This is the fifth and final post in a multi-part series about the Kubernetes Resource Model. Check out parts 1, 2, 3, and 4 to learn more.  

In part 2 of this series, we learned how the Kubernetes Resource Model works, and how the Kubernetes control plane takes action to ensure that your desired resource state matches the running state. 

Up until now, that “running resource state” has existed inside the world of Kubernetes- Pods, for example, run on Nodes inside a cluster. The exception to this is any core Kubernetes resource that depends on your cloud provider. For instance, GKE Services of type Load Balancer depend on Google Cloud network load balancers, and GKE has a Google Cloud-specific controller that will spin up those resources on your behalf. 

But if you’re operating a Kubernetes platform, it’s likely that you have resources that live entirely outside of Kubernetes. You might have CI/CD triggers, IAM policies, firewall rules, databases. The first post of this series introduced the platform diagram below, and asserted that “Kubernetes can be the powerful declarative control plane that manages large swaths” of that platform. Let’s close that loop by exploring how to use the Kubernetes Resource Model to configure and provision resources hosted in Google Cloud.

Why use KRM for hosted resources?

Before diving into the “what” and “how” of using KRM for cloud-hosted resources, let’s first ask “why.” There is already an active ecosystem of infrastructure-as-code tools, including Terraform, that can manage cloud-hosted resources. Why use KRM to manage resources outside of the cluster boundary? 

Three big reasons. The first is consistency. The last post explored ways to ensure consistency across multiple Kubernetes clusters- but what about consistency between Kubernetes resources and cloud resources? If you have org-wide policies you’d like to enforce on Kubernetes resources, chances are that you also have policies around hosted resources. So one reason to manage cloud resources with KRM is to standardize your infrastructure toolchain, unifying your Kubernetes and cloud resource configuration into one language (YAML), one Git config repo, one policy enforcement mechanism. 

The second reason is continuous reconciliation. One major advantage of Kubernetes is its control-loop architecture. So if you use KRM to deploy a hosted firewall rule, Kubernetes will work constantly to make sure that resource is always deployed to your cloud provider- even if it gets manually deleted. 

A third reason to consider using KRM for hosted resources is the ability to integrate tools like kustomize into your hosted resource specs, allowing you to customize resource specifications without templating languages. 

These benefits have resulted in a new ecosystem of KRM tools designed to manage cloud-hosted resources, including the Crossplane project, as well as first-party tools from AWS, Azure, and Google Cloud. 

Let’s explore how to use Google Cloud Config Connector to manage GCP-hosted resources with KRM. 

Introducing Config Connector

Config Connector is a tool designed specifically for managing Google Cloud resources with the Kubernetes Resource Model. It works by installing a set of GCP-specific resource controllers onto your GKE cluster, along with a set of Kubernetes Custom Resources for Google Cloud products, from Cloud DNS to Pub/Sub.

How does it work? Let’s say that a security administrator at Cymbal Bank wants to start working more closely with the platform team to define and test Policy Controller constraints. But they don’t have access to a Linux machine, which is the operating system used by the platform team. The platform team can address this by manually setting up a Google Compute Engine (GCE) Linux instance for the security admin. But with Config Connector, the platform team can instead create a declarative KRM resource for a GCE instance, commit it to the config repo, and Config Connector will spin up the instance on their behalf.

What does this declarative resource look like? A Config Connector resource is just a regular Kubernetes-style YAML file- in this case, a custom resource called Compute Instance. In the resource spec, the platform team can define specific fields, like what GCE machine type to use. 

  apiVersion: compute.cnrm.cloud.google.com/v1beta1
kind: ComputeInstance
metadata:
  annotations:
    cnrm.cloud.google.com/allow-stopping-for-update: "true"
  name: secadmin-debian
  labels:
    created-from: "image"
    network-type: "subnetwork"
spec:
  machineType: n1-standard-1
  zone: us-west1-a
  bootDisk:
    initializeParams:
      size: 24
      type: pd-ssd
      sourceImageRef:
        external: debian-cloud/debian-9
...

Once the platform team commits this resource to the Config Sync repo, Config Sync will deploy the resource to the cymbal-admin GKE cluster, and Config Connector, running on that same cluster, will spin up the GCE resource represented in the file.

