Editor’s note: Today’s guest post comes from Indian social media platform ShareChat. Here’s the story of how they improved performance, app development, and analytics for serving regional content to millions of users using Google Cloud. 

How do you create a social network when your country has 22 major official languages and countless active regional dialects? At ShareChat, we serve more than 160 million monthly active users who share and view videos, images, GIFs, songs, and more in 15 different Indian languages. We also launched a short video platform in 2020, Moj, which already supports over 80 million monthly active users. 

Connecting with people in the language they understand

As mobile data and smartphones have become more affordable in India, we noticed a large new segment of people, many in rural areas, being welcomed onto the internet. However, many of them didn’t speak English, and when it comes to accessing content and information—language plays a significant role. Instead of joining other social media sites where English reigned supreme, new internet users chose to join language or dialect-specific Whatsapp groups where they felt more comfortable instead.

So, we set out to build a platform where people can share their opinions, document their lives, and make new friends, all in their native language. ShareChat simplifies content and people discovery by using a personalized content newsfeed to deliver language-specific content to the right audience.

Given the high-intensity data and high volume of content and traffic, we rely heavily on IT infrastructure. On top of that, a large number of our users rely on 2G networks to post, like, view, or follow each other. Our platform needs to deliver great experiences to people who are spread out across the country and different networks without any reduction in performance.

The right cloud partner to support future growth

ShareChat was born in the cloud—we already knew how to scale systems to serve a large customer base with our existing cloud provider. But like many companies, we struggled with over-provisioning compute and storage to accommodate unpredictable traffic and avoid running out of storage. With demand rising for local language content and an increase in online interactions in response to the COVID-19 crisis, we realized that we would need a more efficient way to scale dynamically and allocate resources as needed.

Google Cloud was a natural choice for us. We wanted to partner with a technology-first company that would make it easy (and cost-effective) to manage a strong technology portfolio that would allow us to build whatever we wanted. Google is at the forefront of technology innovation and provided everything we needed to build, run, and manage our applications (including creating an efficient DevOps pipeline to fix and release new features quickly). 

We had a few issues in mind at the start of discussions with the Google Cloud team, but over time, as we got information and support from them, we realized that these were the partners we wanted in our corner when it came time to tackle our most challenging problems. In the end, we decided to take our entire infrastructure to Google Cloud.

To support millions of users, we deploy and scale using Google Kubernetes Engine. While we analyze our data using a combination of managed data cloud services, such as Pub/Sub for data pipelines, BigQuery for analytics, Cloud Spanner for real-time app serving workloads, and Cloud Bigtable for less-indexed databases. We also rely on Cloud CDN to help us distribute high-quality and reliable content delivery at low latency to our users. 

We now use just half the total core consumption of our legacy environment to run ShareChat’s existing workloads.

Google Cloud delivers better outcomes at every level 

By moving to Google Cloud, we saw major benefits in several key areas: 

Zero-downtime migration for users

At the time of migration, we had over 70 terabytes of data, consisting of 220 tables—some of which were up to 14 terabytes with nearly 50 billion rows. Due to our data’s interdependencies, moving services over one at a time wasn’t an option for us. 

Even though we were migrating such large volumes of data, we didn’t want to impact any of our customers. Latency spikes for out-of-sync data might affect message delivery. For instance, if a message or notification was delayed, we didn’t want to risk a bad user experience causing someone to abandon ShareChat. 

To prepare for the move, we ran a proof-of-concept cluster for over four months to test database performance in a real-world scenario for handling more than a million queries per second. Using an open-source API gateway, we replicated our legacy data environment into Google Cloud for performance testing and capacity analysis. As soon as we were confident Google Cloud could handle the same traffic as our previous cloud environment, we were ready to execute.

Using wrappers, we were able to migrate without having to change anything in our existing application code. The entire migration of 60 million users to Google Cloud took five hours—without any data loss or downtime. Today, ShareChat has grown to 160 million users, and Google Cloud continues to give us the support we need.

Scaling globally to meet unexpected demand

We rely on real-time data to drive everything on ShareChat by tracking everything that goes on in our app—from messages and new groups to content people like or who they follow. Our users create more than a million posts per day, so it’s critical that our systems can process massive amounts of data efficiently. 

