---
title: Query data with Flux
description: Guides that walk through both common and complex queries and use cases for Flux.
url: https://docs.influxdata.com/enterprise_influxdb/v1/flux/guides/
estimated_tokens: 6568
publisher: InfluxData
canonical: https://docs.influxdata.com/enterprise_influxdb/v1/flux/guides/
date: '2025-01-13T07:21:11-07:00'
lastmod: '2025-01-13T07:21:11-07:00'
---

The following guides walk through both common and complex queries and use cases for Flux.

#### Example data variable

Many of the examples provided in the following guides use a `data` variable,
which represents a basic query that filters data by measurement and field.`data` is defined as:

```js
data = from(bucket: "db/rp")
    |> range(start: -1h)
    |> filter(fn: (r) => r._measurement == "example-measurement" and r._field == "example-field")
```

## Flux query guides

* [Query fields and tags](#query-fields-and-tags)
* [Group](#group)
* [Sort and limit](#sort-and-limit)
* [Window & aggregate](#window-amp-aggregate)
* [Transform data with math](#transform-data-with-math)
* [Calculate percentages](#calculate-percentages)
* [Increase](#increase)
* [Moving Average](#moving-average)
* [Rate](#rate)
* [Histograms](#histograms)
* [Fill](#fill)
* [Median](#median)
* [Percentile & quantile](#percentile-amp-quantile)
* [Join](#join)
* [Cumulative sum](#cumulative-sum)
* [First and last](#first-and-last)
* [Exists](#exists)
* [Extract scalar values](#extract-scalar-values)
* [Manipulate timestamps](#manipulate-timestamps)
* [Monitor states](#monitor-states)
* [Query SQL data](#query-sql-data)
* [Conditional logic](#conditional-logic)
* [Regular expressions](#regular-expressions)
* [Geo-temporal data](#geo-temporal-data)
* [Use Flux in Chronograf dashboards](#use-flux-in-chronograf-dashboards)

### [Query fields and tags](/enterprise_influxdb/v1/flux/guides/query-fields/)

Use the `filter()` function to query data based on fields, tags, or any other column value. `filter()` performs operations similar to the `SELECT` statement and the `WHERE` clause in InfluxQL and other SQL-like query languages.

```js
from(bucket: "db/rp")
  |> range(start: -1h)
  |> filter(fn: (r) =>
      r._measurement == "example-measurement" and
      r._field == "example-field" and
      r.tag == "example-tag"
  )
```

### [Group](/enterprise_influxdb/v1/flux/guides/group-data/)

Use the `group()` function to group data with common values in specific columns.

```js
data
    |> group(columns: ["host"], mode: "by")
```

###### Input:

|       \_time       |host |\_value|
|--------------------|-----|-------|
|2020-01-01T00:01:00Z|host1|  1.0  |
|2020-01-01T00:01:00Z|host2|  2.0  |
|2020-01-01T00:02:00Z|host1|  1.0  |
|2020-01-01T00:02:00Z|host2|  3.0  |

###### Output:

|       \_time       |host |\_value|
|--------------------|-----|-------|
|2020-01-01T00:01:00Z|host1|  1.0  |
|2020-01-01T00:02:00Z|host1|  1.0  |

|       \_time       |host |\_value|
|--------------------|-----|-------|
|2020-01-01T00:01:00Z|host2|  2.0  |
|2020-01-01T00:02:00Z|host2|  3.0  |

### [Sort and limit](/enterprise_influxdb/v1/flux/guides/sort-limit/)

Use the `sort()`function to order records within each table by specific columns and the `limit()` function to limit the number of records in output tables to a fixed number, `n`.

```js
data
    |> sort(columns: ["host", "_value"])
    |> limit(n: 4)
```

###### Input:

|       \_time       |host|\_value|
|--------------------|----|-------|
|2020-01-01T00:01:00Z| A  |  1.0  |
|2020-01-01T00:02:00Z| B  |  1.2  |
|2020-01-01T00:03:00Z| A  |  1.8  |
|2020-01-01T00:04:00Z| B  |  0.9  |
|2020-01-01T00:05:00Z| B  |  1.4  |
|2020-01-01T00:06:00Z| B  |  2.0  |

