Skip to content

Commit

Permalink
Reworked image titles and added figcaptions
Browse files Browse the repository at this point in the history
  • Loading branch information
@JimHagan
JimHagan committed Apr 5, 2022
1 parent 9ea466d commit d71a7b8
Show file tree
Hide file tree
Showing 2 changed files with 98 additions and 44 deletions.
117 changes: 78 additions & 39 deletions ...relic-solutions/observability-maturity/operational-efficiency/dg-baselining.mdx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,6 @@ import derivativeexample from 'images/oma-oe-dg-rate-of-growth-time-series.png'
import sumtimeseriesexample from 'images/oma-oe-dg-simple-raw-sum-time-series.png'
import entitytypeexample from 'images/om-oe-dg-facet-entitytype-example.png'
import facetmetricnameexample from 'images/om-oe-dg-facet-metricname-example.png'
import thirtydayingest56weeks from 'images/oma-oe-dg-30-day-ingest-last-56-weeks.png'
import orgwidebaselineview from 'images/oma-dg-org-wide-dashboard-dark.png'
import orgwidetabularview from 'images/oma-dg-org-wide-dashboard-tabular-dark.png'
import baselinebrowserdetail from 'images/oma-dg-browser-detail-dark.png'
Expand Down Expand Up @@ -142,7 +141,7 @@ Understand exactly which groups within the org are contributing which types of d
</td>

<td>
Various Events for third party platform integrations as well as `ContainerSample`
Various events for third party platform integrations as well as `ContainerSample`
</td>

<td>
Expand Down Expand Up @@ -367,13 +366,6 @@ Understand exactly which groups within the org are contributing which types of d
|May 2021|881 k|
|||
```

Daily ingest rate expressed as a fifty six week time series

<img
src={thirtydayingest56weeks}
alt="Thirty Day Ingest Last Fifty Six Weeks"
/>
</Collapser>

<Collapser
Expand All @@ -395,8 +387,12 @@ Understand exactly which groups within the org are contributing which types of d

<img
src={comparewithexample}
alt="Compare With Example"
alt="Example chart showing the use of COMPARE WITH to understand growth patterns."
title="Example chart showing the use of COMPARE WITH to understand growth patterns."
/>
<figcaption>
Example chart showing the use of **COMPARE WITH** to understand growth patterns.
</figcaption>

</Collapser>

Expand All @@ -421,8 +417,13 @@ Understand exactly which groups within the org are contributing which types of d

<img
src={slidingwindowjaggedexample}
alt="Raw Jagged Data"
alt="Daily rate time series without smoothing."
title="Daily rate time series without smoothing."
/>
<figcaption>
Daily rate time series **without** smoothing
</figcaption>


Now if we use a [sliding window](/docs/query-your-data/nrql-new-relic-query-language/nrql-query-tutorials/create-smoother-charts-sliding-windows/) of 4 days to reduce the impact of single day events we will see a clearer picture. Four days is a good choice since it will blur the impact of `weekends` so data for a Sunday will be combined somewhat with data for a Friday etc.

Expand All @@ -432,8 +433,12 @@ Understand exactly which groups within the org are contributing which types of d

<img
src={slidingwindowsmoothexample}
alt="Sliding Window Smoothed"
alt="Daily rate time series with smoothing."
title="Daily rate time series with smoothing."
/>
<figcaption>
Daily rate time series **with** smoothing
</figcaption>

</Collapser>

Expand All @@ -456,21 +461,12 @@ Understand exactly which groups within the org are contributing which types of d

<img
src={derivativeexample}
alt="Derivative Example"
/>

In this scenario the uptick was so blatant a simple time series of the rate will suffice. However the benefit of the derivative is it can be more sensitive at assessing the relative quantity of growth and give us a sense of when it first started. This can be useful if we are in the early stages of a major uptick.

Here is the simple plot of the SUM

