Grafana dashboards — best practices and dashboards-as-code

By first concentrating on monitoring and alerting for the main function of your business and system, you can cover almost all critical problems in subcomponents and infrastructure resources, without having to monitor those explicitly.

That needs explanation…​ The business in this blog article’s example is to process payments. So if payments fail, people cannot pay, and the business is at risk. Anything else is not as important, and therefore not worth to observe as the first thing. If a critical issue arises in our systems or network, the payment_errors_total metric will most likely cover that! To use IT terminology: the metric of payment failures is a significant service level indicator (SLI).

Admittedly, that will not cover if the internet, a customer, or our API are down, because payment requests would not even reach the system and therefore cannot produce logs or error metrics. That can be covered by a metric describing the payment request rate, probably by customer and payment method, since each of those have different typical traffic rates (minimum/maximum, different per timezone or day/night, etc.). We keep this shortcoming out of scope to keep the blog article simple. The point is: choose very few, essential business metrics as a start, not technical metrics.

Often, you would select the most business-relevant Prometheus metric offered by the system you want to monitor. Metrics are stored as time series and therefore very fast to query, as opposed to logs. If you use other observability tools, such as an ELK stack, you can check if Grafana supports the relevant data source. This metric would typically pertain to the methods "RED" (requests/rate, errors, duration) or "USE" (utilization, saturation, errors). The Four Golden Signals of Google’s SRE book additionally distinguishes traffic from saturation. An error metric is a good choice to start, since it is easy to determine which error rate is acceptable, and at which threshold you would consider it a serious problem.

Throughout this blog post, we will use the following example metric and simple terminology from the payments world:

We do not want to have separate metrics credit_card_payment_errors_total and bank_transfer_payment_errors_total! If you have microservices of the same type, as in this example one service per payment method, the metrics really mean the same thing. So rather improve your consistency and use just one metric name. Easy to do if your code is structured in a monorepo, by the way. If you have inconsistent names, it will take extra effort to repeat each dashboard visualization for each of the conventions, instead of just using labels to distinguish and plot the services (or other traits that you define as label dimensions, such as customers).

Metric names should be easy to find in code, so that developers can make sense of them fast in a stressful situation, or find out if/where a metric (still) exists. Here is a bad example where the metric name mysystem_payment_errors_total cannot be found in code: for request_type in ['payment', 'refund', 'status']: prometheus.Counter(name=f’mysystem_{request_type}_errors_total') (Python pseudocode).

Avoid high-cardinality metrics (many label combinations), since those take up lots of space, and queries take longer. Like for the logging rate of your systems, you might want to check for large metrics sporadically, or you may run into unnecessary cost and performance issues.

A graph (nowadays called Time series visualization) for our example metric, showing payment method and error type combinations, looks like this:

Cool graph, right? And we’re already done and have a monitored system! No, this is very, very bad! Great observability requires much more than just clicking together some visuals. The example is not sufficient to monitor a service. A graph visualization is a bad way to get an impression within milliseconds. The eyes have to scan the whole rendered graph, potentially containing multiple lines on varying bounds of the Y axis. You also need to know which thresholds are bad, or configure horizontal lines on the graph which represent warning and errors thresholds. That means lots of lines, colors, and points to look at before getting your question answered: "is this normal or do we have a problem, and where?"

Graphs can be helpful if you set them up nicely, but definitely not in the high-level part of your overview dashboard.

Instead, a Stat visualization, combined with traffic light colors, gives you the answer in milliseconds: green is good, amber (yellow) is noteworthy, red is bad. In addition, I tend to use blue as "noteworthy but may not be problematic" — kind of an early warning sign or unusually high amount of traffic, such as during sales/promotion seasons. So for me personally, I like the order green-blue-amber-red. Grafana allows choosing to color the background instead of the value line, which I recommend since then your whole screen should look green most of the time (click Panel > Display > Color mode > Background), and your eyes do not need to focus on the color of tiny graph lines. Exactly one value is shown — typically a number or human description.

Settings for the above screenshot:

To show the colors, you need to set thresholds on the Field tab. Setting them as static numbers (with the queried unit, e.g. "errors per 2 minutes") may work for the start. That is called Absolute in Grafana.

