# Convert influxdb json to power bi table

**URL:** <https://community.influxdata.com/t/convert-influxdb-json-to-power-bi-table/13072>\
**Category:** Systems\
**Created:** [February 17, 2020, 5:27pm UTC](https://community.influxdata.com/t/convert-influxdb-json-to-power-bi-table/13072 "2020-02-17T17:27:22Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![LochanaMendis](https://avatars.discourse-cdn.com/v4/letter/l/cc9497/32.png) [@LochanaMendis](https://community.influxdata.com/u/LochanaMendis)\
**Post date:** [February 17, 2020, 5:27pm UTC](https://community.influxdata.com/t/convert-influxdb-json-to-power-bi-table/13072/1 "2020-02-17T17:27:22Z")

</div>

Please help to convert json output from rest query from Influxdb to table in power bi

---

<div class="post-metadata">

**Author:** ![Giovanni\_Luisotto](https://sea1.discourse-cdn.com/flex023/user_avatar/community.influxdata.com/giovanni_luisotto/32/4017_2.png) [@Giovanni\_Luisotto](https://community.influxdata.com/u/Giovanni_Luisotto)\
**Post date:** [February 17, 2020, 11:01pm UTC](https://community.influxdata.com/t/convert-influxdb-json-to-power-bi-table/13072/2 "2020-02-17T23:01:21Z")

</div>

In order to help you I need at least the Json structure…  
Also, which is the exact problem on the PowerBI/InfluxDB side?

---

<div class="post-metadata">

**Author:** ![LochanaMendis](https://avatars.discourse-cdn.com/v4/letter/l/cc9497/32.png) [@LochanaMendis](https://community.influxdata.com/u/LochanaMendis)\
**Post date:** [February 18, 2020, 5:13am UTC](https://community.influxdata.com/t/convert-influxdb-json-to-power-bi-table/13072/3 "2020-02-18T05:13:03Z")

