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Shared Dashboards Contest—Add Your Queries Here!

shared-dashboards

#47

This dashboard provides insight into the interaction between Twilio’s voice webhooks and our platform.
The dashboard contains charts that describe the following:

  • Total number of inbound voice calls
  • A breakdown of the number of inbound voice calls by caller type
  • The overall time it takes for our platform to respond to the Twilio Webhook, and the rate of Twilio timeouts
  • A list of timings on the bottlenecks that affect runtime on our platform’s webhook execution

By using this dashboard, we were able to assess the scale of the Twilio timeout problem, accurately identify the specific bottleneck processes in the workflow, and introduce the right optimizations to reduce the number of Twilio timeouts, finally resulting in an improved customer experience.

Screenshot

Dashboard Details

Required Products: Insights Event API
Level of Effort: Low
This dashboard works on top of the Insights Event API, and will require custom attributes or events.

Settings
Use the gear button to edit your dashboard and configure the following settings (below is an example):

  • Dashboard Filter: Enabled
  • Enabled Event Types: ProdApi
  • Enabled Attributes: Custom Event Attributes

Chart Details

Inbound Calls - By Type

Breakdown of the inbound caller population

SELECT count(*) FROM ProdApi SINCE 30 MINUTES AGO FACET cases (where meta_caller_type = 'resident' as 'resident', where meta_caller_type = 'blocked' as 'blocked', where meta_caller_type = 'relay-owner' as 'relay-owner', where meta_caller_type = 'prospect' and meta_is_new_prospect is not true as 'prospect-existing', where meta_caller_type = 'prospect' and meta_is_new_prospect is true as 'prospect-new') WHERE tag = 'relay-voice-inbound' TIMESERIES
This query allows us to monitor the number of requests our platform is receiving from Twilio’s Webhooks, and allows us to see which population type is calling in most frequently.

Count of inbound calls taking > 15 secs to respond

SELECT count(*) as 'Count of Slow Voice Calls' FROM ProdApi WHERE tag = 'relay-voice-inbound' and meta_runtime_seconds > 15 TIMESERIES compare with 1 week ago
This query shows us the number of webhook requests that are taking more than 15 seconds to respond, and so are hitting the Twilio timeouts. The chart compares metrics with the same period from 1 week ago.

Inbound Voice - extract_prospect() runtime in seconds

One of the processes that we suspected to be a bottlenecks contributing to the Twilio timeouts

SELECT average(meta_runtime_extract_prospect) as 'Average runtime', max(meta_runtime_extract_prospect) as 'Highest runtime', min(meta_runtime_extract_prospect) as 'Lowest runtime' FROM ProdApi WHERE tag = 'relay-voice-inbound' and meta_runtime_extract_prospect is not null TIMESERIES
This query shows the timing metrics on one of the processes in the Twilio webhook that was eventually determined to be the main contributor the the Twilio timeouts. We found that even though the average was relatively low, there were very high peak runtimes that co-related with the count of calls that were timing out.


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December 2018 Coffee Chat: Insights & Dashboards & NRQL - OH MY!
#48

This Dashboard provides high level overview of the PVA app. It shows the overall and individual performance matrices for the different important events.

Required Products: APM, Insights,
Custom Event Attributes used.

Sample NRQL queries used:
SELECT timestamp, Event Name, Order No, Total Event Time from TSP:PVA

SELECT timestamp, Event Name, Order No, Total Event Time from TSP:PVA WHERE Event Name = ‘Oms Event’

SELECT timestamp, Event Name, Order No, Total Event Time from TSP:PVA WHERE Event Name = ‘Jesie Event’

SELECT average(Total Event Time) / 1000 FROM TSP:PVA SINCE 1 day AGO TIMESERIES

SELECT average(Total Event Time) / 1000 FROM TSP:PVA where Event Name = ‘Oms Event’ SINCE 1 day AGO TIMESERIES

SELECT average(Total Event Time) / 1000 FROM TSP:PVA where Event Name = ‘Jesie Event’ SINCE 1 day AGO TIMESERIES


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#49

PO-PROD_DASHBOARD

This dashboard is used to track the most frequent errors that occur on Production and also highlights the most time consuming transactions across the containers.
This dashboard helps us track the number of instances these common errors occur on Production.