In our example though, the different payment methods and error types have very different thresholds: for instance, let’s say the credit_card payment method has remote_service_down errors very frequently because the 3rd party provider is unreliable and we cannot help it, so we want to set a higher threshold because it otherwise unnecessarily shows a problem. Or instead of a higher threshold, you could consider querying the error rate increment over 10 minutes, to even out any short spikes. To use relative thresholds, click Percentage and fill some values. They will use the Min/Max settings. For example: if you set Min = 0 and Max = 200, red background color above the 66% threshold will be shown above 66% * 200 = 132 (unit in this example: errors within 2 minutes). Everything above 66% will be red. Everything between 16% and 33% will be blue. And so on.

To set specific thresholds per value combination (here: "payment method / error type"), adjust Max:

Since Grafana live-previews your changes, it should be simple to choose good values for Max. Select a healthy time range for your system, and it should be green (note that the right-most time point is displayed, as we chose Calculation > Last). Select an incident time window, and choose a Max value to make it red. The other values (amber/blue) might then just work, since they are based on percentages. Start with values that work, and adjust them if you later see false positives (red when system is fine) or false negatives (green when system has problems). If you want human descriptions instead of numbers, you can also use the override feature (Field > Value mappings, or for specific fields: Overrides > [create an override] > Add override property > Value Mappings / No value), for instance to replace 0 with the text "no errors".

Example: our query shows the number of errors in 2 minutes. By looking at the graphed data of the last few days (paste query into Explore mode), we might find that 20 errors in 2 minutes are acceptable and should be shown as green. We therefore choose a slightly higher threshold of 25 to still be within the green color. Since we switch to blue color from 16%, we get Max = 25 * 100% / 16% = 156. As a result, red background color — which shouts "something is seriously wrong" — would be shown above Max * 66% = 103 errors in 2 minutes. You should experiment a little so that in healthy times, your dashboard remains green.

Side note: for "higher is better" metrics such as customer conversion rate (100% = all customer purchases succeed), you can just turn around the colors (green on top, red on bottom). The Max setting also defines the upper bound of the graph which is shown as part of the Stat visualization, so if values are higher, the line will cross the top of the Y axis and therefore becomes invisible. Not a big deal if Min/Max cover the expected range. You may also have the rare case of "too high and too low are both bad" metrics, e.g. a counter for payment requests where you always expect some traffic to be made, but also want to be warned if the request rate is soaring. The colors could be adapted to show both low range and high range as red, with green for the expected, normal range.

Pack only few visualizations horizontally, so the font stays large enough. Other people may work on a smaller screen than yours, or do not use full screen sizing. The repeat feature (Panel > Repeat options) makes Grafana create and align them automatically. In our example, the repeat feature is unused, but since we pulled different combinations of payment method and error type out of our (single, consistently named and labeled) metric, that will also show multiple rectangles in one Stat visualization, and try to align them on screen. In the screenshot further above, the titles are barely readable, and the visualization is large and could require scrolling on small screens. To solve that, you could:

Typical pre-built, open source dashboards may show variables like these:

Such low-level investigation dashboards are helpful, but for high-level purposes, your dashboard should show everything at one glance. If you have clusters A and B, of which one serves traffic at a time and the other one is the passive backup, you should not be required to know by heart which cluster is active. Instead, the dashboard should show health across clusters. You can still repeat your visualizations for each cluster, or query for by (cluster) if it proves helpful — but probably rather on low-level dashboards. For our example of an error metric, you want to know if it goes above a threshold, and sum(rate(…​)) does not strictly require distinction by cluster in the high-level visualizations.

Let’s set up the generation of our dashboard from code. First, the necessary tools. jb (jsonnet-bundler) will be used as jsonnet package manager, and go-jsonnet (not the much slower C++ implementation!) for the language itself.

We need jsonnet libraries for the outputs we want to generate. In this case, Grafonnet for Grafana dashboards is enough. If you want to generate non-Grafana resources, consider the kube-prometheus collection which covers much of the Kubernetes landscape (but mind its Kubernetes version compatibility matrix).

Commit jsonnetfile.json and jsonnetfile.lock.json. The lock file ensures that the next user gets the same version of libraries, so you do not need to commit the downloaded modules in the vendor directory. But that probably starts a flame war among Go developers, so please decide yourself…​

Instead of jb, you could also use Git submodules, but probably will regret it after adding more dependencies — I did not test that alternative.

