opensource.google.com

Season of Docs has announced the 2019 program results for standard-length projects. You can view a list of successfully completed technical writing projects on the website along with their final project reports.

During the program, technical writers spent a few months working closely with an open source community. They brought their technical writing expertise to improve the project's documentation while the open source projects provided mentors to introduce the technical writers to open source tools, workflows, and the project's technology.

Since the first days of Google Cloud Platform (GCP), Google has been at the forefront of making your applications more observable. Beyond Stackdriver, our most visible impact in this space is OpenTelemetry, which we initiated in 2017 (as OpenCensus) and has grown into a huge community that includes the majority of APM / monitoring vendors and cloud platforms.

While OpenTelemetry allows developers to easily capture distributed traces and metrics from their own services, there’s also a need to trace requests as they propagate through components that developers don’t directly control, like managed services, load balancers, network hardware, etc. To solve this we co-defined a prototype HTTP header that these components can rely on, gathered partners, and moved the work into the W3C.

This work is now complete, and the W3C Trace Context format is now an official standard. Once implemented in GCP, this will make our services even easier to manage, both with Stackdriver and other third party distributed tracing tools. We explain more in the official post on the W3C blog, which I’ve copied below:

The W3C Distributed Tracing working group has moved the Trace Context specification to the next maturity level. The specification is already being adopted and implemented by many platforms and SDKs. This article describes the Trace Context specification and how it improves troubleshooting and monitoring of modern distributed apps.

W3C Trace Context specification defines the format for propagating distributed tracing context between services. Distributed tracing makes it easy for developers to find the causes of issues in highly-distributed microservices applications by tracking how a single interaction was processed across multiple services. Each step of a trace is correlated through an ID that is passed between services, and W3C Trace Context now defines a standard for these context propagation headers.

Until now, different tracing systems have defined their own headers. Examples include Zipkin’s B3 format and X-Google-Cloud-Trace. Adopting a common context propagation format has been long desired by developers, APM vendors, and cloud platform hosts, as compatibility provides numerous benefits:

• Web and RPC frameworks that use this standard to provide context propagation out of the box will also offer cross-service log correlation, even for developers who haven’t set up distributed tracing.
• API producers can record the trace IDs of requests from API consumers and provide additional spans or metadata to their customers for a given traced request. Producers can also correlate customer trace IDs to internal traces when debugging technical issues raised by consumers.
• Networking infrastructure (proxies, load balancers, routers, etc.) can both ensure that context propagation headers are not removed from requests passing through them, and can record spans or logs for a given trace, without having to support multiple vendor-specific formats. Potential examples of these include router appliances, cloud load balancers, and sidecar proxies like Envoy.
• Instrumentation can be further decoupled from a developer’s choice of APM vendor. For example, using both OpenTelemetry and a given vendor’s agents, a developer can instrument different services in an application, and traces will flow through the system and be processed correctly by the vendor’s backend.
• Web browsers and other clients can use these identifiers to correlate their telemetry with traces collected from backend services. This functionality is currently being defined.

To address this effort, a group of cloud providers, open source contributors, and APM vendors started defining a standard HTTP context propagation header that would replace their homegrown formats. This specification has been discussed and iterated on over the past two years, and the group working on it has grown significantly over that time. Sponsors include Google, Microsoft, Dynatrace, and New Relic (W3C members), and the group was officially moved into the W3C in 2018 for the work to proceed under the guidance of an official standards body and to spur even greater adoption.

TraceContext has since been adopted by OpenTelemetry (which enables it by default and also serves as the reference implementation), Azure services, Dynatrace, Elastic, Google Cloud Platform, Lightstep, and New Relic. We are tracking adoption in this list.

This first phase of work has focused on HTTP, as it is commonly used and has no built-in affordances for trace context propagation (gRPC and some newer RPC systems do). The same group of committee members are also working to define trace context propagation in other formats, starting with AMQP and MQTT for IoT; other upcoming topics include context propagation from clients and web browsers.

By Morgan McLean, OpenTelemetry + Stackdriver

Over the last 15 years GSoC has provided over 15,000 university students, from 109 countries, with an opportunity to hone their skills by contributing to open source projects during their summer break.

It has been over eight years since we started work on Blockly, an open source library for building drag-and-drop block coding apps. In that time, the team has grown from a single developer to a small team and a large community. Blockly is now a standard in the CS education space, used by Scratch, MakeCode, AppInventor, and hundreds of other developers to enable tens of millions of kids around the world to create and express themselves with code.