This KRM workflow for cloud resources opens the door for powerful automation, like custom UIs to automate resource requests within the Cymbal Bank org. 

Integrating Config Connector with Policy Controller 

By using Config Connector to manage Google Cloud-hosted resources as KRM, you can adopt Policy Controller to enforce guardrails across your cloud and Kubernetes resources.  

Let’s say that the data analytics team at Cymbal Bank is beginning to adopt BigQuery. While the security team is approving production usage of that product, the platform team wants to make sure no real customer data is imported. Together, Config Connector and Policy Controller can set up guardrails for BigQuery usage within Cymbal Bank. 

Config Connector supports BigQuery resources, including Jobs, Datasets, and Tables. The platform team can work with the analytics team to define a test dataset, containing mocked data, as KRM, pushing those resources to the Config Sync repo as they did with the GCE instance resource. 

  apiVersion: bigquery.cnrm.cloud.google.com/v1beta1
kind: BigQueryJob
metadata:
  name: cymbal-mock-load-job
  annotations:
    configsync.gke.io/cluster-name-selector: cymbal-admin
spec:
  location: "US"
  jobTimeoutMs: "600000"
  load:
    sourceUris:
      - "gs://cymbal-bank-datasets/cymbal-mock-transactions.csv"

From there, the platform team can create a custom Constraint Template for Policy Controller, limiting the allowed Cymbal datasets to only the pre-vetted mock dataset: 

  rego: |
        package bigquerydatasetallowname
        violation[{"msg": msg}] {
          input.review.object.kind == "BigQueryDataset"
          input.review.object.metadata.name != input.parameters.allowedName
          msg := sprintf("The BigQuery dataset name %v is not allowed", [input.review.object.metadata.name])
        }apiVersion: constraints.gatekeeper.sh/v1beta1

These guardrails, combined with IAM, can allow your organization to adopt new cloud products safely- not only defining who can set up certain resources, but within those resources, what field values are allowed. 

Manage existing GCP resources with Config Connector 

Another useful feature of Config Connector is that it supports importing existing Google Cloud resources into KRM format, allowing you to bring live-running resources into the management domain of Config Connector. 

You can use the config-connector command line tool to do this, exporting specific resource URIs into static files: 

  config-connector export "//sqladmin.googleapis.com/sql/v1beta4/projects/cymbal-bank/instances/cymbal-dev" \
    --output cloudsql/

Output:

  apiVersion: sql.cnrm.cloud.google.com/v1beta1
kind: SQLInstance
metadata:
  annotations:
    cnrm.cloud.google.com/project-id: cymbal-bank
  name: cymbal-dev
spec:
  databaseVersion: POSTGRES_12
  region: us-east1
  resourceID: cymbal-dev
...

From here, we can push these KRM resources to the config repo, and allow Config Sync and Config Controller to start lifecycling the resources on our behalf. The screenshot below shows that the cymbal-dev Cloud SQL database now has the “managed-by-cnrm” label, indicating that it’s now being managed from Config Connector (CNRM = “cloud-native resource management”).

This resource export tool is especially useful for teams looking to try out KRM for hosted resources, without having to invest in writing a new set of YAML files for their existing resources. And if you’re ready to adopt Config Connector for lots of existing resources, the tool has a bulk export option as well. 

Overall, while managing hosted resources with KRM is still a newer paradigm, it can provide lots of benefits for resource consistency and policy enforcement. Want to try out Config Connector yourself? Check out the part 5 demo.

This post concludes the Build a Platform with KRM series. Hopefully these posts and demos provided some inspiration on how to build a platform around Kubernetes, with the right abstractions and base-layer tools in mind. 

Thanks for reading, and stay tuned for new KRM products and features from Google. 

A subsidiary of United States-headquartered corporation Yum! Brands, Pizza Hut prides itself on serving more pizzas than any other pizza business. Founded in 1958, Pizza Hut operates 18,000 restaurants in over 100 countries. In the Indian subcontinent, Pizza Hut and franchise partners Devyani International and Sapphire Foods India operate more than 500 pizza restaurants, including 430 in India itself.

Yum! Brands aims to increase the number of Pizza Hut restaurants in India to 700 by 2022 and has nominated the country as one of the keys to its future growth. The business also operates the KFC and Taco Bell brands in India.

