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Shared Dashboards Contest—Add Your Queries Here!

shared-dashboards

#1

We’re Sharing Dashboards! DashboardBadge3

Participate in our Shared Dashboards contest for a chance to win an Apple Watch! :watch:

You’ve built your New Relic dashboards for a reason. Whether it’s to ensure customers apps are running smoothly, or tracking the velocity of deployments, your dashboards tell the story of your business.

We want to spread the dashboard love by encouraging you to share the chart queries and dashboards that tell that story. On November 26th, 2018, New Relic will kick off our Shared Dashboards contest! You’ll have two weeks to share your most useful queries, Insights charts, and your most awesome dashboards.

Contest winners will be announced, live on-stage, at FutureStack Chicago by New Relic CEO and Founder Lew Cirne, @cirne !

What are you waiting for? Share the story of your business and you can win an Apple Watch!

Participation is as easy as 1-2-3!

  1. Pick your favorite dashboard and post a screen capture to this thread. (Try using this nifty Chrome extension!)

  2. Include the NRQL query statements for two or three of your favorite charts, along with an overview of why this dashboard paints the story of your business. (Here’s an example entry.)

  3. Shamelessly promote your dashboard! Getting other community members to vote on your submission increases your chances of winning. The entry with the most community votes – WINS BIG!

Participation notes: If your dashboard exposes any private data, we will work with you to anonymize your entries. Don’t hesitate to message us with any questions and we’ll do our best to help you participate.

For hints and tips and detailed instructions, check out our Dashboard Contest Level Up post!

Rock the Vote! :ballot_box_with_check:

You, our voters, play an important role in our contest by voting for one of our winners! Every contest entry reply will have a poll attached to it:

Everyone is free to try these queries and dashboards for yourselves and vote for the ones you love! You’ll be learning something new and helping to select one of the grand prize Apple Watch winners.

What Should I Share?

If you need some inspiration, be sure to check out the Dashboard of the Day posts that we will be publishing in the Shared Dashboard category. These dashboards were created to illustrate the different ways you can get value from your data. Feel free to not only take what they publish to use for yourself, but follow a similar template and structure for sharing your own queries and dashboard solutions during this contest.

Copy and Paste this Suggested Template Into Your Reply

# [Effort Level Icon] - Title of Dashboard

_Use this space to describe the value of your dashboard. What's the overall theme/objectives for this dashboard? We want to hear a short Value Statement of the charts and dashboards you are sharing._


>## Screenshot
>![04%20PM|690x325](upload://7XWUIR0x6rDrED46xR3kw5vn8Ox.jpeg) 


### 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.
>
> _Settings_
<sup> Use the gear button to edit your dashboard and configure the following settings (below is an example):</sup>
> * Dashboard Filter: Enabled
> * Enabled Event Types: `PageView`
> * Enabled Attributes: `appName`, `pageUrl`

---

## Chart Details
In this section, please go into the details on the purpose of each of the charts you are sharing. Be sure to also show the query ([NRQL](https://docs.newrelic.com/docs/insights/nrql-new-relic-query-language/using-nrql/introduction-nrql)) that others can cut/paste into the [Insights query bar](https://insights.newrelic.com).

### Chart Title
<sup> _Chart Type_ </sup>
> ```SELECT count(*) from PageView facet appName since 7 days ago```
_Query definition in human English_

### Chart Title
<sup> _Chart Type_ </sup>
> ```SELECT count(*) from PageView facet appName since 7 days ago```
_Query definition in human English_

### Chart Title
<sup> _Chart Type_ </sup>
> ```SELECT count(*) from PageView facet appName since 7 days ago```
_Query definition in human English_

---
 
## Extra Credit - [**Share your dashboard with the community!**](https://docs.newrelic.com/docs/insights/insights-api/manage-dashboards/insights-dashboard-api#schema)

*Want to be a super "NeRD" (New Relic Developer)?*
Use the [API Explorer](https://rpm.newrelic.com/api/explore/dashboards/list) (or our [Postman collection](https://marktofigurethisout)) to load the dashboard definition below into Insights or share the definition of your dashboard and increase your international NeRD cred! (And you'll earn a cool badge for your community profile!)
Here's some [quick tips](https://marktocreatethisdoc) on how to do this. 

