- [Narrator] In this video tutorial, I'll walk you
through the process of creating
and using dashboards for visualizing performance
and availability data collected by Azure Monitor.
Dashboards are near real time views that allow you
to monitor resources quickly launch tasks for day
to day operations.
And they can be customized
to meet the requirements for various user roles
and shared across teams in your organization.
You can use dashboards
to monitor resource utilization, application performance
and end user experience based on metric
and log data collected
and stored by Azure Monitor.
You can often view the performance and availability
of resources directly in their individual resource blades.
But dashboards extend visualization options to
include data correlation across multiple resources and
to provide full stack visibility in centralized views.
For example, you may want to view application performance
and infrastructure health on a single screen,
or compare the performance of two instances
of the same app deployed
across different geographic regions.
Dashboards make it easy to create views
with any combination of tables, chart, maps and shortcuts
based on user defined criteria.
The use cases are completely up to you
and what you consider important,
but I'll highlight a few common scenarios
in the upcoming demonstrations.
Now you do have the option of adding content
to the dashboard using the tile gallery.
However, the easiest way
to build out a monitoring dashboard is
to simply pin the desired visualizations from an insight
or from a view that you've
already created in Metrics Explorer.
The dashboard for this demo combines application
and infrastructure views in a single pane of glass.
I'll start by adding a title
of an application map for an overall app perspective.
This shows which components of the app communicate
with each other.
And if there are any backend dependencies.
Let's go a step further
to see compute resources used by the app.
Now we can see that this component
of the app is running on a virtual machine,
so I can monitor basic VM utilization metrics,
such as CPU, memory, and disk usage.
I'll go ahead and navigate to Metrics Explorer,
create a chart of average and max CPU usage.
And I'll also pin that to my dashboard.
First, I'll click on metrics
and this launches Metrics Explorer
with the scope limited to the VM of interest.
Then I'll choose percentage CPU as my metric
and leave the default aggregation as average.
Then I can add a metric for percentage CPU again,
and this time I'll switch aggregation type to maximum.
And the final step here is to add it
to my existing dashboard,
just like we did before.
Now that I have some content on the dashboard,
let's look at some of the settings that are available to me.
First I'll quickly resize the size
of my application map tile so
that I can see the big picture.
Next, the auto refresh interval controls the frequency
at which data is updated all time range
and granularity control
how far back in time, the dashboard evaluates
and the level of precision used.
The last dashboard setting I'll point out is filters.
Where a single filter can be applied
across the entire dashboard
to all the relevant tiles.
Moving on to tile settings, time span, time granularity,
and time zone can all be set
at the individual tile level to override dashboard settings.
In this next scenario I want to highlight a few
of the different visualization sources that can be used
to create a dashboard.
First up I'll drill down from my dashboard
to the application insights resource to view some
of the available out of the box visualizations.
I'll keep this one simple
and pin the line graph of availability to my dashboard.
This tracks the up or down status
of my apps public facing URL.
The visualization focuses on app availability,
but I'd like to transition
to look at the infrastructure resources supporting the app.
I'll take another look at my app map
to see what backend dependencies may be important.
Here, I can see that my app makes calls
out the various storage services.
I can take advantage of an existing chart from an insight.
In this case I'll navigate
to the storage account of interests
and view the storage insight.
This is an out of the box curated monitoring view built
on workbooks.
What's great about this is: The work
to create frequently used charts
and graphs has already been done,
and I can simply pick the one that I want
and pin it to my dashboard directly.
In this case, I'll use transactions by API name
and spend a moment rearranging my tiles.
For the next part of the demonstration I'll work
on creating shortcuts for commonly used views.
For example, the blades used to evaluate failures
and performance will often be accessed by my team.
So add them both using pin to dashboard,
and I'll do a quick resize on the tiles.
This makes it more convenient for users of the dashboard
to reach these users with a single click.
Keep in mind that other tiles can also be used
in the same way to drill down
to the underlying viewer resource on which it is based.
After some rearranging and resizing,
I can then share my dashboard by publishing it
to a resource group and use the standard Azure R-back
to share access across my team.
So as mentioned before,
by drilling down from the dashboard, I can
jump back to my application insights performance blade.
Here I can see that several charts and tables are based
on data stored in Azure Monitor logs.
I wanna focus on response time
and specifically the line graph of average response time.
From the UI, I can easily get
to the median at 95th percentile or the 99th percentile,
but I can't change the setting to maximum or minimum.
The advantage of working with log queries is
that I can quickly edit the query by adding
and removing filters and summarization modes
as I see fit.
Here I'll add the min and max response times
to go along with the average,
which was included automatically.
Next I'll run the query
to ensure it works as expected.
And just as in all the other cases, I'll pin it
to the dashboard.
So the end result is a dashboard
that contains several application
and infrastructure performance views
from both metric and log data.
This provides various perspectives on the application
and the resources it's consuming.
Remember some of these views
were simply repurposed from existing insights
and others were created using Metrics Explorer
and log queries.
This tutorial highlights that ease of use and flexibility
that dashboards provides for specific situations
and user roles in your organization.