Originally posted on the Google AI Blog

Significant advances in machine learning, speech recognition, and language technologies are rapidly transforming the way in which recommender systems engage with users. As a result, collaborative interactive recommenders (CIRs)—recommender systems that engage in a deliberate sequence of interactions with a user to best meet that user's needs—have emerged as a tangible goal for online services.

Despite this, the deployment of CIRs has been limited by challenges in developing algorithms and models that reflect the qualitative characteristics of sequential user interaction. Reinforcement learning (RL) is the de facto standard ML approach for addressing sequential decision problems, and as such is a natural paradigm for modeling and optimizing sequential interaction in recommender systems. However, it remains under-investigated and under-utilized for use in CIRs in both research and practice. One major impediment is the lack of general-purpose simulation platforms for sequential recommender settings, whereas simulation has been one of the primary means for developing and evaluating RL algorithms in real-world applications like robotics.

To address this, we have developed RᴇᴄSɪᴍ (available here), a configurable platform for authoring simulation environments to facilitate the study of RL algorithms in recommender systems (and CIRs in particular). RᴇᴄSɪᴍ allows both researchers and practitioners to test the limits of existing RL methods in synthetic recommender settings. RecSim’s aim is to support simulations that mirror specific aspects of user behavior found in real recommender systems and serve as a controlled environment for developing, evaluating and comparing recommender models and algorithms, especially RL systems designed for sequential user-system interaction.

As an open-source platform, RᴇᴄSɪᴍ: (i) facilitates research at the intersection of RL and recommender systems; (ii) encourages reproducibility and model-sharing; (iii) aids the recommender-systems practitioner, interested in applying RL to rapidly test and refine models and algorithms in simulation, before incurring the potential cost (e.g., time, user impact) of live experiments; and (iv) serves as a resource for academic-industry collaboration through the release of “realistic” stylized models of user behavior without revealing user data or sensitive industry strategies.

Reinforcement Learning and Recommendation Systems

One challenge in applying RL to recommenders is that most recommender research is developed and evaluated using static datasets that do not reflect the sequential, repeated interaction a recommender has with its users. Even those with temporal extent, such as MovieLens 1M, do not (easily) support predictions about the long-term performance of novel recommender policies that differ significantly from those used to collect the data, as many of the factors that impact user choice are not recorded within the data. This makes the evaluation of even basic RL algorithms very difficult, especially when it comes to reasoning about the long-term consequences of some new recommendation policy—research shows changes in policy can have long-term, cumulative impact on user behavior. The ability to model such user behaviors in a simulated environment, and devise and test new recommendation algorithms, including those using RL, can greatly accelerate the research and development cycle for such problems.

Overview of RᴇᴄSɪᴍ

RᴇᴄSɪᴍ simulates a recommender agent’s interaction with an environment consisting of a user model, a document model and a user choice model. The agent interacts with the environment by recommending sets or lists of documents (known as slates) to users, and has access to observable features of simulated individual users and documents to make recommendations. The user model samples users from a distribution over (configurable) user features (e.g., latent features, like interests or satisfaction; observable features, like user demographic; and behavioral features, such as visit frequency or time budget). The document model samples items from a prior distribution over document features, both latent (e.g., quality) and observable (e.g., length, popularity). This prior, as all other components of RᴇᴄSɪᴍ, can be specified by the simulation developer, possibly informed (or learned) from application data.

The level of observability for both user and document features is customizable. When the agent recommends documents to a user, the response is determined by a user-choice model, which can access observable document features and all user features. Other aspects of a user’s response (e.g., time spent engaging with the recommendation) can depend on latent document features, such as document topic or quality. Once a document is consumed, the user state undergoes a transition through a configurable user transition model, since user satisfaction or interests might change.

We note that RᴇᴄSɪᴍ provides the ability to easily author specific aspects of user behavior of interest to the researcher or practitioner, while ignoring others. This can provide the critical ability to focus on modeling and algorithmic techniques designed for novel phenomena of interest (as we illustrate in two applications below). This type of abstraction is often critical to scientific modeling. Consequently, high-fidelity simulation of all elements of user behavior is not an explicit goal of RᴇᴄSɪᴍ. That said, we expect that it may also serve as a platform that supports “sim-to-real” transfer in certain cases (see below).