### Dashboard Definition


{
  "dashboard": {
    "metadata": { "version": 1 },
    "title": "API Widget Sample",
    "icon":"none|archive|bar-chart|line-chart|bullseye|user|usd|money|thumbs-up|thumbs-down|cloud|bell|bullhorn|comments-o|envelope|globe|shopping-cart|sitemap|clock-o|crosshairs|rocket|users|mobile|tablet|adjust|dashboard|flag|flask|road|bolt|cog|leaf|magic|puzzle-piece|bug|fire|legal|trophy|pie-chart|sliders|paper-plane|life-ring|heart",
    "visibility": "owner|all",
    "editable": "read_only|editable_by_owner|editable_by_all",
    "filter": {
      "event_types": [
        "Transaction"
      ],
      "attributes": [
        "appName"
      ]
    },
    "widgets": [
      {
        "visualization": "billboard|gauge|billboard_comparison",
        "account_id": 12345,
        "data": [
          {
            "nrql": "SELECT count(*) from Transaction since 5 minutes ago"
          }
        ],
        "presentation": {
          "title": "Threshold Event Chart",
          "notes": null,
          "threshold": {
            "red": 18000000,
            "yellow": 8000000
          }
        },
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 1
        }
      },
      {
        "visualization": "facet_bar_chart|faceted_line_chart|facet_pie_chart|facet_table|faceted_area_chart|heatmap",
        "account_id": 12345,
        "data": [
          {
            "nrql": "SELECT count(*) from Transaction since 5 minutes ago facet appName"
          }
        ],
        "presentation": {
          "title": "Facet Chart",
          "notes": null,
          "drilldown_dashboard_id": 64
        },
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 2
        }
      },
      {
        "visualization": "attribute_sheet|single_event|histogram|funnel|raw_json|event_feed|event_table|uniques_list|line_chart|comparison_line_chart",
        "account_id": 12345,
        "data": [
          {
            "nrql": "SELECT latest(appName), latest(duration) from Transaction since 5 minutes ago"
          }
        ],
        "presentation": {
          "title": "Simple Event Chart",
          "notes": null
        },
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 3
        }
      },
      {
        "visualization": "markdown",
        "account_id": 12345,
        "data": [
          {
            "source": "# Dashboard Note\n\n[link goes here](https://www.newrelic.com)"
          }
        ],
        "presentation": {
          "title": "",
          "notes": null
        },
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 1
        }
      },
      {
        "visualization": "metric_line_chart",
        "account_id": 12345,
        "data": [
          {
            "duration": 1800000,
            "end_time": null,
            "entity_ids": [
              238575
            ],
            "metrics": [
              {
                "name": "Apdex",
                "units": null,
                "scope": "",
                "values": [
                  "score"
                ]
              }
            ],
            "order_by": "score",
            "limit": 10
          }
        ],
        "presentation": {
          "title": "Metric Line Chart",
          "notes": null
        },
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 2
        }
      },
      {
        "visualization": "application_breakdown|scope_breakdown|browser_breakdown|background_breakdown|solr_breakdown|gc_runs_breakdown",
        "account_id": 12345,
        "data": [
          {
            "duration": 1800000,
            "end_time": null,
            "entity_ids": [
              238575
            ]
          }
        ],
        "presentation": {
          "title": "Breakdown Metric Chart",
          "notes": null
        },
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 3
        }
      },
      {
        "visualization": "inventory",
        "account_id": 12345,
        "data": [
          {
            "sources": [
              "applications/apm"
            ],
            "filters": {}
          }
        ],
        "presentation": {
          "title": "Inventory",
          "notes": null
        },
        "layout": {
          "width": 2,
          "height": 1,
          "row": 3,
          "column": 1
        }
      },
      {
        "visualization": "traffic_light",
        "account_id": 12345,
        "data": [
          {
            "nrql": "SELECT * from InfrastructureEvent"
          }
        ],
        "presentation": {
          "title": "Traffic Light",
          "notes": null,
          "traffic_lights": [
            {
              "id": "12345",
              "title": "Cat",
              "subtitle": "Cat Status",
              "states": [
                {
                  "type": "wrong",
                  "min": 0,
                  "max": 3
                },
                {
                  "type": "warning",
                  "min": 3,
                  "max": 7
                },
                {
                  "type": "ok",
                  "min": 7,
                  "max": 10
                }
              ]
            }
          ]
        },
        "layout": {
          "width": 1,
          "height": 1,
          "row": 3,
          "column": 3
        }
      }
    ]
  }
}

How Will Winners Be Chosen?

We’ll be giving out a special New Relic developer program t-shirt to anyone who makes a submission during the contest during November 26, 2018 - December 9, 2018.

There will be two contest winners: The Community Choice Winner and our Relic Choice Winner!

The Community Choice Winner is the Explorer with the most votes from other users. The Relic Choice Winner will be chosen by New Relic product leadership, executives, and big wigs! Our two winners will win a New Relic branded series 4 Apple Watch! And these two winners will be showcased during the keynote presentation at FutureStack Chicago by our CEO and founder Lew Cirne (@cirne).

The sooner you post, the sooner the polls go up! Good luck and have fun!

Official Contest Rules

New Relic Shared Dashboard Contest

Official Rules

The New Relic Shared Dashboard Contest (the “Contest”) will be held from November 26, 2018 to December 9, 2018 (the “Contest Period”). Participants may be eligible to win the prize detailed below.

How to Enter:

During the Contest Period, all eligible participants who submit a valid entry at discuss.newrelic.com (an “Entry”) will be entered into the Contest. An Entry must include a NRQL Query, a value statement, components of an Insights dashboard, when possible (and provided no private data is included), and whatever other requirements are stated at discuss.newrelic.com.

The Contest Period shall close at 11:59pm PST on December 7, 2018. There is no limit to the number of Entries a participant can submit. Participant’s submission of the Entry constitutes their acceptance of these Official Rules.

NO PURCHASE OR PAYMENT IS NECESSARY TO ENTER THESE CONTEST.

License Rights; Identifying Participants:

The Contest allows participants to share their dashboards with the wider New Relic community, so Entries may be visible to the public. Each participant grants New Relic and any other participant or member of the public who views its Entry a non-exclusive, royalty-free, irrevocable, perpetual, sublicensable (through multiple tiers) right to use, reproduce, distribute, modify, create derivative works, publicly perform and display and otherwise exploit such Entry (including any screenshots, content, code or other materials provided with the Entry). New Relic may also identify each participant (e.g., using its name and company or New Relic username) in connection with its Entry or the Contest (but has no obligation to do so). New Relic may take down any Entry from the New Relic website at any time, without notice or liability.

Each participant represents and warrants that it has all necessary rights, consents and permissions to submit its Entries and grant the rights above.

Selection of Winners:

Two (2) winners shall be determined from eligible Entries on December 10, 2018. The two (2) participants whose Entries received the most votes by other Contest participants during the Contest Period shall win the Contest. The winner(s) will be notified via discuss.newrelic.com and via email using the email address provided in the Contest. Upon signing an eligibility affidavit and waiver, the winner(s) shall be mailed the prize detailed below. If a winner does not claim their prize within seven (7) days of the notice, they shall forfeit their prize and a new winner shall be selected.

In the unlikely event of a tie, New Relic will supply another poll for extra voting time to be available for an additional 24 hours. All ties will be reviewed and decided at New Relic’s sole discretion.

Prize: Two (2) winners will each receive one (1) Apple Watch valued at $400.00 USD.

Who Can Enter:

The Contest is strictly limited to permanent, legal United States residents who are at least eighteen (18) years of age. Participants are solely responsible for ensuring their participation in the Contest is lawful. New Relic employees are not eligible to participate in the Contest. New Relic reserves the right, at its sole discretion, to disqualify participants for any reason, including if it is determined that one’s participation in the Contest is not lawful.