###### Output:

|       \_time       |host|\_value|
|--------------------|----|-------|
|2020-01-01T00:03:00Z| A  |  1.8  |
|2020-01-01T00:01:00Z| A  |  1.0  |
|2020-01-01T00:06:00Z| B  |  2.0  |
|2020-01-01T00:05:00Z| B  |  1.4  |

### [Window & aggregate](/enterprise_influxdb/v1/flux/guides/window-aggregate/)

This guide walks through windowing and aggregating data with Flux and outlines how it shapes your data in the process.

```js
data
    |> aggregateWindow(every: 20m, fn: mean)
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:00:00Z|  250  |
|2020-01-01T00:04:00Z|  160  |
|2020-01-01T00:12:00Z|  150  |
|2020-01-01T00:19:00Z|  220  |
|2020-01-01T00:32:00Z|  200  |
|2020-01-01T00:51:00Z|  290  |
|2020-01-01T01:00:00Z|  340  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:20:00Z|  195  |
|2020-01-01T00:40:00Z|  200  |
|2020-01-01T01:00:00Z|  290  |
|2020-01-01T01:20:00Z|  340  |

### [Transform data with math](/enterprise_influxdb/v1/flux/guides/mathematic-operations/)

Use the `map()` function to remap column values and apply mathematic operations.

```js
data
    |> map(fn: (r) => ({ r with _value: r._value * r._value }))
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|   2   |
|2020-01-01T00:02:00Z|   4   |
|2020-01-01T00:03:00Z|   3   |
|2020-01-01T00:04:00Z|   5   |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|   4   |
|2020-01-01T00:02:00Z|  16   |
|2020-01-01T00:03:00Z|   9   |
|2020-01-01T00:04:00Z|  25   |

### [Calculate percentages](/enterprise_influxdb/v1/flux/guides/calculate-percentages/)

Use `pivot()` or `join()` and the `map()` function to align operand values into rows and calculate a percentage.

```js
data
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
  |> map(
      fn: (r) => ({
          _time: r._time,
          _field: "used_percent",
          _value: float(v: r.used) / float(v: r.total) * 100.0,
      }),
  )
```

###### Input:

|       \_time       |\_field|\_value|
|--------------------|-------|-------|
|2020-01-01T00:00:00Z| used  |  2.5  |
|2020-01-01T00:00:10Z| used  |  3.1  |
|2020-01-01T00:00:20Z| used  |  4.2  |

|       \_time       |\_field|\_value|
|--------------------|-------|-------|
|2020-01-01T00:00:00Z| total |  8.0  |
|2020-01-01T00:00:10Z| total |  8.0  |
|2020-01-01T00:00:20Z| total |  8.0  |

###### Output:

|       \_time       |   \_field   |\_value|
|--------------------|-------------|-------|
|2020-01-01T00:00:00Z|used\_percent| 31.25 |
|2020-01-01T00:00:10Z|used\_percent| 38.75 |
|2020-01-01T00:00:20Z|used\_percent| 52.50 |

### [Increase](/enterprise_influxdb/v1/flux/guides/increase/)

Use the `increase()` function to track increases across multiple columns in a table. This function is especially useful when tracking changes in counter values that wrap over time or periodically reset.

```js
data
    |> increase()
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|   1   |
|2020-01-01T00:02:00Z|   2   |
|2020-01-01T00:03:00Z|   8   |
|2020-01-01T00:04:00Z|  10   |
|2020-01-01T00:05:00Z|   0   |
|2020-01-01T00:06:00Z|   4   |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:02:00Z|   1   |
|2020-01-01T00:03:00Z|   7   |
|2020-01-01T00:04:00Z|   9   |
|2020-01-01T00:05:00Z|   9   |
|2020-01-01T00:06:00Z|  13   |

### [Moving Average](/enterprise_influxdb/v1/flux/guides/moving-average/)

Use the `movingAverage()` or `timedMovingAverage()` functions to return the moving average of data.

