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.

In our conversations with technology leaders about data-driven transformation using Google Data Cloud –  industry’s leading unified data and AI solution – , one important topic is incorporating continuous intelligence to move from answering questions such as “What has happened? to questions like “What is happening?” and “What might happen?”. The core to this evolution is the need for an underlying data processing that not only provides powerful real-time capabilities for events happening close to origination, but also brings together existing data sources under one unified data platform to enable organizations to draw insights and take actions holistically. Dataflow, Google’s cloud-native data processing and streaming analytics platform, is a key component of any modern data and AI architecture and data transformation journey, along with BigQuery, Google’s internet-scale warehouse with built-in streaming, BI engine and ML; Pub/Sub, a global no-ops event delivery service; and Looker, a modern BI and embedded analytics platform. One of the key evaluation factors is potential economic value of Dataflow to their organization, particularly in the context of engaging other stakeholders is key for many of the leaders that we engage with. So we commissioned Forrester Consulting to conduct a comprehensive study on the impact that Dataflow had on their organization by interviewing actual customers . 

Today we’re excited to share our commissioned study conducted by Forrester Consulting, the Total Economic Impact™ of Google Cloud Dataflow, which allows data leaders to understand and quantify the benefits of Dataflow, and use cases it enables. Forrester conducted interviews with Dataflow customers to evaluate the benefits, costs, and risks of investing in Dataflow across an organization. Based on their interviews, Forrester identified major financial benefits across four different areas: business growth, infrastructure cost savings, data engineer productivity, and administration efficiency. In fact, Forrester found that customers adopting Dataflow can achieve a 55% boost in developer productivity and a 50% reduction in infrastructure costs. In fact, Forrester projects that customers adopting Dataflow can achieve a range of up to 171% Return on Investment (ROI) and a less than six months payback period. Customers can now use figures in the report to compute their own Return on Investment (ROI) and payback period.

“Dataflow is integral to accelerating time-to-market, decreasing time-to-production, reducing time to figure out how to use data for use cases, focusing time on value-add tasks, streamlining ingestion, and reducing total cost of ownership.” – Lead technical architect, CPG

Let’s take a deeper look at the ways that Forrester found that Dataflow can help you achieve your goals and unlock your business potential. 

Benefit #1: Increase data engineer productivity by 55%

Developers can choose among a variety of programming languages to define and execute data workflows. Dataflow also seamlessly integrates with other Google Cloud Platform and open source technologies to maximize value and applicability to a wide variety of use cases. Dataflow streamlined workflows with code reusability,dynamic templates, and the simplicity of a managed service. Engineers trusted pipelines to run correctly and adhere to governance. Data engineers avoided laborious issue-monitoring and remediation tasks that were common in the legacy environments such as poor performance, lack of availability, and failed jobs. Teams valued the language flexibility and open source base.

“Dataflow provided us with ETL replacement that opened limitless potential use cases and enabled us to do smarter data enhancement while data remains in motion.” — Director of data projects, financial services

Benefit #2: Reduce infrastructure costs by up-to 50% for batch and streaming workloads 

Dataflow’s serverless autoscaling and discrete control of job needs, scheduling, and regions eliminated overhead and optimized technology spending. Consolidating global data processing solutions to Dataflow further eliminated excess costs while ensuring performance, resilience, and governance across environments. Dataflow’s unified streaming and batch data platform gives organizations the flexibility to define either workload in the same programming model, run it on the same infrastructure, and manage it from a single operational management tool. 

“Our costs with our cloud data platform using Dataflow are just a fraction of the costs we faced before. Now we only pay for cloud infrastructure consumption because the open source base helps us avoid licensing costs. We spend about $120,000 per year with Dataflow, but we’d be spending millions with our old technologies.” – Lead technical architect, CPG

Benefit #3: Increase top-line revenue by improving customer experience and retention with payback time of < 6 months

Streaming analytics is an essential capability in today’s digital world to gain real-time actionable insights. Likewise, organizations must also have flexible, high- performance batch environments to analyze historical data for building machine learning models, business intelligence, and advanced analytics. Dataflow enabled real-time streaming use cases, improved data enrichment, encouraged data exploration,improved performance and resiliency, reduced errors, increased trust, and eliminated barriers to scale. As a result, organizations provided customers with more accurate, relevant, and in-the-moment data-backed services and insights — boosting customer experience, creating new revenue streams, and improving acquisition, retention, and enrichment.

“It’s already been proven that we are getting more business [with Dataflow] because we can turn around results faster for customers.” – VP of technology, financial services technology

“When we provide data to our customers and partners with Dataflow, we are much more confident in those numbers and can provide accurate data within a minute. Our customers and partners have taken note and commented on this. It’s reduced complaints and prevented churn.” – Senior software engineer, media

Other benefits 

Eliminated administrative overhead and toil

As a cloud-native managed service, all administration tasks such as provisioning, scaling, and updates are automatically handled by Google Cloud. Teams no longer need to manage servers and related software for legacy data processing solutions. Admins also streamlined processes for setting up data sources, adding pipelines, and enforcing governance.

