Based in Mountain View, California, Khan Academy is a not-for-profit that produces and posts a vast collection of free educational online videos about math and science topics ranging from algebra and trigonometry to biology and economics.

Millions of students, educators, and self-learners around the world watch the videos, both on the Khan Academy’s YouTube channel and on its hugely popular website (www.khanacademy.org), where students answer some 1.5 million practice questions per school day. The Khan Academy development team continually tweaks the site based on how visitors choose to learn.

Challenge

Khan Academy’s beginnings date back to 2004, when Sal Khan’s cousin asked him to remotely tutor her daughter in math. Khan was happy to help and more than qualified, given his three MIT degrees and his Harvard MBA.

As more relatives and friends asked Khan for tutoring assistance, he began videotaping short lessons and posting them to YouTube. His knack for distilling complex concepts into easy-to-follow tutorials helped the videos go viral.

Sal knew he needed to offload the technical and maintenance concerns so he could work on what was important. To grow, it was important for him to be able to stop worrying about things like deployment issues and running his own server.

Ben Kamens, lead developer for Khan Academy

Khan maintained a website for his growing video library for several years, but the platform experienced limitations as traffic increased.

At the same time, media attention was growing, and technology industry leaders, including Microsoft founder Bill Gates and Silicon Valley venture capitalist John Doerr, began lending their support and evangelizing Khan’s mission and work. It was at this point that Khan quit his job as a hedge fund analyst to devote his full-time attention to the site’s growing potential.

“With Google App Engine, we don’t need a system administrator or anyone dedicated to deploying our app, so 99 percent of our time is spent working on our application,” says Ben Kamens, lead developer, Khan Academy.

“Sal knew he needed to offload the technical and maintenance concerns so he could work on what was important, which was to make more videos for the Khan Academy library,” explains Ben Kamens, lead developer for Khan Academy. “To grow, it was important for him to be able to stop worrying about things like deployment issues and running his own server.”

Solution

Khan Academy chose App Engine as its hosting and application development platform because App Engine could easily house its growing collection of 2,000-plus videos, resolving the organization’s overall server and maintenance issues with a single solution.

Using App Engine freed the team to focus on the user experience and the array of content that makes the academy such a powerhouse.

“A lot of what the Khan Academy is about is collecting data on student behavior so we can teach them better,” Kamens says. “Did they use a hint? Have they watched the video before? That data is being stored on Google App Engine so we can figure out what the most effective videos are, or where students struggle the most.”

Khan Academy provides individual profiles to students so they can analyze their learning progress, which means the organization needs systems running in the background to collect and track of all this data. Because App Engine takes care of server support, Khan Academy’s five developers can spend almost all of their time improving site functionality.

“If we didn’t have Google App Engine, we’d be spending a lot more time figuring out server setup and working on routers,” Kamens says. “Our ability to focus on the actual product is the benefit of Google App Engine.”

With the App Engine dashboard, which provides information like response time, uptime, and error rates, the development team also has an easy way to watch over site performance. “It lets us see what the average response time is, so we can keep it really low,” Kamens explains.

Results

During the US school year, Khan Academy receives more than 3.8 million unique visits a month — all served through App Engine. To support this much traffic, a typical company would need an internal system administration staff.

“With Google App Engine, we don’t need a system administrator or anyone dedicated to deploying our app, so 99 percent of our time is spent working on our application,” explains Kamens. “Our application lives on Google App Engine, and we bank all of our scalability and traffic concerns on Google App Engine. Even huge traffic spikes from Sal’s media appearances don’t worry us since we know that Google App Engine will handle the spike.”

Kamens also likes the fact that he and his team can turn to the Google App Engine support team for assistance when they need it. He also finds helpful tips on the Google App Engine blog.

The Khan Academy staff has come to completely rely on App Engine. “We are constantly using more of Google App Engine’s capabilities and have been happy with the performance,” Kamens says. “It’s nice to have an easy deployment process since we deploy the site on average at least once a day, [and] sometimes up to nine or 10 times [a day].”

At Google Cloud, we believe in making it simple and secure to consume services whether they’re from Google, a third party or customer-owned. With Private Service Connect, we have adopted a service-centric approach to our network that abstracts the underlying networking infrastructure. And today, we are announcing Private Service Connect is generally available in all Google Cloud regions.

Private Service Connect allows you to create private and secure connections from your cloud networks to services like Cloud Storage or Cloud Bigtable and third-party services like Elastic, MongoDB or Snowflake. It creates service endpoints in your VPCs that provide private connectivity and policy enforcement, allowing you to easily connect to services across different networks and organizations.

Customers told us they want to consume services faster while making sure that the connectivity is private and secure. In the past, achieving this was a challenge: networking teams had to negotiate IP address blocks, mutually agree on policies and coordinate as applications evolved to newer versions. With Private Service Connect, you can delegate the consumption and delivery of services to different teams without having to coordinate between teams.