```sql
SELECT sum(GigabytesIngested) FROM NrConsumption WHERE productLine = 'DataPlatform' and usageMetric = 'BrowserEventsBytes' TIMESERIES 7 days SINCE 6 MONTHS AGO
```

<img
src={sumtimeseriesexample}
alt="Raw Sum Example"
alt="Using a seven day derivative to explore ingest trends"
title="Using a seven day derivative to explore ingest trends"
/>
<figcaption>
Using a seven day derivative to explore ingest trends
</figcaption>

</Collapser>

Expand Down Expand Up @@ -615,9 +611,12 @@ Understand exactly which groups within the org are contributing which types of d

<img
src={entitytypeexample}
alt="Entity Type Example"
alt="Listing of ingest by K8s event type"
title="Listing of ingest by K8s event type"
/>

<figcaption>
Liting of ingest by K8s event type
</figcaption>

</Collapser>

Expand Down Expand Up @@ -653,8 +652,12 @@ Understand exactly which groups within the org are contributing which types of d
```
<img
src={facetmetricnameexample}
alt="Facet Metric Name Example"
alt="Listing of ingest by the metricName attribute of the Metric namespace"
title="Listing of ingest by the metricName attribute of the Metric namespace"
/>
<figcaption>
Listing of ingest by the **metricName** attribute of the **Metric** namespace
</figcaption>

</Collapser>
</CollapserGroup>
Expand Down Expand Up @@ -687,22 +690,35 @@ The main overview tab shows a variety of charts including some powerful time ser

<img
src={orgwidebaselineview}
alt="Org Wide Baseline Time Series View"
alt="Organization wide baseline ingest time series"
title="Organization wide baseline ingest time series"
/>
<figcaption>
Organization wide baseline ingest time series
</figcaption>

The second tab provides a baseline report by sub-account and usage metric.

<img
src={orgwidetabularview}
alt="Org Wide Baseline Tabular View"
alt="Organization wide baseline tabular view"
title="Organization wide baseline tabular view"
/>
<figcaption>
Organization wide baseline tabular view
</figcaption>

The remaining tabs provide detailed views of specific telemetry types such as browser data, apm data, logs, and traces. For example this screenshot shows the browser detail page

<img
src={baselinebrowserdetail}
alt="Baseline Browser Detail"
alt="Baseline Browser ingest"
title="Baseline Browser ingest"
/>
<figcaption>
Example of an ingest detail focused on a single telemetry type (in this case Browser data)
</figcaption>


Detail tabs include:

Expand Down Expand Up @@ -745,8 +761,12 @@ SELECT 360000, rate(sum(GigabytesIngested), 30 day) AS '30 Day Rate' FROM NrCons

<img
src={thirydayratetargetline}
alt="Thirty Day Ingest Target Line"
alt="Thirty day ingest target line"
title="Thirty day ingest target line"
/>
<figcaption>
We an use NRQL to render a line representing our target thirty day ingest **target**.
</figcaption>

We can also apply a daily rate target line. Let's just divide 360000 by 30 and we'll use 12000 as our daily rate target. Update the `Daily Ingest Rate (Compare With 3 Months Prior)` chart:

Expand All @@ -756,8 +776,12 @@ SELECT 12000, rate(sum(GigabytesIngested), 1 day) AS avgGbIngestTimeseries FROM

<img
src={dailyratetargetline}
alt="Daily Ingest Target Line"
alt="Daily ingest target line"
title="Daily ingest target line"
/>
<figcaption>
We an use NRQL to render a line representing our daily ingest **target**.
</figcaption>

### Generate a tabular 30 day ingest report [#generate-report]

Expand All @@ -776,8 +800,11 @@ Below is an example of a sheet we imported into Google Sheets.

<img
src={baselinetabularspreadsheet}
alt="Baseline Tabular Spreadsheet"
alt="Baseline tabular spreadsheet"
/>
<figcaption>
A spreadsheet exported from the baseline dashboard tabular page
</figcaption>

The screenshot shows the table sorted by 30 day ingest total.

Expand Down Expand Up @@ -813,15 +840,21 @@ SELECT rate(sum(GigabytesIngested), 1 day) AS avgGbIngest FROM NrConsumption WHE

<img
src={lookoutviewusagemetric}
alt="Lookout View Usage Metric"
alt="Lookout view usage metric"
/>
<figcaption>
We can use **Lookout** to find anomaly in our ingest by **usageMetric**
</figcaption>

Change the facet field to `consumingAcountName` to get this view:

<img
src={lookoutviewconsumingaccont}
alt="Lookout View Consuming Account"
alt="Lookout view consuming account"
/>
<figcaption>
We can use **Lookout** to find anomaly in our ingest by **consumingAccountName**
</figcaption>

### Install the entity breakdown dashboard (Optional) [#install-entity-breakdown-dashboard]

Expand All @@ -835,8 +868,11 @@ In a previous section you installed the ingest `baseline` dashboard that uses Nr

<img
src={entitybreakdowndash}
alt="Entity Breakdown Dashboard"
alt="Entity breakdown dashboard"
/>
<figcaption>
The entity breakdown dashboard uses **bytecountestimate()** to facet ingest by useful attributes such as application or cluster name
</figcaption>

You can refer back to [this section](/docs/new-relic-solutions/observability-maturity/operational-efficiency/dg-baselining#understand-nr-consumption-metrics) to see exactly which event types are used in these breakdowns.

