The below dashboard shows the appdex for 3 services we have and the response time for them. The response time also includes the past data too so that we can track any anomalies and compare them over the time.
SELECT average(duration*1000) AS AvgDuration FROM Transaction WHERE appName = TIMESERIES COMPARE WITH 1 week ago
0 voters
This dashboard represents the servers status from an infrastructure perspective, and shows information on below 4 critical areas,
The dashboard provide a high level status of the platform from a hosting team perspective.
Alerts:
Alerts events are ingested into insights via a webhook
SELECT count() FROM ALERTS FACET policy_name SINCE 1 day ago
SELECT count() FROM ALERTS FACET condition_name SINCE 1 day ago
SELECT timestamp, incident_id, policy_name, condition_name, details, severity FROM ALERTS SINCE 1 day ago
Servers using 20% and above of their CPU:
SELECT average(cpuPercent) AS ‘CPU %’
FROM SystemSample WHERE ec2Tag_Monitoring LIKE ‘xxxxxx’
FACET hostname where cpuPercent > 10
SINCE 5 minutes ago LIMIT 100
Servers with less than 20% free disk
SELECT average(cpuPercent) AS ‘CPU %’
FROM SystemSample WHERE ec2Tag_Monitoring LIKE ‘xxxxxx’
FACET hostname where cpuPercent > 10
SINCE 5 minutes ago LIMIT 100
The rest are standard NRQL for infrastructure
0 voters
Here is anonymized dashboard for one our k8s clusters
Below are the queries:
SELECT average(cpuPercent) FROM SystemSample TIMESERIES FACET label.displayName
SELECT average(memoryUsedBytes/memoryTotalBytes*100) FROM SystemSample TIMESERIES FACET label.displayName
SELECT average(diskUsedPercent) FROM SystemSample TIMESERIES FACET label.displayName
SELECT timestamp, incident_id, policy_name, condition_name, details, severity FROM alert WHERE policy_name = ‘Engg Alert Policy’ SINCE yesterday
0 voters
Hello Every One,
This Dashboard gives the information about New Relic Exceptions across the environments like Production, UAT, QC & Dev by Sprint wise.
Advantages:
1 Find Common Exceptions in all the environment.
2 Always take top errors from UAT and Give Fix before client face the same issue in production
3 Using this dash board, Find out issues before client raise CLI’s.
Below are the sample NRQL Queries for this dashboard
UAT: Sprint Start and End Date(27th Oct-02 Nov)
SELECT count(*) FROM TransactionError where appName LIKE 'UAT Appname ’ SINCE ‘2018-10-27 00:00:00’ UNTIL ‘2018-11-02 00:00:00’ FACET error.message
QC: Sprint Start and End Date(13th Oct- 26th Oct)
SELECT count(*) FROM TransactionError where appName LIKE ‘QC Appname’ SINCE ‘2018-10-13 00:00:00’ UNTIL ‘2018-10-26 00:00:00’ FACET error.message
Dev: Sprint Start and End Date(24th Sep- 12th Oct)
SELECT count(*) FROM TransactionError where appName LIKE ‘Dev’ SINCE ‘2018-09-2400:00:00’ UNTIL ‘2018-10-12 00:00:00’ FACET error.message
0 voters
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.
This dashboard represent the e-commerces status from a performance perspective, and shows information on below:
The dashboard provide a high level status of the application platform to support, development and management teams.
0 voters
This dashboard provides insight into the interaction between Twilio’s voice webhooks and our platform.
The dashboard contains charts that describe the following:
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
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
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.
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.
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.
0 voters
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
0 voters
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!!!
0 voters
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
0 voters
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.
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.
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
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
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
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
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
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
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": {
"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"
]
}
}
}
0 voters
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.
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
0 voters
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)
0 voters
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
0 voters
Quick overview of impact that failures may cause
Screenshot
Required Products: Insights
Level of Effort: Low
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
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
0 voters
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)
0 voters
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
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
0 voters
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:
Here are sample NRQL statements for my favorite charts:
Transaction Throughput
SELECT count(*) from Transaction where appName = ‘APPNAME’ and transactionType = ‘Web’ FACET name since 1 month ago
Response Code
SELECT count(*) from Transaction where appName =‘APPNAME’ and transactionType =‘Web’ facet httpResponseCode since 1 month ago
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.
0 voters
