Editor’s note: Today we are hearing from Jono MacDougall , Principal Software Engineer at Ravelin. Ravelin delivers market-leading online fraud detection and payment acceptance solutions for online retailers. To help us meet the scaling, throughput, and latency demands of our growing roster of large-scale clients, we migrated to Google Cloud and its suite of managed services, including Cloud Bigtable, the scalable NoSQL database for large workloads.

As a fraud detection company for online retailers, each new client brings new data that must be kept in a secure manner and new financial transactions to analyze. This means our data infrastructure must be highly scalable and constantly maintain low latency. Our goal is to bring these new organizations on quickly without interrupting their business. We help our clients with checkout flows, so we need latencies that won’t interrupt that process—a critical concern in the booming online retail sector. 

We like Cloud Bigtable because it can quickly and securely ingest and process a high volume of data. Our software accesses data in Bigtable every time it makes a fraud decision. When a client’s customer places an order, we need to process their full history and as much data as possible about that customer in order to detect fraud, all while keeping their data secure. Bigtable excels at accessing and processing that data in a short time window. With a customer key, we can quickly access data, bring it into our feature extraction process, and generate features for our models and rules. The data stays encrypted at rest in Bigtable, which keeps us and our customers safe.

Bigtable also lets us present customer profiles in our dashboard to our client, so that if we make a fraud decision, our clients can confirm the fraud using the same data source we use.

We have configured our bigtable clusters to only be accessible within our private network and have restricted our pods access to it using targeted service accounts. This way the majority of our code does not have access to bigtable and only the bits that do the reading and writing have those privileges.

We also use Bigtable for debugging, logging, and tracing, because we have spare capacity and it’s a fast, convenient location. 

We conduct load testings against Bigtable.  We started at a low rate of ~10 Bigtable requests per second and we peaked at ~167000 mixed read and write requests per second  at absolute peak. The only intervention that was done to achieve this was pressing a single button to increase the number of nodes in the database. No other changes were made.

In terms of real traffic to our production system, we have seen ~22,000 req/s (combined read/write) on Bigtable in our live environment as a peak within the last 6 weeks.

Migrating seamlessly to Google Cloud 

Like many startups, we started with Postgres, since it was easy and it was what we knew, but we quickly realized that scaling would be a challenge, and we didn’t want to manage enormous Postgres instances. We looked for a kind of key value store, because we weren’t doing crazy JOINS or complex WHERE clauses. We wanted to provide a customer ID and get everything we knew about it, and that’s where key value really shines.  

I used Cassandra at a previous company, but we had to hire several people just for that chore. At Ravelin we wanted to move to managed services and save ourselves that headache. We were already heavy users and fans of BigQuery, Google Cloud’s serverless, scalable data warehouse, and we also wanted to start using Kubernetes. This was five years ago, and though quite a few providers offer Kubernetes services now, we still see Google Cloud at the top of that stack with Google Kubernetes Engine (GKE). We also like Bigtable’s versioning capability that helped with a use case involving upserts. All of these features helped us choose Bigtable.

Migrations can be intimidating, especially in retail where downtime isn’t an option. We were migrating not just from Postgres to Bigtable, but also from AWS to Google Cloud. To prepare, we ran in AWS like always, but at the same time we set up a queue at our API level to mirror every request over to Google Cloud. We looked at those requests to see if any were failing, and confirmed if the results and response times were the same as in AWS. We did that for a month, fine tuning along the way. 

Then we took the big step and flipped a config flag and it was 100% over to Google Cloud. At the exact same time, we flipped the queue over to AWS so that we could still send traffic into our legacy environment. That way, if anything went wrong, we could fail back without missing data. We ran like that for about a month, and we never had to fail back. In the end, we pulled off a seamless, issue-free online migration to Google Cloud.