“Globally we are the number one pizza chain in the world based on store count and we aim to be the single biggest pizza brand in India,” says Prashant Gaur, Chief Brand and Customer Officer, Pizza Hut India Subcontinent.

Google Cloud Results

• Maximizes customer coverage and helps ensure riders deliver pizzas to customers within required timeframes
• Onboards new stores to delivery in half a day, rather than the one month required previously
• Meets tech-savvy customer demands to interact across new social media messaging channels
• Launch of live tracking delivers superior customer experience, driving positive word of mouth and repeat business

Pizza Hut launched initially in the country in the late 1990s as a dine-in restaurant brand. However, with changing customer needs, Pizza Hut soon included delivery and takeaway to provide customers with the best tasting pizzas whenever and wherever they wanted them. “Pizza is always at the center of the experience, whether through delivery, dine-in, or takeaway,” says Gaur. “Convenience is key in allowing people to access our products.”

Manual processes

While the business had long shifted into a model that featured delivery and takeaway options, it still used some manual processes. For example, some restaurants used manual listings of customer addresses to manage delivery, which ended up excluding some customers and compromising the brand. In other cases it could take the business up to a week to create a trade zone – a delivery zone assigned to a restaurant – for each new outlet, delaying the commencement of delivery services and costing the business money.

In addition, Pizza Hut India identified an opportunity to more closely track whether pizzas were being delivered within targeted timeframes.

“In some cases, we were using a manual, self-reporting mechanism that provided information about the number of orders that reached consumers less than 30 minutes after an order was placed,” explains Ashish Agarwal, Director, Technology and eCommerce, Pizza Hut India Subcontinent. “We wanted to objectively track and verify delivery times.”

Manual order tracking and execution also limited the number of orders that could be processed effectively during demand peaks. Further, customers could not monitor the status of their pizza orders, including estimated time of arrival.

“Millennial consumers expect products and services to be available when and where they want them. Our focus is to retain the heritage of the brand, which is the dine-in environment, legendary service, and great assets in terms of our stores, while responding to this demand,” adds Gaur.

Pizza Hut India also needed improved analytics in order to identify where the best returns could be achieved by opening new restaurants; how to improve the customer experience; and operate more efficiently.

The business decided to implement a transformation program underpinned by two imperatives: the need to deliver operational efficiencies and scale faster by accelerating the opening of new stores, and the need to give consumers the option of connecting with the brand using the most convenient channel.

A ground-breaking initiative

Pizza Hut India evaluated potential technology partners that could help deliver the program and decided to partner with digital consulting services company MediaAgility, and use Google Maps Platform and Google Cloud Platform services.

“Our journey with MediaAgility and Google incorporated two key initiatives that had a specific impact on our brand and consumers,” says Gaur. “The first of these initiatives was the launch of a feature that enabled consumers and our business to track delivery riders in real time.”

This initiative was ground-breaking for a business that had, until recent years, focused on establishing itself as a restaurant brand. However, with delivery an increasingly important part of its revenue mix, Pizza Hut India decided to build customer engagement through the channel. “By allowing customers to track delivery riders in real time, we could improve engagement – but more importantly give them control,” says Gaur.

During the evaluation, MediaAgility demonstrated Google Maps Platform to Pizza Hut India. “We loved Google Maps Platform as its accuracy and value was proven by consumers using Google Maps for their day-to-day needs,” says Agarwal.

Pizza Hut India conducted brainstorming sessions with MediaAgility and its franchise partners to develop its strategy and complete the implementation. The business then completed several proofs of concept to determine how best to deploy and adapt the technology to some operational processes. “We submitted some data points to our franchise partners and our Pizza Hut brand operations team, and they determined which option to adopt,” says Agarwal.

Google Maps Platform powers delivery

MediaAgility, Pizza Hut India, and the franchise partners then completed a three-month implementation that included onboarding all existing restaurants and new restaurants to the delivery platform based on Google Maps Platform and on Google Cloud Platform.

Pizza Hut India now uses Distance Matrix API through Google Maps Platform to provide travel time and distance based on recommended routes between origins and destinations. This service helps provide delivery riders’ estimated time of arrival to customers.