</div>

Hi @Giovanni_Luisotto,

My json structure is as follows

{  
“results”: [  
{  
“statement\_id”: 0,  
“series”: [  
{  
“name”: “Electrical”,  
“tags”: {  
“resource\_name”: “MainActivePowerTotal”  
},  
“columns”: [  
“time”,  
“mean”  
],  
“values”: [  
[  
“2020-02-17T06:10:00Z”,  
null  
],  
[  
“2020-02-17T06:20:00Z”,  
587.67744140625  
],  
[  
“2020-02-17T06:30:00Z”,  
596.9282315882241  
],  
[  
“2020-02-17T06:40:00Z”,  
549.469442267167  
],  
[  
“2020-02-17T06:50:00Z”,  
541.917407989502  
],  
[  
“2020-02-17T07:00:00Z”,  
561.8912205051731  
],  
[  
“2020-02-17T07:10:00Z”,  
545.8261212604801  
],  
[  
“2020-02-17T07:20:00Z”,  
568.2466936809261  
],  
[  
“2020-02-17T07:30:00Z”,  
569.2568552901105  
],  
[  
“2020-02-17T07:40:00Z”,  
575.8338697479992  
],  
[  
“2020-02-17T07:50:00Z”,  
576.8921612995426  
],  
[  
“2020-02-17T08:00:00Z”,  
573.4999496459961  
],  
[  
“2020-02-17T08:10:00Z”,  
560.0957954220655  
],  
[  
“2020-02-17T08:20:00Z”,  
562.0249328613281  
]  
]  
},  
{  
“name”: “Electrical”,  
“tags”: {  
“resource\_name”: “MainCurrentAvg”  
},  
“columns”: [  
“time”,  
“mean”  
],  
“values”: [  
[  
“2020-02-17T06:10:00Z”,  
null  
],  
[  
“2020-02-17T06:20:00Z”,  
907.6358032226562  
],  
[  
“2020-02-17T06:30:00Z”,  
915.0737813313802  
],  
[  
“2020-02-17T06:40:00Z”,  
850.6507209329044  
],  
[  
“2020-02-17T06:50:00Z”,  
815.8712986537388  
],  
[  
“2020-02-17T07:00:00Z”,  
865.1920640733507  
],  
[  
“2020-02-17T07:10:00Z”,  
842.1758460998535  
],  
[  
“2020-02-17T07:20:00Z”,  
873.9825903320312  
],  
[  
“2020-02-17T07:30:00Z”,  
872.6841081891741  
],  
[  
“2020-02-17T07:40:00Z”,  
884.9004669189453  
],  
[  
“2020-02-17T07:50:00Z”,  
894.0495697021485  
],  
[  
“2020-02-17T08:00:00Z”,  
877.8777683803013  
],  
[  
“2020-02-17T08:10:00Z”,  
859.5932713558799  
],  
[  
“2020-02-17T08:20:00Z”,  
875.2303161621094  
]  
]  
},  
{  
“name”: “Electrical”,  
“tags”: {  
“resource\_name”: “MainEnergy”  
},  
“columns”: [  
“time”,  
“mean”  
],  
“values”: [  
[  
“2020-02-17T06:10:00Z”,  
null  
],  
[  
“2020-02-17T06:20:00Z”,  
5102723.4  
],  
[  
“2020-02-17T06:30:00Z”,  
5102773.659090909  
],  
[  
“2020-02-17T06:40:00Z”,  
5102874.0625  
],  
[  
“2020-02-17T06:50:00Z”,  
5102967.76  
],  
[  
“2020-02-17T07:00:00Z”,  
5103057.42  
],  
[  
“2020-02-17T07:10:00Z”,  
5103156.75  
],  
[  
“2020-02-17T07:20:00Z”,  
5103249.166666667  
],  
[  
“2020-02-17T07:30:00Z”,  
5103336.5  
],  
[  
“2020-02-17T07:40:00Z”,  
5103431.604166667  
],  
[  
“2020-02-17T07:50:00Z”,  
5103533.652173913  
],  
[  
“2020-02-17T08:00:00Z”,  
5103625.95  
],  
[  
“2020-02-17T08:10:00Z”,  
5103731.1  
],  
[  
“2020-02-17T08:20:00Z”,  
5103774.625  
]  
]  
},  
{  
“name”: “Electrical”,  
“tags”: {  
“resource\_name”: “MainPowerFactor”  
},  
“columns”: [  
“time”,  
“mean”  
],  
“values”: [  
[  
“2020-02-17T06:10:00Z”,  
null  
],  
[  
“2020-02-17T06:20:00Z”,  
0.9097714920838674  
],  
[  
“2020-02-17T06:30:00Z”,  
0.9139599757535117  
],  
[  
“2020-02-17T06:40:00Z”,  
0.9151813518710252  
],  
[  
“2020-02-17T06:50:00Z”,  
0.9137259721755981  
],  
[  
“2020-02-17T07:00:00Z”,  
0.9180003716832116  
],  
[  
“2020-02-17T07:10:00Z”,  
0.9136283340908232  
],  
[  
“2020-02-17T07:20:00Z”,  
0.9169310607561251  
],  
[  
“2020-02-17T07:30:00Z”,  
0.9202882022392459  
],  
[  
“2020-02-17T07:40:00Z”,  
0.9164717424483526  
],  
[  
“2020-02-17T07:50:00Z”,  
0.9177247940040216  
],  
[  
“2020-02-17T08:00:00Z”,  
0.924390922232372  
],  
[  
“2020-02-17T08:10:00Z”,  
0.9215794191474006  
],  
[  
“2020-02-17T08:20:00Z”,  
0.9185435771942139  
]  
]  
},  
{  
“name”: “Electrical”,  
“tags”: {  
“resource\_name”: “VoltageA”  
},  
“columns”: [  
“time”,  
“mean”  
],  
“values”: [  
[  
“2020-02-17T06:10:00Z”,  
null  
],  
[  
“2020-02-17T06:20:00Z”,  
237.6288833618164  
],  
[  
“2020-02-17T06:30:00Z”,  
237.8891835530599  
],  
[  
“2020-02-17T06:40:00Z”,  
238.23394147087546  
],  
[  
“2020-02-17T06:50:00Z”,  
238.68151047650505  
],  
[  
“2020-02-17T07:00:00Z”,  
236.02180252075195  
],  
[  
“2020-02-17T07:10:00Z”,  
235.0208740234375  
],  
[  
“2020-02-17T07:20:00Z”,  
235.1172063918341  
],  
[  
“2020-02-17T07:30:00Z”,  
235.41241794162326  
],  
[  
“2020-02-17T07:40:00Z”,  
235.31271240234375  
],  
[  
“2020-02-17T07:50:00Z”,  
235.31833618164063  
],  
[  
“2020-02-17T08:00:00Z”,  
235.36876317074424  
],  
[  
“2020-02-17T08:10:00Z”,  
235.18864922774466  
],  
[  
“2020-02-17T08:20:00Z”,  
234.9145263671875  
]  
]  
},  
{  
“name”: “Electrical”,  
“tags”: {  
“resource\_name”: “VoltageB”  
},  
“columns”: [  
“time”,  
“mean”  
],  
“values”: [  
[  
“2020-02-17T06:10:00Z”,  
null  
],  
[  
“2020-02-17T06:20:00Z”,  
237.04432678222656  
],  
[  
“2020-02-17T06:30:00Z”,  
237.54657406277127  
],  
[  
“2020-02-17T06:40:00Z”,  
238.14813714278372  
],  
[  
“2020-02-17T06:50:00Z”,  
238.4341817220052  
],  
[  
“2020-02-17T07:00:00Z”,  
236.1190538406372  
],  
[  
“2020-02-17T07:10:00Z”,  
234.7789579119001  
],  
[  
“2020-02-17T07:20:00Z”,  
234.93265702989368  
],  
[  
“2020-02-17T07:30:00Z”,  
235.25931895862925  
],  
[  
“2020-02-17T07:40:00Z”,  
235.1957550048828  
],  
[  
“2020-02-17T07:50:00Z”,  
235.32901000976562  
],  
[  
“2020-02-17T08:00:00Z”,  
235.26353615208677  
],  
[  
“2020-02-17T08:10:00Z”,  
235.0978963751542  
],  
[  
“2020-02-17T08:20:00Z”,  
null  
]  
]  
},  
{  
“name”: “Electrical”,  
“tags”: {  
“resource\_name”: “VoltageC”  
},  
“columns”: [  
“time”,  
“mean”  
],  
“values”: [  
[  
“2020-02-17T06:10:00Z”,  
null  
],  
[  
“2020-02-17T06:20:00Z”,  
238.33627700805664  
],  
[  
“2020-02-17T06:30:00Z”,  
238.7968485514323  
],  
[  
“2020-02-17T06:40:00Z”,  
239.19335005018445  
],  
[  
“2020-02-17T06:50:00Z”,  
239.64923213078424  
],  
[  
“2020-02-17T07:00:00Z”,  
237.22103881835938  
],  
[  
“2020-02-17T07:10:00Z”,  
235.9083513532366  
],  
[  
“2020-02-17T07:20:00Z”,  
235.97623879568917  
],  
[  
“2020-02-17T07:30:00Z”,  
236.32627563476564  
],  
[  
“2020-02-17T07:40:00Z”,  
236.2939652663011  
],  
[  
“2020-02-17T07:50:00Z”,  
236.29410298665366  
],  
[  
“2020-02-17T08:00:00Z”,  
236.39215850830078  
],  
[  
“2020-02-17T08:10:00Z”,  
236.1125602722168  
],  
[  
“2020-02-17T08:20:00Z”,  
235.96351623535156  
]  
]  
}  
]  
}  
]  
}