Data Flow through components of RᴇᴄSɪᴍ. Colors represent different model components — user and user-choice models (green), document model (blue), and the recommender agent (red)

Applications

We have used RᴇᴄSɪᴍ to investigate several key research problems that arise in the use of RL in recommender systems. For example, slate recommendations can result in RL problems, since the parameter space for action grows exponentially with slate size, posing challenges for exploration, generalization and action optimization. We used RᴇᴄSɪᴍ to develop a novel decomposition technique that exploits simple, widely applicable assumptions about user choice behavior to tractably compute Q-values of entire recommendation slates. In particular, RᴇᴄSɪᴍ was used to test a number of experimental hypotheses, such as algorithm performance and robustness to different assumptions about user behavior.

Future Work

While RᴇᴄSɪᴍ provides ample opportunity for researchers and practitioners to probe and question assumptions made by RL/recommender algorithms in stylized environments, we are developing several important extensions. These include: (i) methodologies to fit stylized user models to usage logs to partially address the “sim-to-real” gap; (ii) the development of natural APIs using TensorFlow’s probabilistic APIs to facilitate model specification and learning, as well as scaling up simulation and inference algorithms using accelerators and distributed execution; and (iii) the extension to full-factor, mixed-mode interaction models that will be the hallmark of modern CIRs—e.g., language-based dialogue, preference elicitation, explanations, etc.

Our hope is that RᴇᴄSɪᴍ will serve as a valuable resource that bridges the gap between recommender systems and RL research — the use cases above are examples of how it can be used in this fashion. We also plan to pursue it as a platform to support academic-industry collaborations, through the sharing of stylized models of user behavior that, at suitable levels of abstraction, reflect a degree of realism that can drive useful model and algorithm development.

Further details of the RᴇᴄSɪᴍ framework can be found in the white paper, while code and colabs/tutorials are available here.

Acknowledgements
We thank our collaborators and early adopters of RᴇᴄSɪᴍ, including the other members of the RᴇᴄSɪᴍ team: Eugene Ie, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu and Craig Boutilier.

By Martin Mladenov, Research Scientist and Chih-wei Hsu, Software Engineer, Google Research

Today is the start of the 10th consecutive year of the Google Code-in (GCI) contest for teens. We anticipate this being the biggest contest yet!

Google and Pixar have collaborated to add Draco compression to USD files to enable significantly smaller meshes for transmission, and real-time asset delivery on the web or in mobile applications.

Draco is an open source compression library to improve the storage and transmission of 3D assets—including compressing points, connectivity information, texture coordinates, color information, normals and any other attributes associated with geometry.

With Draco, applications can present complex 3D assets to the user much more quickly without compromising visual fidelity. For users this means apps can now be downloaded faster, 3D graphics can load quicker, and transmitted over any type of network, regardless of bandwidth.

USD addresses the need to robustly and scalably interchange and augment arbitrary 3D scenes that may be composed from many models and animations. USD also enables assembly and organization of any number of assets into virtual sets, scenes, and shots, transmit them from application to application, and non-destructively edit them (as overrides), with a single, consistent API, in a single scenegraph. USD provides a rich toolset for reading, writing, editing, and rapidly previewing 3D geometry and shading.

We tested Draco compression performance on a representative set of of USD objects and found that Draco on average compressed objects by more than 15X. On a typical 4G network, these assets would load 2.5X faster, all while using less of your users’ data plan.


Compressing USD objects with Draco enables a wide range of use cases moving forward, especially when delivering run-time assets to consumer devices. Anything from 3D commerce to complex AR scenes can benefit from reduced data requirements and quicker time to launch.

We look forward to seeing what people do with this combination of Draco compression and USD format. Check out the code on GitHub and let us know what you think and how you plan to use it!

By F. Sebastian Grassia, Pixar and Jamieson Brettle, Chrome Media

It's hard to believe it was just five years ago Googlers decided to open up Kubernetes to the world, partnering with Red Hat, and eventually many others, to build a community that would reshape the world of infrastructure. Kubernetes' impact has been accelerated by an incredible 35,000 contributors, who give their time to make the project a cornerstone of cloud native computing, and the center of three huge events a year around the world.

No surprise then that we're excited to be at KubeCon + CloudNativeCon North America 2019 in San Diego: to celebrate Kubernetes, plus a constellation of other open source projects. You can meet leaders from these projects at the Google Cloud Community Lounge, part of Google's numerous activities at KubeCon + CloudNativeCon.

Happy Birthday, Go!

First of all, let's take a moment to celebrate the project that started it all, and wish the Go programming language a happy 10th birthday! As Kubernetes co-founder Joe Beda pointed out in 2014, Go is "Goldilocks for system software", and has been foundational to Kubernetes' success.

