Share Chat: Building a Scalable Data-Driven Social Network for Non-English Speakers Globally

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With 22 major official languages and numerous additional regional dialects used in rural regions, India is a multilingual nation. More than 227 million people in India use the internet in rural regions, and as internet access becomes more widely available, this number is likely to rise rapidly, according to the Internet and Mobile Association of India.

ShareChat, which was conceptualised, planned, and constructed in India, connects Indians in rural areas and from all over the world on a single platform. More than 160 million users share and consume content each month in 15 different Indian languages, including videos, photos, GIFs, songs, and much more. With over 80 million monthly active users, Moj has become the most popular short video platform in India. It was also introduced by ShareChat in July 2020.

Because ShareChat uses a tailored content feed as its mobile app homepage, it makes it easier to find information and people because it is aware that many new internet users don’t know how to utilise search phrases to find it.

To surface the appropriate content to the appropriate user, the company’s data science team uses machine learning models that recognise the language of the content and user involvement.

Easily Scaling Apps to Handle Surges in Demand

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Data is the engine that drives ShareChat’s application. ShareChat’s social network platform is driven by real-time data, which keeps track of every user activity in the app, from chat messages and the creation of new groups to what people enjoy and who they follow.

More than a million postings are made on ShareChat every day, so its systems must analyse gigabytes of data with extreme efficiency. Due to Cloud Spanner’s relational database’s global consistency and secondary indexing, ShareChat switched from a NoSQL database to it.

ShareChat transferred 120 tables with 17 indexes from 220 database tables to Cloud Spanner, which supports transactional workloads, cutting costs by 30%. Even in the event that one region fails, Cloud Spanner’s capacity to seamlessly duplicate data in several places in real time enables ShareChat to instantly get a copy of any documents they demand.

The business was able to extend horizontally with no code changes as its traffic increased by 500 per cent in a matter of days. This occurred concurrently with the Moj app launch, and within a week it was seamlessly expanded to another region. The programme handled the additional load that was thrown at it without any assistance thanks to Cloud Spanner.

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Big-Bang Migration with No User Downtime

To ensure a seamless migration, ShareChat put together a dream team with the necessary knowledge and tools. The engineers at ShareChat collaborated with CloudCover, a company with experience in large-scale cloud migrations, to plan, test, and carry out the migration procedure.

At the time of the conversion, ShareChat already contained 220 tables and more than 70 terabytes of data. Some of these tables had about 50 billion rows and 14 terabytes of storage space.

ShareChat decided against a planned rollout and instead went with a big-bang move. Due to the interdependencies between data, consumers may encounter latency spikes from out-of-sync data if services were migrated one at a time, which would compromise their timely delivery. Users might receive delayed alerts, such as invitations to events that are two weeks old, if the data isn’t in sync, for instance. Users may discontinue using ShareChat as a result of errors like that.

Even though we are moving gigabytes of data, none of our clients should be impacted, claims Venkatesh. “Google Cloud greatly boosted our confidence that this could be accomplished and that it would collaborate with us as a partner. We didn’t experience this with our previous cloud partners.

Given that the platform must be able to process more than one million queries per second, ShareChat ran a proof-of-concept cluster over the course of four months to confirm database performance in a real-world situation. To copy all data from the legacy environment to Google Cloud for in-depth performance tests and capacity assessments, it employed an open source API gateway.

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Enhancing Share Chat’s Commercial Ad Selection

Although ShareChat can currently be downloaded for free, the next stage of its development will focus on improving how it makes money from its service in order to benefit both users and investors. The app presently makes money from advertisements to fund app development. The business links content producers and brands to develop regional language campaigns.

Hyper-local material will be used by partners to improve their brand’s experience with rural ShareChat users. To assist advertisers in maximising marketing expenditure, the Business and Strategy team uses BigQuery and Data Studio to show campaign performance information.

In addition, ShareChat makes use of BigQuery and Data Studio to display resource utilisation data via GKE usage metering, a Google Kubernetes Engine feature. ShareChat may publish a cost breakdown for several departments to manage over-provisioned resources and cut waste by simply labelling the services in BigQuery.

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Automating Dev Ops to Make Things Easier

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ShareChat typically processes 80,000 requests per second (RPS), or around 7 billion RPS per day. Push alerts, like the daily trending topic sent to users at 5 PM, can cause a spike in RPS of 130,000 in a couple of seconds given the magnitude of its subscriber base.

The Google Kubernetes Engine may be pre-scaled for traffic spikes around scheduled holidays like Diwali when millions of Indians send wishes, adds Venkatesh, rather than over-provisioning servers. “Moving to a native Kubernetes environment substantially improves our capacity to embrace agile work practises, such as automated deployment and to save time writing scripts.”

Without changing any code, ShareChat is possible to attach ancillary activities like logging into the application thanks to special Kubernetes features like sidecar proxy. Unlike the prior cloud provider, Google Kubernetes automatically handles Kubernetes upgrades. In order to reduce security risks and take advantage of new features, clusters and nodes automatically upgrade to run the most recent Kubernetes version.