How it works

Private Service Connect makes it easy to consume services by leveraging service endpoints that are locally managed. The services can be in different projects or managed by different organizations. Access to the service is controlled by strict governance and IAM policies. Application teams and developers can focus on delivering their services easily by exposing their ‘service attachment’. No more worrying about networking constructs—Private Service Connect takes care of connecting to the service on the Google backbone for them.  

Benefits to our partners

Being able to consume services from a variety of software vendors and service providers makes it possible for enterprises to innovate faster. For that, developers need to be able to compose services from third-party vendors, Google managed services, as well as their own services. To help, third-party partners can use Private Service Connect to deliver multi-tenant services securely and at massive scale, and make the connectivity to their services appear as if they are running on the enterprises’ network. Private Service Connect will also integrate with Service Directory to register many producer services, making service consumption even simpler. 

“In today’s environment, where seamless access to real-time market information and the ability to handle increasingly vast volumes of data is essential, our clients are demanding native connectivity in the cloud. Google’s Private Service Connect offers the performance and reliability required by the types of mission critical apps that rely on Bloomberg’s tick for tick market data feed, B-PIPE.” —Cory Albert, Global Head of Cloud Strategy, Enterprise Data at Bloomberg 

“One of the key goals for Elastic on Google Cloud is to monitor and protect our customers’ data. Google Cloud’s Private Service Connect with Elastic Cloud furthers our commitment to our customers that together we make it quick, easy and secure to gain insights and intelligence from their data.” —Uri Cohen, Product Lead for Elastic Cloud

“MongoDB’s partnership with Google is an integral part of our strategy to support modern apps and mission-critical databases and to become a cloud data company. Private Service Connect allows our customers to connect to MongoDB Atlas on Google Cloud seamlessly and securely and we’re excited for customers to have this additional and important capability.”—Andrew Davidson, VP of Cloud Product, MongoDB

Check out the Google Cloud Console to try it today.

Founded in 2006, PaGaLGuY started as a forum that enabled students, typically aged between 20 and 30 to discuss and seek advice on academic issues. By 2011, PaGaLGuY had increased its traffic to about 250,000 page views per month. The business is now one of India’s largest education networks and provides an app that users can download to their Android and iOS devices.

Over the past six years, PaGaLGuY has extended its service to include video advice from experts on education topics and grown the number of views of its pages to 1.5 million per day. As Head of Technology for the business, Sandeep Kalidindi has played a key role in ensuring PaGaLGuY is as engaging as possible to users. “Because the product is advertising based, the greater the user engagement, the greater the advertising revenue,” Kalidindi explains.

Google Cloud Platform Results

• Supported growth to 1.5 million page views per day and demand spikes that see requests increase from about 90 per second to about 1,200 per second
• Reduced API latency from about 1 second to about 40 milliseconds
• Reduced system administration time from three to four days per week to 30 minutes every two weeks

In 2015, PaGaLGuY’s senior management team decided to deliver an even more relevant experience for users of the education network. “The core thing we had to do was personalise the experience for each and every student that visited PaGaLGuY,” Kalidindi says. “So we had to capture each student’s data to customise what they see when they open the site.”

The business also found traffic to the network was straining its infrastructure. During demand peaks, created by exams involving as many as 5 million students, PaGaLGuY would be inaccessible for periods of 30 minutes to one hour. Furthermore, average API latency had climbed to an unacceptable 1 second, compromising performance.

PaGaLGuY needed to access extensive compute resources to undertake its planned change. Had the business relied on a physical technology architecture to undertake the transformation, it would have had to purchase capacity equivalent to 16 new servers. “There was no way with a small team we could grow to that extent in a short time,” Kalidindi says. “This was the right moment for us to explore cloud services.”

The business established two primary requirements the selected cloud service needed to meet. First, PaGaLGuY had to be able to scale the platform with costs rising only in proportion to the increase in resources consumed. Accordingly, the business would have to minimise the number of employees required to manage the cloud environment. Second, the platform had to give PaGaLGuY easy access to student data and the ability to undertake prompt, granular analysis.

PaGaLGuY reviewed available public cloud services and determined that Google Cloud Platform (GCP) was the best fit for its business. “Google Cloud Platform was considerably more mature than the alternatives, with a high degree of automation and a suite of managed services,” Kalidindi says. PaGaLGuY management then discussed with Google how to optimise cost, performance and availability of its personalised education network on GCP.

With assistance from Google and business transformation specialists Searce, PaGaLGuY was able to deliver the platform into production on GCP in 10 months. “Searce was very proactive in ensuring the environment met our needs and allowing us to gain priority access to Google services in development,” Kalidindi says. “Their team was integral to the success of the migration.”

PaGaLGuY has been running in production in GCP for two years. The education network’s GCP architecture comprises a scalable back-end built on Google App Engine; a managed environment for its containerised applications in Google Kubernetes Engine; messaging-oriented middleware through Google Cloud Pub/Sub; a relational database in Google Cloud SQL; a managed data analytics warehouse running in Google BigQuery; stream and batch data processing through Google Cloud Dataflow; and object storage in Google Cloud Storage.

