Google Announces Cloud Functions’ Native Integration with Secret Manager
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Google Cloud Functions provides a simple and intuitive developer experience to execute code from Google Cloud, Firebase, Google Assistant, or any web, mobile, or backend application. Oftentimes that code needs secrets—like API keys, passwords, or certificates—to authenticate to or invoke upstream APIs and services.
While Google Secret Manager is a fully-managed, secure, and convenient storage system for such secrets, developers have historically leveraged environment variables or the filesystem for managing secrets in Cloud Functions. This was largely because integrating with Secret Manager required developers to write custom code… until now. We listened to customer feedback and today we are announcing Cloud Functions has a native integration with Secret Manager!
This native integration has many key benefits including:
- Zero required code changes. Cloud functions that already consume secrets via environment variables or files bundled with the source upload simply require an additional flag during deployment. The Cloud Functions service resolves and injects the secrets at runtime and the plaintext values are only visible inside the process.
- Easy environment separation. It’s easy to use the same codebase across multiple environments, e.g., dev, staging, and prod, because the secrets are decoupled from the code and are resolved at runtime.
- Supports the 12-factor app pattern. Because secrets can be injected into environment variables at runtime, the native integration supports the 12-factor pattern while providing stronger security guarantees.
- Centralized secret storage, access, and auditing. Leveraging Secret Manager as the centralized secrets management solution enables easy management of access controls, auditing, and access logs.
Cloud Functions’ native integration with Secret Manager is available in preview to all Google Cloud customers today. Let’s take a deeper dive into this new integration.
Example
Suppose the following cloud function invoked via HTTP uses a secret token to invoke an upstream API:
const https = require('https');const token = process.env.TOKEN;exports.secretDemo = (req, res) => {https.get(`https://upstream-api.example.com?token=${token}`, (innerRes) => {innerRes.on('end', () => { res.send('OK') });}).on('error', (err) => {res.send(`Error: ${err}`);});}
Without Cloud Functions’ native integration with Secret Manager, this function is deployed via:
$ gcloud functions deploy "secretDemo" \--runtime "nodejs14" \--trigger-http \--allow-unauthenticated \--set-env-vars "TOKEN=abcd1234"
This approach has a number of drawbacks, the biggest of which being that anyone with viewer permissions on the Google Cloud project can see the environment variables set on a cloud function.
To improve the security of this code, migrate the secret to Secret Manager in the same project:
$ gcloud secrets create "token" \--replication-policy "automatic" \--data-file - <<< "abcd1234"
Finally, without changing any code in the function re-deploy with slightly different flags:
$ gcloud beta functions deploy "secretDemo" \--runtime "nodejs14" \--trigger-http \--allow-unauthenticated \--set-secrets "TOKEN=my_token:latest"
It’s truly that easy to migrate from hard-coded secrets to using secure secret storage with Secret Manager! To add even more layers of security, consider running each cloud function with a dedicated service account and practice the principle of least privilege. Learn more in the Secret Manager Best Practices guide.
Making security easy
Security and proper secrets management are core pillars of modern software development, and we’re excited to provide customers a way to improve the security of their cloud functions. To learn more about the new native integration, check out the Cloud Functions documentation. You can also learn more about Cloud Functions or learn more about Secret Manager.
The Future of Cloud Computing: Choose Your Own Services and Payment Options
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As the saying goes, “it’s hard to make predictions, especially about the future.” Some organizations find it challenging to predict what cloud resources they’ll need in months or years ahead. Every organization is on its own unique cloud journey. To help, we’re developing new ways for customers to consume and pay for Google Cloud services. We’re doing this by removing barriers to entry, aligning cost to consumption and providing contractual and product flexibility. Read on to learn how we’re rolling out several new go-to-market programs across these key areas to help our customers purchase and consume Google Cloud services more easily.
Removing barriers to entry with Google Cloud Flex Agreements
Many customers choose multi-year commitments because they provide better line-of-sight into IT spend and budgeting. However, these commitments can create difficulty for those who don’t have clear visibility into their future cloud consumption needs. That’s why today we’re launching Flex Agreements, which enable customers to migrate their workloads to the cloud with no up-front commitments. As part of this new licensing option, Google Cloud customers still get access to unique incentives, such as monthly spend discounts1, committed use discounts, cloud credits, and access to professional services, based on monthly spend and workloads migrated to Google Cloud.
Flex Agreements are just one example of how we are removing barriers to help customers start using Google Cloud. In 2022, we launched the Innovators Plus annual subscription, which gives developers a curated toolkit to accelerate their expertise, including access to live and on-demand training through Google Cloud Skills Boost, Google Cloud credits, and more.
We also recently expanded trials for Google Cloud products. For example, the new Spanner free trial instance is good for 90 days, allowing developers to create Google Standard SQL or PostgreSQL databases, explore Spanner capabilities, and prototype applications—with no commitment or contract needed.
Contractual and feature flexibility
Contractual flexibility has always been one of our core principles. Committed Use Discounts (CUDs), for example, provide discounted prices in exchange for a commitment to use a minimum level of resources for a specified term. Last year, we introduced Flexible CUD, spend-based commitments that offer predictable and simple flat-rate discounts that apply across multiple virtual machine families and regions.
In addition to contractual flexibility, our customers also need the flexibility to choose features and functionality based on their stages of cloud adoption and the complexity of their business requirements. Therefore, over the next few quarters, we will launch new product pricing editions—Standard, Enterprise, and Enterprise Plus—in parts of our cloud portfolio. This new commercial packaging model will help give customers more choice and flexibility to optimize their cloud spend.
For customers running workloads such as those in regulated industries like banking and public sector, the higher-end Enterprise Plus tier will offer compute, storage, networking and analytics services with high availability, multi-region support, regional failover and disaster recovery, advanced security, and a broad range of regulatory compliance support. The Enterprise pricing tier will include a broad range of features designed for customers with workloads that demand a high level of scalability, flexibility, and reliability. The Standard pricing tier will offer cost-efficient and easy-to-use managed services that include all essential capabilities such as autoscaling to meet the core workload requirements of customers.
Align costs to consumption with autoscaling
At Google Cloud, a core requirement for the products we build is providing customers industry-leading capabilities to automatically scale (autoscale) services up and down to match capacity with real-time demand. Autoscaling improves uptime, reduces infrastructure costs, and removes the operational burden of managing resources.
Many Google Cloud products include autoscaling capabilities to help customers manage unplanned variations in demand. For example, Dataflow vertical and horizontal autoscaling, in combination with granular adaptive resource configuration (aka “right-fitting”), has resulted in up to 50% saving in infrastructure costs for streaming by automatically choosing the right number of instances required to run the jobs and dynamically re-allocating more or fewer instances during the runtime of jobs. Bigtable also provides native autoscaling capabilities, and Spanner’s autoscale is an open source tool that works across regional and multi-regional Spanner deployments.
Similarly, we added multiple features such as Cluster Autoscaler, Horizontal Pod Autoscaling, Vertical Pod Autoscaling, and Node Auto-Provisioning to GKE for elasticity and cost efficiency.
For L.L.Bean, the ability to quickly scale capacity to meet changing usage patterns (e.g., during the holidays), as well as to rapidly perform load tests to test capacity, are “night and day” with Google Cloud compared to L.L.Bean’s legacy on-premises IT system.
“We won’t have to pay for peak capacity to have it available during peak shopping times. We just scale capacity up or down as needed.” — Randy Dyer, Enterprise Architect, L.L.Bean
We are now taking these capabilities to the next level by enabling autoscaling in BigQuery at a more granular level so you never pay more than what you use. This allows you to provision additional capacity in smaller increments, so you never overprovision and overpay for underutilized capacity. BigQuery customers can now try the new BigQuery autoscaler (currently in public preview) in their Google Cloud console.
A commitment to flexibility and choice
At Google Cloud, we remain deeply committed to the success of our customers and partners, and we are uniquely positioned to help organizations transform their business. By providing you with more flexibility and choice in how to purchase our products, we are empowering you to be more efficient and resilient.
Join Google Data Cloud & AI Summit to hear the latest announcements around innovations in Google Data Cloud for databases, data analytics, business intelligence, and AI. Gain expert insights, new solutions, and strategies that can help you transform customer experiences with modern apps, boost revenue, and reduce costs.
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Cloud Bigtable brings database stability and performance to Precognitive
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At Precognitive, we were able to start with a blank technology slate to support our fraud detection software products. When we started building the initial version of our platform in 2017, we had some decisions to make: What coding language to use? What cloud infrastructure provider to choose? What database to use? The majority of the decisions were straightforward, but we struggled to decide upon a database. We had plenty of collective experience with relational databases, but not with a wide-column database like Cloud Bigtable—which we knew we’d need to scale our behavior and device workloads. At launch, our products were supported by a self-managed database, but we quickly migrated to Cloud Bigtable, and we love it.
To efficiently support our bursty, real-time fraud detection workloads, we needed a cloud database that could satisfy the following key requirements:
- Stability to keep up with increased adoption of our products
- Intelligent scaling that avoids bottlenecks
- Native integrations with BigQuery and Cloud Dataproc
- Managed services that free up our engineers’ time to work on our products
Adding Cloud Bigtable as our performance database
As we scaled our services and added customers, our data collection services for our Device Intelligence and Behavioral Analytics products were seeing thousands of events per second. Cloud Bigtable provided a stable managed database that could handle the volume we were receiving during peak hours. We weren’t always able to handle this scale, as an early version of our product utilized a self-managed database.
Every month, two or three engineers spent hours managing the database instances. Whenever the instances crashed, it would cost at least one engineer a day or two of productivity attempting to restore the instances and recovering any data from our backup database. Managing this database internally was taking precious time away from product development.
We circled back to Cloud Bigtable. After two weeks of R&D, we decided to switch the Device Intelligence and Behavioral Analytics services to Cloud Bigtable.
Cloud Bigtable solved our scaling issues. Cloud Bigtable had been attractive to us from the start because it was fully managed, and offered regional replication and other features we were lacking in our own managed instances. Cloud Bigtable provides horizontal scaling and automatically rebalances row keys (equivalent to a shard key) over time to prevent “hot” nodes. In addition, Cloud Bigtable provides a connector to BigQuery and Cloud Dataproc that allows us to analyze the terabytes of data we are processing and use that data for unsupervised machine learning.
The perks of using Cloud Bigtable
After the migration to Cloud Bigtable, we noticed a number of additional benefits: improved I/O performance, a significant cost reduction, and a sizable decrease in hours spent on database maintenance.
We measured some of our typical metrics before and after implementing Cloud Bigtable. Our request latency dropped by about 30 ms on average (to sub-10 ms) for API requests. Prior to the change, we were seeing latencies of 40+ ms on average. This latency drop on our Behavioral Analytics and Device Intelligence products allowed us to trim about an additional 10 to 15 ms off our average response time across all dependent services.
Before we moved to Cloud Bigtable, we had to scale our database instances every time a new customer was onboarded. We were over-scaling in an attempt to avoid constantly resizing our database servers. By sunsetting our self-managed database and switching to Cloud Bigtable, we cut database infrastructure costs by approximately 35% and can now scale as needed, with a couple of clicks, during onboarding.
We have spent zero hours managing a Cloud Bigtable database since launch, and we put the time we are saving every month toward product development.
Moving forward with Cloud Bigtable
As an engineering team, we love working with Cloud Bigtable. We are not only seeing improved developer experience and reduced latency, which keeps the engineers happy, but also reduced costs, which keeps the business happy. We’re able to build more product, too, with the time we’ve saved by switching to Cloud Bigtable. Stay tuned to our engineering blog for more on the lessons we’ve learned and our contributions to the wider Cloud Bigtable community.
Innovation in the Clouds: Sky’s Blue-Sky Approach to FinOps
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Google Cloud’s partnership with Sky Group, one of Europe’s largest media and entertainment companies, dates back more than four years to when Sky first became a Google Cloud customer moving diagnostic data from millions of its Sky Q TV boxes to its Google Cloud data platform.
In June 2019, a few years into their cloud adoption journey, Sky was faced with a challenge they had anticipated from the start. Their recent bill across all major cloud providers had been increasing rapidly, reaching their planned yearly budget after only six months. Sky wasn’t sure if they’d undershot their forecasts, if they were overspending, or both.
“In the beginning, we were given a brief to investigate internal cloud spend with the aim of finding out where we could make savings, but in reality we didn’t know what we would expect to find,” said Nathan King, a cloud architect in the Cloud Enablement Center and now Head of Cloud Financial Management (FinOps) at Sky since the start of 2020.
Nathan assembled a small team who started to explore Google Cloud spend using the Cloud Billing tool. At first, they drilled into their biggest Google Cloud cost categories and discovered some immediate cost optimizations with BigQuery, Compute Engine and Cloud Storage. Over the course of the next six months, through careful analysis, they managed to find over $1.5m in immediate savings, exceeding expectations.
Yet they soon realized this was just the tip of the iceberg—it was clear there were millions of pounds more savings to be made, but actually achieving them at scale would require careful planning. “We formed a FinOps function to target these savings, but with 600 to 700 projects for Google Cloud alone, spanning four Google Cloud organizations, it would have been a manual process and difficult for teams to digest our recommendations,” Nathan said.
After attending a Google-led FinOps workshop and shaping their FinOps strategy, Nathan’s team focused on iterating through the FinOps lifecycle phases of Inform, Optimize, Operate and generating savings over time. Here’s how they did it:
Inform: Make Information Visible
The first step was focused on developing a clear vision for cost allocation and recharge, which required partnering closely with the finance, procurement and tax teams (particularly for international and affiliates) to understand the supporting business logic and processes. With a lot of hard work, the team managed to break down barriers to implement and embed new processes into broader business functions like finance.
WIth the recharge model in place, the team ran a number of pilots to find the right FinOps tooling to meet their needs. They ran a number of pilots, including using Data Studio and visualizing BigQuery exports. Given their ambitions to scale across the enterprise globally, the team chose Google Cloud’s Looker to realize their vision, building intuitive dashboards to visualize spend and recommendations across all cloud providers. “We wanted one view across all clouds, where customers can dynamically see cloud spend and intelligent optimization recommendations in just one place,” Nathan said.
After less than three weeks of development, the Looker dashboards were ready to go and have been a game changer ever since. “The moment our leadership and different departments started seeing the Looker dashboards, the value we were adding as a FinOps team became immediately clear,” Nathan said.
There are different report pages for each stakeholder group, each custom developed and automated using Looker and BigQuery. The BigQuery Optimization page, for example, provides insights on Slots consumed across the organization, down to granular query data like the cost of each query, how it was written, who submitted it and number of slots utilized. The dashboards also highlight potential areas of optimization, like BigQuery datasets without retention policies set or where data isn’t partitioned.
A recent breakthrough has been building pages for business teams, showing the related cloud spend contributing to a business unit of value, such as the cost per live stream or per subscriber in Sky’s case. Although this is an inherently difficult metric to capture, the opportunity has been made possible with the FinOps team’s progress and is starting to drive business investment decisions.
Optimize: Drive Cloud Efficiency
The second stage of the FinOps lifecycle focuses on delivering optimizations. As Sky’s FinOps dashboards were operationalized and highlighted savings opportunities, they enabled users to generate more than $3 million in Google Cloud savings alone in 2020 and over $800,000 in other cloud providers.
The team began with focusing on the top four products by spend: BigQuery, Compute Engine, Cloud Dataflow and Cloud Storage. Working with their Google account team and studying Google whitepapers and blog posts like Cloud cost optimization: principles for lasting success, they developed their own best practice guidance and embedded recommendations into the dashboards.
Creating their own recommenders and leveraging Google Cloud’s recommenders, the team discovered a plethora of cost optimization opportunities. “Key examples were overly expensive queries, storage buckets set without retention policies, and VMs without autoscaling enabled,” Nathan said. Teams were then empowered to make their own savings, like the NowTV business unit that had been forecast to overspend for the year until they received their dashboard with thousands of optimization recommendations. After just three weeks, the team had implemented more than 90% of recommendations and brought their spend under budget for the year, saving more than 50%.
The FinOps team still searches for new recommendations every day and have been collaborating with Google product managers to take their insights to the next level. “We’ve loved partnering with Google product managers, who encourage us to give feedback on new features before they go to market. We’ve also shared some of our in-house recommenders to influence the features being developed by Google, including the Idle VM and Idle Persistent Disk Recommenders as part of Active Assist,” Nathan said.
Operate: Embed FinOps & Drive Self-Sufficiency
Now that teams could visualize their cloud spend and make real-time decisions based on cost optimization recommendations, the FinOps team has begun working on embedding processes, leveraging machine learning, and improving efficiency in their own ways of working.
Looker’s extensive capabilities continue to play a role in this. “Before we started using Looker, our most popular report was an electricity bill showing customers’ detailed monthly cloud spend, previous month comparisons and forecasts for months ahead,” Nathan said. “This report took days, sometimes weeks to run. With Looker, we’ve automated the entire process and brought that time down to just minutes.”
More teams are embedding the dashboards into their own processes, like finance, which now uses the interactive dashboards in meetings instead of static report snapshots, or in-house Google Cloud architects, who use the recommendations to optimize their cloud spend before deploying any technology.
As the FinOps team continues to operate like a product function, designing with CX/UX in mind and iteratively releasing new features like anomaly reporting, budget alerts, and forecasting based on machine learning, it’s becoming clear that Cloud Financial Management is a key capability and mindset that can impact wide-reaching parts of the business at scale.
Elevating Sky’s FinOps journey to the next level
Indeed, as more business teams collaborate with the FinOps function, the opportunities are growing. “The FinOps team has changed the way we view and manage cloud spend, enabling us to partner with finance and show digestible reports to the CFO. We’re now looking further to broaden our range of insights, like elevating our dashboards to understand how using Google Cloud is supporting Sky’s Net carbon zero ambitions by incorporating Google’s data center sustainability metrics,” says Vince Marco, Architecture Manager at Sky.
So, after being unsure of drivers for their increasing cloud spend in 2019, 18 months later Sky is far more confident about its investment decisions. The team knows that every dollar spent is being used optimally and driving maximum value for its investment.
If you’re an enterprise using cloud, but want to better manage cloud costs, consider setting up a FinOps capability and creating a FinOps mindset. Looker can help you get started by providing reporting and insights into cloud expenditures to identify initial savings. As you learn more and scale, empower teams to make their own savings utilizing built-in actionality for monitoring and customizing for business billing activity nuances and department-specific chargebacks. Reimagine how cloud finances can be managed and optimized as Sky is doing.
To learn more about Looker’s Cloud Cost Management Block visit Looker Marketplace.
Google and NetApp Partnership Expansion to Ease Customers’ Cloud Migration
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As organizations continue to evolve to meet the needs of their stakeholders, they are increasingly looking to adopt the cloud and digitally transform their business. Since our partnership with NetApp began in 2018, we’ve been focused on delivering the infrastructure, tools, and services to help customers on their cloud migration journey and benefit from their data to better serve their customers.
“Customers are looking to adopt flexible and scalable solutions that meet the needs of their business,” said Ronen Schwartz, SVP & GM, Cloud Volumes, NetApp. “Our long-standing partnership with Google Cloud continues to deliver innovative solutions for customers as they look to migrate and run their business critical applications in the cloud while unlocking the power of their data with industry-leading cloud services.”
Innovative, industry-first solutions have become the standard for our partnership. From the general availability of NetApp Cloud Volumes Service (CVS) for Google Cloud in 2019, to our collaboration on new architectures in 2020 with the release of the software-defined CVS powered by Google Kubernetes Engine (GKE), we continue to deliver first-of-its-kind enterprise file storage services together. In 2021, the pace of innovation has only increased—together, we delivered volume replication powered by SnapMirror, which efficiently copies volume data from one Google Cloud region to another for data consistency, development, testing, or disaster recovery.
We’ve also made our services available globally—you can now run Cloud Volumes ONTAP in all 28 Google Cloud regions, and CVS is now available in 19 regions, allowing you to access our services wherever you are around the globe. All of this is backed up by a 99.99% SLA for CVS, enabling high availability for your enterprise workloads.
Hundreds of customers including MailerLite, a leader in marketing automation, have benefitted from our joint services. MailerLite was looking to move millions of files to the cloud without disruption as they were facing availability, management, and maintenance challenges as their business scaled. By leveraging NetApp CVS on Google Cloud, MailerLite was able to migrate over 100 million files to Google Cloud in about an hour, removing their ongoing infrastructure overhead, saving developer hours, and eliminating the scalability limitations they previously experienced.
Expanding our partnership
Last week at Google Cloud Next ‘21, we announced we are expanding our strategic partnership in two ways to make it even easier for customers to migrate and run business critical applications on Google Cloud. First, NetApp will provide storage infrastructure for the newly announced Google Distributed Cloud Hosted, where data resides in customer-owned data centers and colocation facilities. This enables a safe and secure way to modernize an on-premises deployment while still meeting data security and privacy requirements.
Additionally, for customers looking to migrate their VMware workloads to Google Cloud, we announced preview access for NetApp Cloud Volumes as datastores for Google Cloud VMware Engine. By providing customers with the choice to scale compute and storage independently, customers can now reduce costs and complexity when migrating and running storage-bound workloads. This also allows customers to address regional disaster recovery use cases, making it easier and more cost-effective to move on-premises workloads into Google Cloud. To get started, you can sign up for preview access using the Registration for Preview Access form.
Looking forward
We’re excited for what’s to come with our partnership and committed to delivering value to customers across industries. Google Cloud is a Gold sponsor at this week’s NetApp Insight, and we invite you to join our spotlight session featuring Google Cloud and NetApp leaders. Additionally, we are leading multiple breakout sessions where you will hear from our product team on how you can accelerate cloud adoption with Google Cloud and NetApp. You can also learn more and get started today by visiting our NetApp on Google Cloud website.
Sainsbury’s Uses AI to Figure Out How the World Eats
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Retail will forever be an industry that must constantly reinvent itself in response to, and anticipation of, ever-changing consumer demands.
Digital transformation is fueling these changes and we’ve previously spoken about how businesses including Ulta Beauty and Kohl’s are taking advantage of Google Cloud to put data at the center of what they do and deliver the best possible shopping experience and product offerings for their customers.
Leveraging Google Cloud machine learning platform, Sainsbury is able to develop predictive analytics models to spot trends and adjust inventory, providing shoppers with a better experience.
Sainsbury’s, one of Britain’s best-known supermarkets, is another great example of a business transforming the way it engages with its customers with the cloud.
With over 150 years of service, Sainsbury’s vision is to be the most trusted retailer, where people love to work and shop. It makes customers’ lives easier, by offering great quality and service at fair prices.
The food industry and the way that customers shop is rapidly changing. From foodie hashtags on Instagram, to the latest cooking fads, customers want to stay connected to the latest trends and Sainsbury’s is empowering them do that.
To help Sainsbury’s achieve this goal, its Commercial and Technology teams, in partnership with Accenture, are building cutting-edge machine learning solutions on Google Cloud Platform (GCP) to provide new insights on what customers want and the trends driving their eating habits.
With the help of Google Cloud Platform, we are generating new insights into how the world eats and lives, to help us stay ahead of market trends and provide an even better shopping experience for our customers.
–Phil Jordan, Group CIO, Sainsbury’s
Sainsbury’s solution relies on data from multiple structured and unstructured sources. Using Google Cloud’s powerful cloud-based analytics tools to ingest, clean and classify that data, and a custom-built front-end interface for internal users to seamlessly navigate through a variety of filters and categories, Sainsbury’s is able to gain advanced insights in real time.
As a result, Sainsbury’s has been able to develop predictive analytics models to spot trends and adjust inventory, providing shoppers with a better experience.
Phil Jordan, Group CIO of Sainsbury’s believes this project will have a big impact.
“The grocery market continues to change rapidly. We know our customers want high quality at great value and that finding innovative and distinctive products is increasingly important to them. With the help of Google Cloud Platform, we are generating new insights into how the world eats and lives, to help us stay ahead of market trends and provide an even better shopping experience for our customers.”
This project is also a great example of the successes Google Cloud customers have when they work with the company’s partners.
“We’re delighted to partner with Google Cloud to help the Sainsbury’s Commercial team apply predictive analytics to the identification of new and emerging trends in grocery,” says Adrian Bertschinger, Managing Director for Retail, Accenture.
“The food sector is experiencing significant, rapid disruption, and this new, cloud-based insights platform will help Sainsbury’s identify trends much earlier and adapt their product assortment in a faster, more informed way—all for the benefit of customers.”
Whatever the next food or shopping trend may be, Sainsbury’s is looking to the cloud to help them stay a step ahead.
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