This put up is about Dashify, the Cisco Observability Platform’s dashboarding framework. We’re going to describe how AppDynamics, and companions, use Dashify to construct customized product screens, after which we’re going to dive into particulars of the framework itself. We’ll describe its particular options that make it essentially the most highly effective and versatile dashboard framework within the trade.

What are dashboards?

Dashboards are data-driven consumer interfaces which can be designed to be considered, edited, and even created by product customers. Product screens themselves are additionally constructed with dashboards. For that reason, an entire dashboard framework supplies leverage for each the tip customers seeking to share dashboards with their groups, and the product-engineers of COP options like Cisco Cloud Observability.

Within the observability house most dashboards are centered on charts and tables for rendering time sequence information, for instance “common response time” or “errors per minute”. The picture beneath exhibits the COP EBS Volumes Overview Dashboard, which is used to know the efficiency of Elastic Block Storage (EBS) on Amazon Net Providers. The dashboard options interactive controls (dropdowns) which can be used to further-refine the situation from all EBS volumes to, for instance unhealthy EBS volumes in US-WEST-1.

A number of different dashboards are supplied by our Cisco Cloud Observability app for monitoring different AWS techniques. Listed below are only a few examples of the quickly increasing use of Dashify dashboards throughout the Cisco Observability Platform.

• EFS Volumes
• Elastic Load Balancers
• S3 Buckets
• EC2 Cases

Why Dashboards

No observability product can “pre-imagine” each approach that clients wish to observe their techniques. Dashboards permit end-users to create customized experiences, constructing on current in-product dashboards, or creating them from scratch. I’ve seen massive organizations with greater than 10,000 dashboards throughout dozens of groups.

Dashboards are a cornerstone of observability, forming a bridge between a distant information supply, and native show of information within the consumer’s browser. Dashboards are used to seize “eventualities” or “lenses” on a specific downside. They will serve a comparatively mounted use case, or they are often ad-hoc creations for a troubleshooting “conflict room.” A dashboard performs many steps and queries to derive the information wanted to deal with the observability situation, and to render the information into visualizations. Dashboards might be authored as soon as, and utilized by many various customers, leveraging the know-how of the writer to enlighten the viewers. Dashboards play a essential function in low-level troubleshooting and in rolling up high-level enterprise KPIs to executives.

The purpose of dashboard frameworks has all the time been to offer a approach for customers, versus ‘builders’, to construct helpful visualizations. Inherent to this “democratization” of visualizations is the notion that constructing a dashboard should someway be simpler than a pure JavaScript app growth strategy. Afterall, dashboards cater to customers, not hardcore builders.

The issue with dashboard frameworks

The diagram beneath illustrates how a standard dashboard framework permits the writer to configure and prepare parts however doesn’t permit the writer to create new parts or information sources. The dashboard writer is caught with no matter parts, layouts, and information sources are made accessible. It is because the areas proven in purple are developed in JavaScript and are supplied by the framework. JavaScript is neither a safe, nor simple know-how to be taught, subsequently it’s not often uncovered on to authors. As an alternative, dashboards expose a JSON or YAML primarily based DSL. This sometimes leaves discipline groups, SEs, and energy customers within the place of ready for the engineering staff to launch new parts, and there’s nearly a deep characteristic backlog.

I’ve personally seen this situation play out many occasions. To take an actual instance, a staff constructing dashboards for IT providers needed rows in a desk to be coloured in response to a “warmth map”. This required a characteristic request to be logged with engineering, and the core JavaScript-based Desk part needed to be modified to assist warmth maps. It turned typical for the core JS parts to grow to be a mishmash of domain-driven spaghetti code. Ultimately the code for Desk itself was exhausting to search out amidst the handfuls of props and hidden behaviors like “warmth maps”. No person was pleased with the scenario, but it surely was typical, and core part groups largely spent their dash cycles constructing area behaviors and attempting to know the spaghetti. What if dashboard authors themselves on the power-user finish of the spectrum may very well be empowered to create parts themselves?

Enter Dashify

Dashify’s mission is to take away the barrier of “you may’t do this” and “we don’t have a part for that”. To perform this, Dashify rethinks among the foundations of conventional dashboard frameworks. The diagram beneath exhibits that Dashify shifts the boundaries round what’s “inbuilt” and what’s made utterly accessible to the Writer. This radical shift permits the core framework staff to give attention to “pure” visualizations, and empowers area groups, who writer dashboards, to construct area particular behaviors like “IT warmth maps” with out being blocked by the framework staff.

To perform this breakthrough, Dashify needed to clear up the important thing problem of how one can simplify and expose reactive conduct and composition with out cracking open the proverbial can of JavaScript worms. To do that, Dashify leveraged a brand new JSON/YAML meta-language, created at Cisco within the open supply, for the aim of declarative, reactive state administration. This new meta-language is named “Said,” and it’s getting used to drive dashboards, in addition to many different JSON/YAML configurations throughout the Cisco Observability Platform. Let’s take a easy instance to point out how Said allows a dashboard writer to insert logic straight right into a dashboard JSON/YAML.

Suppose we obtain information from an information supply that gives “well being” about AWS availability zones. Assume the well being information is up to date asynchronously. Now suppose we want to bind the altering well being information to a desk of “alerts” in response to some enterprise guidelines:

1. solely present alerts if the proportion of unhealthy cases is larger than 10%
2. present alerts in descending order primarily based on proportion of unhealthy cases
3. replace the alerts each time the well being information is up to date (in different phrases declare a reactive dependency between alerts and well being).

This snippet illustrates a desired state, that adheres to the foundations.

