Editor’s note: Today’s post documents the solution to a problem of how to scale website infrastructure as it moves from on-prem to the cloud. It is the result of collaboration between technical teams of Les Echos Le Parisien Annonces (a division of Groupe Les Echos, subsidiary company of LVMH) and Google in the spirit of finding the best tool for the job together.

All technology starts aging from the first day you set it up, and over time becomes in need of some renewal. At Les Echos Le Parisien Annonces (a division of Groupe Les Echos, subsidiary company of LVMH), we complement the main site (“publication annonce légale”) with a number of local market sites for different regions within metropolitan France, as well as French readers in territories across the world. Each local market site offers a variety of services and content, which are served by a set of CMS-driven sites.

Historically these sites were served from dedicated on-prem infrastructure. As the number of sites grew, vertical scaling was used to increase the capacity of the machine. Over time, this approach presented challenges. First, each new site involved modifying multiple shared configurations, and modifying several monolithic parts of the architecture. Because all the sites shared parts of the serving stack, an issue that developed for one site could take down all of the sites. The reliability of the serving path was not able to reach the availability we and our customers expected. Les Echos and Google put their heads together to solve this with a new serverless deployment pattern where each new site can be quickly and independently launched.

Each site, running a conventional PHP CMS solution, is now containerized and deployed as its own Cloud Run service with an independent configuration and database. This means each site scales vertically, including down to zero at times of day when that locale is quiet. Because Cloud Run is available across GCP regions, we can deploy sites closest to the market we want to reach. To further enhance our customer experience, each of these sites is run behind Cloud Global Load Balancing with Cloud Armor and Cloud CDN, providing extra security and performance. The initial pilot of 30 sites has performed well, and the remainder of the 150 current sites are now being migrated. Adding new sites has gone from taking hours to minutes. This allows Les Echos to quickly explore new opportunities in markets that previously would have taken much longer to assess and qualify for investing in a new site.

Adopting this serverless approach has been a successful step in the modernization journey to Google Cloud, allowing us to use containers to take existing software and both re-architect and re-platform to support growth while reducing toil.

Introduction

In my previous post, I talked about how you can use a parent workflow to execute child workflows in parallel for faster overall processing time and easier detection of errors. Another useful pattern is to use a Cloud Tasks queue to create Workflows executions and that’s the topic of this post.

When your application experiences a sudden surge of traffic, it’s natural to want to handle the increased load by creating a high number of concurrent workflow executions. However, Google Cloud’s Workflows enforces quotas to prevent abuse and ensure fair resource allocation. These quotas limit the maximum number of concurrent workflow executions per region, per project, for example, Workflows currently enforces a maximum of 2000 concurrent executions by default. Once this limit is reached, any new executions beyond the quota will fail with an HTTP 429 error.

A Cloud Tasks queue can help. Rather than creating Workflow executions directly, you can add Workflows execution tasks to the Cloud Tasks queue and let Cloud Tasks drain the queue at a rate that you define. This allows for better utilization of your workflow quota and ensures the smooth execution of workflows.

Let’s dive into how to set this up. 

Create a Cloud Tasks queue

We’ll start by creating a Cloud Tasks queue. The Cloud Tasks queue acts as a buffer between the parent workflow and the child workflows, allowing us to regulate the rate of executions.

Create the Cloud Tasks queue (initially with no dispatch rate limits) with the desired name and location:

QUEUE=queue-workflow-child
LOCATION=us-central1

gcloud tasks queues create $QUEUE --location=$LOCATION

Now that we have our queue in place, let’s proceed to set up the child workflow.

Create and deploy a child workflow

The child workflow performs a specific task and returns a result to the parent workflow.

Create workflow-child.yaml to define the child workflow:

main:

  params: [args]

  steps:

    - init:

        assign:

          - iteration: ${args.iteration}

    - wait:

        call: sys.sleep

        args:

            seconds: 10

    - return_message:

        return: ${"Hello world" + iteration}

In this example, the child workflow receives an iteration argument from the parent workflow, simulates work by waiting for 10 seconds, and returns a string as the result.

Deploy the child workflow:

gcloud workflows deploy workflow-child --source=workflow-child.yaml --location=$LOCATION

Create and deploy a parent workflow

Next, create a parent workflow in workflow-parent.yaml.

The workflow assigns some constants first. Note that it’s referring to the child workflow and the queue name between the parent and child workflows:

main:
  steps:
    - init:
        assign:
          - project_id: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
          - project_number: ${sys.get_env("GOOGLE_CLOUD_PROJECT_NUMBER")}
          - location: ${sys.get_env("GOOGLE_CLOUD_LOCATION")}
          - workflow_child_name: "workflow-child"
          - queue_name: "queue-workflow-child"

In the next step, Workflows creates and adds a high number of tasks (whose body is an HTTP request to execute the child workflow) to the Cloud Tasks queue:

- enqueue_tasks_to_execute_child_workflow:
        for:
          value: iteration
          range: [1, 100]
          steps:
              - iterate:
                  assign:
                    - data:
                        iteration: ${iteration}
                    - exec:
                        # Need to wrap into argument for Workflows args.
                        argument: ${json.encode_to_string(data)}
              - create_task_to_execute_child_workflow:
                  call: googleapis.cloudtasks.v2.projects.locations.queues.tasks.create
                  args:
                      parent: ${"projects/" + project_id + "/locations/" + location + "/queues/" + queue_name}
                      body:
                        task:
                          httpRequest:
                            body: ${base64.encode(json.encode(exec))}
                            url: ${"https://workflowexecutions.googleapis.com/v1/projects/" + project_id + "/locations/" + location + "/workflows/" + workflow_child_name + "/executions"}
                            oauthToken:
                              serviceAccountEmail: ${project_number + "-compute@developer.gserviceaccount.com"}

Note that task creation is a non-blocking call in Workflows. Cloud Tasks takes care of running those tasks to execute child workflows asynchronously.