We chose to migrate to Spanner for its global consistency and secondary index. Unlike our legacy NoSQL database, we could scale without having to rethink existing tables or schema definitions and keep our data systems in sync across multiple locations. It’s also cost-effective for us—moving over 120 tables with 17 indexes into Cloud Spanner reduced our costs by 30%.

Spanner also replicates data seamlessly in multiple locations in real time, enabling us to retrieve documents if one region fails. For instance, when our traffic unexpectedly grew by 500% over just a few days, we were able to scale horizontally with zero lines of code change. We were also launching our Moj video app simultaneously, and we were able to move it to another region without a single issue. 

Simplifying development and deployment

On average, we experience about 80,000 requests per second (RPS) –nearly 7 billion RPS per day. That means daily push notifications sent out to the entire user base about daily trending topics can often result in a spike of 130,000 RPS in just a few seconds. 

Instead of over-provisioning, Google Kubernetes Engine (GKE) enables us to pre-scale for traffic spikes around scheduled events, such as holidays like Diwali, when millions of Indians send each other greetings. 

Migrating to GKE has also enabled us to adopt more agile ways of work, such as automating deployment and saving time with writing scripts. Even though we were already using container-based solutions, they lacked transparency and coverage across the entire deployment funnel. 

Kubernetes features, such as sidecar proxy, allows us to attach peripheral tasks like logging into the application without requiring us to make code changes. Kubernetes upgrades are managed by default, so we don’t have to worry about maintenance and stay focused on more valuable work. Clusters and nodes automatically upgrade to run the latest version, minimizing security risks and ensuring we always have access to the latest features.

Low latency and real-time ML predictions

Even though many of our users may be accessing ShareChat outside of metropolitan areas, it doesn’t mean they’re more patient if the app loads slowly or their messages are delayed. We strive to deliver a high-performance experience, regardless of where our users are. 

We use Cloud CDN to cache data in five Google Cloud Point of Presence (PoP) locations at the edge in India, allowing us to bring content as close as possible to people and speeding up load time. Since moving to Cloud CDN, our cache hit ratio has improved from 90% to 98.5%—meaning our cache can handle 98.5% of content requests. 

As we expand globally, we’d like to use machine learning to reach new people with content in different languages. We want to build new algorithms to process real-time datasets in regional languages and accurately predict what people want to see. Google Cloud gives us an infrastructure optimized to handle compute-intensive workloads that will be useful to us both now—and in the future.  

The confidence to build the best platform

Our current system now performs better than before we migrated, but we are continuously building new features on top of it. Google’s data cloud has provided us with an elegant ecosystem of services that allows us to build whatever we want, more easily and faster than ever before. 

Perhaps the biggest advantage of partnering with Google Cloud has been the connection we have with the engineers at Google. If we’re working to solve a specific problem statement and find a specific solution in a library or a piece of code, we have the ability to immediately connect with the team responsible for it. 

As a result, we have experienced a massive boost in our confidence. We know that we can build a really good system because we not only have a good process in place to solve problems—we have the right support behind us.

The ability to gain real-time insights from IoT data can redefine competitiveness for businesses. Intelligence allows connected devices and assets to interact efficiently with applications and with human beings in an intuitive and non-disruptive way. After your IoT project is up and running, many devices will be producing lots of data. You need an efficient, scalable, affordable way to both manage those devices and handle all that information. 

IoT Core is a fully managed service for managing IoT devices. It supports registration, authentication, and authorization inside the Google Cloud resource hierarchy as well as device metadata stored in the cloud, and the ability to send device configuration from other GCP or third-party services to devices. 

Main components

The main components of Cloud IoT Core are the device manager and the protocol bridges:

• The device manager  registers devices with the service, so you can then monitor and configure them. It provides:
  • Device identity management 
  • Support for configuring, updating, and controlling individual devices
  • Role-level access control
  • Console and APIs for device deployment and monitoring
• Two protocol bridges (MQTT and HTTP) can be used by devices to connect to Google Cloud Platform for:
  • Bi-directional messaging
  • Automatic load balancing
  • Global data access with Pub/Sub

How does Cloud IoT Core work?

Device telemetry data is forwarded to a Cloud Pub/Sub topic, which can then be used to trigger Cloud Functions as well as other third-party apps to consume the data. You can also perform streaming analysis with Dataflow or custom analysis with your own subscribers.