Release

All participants in the Contest release New Relic, Inc. and its respective employees, officers, directors and shareholders (“Released Parties”) from and against all liability, claims, and damages arising in connection with their participation or inability to participate, entry in the Contest and/or acceptance, receipt, ownership or use of the prize awarded in the Contest, including but not limited to personal injury, death, damage to property or loss of property, use of an Entry, and identification of participants. Before claiming their prize, the winner must execute an affidavit and waiver verifying their eligibility and acknowledging: (i) their responsibility for any taxes incurred in connection with the prize awarded and (ii) that New Relic will not be liable for any injuries caused while operating prizes awarded from the Contest.

Each participant further understands and agrees that all rights under Section 1542 of the Civil Code of California (“Section 1542”) and any similar law of any state or territory of the United States that may be applicable with respect to the foregoing release are hereby expressly and forever waived. You acknowledge that Section 1542 provides that: “A GENERAL RELEASE DOES NOT EXTEND TO CLAIMS WHICH THE CREDITOR DOES NOT KNOW OR SUSPECT TO EXIST IN HIS OR HER FAVOR AT THE TIME OF EXECUTING THE RELEASE, WHICH, IF KNOWN BY HIM OR HER MUST HAVE MATERIALLY AFFECTED HIS OR HER SETTLEMENT WITH THE DEBTOR.” The releases hereunder are intended to apply to all claims not known or suspected to exist with the intent of waiving the effect of laws requiring the intent to release future unknown claims.

Limitation of Liability

Released Parties are not responsible for any claims, damages, expenses, costs or losses to any person or property of any kind arising from or in connection with: (1) typographical or other errors in the printing of these Official Rules; (2) technical failures of any kind, including but not limited to malfunctions, interruptions or disconnections in phone lines hardware, software, or failure of any email or entries to be received by New Relic due to technical problems, human error or traffic congestion, unavailable network connections on the Internet or any website; (3) unauthorized third party tampering with the Contest; (4) technical or human error in the administration of the prize; (5) late, lost, undeliverable, damaged or stolen mail, or (6) all activities under the Contest.

General:

The Contest is governed by and will be construed in accordance with the laws of the State of California and the forum and venue for any dispute shall be San Francisco, California.

Participant acknowledges and agrees that survey information collected during the Contest Period will be used for marketing and other internal purposes. New Relic reserves the right to terminate this Contest for any or no reason and at any time without further notice.


Relic Solution: Sharing Dashboards
Welcome New Explorers | November 28 2018
#5

Dashboard: - Website Performance-Prod

This dashboard represent the production website status from a performance perspective, and shows information on below 4 critical areas,

  • End user experience :- User journey synthetic check results, user sessions & page view details.
  • Application performance:- Application services & database, response time and throughput.
  • Application Transaction details:- Errors, Apdex score & error codes.
  • Infrastructure performance:- Server CPU, Memory & Filesystem usage, load and infrastructure events.

The dashboard provide a high level status of the application platform to support, development and management teams.

Screenshot

Dashboard Details

Required Products: APM, Browser, Synthetics, Infrastructure.
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 (below is an example):

  • Dashboard Filter: Enabled
  • Enabled Event Types: PageView, InfrastructureEvent, TransactionError, SystemSample, SyntheticCheck.
  • Enabled Attributes: appName, hostname, apmApplicationNames, httpResponseCode, entityId.

Chart Details

User Journey - Synthetics check - Success %.

Gauge

SELECT percentage(count(*), WHERE result = 'SUCCESS') AS 'User Journey Success %' FROM SyntheticCheck SINCE 1 day ago WHERE monitorName = 'My-Synthetic-check'
The success pecentage of My-synthetic-check scripted browser syntheic check, which simulates complete user journey on the website from 2 different loactions on regular intervals.

Avg External Service response time.

Line Chart Comparison

SELECT average(externalDuration) FROM Transaction SINCE 1 hour ago WHERE appName LIKE '%My-APP%' TIMESERIES AUTO COMPARE WITH 1 day ago
Average duration of external service calls for the past 1 hour compared to previous day.

Server Memory Usage

Linked bar chart

SELECT average(memoryUsedBytes/memoryTotalBytes*100) AS '' FROM SystemSample FACET entityId LIMIT 100 SINCE 1 hour ago
Facetable bar chart of Server memory usage data.(Facet by entityId instead of hostname for security reasons).


Vote for this Dashboard!

  • :star: :star: :star:
  • :star: :star:
  • :star:

0 voters


December 2018 Coffee Chat: Insights & Dashboards & NRQL - OH MY!
#6

We have this dashboard spanning multiple TVs mounted in the IT area.

The objective was to show overall performance of all of our sites with easily discernible attributes; and be filterable to the individual APM application level.

It’s a bit of a hodgepodge of metrics, but shows several key areas that were requested to be highly visible to anyone that looks at the TVs.

Screenshot

_


Dashboard Details

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


Pageview Comparison

billboard_comparison

SELECT count(*) FROM PageView SINCE 24 hour ago COMPARE WITH 1 week ago WHERE appName = 'ATPWT'
Compare page view from today with last week


Media Page Timing (browser)

Chart Type

SELECT count(*) from PageView facet appName since 7 days ago
Page View by device type


Average Response Time

attribute_sheet

SELECT count(*) as 'Page Views',average(duration) as 'AVG',percentile(duration,50,75) as '%' FROM PageView facet pageUrl SINCE 1 day ago LIMIT 30
Show Top Pages by duration


Error Messages

facet_bar_chart

SELECT count(*) as 'Errors' FROM TransactionError facet `error.message` SINCE 1 day ago limit 30
Show Recent Error Messages


Dashboard Definition:

Dashboard JSON

{
“dashboard”: {
“title”: “Apps, Pages and Hosts”,
“metadata”: {
“version”: 1
},
“widgets”: [
{
“visualization”: “application_breakdown”,
“layout”: {
“width”: 2,
“height”: 1,
“row”: 1,
“column”: 1
},
“widget_id”: 5855888,
“account_id”: 268725,
“data”: [
{
“duration”: 1800000,
“end_time”: null,
“entity_ids”: [
148914409
]
}
],
“presentation”: {
“title”: “Response Time”,
“notes”: null
}
},
{
“visualization”: “facet_bar_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 1,
“column”: 3
},
“widget_id”: 5827857,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() FROM Transaction FACET name SINCE 1 day AGO"
}
],
“presentation”: {
“title”: “APM Transaction Name”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “facet_pie_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 2,
“column”: 1
},
“widget_id”: 5827853,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) as ‘Transactions’ FROM Transaction where appName !=‘ATP-Dev’ facet appName SINCE 1 day ago”
}
],
“presentation”: {
“title”: “Filter by App”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “facet_pie_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 2,
“column”: 2
},
“widget_id”: 5827867,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() as ‘Transactions’ FROM Transaction FACET response.status WHERE response.status >= ‘400’ SINCE 1 day ago"
}
],
“presentation”: {
“title”: “Errors by Response Status”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “facet_bar_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 2,
“column”: 3
},
“widget_id”: 5827856,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) as ‘Page Views’,average(duration) as ‘AVG’,percentile(duration,50,75) as ‘%’ FROM PageView facet pageUrl SINCE 1 day ago LIMIT 30”
}
],
“presentation”: {
“title”: “Top Pages (Browser)”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “facet_bar_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 3,
“column”: 1
},
“widget_id”: 5827871,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() as ‘Errors’ FROM TransactionError facet error.class SINCE 1 day ago"
}
],
“presentation”: {
“title”: “Error Class”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “facet_bar_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 3,
“column”: 2
},
“widget_id”: 5827866,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) as ‘Errors’ FROM TransactionError facet error.message SINCE 1 day ago limit 30”
}
],
“presentation”: {
“title”: “Error Messages”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “facet_bar_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 3,
“column”: 3
},
“widget_id”: 5827865,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() as ‘Errors’ FROM TransactionError facet request.referer SINCE 1 day ago"
}
],
“presentation”: {
“title”: “Error Referer”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “gauge”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 4,
“column”: 1
},
“widget_id”: 7712209,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) FROM PageView SINCE 1 minute ago”
}
],
“presentation”: {
“title”: “Page Views”,
“notes”: null,
“threshold”: {
“red”: 172834
}
}
},
{
“visualization”: “billboard_comparison”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 4,
“column”: 2
},
“widget_id”: 7707510,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() FROM PageView SINCE 24 hour ago COMPARE WITH 1 week ago WHERE appName = ‘ATPWT’"
}
],
“presentation”: {
“title”: “Page View Comp (24h / 1w)”,
“notes”: null
}
},
{
“visualization”: “attribute_sheet”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 4,
“column”: 3
},
“widget_id”: 5827855,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) as ‘Transactions’, uniquecount(host) as ‘Hosts’ FROM Transaction where appName != ‘ATP-Dev’ SINCE 1 day ago”
}
],
“presentation”: {
“title”: “Count of Transactions and Hosts”,
“notes”: null
}
},
{
“visualization”: “facet_pie_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 5,
“column”: 1
},
“widget_id”: 7710103,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() FROM PageView FACET deviceType WHERE deviceType != ‘Unknown’ SINCE 1 day AGO"
}
],
“presentation”: {
“title”: “PageViews over time by Device Type”,
“notes”: null,
“drilldown_dashboard_id”: 640548
}
},
{
“visualization”: “gauge”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 5,
“column”: 2
},
“widget_id”: 7712210,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT uniqueCount(session) FROM PageView SINCE 1 day ago”
}
],
“presentation”: {
“title”: “Sessions”,
“notes”: null,
“threshold”: {
“red”: 28
}
}
},
{
“visualization”: “heatmap”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 5,
“column”: 3
},
“widget_id”: 7712212,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT histogram(duration, 10, 50) FROM PageView SINCE 1 day ago FACET countryCode Limit 50”
}
],
“presentation”: {
“title”: “Countries”,
“notes”: null,
“drilldown_dashboard_id”: null
}
},
{
“visualization”: “facet_bar_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 6,
“column”: 1
},
“widget_id”: 5827854,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) as ‘Transactions’ FROM Transaction facet host SINCE 1 day ago limit 300”
}
],
“presentation”: {
“title”: “Transactions by HOST”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “faceted_line_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 6,
“column”: 2
},
“widget_id”: 5827859,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() as ‘Transactions’ FROM Transaction FACET response.status WHERE response.status >= ‘400’ and appName != ‘ATP-Dev’ SINCE 1 day ago TIMESERIES"
}
],
“presentation”: {
“title”: “Error Status OverTime”,
“notes”: null
}
},
{
“visualization”: “facet_bar_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 6,
“column”: 3
},
“widget_id”: 6267497,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) as ‘Transactions’ FROM Transaction FACET request.uri WHERE response.status = ‘404’ SINCE 1 day ago LIMIT 100”
}
],
“presentation”: {
“title”: “404 URLs”,
“notes”: null,
“drilldown_dashboard_id”: null
}
},
{
“visualization”: “faceted_line_chart”,
“layout”: {
“width”: 2,
“height”: 1,
“row”: 7,
“column”: 1
},
“widget_id”: 5827858,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count() FROM Transaction where appName != ‘ATP-Dev’ FACET host SINCE 1 day ago TIMESERIES AUTO"
}
],
“presentation”: {
“title”: “Application Throughput by Host”,
“notes”: null
}
},
{
“visualization”: “facet_table”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 7,
“column”: 3
},
“widget_id”: 5827861,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT average(duration), percentile(duration, 50) FROM Transaction where appName != ‘ATP-Dev’ since 1 day ago facet host”
}
],
“presentation”: {
“title”: “Duration Per Host”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “faceted_area_chart”,
“layout”: {
“width”: 3,
“height”: 1,
“row”: 8,
“column”: 1
},
“widget_id”: 5827860,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT percentile(duration, 50) FROM Transaction since 1 day ago facet host timeseries”
}
],
“presentation”: {
“title”: “App Duration per Host”,
“notes”: null
}
},
{
“visualization”: “faceted_area_chart”,
“layout”: {
“width”: 3,
“height”: 1,
“row”: 9,
“column”: 1
},
“widget_id”: 5827869,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) FROM TransactionError since 1 day ago facet host timeseries”
}
],
“presentation”: {
“title”: “Error Rate by HOST”,
“notes”: null
}
},
{
“visualization”: “facet_table”,
“layout”: {
“width”: 3,
“height”: 1,
“row”: 10,
“column”: 1
},
“widget_id”: 5827862,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count(), average(duration) as ‘AVG’, percentile(duration, 50,75,99) FROM Transaction where appName != ‘ATP-Dev’ FACET name SINCE 1 day AGO limit 100"
}
],
“presentation”: {
“title”: “APM Transaction Performance”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “facet_table”,
“layout”: {
“width”: 3,
“height”: 1,
“row”: 11,
“column”: 1
},
“widget_id”: 5827863,
“account_id”: 268725,
“data”: [
{
“nrql”: "SELECT count(
) as ‘Pageviews’,average(duration) as ‘AVG’,average(duration - backendDuration) as ‘Front’,average(backendDuration) as ‘Back’, average(webAppDuration) as ‘AppTime’ FROM PageView where appName != ‘ATP-Dev’ FACET pageUrl SINCE 1 day ago limit 100”
}
],
“presentation”: {
“title”: “Average Response Times (Browser)”,
“notes”: null,
“drilldown_dashboard_id”: null
}
},
{
“visualization”: “facet_table”,
“layout”: {
“width”: 3,
“height”: 1,
“row”: 12,
“column”: 1
},
“widget_id”: 5827864,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT count(*) as ‘Pageviews’,percentile(duration,50) as ‘Median’,percentile(duration - backendDuration,50) as ‘Front’,percentile(backendDuration,50) as ‘Back’, percentile(webAppDuration, 50,99) as ‘AppTime’ FROM PageView FACET pageUrl SINCE 1 day ago limit 100”
}
],
“presentation”: {
“title”: “Median Page Timing (Browser)”,
“notes”: null,
“drilldown_dashboard_id”: 640532
}
},
{
“visualization”: “line_chart”,
“layout”: {
“width”: 2,
“height”: 1,
“row”: 13,
“column”: 1
},
“widget_id”: 5827868,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT percentile(webAppDuration, 50, 75, 95) FROM PageView TIMESERIES 10 minutes SINCE 1 day ago”
}
],
“presentation”: {
“title”: “AppTime”,
“notes”: null
}
},
{
“visualization”: “faceted_line_chart”,
“layout”: {
“width”: 1,
“height”: 1,
“row”: 13,
“column”: 3
},
“widget_id”: 5836791,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT average(duration) FROM Transaction where appName != ‘ATP-Dev’ facet appName SINCE 1 day ago TIMESERIES AUTO”
}
],
“presentation”: {
“title”: “Average Duration Applications”,
“notes”: null
}
},
{
“visualization”: “faceted_area_chart”,
“layout”: {
“width”: 3,
“height”: 1,
“row”: 14,
“column”: 1
},
“widget_id”: 5827870,
“account_id”: 268725,
“data”: [
{
“nrql”: “SELECT average(databaseDuration) FROM Transaction where appName != ‘ATP-Dev’ since 1 day ago facet host TIMESERIES”
}
],
“presentation”: {
“title”: “DB Perf per Host”,
“notes”: null
}
}
],
“filter”: {
“event_types”: [
“PageView”,
“Transaction”,
“TransactionError”
],
“attributes”: []
}
}
}