```js
data
    |> movingAverage(n: 3)
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.2  |
|2020-01-01T00:03:00Z|  1.8  |
|2020-01-01T00:04:00Z|  0.9  |
|2020-01-01T00:05:00Z|  1.4  |
|2020-01-01T00:06:00Z|  2.0  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:03:00Z| 1.33  |
|2020-01-01T00:04:00Z| 1.30  |
|2020-01-01T00:05:00Z| 1.36  |
|2020-01-01T00:06:00Z| 1.43  |

```js
data
    |> timedMovingAverage(every: 2m, period: 4m)
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.2  |
|2020-01-01T00:03:00Z|  1.8  |
|2020-01-01T00:04:00Z|  0.9  |
|2020-01-01T00:05:00Z|  1.4  |
|2020-01-01T00:06:00Z|  2.0  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:02:00Z| 1.000 |
|2020-01-01T00:04:00Z| 1.333 |
|2020-01-01T00:06:00Z| 1.325 |
|2020-01-01T00:06:00Z| 1.150 |

### [Rate](/enterprise_influxdb/v1/flux/guides/rate/)

Use the `derivative()` function to calculate the rate of change between subsequent values or the `aggregate.rate()` function to calculate the average rate of change per window of time. If time between points varies, these functions normalize points to a common time interval making values easily comparable.

```js
data
    |> derivative(unit: 1m, nonNegative: true)
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:00:00Z|  250  |
|2020-01-01T00:04:00Z|  160  |
|2020-01-01T00:12:00Z|  150  |
|2020-01-01T00:19:00Z|  220  |
|2020-01-01T00:32:00Z|  200  |
|2020-01-01T00:51:00Z|  290  |
|2020-01-01T01:00:00Z|  340  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:04:00Z|       |
|2020-01-01T00:12:00Z|       |
|2020-01-01T00:19:00Z| 10.0  |
|2020-01-01T00:32:00Z|       |
|2020-01-01T00:51:00Z| 4.74  |
|2020-01-01T01:00:00Z| 5.56  |

```js
import "experimental/aggregate"

data
    |> aggregate.rate(every: 20m, unit: 1m)
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:00:00Z|  250  |
|2020-01-01T00:04:00Z|  160  |
|2020-01-01T00:12:00Z|  150  |
|2020-01-01T00:19:00Z|  220  |
|2020-01-01T00:32:00Z|  200  |
|2020-01-01T00:51:00Z|  290  |
|2020-01-01T01:00:00Z|  340  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:20:00Z|       |
|2020-01-01T00:40:00Z| 10.0  |
|2020-01-01T01:00:00Z| 4.74  |
|2020-01-01T01:20:00Z| 5.56  |

### [Histograms](/enterprise_influxdb/v1/flux/guides/histograms/)

Use the `histogram()` function to create cumulative histograms with Flux.

```js
data
    |> histogram(
        column: "_value",
        upperBoundColumn: "le",
        countColumn: "_value",
        bins: [100.0, 200.0, 300.0, 400.0],
    )
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:00:00Z| 250.0 |
|2020-01-01T00:01:00Z| 160.0 |
|2020-01-01T00:02:00Z| 150.0 |
|2020-01-01T00:03:00Z| 220.0 |
|2020-01-01T00:04:00Z| 200.0 |
|2020-01-01T00:05:00Z| 290.0 |
|2020-01-01T01:00:00Z| 340.0 |

###### Output:

| le  |\_value|
|-----|-------|
|100.0|  0.0  |
|200.0|  3.0  |
|300.0|  6.0  |
|400.0|  7.0  |

### [Fill](/enterprise_influxdb/v1/flux/guides/fill/)

Use the `fill()` function to replace *null* values.

```js
data
    |> fill(usePrevious: true)
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z| null  |
|2020-01-01T00:02:00Z|  0.8  |
|2020-01-01T00:03:00Z| null  |
|2020-01-01T00:04:00Z| null  |
|2020-01-01T00:05:00Z|  1.4  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z| null  |
|2020-01-01T00:02:00Z|  0.8  |
|2020-01-01T00:03:00Z|  0.8  |
|2020-01-01T00:04:00Z|  0.8  |
|2020-01-01T00:05:00Z|  1.4  |

```js
data
    |> fill(value: 0.0)
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z| null  |
|2020-01-01T00:02:00Z|  0.8  |
|2020-01-01T00:03:00Z| null  |
|2020-01-01T00:04:00Z| null  |
|2020-01-01T00:05:00Z|  1.4  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  0.0  |
|2020-01-01T00:02:00Z|  0.8  |
|2020-01-01T00:03:00Z|  0.0  |
|2020-01-01T00:04:00Z|  0.0  |
|2020-01-01T00:05:00Z|  1.4  |