Saved business operations costs for support teams and data end users

Dataflow improved the speed, quality, reliability, and ease of access to data for insights for general business users, saving time and empowering users to drive better data-backed outcomes. It also reduced support inquiry volume while automating manual job creation.

What’s next?

Download the Forrester Total Economic Impact study today to dive deep into the economic impact Dataflow can deliver your organization. We would love to partner with you to explore the potential Dataflow can unlock in your teams. Please reach out to our sales team to start a conversation about your data transformation with Google Cloud.

At 340 square miles, the City of Memphis is among the largest in the United States in terms of land area. Memphis has over 6,800 lane-miles of city streets, enough to drive back and forth to Los Angeles four times. Keeping these streets well maintained and safe for citizens and visitors is a major priority for the city.

Lots of traffic, lots of roads, and a four-season climate prone to wintertime freeze-thaw-refreeze cycles means the opportunity for potholes. Although the city aims to fill potholes within five business days of notification, it can take longer, especially during winter and early spring. Last year, the city’s Public Works crews repaired some 63,000 potholes, only 20% of which were reported by residents. Approximately 32,000-man-hours each year are spent repairing potholes, with seasonal fluctuations requiring ten to twelve Street Maintenance crews working steadily during the winter months. Still, many went unreported, leading the city to flag pothole request resolution under “needs improvement” on its open data portal website.

Like many large cities, Memphis also struggles with vacant and blighted properties. Nearly 15,000 properties in Memphis are likely vacant, and city officials contend that many are owned by out-of-town investors who live elsewhere and do not take necessary restoration or maintenance steps. These properties can decrease the value of surrounding real estate and discourage new businesses and other residents from moving to an area. Citizen frustration and concerns over the number of blighted properties has made blight eradication a major focus of the City of Memphis.

Historically, residents reported potholes and blighted properties by calling 311, or more recently by using the Memphis 311 app. However, these reports only covered about 20 percent of the problems — often the worst cases. And by the time residents took the initiative to submit a 311 report, they usually weren’t feeling good about the situation.

Recognizing that potholes and vacant properties are often the most visible indicators of whether a city government is doing its job efficiently, Memphis Mayor Jim Strickland and CIO Mike Rodriguez began looking for ways they could apply technology to fix the problems. Mike approached Google for ideas, and Google recommended conducting a machine learning proof-of-concept (POC) with SpringML, a Google Cloud Partner.

“Memphis is focused on easy living, and we want to do everything we can to keep our citizens happy,” says Mike Rodriguez. “Working with Google and SpringML to reduce potholes and urban blight using machine learning and artificial intelligence was an easy decision.”

Bringing machine learning to city operations and budgets

The city’s goal is to detect potholes and abandoned properties by analyzing video footage of roads and residential properties. It wanted to classify potholes by width and depth, and share the information with workers who can repair them. For abandoned properties, it wanted to enable more strategic deployment of resources for homeowners citywide and take action to hold neglectful property owners accountable.

The POC began by training TensorFlow models for ML object detection using preconfigured AI Platform Deep Learning VM Images on Compute Engine. SpringML helped set up cameras and developed a user interface to collect pothole data and automate the 311 ticketing process.

Together, the teams analyzed 30 days of video from a moving city bus and high-resolution video from 360-degree cameras mounted to a code enforcement vehicle, overlaid with data from 311 reports. As the models were refined, accuracy quickly climbed from 50 percent to over 90 percent as models were taught to differentiate a pothole from a manhole cover or other object.

The city also imported routes, potholes, and paving data along with geolocation data from ArcGIS and Google Maps into BigQuery to better understand street conditions and the proximity of potholes to one another. BigQuery also analyzes city property records, tax records, 311 reports, and third-party survey data on-demand to predict where homes are starting to become run down and where neighborhood decay is most likely to occur. The SpringML team created a pilot analysis to begin vacant property protections and developed a user interface tool to interact with the model’s results.

“Google Cloud Platform made it possible for us to experiment with machine learning and artificial intelligence to help solve our city’s problems while working within the budget constraints of a municipal IT organization,” says Mike. “Google turned a ‘nice to have’ into a ‘let’s do this!'”

Identifying 75 percent more potholes

Memphis expects to substantially reduce the number of potholes on its streets, creating a better driving experience for residents and visitors alike. Because drivers won’t be as likely to swerve to miss a pothole, streets will be safer and friendlier to bicycles and scooters. Fewer potholes will also save the city between $10,000 and $20,000 annually in city claims that it pays out in cases where vehicle damage results from a pothole that was not addressed in a timely manner.

“Historically, Public Works has relied primarily upon Street Maintenance crews to proactively locate and fill potholes. As Memphis has over 6,800 lane-miles of public streets, it is a daunting task to reliably survey the entire system in an efficient and systematic way,” says Robert Knecht, Public Works Director for the City of Memphis. “The outcome of the data collected will be invaluable to Public Works so that it can ensure it is managing the city’s street system in a more proactive manner.”