Expand All @@ -858,8 +894,11 @@ Individual dashboards installed by this package include:

<img
src={infraintegrationsdash}
alt="Infra Integrations Dashboard"
alt="Infra integrations dashboard"
/>
<figcaption>
This [quickstart](https://onenr.io/0EPwJJO9Ow7) contains a highly granular set of dashboard breaking down data by nearly every cloud integration, on-host integration, as well as the K8s integration
</figcaption>

## Exercise [#exercise]

Expand Down
25 changes: 20 additions & 5 deletions ...relic-solutions/observability-maturity/operational-efficiency/dg-optimizing.mdx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -80,8 +80,13 @@ Use the following examples to help you visualize how you would assess your own t

<img
src={valuedriversuptime}
alt="Value Drivers Uptime"
alt="Observability value drivers with a focus on Uptime and Reliability"
title="Observability value drivers with a focus on Uptime and Reliability"
style={{width: "400px"}}
/>
<figcaption>
Observability value drivers with a focus on **Uptime and Reliability**
</figcaption>

Their estate includes:

Expand Down Expand Up @@ -121,8 +126,13 @@ Use the following examples to help you visualize how you would assess your own t

<img
src={valuedriverscustomer}
alt="Value Drivers Customer"
alt="Observability value drivers with a focus on Customer Experience"
title="Observability value drivers with a focus on Customer Experience"
style={{width: "400px"}}
/>
<figcaption>
Observability value drivers with a focus on **Customer Experience**
</figcaption>

Their estate includes:

Expand Down Expand Up @@ -165,8 +175,13 @@ Use the following examples to help you visualize how you would assess your own t

<img
src={valuedriversinnovation}
alt="Value Drivers Innovation"
alt="Observability value drivers with a focus on Innovation and Growth"
title="Observability value drivers with a focus on Innovation and Growth"
style={{width: "400px"}}
/>
<figcaption>
Observability value drivers with a focus on **Innovation and Growth**
</figcaption>

Their estate includes:

Expand Down Expand Up @@ -633,12 +648,12 @@ The different approaches:
* Logs generated per cluster
</Callout>

It's not surprising that a complex and decentralized system like Kubernetes has the potential to generate a lot of telemetry fast. There are a few good approaches to managing data ingest in Kubernetes. These will be very straightforward if you are using observability as code in your K8s deployments. We highlly recommend you install this `Kubernetes Data Ingest Analysis` dashboard for making any decisions about reducing K8s ingest. The dashbaord is available in this [quickstart](https://newrelic.com/instant-observability/infrastructure-integrations-data-analysis/8e31a0ae-81c0-4df0-a119-a0ada9ec16fa).
It's not surprising that a complex and decentralized system like Kubernetes has the potential to generate a lot of telemetry fast. There are a few good approaches to managing data ingest in Kubernetes. These will be very straightforward if you are using observability as code in your K8s deployments. We highly recommend you install this `Kubernetes Data Ingest Analysis` dashboard for making any decisions about reducing K8s ingest. The dashbaord is available in this [quickstart](https://newrelic.com/instant-observability/infrastructure-integrations-data-analysis/8e31a0ae-81c0-4df0-a119-a0ada9ec16fa).

### Scrape interval

Depending on your observability objectives you may consider adjusting the scrape interval. The default is 15s. Now the Kubernetes cluster explorer only refreshes every 45s. If your primary use of the K8s data is to support the KCE visualizations you may consider changing your scrape interval to 20s. That change from 15s to 20s can have a substantial impact.
See [this section](/docs/kubernetes-pixie/kubernetes-integration/installation/install-kubernetes-integration-using-helm/#scrape-interval) of the New Relic docs for more details.
See more details about managing [scrape interval](/docs/kubernetes-pixie/kubernetes-integration/installation/install-kubernetes-integration-using-helm/#scrape-interval) in our Helm configuration documentation.

### Kube state metrics

Expand Down

0 comments on commit d71a7b8

Please sign in to comment.