First write a minimal dashboard as code and save as dashboards/payment-gateway.jsonnet:

Play around manually:

The last command outputs a valid Grafana dashboard as JSON. To manually apply it, open the test dashboard in your Grafana instance, then ⚙️ > JSON Model > copy-paste generated JSON > Save Changes. You should now see the dashboard as described by code — containing only a text panel that says "Yippie". This is the simplest development workflow. But it is very tiring to always go and copy-paste some generated blob and save it with many clicks. And the workflow is not visual (WYSIWYG). The next section explains a better way.

While there is no WYSIWYG editor for the whole conversion from jsonnet to a visual dashboard in Grafana, here is an alternative which works right now (in 2022):

Preparation in your shell:

Next, store the following script as watch.sh:

Run the script, passing the source files as argument.

The script listens for changes to the source file and then overwrites the dashboard in your Grafana instance with an API request. Open your Grafana instance and find the dashboard by its title. Mind that the uid field in source code must be set to a fixed value per dashboard in order to overwrite the dashboard instead of creating new ones.

This workflow gives you results within seconds and you only need to refresh in your browser to see saved changes. You may want to keep an eye on your terminal, since jsonnet is a compiled language and therefore spits out errors if you make coding mistakes.

Grafana provisioning allows automatic reloading of dashboards from a certain place. We want to load the committed, generated dashboards. On the other hand, we will not fully recreate Grafana and its database on every commit to some control repository — I’d call that murder by GitOps, and the sheer idea does not sound useful, as users and their settings are stored in the database, so we do not want to manage everything as code.

We can set up Grafana in various ways: via Ansible on a single server, with containers on Docker or Kubernetes, manually run on the company’s historic Raspberry Pi in the CEO’s closet, etc. They luckily all work the same way for configuration: local files.

In the dashboard provisioning section, it says you can put "one or more YAML configuration files in the provisioning/dashboards directory". We try that with a realistic setup of Grafana, and for the sake of simplicity, we assume a GitOps model, meaning that Grafana loads dashboards from committed files. You have to adapt this article yourself to your respective setup.

The following instructions show you how to do this on a self-hosted Kubernetes setup of Grafana (see their setup instructions). As I do not have a Grafana Cloud account right now, I cannot tell if the cloud offering provides this much flexibility, or any good way of using the GitOps model — if they do, the documentation misses this important piece (as of 2022-04). We use kind to simulate a production Kubernetes cluster.

To install from Grafana’s Helm chart, you need to configure it. Store the following content in grafana-values.yaml:

And continue installation:

Now open http://127.0.0.1:7878/ and log in with admin:admin.

Grafana reads dashboards from a directory structure. In the Kubernetes world, we can put generated dashboards into a ConfigMap in order to mount it into the Grafana directory structure. They however have a 1 MiB limit each. An example high-level dashboard of mine takes 115 kiB when Base64-encoded, so you also will not be able to say "we can fix this problem later", since you will reach the limit very soon. We will render all dashboards into one manifest file. That YAML file will contain one ConfigMap object per dashboard. Committing that file in a GitOps fashion is easy to add in your CI (git clone && git add && git commit && git push), but that is out of scope for this article. You could also do CIOps (kubectl apply from CI; not recommended), or just rsync -r --delete if you have Grafana on physical, mutable hardware and not on Kubernetes, or whatever other way of deployment to the directory structure. Make sure you overwrite during deployment instead of only adding new files/dashboards, since deletion and cleanup of technical debt is just as important as it is for writing software. Treat your Grafana instance and database as something that gets reset regularly, from a committed state. This avoids people making manual, unreviewed edits.

As you can see in the configuration, we use k8s-sidecar to automatically collect all dashboard JSON files into one directory for use by Grafana. Each ConfigMap must have the label collect-me: grafana-dashboard to get picked up. The following script creates such ConfigMap manifests. I do not explain here how to integrate it with your specific CI tool, but that should be easy if it works locally. Save the script as render-and-configmap.sh.

Ensure you are still pointing KUBECONFIG to the desired Kubernetes cluster, and test dashboard deployment like so:

Head over to the Grafana instance running in Kubernetes, and you see that the dashboard was already loaded. Integrate this with your CI pipeline, et voilà, you have a GitOps workflow!