Required Products: APM , Insights
Level of Effort: Low

Sample NRQL queries used,

SELECT count(*) FROM TransactionError where appName LIKE ‘%appName%’ SINCE 3 days ago FACET error.message

SELECT count(*) FROM TransactionError where appName like ‘%prod%order%’ and appName not LIKE ‘%prodstg%’ FACET error.message SINCE 3 day ago

SELECT count(*) from TransactionError where error.message like ‘%error message%’ and appName like ‘%prod%order%’ and appName not like ‘%prodstg%’ FACET dateOf(timestamp) SINCE 30 day ago

Feedback appreciated!!!


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#50

This is my staging dashboard, Here I can monitor Success/Failure transactions ratio, Http status codes, weekly load on server, error messages,cpu utilization,Requests information based on locations, etc.

Queries

SELECT sum(duration) FROM SyntheticCheck FACET monitorName

SELECT percentage(count(*), WHERE result = ‘SUCCESS’) AS ‘User Journey Success %’ FROM SyntheticCheck SINCE 1 day ago WHERE monitorName = ‘<your monitorName>’

SELECT count(*) FROM Transaction since 1 day ago FACET httpResponseCode

SELECT count(*) FROM Transaction since 7 day ago FACET weekdayOf(timestamp)

SELECT count(*) FROM Transaction since 1 day ago FACET appName

SELECT count(*) FROM PageView SINCE 24 hours ago COMPARE WITH 1 weeks ago WHERE appName = ‘<your appName>’

SELECT count(*) as ‘Errors’ FROM TransactionError facet error.message SINCE 1 day ago limit 30

SELECT count(*) from PageView facet appName since 7 days ago

SELECT histogram(duration,60,30 ) from PageView SINCE 1 day ago FACET appName limit 20


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#51

Dashboard App Release Monitoring

This dashboard shows us the health of our latest App release. It is tied to a specific version and some widgets compare the latest release with the previous one.

Most important widgets

  • Crash Rate over the last 60 days (includes 2 App releases)
  • Crash Rate per 1000 sessions
  • Annoyed User Rate
    – This shows us the percentage of all users for which the app crashed at least once
  • Non Crash Free Users
    – Line chart which shows us the annoyed user rate over time
  • Adoption Rate
    – Line chart which shows us when and how much users have update their App to the latest version
  • User Affected crashes
    – This widget is really important for us as it sorts the crashes based on how much users are affected. This helps us in deciding which bugs we need to fix in combination with the crashes sorted by occurrence.

Dashboard Details

Required Products: APM
Level of Effort: Low
This dashboard will work on the standard New Relic data models and does not require any custom attributes or events.


Chart Details

Crash Rate

Line Chart

SELECT percentage(uniqueCount(sessionId), WHERE category = 'Crash') as `Crash rate` FROM MobileSession, MobileCrash WHERE (appName = 'android-runtastic-lite' OR appName = 'android-runtastic-pro' AND appVersion NOT LIKE '%debug') FACET appName TIMESERIES SINCE 60 days AGO LIMIT 1000
Show the crash rate over the last 60 days

Crash Rate Lite

Billboard

SELECT (count(crashException) / uniqueCount(sessionId))*1000 as 'per 1000 sessions' FROM Mobile WHERE appName = 'android-runtastic-lite' AND appVersion = '8.10' AND crashLocationClass NOT LIKE 'android.app.UiAutomation' SINCE 1 week ago COMPARE WITH 1 week ago
Crash rate per 1000 sessions

Annoyed User Rate

Pie Chart

SELECT uniqueCount(rt_uidt) AS 'UNIQUE USERS' FROM Mobile WHERE appName = 'android-runtastic-lite' AND appVersion = '8.10' AND (category='Crash' or category='Session') SINCE 1 week ago FACET category
Show the percentage of users which had at least one crash for a specific app version over the last week

