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Refactor image tags from markdown to HTML
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@JimHagan
JimHagan committed Apr 5, 2022
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75 changes: 61 additions & 14 deletions ...relic-solutions/observability-maturity/operational-efficiency/dg-baselining.mdx
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Expand Up @@ -27,6 +27,18 @@ 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'
import thirydayratetargetline from 'images/oma-oe-dg-ingest-target-30-day-rate-ts.png'
import dailyratetargetline from 'images/oma-oe-dg-ingest-target-line.png'
import baselinetabularspreadsheet from 'images/oma-oe-dg-baseline-sheet.png'
import lookoutviewusagemetric from 'images/oma-oe-dg-lookout-view-telem-type.png'
import lookoutviewconsumingaccont from 'images/oma-oe-dg-lookout-view-consuming-account.png'
import infraintegrationsdash from 'images/oma-oe-dg-infra-integrations-nr-io.png'
import entitybreakdowndash from 'images/oma-oe-dg-entity-breakdown-dash.png'


<img
src={baseliningicon}
Expand Down Expand Up @@ -102,7 +114,7 @@ Understand exactly which groups within the org are contributing which types of d
</td>

<td>
`SystemSample`, `StorageSample`, `ContainerSample`, `InfrastructureEvent`
`SystemSample`, `StorageSample`, `InfrastructureEvent`
</td>

<td>
Expand Down Expand Up @@ -130,7 +142,7 @@ Understand exactly which groups within the org are contributing which types of d
</td>

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

<td>
Expand Down Expand Up @@ -172,7 +184,7 @@ Understand exactly which groups within the org are contributing which types of d
</td>

<td>
`Browser`, `Browser:EventLog`, `Browser:JSErrors`, `PcvPerf`, `PageViewTiming`
`Browser`, `BrowserInteraction`, `Browser:EventLog`, `Browser:JSErrors`, `JavaScriptError`, `PageView`, `PageViewTiming`, `PcvPerf`
</td>

<td>
Expand Down Expand Up @@ -356,7 +368,12 @@ Understand exactly which groups within the org are contributing which types of d
|||
```

![30 Day Ingest Last 56 Weeks](images/oma-oe-dg-30-day-ingest-last-56-weeks.png)
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 Down Expand Up @@ -668,15 +685,24 @@ That will bring you to the newly installed dashboard.

The main overview tab shows a variety of charts including some powerful time series views.

![Org Wide View](images/oma-dg-org-wide-dashboard-dark.png)
<img
src={orgwidebaselineview}
alt="Org Wide Baseline Time Series View"
/>

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

![Org Wide Tabular](images/oma-dg-org-wide-dashboard-tabular-dark.png)
<img
src={orgwidetabularview}
alt="Org Wide Baseline Tabular View"
/>

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

![Browser Detail Page](images/oma-dg-browser-detail-dark.png)
<img
src={baselinebrowserdetail}
alt="Baseline Browser Detail"
/>

Detail tabs include:

Expand Down Expand Up @@ -717,15 +743,21 @@ To make the target easier to measure against we added a threshold line chart by
SELECT 360000, rate(sum(GigabytesIngested), 30 day) AS '30 Day Rate' FROM NrConsumption WHERE productLine='DataPlatform' since 30 days ago limit max compare with 1 month ago TIMESERIES 7 days
```

![Ingest target 30 day](images/oma-oe-dg-ingest-target-30-day-rate-ts.png)
<img
src={thirydayratetargetline}
alt="Thirty Day Ingest Target Line"
/>

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:

```sql
SELECT 12000, rate(sum(GigabytesIngested), 1 day) AS avgGbIngestTimeseries FROM NrConsumption WHERE productLine='DataPlatform' TIMESERIES AUTO since 9 months ago limit max COMPARE WITH 3 months ago
```

![Ingest Target Daily](images/oma-oe-dg-ingest-target-line.png)
<img
src={dailyratetargetline}
alt="Daily Ingest Target Line"
/>

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

Expand All @@ -742,7 +774,10 @@ SELECT sum(GigabytesIngested) AS 'gb_ingest_30_day_sum', rate(sum(GigabytesInges

Below is an example of a sheet we imported into Google Sheets.

![DG Baseline Sheet](images/oma-oe-dg-baseline-sheet.png)
<img
src={baselinetabularspreadsheet}
alt="Baseline Tabular Spreadsheet"
/>

