# AggregateWindow with mixed types

**URL:** <https://community.influxdata.com/t/aggregatewindow-with-mixed-types/23385>\
**Category:** InfluxDB 2\
**Tags:** schema, query, flux\
**Created:** [January 16, 2022, 12:54am UTC](https://community.influxdata.com/t/aggregatewindow-with-mixed-types/23385 "2022-01-16T00:54:07Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![AnalogJ](https://sea1.discourse-cdn.com/flex023/user_avatar/community.influxdata.com/analogj/32/9243_2.png) [@AnalogJ](https://community.influxdata.com/u/AnalogJ)\
**Post date:** [January 16, 2022, 12:54am UTC](https://community.influxdata.com/t/aggregatewindow-with-mixed-types/23385/1 "2022-01-16T00:54:07Z")

</div>

Hi,  
I’m trying to create an aggregateWindow (for downsampling) that contains data points with fields that have multiple types (string, int & float).

Here’s an incredibly simplified version of what’s happening

First we’ll create a empty influxdb v2 db container

```auto
docker run --rm -p 8086:8086 \
  -e DOCKER_INFLUXDB_INIT_MODE=setup \
  -e DOCKER_INFLUXDB_INIT_USERNAME=admin \
  -e DOCKER_INFLUXDB_INIT_PASSWORD=password12345 \
  -e DOCKER_INFLUXDB_INIT_ORG=scrutiny \
  -e DOCKER_INFLUXDB_INIT_BUCKET=metrics \
  -e DOCKER_INFLUXDB_INIT_ADMIN_TOKEN=my-super-secret-auth-token \
  influxdb:2.0

```

After that, we’ll populate influxDB with 4 data points: 2 points for each “device\_wwn”

```auto
curl --request POST \
"http://localhost:8086/api/v2/write?org=scrutiny&bucket=metrics&precision=ns" \
  --header "Authorization: Token my-super-secret-auth-token" \
  --header "Content-Type: text/plain; charset=utf-8" \
  --header "Accept: application/json" \
  --data-binary '
    smart,device_wwn=diskdeviceid01,protocol="NVMe" temperature=70.00,attr.power_cycles.attribute_id="power_cycles",attr.power_cycles.value=100,attr.host_reads.attribute_id="host_reads",attr.host_reads.value=1000 1642291849000000000
    smart,device_wwn=diskdeviceid01,protocol="NVMe" temperature=80.00,attr.power_cycles.attribute_id="power_cycles",attr.power_cycles.value=110,attr.host_reads.attribute_id="host_reads",attr.host_reads.value=2000 1642291909000000000
    smart,device_wwn=diskdeviceid02,protocol="ATA" temperature=70.00,attr.1.attribute_id="1",attr.1.value=100,attr.2.attribute_id="2",attr.2.value=1000 1642291649000000000
    smart,device_wwn=diskdeviceid02,protocol="ATA" temperature=80.00,attr.1.attribute_id="1",attr.1.value=110,attr.2.attribute_id="2",attr.2.value=2000 1642291709000000000
    '

```

Finally we’ll attempt to aggregate/downsample the data we just wrote (down to 1 data point per unique “device\_wwn”:

Ideally the two datapoints should be:

```auto
smart,device_wwn=diskdeviceid01,protocol="NVMe" temperature=75.00,attr.power_cycles.attribute_id="power_cycles",attr.power_cycles.value=105,attr.host_reads.attribute_id="host_reads",attr.host_reads.value=1500 1642291909000000000

smart,device_wwn=diskdeviceid02,protocol="ATA" temperature=75.00,attr.1.attribute_id="1",attr.1.value=105,attr.2.attribute_id="2",attr.2.value=15000 1642291709000000000

```

This aggregateWindow query fails

```auto
curl -vvv --request POST "http://localhost:8086/api/v2/query?org=scrutiny" \
  --header 'Authorization: Token my-super-secret-auth-token' \
  --header 'Accept: application/csv' \
  --header 'Content-type: application/vnd.flux' \
  --data 'import "influxdata/influxdb/schema"

smart_data = from(bucket: "metrics")
|> range(start: -2y, stop: now())
|> filter(fn: (r) => r["_measurement"] == "smart" )
|> filter(fn: (r) => r["_field"] !~ /(_measurement|protocol|device_wwn)/)

smart_data
|> aggregateWindow(fn: mean, every: 1w)
|> group(columns: ["device_wwn"])
|> schema.fieldsAsCols()'

{"code":"invalid","message":"unsupported input type for mean aggregate: string"}%

```

But if we filter out the “attribute\_id” field (which is of type string), everything works:

```auto
curl -vvv --request POST "http://localhost:8086/api/v2/query?org=scrutiny" \
  --header 'Authorization: Token my-super-secret-auth-token' \
  --header 'Accept: application/csv' \
  --header 'Content-type: application/vnd.flux' \
  --data 'import "influxdata/influxdb/schema"

smart_data = from(bucket: "metrics")
|> range(start: -2y, stop: now())
|> filter(fn: (r) => r["_measurement"] == "smart" )
|> filter(fn: (r) => r["_field"] !~ /(_measurement|protocol|device_wwn|attribute_id)/)

smart_data
|> aggregateWindow(fn: mean, every: 1w)
|> group(columns: ["device_wwn"])
|> schema.fieldsAsCols()'

```

As I mentioned above, this is an incredibly simplified version of my dataset, and we have dozens of fields for each point, with 1/3 being string values (which are constants). I need to find a way to have them copied into the aggregated data.

---

<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:** [January 18, 2022, 5:03pm UTC](https://community.influxdata.com/t/aggregatewindow-with-mixed-types/23385/2 "2022-01-18T17:03:42Z")

</div>

Hello @AnalogJ,  
You can use this package:

> **[types.isType() function | Flux 0.x Documentation](https://docs.influxdata.com/flux/v0.x/stdlib/types/istype/#type)**
>
> types.isType() tests if a value is a specified type.

But I’m having trouble thinking of way that you can programmatically filter out specific string fields and match them back into their aggregate value/series.  
You could maybe filter out the string types and convert them to tags with a map() function and filtering out those string fields before performing the aggregateWindow().