Non Crash Free Users

Line Chart

SELECT (filter(uniqueCount(uuid), WHERE category='Crash') / uniqueCount(uuid)) * 100 as `Crash-free users` FROM MobileSession, MobileCrash WHERE (appName = 'android-runtastic-lite' OR appName = 'android-runtastic-pro') FACET appName TIMESERIES SINCE 60 days AGO
Show the time series for users which had at least one crash over the last 60 days

App Sessions

Billboard

SELECT uniqueCount(sessionId) FROM Mobile WHERE appName = 'android-runtastic-lite' AND appVersion = '8.10' SINCE 1 week ago COMPARE WITH 1 week ago
Show the unique app sessions for a specific app version over the last week

Adoption Rate

Line Chart

SELECT uniqueCount(uuid) FROM MobileSession WHERE appName = 'android-runtastic-lite' OR appName = 'android-runtastic-pro' FACET appVersion SINCE 2 month ago TIMESERIES 1 day LIMIT 3
Show the amount of unique users which are using a specific version of the app over the last 60 days

User Affected Crashes

Pie Chart

SELECT (filter(uniqueCount(rt_uidt), WHERE category='Crash')) as `Users affected` FROM MobileSession, MobileCrash WHERE (appVersion = '8.10' AND appName='android-runtastic-lite') FACET crashLocation SINCE 1 week AGO LIMIT 5
_Show the crashes for a specific version of the app ordered by how much unique users are affected _