Once done with the cluster, you can delete it:

You will surely have different environments, such as dev/staging/prod. They have varying URLs, potentially different set of running systems, and thresholds for production not always make sense in pre-production environments. Pass --ext-str myCompanyEnv=prod to the jsonnet tool to pass in a variable which you can use inside the source code. This allows previewing dashboards with development data before a software feature even goes live, and you will have very consistent views across environments. Example usage:

Alternatively, --ext-code-file seems also a viable option, but I have no experience with it (see external blog post Grafana dashboards and Jsonnet which showcases this parameter).

This can also be interesting if you have a dev/prod split for your Grafana instances.

A simple dashboard should be a matter of only few lines of code. Follow the jsonnet tutorial to learn more how to achieve that. You will see some similarities with Python, for example string formatting with %, slicing, array comprehension, modules/imports and other syntax that can make your code easier and shorter.

If you think your users are not technical enough, jsonnet may not be a good fit unless you either provide high-level functions, or replace the whole jsonnet+Grafonnet rendering idea with your own custom solution that does the same thing: output dashboard definitions as Grafana-compatible JSON.

Use the folder structure to organize your dashboards. You and your colleagues will surely play around with test dashboards, and mixing them with production-ready, usable ones is not helpful. Also, particularly if you have many systems to watch, you want everything categorized for easy access. Product-unrelated dashboards, such as monitoring for Kubernetes clusters or infrastructure, can go into a separate category. Unfortunately, you cannot set the parent folder through jsonnet as of 2022-04, but it has to be achieved as part of deploying the generated dashboards.

For very detailed dashboards, you may have a lot of graphs. While this is typically discouraged, your software may really have so many important metrics. In such case, group them into rows. They are collapsible and ease navigation. Maybe Grafana could consider adding a "Table of contents" feature to jump around quickly on a dashboard, using a navigation sidebar.

In the developer workflow above, we explicitly set dashboards to editable. You may want this for the GitOps/CI workflow as well. This is helpful because incidents sometimes require a bit of playing around with shown data. Users should however not save changes, since they are supposed to be overwritten regularly by deploying dashboards from committed code.

By default, hovering over a graph with many series shows them in a box in alphabetical order of the display label, e.g. sorted by {{customer_name}}. Typically however, such as for error rate metrics, you want the top values shown first, since the bottom of the tooltip may be cut off in case of many entries. Go to setting Display > Hover tooltip > Sort order and adjust to your liking (e.g. Decreasing). With jsonnet, use grafana.graphPanel.new(sort="decreasing") (not documented as of 2022-04).

Do not confuse the tooltip with the legend (which also has a configurable sort order!).

Many metrics only produce non-negative numbers. Graphs with such a metric on the Y axis should therefore have Visualization > Axes > Y-Min set to 0 instead of auto in order to save screen space by not showing the negative area. Another problem is that you often want 0 to be the lower bound, or else Grafana chooses the display range based on the available data. In jsonnet: grafana.graphPanel.new(min=0). Setting the maximum may be helpful if you know the number range (e.g. disk full 0-100%) and want to have a consistent display.

See how the bad example on the left makes you think of a fluctuating metric. The corrected example on the right shows that in reality, the value is quite stable. In general, make trends easier to recognize for the eyes.

Be cautious with your use case, though. If you want to display and warn when a disk gets full, for example, you better extrapolate and display the trend. Or use a Stat visualization with a warning color once the trend reaches "disk becomes full within 30 days". The respective alerts need to be designed in a similar way. Otherwise, if a short time range is selected, the user may not see that the disk usage is going up a lot, as the difference between 200 GiB and 210 GiB may not look dramatic with Y-Min set to zero.

Use grafana.graphPanel.new(…​).addLinks(…​) to create panel links:

For links to detailed dashboards, consistently pre-select a reasonable time frame such as now-30m.

If you consistently tag dashboards, you can use dashboard links to put clickable links to related dashboards on top. You can also add external links such as other company tools. I have not used this feature yet and typically rather repeat the links on each panel since that does not require scrolling all the way to the top. With jsonnet, it is easy to provide a consistent set of links (as dashboard or panel links).

In rare cases, you want a repetitive variable such as datacenter = cluster="dc"\,host=~"server.*" so that queries become less verbose: sum by (payment_method, error_type) (rate(payment_errors_total{$datacenter}[2m])). If the value is used in a label filter of a Prometheus query, as in this example, remember that commas need to be escaped with a backslash, or else Grafana treats the comma as separator between different choices for the variable value.

