# Live Dashboards NQL examples

{% hint style="warning" %}
When configuring widgets using NQL, [do not hardcode absolute time values](https://docs.nexthink.com/platform/user-guide/managing-live-dashboards#caution-on-using-hardcoded-time-values-in-widgets) or static timestamps.
{% endhint %}

This list of NQL query examples is designed to help you create live dashboard widgets.

## KPI widget <a href="#livedashboardsnqlexamples-kpiwidget" id="livedashboardsnqlexamples-kpiwidget"></a>

<details>

<summary>NQL structure</summary>

```
...
summarize <kpi> = <sum() | count() | avg() | max() | min()>
```

</details>

#### Examples

<details>

<summary>Display the total web application errors during the last 7 days.</summary>

{% code overflow="wrap" lineNumbers="true" %}

```
web.errors during past 7d
| summarize total_errors = number_of_errors.sum() 
```

{% endcode %}

</details>

<details>

<summary>Display the ratio of backend page load duration during the last 7 days.</summary>

{% code overflow="wrap" %}

```
web.page_views during past 7d
| summarize 
  backend_dur_ratio = page_load_time.backend.sum() /
  page_load_time.overall.sum()
```

{% endcode %}

</details>

<details>

<summary>Display the estimated savings from remote action remediation in USD.</summary>

```
remote_action.executions during past 30d
| where status == success
| where purpose == remediation
| summarize amt_saved = (number_of_executions.sum()) * (20)
| list amt_saved.as(format = currency,code = usd)
```

</details>

***

## Line chart <a href="#livedashboardsnqlexamples-linechart" id="livedashboardsnqlexamples-linechart"></a>

<details>

<summary>NQL structure</summary>

```
<event table> <time_duration>
...
summarize <kpi1>, <kpi2>, ... by <time_duration_granularity>
(list <time>, <kpi1>, <kpi2>, ...)
```

</details>

#### Examples

<details>

<summary>Display average daily backend page load duration, client page load duration, and network duration over the last 7 days, without specifying the list line.</summary>

```
web.page_views during past 7d
| summarize 
    backend_duration = page_load_time.backend.avg() , 
    client_duration = page_load_time.client.avg() , 
    network_duration = page_load_time.network.avg() by 1d
 
```

</details>

<details>

<summary>Display average daily backend page load duration, client page load duration, and network duration over the last 7 days, using the list line to indicate the parameters that should be included.</summary>

```
web.page_views during past 7d
| summarize 
    backend_duration = page_load_time.backend.avg() , 
    client_duration = page_load_time.client.avg() , 
    network_duration = page_load_time.network.avg() by 1d
| list end_time, backend_duration, client_duration, network_duration
```

</details>

<details>

<summary>Display the estimated daily total energy consumption in kilowatt-hours (kWh).</summary>

{% code overflow="wrap" %}

```nql_/apigateway/nql-editor
execution.events during past 15d
| where device.operating_system.name != "*server*"
| where 
  (device.hardware.type == laptop 
  or device.hardware.type == desktop)
| where binary.name in ["nxtsvc.exe", "nxtsvc"]
| summarize 
  Total_energy_consumption = 
  (((execution_duration.sum()) ^ (1)) / (3600)) * (30) 
  by 1d
| list 
  start_time, 
  Total_energy_consumption.as(format = energy)
```

{% endcode %}

</details>

***

## Bar chart with disabled default breakdowns <a href="#livedashboardsnqlexamples-barchart-disabled-breakdown" id="livedashboardsnqlexamples-barchart-disabled-breakdown"></a>

<details>

<summary>NQL structure</summary>

In this case, as the [default breakdowns are disabled](https://docs.nexthink.com/platform/user-guide/widget-types/bar-chart#barcharts-without-breakdowns), you should always specify `by <segmentation1>,...` in the query.

```
...
summarize <kpi1>, <kpi2>, ... by <segmentation1>, <segmentation2>, ...
```