### [Median](/enterprise_influxdb/v1/flux/guides/median/)

Use the `median()` function to return a value representing the `0.5` quantile (50th percentile) or median of input data.

```js
data
    |> median()
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.0  |
|2020-01-01T00:03:00Z|  2.0  |
|2020-01-01T00:04:00Z|  3.0  |

###### Output:

|\_value|
|-------|
|  1.5  |

### [Percentile & quantile](/enterprise_influxdb/v1/flux/guides/percentile-quantile/)

Use the `quantile()` function to return all values within the `q` quantile or percentile of input data.

```js
data
    |> quantile(q: 0.99, method: "estimate_tdigest")
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.0  |
|2020-01-01T00:03:00Z|  2.0  |
|2020-01-01T00:04:00Z|  3.0  |

###### Output:

|\_value|
|-------|
|  3.0  |

### [Join](/enterprise_influxdb/v1/flux/guides/join/)

This guide walks through joining data with Flux and outlines how it shapes your data in the process.

```
t1 = from(bucket: "example-bucket")
    |> range(start: 2020-01-01T00:00:00Z)
    |> filter(fn: (r) => r.m == "foo")

t2 = from(bucket: "example-bucket")
    |> range(start: 2020-01-01T00:00:00Z)
    |> filter(fn: (r) => r.m == "bar")

join(tables: {t1: t1, t2: t2}, on: ["_time"])
```

###### Input:

###### t1

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|   1   |
|2020-01-01T00:02:00Z|   2   |
|2020-01-01T00:03:00Z|   1   |
|2020-01-01T00:04:00Z|   3   |

###### t2

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|   5   |
|2020-01-01T00:02:00Z|   2   |
|2020-01-01T00:03:00Z|   3   |
|2020-01-01T00:04:00Z|   4   |

###### Output:

|       \_time       |\_value\_t1|\_value\_t2|
|--------------------|-----------|-----------|
|2020-01-01T00:01:00Z|     1     |     5     |
|2020-01-01T00:02:00Z|     2     |     2     |
|2020-01-01T00:03:00Z|     1     |     3     |
|2020-01-01T00:04:00Z|     3     |     4     |

### [Cumulative sum](/enterprise_influxdb/v1/flux/guides/cumulativesum/)

Use the `cumulativeSum()` function to calculate a running total of values.

```js
data
    |> cumulativeSum()
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|   1   |
|2020-01-01T00:02:00Z|   2   |
|2020-01-01T00:03:00Z|   1   |
|2020-01-01T00:04:00Z|   3   |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|   1   |
|2020-01-01T00:02:00Z|   3   |
|2020-01-01T00:03:00Z|   4   |
|2020-01-01T00:04:00Z|   7   |

### [First and last](/enterprise_influxdb/v1/flux/guides/first-last/)

Use the `first()` or `last()` functions to return the first or last point in an input table.

```js
data
    |> first()
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.0  |
|2020-01-01T00:03:00Z|  2.0  |
|2020-01-01T00:04:00Z|  3.0  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |

```js
data
    |> last()
```

###### Input:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.0  |
|2020-01-01T00:03:00Z|  2.0  |
|2020-01-01T00:04:00Z|  3.0  |

###### Output:

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:04:00Z|  3.0  |

### [Exists](/enterprise_influxdb/v1/flux/guides/exists/)

Use the Flux `exists` operator to check if a record contains a key or if that key’s value is `null`.

##### Filter null values

```js
data
  |> filter(fn: (r) => exists r._value)
```

### [Extract scalar values](/enterprise_influxdb/v1/flux/guides/scalar-values/)

Use Flux stream and table functions to extract scalar values from Flux query output. This lets you, for example, dynamically set variables using query results.

```js
scalarValue = {
  _record =
    data
      |> tableFind(fn: key => true)
      |> getRecord(idx: 0)
  return _record._value
}
```

### [Manipulate timestamps](/enterprise_influxdb/v1/flux/guides/manipulate-timestamps/)

Use Flux to process and manipulate timestamps.