I require the resource names as collumns and its corresponding mean values as rows. The time is common to all resource names.

Further is there a function to dynamically convert any multi arrayed json to a table?

---

<div class="post-metadata">

**Author:** ![Anaisdg](https://sea1.discourse-cdn.com/flex023/user_avatar/community.influxdata.com/anaisdg/32/6401_2.png) [@Anaisdg](https://community.influxdata.com/u/Anaisdg)\
**Post date:** [February 18, 2020, 10:36pm UTC](https://community.influxdata.com/t/convert-influxdb-json-to-power-bi-table/13072/4 "2020-02-18T22:36:57Z")

</div>

Hello @LochanaMendis,  
Welcome 🙂 perhaps this thread or community user can provide you with additional help:

> [@Sharing: Example Power BI Power Query to execute Flux query](https://community.influxdata.com/t/sharing-example-power-bi-power-query-to-execute-flux-query/11200):
>
> (So many uses of “power” and “query” follow… for great justice) We currently use Influx OSS and InfluxCloud v1. Grafana, Chronograf, and Power BI are the main applications we run against InfluxDB. We’re preparing for an eventual transition to InfluxDB v2. One piece of the migration is updating our Power BI reports to use the InfluxDB v2 API. Unfortunately, Microsoft still does not offer native support for InfluxDB in their Power BI tool. So, raw Power Query code is necessary to submit queries …

---

<div class="post-metadata">

**Author:** ![Giovanni\_Luisotto](https://sea1.discourse-cdn.com/flex023/user_avatar/community.influxdata.com/giovanni_luisotto/32/4017_2.png) [@Giovanni\_Luisotto](https://community.influxdata.com/u/Giovanni_Luisotto)\
**Post date:** [February 19, 2020, 9:52am UTC](https://community.influxdata.com/t/convert-influxdb-json-to-power-bi-table/13072/5 "2020-02-19T09:52:08Z")