</details>

#### Examples

<details>

<summary>Display the number of hard resets and the number of device over the last 7 days, broken down by: platform, hardware manufacturer and model.</summary>

```
device_performance.hard_resets  during past 7d
| summarize
    num_hard_resets = number_of_hard_resets.sum() ,
    num_devices = device.count()
   by
    device.operating_system.platform ,
    device.hardware.manufacturer ,
    device.hardware.model
| sort num_hard_resets desc
```

</details>

<details>

<summary>Display the number of web transactions by application.</summary>

```
web.transactions 
| summarize nb_transactions = number_of_transactions.sum() 
   by application.name 
| sort nb_transactions desc
```

</details>

<details>

<summary>Display the Internet Service Provider (ISP) count excluding unknown ISP.</summary>

```
devices
| where device.public_ip.isp != null
| summarize 
  devices = device.name.count() 
  by device.public_ip.isp
| sort devices desc
```

</details>

<details>

<summary>Display the estimated savings in USD achieved through the workflows, categorized by each trigger method.</summary>

{% code overflow="wrap" %}

```nql_/apigateway/nql-editor
workflow.executions during past 30d
| where status == success
| summarize amt_saved = (number_of_executions.sum()) * (100) 
  by trigger_method
| list trigger_method, amt_saved.as(format = currency,code = usd)
| sort amt_saved desc
```

{% endcode %}

</details>

## Bar chart with enabled default breakdowns <a href="#livedashboardsnqlexamples-barchart-enabled-breakdown" id="livedashboardsnqlexamples-barchart-enabled-breakdown"></a>

<details>

<summary>NQL structure</summary>

In this case, as the [default breakdowns are enabled](https://docs.nexthink.com/platform/user-guide/widget-types/bar-chart#barcharts-with-breakdowns), you can omit `by <segmentation1>,...` from the query since the system defaults to the options in the breakdown dropdown.

```
...
summarize <kpi1>, <kpi2>, ...
```

</details>

#### Examples

<details>

<summary>Number of web page view by default breakdown options</summary>

Since [default breakdowns are enabled](https://docs.nexthink.com/platform/user-guide/widget-types/bar-chart#barcharts-with-breakdowns), the system displays `web.page_views` according to the available default options in the breakdown dropdown.\
In this case, by: **URL**, **Adapted type**, **Number of active tabs**, **Number of large resources**, **Experience level.**

```
web.page_views
| summarize web.page_views = number_of_page_view.sum() 
```

</details>

***

## Single-metric gauge chart <a href="#livedashboardsnqlexamples-single-metricgaugechart" id="livedashboardsnqlexamples-single-metricgaugechart"></a>

### Ratio of devices or users when there is a bad event

Create a single-metric gauge chart displaying the ratio of devices or users when there is a bad event, for example, a crash. It allows to see how devices or users are affected by the issue.

<details>

<summary>NQL structure</summary>

```
<devices|users>
| include <event table>
| compute temp_bad_number = <device|user>.count()
| summarize 
   <metric> = temp_bad_number.sum(), 
   <total> = count()
```

</details>

#### Example

<details>

<summary>Display the ratio of devices with execution crashes out of all the devices in the company.</summary>

```
devices
| include execution.crashes
| compute crash_cnt = device.count()
| summarize 
   devices_with_crashes = crash_cnt.sum(), 
   total_devices = count()
```

</details>

<details>

<summary>Display only the crashes that happened while the application was running in the foreground.</summary>

```
devices
| include execution.crashes
| compute crash_cnt = device.countif(process_visibility == foreground)
| summarize
   devices_with_crashes = crash_cnt.sum(),
   total_devices = count()
```

</details>

### Ratio of events

Display the ratio of events when there is an event such as a crash, freeze, hard reset, system reset.

<details>

<summary>NQL structure</summary>

```
<devices|users>
| include <bad event table>
| compute temp_metric_number = count()
| include <total event table>
| compute temp_total_number = count()
| summarize 
    <metric> = temp_metric_number.sum(), 
    <total> = temp_total_number.sum()
```

</details>

#### Example

<details>

<summary>Display the ratio of poor quality collaboration sessions out of the total number of sessions.</summary>

```
devices 
| include collaboration.sessions 
| where video.quality == poor or audio.quality == poor 
| compute num_poor_quality_sessions = id.count() 
| include collaboration.sessions 
| compute num_total_sessions = id.count() 
| summarize
    poor_quality = num_poor_quality_sessions.sum(), 
    acceptable_quality = num_total_sessions.sum()
```

</details>

### Score metric

Display the DEX score metric.