How about this: "Kubernetes' success is in part due to Go and the Go community. Go hits the sweet spot for distributed systems software. It is low enough to do the job but high level enough to attract a thriving community."

— Joe Beda (@jbeda) June 29, 2018

Since Kubernetes' adoption of Go, it has become known as the language of the cloud. Most of the projects you'll see this year at KubeCon are built with, or are compatible with, Go. And the Go community is a vibrant ecosystem in its own right: over the last ten years it has grown to see over 20 conferences a year, as well as 2,100 contributors. Read more about the history and evolution of Go in these 10th birthday blog posts from Russ Cox and Steve Francia.

Come join us in celebrating Go's birthday during the booth crawl, from 6.40pm to close on Tuesday November 19! Sweet treats will be served, meet with some of the Go team and other enthusiasts.

Google Open Source At KubeCon

KubeCon is a great chance to learn about the many open source projects that Google has founded or contributes to, and meet face-to-face with our engineers and community leaders. We want to hear about your use cases, meet with contributors (aspiring or experienced!), and connect with the whole community. Google's participation in the conference is not limited to technical talks. In fact, we sponsor and participate in many important community gatherings like the Diversity Lunch+Hack, and project governance like the Steering Committee and Technical Oversight Committee. We recognize and care about fostering a healthy, inclusive environment that extends far beyond just technology concerns.

Of course, Kubernetes is at the center of the conference. And, Google is deeply committed to the Kubernetes ecosystem, including creating and contributing to sub-projects such as kubebuilder, kustomize, KIND, and krew, as well as building testing infrastructure and fostering a community that inspires the whole world of open source. We're still here and still trailblazing. Come and talk with us at the community lounge, attend our talks and tutorials throughout the week, and drop in on hallway discussions galore!

gRPC, developed at Google and donated to CNCF, is a modern, open source, high-performance RPC framework that can run in any environment. Use gRPC to efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking and authentication. Meet some of the gRPC maintainers, connect with the community, and pick up the latest limited edition PanCakes sticker at the Community Lounge on Wednesday!

Knative is an open source serverless platform hosted on Kubernetes. It abstracts away much of the complexity, allowing developers to focus on their code and build highly scalable, secure stateless applications. Knative codifies the best practices shared by successful real-world implementations, and solves the "boring but difficult" parts of deploying and managing cloud native services on Kubernetes.

Knative recently cut their 10th release of the project, providing a stable v1 API for the serving project, and additional work to enable production-readiness. Meet some of the maintainers and learn more about Knative in a hands-on workshop on the Monday November 18.

Istio is an open source service mesh, providing observability, control and security over communication in a distributed application -- running on Kubernetes or on VMs. Started in 2017 by Google and IBM, and reaching 1.0 in 2018, Istio has developed a vibrant community. Over the last 12 months, more than 600 developers from across 125+ companies have committed PRs! GitHub acknowledged this recently, reporting that Istio is in the top 4 of all projects across GitHub for contributor growth for committed PRs! The most recent quarterly release was Istio 1.4, which featured scalability and performance improvements as well as greatly improved experience for getting started.

Kubeflow is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. The community is building a powerful development experience for data scientists to build, train and deploy from notebooks, as well as the enterprise capabilities ML operations teams rely on to deploy and scale advanced workflows in a variety of infrastructures. The Kubeflow project is supported by 180+ contributors from 25+ organizations.

Kubeflow just released v0.7, featuring beta functionality for the Kubeflow 1.0 release, expected in early 2020. Learn how Kubeflow is being used and built in the 17+ talks featuring the project in the Machine Learning + Data track. If you're getting started, attend our Kubeflow OSS Hands-On Workshop on November 18, 1-3pm. Connect with the Kubeflow maintainers attending KubeCon by following @kubeflow.

Agones is an open source, multiplayer-dedicated, game server scaling and orchestration platform built for Kubernetes. Google founded the project in early 2018, along with Ubisoft, and recently Agones reached its 1.0 release milestone. It is being used in production for several games, with more to come!

If you're a game developer or just interested in non-traditional Kubernetes workloads, please join us for a workshop on Monday November 18, where we'll combine forces with our sibling project Open Match to turn a Kubernetes cluster into a game services backend. We're very excited about the future of open source and game development, and invite you to join us Tuesday morning in the Google Cloud Community Lounge for an open source in gaming meetup!