However how can we construct a dashboard that repeatedly adheres to the three guidelines? If the well being information modifications, how can we be certain the alerts shall be up to date? These questions get to the guts of what it means for a system to be Reactive. This Reactive situation is at finest troublesome to perform in as we speak’s common dashboard frameworks.

Discover we’ve framed this downside by way of the information and relationships between completely different information objects (well being and alerts), with out mentioning the consumer interface but. Within the diagram above, observe the “information manipulation” layer. This layer permits us to create precisely these sorts of reactive (change pushed) relationships between information, decoupling the information from the visible parts.

Let’s take a look at how simple it’s in Dashify to create a reactive information rule that captures our three necessities. Dashify permits us to switch *any* piece of a dashboard with a reactive rule, so we merely write a reactive rule that generates the alerts from the well being. The Said rule, starting on line 12 is a JSONata expression. Be at liberty to strive it your self right here.

One of the vital fascinating issues is that it seems you don’t need to “inform” Dashify what information your rule is dependent upon. You simply write your rule. This simplicity is enabled by Said’s compiler, which analyzes all the foundations within the template and produces a Reactive change graph. In the event you change something that the ‘alerts’ rule is , the ‘alerts’ rule will hearth, and recompute the alerts. Let’s rapidly show this out utilizing the said REPL which lets us run and work together with Said templates like Dashify dashboards. Let’s see what occurs if we use Said to alter the primary zone’s unhealthy depend to 200. The screenshot beneath exhibits execution of the command “.set /well being/0/unhealthy 200” within the Said JSON/YAML REPL. Dissecting this command, it says “set the worth at json pointer /well being/0/unhealthy to worth 200”. We see that the alerts are instantly recomputed, and that us-east-1a is now current within the alerts with 99% unhealthy.

By recasting a lot of dashboarding as a reactive information downside, and by offering a sturdy in-dashboard expression language, Dashify permits authors to do each conventional dashboard creation, superior information bindings, and reusable part creation. Though fairly trivial, this instance clearly exhibits how Dashify differentiates its core know-how from different frameworks that lack reactive, declarative, information bindings. In truth, Dashify is the primary, and solely framework to characteristic declarative, reactive, information bindings.

Let’s take one other instance, this time fetching information from a distant API. Let’s say we wish to fetch information from the Star Wars REST api. Enterprise necessities:

1. Developer can set what number of pages of planets to return
2. Planet particulars are fetched from star wars api (https://swapi.dev)
3. Record of planet names is extracted from returned planet particulars
4. Consumer ought to have the ability to choose a planet from the listing of planets
5.  ‘residents’ URLs are extracted from planet data (that we obtained in step 2), and resident particulars are fetched for every URL
6. Full names of inhabitants are extracted from resident particulars and introduced as listing

Once more, we see that earlier than we even contemplate the consumer interface, we are able to solid this downside as an information fetching and reactive binding downside. The dashboard snippet beneath exhibits how a price like “residents” is reactively sure to selectedPlanet and the way map/cut back type set operators are utilized to whole outcomes of a REST question. Once more, all of the expressions are written within the grammar of JSONata.

To exhibit how one can work together with and check such a snippet, checkout This github gist exhibits a REPL session the place we:

1. load the JSON file and observe the default output for Tatooine
2. Show the reactive change-plan for planetName
3. Set the planet title to “Coruscant”
4. Name the onSelect() operate with “Naboo” (this demonstrates that we are able to create capabilities accessible from JavaScript, to be used as click on handlers, however produces the identical consequence as straight setting planetName)

From this concise instance, we are able to see that dashboard authors can simply deal with fetching information from distant APIs, and carry out extractions and transformations, in addition to set up click on handlers. All these artifacts might be examined from the Said REPL earlier than we load them right into a dashboard. This exceptional financial system of code and ease of growth can’t be achieved with another dashboard framework.
In case you are curious, these are the inhabitants of Naboo:

What’s subsequent?

Now we have proven lots of “information code” on this put up. This isn’t meant to suggest that constructing Dashify dashboards requires “coding”. Slightly, it’s to point out that the foundational layer, which helps our Dashboard constructing GUIs is constructed on very stable basis. Dashify just lately made its debut within the CCO product with the introduction of AWS monitoring dashboards, and Knowledge Safety Posture Administration screens. Dashify dashboards are actually a core part of the Cisco Observability Platform and have been confirmed out over many advanced use instances. In calendar Q2 2024, COP will introduce the dashboard enhancing expertise which supplies authors with inbuilt visible drag-n-drop type enhancing of dashboards. Additionally in calendar Q2, COP introduces the flexibility to bundle dashify dashboards into COP options permitting third get together builders to unleash their dashboarding abilities. So, climate you skew to the “give me a gui” finish of the spectrum or the “let me code” way of life, Dashify is designed to fulfill your wants.

Summing it up

Dashboards are a key, maybe THE key know-how in an observability platform. Present dashboarding frameworks current unwelcome limits on what authors can do. Dashify is a brand new dashboarding framework born from many collective years of expertise constructing each dashboard frameworks and their visible parts. Dashify brings declarative, reactive state administration into the arms of dashboard authors by incorporating the Said meta-language into the JSON and YAML of dashboards. By rethinking the basics of information administration within the consumer interface, Dashify permits authors unprecedented freedom. Utilizing Dashify, area groups can ship advanced parts and behaviors with out getting slowed down within the underlying JavaScript frameworks. Keep tuned for extra posts the place we dig into the thrilling capabilities of Dashify: Customized Dashboard Editor, Widget Playground, and Scalable Vector Graphics.

Associated sources

Share: