noramullen1
GitHub
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1062486607
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@ -15,8 +15,3 @@ weight: 104
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The following articles walk through common task use cases.
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{{< children >}}
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{{% note %}}
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This list will continue to grow.
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If you have suggestions, please [submit them to the InfluxData Community](https://community.influxdata.com/c/influxdb2).
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{{% /note %}}
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@ -0,0 +1,49 @@
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---
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title: Calculate a weekly mean
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description: >
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Calculate a weekly mean and add it to a new bucket.
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menu:
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influxdb_2_0:
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name: Calculate a weekly mean
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parent: Common tasks
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weight: 202
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influxdb/v2.0/tags: [tasks]
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---
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{{% note %}}
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This example uses [NOAA water sample data](/influxdb/v2.0/reference/sample-data/#noaa-water-sample-data).
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{{% /note %}}
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This example calculates a temperature weekly mean and stores it in a separate bucket.
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The following query:
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- Uses [`filter()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/filter/) to filter the `average_temperature` measurement.
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- Uses [`range()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/range/) to define a time range.
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- Uses [`aggregateWindow()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/aggregates/aggregatewindow/) to group average temperature by week and compute the mean.
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- Sends the weekly mean to a new bucket (`weekly_means`)
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```js
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option task = {
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name: "weekly-means",
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every: 1w,
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}
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from(bucket: "noaa")
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|> filter(fn: (r) => r._measurement == "average_temperature")
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|> range(start: 2019-09-01T11:24:00Z)
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|> aggregateWindow(every: 1w, fn: mean)
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|> to(bucket: "weekly_means")
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```
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### Example results
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| _start | _stop | _field | _measurement | location | _value | _time |
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|:------ |:----- |:------ |:------------ |:-------- | ------: |:----- |
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| 2019-09-01T11:24:00Z | 2020-10-19T20:39:49Z | degrees | average_temperature | coyote_creek | 80.31005917159763 | 2019-09-05T00:00:00Z |
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| 2019-09-01T11:24:00Z | 2020-10-19T20:39:49Z | degrees | average_temperature | coyote_creek | 79.8422619047619 | 2019-09-12T00:00:00Z |
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| 2019-09-01T11:24:00Z | 2020-10-19T20:39:49Z | degrees | average_temperature | coyote_creek | 79.82710622710623 | 2019-09-19T00:00:00Z |
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| _start | _stop | _field | _measurement | location | _value | _time |
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|:------ |:----- |:------ |:------------ |:-------- | ------: |:----- |
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| 2019-09-01T11:24:00Z | 2020-10-19T20:39:49Z | degrees | average_temperature | santa_monica | 80.19952494061758 | 2019-09-05T00:00:00Z |
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| 2019-09-01T11:24:00Z | 2020-10-19T20:39:49Z | degrees | average_temperature | santa_monica | 80.01964285714286 | 2019-09-12T00:00:00Z |
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| 2019-09-01T11:24:00Z | 2020-10-19T20:39:49Z | degrees | average_temperature | santa_monica | 80.20451
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@ -0,0 +1,43 @@
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---
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title: Convert results to JSON
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seotitle: Convert results to JSON and send them to a URL
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description: >
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Use `json.encode()` to convert query results to JSON and `http.post()` to send them
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to a URL endpoint.
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menu:
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influxdb_2_0:
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name: Convert results to JSON
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parent: Common tasks
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weight: 203
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influxdb/v2.0/tags: [tasks]
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---
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{{% note %}}
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This example uses [NOAA water sample data](/influxdb/v2.0/reference/sample-data/#noaa-water-sample-data).
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{{% /note %}}
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Send each record to a URL endpoint using the HTTP POST method. This example uses [`json.encode()`](/influxdb/v2.0/reference/flux/stdlib/json/encode/) to convert a value into JSON bytes, then uses [`http.post()`](/influxdb/v2.0/reference/flux/stdlib/http/post/) to send them to a URL endpoint.
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The following query:
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- Uses [`filter()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/filter/) to filter the `average_temperature` measurement.
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- Uses [`mean()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/aggregates/mean/) to calculate the average value from results.
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- Uses [`map()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/map/) to create a new column, `jsonStr`, and build a JSON object using column values from the query. It then byte-encodes the JSON object and stores it as a string in the `jsonStr` column.
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- Uses [`http.post()`](/influxdb/v2.0/reference/flux/stdlib/http/post/) to send the `jsonStr` value from each record to an HTTP endpoint.