Dashboard Definition

{
  "dashboard": {
    "id": 751657,
    "title": "Android Runtastic Insights 8.10",
    "description": null,
    "icon": "bar-chart",
    "created_at": "2018-10-17T10:10:29Z",
    "updated_at": "2018-11-13T09:55:36Z",
    "visibility": "all",
    "editable": "editable_by_all",
    "ui_url": "https://insights.newrelic.com/accounts/38723/dashboards/751657",
    "api_url": "https://api.newrelic.com/v2/dashboards/751657",
    "owner_email": "thomas.richtsfeld@runtastic.com",
    "metadata": {
      "version": 1
    },
    "widgets": [
      {
        "visualization": "faceted_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 1
        },
        "widget_id": 7110611,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT (filter(uniqueCount(uuid), WHERE category='Crash') / uniqueCount(uuid)) * 100 as `Crash-free users` FROM MobileSession, MobileCrash WHERE (appName = 'android-runtastic-lite' OR appName = 'android-runtastic-pro') FACET appName TIMESERIES SINCE 2 weeks AGO"
          }
        ],
        "presentation": {
          "title": "Non Crash free users",
          "notes": null
        }
      },
      {
        "visualization": "billboard_comparison",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 2
        },
        "widget_id": 7109480,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT (count(crashException) / uniqueCount(sessionId))*1000 as 'per 1000 sessions' FROM Mobile WHERE appName = 'android-runtastic-lite' AND appVersion = '8.10' AND crashLocationClass NOT LIKE 'android.app.UiAutomation' SINCE 1 week ago COMPARE WITH 1 week ago"
          }
        ],
        "presentation": {
          "title": "Crash Rate Lite",
          "notes": "Without RN crash"
        }
      },
      {
        "visualization": "billboard_comparison",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 3
        },
        "widget_id": 7109483,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT (count(crashException) / uniqueCount(sessionId))*1000 as 'per 1000 sessions' FROM Mobile WHERE appName = 'android-runtastic-pro' AND appVersion = '8.10' AND crashLocationClass NOT LIKE 'android.app.UiAutomation' AND crashFingerprint NOT LIKE '561574351be24c03916cb089f74fc825-38723-188441194' SINCE 1 week ago COMPARE WITH 1 week ago"
          }
        ],
        "presentation": {
          "title": "Crash Rate Pro",
          "notes": null
        }
      },
      {
        "visualization": "faceted_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 1
        },
        "widget_id": 7365468,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT uniqueCount(uuid) FROM MobileSession WHERE appName = 'android-runtastic-lite' OR appName = 'android-runtastic-pro' FACET appVersion SINCE 1 month ago TIMESERIES 1 day LIMIT 3"
          }
        ],
        "presentation": {
          "title": "Adoption Rate",
          "notes": null
        }
      },
      {
        "visualization": "facet_pie_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 2
        },
        "widget_id": 7110182,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT (filter(uniqueCount(rt_uidt), WHERE category='Crash')) as `Users affected` FROM MobileSession, MobileCrash WHERE (appVersion = '8.10' AND appName='android-runtastic-lite') FACET crashLocation SINCE 1 week AGO LIMIT 5"
          }
        ],
        "presentation": {
          "title": "User Affected Crashes Lite",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "facet_pie_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 3
        },
        "widget_id": 7110241,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT (filter(uniqueCount(rt_uidt), WHERE category='Crash')) as `Users affected` FROM MobileSession, MobileCrash WHERE (appVersion = '8.10' AND appName='android-runtastic-pro') FACET crashLocation SINCE 1 week AGO LIMIT 5"
          }
        ],
        "presentation": {
          "title": "User Affected Crashes Pro",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "facet_bar_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 3,
          "column": 1
        },
        "widget_id": 7365465,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT average(rt_startup_time_to_ui) as milliseconds from performance WHERE appName LIKE 'android-runtastic-%' AND (appVersion = '8.10' OR appVersion = '8.9.2' OR appVersion = '8.8')  FACET appVersion"
          }
        ],
        "presentation": {
          "title": "Avg. Time to UI",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "facet_pie_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 3,
          "column": 2
        },
        "widget_id": 7109486,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT count(*) FROM MobileCrash WHERE (appName='android-runtastic-lite') WHERE appVersion = '8.10' AND crashFingerprint NOT IN(  ) AND crashLocationClass NOT LIKE 'android.app.UiAutomation' FACET `crashLocation` SINCE 1 Week ago LIMIT 5"
          }
        ],
        "presentation": {
          "title": "Top 5 Crashes Lite",
          "notes": null,
          "drilldown_dashboard_id": 751657
        }
      },
      {
        "visualization": "facet_pie_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 3,
          "column": 3
        },
        "widget_id": 7109487,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT count(*) FROM MobileCrash WHERE (appName='android-runtastic-pro') WHERE appVersion = '8.10' AND crashLocationClass NOT LIKE 'android.app.UiAutomation' FACET `crashLocation` SINCE 1 Week ago LIMIT 5"
          }
        ],
        "presentation": {
          "title": "Top 5 Crashes Pro",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "facet_bar_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 4,
          "column": 1
        },
        "widget_id": 7365466,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT average(rt_startup_operation_execution_time) as milliseconds from performance WHERE appName LIKE 'android-runtastic-%' AND (appVersion = '8.10' OR appVersion = '8.9.2' OR appVersion = '8.8')  FACET appVersion"
          }
        ],
        "presentation": {
          "title": "Avg. AppStartHandler duration",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "facet_pie_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 4,
          "column": 2
        },
        "widget_id": 7109482,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT uniqueCount(rt_uidt) AS 'UNIQUE USERS' FROM Mobile WHERE appName = 'android-runtastic-lite' AND appVersion = '8.10' AND (category='Crash' or category='Session') SINCE 1 week ago FACET category"
          }
        ],
        "presentation": {
          "title": "Annoyed User Rate Lite",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "facet_pie_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 4,
          "column": 3
        },
        "widget_id": 7109485,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT uniqueCount(rt_uidt) as `unique users` FROM Mobile WHERE appName = 'android-runtastic-pro' AND appVersion = '8.10' AND (category='Crash' or category='Session') SINCE 1 week ago FACET category"
          }
        ],
        "presentation": {
          "title": "Annoyed User Rate Pro",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "faceted_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 5,
          "column": 1
        },
        "widget_id": 7110709,
        "account_id": 38723,
        "data": [
          {
            "nrql": "SELECT percentage(uniqueCount(sessionId), WHERE category = 'Crash') as `Crash rate` FROM MobileSession, MobileCrash WHERE (appName = 'android-runtastic-lite' OR appName = 'android-runtastic-pro' AND appVersion NOT LIKE '%debug') FACET appName TIMESERIES SINCE 60 days AGO LIMIT 1000"
          }
        ],
        "presentation": {
          "title": "Crash rate",
          "notes": null
        }
      }
    ],
    "filter": {
      "event_types": [
        "MobileCrash"
      ],
      "attributes": [
        "appBuild",
        "crashLocation"
      ]
    }
  }
}

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#52

This Dashboard is use to monitoring the server by there HTTP response and error massage .