Directions Service calculates directions by communicating with the Google Maps API Directions Service, which receives direction requests and returns efficient paths based on travel time and factors such as distance and number of turns. This service provides a view of the delivery rider’s position relative to the customer’s location.

Through the Nearest Roads service included in Roads API, Pizza Hut India obtains individual road segments for given GPS coordinates, while Snap to Roads provides the best-fit road geometry for given GPS coordinates.

The business employs Maps Javascript API to customize maps with dedicated content and imagery for websites and mobile devices – showing a map view to store managers and customers – and uses Maps SDK for Android to add maps based on Google Maps to its applications.

Google Cloud Platform runs the delivery platform

Pizza Hut India is running its delivery platform on a Google Cloud Platform architecture that comprises App Engine to run its web applications; virtual machine instances provided through Compute Engine; Cloud Datastore to run a NoSQL document database; Firebase to develop and run its mobile applications; Cloud Storage to store data; and a BigQuery analytics data warehouse.

Realizing goals

With Google Maps Platform and Google Cloud Platform, Pizza Hut India and its franchise partners are realizing the goals of the transformation program.

Rather than take up to seven days to manually map trade zones for stores, Pizza Hut India uses Google Maps Platform to create and update them as required. “After we started working with MediaAgility and Google, we digitized those maps and created trade zones – zones within which Pizza Hut India restaurants will deliver – based on estimated travel times during the busiest time of the week,” says Gaur. “This minimizes the risk of late deliveries.

“The other benefit was reduced time to activate new stores,” he adds. “If you are launching 100 stores per year, this becomes a big, big task. Now we can bring new stores into the market much more quickly.”

With MediaAgility and Google Maps Platform, the business can also help ensure newly built establishments – such as blocks of flats – are captured within trade zones, allowing stores to deliver to residents.

Delivery accounts for increased transactions

Pizza Hut India now automatically allocate orders to delivery riders using a rider tracking application on their mobile phones. When a rider starts his or her journey, Google Maps Platform enables point-by-point tracking by consumers and store managers. The Pizza Hut India delivery operations team monitors delivery performance through a real-time dashboard.

Pizza Hut India is meeting customer expectations of live, map-based streaming of delivery status on their devices – enhancing customer experience and loyalty, and adding accountability to the process. The business is reaping the rewards of its investment, with delivery now a large slice of its overall offering. “We have taken significant strides in the past three to four years to change our customers’ online ordering experience, and the last-mile delivery experience to the customers’ homes,” says Gaur.

Launching delivery tracking has also had a dramatic impact on Pizza Hut India’s internal key performance indicators. “The proportion of calls to our call center that are following up on an order, as opposed to placing an order, has fallen dramatically,” says Gaur.

Advanced analytics

Further, Pizza Hut India is running advanced analysis of data in the BigQuery data warehouse, enabling the business to determine which restaurants are doing well, which deliveries may be delayed, and what locations are most promising to open a new store. These insights enable the business to boost productivity, expand effectively, and operate more efficiently.

The business can now onboard new stores to delivery in half a day, rather than the month required previously. “The process enabled by Google Cloud and MediaAgility is also delivering significant operational cost savings as well as helping us open and grow new stores quickly,” says Agarwal.

“MediaAgility acted as our development partner across the project, helping us deploy everything from zones to rider tracking,” he says. “It’s been with us from the strategy discussion through the implementation and stabilization phases.”

Chatbot in development

MediaAgility has also helped Pizza Hut India create a chatbot using the Dialogflow development suite for creating conversational interfaces. “This project is about giving millennials and other customers one more channel to reach out and connect to the brand,” explains Agarwal.

Pizza Hut India is now ideally placed to continue growing and position itself as a brand of choice for tech-savvy consumers. “We’re extremely pleased with the contribution of all the parties involved in this project and look forward to continue transforming our business to meet the demands of the digital age,” says Agarwal.

While Gaur acknowledges it is difficult to predict change over the next two to five years, he believes delivery is likely to become more prominent in Pizza Hut India’s combination of offerings. “When we began our online journey in 2016, we predicted delivery would be about 25 percent of the mix by 2020,” he says. “With Google and MediaAgility, I think it will keep growing.”