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```js
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import "http"
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import "json"
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from(bucket: "noaa")
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|> filter(fn: (r) => r._measurement == "average_temperature")
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|> mean()
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|> map(fn: (r) => ({ r with
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jsonStr: string(v: json.encode(v: {"location":r.location,"mean":r._value}))}))
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|> map(fn: (r) => ({r with
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status_code: http.post(
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url: "http://somehost.com/",
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headers: {x:"a", y:"b"},
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data: bytes(v: r.jsonStr)
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)
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}))
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```
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@ -0,0 +1,21 @@
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---
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title: Common queries
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seotitle: Common queries with Flux
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description: >
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This collection of articles walks through common use cases for Flux queries.
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influxdb/v2.0/tags: [queries]
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menu:
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influxdb_2_0:
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name: Common queries
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parent: Query data
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weight: 104
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---
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The following articles walk through common queries using the [NOAA water database data](https://influx-testdata.s3.amazonaws.com/noaa.csv).
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{{< children >}}
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{{% note %}}
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This list will continue to grow.
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If you have suggestions, please [submit them to the InfluxData Community](https://community.influxdata.com/c/influxdb2).
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{{% /note %}}
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---
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title: Use values to calculate a new column
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description: >
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Use the `map()` function to create a new column calculated from existing values in each row.
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influxdb/v2.0/tags: [queries]
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menu:
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influxdb_2_0:
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name: Calculate a new column
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parent: Common queries
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weight: 104
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---
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{{% note %}}
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This example uses [NOAA water sample data](/influxdb/v2.0/reference/sample-data/#noaa-water-sample-data).
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{{% /note %}}
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This example converts temperature from Fahrenheit to Celsius and maps the Celsius value to a new `celsius` column.
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The following query:
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- Uses [`filter()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/filter/) to filter the `average_temperature` measurement.
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- Uses [`map()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/map/) to create a new column calculated from existing values in each row.
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```js
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from(bucket: "noaa")
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|> filter(fn: (r) => r._measurement == "average_temperature")
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|> map(fn: (r) => ({r with
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celsius: ((r._value - 32.0) * 5.0 / 9.0)
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})
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)
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```
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### Example results
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| _start | _stop | _field | _measurement | location | _time | _value | celsius |
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|:------ |:----- |:------: |:------------: |:--------: |:----- | ------:| -------:|
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:00:00Z | 82 | 27.78 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:06:00Z | 73 | 22.78 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:12:00Z | 86 | 30.00 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:18:00Z | 89 | 31.67 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:24:00Z | 77 | 25.00 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:30:00Z | 70 | 21.11 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:36:00Z | 84 | 28.89 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:42:00Z | 76 | 24.44 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:48:00Z | 85 | 29.44 |
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| 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | degrees | average_temperature | coyote_creek | 2019-08-17T00:54:00Z | 80 | 26.67 |
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| ••• | ••• | ••• | ••• | ••• | ••• | ••• | ••• |
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---
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title: Compare the last measurement to another bucket's mean
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seotitle: Compare the last measurement to a mean stored in another bucket
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description: >
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Compare the value from the latest point to an average value stored in another bucket. This is useful when using the average value to calculate a threshold check.
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influxdb/v2.0/tags: [queries]
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menu:
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influxdb_2_0:
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name: Compare values from different buckets
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parent: Common queries
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weight: 104
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---
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{{% note %}}
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This example uses [NOAA water sample data](/influxdb/v2.0/reference/sample-data/#noaa-water-sample-data).
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{{% /note %}}
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This example compares the value from the latest point to an average value stored in another bucket. This is useful when using the average value to calculate a [threshold check](/influxdb/v2.0/monitor-alert/checks/create/#threshold-check).
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The following query:
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- Uses [`range()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/range/) to define a time range.
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- Gets the last value in the `means` bucket and compares it to the last value in the `noaa` bucket using [`last()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/selectors/last/).
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- Uses [`join()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/join/) to combine the results
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- Uses [`map()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/map/) to calculate the differences
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```js
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means = from(bucket: "weekly_means")
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|> range(start: 2019-09-01T00:00:00Z)
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|> last()
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|> keep(columns: ["_value", "location"])
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latest = from(bucket: "noaa")
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|> range(start: 2019-09-01T00:00:00Z)
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|> filter(fn: (r) => r._measurement == "average_temperature")
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|> last()
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|> keep(columns: ["_value", "location"])
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join(tables: {mean: means, reading: latest}, on: ["location"])
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|> map(fn: (r) => ({r with deviation: r._value_reading - r._value_mean}))
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```
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### Example results
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| location | _value_mean | _value_reading | deviation |
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|:-------- | -----------: | --------------:| ---------: |
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| coyote_creek | 79.82710622710623 | 89 | 9.172893772893772 |
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| santa_monica | 80.20451339915374 | 85 | 4.79548660084626 |
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---
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title: Find and count unique values
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description: >
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Count the number of unique values in a specified column.