### [Monitor states](/enterprise_influxdb/v1/flux/guides/monitor-states/)

Flux provides several functions to help monitor states and state changes in your data.

### [Query SQL data](/enterprise_influxdb/v1/flux/guides/sql/)

The Flux `sql` package provides functions for working with SQL data sources. Use `sql.from()` to query SQL databases like PostgreSQL and MySQL

```js
import "sql"

sql.from(
    driverName: "postgres",
    dataSourceName: "postgresql://user:password@localhost",
    query: "SELECT * FROM example_table",
)
```

### [Conditional logic](/enterprise_influxdb/v1/flux/guides/conditional-logic/)

This guide describes how to use Flux conditional expressions, such as `if`, `else`, and `then`, to query and transform data. **Flux evaluates statements from left to right and stops evaluating once a condition matches.**

```js
if color == "green" then "008000" else "ffffff"
```

### [Regular expressions](/enterprise_influxdb/v1/flux/guides/regular-expressions/)

This guide walks through using regular expressions in evaluation logic in Flux functions.

```js
data
    |> filter(fn: (r) => r.tag =~ /^foo[1-3]/)
```

###### Input:

|       \_time       |tag |\_value|
|--------------------|----|-------|
|2020-01-01T00:01:00Z|foo1|  1.0  |
|2020-01-01T00:02:00Z|foo5|  1.2  |
|2020-01-01T00:03:00Z|bar3|  1.8  |
|2020-01-01T00:04:00Z|foo3|  0.9  |
|2020-01-01T00:05:00Z|foo2|  1.4  |
|2020-01-01T00:06:00Z|bar1|  2.0  |

###### Output:

|       \_time       |tag |\_value|
|--------------------|----|-------|
|2020-01-01T00:01:00Z|foo1|  1.0  |
|2020-01-01T00:04:00Z|foo3|  0.9  |
|2020-01-01T00:05:00Z|foo2|  1.4  |

### [Geo-temporal data](/enterprise_influxdb/v1/flux/guides/geo/)

Use the Flux Geo package to filter geo-temporal data and group by geographic location or track.

```js
import "experimental/geo"

sampleGeoData
  |> geo.filterRows(region: {lat: 30.04, lon: 31.23, radius: 200.0})
  |> geo.groupByArea(newColumn: "geoArea", level: 5)
```

### [Use Flux in Chronograf dashboards](/enterprise_influxdb/v1/flux/guides/flux-in-dashboards/)

This guide walks through using Flux queries in Chronograf dashboard cells, what template variables are available, and how to use them.
| _time | host | _value |
| --- | --- | --- |
| _time | host | _value |
| 2020-01-01T00:01:00Z | host1 | 1.0 |
| 2020-01-01T00:01:00Z | host2 | 2.0 |
| 2020-01-01T00:02:00Z | host1 | 1.0 |
| 2020-01-01T00:02:00Z | host2 | 3.0 |

| _time | host | _value |
| --- | --- | --- |
| _time | host | _value |
| 2020-01-01T00:01:00Z | host1 | 1.0 |
| 2020-01-01T00:02:00Z | host1 | 1.0 |

| _time | host | _value |
| --- | --- | --- |
| _time | host | _value |
| 2020-01-01T00:01:00Z | host2 | 2.0 |
| 2020-01-01T00:02:00Z | host2 | 3.0 |

| _time | host | _value |
| --- | --- | --- |
| _time | host | _value |
| 2020-01-01T00:01:00Z | A | 1.0 |
| 2020-01-01T00:02:00Z | B | 1.2 |
| 2020-01-01T00:03:00Z | A | 1.8 |
| 2020-01-01T00:04:00Z | B | 0.9 |
| 2020-01-01T00:05:00Z | B | 1.4 |
| 2020-01-01T00:06:00Z | B | 2.0 |