PaGaLGuY has leveraged GCP services to break down its platform application from a monolithic build to a series of microservices running in Google App Engine that enable independent deployment cycles, minimise test and quality assurance overheads and provide clearer monitoring and logging.

Running on GCP has enabled PaGaLGuY to add new personalisation features and grow fourfold without having to add any new engineers or administrators to accommodate the increased traffic. The business has also used the platform to seamlessly collect and aggregate students’ data for analysis, reporting and delivering a more targeted user experience. Furthermore, PaGaLGuY has been able to provide its management team with direct access to Google BigQuery to scrutinise data rather than require them to wait at least a day to view reports created by the product or technology teams.

Support demand peaks of 1,200 requests per second

“Thanks to Google Cloud Platform, we can easily support demand peaks that see requests per second rise from an average 90 per second to about 1,200 per second for as long as 45 minutes,” Kalidindi says. Due to GCP’s scalability, PaGaLGuY can ensure its education network remains available and performance remains consistent during those periods.

Latency cut to 40 milliseconds

The business has also reduced average API latency from 1 second to about 40 milliseconds. Furthermore, using GCP has enabled PaGaLGuY to automate most of its processes and reduce system administration requirements from three to four days a week across its team members to about half an hour per week.

The performance of GCP has transformed PaGaLGuY’s culture and processes. “Once our team was exposed to Google Cloud Platform and understood the superiority of the platform, our mindset changed from ‘let us do everything on our own’ to ‘let us do what we do best’ and delegate the remainder,” Kalidindi says. The quality of the service provided by GCP means PaGaLGuY effectively considers the cloud provider as part of its team. “We are always eager to see what new services are being launched and are extremely excited about what Google Cloud Platform can provide as part of its roadmap.” he concludes.

We’re excited to announce that Spring Cloud GCP version 4.0 is now generally available! In this post, we’ll be describing what the new major version has to offer, and demonstrating the process of using the migration guide on one of our reference architectures, Bank of Anthos.

What’s new?

With this release, Spring Cloud GCP officially supports Spring Boot 3.x. However, this migration involves a number of breaking changes as outlined in the migration guide. The full list of changes made is available on Github, but the one of the most significant differences is that Java 17 is now a minimum requirement.

Another notable feature of this release is the addition of starter artifacts – Spring Boot starters for Google Cloud – that provide dependencies and auto-configurations for 80+ Google Client libraries. Just as the name suggests, these starters can serve as helpful starting points when working with a new client library. For now, they’re in preview. 

These starters are not included in the BOM by default and need to be added as a dependency to your project before they can be used. For example, if you wanted to get started with Cloud Text-to-Speech, you would include the following:

<dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-texttospeech-spring-starter</artifactId>
    <version>4.0.0-preview</version>
  </dependency>

The upgrade process

We prepared a migration guide to help answer any questions involved with moving from 3.x to 4.x.  Let’s follow those instructions to migrate Bank of Anthos.

We’ll start by cloning and building the application before the upgrade, according to the quickstart and development guide:

PROJECT_ID=<YOUR-PROJECT-ID>
gcloud services enable container --project ${PROJECT_ID}

git clone https://github.com/GoogleCloudPlatform/bank-of-anthos.git
cd bank-of-anthos/

gcloud services enable container.googleapis.com monitoring.googleapis.com \
  --project ${PROJECT_ID}

REGION=us-central1
gcloud container clusters create-auto bank-of-anthos \
  --project=${PROJECT_ID} --region=${REGION}

gcloud container clusters get-credentials bank-of-anthos \
  --project=${PROJECT_ID} --region=${REGION}

skaffold run --default-repo=gcr.io/${PROJECT_ID}/bank-of-anthos

At the end of this, you should see a “deployment stabilized” message:

Deployments stabilized in 9.657 seconds

With the environment set up for development, we’re ready to move on to the actual migration. The migration is already complete at time of writing, but here’s the full list of changes made:

• We began by checking the current Java, Spring Boot and Spring Cloud GCP versions.
  • Our Java version is okay, but the other two are out of date, and will need to be updated.
    We can also add the Spring Cloud GCP BOM to manage our versions going forward while we’re here.
• Then, in no particular order, we can make the following code changes:
  • Remove the explicit type declarations from our ResponseEntity instantiations.
  • Replace all javax.* imports with Jakarta.* counterparts, since Spring 3.0 makes use of Jakarta EE 9.0

With those changes in place, the app is upgraded and ready to re-deploy!

Live example

For a finished example of this migration, check out the Bank of Anthos repository on Github. It’s an excellent sample application that showcases a polyglot Java & Python app, served on Kubernetes and Google Cloud. All CI/CD and configurations are open source, so it may be instructive as you approach this migration.

Thanks for reading, and feel free to provide comments or feedback on Twitter, or in the issues section of the repository.