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influxdb/v2.0/tags: [queries]
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menu:
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influxdb_2_0:
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name: Count unique values
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parent: Common queries
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weight: 104
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---
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{{% note %}}
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These examples use [NOAA water sample data](/influxdb/v2.0/reference/sample-data/#noaa-water-sample-data).
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{{% /note %}}
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The following examples identify and count unique locations that data was collected from.
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## Find unique values
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This query:
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- Uses [`group()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/group/) to ungroup data and return results in a single table.
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- Uses [`keep()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/keep/) and [`unique()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/selectors/unique/) to return unique values in the specified column.
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```js
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import "experimental/csv"
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csv.from(bucket: "noaa")
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|> group()
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|> keep(columns: ["location"])
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|> unique(column: "location")
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```
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### Example results
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| location |
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|:-------- |
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| coyote_creek |
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| santa_monica |
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## Count unique values
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This query:
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- Uses [`group()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/group/) to ungroup data and return results in a single table.
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- Uses [`keep()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/keep/), [`unique()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/selectors/unique/), and then [`count()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/aggregates/count/) to count the number of unique values.
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```js
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from(bucket: "noaa")
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|> group()
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|> unique(column: "location")
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|> count(column: "location")
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```
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### Example results
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| location |
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| ---------:|
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| 2 |
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---
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title: Recalculate the _value column
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description: Recalculate the `_value` column without creating a new one.
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influxdb/v2.0/tags: [queries]
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menu:
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influxdb_2_0:
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name: Recalculate the _value column
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parent: Common queries
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weight: 104
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---
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{{% note %}}
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This example uses [NOAA water sample data](/influxdb/v2.0/reference/sample-data/#noaa-water-sample-data).
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{{% /note %}}
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Recalculate the `_value` column without creating a new one. Use the `with` operator in [`map()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/map/) to overwrite the existing `_value` column.
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The following query:
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- Uses [`filter()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/filter/) to filter the `average_temperature` measurement.
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- Uses [`map()`](/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/map/) to convert Fahrenheit temperature values into Celsius.
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```js
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from(bucket: "noaa")
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|> filter(fn: (r) => r._measurement == "average_temperature")
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|> map(fn: (r) => ({r with _value: (float(v: r._value) - 32.0) * 5.0 / 9.0} ))
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```
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| _field | _measurement | _start | _stop | _time | location | _value |
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|:------ |:------------ |:------ |:----- |:----- |:-------- | ------: |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:00:00Z | coyote_creek | 27.77777777777778 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:06:00Z | coyote_creek | 22.77777777777778 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:12:00Z | coyote_creek | 30 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:18:00Z | coyote_creek | 31.666666666666668 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:24:00Z | coyote_creek | 25 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:30:00Z | coyote_creek | 21.11111111111111 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:36:00Z | coyote_creek | 28.88888888888889 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:42:00Z | coyote_creek | 24.444444444444443 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:48:00Z | coyote_creek | 29.444444444444443 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T00:54:00Z | coyote_creek | 26.666666666666668 |
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| degrees | average_temperature | 1920-03-05T22:10:01Z | 2020-03-05T22:10:01Z | 2019-08-17T01:00:00Z | coyote_creek | 21.11111111111111 |
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||||
| ••• | ••• | ••• | ••• | ••• | ••• | ••• |
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||||
|
|
@ -53,3 +53,20 @@ to query and analyze the geo-temporal data in this sample data set.
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</a>
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_Used in [Work with geo-temporal data](/influxdb/v2.0/query-data/flux/geo/)._
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|
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### NOAA water sample data
|
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|
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This data set is publicly available data from the [National Oceanic and Atmospheric Administration’s (NOAA) Center for Operational Oceanographic Products and Services](http://tidesandcurrents.noaa.gov/stations.html).
|
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[The CSV data](https://influx-testdata.s3.amazonaws.com/noaa.csv) includes 15,258 observations of water levels (ft) collected every six minutes at two stations (Santa Monica, CA (ID 9410840) and Coyote Creek, CA (ID 9414575)) over the period from August 18, 2015 through September 18, 2015.
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|
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To avoid having to re-download this 10MB dataset every time you run a query, we recommend that you [create a new bucket](/influxdb/v2.0/organizations/buckets/create-bucket/) (`noaa`) and write the NOAA data to it. To do so, run the following:
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|
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```js
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import "experimental/csv"
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|
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csv.from(url: "https://influx-testdata.s3.amazonaws.com/noaa.csv")
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|> to(bucket: "noaa", org: "your-org")
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```
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_Used in [Common queries](/influxdb/v2.0/query-data/common-queries/) and [Common tasks](/influxdb/v2.0/process-data/common-tasks/)._
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Reference in New Issue