</div>

I’ve found a way to read those data in a decent way, without using custom M code

Please note that the json you pasted is not a valid json (at least to me) since it does not use the standard double quotes ("). Anyway I saved it to a flie and loaded it, below the code

```auto
let
    Source = Json.Document(File.Contents("C:\Users\gluisotto\Desktop\TestDataInflux.json")),
    results = Source[results],
    results1 = results{0},
    series = results1[series],
    #"Converted to Table" = Table.FromList(series, Splitter.SplitByNothing(), null, null, ExtraValues.Error),
    #"Expanded Column1" = Table.ExpandRecordColumn(#"Converted to Table", "Column1", {"name", "tags", "columns", "values"}, {"Column1.name", "Column1.tags", "Column1.columns", "Column1.values"}),
    #"Expanded Column1.tags" = Table.ExpandRecordColumn(#"Expanded Column1", "Column1.tags", {"resource_name"}, {"Column1.tags.resource_name"}),
    #"Expanded Column1.values" = Table.ExpandListColumn(#"Expanded Column1.tags", "Column1.values"),
    #"Extracted Values" = Table.TransformColumns(#"Expanded Column1.values", {"Column1.values", each Text.Combine(List.Transform(_, Text.From), ","), type text}),
    #"Split Column by Delimiter" = Table.SplitColumn(#"Extracted Values", "Column1.values", Splitter.SplitTextByDelimiter(",", QuoteStyle.Csv), {"Column1.values.1", "Column1.values.2"}),
    #"Changed Type" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"Column1.values.1", type datetime}, {"Column1.values.2", type number}})
in
    #"Changed Type"

```

As you will see, the result is not bad, but is still a bit raw:

- the columns are not renamed dynamically, I’ve looked a bit on the web but did not find a quick way to so. (but since the query itself is static you can also set the names manually, if you change the query, you will also change the field names)
- There is the column “columns”, of type list which contains the names of the value columns, you may want to remove that form the final result

A nicer result might be obtained with this code, which also pivotes the table, to turns your table structure from a list of rows with “attribute | value” to column for each “attribute” (the tag “resource\_name” in this case)

```auto
let
    Source = Json.Document(File.Contents("C:\Users\gluisotto\Desktop\TestDataInflux.json")),
    results = Source[results],
    results1 = results{0},
    series = results1[series],
    #"Converted to Table" = Table.FromList(series, Splitter.SplitByNothing(), null, null, ExtraValues.Error),
    #"Expanded Column1" = Table.ExpandRecordColumn(#"Converted to Table", "Column1", {"name", "tags", "columns", "values"}, {"Column1.name", "Column1.tags", "Column1.columns", "Column1.values"}),
    #"Expanded Column1.tags" = Table.ExpandRecordColumn(#"Expanded Column1", "Column1.tags", {"resource_name"}, {"Column1.tags.resource_name"}),
    #"Expanded Column1.values" = Table.ExpandListColumn(#"Expanded Column1.tags", "Column1.values"),
    #"Extracted Values" = Table.TransformColumns(#"Expanded Column1.values", {"Column1.values", each Text.Combine(List.Transform(_, Text.From), ","), type text}),
    #"Split Column by Delimiter" = Table.SplitColumn(#"Extracted Values", "Column1.values", Splitter.SplitTextByDelimiter(",", QuoteStyle.Csv), {"Column1.values.1", "Column1.values.2"}),
    #"Changed Type" = Table.TransformColumnTypes(#"Split Column by Delimiter",{{"Column1.values.1", type datetime}, {"Column1.values.2", type number}}),
    #"Removed Columns" = Table.RemoveColumns(#"Changed Type",{"Column1.columns"}),
    #"Renamed Columns" = Table.RenameColumns(#"Removed Columns",{{"Column1.values.1", "Time"}}),
    #"Reordered Columns" = Table.ReorderColumns(#"Renamed Columns",{"Column1.name", "Time", "Column1.tags.resource_name", "Column1.values.2"}),
    #"Pivoted Column" = Table.Pivot(#"Reordered Columns", List.Distinct(#"Reordered Columns"[Column1.tags.resource_name]), "Column1.tags.resource_name", "Column1.values.2", List.Sum)
in
    #"Pivoted Column"

```