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#7

We have a dashboard for all app performance stats

APM DashBoard :
1)Trasaction server times
2)Throughput
3)Error rate
4)Appdex score
5)App activity
6) Server node statistics

Based on that we can find out the performance of API calls and improve it. we can see the app memory usages and their behavior.App profiling is done based on the graph spikes.We use synthetic pings to monitor app and create incidents which will be communicated through slack and Outlook.

Level of effort :low


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#8

Current Active sessions of the Application
and JS Errors.


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#9

Track disk usage and growth

This shows disk usage over time for various periods. I built this because we were having unexpected disk growth that was eating the system and causing it to crash. I wanted to see at a glance what the growth looked like over time, where the spikes were, if any, and try to find a pattern.

Queries

SELECT average(diskUsedPercent) FROM StorageSample TIMESERIES FACET `entityAndMountPoint` WHERE (`entityAndMountPoint` in ('6059150612827629914//')) LIMIT 100 SINCE 7 days ago
SELECT average(diskUsedPercent) FROM StorageSample TIMESERIES FACET `entityAndMountPoint` WHERE (`entityAndMountPoint` in ('6059150612827629914//')) LIMIT 100 SINCE 30 days ago
SELECT average(diskUsedPercent) FROM StorageSample TIMESERIES FACET `entityAndMountPoint` WHERE (`entityAndMountPoint` in ('6059150612827629914//')) LIMIT 100 SINCE 4 hours ago


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#10

Hello!!
My name is Luis Sabatini, I present my favorite Dashboard. In this panel you can observe the monitoring of servers at transaction level, stored procedures with more load, more used services and different distributions.

Greetings from Chile


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#11

Dashboard - Engine Metrics

This dashboard represents some basic health check stats for one of my team’s primary applications. It’s a COTS product, so I’ve kept the information represented to some very basic metrics that we can use to get an “At A Glance” status.