Flexing Bigtable’s features

For our database structure, we originally had everything spread across rows, and we’d use a hash of a customer ID as a prefix. Then we could scan each record of history, such as orders or transactions. But eventually we got customers that were too big, where the scanning wasn’t fast enough. So we switched and put all of the customer data into one row and the history into columns. Then each cell was a different record, order, payment method, or transaction. Now, we can quickly look up the one row and get all the necessary details of that customer. Some of our clients send us test customers who place an order, say, every minute, and that quickly becomes problematic if you want to pull out enormous amounts of data without any limits on your row size. The garbage collection feature makes it easy to clean up big customers.  

We also use Bigtable replication to increase reliability, atomicity, and consistency. We need strong consistency guarantees within the context of a single request to our API since we make multiple bigtable requests within that scope. So within a request we always hit the same replica of Bigtable and if we have a failure, we retry the whole request. That allows us to make use of the replica and some of the consistency guarantees, a nice little trade-off where we can choose where we want our consistency to live.https://www.youtube.com/embed/0-eH5u7rrQQ?enablejsapi=1&

We also use BigQuery with Bigtable for training on customer records or queries with complicated WHERE clauses. We put the data in Bigtable, and also asynchronously in BigQuery using streaming inserts, which allows our data scientists to query it in every way you can imagine, build models, and investigate patterns and not worry about query engine limitations. Since our Bigtable production cluster is completely separate, doing a query on BigQuery has no impact on our response times. When we were on Postgres many years ago, it was used for both analysis and real time traffic and it was not the optimal solution for us. We also use Elasticsearch for powering text searches for our dashboard.

If you’re using Bigtable, we recommend three features:

• Key visualizer. If we get latency or errors coming back from Bigtable, we look at the key visualizer first. We may have a hotkey or a wide row, and the visualizer will alert us and provide the exact key range where the key lives, or the row in question. Then we can go in and fix it at that level. It’s useful to know how your data is hitting Bigtable and if you’re using any anti-patterns or if your clients have changed their traffic pattern that exacerbated some issue.
• Garbage collection. We can prevent big row issues by putting size limits in place with the garbage collection policies.  
• Cell versioning. Bigtable has a 3d array, with rows, columns, and cells, which are all the different versions. You can make use of the versioning to get history of a particular value or to build a time series within one row. Getting a single row is very fast in Bigtable so as long as you can keep the data volume in check for that row, making use of cell versions is a very powerful and fast option. There are patterns in the docs that are quite useful and not immediately obvious. For example, one trick is to reverse your timestamps (MAXINT64 – now) so instead of the latest version, you can get the oldest version effectively reversing the cell version sorting if you need it.

Google Cloud and Bigtable help us meet the low-latency demands of the growing online retail sector, with speed and easy integration with other Google Cloud services like BigQuery. With their managed services, we freed up time to focus on innovations and meet the needs of bigger and bigger customers. 

Learn more about Ravelin and Bigtable, and check out our recent blog, How BIG is Cloud Bigtable?

As the saying goes, “it’s hard to make predictions, especially about the future.” Some organizations find it challenging to predict what cloud resources they’ll need in months or years ahead. Every organization is on its own unique cloud journey. To help, we’re developing new ways for customers to consume and pay for Google Cloud services. We’re doing this by removing barriers to entry, aligning cost to consumption and providing contractual and product flexibility. Read on to learn how we’re rolling out several new go-to-market programs across these key areas to help our customers purchase and consume Google Cloud services more easily.

Removing barriers to entry with Google Cloud Flex Agreements

Many customers choose multi-year commitments because they provide better line-of-sight into IT spend and budgeting. However, these commitments can create difficulty for those who don’t have clear visibility into their future cloud consumption needs. That’s why today we’re launching Flex Agreements, which enable customers to migrate their workloads to the cloud with no up-front commitments. As part of this new licensing option, Google Cloud customers still get access to unique incentives, such as monthly spend discounts¹, committed use discounts, cloud credits, and access to professional services, based on monthly spend and workloads migrated to Google Cloud.

Flex Agreements are just one example of how we are removing barriers to help customers start using Google Cloud. In 2022, we launched the Innovators Plus annual subscription, which gives developers a curated toolkit to accelerate their expertise, including access to live and on-demand training through Google Cloud Skills Boost, Google Cloud credits, and more. 