| _time | host | _value |
| --- | --- | --- |
| _time | host | _value |
| 2020-01-01T00:03:00Z | A | 1.8 |
| 2020-01-01T00:01:00Z | A | 1.0 |
| 2020-01-01T00:06:00Z | B | 2.0 |
| 2020-01-01T00:05:00Z | B | 1.4 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:00:00Z | 250 |
| 2020-01-01T00:04:00Z | 160 |
| 2020-01-01T00:12:00Z | 150 |
| 2020-01-01T00:19:00Z | 220 |
| 2020-01-01T00:32:00Z | 200 |
| 2020-01-01T00:51:00Z | 290 |
| 2020-01-01T01:00:00Z | 340 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:20:00Z | 195 |
| 2020-01-01T00:40:00Z | 200 |
| 2020-01-01T01:00:00Z | 290 |
| 2020-01-01T01:20:00Z | 340 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 2 |
| 2020-01-01T00:02:00Z | 4 |
| 2020-01-01T00:03:00Z | 3 |
| 2020-01-01T00:04:00Z | 5 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 4 |
| 2020-01-01T00:02:00Z | 16 |
| 2020-01-01T00:03:00Z | 9 |
| 2020-01-01T00:04:00Z | 25 |

| _time | _field | _value |
| --- | --- | --- |
| _time | _field | _value |
| 2020-01-01T00:00:00Z | used | 2.5 |
| 2020-01-01T00:00:10Z | used | 3.1 |
| 2020-01-01T00:00:20Z | used | 4.2 |

| _time | _field | _value |
| --- | --- | --- |
| _time | _field | _value |
| 2020-01-01T00:00:00Z | total | 8.0 |
| 2020-01-01T00:00:10Z | total | 8.0 |
| 2020-01-01T00:00:20Z | total | 8.0 |

| _time | _field | _value |
| --- | --- | --- |
| _time | _field | _value |
| 2020-01-01T00:00:00Z | used_percent | 31.25 |
| 2020-01-01T00:00:10Z | used_percent | 38.75 |
| 2020-01-01T00:00:20Z | used_percent | 52.50 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1 |
| 2020-01-01T00:02:00Z | 2 |
| 2020-01-01T00:03:00Z | 8 |
| 2020-01-01T00:04:00Z | 10 |
| 2020-01-01T00:05:00Z | 0 |
| 2020-01-01T00:06:00Z | 4 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:02:00Z | 1 |
| 2020-01-01T00:03:00Z | 7 |
| 2020-01-01T00:04:00Z | 9 |
| 2020-01-01T00:05:00Z | 9 |
| 2020-01-01T00:06:00Z | 13 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.2 |
| 2020-01-01T00:03:00Z | 1.8 |
| 2020-01-01T00:04:00Z | 0.9 |
| 2020-01-01T00:05:00Z | 1.4 |
| 2020-01-01T00:06:00Z | 2.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:03:00Z | 1.33 |
| 2020-01-01T00:04:00Z | 1.30 |
| 2020-01-01T00:05:00Z | 1.36 |
| 2020-01-01T00:06:00Z | 1.43 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.2 |
| 2020-01-01T00:03:00Z | 1.8 |
| 2020-01-01T00:04:00Z | 0.9 |
| 2020-01-01T00:05:00Z | 1.4 |
| 2020-01-01T00:06:00Z | 2.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:02:00Z | 1.000 |
| 2020-01-01T00:04:00Z | 1.333 |
| 2020-01-01T00:06:00Z | 1.325 |
| 2020-01-01T00:06:00Z | 1.150 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:00:00Z | 250 |
| 2020-01-01T00:04:00Z | 160 |
| 2020-01-01T00:12:00Z | 150 |
| 2020-01-01T00:19:00Z | 220 |
| 2020-01-01T00:32:00Z | 200 |
| 2020-01-01T00:51:00Z | 290 |
| 2020-01-01T01:00:00Z | 340 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:04:00Z |  |
| 2020-01-01T00:12:00Z |  |
| 2020-01-01T00:19:00Z | 10.0 |
| 2020-01-01T00:32:00Z |  |
| 2020-01-01T00:51:00Z | 4.74 |
| 2020-01-01T01:00:00Z | 5.56 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:00:00Z | 250 |
| 2020-01-01T00:04:00Z | 160 |
| 2020-01-01T00:12:00Z | 150 |
| 2020-01-01T00:19:00Z | 220 |
| 2020-01-01T00:32:00Z | 200 |
| 2020-01-01T00:51:00Z | 290 |
| 2020-01-01T01:00:00Z | 340 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:20:00Z |  |
| 2020-01-01T00:40:00Z | 10.0 |
| 2020-01-01T01:00:00Z | 4.74 |
| 2020-01-01T01:20:00Z | 5.56 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:00:00Z | 250.0 |
| 2020-01-01T00:01:00Z | 160.0 |
| 2020-01-01T00:02:00Z | 150.0 |
| 2020-01-01T00:03:00Z | 220.0 |
| 2020-01-01T00:04:00Z | 200.0 |
| 2020-01-01T00:05:00Z | 290.0 |
| 2020-01-01T01:00:00Z | 340.0 |