Screenshot

Dashboard Details

Required Products: APM, Infrastructure, Synthetics
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 (below is an example):

  • Dashboard Filter: Disabled
  • Enabled Event Types: N/A
  • Enabled Attributes: N/A

Chart Details

Average cpuPercent

Faceted Line Chart

SELECT average(cpuPercent) FROM SystemSample TIMESERIES FACET `entityName` WHERE entityName LIKE '%hostnameFilter%' LIMIT 100 SINCE 1 day ago
Show me the average CPU percent utilization per host for the last 1 day, filtered by hostname match.

Average Percent Memory Used

Faceted Line Chart

SELECT average(memoryUsedBytes/memoryTotalBytes*100) FROM SystemSample TIMESERIES FACET `entityName` WHERE `entityName` LIKE '%hostnameFilter%' LIMIT 100 SINCE 1 day ago
Show me the average Memory percent utilization per host for the last 1 day, filtered by hostname match.

Average loadAverageFiveMinutes

Faceted Line Chart

SELECT average(loadAverageFiveMinute) FROM SystemSample TIMESERIES FACET `entityName` WHERE `entityName` LIKE '%hostnameFilter%' LIMIT 100 SINCE 1 day ago
Show me the average loadAverageFiveMinutes per host for the last 1 day, filtered by hostname match.

Synthetics Checks

Facet Table

SELECT average(duration), percentage(count(*), WHERE result='SUCCESS') FROM SyntheticCheck FACET monitorName SINCE 1 day ago
Show me the average Duration and Availability Percentage per monitor for all Synthetics checks in the last 1 day.

Apdex score

Metric Line Chart

This widget uses the APM Apdex Score metric, with the View by host setting disabled.
Shows the overall Apdex scores for the App Server for the last 1 day.

Errors/all

Metric Line Chart

This widget uses the APM Error Rate metric, with the View by host setting disabled.
Shows the combined error rate for all errors in the last 1 day.


Dashboard Definition

{
  "dashboard": {
    "id": 000000,
    "title": "Engine Metrics",
    "description": null,
    "icon": "bar-chart",
    "created_at": "2018-06-08T22:12:40Z",
    "updated_at": "2018-08-09T00:39:24Z",
    "visibility": "all",
    "editable": "editable_by_all",
    "ui_url": "https://insights.newrelic.com/accounts/000000/dashboards/000000",
    "api_url": "https://api.newrelic.com/v2/dashboards/000000",
    "owner_email": "first.last@email.com",
    "metadata": {
      "version": 1
    },
    "widgets": [
      {
        "visualization": "faceted_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 1
        },
        "widget_id": 000000,
        "account_id": 000000,
        "data": [
          {
            "nrql": "SELECT average(cpuPercent) FROM SystemSample TIMESERIES FACET `entityName` WHERE entityName LIKE '%hostnameFilter%' LIMIT 100 SINCE 1 day ago"
          }
        ],
        "presentation": {
          "title": "Average cpuPercent",
          "notes": "WHERE entityName LIKE '%hostnameFilter%'"
        }
      },
      {
        "visualization": "faceted_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 2
        },
        "widget_id": 000000,
        "account_id": 000000,
        "data": [
          {
            "nrql": "SELECT average(memoryUsedBytes/memoryTotalBytes*100) FROM SystemSample TIMESERIES FACET `entityName` WHERE (`entityName` LIKE '%hostnameFilter%') LIMIT 100 SINCE 1 day ago"
          }
        ],
        "presentation": {
          "title": "Average Percent Memory Used",
          "notes": "WHERE (`entityName` LIKE '%hostnameFilter%')"
        }
      },
      {
        "visualization": "faceted_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 1,
          "column": 3
        },
        "widget_id": 000000,
        "account_id": 000000,
        "data": [
          {
            "nrql": "SELECT average(loadAverageFiveMinute) FROM SystemSample TIMESERIES FACET `entityName` WHERE (`entityName` LIKE '%hostnameFilter%') LIMIT 100 SINCE 1 day ago"
          }
        ],
        "presentation": {
          "title": "Average loadAverageFiveMinutes",
          "notes": "WHERE (`entityName` LIKE '%hostnameFilter%')"
        }
      },
      {
        "visualization": "facet_table",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 1
        },
        "widget_id": 000000,
        "account_id": 000000,
        "data": [
          {
            "nrql": "SELECT average(duration), percentage(count(*), WHERE result='SUCCESS') FROM SyntheticCheck FACET monitorName SINCE 1 day ago"
          }
        ],
        "presentation": {
          "title": "Synthetics Checks",
          "notes": null,
          "drilldown_dashboard_id": null
        }
      },
      {
        "visualization": "metric_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 2
        },
        "widget_id": 000000,
        "account_id": 000000,
        "data": [
          {
            "duration": 86400000,
            "end_time": null,
            "entity_ids": [
              000000
            ],
            "metrics": [
              {
                "name": "Apdex",
                "units": null,
                "scope": "",
                "values": [
                  "score"
                ]
              }
            ],
            "facet": null,
            "order_by": null,
            "limit": null
          }
        ],
        "presentation": {
          "title": "Apdex score",
          "notes": null
        }
      },
      {
        "visualization": "metric_line_chart",
        "layout": {
          "width": 1,
          "height": 1,
          "row": 2,
          "column": 3
        },
        "widget_id": 000000,
        "account_id": 000000,
        "data": [
          {
            "duration": 86400000,
            "end_time": null,
            "entity_ids": [
              000000
            ],
            "metrics": [
              {
                "name": "Errors/all",
                "units": null,
                "scope": "",
                "values": [
                  "error_rate"
                ]
              }
            ],
            "facet": null,
            "order_by": null,
            "limit": null
          }
        ],
        "presentation": {
          "title": "Errors/all",
          "notes": null
        }
      }
    ],
    "filter": null
  }
}

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#12

Screenshot

NOTE: The application names and Monitor Names have been removed.

This dashboard helps the operations team to determine the service SLA for each instance and farm weekly. It also helps us to find which is the client that has suffered any kind of downtime during the operation time.

It also helps us to study the farms usage in order to plan any hardware upgrade needed.

Details

Required Products: APM, Synthetics, Infrastructure.