Even if you use jsonnet, you should use variables instead of filling a hardcoded value into each query. This allows users to change all visualizations on a dashboard at once (at ⚙️ > Variables).

Consider hiding those variables on the dashboard if their sole purpose is to avoid repeated, hardcoded values. See also below for some rationale.

You do not want to be looking at a development dashboard while debugging a production issue, so make that mistake impossible to happen. Possible solutions:

The grid position must be specified explicitly:

See Panel size and position documentation — width is split in 24 columns, height is 30 pixels each. I recommend you use 12 or 24 columns width for readability on small screens, and set a reasonable, consistent height for all visualizations on a dashboard.

As of 2022-04, you cannot easily align visualizations using jsonnet. You can hardcode x/y absolute values to your liking, but that is a hassle since you do not want to develop a user interface in an absolute grid, right? I recommend setting both to 0 in order to automatically align the visualizations on screen.

Choose the right unit, e.g. Seconds, Requests per second, etc.

Mind subtle differences between the built-in choices, e.g. Duration / seconds will show long text such as "412 milliseconds" which makes it hard to put much information on one screen — consider using Time / seconds instead.

Also, do not confuse the order of magnitude: if your data is provided in seconds, do not choose Time / milliseconds since that would show falsified values.

Grafana’s default is to use the browser timezone. Particularly for international companies or those who have international customers, consistent values and avoidance of confusion are important. Employees usually do not open the user settings page, for example to choose light/dark mode or their timezone preference, resulting in inconsistent customer and incident communication regarding dates and times. I am working from Germany and keep seeing confusion between CET/CEST once daylight saving time toggles, and sometimes even do such mistakes myself. Let’s avoid that and communicate only in UTC, and default to UTC in tools such as Grafana.

By writing a jsonnet wrapper function instead of using raw calls to grafana.dashboard.new, you can set that as your default for generated dashboards.

Somewhat related xkcd comic: ISO 8601. Did you know that the Z suffix in 2022-04-21T17:13Z stands for UTC ("Zulu time") and is therefore a pretty good abbreviation? Most non-technical people rather know the suffix "UTC", so that one is preferable.

Even if you rely on one Prometheus-compatible source in the beginning, you will very likely add more data sources, or migrate to another one, in the future. Therefore, explicitly define the source in each visualization. In jsonnet, use for example grafana.statPanel.new(datasource='thanos'). In general, do not ever name something default, anywhere. The same applies to the words "old" and "new", since "new" is always the next "old".

Those are hard to set up since the UI does not give guidance. You have to set several options correctly to see reasonable results:

Instead of a histogram, laying out the information as percentiles on a Stat visualization may give a faster overview and is preferable on high-level dashboards. For example, p50 (median), p95 and p99 percentiles are often useful. Make sure you use the rate function inside histogram_quantile.

Example Prometheus query: histogram_quantile(0.95, sum(rate(prometheus_http_request_duration_seconds_bucket[2m])) by (le))

Grafana annotations can mark interesting time points on graphs. Among many imaginable events to enrich on your dashboards, software and infrastructure deployments are the most interesting ones since change in a technology-driven company usually means risk and the potential for failure. In an incident, the starting point is often known quite soon by looking at dashboards. If graphs additionally show whether and when changes were made, you have better chances to find the cause.

Do not bother adding annotations manually (e.g. time window of every deployment), since people will forget the procedure, get the timezone wrong, and it only adds an unnecessary burden which should be automated.

Here is an example how to consistently show Argo CD sync events on your dashboards. Those mostly relate to real deployments. When I developed that query, no better, human-level event type was available. You may want to tweak this to your own use cases.

You can now use grafana.dashboard.new(…​).addAnnotation(deployments) to add the annotations to your dashboard.

Each visualization’s title should be self-describing. Bad: "Error rate". Good: "Payment methods — error rate of requests to provider".

One reason is because you can link to a single visualization which is helpful during incidents to tell others what exactly you are looking at (or to present one detail on a really large TV):

And again, it helps the eyes to quickly get a full picture instead of having to look at multiple locations on screen.

In Grafana, the dashboard title is always displayed, even for such single-visualization URLs. So if your dashboard is nicely titled "Payment gateway (high-level)", that will already be a good starting point and you may not even need or want verbose visualization titles.