#53

Love it! :santa: :evergreen_tree: :gift:


#54

Hey @souravc Feel free to add your dashboard’s Value Statement and some of your NRQL queries to qualify to win the T-Shirt / Apple Watch.


#55

Please find the NRQL queries:

SELECT count(error.message) FROM Transaction,TransactionError WHERE httpResponseCode !=‘200’ AND request.headers.accept NOT LIKE ‘text/html, image/gif, image/jpeg,’ FACET appName,httpResponseCode SINCE 30 minutes ago

SELECT count(error.message) FROM TransactionError,Transaction FACET error.message SINCE yesterday

select count (*) from Transaction WHERE httpResponseCode IS NULL FACET appName SINCE 30 minutes ago

SELECT count(aggregateFacet) from TransactionError WHERE aggregateFacet like ‘%HttpClientError%’ COMPARE WITH 30 minutes ago TIMESERIES

SELECT count(httpResponseCode) FROM Transaction,TransactionError FACET httpResponseCode,appName SINCE 1 day ago


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#56

Transactions - EC2 usage

With this dashboard, we are tracking the state of Transactions during the day, compared to the same time 1 day earlier, over 1 week, state of the response codes and CU usage by month. All of them are important for getting a general picture of the activity of the site. The other graphs (APM …) giving us the details.

Transaction
SELECT count(*) FROM Transaction SINCE 1 day ago WHERE appName =‘us-new-alison-fe’ TIMESERIES AUTO

Transactions Over Time
SELECT count(*) FROM Transaction SINCE 1 day ago COMPARE WITH 1 day ago WHERE appName =‘us-new-alison-fe’ TIMESERIES AUTO

Transactions Over Time 1 week
SELECT count(*) FROM Transaction SINCE 7 days ago WHERE appName =‘us-new-alison-fe’ TIMESERIES 1 hour

** HTTP response codes since 1 minute ago**
SELECT count(*) FROM Transaction WHERE appName =‘us-new-alison-fe’ FACET httpResponseCode SINCE 1 minute ago

HTTP response codes since 1 week ago
SELECT count(*) FROM Transaction WHERE appName =‘us-new-alison-fe’ FACET httpResponseCode SINCE 1 week AGO TIMESERIES

CU Usage by month
SELECT SUM(apmComputeUnits) as usage FROM NrDailyUsage WHERE productLine = ‘APM’ AND usageType = ‘Host’ SINCE last year FACET monthOf(timestamp)


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#57

This dashboard makes easier to analyze users actions on our site, transactions, load time, errors and more.

I captured following actions by using NRQL (New Relic query language)

• Active users
• Sessions Explorer
• Sessions by city
• Transactions occur most frequently
• Analyze peak traffic hours
• Page load time
• Error occurrence by message
• Error frequency by host
• Error details and frequency

NRQL queries used as below:

select uniquecount(session) from PageView where appName = ‘…’ since 60 minutes ago

SELECT uniqueCount(session) FROM PageView SINCE 60 MINUTES AGO COMPARE WITH 1 WEEK AGO WHERE appName = ‘…’ TIMESERIES

select count(session) from PageView facet city where appName =’…’ since 1 week ago

select average(duration) from PageView, Transaction FACET name where appName = ‘…’ SINCE 1 week ago

select count(session) from PageView since 1 days ago facet hourof(timestamp) where appName = ‘…’

select min(duration), max(duration), average(duration) from PageView where appName =’…’

SELECT count(error.message) FROM TransactionError FACET error.message where appName = ‘…’ since 1 week ago

SELECT count(error.message) FROM TransactionError FACET host WHERE appName = ‘…’ since 1 week ago

SELECT count(error.message) FROM TransactionError FACET error.message TIMESERIES where appName = ‘…’ since 1 day ago