We also recently expanded trials for Google Cloud products. For example, the new Spanner free trial instance is good for 90 days, allowing developers to create Google Standard SQL or PostgreSQL databases, explore Spanner capabilities, and prototype applications—with no commitment or contract needed. 

Contractual and feature flexibility  

Contractual flexibility has always been one of our core principles. Committed Use Discounts (CUDs), for example, provide discounted prices in exchange for a commitment to use a minimum level of resources for a specified term. Last year, we introduced Flexible CUD, spend-based commitments that offer predictable and simple flat-rate discounts that apply across multiple virtual machine families and regions.

In addition to contractual flexibility, our customers also need the flexibility to choose features and functionality based on their stages of cloud adoption and the complexity of their business requirements. Therefore, over the next few quarters, we will launch new product pricing editions—Standard, Enterprise, and Enterprise Plus—in parts of our cloud portfolio. This new commercial packaging model will help give customers more choice and flexibility to optimize their cloud spend.

For customers running workloads such as those in regulated industries like banking and public sector, the higher-end Enterprise Plus tier will offer compute, storage, networking and analytics services with high availability, multi-region support, regional failover and disaster recovery, advanced security, and a broad range of regulatory compliance support. The Enterprise pricing tier will include a broad range of features designed for customers with workloads that demand a high level of scalability, flexibility, and reliability. The Standard pricing tier will offer cost-efficient and easy-to-use managed services that include all essential capabilities such as autoscaling to meet the core workload requirements of customers.

Align costs to consumption with autoscaling

At Google Cloud, a core requirement for the products we build is providing customers industry-leading capabilities to automatically scale (autoscale) services up and down to match capacity with real-time demand. Autoscaling improves uptime, reduces infrastructure costs, and removes the operational burden of managing resources.  

Many Google Cloud products include autoscaling capabilities to help customers manage unplanned variations in demand. For example, Dataflow vertical and horizontal autoscaling, in combination with granular adaptive resource configuration (aka “right-fitting”), has resulted in up to 50% saving in infrastructure costs for streaming by automatically choosing the right number of instances required to run the jobs and dynamically re-allocating more or fewer instances during the runtime of jobs. Bigtable also provides native autoscaling capabilities, and Spanner’s autoscale is an open source tool that works across regional and multi-regional Spanner deployments. 

Similarly, we added multiple features such as Cluster Autoscaler, Horizontal Pod Autoscaling, Vertical Pod Autoscaling, and Node Auto-Provisioning to GKE for elasticity and cost efficiency. 

For L.L.Bean, the ability to quickly scale capacity to meet changing usage patterns (e.g., during the holidays), as well as to rapidly perform load tests to test capacity, are “night and day” with Google Cloud compared to L.L.Bean’s legacy on-premises IT system.

“We won’t have to pay for peak capacity to have it available during peak shopping times. We just scale capacity up or down as needed.” — Randy Dyer, Enterprise Architect, L.L.Bean

We are now taking these capabilities to the next level by enabling autoscaling in BigQuery at a more granular level so you never pay more than what you use. This allows you to provision additional capacity in smaller increments, so you never overprovision and overpay for underutilized capacity. BigQuery customers can now try the new BigQuery autoscaler (currently in public preview) in their Google Cloud console.

A commitment to flexibility and choice

At Google Cloud, we remain deeply committed to the success of our customers and partners, and we are uniquely positioned to help organizations transform their business. By providing you with more flexibility and choice in how to purchase our products, we are empowering you to be more efficient and resilient.

Join Google Data Cloud & AI Summit to hear the latest announcements around innovations in Google Data Cloud for databases, data analytics, business intelligence, and AI. Gain expert insights, new solutions, and strategies that can help you transform customer experiences with modern apps, boost revenue, and reduce costs.

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The Opportunity for Streamed AR/VR Content 

What if you could get a high quality AR/VR experience without a dedicated physical computer—or even without a physical tether?  