For averaging queries like sum(rate(metric[5m])), which may constitute most of your dashboards, you should consider adding the interval hint (e.g. abbreviated [5m]) to the visualization title — and/or the Y axis — so that users are aware how fast a recovered metric or an error peak will become visible.

The Prometheus naming practices page gives very good guidance, such as to use lower_snake_case, name counters xxx_total or specify the unit such as xxx_seconds.

The 3 current pillars of observability — metrics, logs and traces — may not remain considered the best solution forever. We can expect tooling to try and combine them in the future, such as "metrics from logs" features. You want to observe your applications with minimum instrumentation effort? Then sometimes, a LogQL query such as sum(count_over_time(…​ [15m])) to look for specific log lines may be what you want (temporarily), instead of developing and maintaining a new metric. Beware however that log text tends to change much more frequently than metric names, and how much slower and more expensive it is to query logs. A totally uninstrumented application can easily be monitored if you have access to its logs. Later on, you can make the dashboards more efficient once you learned which indicators are important to show the application health, and which ones are not. Very helpful if you are just getting started with observability.

You can use > 0 or topk(…​) > 5 to display only offenders in your high-level dashboard (please also read Keep panels small on screen above).

For example, the customers with the highest concurrency of API requests. Use Value mappings feature to map "null" to e.g. "currently low concurrency" for better understanding in humans (since Stat visualizations always show something). Together with green/yellow/red thresholds, this explains in 2 seconds what the current value is and whether it is problematic. As explained before, use Calculation > Last if only the latest value is relevant — you do not care about the Average API concurrency over 3 hours while debugging an incident, right?

In our payment example, we could alternatively show the payment methods with the highest rate of errors. Or depending on the business, define each payment method’s business importance in code and then only show the most critical products with a label filter (e.g. importance="boss_says_this_is_super_critical"; or name them "Tier1", "Tier2", etc.).

Note that topk(5, …​) may not show you the top 5 items if evaluated for a graph, since the "top 5" are checked for many time points and all resulting items are shown. The same applies to Stat visualizations — unless you choose Instant to only choose the end time point, but that can falsify the desired data to show. If you really want only up to 5 items to be shown, use the @ end() modifier to evaluate only the latest top-5 values in the range (thanks to a blog reader for pointing this out! – available since Prometheus v2.25.0).

In a modern infrastructure, you might run synthetic test traffic to verify the end-to-end health of your applications. Since those are not from real customers, you should check if that should be shown differently or excluded from certain dashboards or visualizations.

If your business is mostly in a certain region or timezone of the world, such as European payments, traffic goes down at night. Consider different error and request thresholds at day and night, respectively. Visualizations should be clearly distinguished with e.g. 🔆 or 🌒 in the title.

Prometheus allows the time distinction with and/unless hour() >=6 <21. This can be tricky, though: in special cases such as calculations sum(…​)/sum(…​) and hour() >=6 <21, label set matching will surprise you with an empty result. Example to fix that: (sum by (something) (rate(some_metric[15m]))) / sum by (something) (rate(some_metric2[15m])) and ignoring(something) hour() >=6 <21.

This is cumbersome and should be avoided for the start, unless you really need such a strong distinction by time.

The same applies to calculations like rate(…​) / rate(…​). Why? Any change to the labels will make them explode into many series. Combine rate with sum or sum by.

A counter in Prometheus represents a value that can only increase. In contrast, a gauge can take an arbitrary value.

In regular scrape intervals, a metric’s value gets collected by Prometheus. A longer scrape interval means less storage and cost, but can mean that a short spike of a gauge’s value is not stored at all, and therefore also will not produce an alert. So prefer a counter if possible for your for use case, since its value does not lose increments (but on the other hand, it only supports increments).

Good example for using a gauge: queue size. Items can be processed, i.e. removed from the queue, or added. The more interesting metrics for queues however are error rate and per-item processing time.

To find a root cause quickly in case of problems, dashboards must allow drilling down into details. In our example of payment methods as products, each of them could fail separately, or several/all at once. This must be visible in visualizations and alert messages.

Examples why this distinction is important:

Other ideas for details to drill down into: per customer, per Kubernetes cluster, per cloud region, per API endpoint. For some of these, you may be able to leverage variables (mind Avoid dropdowns for variable values), while some value ranges may simply be too large — for instance if you have a million customers — and you should rather show the top N problematic ones (Show only offenders or top N problematic items).