Live Dashboards NQL examples

This list of NQL query examples is designed to help you create Live Dashboards widgets. Go through the queries below and pick the one most similar to the widget you would like to create and the information that you want to display. Copy the query and adjust it to your use case.

KPI widget

NQL structure to create a KPI widget.

CODE

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

Display the total web application errors during the last 7 days.

CODE

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

Display the ratio of backend page load duration during the last 7 days.

CODE


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

Line chart

NQL structure to create a line chart.

CODE

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

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

CODE

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

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.

CODE

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

Bar chart

NQL structure to create a bar chart.

CODE

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

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

CODE

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

Display the number of web transactions by application.

CODE

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

Display the Internet Service Provider (ISP) count excluding unknown ISP.

CODE

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

Single-metric gauge chart

NQL structure to 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.

CODE

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

Display the ratio of devices with execution crashes out of all the devices in the company.

CODE

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

NQL structure to display the ratio of events when there is an event such as a crash, freeze, hard reset, system reset.

CODE

<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()

Display the ratio of poor quality collaboration sessions out of the total number of sessions.

CODE

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()

NQL structure to display the score metric.

CODE

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

Display the DEX score.

CODE

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

Multi-metric gauge chart

NQL structure of a multi-metric gauge chart displaying the ratio of devices or users with bad events against objects without them.

CODE

<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()

Display the ratio of devices with crashes against those without them.

CODE

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

Display the ratio of devices with bad events against devices without them.

CODE

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()

Display the ratio of devices with hard resets against the ones without them.

CODE

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()

NQL structure to display the ratio of events with a good state against events with a bad state.

CODE

<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()

Display the ratio of page views with good experience against the ones with a frustrating experience.

CODE

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()

NQL structure to display a ratio of users or devices with a good state against the ones with a bad state.

NONE

<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()

Display the ratio of page views with good experience against the ones with a frustrating experience.

CODE

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()