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

Expand Down Expand Up @@ -776,11 +811,17 @@ This view is based on the query
SELECT rate(sum(GigabytesIngested), 1 day) AS avgGbIngest FROM NrConsumption WHERE productLine='DataPlatform' FACET usageMetric
```

![Lookout View Usage Metric](images/oma-oe-dg-lookout-view-telem-type.png)
<img
src={lookoutviewusagemetric}
alt="Lookout View Usage Metric"
/>

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

![Lookout View Consuming Account](images/oma-oe-dg-lookout-view-consuming-account.png)
<img
src={lookoutviewconsumingaccont}
alt="Lookout View Consuming Account"
/>

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

Expand All @@ -792,7 +833,10 @@ In a previous section you installed the ingest `baseline` dashboard that uses Nr
4. Click `Done` since there is no agent to install.
5. When the quickstart is done installing open the `Data Governance Entity Breakdowns` dashboard.

![Entity breakdown dashboard](images/oma-oe-dg-entity-breakdown-dash.png)
<img
src={entitybreakdowndash}
alt="Entity Breakdown Dashboard"
/>

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 @@ -812,7 +856,10 @@ Individual dashboards installed by this package include:
* On-Host Integrations
* Kubernetes

![Infrastructure Integrations IO](images/oma-oe-dg-infra-integrations-nr-io.png)
<img
src={infraintegrationsdash}
alt="Infra Integrations Dashboard"
/>

## Exercise [#exercise]

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88 changes: 76 additions & 12 deletions ...relic-solutions/observability-maturity/operational-efficiency/dg-optimizing.mdx
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Expand Up @@ -20,6 +20,11 @@ redirects:
---

import optimizingicon from 'images/oma-oe-dg-optimizing-icon.png'
import valuedriversuptime from 'images/oma-oe-dg-value-driver-uptime.png'
import valuedriverscustomer from 'images/oma-oe-dg-value-driver-customer.png'
import valuedriversinnovation from 'images/oma-oe-dg-value-driver-innovation.png'
import kubestatemetrics from 'images/oma-oe-dg-update-k8s-kube-state-metrics.png'
import kubernetesscrapeinterval from 'images/oma-oe-dg-update-k8s-scrape-interval.png'

<div>
<img
Expand Down Expand Up @@ -73,7 +78,10 @@ Use the following examples to help you visualize how you would assess your own t
>
An account is ingesting about 20% more than they had budgeted for. They have been asked by a manager to find some way to reduce consumption. Their most important value driver is `Uptime, performance, and reliability`

![Value Drivers Uptime](images/oma-oe-dg-value-driver-uptime.png)
<img
src={valuedriversuptime}
alt="Value Drivers Uptime"
/>

Their estate includes:

Expand Down Expand Up @@ -111,7 +119,10 @@ Use the following examples to help you visualize how you would assess your own t
>
A team responsible for a new user facing platform with an emphasis on mobile monitoring and browser monitoring is running 50% over budget. They will need to right size their ingest, but they are adamant about not sacrificing any `Customer Experience` observability.

![Value Drivers Uptime](images/oma-oe-dg-value-driver-customer.png)
<img
src={valuedriverscustomer}
alt="Value Drivers Customer"
/>

Their estate includes:

Expand Down Expand Up @@ -152,7 +163,10 @@ Use the following examples to help you visualize how you would assess your own t
>
A team is in the process of refactoring a large Python monolith to four microservices. The monolith shares much infrastructure with the new architecture including a customer database, and a cache layer. They are 70% over budget and have two months to go before they can officially decommission the monolith.

![Value Drivers Uptime](images/oma-oe-dg-value-driver-innovation.png)
<img
src={valuedriversinnovation}
alt="Value Drivers Innovation"
/>

Their estate includes:

Expand Down Expand Up @@ -590,13 +604,12 @@ 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.
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).

### 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.

![Update Scrape Interval](images/oma-oe-dg-update-k8s-scrape-interval.png)
See [this section](/docs/kubernetes-pixie/kubernetes-integration/installation/install-kubernetes-integration-using-helm/#scrape-interval) of the New Relic docs for more details.

### Kube state metrics

Expand Down Expand Up @@ -629,12 +642,63 @@ The different approaches:
StatefulSet data
```

_Updating the manifest_
![Kube State Metrics - Manifest](images/oma-oe-dg-update-k8s-kube-state-metrics.png)

_Updating the Helm chart_

![Kube State Metrics - Helm](images/oma-oe-dg-update-k8s-kube-state-metrics.png)
_Example of updating state metrics in manifest (Deployment)_

```shell
[spec]
[template]
[spec]
[containers]
[name=kube-state-metrics]
[args]
#- --collectors=daemonsets
#- --collectors=deployments
#- --collectors=endpoints
#- --collectors=namespaces
#- --collectors=replicasets
#- --collectors=services
#- --collectors=statefulsets
```

_Example of updating state metrics in manifest (ClusterRole)_

```shell
[rules]
# - apiGroups: ["extensions", "apps"]
# resources:
# - daemonsets
# verbs: ["list", "watch"]

# - apiGroups: ["extensions", "apps"]
# resources:
# - deployments
# verbs: ["list", "watch"]

# - apiGroups: [""]
# resources:
# - endpoints
# verbs: ["list", "watch"]

# - apiGroups: [""]
# resources:
# - namespaces
# verbs: ["list", "watch"]

# - apiGroups: ["extensions", "apps"]
# resources:
# - replicasets
# verbs: ["list", "watch"]

# - apiGroups: [""]
# resources:
# - services
# verbs: ["list", "watch"]

# - apiGroups: ["apps"]
# resources:
# - statefulsets
# verbs: ["list", "watch"]
```

### Config `lowDataMode` in `nri-bundle` chart [#low-data-mode]

Expand Down

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