Deploy the parent workflow:

gcloud workflows deploy workflow-parent --source=workflow-parent.yaml --location=$LOCATION

Execute the parent workflow with no dispatch rate limits

Time to execute the parent workflow:

gcloud workflows run workflow-parent --location=$LOCATION

As the parent workflow is running, you can see parallel executions of the child workflow, all executed roughly around the same:

In this case, 100 executions is a well under the concurrency limit for Workflows. Quota issues may arise if you submit 1000s of executions all at once. This is when Cloud Tasks queue and its rate limits become useful.

Execute the parent workflow with dispatch rate limits

Let’s now apply a rate limit to the Cloud Tasks queue. In this case, 1 dispatch per second:

gcloud tasks queues update $QUEUE --max-dispatches-per-second=1 --location=$LOCATION

Execute the parent workflow again:

gcloud workflows run workflow-parent --location=$LOCATION

This time, you see a more smooth execution rate (1 execution request per second):

Summary

By introducing a Cloud Tasks queue before executing a workflow and playing with different dispatch rates and concurrency settings, you can better utilize your Workflows quota and stay below the limits without triggering unnecessary quota related failures. 

Check out the Buffer HTTP requests with Cloud Tasks codelab, if you want to get more hands-on experience with Cloud Tasks. As always, feel free to contact me on Twitter @meteatamel for any questions or feedback.

If you’re looking to migrate and modernize your Microsoft and Windows workloads, Google Cloud is your premiere destination. No matter what migration strategy you’ve selected or what value you’re looking to achieve, with Google Cloud you’re able to:

• simplify your migration and modernization journey
• reduce your on-prem footprint and increase agility
• optimize license usage to reduce costs
• modernize to reduce single-vendor dependencies
• rely on enterprise-class support backed by Microsoft

Whether you’re looking to migrate applications running on Windows virtual machines, adopt Windows containers in Google Kubernetes Engine (GKE), convert SQL databases to Cloud SQL, or something else, Google Cloud offers you the first-class experience you need. 

But we don’t want you to take our word for it. Try it out yourself with our new online Microsoft and Windows on Google Cloud Demo Center without any commitment or friction.

The demo center uses hands-on guided simulations to walk you through several scenarios for solving business critical challenges with Google Cloud’s Microsoft and Windows solutions. Because these are all simulated, you’ll see how it works without any deployment, configuration, or commitment. It’s a seamless way for you to see exactly how Google Cloud can help you.

Run dedicated hardware and optimize with sole tenant

Sometimes you might want to run your workloads on dedicated hardware (with oversubscription options) for compliance, licensing, and management. Google Cloud provides sole-tenant nodes that allow you to easily deploy your virtual machines onto dedicated machines to avoid “noisy neighbor” issues, address regulatory or licensing constraints, and optimize inter-VM communications.

Plus, the CPU Overcommit option allows oversubscribing sole-tenant node resources by up to 2x, therefore helping save on per-physical core licensing for many licensed workloads like SQL Server.

Learn how to set up a sole tenant group and node.

Optimize license costs with premium images & custom VMs

One of the easiest ways to optimize your cloud experience with virtual machines (VM) is to pick the right VM image. Google Cloud provides premium license-included VM images that are thoroughly tested and optimized, including SQL Server options with pay-as-you-go licensing. These are great for workloads that don’t need to run all the time or when you do not have spare licenses for bring your own license (BYOL). 

Explore some Windows & SQL images and learn how CPU/Memory options can help optimize deployment and save on licensing.

Modernize your databases with Managed SQL Server

Sometimes you need to manage your SQL Server instance to achieve certain business or operational goals. But more often, managing SQL Server deployments can be undifferentiated: backups, high-availability, updates, and patching are just some of the many things you have to take care of when going the do-it-yourself route. One way to modernize your database tier is to migrate to a managed service like Cloud SQL, which is a fully managed Relational Database service for SQL. 

Explore the process of creating an instance in just a few clicks!

Extract apps from VMs and move to containers in GKE with Migrate for Anthos

Many Windows workloads running on virtual machines such as Internet Information Services (IIS) are ideal candidates for migrating to containers without major changes like rewriting or rearchitecting. However, doing this migration manually can be tedious, which is why Migrate for Anthos can help easily re-platform a .NET app running on IIS into a container-based app. 

Simulate intelligently extracting, migrating, and modernizing applications to run natively on containers in GKE and Anthos clusters.

Move .NET applications to GKE on Windows without code changes

When you’re looking to go fully cloud native, you can leverage Windows containers in GKE without rewriting your .NET applications. Simply create clusters with Windows nodes and deploy containerized Windows workloads in a few clicks, even alongside Linux containers. These deployments reduce operational overhead with features such as auto-upgrade, auto-repair, and release channels. 

Learn  how easy it is to build a GKE cluster with a Windows node and deploy an app. 

Now that you’ve gotten a feel for what’s available, go check out the Demo Center. You can also visit us at Windows and Microsoft on Google Cloud to learn more.

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.”