| le | _value |
| --- | --- |
| le | _value |
| 100.0 | 0.0 |
| 200.0 | 3.0 |
| 300.0 | 6.0 |
| 400.0 | 7.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | null |
| 2020-01-01T00:02:00Z | 0.8 |
| 2020-01-01T00:03:00Z | null |
| 2020-01-01T00:04:00Z | null |
| 2020-01-01T00:05:00Z | 1.4 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | null |
| 2020-01-01T00:02:00Z | 0.8 |
| 2020-01-01T00:03:00Z | 0.8 |
| 2020-01-01T00:04:00Z | 0.8 |
| 2020-01-01T00:05:00Z | 1.4 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | null |
| 2020-01-01T00:02:00Z | 0.8 |
| 2020-01-01T00:03:00Z | null |
| 2020-01-01T00:04:00Z | null |
| 2020-01-01T00:05:00Z | 1.4 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 0.0 |
| 2020-01-01T00:02:00Z | 0.8 |
| 2020-01-01T00:03:00Z | 0.0 |
| 2020-01-01T00:04:00Z | 0.0 |
| 2020-01-01T00:05:00Z | 1.4 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.0 |
| 2020-01-01T00:03:00Z | 2.0 |
| 2020-01-01T00:04:00Z | 3.0 |

| _value |
| --- |
| _value |
| 1.5 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.0 |
| 2020-01-01T00:03:00Z | 2.0 |
| 2020-01-01T00:04:00Z | 3.0 |

| _value |
| --- |
| _value |
| 3.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1 |
| 2020-01-01T00:02:00Z | 2 |
| 2020-01-01T00:03:00Z | 1 |
| 2020-01-01T00:04:00Z | 3 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 5 |
| 2020-01-01T00:02:00Z | 2 |
| 2020-01-01T00:03:00Z | 3 |
| 2020-01-01T00:04:00Z | 4 |

| _time | _value_t1 | _value_t2 |
| --- | --- | --- |
| _time | _value_t1 | _value_t2 |
| 2020-01-01T00:01:00Z | 1 | 5 |
| 2020-01-01T00:02:00Z | 2 | 2 |
| 2020-01-01T00:03:00Z | 1 | 3 |
| 2020-01-01T00:04:00Z | 3 | 4 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1 |
| 2020-01-01T00:02:00Z | 2 |
| 2020-01-01T00:03:00Z | 1 |
| 2020-01-01T00:04:00Z | 3 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1 |
| 2020-01-01T00:02:00Z | 3 |
| 2020-01-01T00:03:00Z | 4 |
| 2020-01-01T00:04:00Z | 7 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.0 |
| 2020-01-01T00:03:00Z | 2.0 |
| 2020-01-01T00:04:00Z | 3.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.0 |
| 2020-01-01T00:03:00Z | 2.0 |
| 2020-01-01T00:04:00Z | 3.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:04:00Z | 3.0 |

| _time | tag | _value |
| --- | --- | --- |
| _time | tag | _value |
| 2020-01-01T00:01:00Z | foo1 | 1.0 |
| 2020-01-01T00:02:00Z | foo5 | 1.2 |
| 2020-01-01T00:03:00Z | bar3 | 1.8 |
| 2020-01-01T00:04:00Z | foo3 | 0.9 |
| 2020-01-01T00:05:00Z | foo2 | 1.4 |
| 2020-01-01T00:06:00Z | bar1 | 2.0 |

| _time | tag | _value |
| --- | --- | --- |
| _time | tag | _value |
| 2020-01-01T00:01:00Z | foo1 | 1.0 |
| 2020-01-01T00:04:00Z | foo3 | 0.9 |
| 2020-01-01T00:05:00Z | foo2 | 1.4 |