In the past, interacting with VR required a dedicated, high-end workstation and, depending on the headset, wall-mounted sensors and a dedicated physical space. Complex tasks in VR can push the limits of sensor reach, cable-length, and spatial boundaries, entangling the artist and restricting their movement. This solution was not scalable beyond a handful of advanced use cases.

Recently, tetherless VR headsets from manufacturers such as HTC and Oculus have emerged that free the user from these odious restrictions, enabling a new freedom to experience VR and AR from just about anywhere. The enhanced portability and reduced cost has led to substantially increased adoption among consumers and opened up many new exploratory use cases in enterprise.

However, while these headsets are more accessible and portable, a tradeoff in compute power was required in order to achieve these goals. The limited on-device compute power of this new generation of Head Mounted Displays (HMDs) is acceptable for many consumer applications such as casual gaming. However, advanced enterprise workloads with heavy graphics, compute, or memory requirements can be difficult or even impossible to run on these lightweight devices.

By combining NVIDIA CloudXR with their powerful GPUs running NVIDIA RTX Virtual Workstations (vWS) in Google Cloud data centers, you can experience high-fidelity VR and AR applications from just about anywhere with a good internet connection. The heavy computations are performed in the cloud on a GPU-attached VM and the content streams to any CloudXR compatible headset. 

The combination of Google Cloud’s private fiber optic network — the same network we built for global delivery of YouTube content — and CloudXR’s QoS technologies provides the user with the highest possible quality of service. In fact, the streaming experience is comparable to that of a headset tethered to a powerful physical workstation, but without the friction of hardware and cables. 

This combination of power and portability sets the stage to unlock the potential of high-quality gaming and enterprise AR/VR experiences anywhere on Earth.

Case Study: Creating a Masterpiece

Digital character creation is a core skill for many 3D artists today. One of the preferred methods of 3D character creation is digital sculpting, which lets artists create both hard-surface and organic shapes with high levels of accuracy and control.

Sculpting is one of the many tasks in the character designer’s skillset. Artists must also master the texturing, rigging, and posing of characters as part of the digital character creation pipeline. 

Mastering all these tasks can be challenging, and often, the technology gets in the way; working in multiple applications requires switching contexts and workflows, breaking the artist’s creative flow. The traditional user experience can also be unintuitive, forcing the creator to translate what they want to do with their hands and head into mouse movement and keyboard presses.

Masterpiece Studio Pro revolutionizes this character creation workflow by giving artists the first fully immersive 3D creation pipeline. Artists work in VR, giving them a far more intuitive and seamless way to work which combines the best of the digital and physical worlds.

In Masterpiece Studio Pro, the artist can work on the character or object at any scale, using familiar tools and hand gestures to sculpt a model, much as they would a real clay figure. Performing other tasks such as skeleton creation is simple, allowing the artist to work directly with the limbs of a character to place and adjust the joints.

Bringing It All Together

NVIDIA CloudXR streaming from the cloud provides tremendous opportunities for new creative use cases within gaming and across the enterprise. This solution joins Virtual Studio for Gaming as the latest in our series to help developers build better games.

The Masterpiece Studio use case is a powerful demonstration of new modalities for content creation and collaboration. To try CloudXR with NVIDIA RTX vWS on Google Cloud for yourself, see this tutorial. Masterpiece Studio Pro also offers extensive learning materials and a free trial.

In the past year, Google has worked to surface timely and reliable health information, amplify public health campaigns, and help nonprofits get urgent support to Indians in need. Now, we are continuing to focus on helping India’s businesses accelerate their digital transformation, deepening our commitment to India’s digitization and economic recovery. To support customers and the public sector in India and across Asia Pacific, we’re excited to announce that our new Google Cloud region in Delhi National Capital Region (NCR) is now open. 

Designed to help both Indian and global companies alike build highly available applications for their customers, the Delhi NCR region is our second Google Cloud region in India and 10th to open in Asia Pacific. 