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#58

Quality KPIs

Quick overview of impact that failures may cause

Screenshot

Dashboard Details

Required Products: Insights
Level of Effort: Low


Chart Details

Failure impact

SELECT percentage(count(*), WHERE aggregateFacet is not null) FROM Transaction, TransactionError WHERE appName LIKE '-%' WHERE `response.status` != 301 SINCE 1 day ago TIMESERIES
Percentage of errors our customer hit

Resolution time & occurrences

SELECT (max(timestamp) - min(timestamp)) / 3600000 as 'Resolution time [h]', sum(databaseCallCount) as 'Occurences' FROM TransactionError WHERE appName LIKE '%' SINCE 1 day ago TIMESERIES
Resolution time after failure


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#59

My dashboard is used to track the various errors which occur on Production. Also have the most time consuming transactions across the site.
This helps us to track the common errors and the site performance of Production.

Following are my NRQLs which I used to get the transaction details as per business needs:

SELECT average(duration) FROM PageView WHERE appName = ‘My_App_Name’ SINCE today COMPARE WITH 1 day ago

SELECT count(*) from PageView where appName = ‘My_App_Name’ FACET regionCode

SELECT uniqueCount(session) from PageView where appName = ‘My_App_Name’ TIMESERIES

SELECT count(*) FROM Transaction since 7 day ago FACET weekdayOf(timestamp)


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#60

Website Performance Dashboard

This Dashboard displays performance of website with all External Web services and servers. T he Dashboard covers performance of all the layers of website in a Single View. This is displayed on TV screen and is very helpful for 1st line support guys.

Some matrix shown here are

  • Response Time (All Layers)
  • Response Time Comparison with Previous Day
  • User Session
  • Requests Per Server.
  • User Demographic detail
  • Top 10 Slowest Pages
  • Top 10 Most Requested Page Slowest Pages

NRQL

SELECT Count() FROM Transaction WHERE appName in (‘XXXX’, ‘YYYY’,‘ZZZZ’) FACET host
SELECT count(
) from PageView where appName = ‘XXXX’ since 1 day ago facet pageUrl

SELECT uniqueCount(session) from PageView where appName = ‘XXXX’ TIMESERIES

SELECT average(duration) FROM PageView WHERE appName=‘XXXX’ SINCE today COMPARE WITH 1 day ago

SELECT count(*) from PageView where appName=‘XXXX’ FACET countryCode

SELECT percentile(duration, 75) from PageView where appName = ‘XXXX’ since 1 month ago facet pageUrl


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#61

Want to monitor your API?! Here is an API performance dashboard my team displays for both Production and Test environments by API versions. This is viewed by the Product Manager, Business Owner, Engineers, and QA team members.

This dashboard keeps us informed real time of transaction patterns that positions us to identify potential issues we’d like to get ahead of before a consumer alerts us. You can also use this dashboard to have conversations with specific consumers about their API usage simply by using the filter feature (just enter the consumer’s ID in the box as the top of the page). Care to filter the dashboard view by transaction or response codes?! The charts in rows 2 and 3 are clickable and allow filtering as well. Additionally, the team uses this dashboard weekly, to review performance from a weekly/monthly view. This helps us iteratively identify improvements (another way to identify features and bugs).

Here are questions you can answer by looking at this dashboard:

  1. Total number of transactions for a given period (ie, last 30 mins, last month)
  2. Traffic for each specific transaction
  3. Responses returned for each transaction
  4. Consumer usage
  5. Response time across all transactions
  6. Error % across all transactions
  7. Throughput across all transactions

Here are sample NRQL statements for my favorite charts:

  1. Transaction Throughput
    SELECT count(*) from Transaction where appName = ‘APPNAME’ and transactionType = ‘Web’ FACET name since 1 month ago

  2. Response Code
    SELECT count(*) from Transaction where appName =‘APPNAME’ and transactionType =‘Web’ facet httpResponseCode since 1 month ago

  3. Consumer Usage
    SELECT COUNT(*) FROM Transaction WHERE appName=‘APPNAME’ AND transactionType = ‘Web’ FACET cases(WHERE REQUESTER = ‘REQUESTER1’ as ‘Mobile App (REQUESTER1)’, where REQUESTER = ‘REQUESTER2’ as ‘CA Portal (REQUESTER2)’) or REQUESTER since 1 month ago

**Charts in rows 1-3 are custom built using nrql statements, 4-6 are copies of charts from the app’s APM overview page.
**Also no need to reinvent the wheel of the great work of the APM overview page, below the last row of charts, we added links to easily access for each app version.