Memphis will be able to better prioritize road maintenance based on condition and impact, increasing the efficiency of its Public Works road crews. Analyzing video of streets also gave the city visibility into issues it wasn’t previously aware of, such as curbs, gutters, and manhole covers that had been mistakenly paved over and need to be excavated. The ML process is easily transferrable to other concerns as well, helping the city identify illegal signs or spools of cable hanging on light posts that could be potentially unsafe.

Helping communities recover and thrive

Memphis is also having success in analyzing predictive trends to combat high rates of abandoned and blighted properties, surpassing 97.5 percent accuracy. “In the past, Public Works experimented with comprehensive, city-wide blight identification by using approximately 200 volunteers to survey and photograph over 237,000 city parcels. This effort was costly, took a long time to complete, and resulted in inconsistent data collection,” says Robert. “Blighted property conditions can change quickly in a city the size of Memphis. Now, with this new technology, Memphis will be able to make a significant difference in the efforts to proactively and comprehensively identify and manage blighted and substandard properties.”

Code Enforcement with better data-driven detection mechanisms enables the city to also identify cases where homeowners are not physically or financially able to keep up with the challenges of homeownership and make them aware of resources that are available to assist them. Memphis Code Enforcement can do a better job of finding people living in derelict properties that pose hazards to inhabitants’ health and safety, and help them fix those problems or find a new place to live.

“Using SpringML and Google Cloud Platform to detect indicators of vacant or blighted properties will help Memphis create safer neighborhoods that will be more attractive to businesses and home buyers,” says Mike. “Property values and employment will go up, crime will go down, and social services can be more focused and effective.”

Revolutionizing service delivery for citizens

Memphis is proving the viability of a cost-effective, cloud-based machine learning model that other cities can follow. The city is already looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents.

As part of his commitment to a transparent government, Memphis Mayor Jim Strickland created an open data policy that commits to releasing raw data and sharing it with citizens in a variety of downloadable formats. Going forward, this transparency will help citizens understand how their needs are being served and uncover new, innovative use cases for AI and ML.

“Our goal is to become a smart city, and technologies such as Google Cloud Platform and SpringML put us ahead of the game,” says Mayor Strickland. “Google understands data, and there isn’t a better company to help us analyze our data resources for actionable insights.”

Last year, we announced our most ambitious sustainability goal yet: to operate everywhere on 24/7 carbon-free energy by 2030. We’ve set this goal to ensure that Google Cloud continues to be the cleanest cloud in the industry, and to show that full-scale decarbonization of electricity use is possible. 

Since setting our target, we’ve made tremendous progress in how we track, buy, and use electricity; advocate for clean energy policies; and support the development of new technologies to help us reach this goal. And we’ve done it all while maintaining a commitment to transparency that we hope will make it easier for other organizations wishing to fully decarbonize their operations as well.

In the spirit of transparency, today we’re releasing the 2020 carbon-free energy percentages (CFE%) for all Google data centers, as well as overall progress on the road to our 2030 goal: In 2020, Google achieved 67% round-the-clock carbon free energy across all its data centers, up from 61% in 2019. In other words, of all the electricity consumed by Google data centers in 2020, two-thirds of it was matched with local, carbon-free sources on an hourly basis.

Though we saw a significant jump in global CFE% in 2020, we expect the numbers to vary from year to year. Ultimately, CFE% is dependent on the amount of new clean energy that comes online in a given year; we may even occasionally see short-term drops in the numbers. What’s most important is that we continue to maintain a long-term trajectory toward our 2030 goal. With meaningful progress in clean energy policy, technologies, and transactional models, we believe 24/7 carbon-free energy is achievable.

Tracking these numbers also allows us to give Google Cloud customers greater control in their own sustainability efforts. Earlier this year we announced Google Cloud Region Picker, a system that helps our customers assess factors like cost, speed, and CFE% as they choose where to run their applications. 

To outline some of the events that have helped us get to 67% CFE%, we’ve developed an animation that shows every hour of electricity use in 2020, at all our Google data centers around the world. 

Visualizing clean energy: every hour, every day, everywhere

Imagining what every hour in a year looks like is hard enough (there are 8,760 of them, in case you’re wondering). With 23 data centers and 25 cloud regions around the world, we’re aiming to source clean energy for over 200,000 operational hours each year.

https://youtube.com/watch?v=f9ecEokcFlk%3Fenablejsapi%3D1%26

The “A year in carbon-free energy” animation points out significant projects that came online in 2020 to bring our data centers closer to operating entirely on round-the-clock carbon-free energy. It also reflects an unparalleled level of transparency about our carbon-free energy data, showing hour-by-hour where we need to develop new clean energy projects, advocate for policy changes, and in some cases, look to new technologies that can help fill in the gaps left by variable renewable resources. 

In the animation, you’ll notice sites with a lot of green at midday (e.g. in Chile or the U.S. Southeast) – a sign that solar is making a big contribution. Other data centers, such as our facilities in the U.S. Midwest, rely more heavily on wind power and are subject to seasonal fluctuations in wind speed. 

Preventing the worst impacts of climate change will require decarbonizing the world’s electric grids, as fast as possible. Google is committed to doing as much as possible to clear a path for others and drive collective action to achieve this goal. We’re thrilled to be in good company as we move, together, toward a carbon-free future.