Cloud IoT Core supports direct device connections as well as gateway-based architectures. In both cases the real time state of the device and the operational data is ingested into Cloud IoT Core and the key and certificates at the edge are also managed by Cloud IoT Core. From Pub/Sub the raw input is fed into Dataflow for transformation, and the cleaned output is populated in Cloud Bigtable for real-time monitoring or BigQuery for warehousing and machine learning. From BigQuery the data can be used for visualization in Looker or Data Studio and it can be used in Vertex AI for creating machine learning models. The models created can be deployed at the edge using Edge Manager (in experimental phase). Device configuration updates or device commands can be triggered by Cloud Functions or Dataflow to Cloud IoT Core, which then updates the device.  

Design principles of Cloud IoT Core

As a managed service to securely connect, manage, and ingest data from global device fleets, Cloud IoT COre is designed to be:

• Flexible, providing easy provisioning of device identities and enabling devices to access most of Google Cloud
• IThe industry leader in IoT scalability and performance
•  Interoperable, with supports for the most common industry-standard IoT protocols

Use cases

IoT use cases range across numerous industries. Some typical examples include:

• Asset tracking, visual inspection, and quality control in retail, automotive, industrial, supply chain and logistics
• Remote monitoring and predictive maintenance in oil & gas, utilities, manufacturing, and transportation
• Connected homes and consumer technologies.
• Vision intelligence in retail, security, manufacturing, and industrial sectors
• Smart living in commercial, residential, and smart spaces 
• Smart factories with predictive maintenance and real-time plant floor analytics

 For a more in-depth look into Cloud IoT Core check out the documentation.  

https://youtube.com/watch?v=76v16P-Wqe4%3Fenablejsapi%3D1%26

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Built for a CapEx world, most organizations’ finance systems aren’t set up to take advantage of cloud’s dynamic, OpEx-driven consumption patterns.

It may not be a household name yet, but chances are you’ve crossed paths with OpenX today. OpenX, a leader in programmatic advertising, operates one of the world’s largest ad exchanges, serving over 250 billion ad requests per day, connecting more than 30,000 brands and reaching nearly one billion consumers. To make it happen, in 2019, OpenX migrated entirely out of its data centers and became the first major ad-exchange platform to move completely to the cloud.

The OpenX CTO, Paul Ryan, knew that this cloud transformation initiative had the potential to increase costs faster than its revenues. To be successful, he needed his engineering, finance, and business teams to forge a new “cost-aware” culture, complete with effective cost visibility and controls. In other words, he needed Cloud FinOps — an operational framework and cultural shift that brings technology, finance, and business together to drive financial accountability and realize business benefits through cloud transformation.

Ryan laid out a cloud migration roadmap that included cost governance and controls around project ownership, established cost responsibility with engineering teams to accurately forecast cloud consumption, and challenged developers to lower per-unit costs — while at the same time improving performance, scalability, speed and global reach.

It worked! In just 9 months, OpenX reduced their per-unit cost by over 60%. The framework allowed OpenX to launch new regions in a matter of days, reduce their time to market for new features by over 50%, and complete their migration in record time — seven months! “We are now able to stop worrying about legacy infrastructure and focus more on our growth categories,” said Ryan. “Our tech stack is getting smarter and more sophisticated by the day, and we have the flexibility to scale our infrastructure in real-time as the business scales and evolves.”

Unblocking Cloud’s Potential

Cloud holds the key to a successful digital transformation. In fact, McKinsey forecasts that by 2030, the Fortune 500 alone may realize over $1 trillion of EBITDA value drivers associated with public cloud enablement. But unlike OpenX, many companies struggle to achieve near-term value objectives from their cloud investments. Surveys reflect that more than 30% of cloud spend in 2021 was wasted or inefficient, while upwards of 80% of CIOs have yet to achieve the business benefits of migrating to the cloud.

Traditional IT finance processes are ill-suited for cloud infrastructure: Traditional planning and budgeting processes are challenged to address dynamic consumption patterns and complex migrations. Centralized IT budgets using traditional allocations fail to provide the necessary visibility into sources of cost overruns. CapEx-focused cost controls have little ability to manage largely OpEx-driven spend. Trend-based forecasting often inaccurately predicts cloud costs. And developer teams lack access to cost-aware architecture patterns to deploy the applications more efficiently.