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

Error Number per app

Table

SELECT count(*) from TransactionError where response.status !=‘400’ and response.status != ‘302’ LIMIT 1000 SINCE 1 week ago FACET appName

The error count for each client application running on our farms. 400 and 302 are responses that we are waiting so that is why I’ve removed from the counter.

Error Number count

Billboard

SELECT count(*) FROM TransactionError WHERE response.status !=‘400’ and response.status != ‘302’ SINCE 1 week ago COMPARE WITH 1 week ago

The error count total compared with 1 week earlier.

PROD APPS % Uptime per farm

Billboard

SELECT filter(percentage(count(*), WHERE result =‘SUCCESS’), WHERE monitorName IN (SPECIFIC SYNTHETICS MONITOR NAME FOR EACH FARM)) as ‘FARM0X’ FROM SyntheticCheck SINCE 1 month ago COMPARE WITH 1 month ago

The farm uptime according to synthetics monitor error counts

PROD FARMS - CPU % Usage

Line chart

SELECT filter(average(cpuPercent), where hostname in (FARM SERVERS NAMES)) as ‘FARM01’, filter(average(cpuPercent), where hostname in (FARM SERVER NAME)) as ‘FARM02’,filter(average(cpuPercent), where hostname in (FARM SERVERS NAMES)) as ‘FARM03’,filter(average(cpuPercent), where hostname in (FARM SERVERS NAMES)) as ‘FARM04’,filter(average(cpuPercent), where hostname in (FARM SERVERS NAMES)) as ‘FARM05’,filter(average(cpuPercent), where hostname in (FARM SERVERS NAMES)) as ‘FARM07’,filter(average(cpuPercent), where hostname in (FARM SERVERS NAMES)) as ‘FARM08’,filter(average(cpuPercent), where hostname in (FARM SERVERS NAMES)) as ‘FARM09’ FROM SystemSample TIMESERIES LIMIT 100 SINCE 1 month ago

CPU Usage for each farm in the application


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#14

This is a basic dashboard that we have to monitor one of our stack deployments, these are displayed on many monitors around our building (for different stacks/DCs). From information shown (such as ‘Recent Response Times’) we can quickly gauge an issue in each stack - before diving in deeper.

Dashboard Details
Required Products: APM, Infrastructure

  • Recent Response Times
    SELECT average(totalTime) AS TotalTime, average(queueDuration) AS QueueDuration, average(databaseDuration) AS DatabaseDuration, average(externalDuration) AS ExternalDuration, average(webDuration) AS WebDuration FROM Transaction TIMESERIES 10 seconds SINCE 1 HOUR AGO WHERE host LIKE '82%'

  • Spins / Sec
    SELECT count(stake) / 10 FROM xxxxx FACET eventSubType where eventSubType IN('PLAY','PLAY_FREEBETS') and host like '82%' TIMESERIES 10 seconds SINCE 1 hour ago

  • Throughput / Sec
    SELECT COUNT(*) / 10 AS ThroughputPerSec FROM Transaction FACET name WHERE name LIKE 'WebTransaction/WebService/%' AND host like '82%' TIMESERIES 10 seconds SINCE 1 hour ago LIMIT 100

  • API Throughput / Sec
    SELECT COUNT(*) / 10 FROM Transaction WHERE name LIKE 'WebTransaction/WebService/%API%' AND host LIKE '82%' TIMESERIES 10 seconds FACET host LIMIT 100

  • Total Throughput / Sec [vs Last Week]
    SELECT COUNT (*) / 60 FROM Transaction WHERE name LIKE 'WebTransaction/WebService/xxxxx%' AND host like '82%' TIMESERIES 1 minute SINCE 1 hour ago COMPARE WITH 1 week ago

  • Slowest Times
    SELECT MAX(totalTime) AS SlowestResponse, MAX(databaseDuration) AS SlowestDB, MAX(externalDuration) AS SlowestWallet, MAX(queueDuration) AS SlowestIIS FROM Transaction SINCE 1 HOUR AGO WHERE host like '82%' TIMESERIES AUTO

  • Database Calls / Sec Per Host
    SELECT SUM(databaseCallCount) / 10 AS CallCount FROM Transaction TIMESERIES 10 seconds SINCE 1 HOUR AGO FACET host LIMIT 100 WHERE host like '82%'


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#15

Are Data Apps considered valid entries for the contest?


#16

Overview dashboard with queries to

  • Count the most frequent errors: SELECT count(*) from Transaction where errorMessage is not null facet errorMessage
  • Count activity of a process: SELECT filter(count(*), where errorMessage is not null) as with_error, filter(count(*), where errorMessage is null) as success from Transaction where name like '%Processor%' and appName = 'website' since 2 hours ago TIMESERIES
  • Count activity of important process in different platforms: SELECT count(*) from Transaction where name = 'Process' and appName not like '%staging%' FACET appName TIMESERIES

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#17

As long as you also include your value statement and the associated NRQL, I say yes! Please post Data Apps if you’ve got em! @adam.ahndan! :blush:


#18

Continuing the discussion from Shared Dashboards Contest—Add Your Queries Here!:


Know your customers (KYC) dashboard:

Hello!
My name is Sheik. I have shared my very favorite dashboard which is called “Know Your Customers (KYC) dashboard” which i built for an eCommerce application. Basically any e-commerce application will have a shopping experience and a buying experience.

The purpose of this dashboard is to understand how our customers are experiencing our website when they shop and buy products and on what ways we can serve them better respective to their location, timezone and interest.

Query Sample:

SELECT average(duration) as ’ End User Time’, average(backendDuration) as ’ App Server Time ’ FROM PageView WHERE appName LIKE ‘$Instance’ SINCE 2 minutes ago UNTIL 10 seconds ago TIMESERIES

Hope everyone like it!


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#19

Here we have our dashboard which is monitoring Manchester Metropolitan University’s Moodle instance

Some of our NRQL which is wired up to alerts, we ignore the goings on in the early AM as that is when the syncing of our data across servers occurs.