<details>

<summary><strong>NQL structure</strong></summary>

```
<score table>
| summarize <metric> = <score_field>.avg(), <total> = <total>
```

</details>

**Example**

<details>

<summary>Display the DEX score metric</summary>

```
dex.scores
| summarize score = value.avg() , total = 100
```

</details>

***

## Multi-metric gauge chart <a href="#livedashboardsnqlexamples-multi-metricgaugechart" id="livedashboardsnqlexamples-multi-metricgaugechart"></a>

### Ratio of devices or users with bad events against objects without them

<details>

<summary><strong>NQL structure</strong></summary>

```
<devices|users>
| include <event table>
| compute temp_bad_number = <device|user>.count()
| summarize 
   <good_label> = count() - temp_bad_number.sum(), 
   <bad_label> = temp_bad_number.sum()
```

</details>

**Example**

<details>

<summary>Display the ratio of devices with crashes against those without them.</summary>

```
devices
| include execution.crashes
| compute crash_cnt = device.count()
| summarize 
    without_crashes = count() - crash_cnt.sum(), 
    with_crashes = crash_cnt.sum()
```

</details>

### Ratio of devices with bad events against devices without them

<details>

<summary><strong>NQL structure</strong></summary>

```
devices
| include <bad event table>
| compute temp_bad_number = count()
| include <total event table>
| compute temp_total_number = count()
| summarize 
   <good_label> = temp_total_number.sum() - temp_bad_number.sum(), 
   <bad_label> = temp_bad_number.sum()
```

</details>

**Example**

<details>

<summary>Display the ratio of devices with hard resets against the ones without them.</summary>

```
devices
| include device_performance.hard_resets
| compute hard_reset_cnt = number_of_hard_resets.sum()
| include device_performance.events
| compute total_cnt = count()
| summarize 
   no_hard_resets = total_cnt.sum() - hard_reset_cnt.sum(), 
   hard_resets = hard_reset_cnt.sum()
```

</details>

### Ratio of users or devices with a good state against the ones with a bad state

<details>

<summary><strong>NQL structure</strong></summary>

```
<devices|users>
| include <event table>
| where <condition is bad>
| compute temp_bad_number = <device|user>.count()
| include <event table>
| where <condition is good>
| compute temp_good_number = <device|user>.count()
| summarize 
   <good_label> = temp_good_number.sum(), 
   <bad_label> = temp_bad_number.sum()
```

</details>

**Example**

<details>

<summary>Display the ratio of users with good page views experience against the ones with a frustrating experience.</summary>

```
users
| include web.page_views
| where experience_level == frustrating
| compute frustrating_cnt = user.count()
| include web.page_views
| where experience_level == good 
| compute good_cnt = user.count()
| summarize 
   good = good_cnt.sum(), 
   frustrating = frustrating_cnt.sum()
```

</details>

### Ratio of events with a good state against events with a bad state

<details>

<summary><strong>NQL structure</strong></summary>

```
<devices|users>
| include <event table>
| where <condition is bad>
| compute temp_bad_number = <sum|count>
| include <event table>
| where <condition is good>
| compute temp_good_number = <sum|count>
| summarize 
   <good_label> = temp_good_number.sum(), 
  <bad_label> = temp_bad_number.sum()
```

</details>

**Example**

<details>

<summary>Display the ratio of page views with good experience against the ones with a frustrating experience.</summary>

```
users
| include web.page_views
| where experience_level == frustrating
| compute frustrating_cnt = number_of_page_views.sum() 
| include web.page_views
| where experience_level == good 
| compute good_cnt = number_of_page_views.sum() 
| summarize 
   good = good_cnt.sum(), 
   frustrating = frustrating_cnt.sum()
```

</details>

***

RELATED TOPIC:

[Widget types](https://docs.nexthink.com/platform/user-guide/live-dashboards/widget-types)


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