Enter Cloud FinOps

At Google we’ve worked with many companies, like OpenX, to help organizations realize the transformational benefits of the cloud by cultivating a culture of transparency and embedding agile processes to manage costs. We’ve distilled these learnings into a Cloud FinOps operational framework that gives organizations the financial governance and accountability they need to grow their business sustainably.

At a high level, a Cloud FinOps approach depends on five key areas:

1. Accountability and Enablement

Accountability and enablement aim at instilling a cost-conscious culture across the organization. Oftentimes, this means standing up a cross-functional and dedicated team with members from technology, finance and engineering to establish cloud financial best practices and governance. In various organizations, we’ve seen this through an extension of a Cloud Center of Excellence, a Cloud Business Office or simply a Cloud FinOps team. Enablement focuses on empowering IT, finance and business leaders through training to help them better understand the economics of cloud services and the strategies to efficiently deploy and manage them. Cloud financial training guides teams on how to design cost-effective cloud environments, for example, embracing ”cloud-native” design principles such as auto-scaling/elasticity and Infrastructure as a Code.

2. Measurement and Business Value Realization

Effective measurements not only create awareness and enable agile processes, but also support a culture that celebrates success and rewards teams for achieving business objectives. As such, measurement in the service of business value realization is about developing a comprehensive set of long-term benefits and cost KPIs to quantify the total net value of the return on digital transformation. Organizations often start with cost-related KPIs and eventually evolve those KPIs into business value metrics that are mapped to targeted business outcomes.

3. Cloud Cost Optimization

Cloud cost optimization is an iterative and continuous process that provides a consistent methodology to manage cloud consumption cost-effectively. There are three key areas of optimization:

Resource optimization – Model cost-effective cloud usage based on utilization and consumption patterns.
Pricing optimization – Manage cloud spend through a continuous analysis of various pricing models. In a Google Cloud context, that might mean Committed Use Discounts, BigQuery flat rate reservations, etc.
Architecture optimization – Build applications with a cost-aware architecture by leveraging newer generation compute instances (like Tau VMs, which offer an industry-leading 42% better price-performance versus comparable offerings), or using managed services and serverless technology to offload operational overhead.
For example, video hosting, sharing and services platform provider Vimeo built transcoding pipelines by using Google Cloud Spot VMs to optimize their infrastructure spend. To do so, they created fault-tolerant workloads that could withstand preemptions, and in exchange, got up to a 91% discount compared to using regular on-demand instances.

4. Planning and Forecasting

In the cloud, accurately forecasting your finances requires rethinking of traditional approaches to depreciation and trend-based forecasting of maintenance and licensing costs. One way to improve the accuracy of your dynamic cloud needs is to use workload-specific forecasting models that leverage a combination of trend-based models for steady-state workloads, driver-based models for scaling applications, as well as monthly variance analysis. In other words, you can define cloud budgets and forecasts by monitoring cloud consumption trends, allocating cloud cost pools with a proper tagging strategy that’s mapped to a chart of accounts in a general ledger, and conducting a cost-benefit analysis based on cloud infrastructure, implementation, and support costs.

5. Tools and Accelerators

Without the proper tools and processes in place, understanding and managing cloud costs can be complex — and this especially true as organizations scale their business in the cloud. By deploying proper cloud cost management tools and accelerators such as Looker Cloud Cost Management and automation scripts to set guardrails and enforce cost control policies, organizations can effectively manage and track cloud spend with access to near-real-time billing and cost data to make better informed business decisions.

The key objectives of Google Cloud Cost Management tools are to make it as simple as possible for organizations to get visibility into their current and forecasted costs with built-in reporting and customizable dashboards; help drive greater accountability for cloud spending across the organization by providing flexible ways to organize cloud resources and allocate costs; provide strong financial governance controls to reduce the risk of overspending; and offer intelligent recommendations for optimizing cloud costs and usage.

Start Saving with Cloud

Businesses are continuously seeking to better operate and manage their cloud environments and the need is ever increasing to transparently manage cloud spend, optimize costs, and obtain their desired business agility. By enhancing your Cloud FinOps capabilities and adopting principles of continuous cost optimization, you too can accelerate the business value of cloud computing.

Special thanks to Bruce Warner, Daniel Petibone, Nihar Jhawar and FinOps Foundation community for their contributions and sharing their domain expertise to this important Cloud FinOps topic.