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.

“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, Google Cloud Product Manager Rachel Tsao explores how to save on compute costs with modern container platforms.

Many tech companies and startups are built to operate under a certain degree of pressure and to efficiently manage costs and resources. These pressures have only increased with inflation, geopolitical shifts, and supply chain concerns, however, creating urgency for companies to find ways to preserve capital while increasing flexibility. The right approach to containers can be crucial to navigating these challenges.

In the last few years, development teams have shifted from virtual machines (VMs) to containers, drawn to the latter because they are faster, more lightweight, and easier to manage and automate. Containers also consume fewer resources than VMs, by leveraging shared operating systems. Perhaps most importantly, containers enable portability, letting developers put an application and all its dependencies into a single package that can run almost anywhere.

Containers are central to an organization’s agility, and in our conversations with customers about why they choose Google Cloud, we hear frequently that services like Google Kubernetes Engine (GKE) and Cloud Run help tech companies and startups to not only go to market quickly, but also save money. In this article, we’ll explore five ways to help your business quickly and easily reduce compute costs with containers.

5 ways to control compute costs with containers

Whether your company is an established player that is modernizing its business or a startup building its first product, managed containerized products can help you reduce costs, optimize development, and innovate. The following tips will help you to evaluate core features you should expect of container services and include specific advice for GKE and Cloud Run.

1. Identify opportunities to reduce cluster administration

Most companies want to dedicate resources to innovation, not infrastructure curation. If your team has existing Kubernetes knowledge or runs workloads that need to leverage machine types or graphics processing units (GPUs), you may be able to simplify provisioning with GKE Autopilot. GKE Autopilot provisions and manages the cluster’s underlying infrastructure, all while you pay for only the workload, not 24/7 access to the underlying node-pool compute VMs. In this way, it can reduce cluster administration while saving you money and giving you hardened security best practices by default.

2. Consider serverless to maximize developer productivity

Serverless platforms continue the theme of empowering your technical talent to focus on the most impactful work. Such platforms can promote productivity by abstracting away aspects of infrastructure creation, letting developers work on projects that drive the business while the platform provider oversees hardware and scalability, aspects of security, and more.

For a broad range of workloads that don’t need machine types or GPUs, going serverless with Cloud Run is a great option for building applications, APIs, internal services, and even real-time data pipelines. Analyst research supports that Cloud Run customers achieve faster deployments with less time spent monitoring services, resulting in reinvested productivity that lets these customers do more with fewer resources.

Designed with high scalability in mind, and an emphasis on the portability of containers, Cloud Run also supports a wide range of stateless workloads, including jobs that run to completion. Moreover, it lets you maximize the skills of your existing team, as it does not require cluster management, a Kubernetes skillset or prior infrastructure experience. Additionally, Cloud Run leverages the Knative spec and a container image as a deployment artifact, enabling an easy migration to GKE if your workload needs change.

With Cloud Run, gone are the days of infrastructure overprovisioning! The platform scales down to zero automatically, meaning your services always have the capacity to meet demand, but do not incur costs if there is no traffic.

3. Save with committed use discounts

Committed use discounts provide discounted pricing in exchange for committing to a minimal level of usage in a region for a specified term. If you are able to reliably predict your resource needs, for instance, you can get a 17% discount for Cloud Run (for either one year or three years), and either a 20% discount (for one year) or a 45% discount (for three years) on GKE Autopilot.

4. Leverage cost management features

Minimum and maximum instances are useful for ensuring your services are ready to receive requests but do not cause cost overages. For Google Cloud customers, best practices for cost management include building your container with Cloud Build, which offers pay-for-use pricing and can be more cost efficient than steady-state build farms.

Relatedly, if you choose to leverage serverless containers with Cloud Run, you can set minimum instances to avoid the lag (i.e., the cold start) when a new container instance is starting up from zero. Minimum instances are billed at one-tenth of the general Cloud Run cost. Likewise, if you are testing and want to avoid costs spiraling, you can set a maximum number of instances to ensure your containers do not scale beyond a certain threshold. These settings can be turned off anytime, resulting in no costs when your service is not processing traffic. To have better oversight of costs, you can also view built-in billing reports and set budget alerts on Cloud Billing.

5. Match workload needs to pricing models

GKE Autopilot is great for running highly reliable workloads thanks to its Pod-level SLA. But if you have workloads that do not need a high level of reliability (e.g., fault tolerant batch workloads, dev/test clusters), you can leverage spot pricing to receive a discount of 60% to 91% compared to regularly-priced pods. Spot Pods run on spare Google Cloud compute capacity as long as resources are available. GKE will evict your Spot Pod with a grace period of 25 seconds during times of high resource demand, but you can automatically redeploy as soon as there is available capability. This can result in significant savings for workloads that are a fit.

Innovation requires balance

Put into practice, these tips can help you and your business to get the most out of containers while controlling management and resource costs. That said, it is worth noting that while managing cloud costs is important, the relationship between “cloud” and “cost” is often complex. If you are adopting cloud computing with only the primary goal of saving money, you may soon run into other challenges. Cloud services can save your business money in many ways, but they can also help you get the most value for your money. This balance between cost efficiency and absolute cost is important to keep in mind so that even in challenging economic landscapes, your tech company or startup can continue growing and innovating.

Beyond cost savings, many tech and startup companies are seeking improved business agility, which is the ability to deploy new products and features frequently and with high quality. With deployment best practices built into GKE Autopilot and Cloud Run, you can transform the way your team operates while maximizing productivity with every new deployment.

You can learn if your existing workloads are appropriate for containers with this fit assessment and these guides for migrating to containers. For new workloads, you can leverage these guides for GKE Autopilot and Cloud Run. And for more tips on cost optimization, check out our Architecture Framework for compute, containers, and serverless.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program and apply for our Google for Startups Cloud Program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.