3,3"
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},
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"hide": false,
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"lines": 10,
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"points": [],
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"pulseWave": {
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"offCount": 3,
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"offValue": 1,
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"onCount": 3,
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"onValue": 2,
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"timeStep": 60
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},
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"refId": "D",
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"scenarioId": "predictable_csv_wave",
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"stringInput": ""
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},
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"params": [
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"D",
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"10m",
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"now"
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]
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},
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"reducer": {
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"params": [],
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"type": "percent_diff_abs"
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},
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"type": "query"
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}
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]
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}
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