Errors > 20% : ignores 2 to 4:59 am
select filter(count(*), where 'error.message' is not null)*100 / filter(count(*), where duration is not null) as 'Errors' from Transaction, TransactionError where hourOf(timestamp) IN ('0:00','1:00','5:00','6:00','7:00','8:00','9:00','10:00','11:00','12:00','13:00','14:00','15:00','16:00','17:00','18:00','19:00','20:00','21:00','22:00','23:00') with TIMEZONE 'Europe/London'

APDEX < 0.5 for 1 minute
SELECT apdex(duration, 0.5) FROM Transaction where hourOf(timestamp) IN ('0:00','1:00','2:00','3:00','4:00','5:00','6:00','7:00','8:00','9:00','10:00','11:00','12:00','13:00','14:00','15:00','16:00','17:00','18:00','19:00','20:00','21:00','22:00','23:00') with TIMEZONE 'Europe/London'

Database spike alert : ignores 2 to 4:59 am
SELECT average(databaseDuration) * 92 FROM Transaction where hourOf(timestamp) IN ('0:00','1:00','5:00','6:00','7:00','8:00','9:00','10:00','11:00','12:00','13:00','14:00','15:00','16:00','17:00','18:00','19:00','20:00','21:00','22:00','23:00') with TIMEZONE 'Europe/London'

We have the above NRQL wired into alerts as well as synthetics pinging & logging in from around the globe.

Hope this prove useful to someone out there. I plan on seeing whether I can use some of your NRQL to make it even better.

Great product and great community, cheers.


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#20

This is our Production Performance Monitoring Board:

We monitor Top Slowest Transactions

External Calls: Tax , Inventory and Loyalty services

Infrastructure: Load Balancing, Application Response by Host and Throughput by Host, CPU utilization

User Experience: Mobile, Desktop and DOM Load Time

Some of my favorite Queries below

Slowest Transactions on App:

SELECT average(duration) FROM Transaction WHERE transactionType=‘Web’ and appName =’$AppName’ FACET name SINCE 1 day ago LIMIT 10

Load Balancing on App instances:

SELECT count(*) from Transaction where appName = ‘$AppName’ FACET host SINCE 1 day ago

_emphasized text_Desktop DOM Time:
Select average(domProcessingDuration) as ‘DOM Load’ FROM PageView where
appName = ‘$AppName’ and deviceType='Desktop’and pageFACA<'4’since 2 minutes ago

Desktop Page Load Time:

SELECT average(duration) as ‘Page Load’ FROM PageView WHERE appName =’$AppName’ and deviceType=‘Desktop’ and pageFACA<‘4’ since 2 minutes ago

Page Views Trend

SELECT count(*) as ‘Page View Count’FROM PageView where appName=’$AppName’ and pageFACA<‘4’ since 3 hours ago TIMESERIES 1 minute COMPARE WITH 1 week ago


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#21

Nice to See you Sheik, an Excellent Dashboard


#22


We started using newrelic to monitor A/B Tests regarding large scale performance improvements. This first A/B test was created as a proof of concept to see if the data would be useful.

Some of the queries are as follows
SELECT average(duration)from Transaction where appName = 'Production - Advisor Web' AND 'ABTest1-Activated' is NOT NULL FACET ABTest1 TIMESERIES AUTO

SELECT count(*) FROM Transaction WHERE appName = 'Production - Advisor Web' where 'ABTest1-Activated' is not NULL FACET 'ABTest1-Activated'

SELECT average(duration) from Transaction WHERE appName = 'Production - Advisor Web' and hasAjaxXRequestedWithHeader = 'True' and 'ABTest1-Activated' is NOT NULL FACET 'ABTest1-Activated' TIMESERIES AUTO


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#23

Dashboard: GLAIM_Dashboard

This dashboard shows the activity of the probes which monitor IBM Global Logistcs applications which are key to the business.

Dashboard Details

Required Products: Synthetics, Infrastructure.
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 (below is an example):

  • Dashboard Filter: Enabled
  • Enabled Event Types: ProcessSample,ProcessSample,StorageSample,SyntheticCheck,SyntheticRequest,
  • Enabled Attributes: agentName, checkId,device, duration,

Chart Details

Count Checks

Billboard

SELECT count(*) FROM SyntheticCheck
The total count of checks performed by the monitors on hourly basis

Total CPU System Percent

Gauge

SELECT sum(cpuSystemPercent) FROM ProcessSample
Total CPU system percent usage out of the total amount of processing power

Count Requests

Billboard

SELECT count(*) from SyntheticRequest
Total number to requests sent by all the monitors

Response Body Size

Pie Chart

SELECT sum(totalResponseBodySize) from SyntheticCheck FACET monitorName
Amount of responces body size grouping by the name of the monitors

Apdex

Table

SELECT average(duration), apdex(duration, t:7000) FROM SyntheticCheck FACET monitorName
Average duration and apdex time for each monitor check performed

Request Body Size

Pie Chart

SELECT sum(totalRequestBodySize) FROM SyntheticCheck FACET monitorName
Amount of requests body size grouping by the name of the monitors

Checks Duration

Bar Chart

SELECT sum(duration) FROM SyntheticCheck FACET monitorName
Average duration time for each monitor check (displayed graphically as bars)


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#24

*New Relic provides many built in dashboard but my requirement was to create dashboard and group them in single screen.

  • I used data Apps in insights which is really very useful. I had more than 10 java micro services and Angular UI. I build dashboard for each of them using data apps and can navigate easily using tabs. so one place to track all my apps.

If you see in above dashboard, i have API like user, config, order , gateway etc. and can navigate easily. Charts are standard charts of CPU, memory, Threads etc.

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

Queries
SELECT appName, cpuPercent FROM Transaction, SystemSample where appName like ‘WO-PRD%’ SINCE LAST WEEK

SELECT appName, transactionType FROM Transaction where appName like ‘WO-PRD%’ SINCE last week

SELECT appName, queueDuration FROM Transaction where appName like ‘WO-PRD%’ SINCE last week


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