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#62

Screenshot

Dashboard Details

Required Products: Browser

Level of Effort: Low

This dashboard will work on the standard New Relic data models and does not require any custom attributes or events.

Settings

Use the gear button to edit your dashboard and configure the following settings:

  • Dashboard Filter: Enabled

  • Enabled Event Types: PageView

  • Enabled Attributes: pageUrl


NRQL Query

SELECT funnel( session , WHERE pageUrl=‘URL’ AS ‘Homepage’, WHERE pageUrl=‘URL’ AS ‘Page Name’, WHERE pageUrl=‘URL’ AS ‘Page Name’, WHERE pageUrl=‘URL’ AS ‘Page Name’, WHERE pageUrl=‘URL’ AS ‘Page Name’, WHERE pageUrl=‘URL’ AS ‘End URL’) FROM PageView SINCE 1 day ago

NRQL Query Details

The query shows the number of page views for a given URL ; the starting place for the navigation path. Of the those who visited the starting URL, how many of those then navigated to the next URL in the query. Of those who visited the first two URLs, how many of those then navigated to the next URL in the query. This sequence will continue until the last URL has been reached, displaying the percentage of users to travel the entire path. The biggest advantage of this dashboard is to see which path generates the most success. For example, for an ecommerce site, which path generates the most sales? This could be used by a marketing team to creatively push users to a particular path.


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#63

Dashboard: PVA - External Services Performance
Products used: APM, Insight Metrics Explorer (Under Data Explorer)

This Dashboard shows how the external service that PVA is dependent on is performing.

Sample NRQL Queries:
Metrics explorer doesn’t give the ability to view the underlying data through NRQL.


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#64

Company Website Performance Dashboard

We use this dashboard to monitor not only our website performance but traffic from different demographic areas. It enables us to quickly skim through the areas that we need to improve, and gives are re-conformation of doing a better job. Some of the NRQL queries are listed below:

  1. Our home page load time : SELECT average(duration) FROM PageView WHERE pageUrl='companyurl' COMPARE WITH 1 week ago
  2. Views by different article urls: SELECT count(pageUrl) FROM PageView FACET pageUrl SINCE 60 MINUTES AGO TIMESERIES
  3. Traffic from the ‘US’: SELECT count(*) FROM PageView WHERE countryCode = 'US' COMPARE WITH 1 week ago
  4. Traffic from outside the ‘US’: SELECT count(*) FROM PageView WHERE countryCode = 'US' COMPARE WITH 1 week ago

FEE DASHBOARD


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#65

Dashboard - Continuous Testing Data App
I have set up continuous testing using JMeter to test some apps in QA, Stage, and Perf environments. The results are then fed to Insights.
One use of this data is to compare the performance of various builds in all environments. A simple data app starts with a heatmap of response time for one key metric across all environments in two cloud regions. Selecting any one of those from the main page of the data app links to a facet showing the detail - average response time and build number.

Value statement : This intuitive interface allows anyone from developers to managers to quickly compare performance over builds. Data is sortable and defaulting the timerange to ‘this quarter’ shows a large amount of data.

Main Page

Detail Page

Main heatmap for one region

SELECT histogram((elapsed - Connect), 500, 100) FROM FNDperf WHERE url like ‘%west%’ AND responseCode = 200 AND label =‘GRANT LOCAL Guest Token’ SINCE 1 week ago FACET url

One call on the detail page

SELECT average(elapsed - Connect) AS ‘Avg GRANT’ FROM FNDperf WHERE label = ‘GRANT LOCAL Guest Token’ AND responseCode = 200 FACET buildNum SINCE this quarter


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#66

How did you go about forming that top left chart (Front End Performance). Is that a NRQL statement or did you export that from the Browser function to this dashboard somehow?