Abstract

In this blog, we describe our journey to the cloud and share some lessons we learned along the way. Our hope is that you’ll find this information helpful as you go through the decision, execution, and completion of your own migration to the cloud.

Introduction

LiveRamp is a data enablement platform powered by identity, centered on privacy, integrated everywhere. Everything we do centers on making data safe and easy for businesses to use. Our Safe Haven platform powers customer intelligence, engages customers at scale, and creates breakthrough opportunities for business growth.

Businesses safely and securely bring us their data for enrichment and use the insights gained to deliver better customer experiences and generate more valuable business outcomes. Our fully interoperable and neutral infrastructure delivers end-to-end addressability for the world’s top brands, agencies, and publishers. Our platforms are designed to handle the variability and surge of the workload and guarantee service-level agreements (SLAs) to businesses.

We process petabytes of batch and streaming data daily. We ingest, process (join and enhance), and distribute this data. We receive and distribute data from thousands of partners and customers on a daily basis. We maintain the world’s largest and most accurate identity graph and work with more than 50 leading demand-side and supply-side platforms.

Our decision to migrate to Google Cloud and Dataproc

As an early adopter of Apache Hadoop, we had a single on-prem production managed Hadoop cluster that was used to store all of LiveRamp’s persistent data (HDFS) and run the Hadoop jobs that make up our data pipeline (YARN). The cluster consisted of around 2500 physical machines with a total of 30PB or raw storage, ~90,000 vcores, and ~300TB of memory. Engineering teams managed and ran multiple MapReduce jobs on these clusters.

The sheer volume of applications that LiveRamp ran on this cluster caused frequent resource contention issues, not to mention potentially widespread outages if an application was tuned improperly. Our business was scaling and we were running into constraints related to data center space and power in our on-premises environment. These constraints restricted our ability to meet our business objectives so a strategic decision was made to leverage elastic environments and migrate to the cloud. The decision required financial analysis and a detailed understanding of the available options, from do-it-yourself and vendor-managed distributions to leveraging cloud-managed services.

LiveRamp’s target architecture

We ultimately chose Google Cloud and Dataproc, a managed service for Hadoop, Spark, and other big data frameworks. During the migration we made a few fundamental changes to our Hadoop infrastructure:

Instead of 1 large persistent cluster managed by a central team, we have decentralized the cluster ownership to individual teams. This gave the teams flexibility to recreate, perform upgrades or change configurations as they see fit. This also gives us better cost attribution, less blast radius for errors, and less chance that – a rogue job from one team will impact the rest of the workloads.

Persistent data is no longer stored in HDFS on the clusters, it is in Google Cloud Storage, which, conveniently, served as a drop in replacement, as GCS is compatible with all the same APIs as HDFS. This means we can delete all the virtual machines that are part of the cluster without losing any data.

Introduced autoscaling clusters to control compute cost, and to dramatically decrease request latency. On premise you’re paying for the machines so you might as well use them. Cloud compute is elastic so you want to burst when there is demand and scale down when you can.

For example, one of our teams runs about 100,000 daily Spark jobs on 12 Dataproc clusters that each independently scale up to 1000 VMs. This gives that team a current peak capacity of about 256,000 cores. Because the team is bound to its own GCP Project inside of a GCP Organization, the cost attributed to that team is now very easy to report. The team uses architecture represented below to distribute the jobs across the clusters. This architecture allows them to bin similar workloads together so that they can be optimized together. Below is the logical architecture of the above workload:

Our approach

Overall migration and post migration stabilization/optimization of the largest of our workloads took us about several years to complete. We broadly broke down the migration into multiple phases.

Initial Proof-Of-Concept

When analyzing solutions for cloud-hosted big data services, any product had to meet our clear acceptance criteria:

1. Cost: Dataproc is not particularly expensive compared to similar alternatives, but our discount with the existing managed Hadoop partner made it expensive. We have initially accepted that the cost would remain the same. We did see cost benefits post migration, after several rounds of optimizations.
2. Features: Some key features (compared to current state) that we were looking for are built-in autoscaler, ease of creating/updating/deleting clusters, managed big data technologies etc.
3. Integration with GCP: As we had already decided to move other LiveRamp-owned services to GCP, a big data platform with robust integration with GCP was a must. Basically, we’d like to be able to leverage GCP features without a lot of effort on our end (custom vms, preemptible vms, etc).
4. Performance: Cluster creation, deletion, scale up, and scale down should be fast. This will allow teams to iterate and react quickly. These are some rough estimates of how fast the cluster operations should be:

• Cluster creation: <15 minutes
• Cluster Deletion: <15 minutes
• Adding 50 nodes: <20 minutes
• Removing 200 nodes: <10 minutes

5. Reliability: Bug free and low downtime software that has concrete SLAs on clusters and a strong commitment to the correct functioning of all of its features.

An initial prototype to better understand Dataproc and Google Cloud helped us prove that target technologies and architecture will give us reliability and cost improvements. This also fed into our decisions around target architecture. This was then reviewed by the Google team before we embarked on the migration journey.

Overall migration

Terraform module

Our ultimate goal is to create self-service tooling that allows our data engineers to deploy infrastructure as easily and safely as possible. After defining some best practices around cluster creation and configuration, the central team’s first step was to build a terraform module that can be used by all the teams to create their own clusters. This module will create a dataproc cluster along with all supporting buckets, pods and datadog monitors:

• A dataproc cluster autoscaling policy that can be customized
• A dataproc cluster with LiveRamp defaults preconfigured
• Sidecar applications for recording job metrics from the job history server and for monitoring the cluster health
• Pre configured datadog cluster health monitors for alerting

This Terraform module is also composed of multiple supporting modules underneath. This allows users to call the supporting modules directly in your project terraform as well if such a need arises. The module can be used to create a cluster by just setting the parameters like project id, path to application source (Spark or Map/Reduce), subnet, VM instance type, auto scaling policy etc.

Workload migration

Based on our analysis of Dataproc, discussions with GCP team and the POC, we used following criteria:

• We prioritized applications that can use preemptibles to achieve cost parity to our existing workloads
• We prioritized some of our smaller workloads initially to build momentum within the organization. For example, we left the single workload that accounted for ~40% of our overall batch volume to the end, after we had gained enough experience as an organization.
• We combined the migration to Spark along with the migration to Dataproc. This has initially resulted in some extra dev work but helped reduce the effort for testing and other activities.

Our initial approach was to lift and shift from existing managed providers and Map/Reduce to Dataproc and Spark. We then later focused on optimizing the workloads for cost and reliability.

What’s working well

Cost Attribution

As is true with any business, it’s important to know where your cost centers are. Moving from a single cluster, made opaque by the number of teams loading work onto it, to GCP’s Organization/Project structure has made cost reporting very simple. The tool breaks down cost by project, but also allows us to attribute cost to a single cluster via tagging. As we sometimes deploy a single application to a cluster, this helps us to make strategic decisions on cost optimizations at an application level very easily.

Flexibility

The programmatic nature of deploying Hadoop clusters in a cloud like GCP dramatically reduces the time and effort involved in making infrastructure changes. LiveRamp’s use of a self-service Terraform module means that a data engineering team can very quickly iterate on cluster configurations. This allows a team to create a cluster that is best for their application while also adhering to our security and health monitoring standards. We also get all the benefits of infrastructure as code: highly complicated infrastructure state is version controlled and can be easily recreated and modified in a safe way.

Support

When our teams face issues with services that run on Dataproc, the GCP team is always quick to respond. They work very closely with LiveRamp to develop new features for our needs. They proactively provide LiveRamp with preview access to new features that help LiveRamp to stay ahead of the curve in the Data Industry.

Cost Savings

We have achieved around 30% cost savings in certain clusters by achieving the right balance between on-demand and PVMs. The cost savings were a result of our engineers building efficient A/B testing frameworks that helped us run the clusters/jobs in several configurations to arrive at the most reliable, maintainable and cost efficient configuration. Also, one of the applications is now 10x + faster.

Five lessons learned

Migration was a successful exercise that took about six months to complete, across all our teams and applications. While many aspects went really well, we also learned a few things along the way that we hope will help you when planning your own migration journey.

1. Benchmark, benchmark, benchmark

It’s always a good idea to benchmark the current platform against the future platform to compare costs and performance. On-premises environments have a fixed capacity, while cloud platforms can scale to meet workload needs. Therefore, it’s essential to ensure that the current behavior of the key workload is clearly understood before the migration.

2. Focus on one thing at a time

We initially focused on reliability while remaining cost-neutral during the migration process, and then focused on cost optimization post-migration. Google teams were very helpful and instrumental in identifying cost optimization opportunities.

3. Be aware of alpha and beta products

Although there usually aren’t any guarantees of a final feature set when it comes to pre-released products, you can still get a sense of their stability and create a partnership if you have a specific use case. In our specific use case, Enhanced Flexibility Mode was in alpha stage in April 2019, beta in August 2020, and released in July 2021. Therefore, it was helpful to check in on the product offering and understand its level of stability so we could carry out risk analysis and decide when we felt comfortable adopting it.

4. Think about quotas

Our Dataproc clusters could support much higher node counts than was possible with our previous vendor. This meant we often had to increase IP space and change quotas, especially as we tried out new VM and disk configurations.

5. Preemptable and committed use discounts (CUDs)

CUDs make compute less expensive while preemptables make compute significantly less expensive. However, preemptibles don’t count against your CUD purchases, so make sure you understand the impact on your CUD utilization when you start to migrate to preemptables.

We hope these lessons will help you in your Data Cloud journey.

Customers tell us that sharing and exchanging data with other organizations is a critical element of their analytics strategy, but it’s hamstrung by unreliable data and processes, and only getting harder with security threats and privacy regulations on the rise. 

Furthermore, traditional data sharing techniques use batch data pipelines that are expensive to run, create late arriving data, and can break with any changes to the source data. They also create multiple copies of data, which brings unnecessary costs and can bypass data governance processes. These techniques do not offer features for data monetization, such as managing subscriptions and entitlements. Altogether, these challenges mean that organizations are unable to realize the full potential of transforming their business with shared data.

To address these limitations, we are introducing Analytics Hub, a new fully managed service, available in Q3, in preview, that helps you unlock the value of data sharing, leading to new insights and increased business value. With Analytics Hub you get:

• A rich data ecosystem by publishing and subscribing to analytics-ready datasets. 
• Control and monitoring over how your data is being used, because data is shared in one place.
• A self-service way to access valuable and trusted data assets, including data provided by Google. For example, a unique dataset from Google Search Trends will be available, that you can query and combine with your own data.
• An easy way to monetize your data assets without the overhead of building and managing the infrastructure. 

Built on a decade of cross-organizational sharing

While Analytics Hub is a new service, it builds on BigQuery, Google’s petabyte-scale, serverless cloud data warehouse. BigQuery’s unique architecture provides separation between compute and storage, enabling data publishers to share data with as many subscribers as you want without having to make multiple copies of your data. With BigQuery, there are no servers to deploy or manage, which means that data consumers get immediate value from shared data. Data can be provided and consumed in real-time using the streaming capabilities of BigQuery and you can leverage the built in machine learning, geospatial, and natural language capabilities of BigQuery or take advantage of the native business intelligence support with tools like Looker, Google Sheets, and Data Studio.

BigQuery has had cross-organizational, in-place data sharing capabilities since it was introduced in 2010. We took a look at usage metrics in BigQuery and found that over a 7 day period in April, we had over 3,000 different organizations sharing over 200 petabytes of data. These numbers don’t include data sharing between departments within the same organization.

As you can see, data sharing in BigQuery is already popular. But we want to make it easier and even more scalable.

Raising the bar on data sharing 

To make data sharing easier and more scalable in BigQuery, Analytics Hub introduces the  concepts of shared datasets and exchanges. As a data publisher, you create shared datasets that contain the views of data that you want to deliver to your subscribers. Next, you create exchanges, which are used to organize and secure shared datasets. By default, exchanges are completely private, which means that only the users and groups that you give access to can view or subscribe to the data. You can also create internal exchanges or leverage public exchanges provided by Google. Finally, you publish shared datasets into an exchange to make them available to subscribers. 

Data subscribers search through the datasets that are available across all exchanges for which they have access and subscribe to relevant datasets. This creates a linked dataset in their project that they can query and join with their own data. Subscribers pay for the queries that they run against the data while the publisher pays for the storage of the data. Data providers can add new data, new tables, or new columns to the shared dataset and these will be immediately available to subscribers. In addition, the publisher can track subscribers, disable subscriptions, and see aggregated usage information for the shared data. 

Analytics Hub makes it easy for you to publish, discover, and subscribe to valuable datasets that you can combine with your own data to derive unique insights. Here are some types of data that will be available through Analytics Hub:

• Public datasets: Easy access to the existing repository of over 200 public datasets, including data about weather and climate, cryptocurrency, healthcare and life sciences, and transportation. 
• Google datasets: Unique, freely-available datasets from Google. One example of this is the COVID-19 community mobility dataset. Another example is the forthcoming Google Trends dataset, which will provide the top 25 search terms and top 25 rising search terms over a 5 year window in 210 distinct locations in the US. Trends data can be used by everyone in the organization to gain insights into what customers care about.
• Commercial (paid for) datasets: We are working with leading commercial data providers to bring their data products to Analytics Hub. If you are interested in delivering your data via Analytics Hub, we’re also introducing Data Gravity, an initiative that provides storage benefits and new distribution paths for data published through Analytics Hub. 
• Internal datasets: We know that data sharing can be challenging in larger organizations. Analytics Hub can be used for internal data, for example, to share standardized customer demographics with your sales engineering and data science teams.

Customers and partners using Analytics Hub

“Google Search Trends data has always been an important tool for our WPP agency data teams. At WPP we believe that data variety is a superpower which is why we are excited to use the new Trends dataset availability within BigQuery, plus the launch of Analytics Hub. The best creativity in the world is informed by data insights, and influenced by what people search for, so the operational efficiencies we’ll gain via the Analytics Hub and the insights we can drive with Trends data are just phenomenal.”
—Di Mayze Global Head of Data and AI, WPP

“Equifax Ignite is our shared data analytics environment within our Equifax data fabric. We are excited to partner with Google to leverage Analytics Hub and BigQuery to deliver data to over 400 statisticians and data modelers as well as securely sharing data with our partner financial institutions.”
—Kumar Menon, SVP Data Fabric and Decision Science, Equifax

“The flow of data and insights between our teams at Deloitte and our clients is paramount for building truly transformational data cultures. With its purpose-built architecture for secure data exchanges and sharing analytics resources, Google Cloud’s Analytics Hub can help provide significant operational efficiencies for how Deloitte teams support our clients’ data-driven initiatives within their industry ecosystems. It will also help minimize the worries about scale, privacy and security, or the administrative burden associated with each.”
—Navin Warerkar, Managing Director, Deloitte Consulting LLP, and US Google Cloud Data & Analytics GTM Lead

“Crux Informatics is proud to partner with Google to support the launch of Analytics Hub, removing friction for those who need access to analytics-ready data. With thousands of datasets from over 140 sources, Crux Informatics will accelerate access to data on Analytics Hub and together provide a more efficient and cost effective solution to deliver datasets in Google Cloud’s ecosystem.”
—Will Freiberg, CEO, Crux Informatics

Next steps for Analytics Hub

This is just the beginning for Analytics Hub. As we get to preview and general availability, we will be adding additional capabilities, including workflows for publishing and subscribing, publishing analytics assets (Looker Blocks, Data Studio reports, Connected Google Sheets) along with the shared data, the ability for data publishers to specify query restrictions on the usage of their data, and making it easy for data publishers to create sandbox environments for subscribers to work with their data, even if they are not yet on Google Cloud. We will provide features in Analytics Hub for monetization of data, including managing subscriptions, data entitlements, and billing.

Please sign up for the preview, which is scheduled to be available in the third quarter of 2021. In the meantime, you can learn more about BigQuery and how to leverage its built-in data sharing capabilities. Please go to g.co/cloud/analytics-hub to register your interest in Analytics Hub.

We’re all accustomed to Google magic in our daily lives. When we need information like the time a store closes, the best route to a destination, help cooking a favorite recipe — we just ask Google. Google is known for providing useful information everywhere, wherever you are.

But for business, it isn’t so easy to get answers to important questions. In a recent study by HBR, only 30% of respondents report having a well-articulated data strategy and, at most, 24% of respondents said they thought their organization was data-driven. If you asked, most of these executives would probably tell you they’re drowning in data, so how come they’re struggling to be data-driven?

Data’s journey begins in operational databases, where the data is born as users and systems interact with applications. Operational databases are the backbone of applications and are essential ingredients for building innovative customer experiences. At Google Cloud, we have been focused on building a unified and open platform that provides you the easiest and fastest way to innovate with data. Google’s data cloud enables data teams to manage each stage of the data lifecycle from operational transactions in databases to analytical applications across data warehouses, lakes, data marts, and to rich data-driven experiences.

For example, Merpay, a mobile payment service in Japan, wanted to build its experience on a database that delivered on requirements around availability, scalability and performance. By using Cloud Spanner, Merpay was able to reduce overhead costs and ultimately devote engineering resources to developing new tools and solutions for their customers.

If you’re looking to bring the same kinds of transformation to your organization, let’s dive into the latest database announcements and how they can help you do that:

Tap into transformative database capabilities

Google’s data cloud allows organizations to build transformative applications quickly with our one-of-a-kind databases such as Cloud Spanner, Google Cloud’s fully managed relational database with global scale, strong consistency, and industry leading 99.999% availability. Leading businesses across industries such as Sabre, Vimeo, Uber, ShareChat, Niantic, and Macy‘s use Spanner to power their mission-critical applications and delight customers. Recent innovations in Spanner such as granular instance sizing and PostgreSQL interface lower the barrier to entry for Spanner, thus making it accessible for any developer and for any application, big or small.

In that effort, we are excited to announce Spanner free trial instances to give developers an easy way to explore Spanner at no cost. The new free trial for Spanner provides a zero-cost, hands–on experience to try out Spanner and comes with guided tutorials and a sample database. The Spanner free trial provides a Spanner instance with 10GB storage for 90 days. Customers can upgrade to a paid instance anytime during the 90-day period to continue exploring Spanner and unlock the full capabilities of Spanner, such as unlimited scaling and multi-region deployments. To learn more about the new free trial, you can read this blog and watch this short video. Start your free trial today in the Google Cloud console.

We’re also excited to announce the preview of fine-grained access control for Spanner that lets you authorize access to Spanner data at the table and column level. Spanner already provides access control with Identity and Access Management (IAM) which enables a simple and consistent access control interface for all Google Cloud services. With fine-grained access control, it’s now easier than ever to protect your transactional data in Spanner and ensure appropriate security controls are in place when granting access to data. Learn more about fine-grained access control in this blog.

Generate exponential value from data with a unified data cloud

Google’s common infrastructure for data management has many advantages. Thanks to our highly durable distributed file systems, disaggregated compute and storage at every layer, and high-performance Google-owned global networking, we’re able to provide best-in-class, tightly integrated operational and analytical data services with superior availability and scale at the best price- performance and operational efficiency.

In that effort, today we’re excited to announce Datastream for BigQuery, now in preview. Developed in close partnership with the BigQuery team, Datastream for BigQuery delivers a unique, truly seamless and easy-to-use experience that enables real-time insights in BigQuery with just a few steps.

Datastream efficiently replicates updates directly from source systems into BigQuery tables in real-time by using BigQuery’s newly developed Change Data Capture (CDC) and Storage Write API’s UPSERT functionality. You no longer have to waste valuable resources building and managing complex data pipelines, self-managing staging tables, tricky DML merge logic, or manual conversion from database-specific data types into BigQuery data types. Just configure your source database, connection type, and destination in BigQuery and you’re all set.

Klook, a Hong Kong-based travel company, is one industry-leading enterprise using Datastream for BigQuery to help drive better business decisions. “Prior to adopting Datastream, we had a team of data engineers dedicated to the task of ingesting data into BigQuery, and we spent a lot of time and effort making sure that the data was accurate,” said Stacy Zhu, senior manager for data at Klook. “With Datastream, our data analysts can have accurate data readily available to them in BigQuery with a simple click. We enjoy Datastream’s ease of use and its performance helps us achieve large scale ELT data processing.”

Achievers, an award-winning employee engagement software and platform, recently adopted Datastream, as well. “Achievers had been heavily using Google Cloud VMs (GCE), and Google Kubernetes Engine (GKE)” says Daljeet Saini, lead data architect at Achievers. “With the help of Datastream, Achievers will be streaming data into BigQuery and enabling our analysts and data scientists to start using BigQuery for smart analytics, helping us take the data warehouse to the next level.”

To make it easier to stream database changes to BigQuery and other destinations, Datastream is also adding support for PostgreSQL databases as a source, also in preview. Datastream sources now include MySQL, PostgreSQL, AlloyDB for PostgreSQL, and Oracle databases, which can be hosted on premises, on Google Cloud services such as Cloud SQL or Bare Metal Solution for Oracle, or anywhere else on any cloud.

Key use cases for Datastream include real-time analytics, database replication via continuous change data capture, and enablement of event-driven application architectures. In real-world terms, real-time insights can help a call center provide better service by measuring call wait times continuously, rather than retrospectively at the end of the week or month. And retailers or logistics companies that do inventory management based on real-time data can become far less wasteful than if it were based on periodic reports.

Accelerate your modernization journey to the cloud

In recent years, application developers and IT decision makers have increasingly adopted open-source databases to ensure application portability and extensibility and prevent lock-in. They’re no longer willing to tolerate the opaque costs or the overpriced and restrictive licensing of legacy database vendors.

In particular, PostgreSQL has become the emerging standard for cloud-based enterprise applications. Many organizations are choosing to standardize on PostgreSQL to reduce the learning curve for their teams and avoid the lock-in from the previous generation of databases. Google’s data cloud offers several PostgreSQL database options including AlloyDB for PostgreSQL, a PostgreSQL-compatible database that provides a powerful option for migrating, modernizing, or building commercial-grade workloads

To help developers and data engineers modernize and migrate their applications and databases to the cloud, we’re pleased to announce, in preview, that our Database Migration Service (DMS) now supports PostgreSQL migrations to AlloyDB. DMS makes the journey from PostgreSQL to AlloyDB easier and faster, with a serverless migration you can set up in just a few clicks. Together with Oracle to PostgreSQL data and schema migration support, also in preview, DMS gives you a way to modernize legacy databases and adopt a modern, open technology platform. Learn more about PostgreSQL to AlloyDB migration with DMS in this blog.

Among organizations adopting PostgreSQL is SenseData. A platform created to improve the relationship between companies and customers, SenseData is a market leader in Latin America in the field of Customer Success.

“At Sensedata, we’ve built our customer success platform on PostgreSQL, and are looking to increase our platform performance and scale for the next phase of our growth,” said Paulo Souza, co-founder and CTO of SenseData. “We have a mixed database workload, requiring both fast transactional performance and powerful analytical processing capabilities, and our initial testing of AlloyDB for PostgreSQL has given impressive results, with more than a 350% performance improvement in our initial workload, without any application changes. We’re looking forward to using Database Migration Service for an easy migration of multiple terabytes of data to AlloyDB.”

CURA Grupo is one of Brazil’s largest medical diagnostics conglomerates with over 1,600 employees, 500 qualified physicians, and 6 million examinations conducted every year. With more than 30,000 examinations performed every day, storing the database on premises was becoming increasingly unfeasible. CURA Grupo used Database Migration Service to migrate to Google Cloud, along with synchronization between their on-prem server’s database and the cloud. The result was an easy transition with just around 20 minutes of downtime.

To learn more about these exciting innovations and more, join us at Next 22 happening on October 11-13. You will also hear from customers such as PLAID, Major League Baseball, DaVita, CERC, Credit Karma, Box, and Forbes who are all innovating faster with Google Cloud databases.

Meesho is an Indian online marketplace that serves millions of customers every day. Recently, the company decided to adopt a multi-cloud strategy, leveraging Google Cloud’s scalable and reliable infrastructure to drive operational efficiency, modernize and scale for growth. To do so, they needed to migrate billions of static files and images to Google Cloud, to render the static content that serves their web and mobile applications. But with over a petabyte of data in their object storage system, and 10 billion requests per day, Meesho needed to perform this gigantic migration gradually, with zero downtime — a huge challenge. 

In this blog post, we look at how Meesho did this using Storage Transfer Service, Cloud Storage and Cloud CDN. We also look at how it saved on storage capacity by resizing static images as needed on the fly, using Cloud Run. 

CDN migration requirements

Migrating from one cloud to another isn’t easy. To pull it off, Meesho identified the following requirements: 

• Petabyte-scale data transfer: Meesho needed to migrate billions of image files from their existing object storage server to Cloud Storage.
• Dynamic image resizing: To save on storage costs, Meesho wanted the ability to dynamically resize the images based on the end user platform and store the smaller images in the Cloud CDN cache.
• High-throughput data transfer: To support consumer demand, Meesho needed images to be served at a throughput of thousands of requests per second.
• Zero downtime: Since any downtime involves potential loss of revenue, Meesho needed to perform the migration without taking any systems offline. 

Migration architecture

The above figure depicts the CDN migration architecture implemented in Meesho. The existing DNS server points to both the source load balancer as well as Google External HTTP Load Balancer with weighted distribution. The source load balancer points to the source object storage. Images were transferred from the source object storage to Google Cloud Storage.

The Google External HTTP Load Balancer was deployed with Cloud CDN to serve static images that are stored in the CDN cache to users. The Google Load Balancer public IP is configured as an end point on their existing DNS server. The Load Balancer is connected to Cloud Run, which talks to the Cloud Storage bucket. When a request reaches the Load Balancer in the edge, it first checks if the content is available in Cloud CDN, and returns the object from the closest edge network. If the image is not available in the Cloud CDN cache, the request is sent to Cloud Run which obtains the image from the Cloud Storage bucket and performs dynamic resizing of the image if necessary.

Data transfer

Meesho used Google Cloud’s Storage Transfer Service to transfer data from their current object storage to Cloud storage bucket over the internet. Since the number of files and total size of the data to be transferred was huge, Meesho executed multiple parallel transfers by specifying folders and subfolders as prefixes in a Storage Transfer Service job.

Dynamic image resizing

Meesho delivers static images to multiple end user platforms — mobile, laptop — at multiple resolutions. Rather than store each image at multiple image resolutions, Meesho opted to store a single high-resolution mezzanine image. It then attached Cloud Run as a serverless network endpoint group to a Cloud Load Balancer. Application requests for images specify the name of the object, the format of the image, and its resolution (for example, abc.jpeg with 750*450 resolution). If the specific image exists for the requested resolution, then it is returned from the Cloud Storage bucket to the end user and stored in the Cloud CDN cache. If an image for a specified resolution and/or format is not found, the mezzanine image (in our example, abc.jpeg) is resized to the specified resolution and format, then stored in Cloud Storage bucket and returned to the end user. The dynamic resizing and formatting is only performed the first time for a specific resolution. 

In this architecture, it is important to configure Cloud Run to scale appropriately as it handles a bulk of “CDN cache-miss” requests. Meesho performed the following configuration steps:

• Configured the number of concurrent requests that a single instance of Cloud Run can handle 
• Ensured a sufficient minimum of Cloud Run instances were available to serve user traffic to avoid cold-start latency
• Reviewed limits of Cloud Run maximum instance size for the region and increased the limits if necessary to handle peak load 
• Set up smaller start-up times for Cloud Run containers, so that the application could quickly autoscale to handle a surge in traffic
• Optimized the memory and CPU configuration to handle processing requirements

CDN configuration

Cloud CDN was configured to ensure a high cache hit ratio > 99 %. This not only sped up the rendering of the images, but also reduced the load on Cloud Run, saving cost and improving performance.

Achieving zero downtime

Meesho followed well-established DevOps principles to achieve a zero-downtime migration: 

• Metrics and alerts were configured in Cloud Monitoring to oversee the load balancer.
• The DNS server was configured to point to Cloud Load Balancer IP addresses in addition to their current load balancer, which served status assets. 
• Weight-based DNS load balancing was employed to gradually shift the traffic to Google Cloud, while monitoring application performance and HTTP response codes. 
• The initial migration process distributed .1% of traffic during non-peak hours. The metrics, end user performance and response codes were continuously monitored.
• Traffic was gradually incremented over a two-week period by increasing the weight of the Google Cloud Load balancer in DNS. By gradually shifting traffic, Meesho ensured a healthy cache-hit ratio, allowing Cloud Run to learn traffic patterns gradually and scale seamlessly.

Meesho learned a lot through this experience, and has the following advice for anyone undertaking a similar migration:

• While transferring data using Storage Transfer Service of Google Cloud, split the transfer process into multiple transfers.
• Ensure that applications do not pin certificates, which could create problems while migrating to the newer certificates in Google Cloud.
• Plan a gradual migration process to gradually increase the traffic to Google Cloud. 

Summary

When all is said and done, Meesho considers its migration to Google Cloud a big success. After migrating the static images to Cloud CDN, Meesho held two major sales that each had three times the normal peak traffic, all with no issues. The CDN migration helped Meesho reduce its costs, improve performance and reduce load balancer errors when fetching static images. To learn more about Cloud CDN and how you can use it in your environment, check out the documentation.