Financing digital transformation poses a significant problem for many enterprises we work with. It’s not because the C-suite doesn’t support it: the need to become more agile, digital, and data-driven is nearly ubiquitous in executive public pronouncements. But most large organizations look at IT investment in a way that’s ineffective for a successful digital transformation effort.  

A varied combination of  factors affect an organization’s success in the digital ecosystem, and a lack of alignment between IT and business leaders can hamper even the most digitally sophisticated organizations. Empirical research by the Apigee Institute shows clear evidence that a solid digital transformation effort, aligned across business and marketing, whose progress is carefully tied to core enterprise metrics, must be supported by an investment strategy that uses a “real options” portfolio approach.

Let’s explore ways to think about funding digital transformation.

Planning for the Unimaginable

Common IT investment strategies are focused on straight-line net present value (NPV) business case calculations, which emphasize linear solutions and impacts. These methods ignore the value of enabling a range of options and potential future use cases, which an API program, with its reusable, highly consumable digital assets, makes available for the enterprise. In addition, these methods often ignore the value of the new categories of business data generated by such programs, as well as business capabilities that were impossible to imagine before the capabilities came online.

One media company we’re working with was astounded by a project that now makes key sales provisioning available to large distribution partners within two months, when previous estimates predicted two years and many millions of dollars, each.

More importantly, that functionality will now be reused across all of their numerous current partners and sales channels, and is enabling exciting new partnerships that were too costly before. This functionality was an unexpected by-product of their original API effort—it wasn’t even the main focus of the initial project.  

There was no way that the impact of this capability or its implications on new business could have been predicted at the outset, and no way to formally model the outsized impact this functionality will have on the enterprise. Any attempts to do so would have been deemed ludicrous.

Breaking from the Status Quo

So how do you fund an API-driven digital transformation, which by nature emphasizes speed and optionality to unlock new ways of doing business, instead of the business-as-usual single-use, point-to-point integrations that perpetuate status quo thinking? For a world where the latter is the dominant paradigm and you don’t have a clear C-level mandate, here are three suggestions.

1.  Enable your web/e-commerce teams. Many such teams find their responsibilities expanded to include mobile, kiosk, and many other digital initiatives, while being trapped in the traditional web server, do-it-yourself paradigm. These teams are actually quite open to technology, and often harbour weekend hackers who love to explore the new capabilities that are expanding the digital ecosystem at light speed. Tapping this passion by funding new approaches and quickly realizing immediate projects can create first the technical, and then the business proof points that resonate with a strategy team. These capabilities must be nurtured to move out of the lab into production, where real results are created.  

2. Focus on partnerships. Opening and accelerating  your relationships with partners via APIs (read more about partner API initiatives here) is high-impact and relatively easy to model. This business area can prove out an API platform, and in our experience creates clear business value that can support future justifications for other business units. What’s best about these wins is that sales operations groups are typically very tactical and demanding, so when they see the value, these groups will send a message to the rest of the organization that APIs have business value. Further, your enterprise’s core partnerships have the focus of executive attention. Adding value here will elevate the stature of the API program in the C-suite.

3. Work on your CFO/financial organization. Using your successes in e-commerce and partnerships, you can propose a more robust investment using a real options approach that leads to treatment of APIs as products. This path is predicated on what research has proven to work: the adoption of a digital KPI target, coupled with a portfolio of programs whose net result is to achieve the target KPI.  

Building Toward the Big Vision

But what about the visions of ground-breaking business models, ubiquitous connectivity, and enablement of an ever-shifting set of opportunities on big platforms and with unlimited partnership possibilities? Yes, these are still the endpoints to work toward, and fundamentally comprise the strategic reasons to embark on a digital transformation. If your enterprise has the C-Level leadership (in the form of a CDO or CDO-like figure), supported by a well-funded, sophisticated investment approach, by all means, build that new world now.

However, should that vision be shared only within your business unit, a forward-thinking IT group, or a dedicated innovation team, the most important thing to do is to build your team’s capability—and your program’s credibility—while delivering value along the way. The approach we suggest here has been proven in many of the organizations we work with; once your organization begins working faster and at lower costs by orders of magnitude, you’ll have created enough leverage to change the way your organization invests in its API program.