What customers and partners are saying

Navigating this past year has been a challenge for companies as they grapple with changing customers demands and economic uncertainty. Technology has played a critical role, and we’ve been fortunate to partner with and serve people, companies, and government institutions around the world to help them adapt. The Google Cloud region in Delhi NCR will help our customers adapt to new requirements, new opportunities and new ways of working, like we’ve helped so many companies do in the region: 

• InMobi scaled a personalized AI platform to support 120+ million active users. “With the arrival of the Google Cloud Delhi NCR, InMobi Group sees the opportunity to continue closing the gap between our users and products,” says Mohit Saxena, Co-founder and Group CTO of Inmobi. “Glance, especially, has been serving AI-powered personalised content to over 120 million active users. We can’t wait to continue giving them truly meaningful experiences that are speedy, scale well, and are relevant to them, by expanding the use of our current tools working on Google Cloud with the opening of a new region.”
• Groww now supports a sizable user base. “Google Cloud provides great technology that enables us to build and scale infrastructure to millions of users, and the new Google Cloud region in Delhi NCR will continue to help more businesses and startups in India access powerful cloud-based infrastructure, products and services,” says Neeraj Singh, Co-founder and Chief Technology Officer, Groww.
• HDFC Bank is positioned for the future. “At HDFC Bank, we are harnessing technology platforms to both run and build the bank. As we progress to be future ready, the objective is to invest in future technologies that give us scale, efficiency and resiliency. Towards this the Google Cloud region in Delhi NCR will enable us to enhance our resiliency and help us in building an active-active design framework for our new generation applications on cloud,” says Ramesh Lakshminarayanan, CIO, HDFC Bank.  
• Dr. Reddy’s Lab built a modern data platform with Google Cloud. “At Dr Reddy’s, we pride ourselves in helping patients regain good health, acting quickly to provide innovative solutions to address patients’ unmet needs and in accelerating access to medicines to people worldwide. Our Google Cloud-powered data platform is helping us realize these objectives and we welcome Google’s investment in the new Delhi NCR region as helping us and other businesses in India make further contributions to our social and economic future,” says Mukesh Rathi, Senior Vice President & CIO, Dr. Reddy’s Laboratories.
• “To survive the disruption caused by the pandemic and to succeed in the long term, organizations need to become digital natives, so they can be more agile, explore new business models and build new capabilities that boost resilience. A cloud-first strategy plays a key role in enabling businesses to do this,” said Piyush N. Singh, Lead – India market unit & lead – Growth and Strategic Client Relationships, Asia Pacific and Latin America, Accenture. “Harnessing the potential of cloud requires the right data infrastructure and this expansion by Google Cloud will undoubtedly help Indian enterprises in their digital transformation journeys.”

A global network of regions

Delhi NCR joins 25 existing Google Cloud regions connected via our high-performance network, helping customers better serve their users and customers throughout the globe. As the second region in India, customers benefit from improved business continuity planning with distributed, secure infrastructure needed to meet IT and business requirements for disaster recovery, while maintaining data sovereignty.

With this new region, Google Cloud customers operating in India also benefit from low latency and high performance of their cloud-based workloads and data. Designed for high availability, the region opens with three availability zones to protect against service disruptions, and offers a portfolio of key products, including Compute Engine, App Engine, Google Kubernetes Engine, Cloud Bigtable, Cloud Spanner, and BigQuery. 

Supporting India’s recovery with training and education

Google and Google Cloud will also continue to support our customers with people and education programs. We’re investing in local talent and the local developer community to help enterprises digitally transform and support economic recovery. 

Through the India Digitization Fund, we expanded our efforts to support India’s recovery from COVID-19—in particular, through programs to support education and small businesses. In addition to expanding internet access, and investments to help start-ups accelerate India’s digital transformation, we’ve grown our Grow with Google efforts. Businesses can access digital tools to maintain business continuity, find resources like quick help videos, and learn digital skills—in both English and in Hindi.

Helping customers build their transformation clouds

Google Cloud is here to support businesses, helping them get smarter with data, deploy faster, connect more easily with people and customers throughout the globe, and protect everything that matters to their businesses. The cloud region in Delhi NCR offers new technology and tools that can be a catalyst for this change. To learn more, visit the Google Cloud locations page, and be sure to watch the region launch event here.