Krew makes it easy to discover and install kubectl plugins. Initially developed at Google, and now a Kubernetes subproject, over 60 open source kubectl plugins are distributed through Krew, and can be installed with a single command. Learn more in our breakout session, and come and talk about Krew in a Community Lounge Q&A session.

KIND makes it easy and cheap to test Kubernetes locally with Docker container "nodes". Started in the Kubernetes SIG-Testing group, this project has flourished and is now used for testing Kubernetes Pull Requests and many subprojects in the ecosystem including kubeadm, CSI, Istio, and Cluster API. KIND v0.6.0 will release before KubeCon, and the team will be presenting a deep dive and running a workshop on contributing to Kubernetes using KIND.

Envoy is a fundamental building block of service mesh architectures, providing a programmable data plane for services. Service mesh helps deploy multi-cloud applications and microservices at scale, by decoupling applications from networking, and service development from operations. Google is a lead contributor to the Envoy project, a critical part of Google Cloud's enterprise-grade service mesh products such as Anthos Service Mesh, Traffic Director and L7 ILB. We are excited to be a sponsor for EnvoyCon on Monday November 18, and would love for you to join us for an intro to Envoy on Thursday November 21.

OpenTelemetry provides the best way to capture metrics and distributed traces from your applications. Google is one of the founders of OpenTelemetry, and the community has grown to include most major monitoring, APM, and cloud vendors. OpenTelemetry is fully supported by the Google Cloud Platform and Stackdriver. Hear more about OpenTelemetry in the KubeCon keynotes, and go deeper in the overview and advanced breakout sessions.

Tekton defines reusable building blocks on Kubernetes for Cloud Native CI/CD. In March Tekton was donated to the CDF (the Continuous Delivery Foundation), and is being developed in collaboration with companies such as Red Hat, IBM, Cloudbees and other friends.

With the first release of Tekton Triggers and Tekton Pipelines, it's now possible to create an entire bespoke CI/CD system from scratch! Hear more about Tekton at the Continuous Delivery Summit on Monday, and catch the breakout sessions Mario's Adventures in Tekton Land and Russian Doll: Extending Containers with Nested Processes. Meet the Tekton maintainers at the Google Cloud Community Lounge on Thursday November 21 from 12.30-2.25pm, and follow @tektoncd for updates during the conference!

gVisor is a container-native sandbox for defense-in-depth anywhere. It provides a per-sandbox user-space kernel to improve isolation and mitigate privilege escalation vulnerabilities. gVisor integrates with Kubernetes and other container orchestration systems, while preserving the portability and resource efficiency of containers. Connect with the gVisor team in the Google Cloud Community Lounge during the Cloud Native Security meetup from 3.55-4.25pm on Thursday November 21.

Skaffold is a tool from Google that speeds up the feedback loop (build, tag, push, deploy) when developing applications on Kubernetes. Using Skaffold, you create a configuration for your project, and on every source code change Skaffold builds the images, deploys the app, abd starts tailing logs from pods and forwarding ports. For continuous delivery pipelines Skaffold provides a one-off deployment, with the ability to wait for health-checks. Skaffold is now GA, and Patrick Flynn and Balint Pato are available to chat about Skaffold and the Kubernetes Developer Experience from 4-5pm on Tuesday November 19, at the Google Cloud Community Lounge.

Additionally, join us for a breakout session on Binary Authorization in Kubernetes, which brings together Grafeas, an artifact metadata API for software supply chains, and Kritis, a deploy-time policy enforcer for Kubernetes applications.

We're excited to meet with so many users, friends, and fellow contributors at KubeCon, and hope you can join us in our talks and the Google Cloud Community Lounge.

Still can't get enough?

If you're interested in keeping up with the Kubernetes ecosystem, including all the news from KubeCon, subscribe to the Kubernetes Podcast from Google. Every week we release an episode covering news from the community, and in-depth interviews with leaders from the Kubernetes and cloud native ecosystem. Subscribe via your favorite podcast platform.

Though we’re still waiting on the full materialization of the promise of the Semantic Web, search engines—including Google—are heavy consumers of structured data on the web through Schema.org. In 2015, pages with Schema.org markup accounted for 31.3% of the web. Among SEO communities, interest in Schema.org and structured data has been on the rise in recent years.

Ten years ago, we announced the Go release here on this blog. This weekend we marked Go's 10th birthday as an open-source programming language and ecosystem for building modern networked software.

Go's original target was networked system infrastructure, anticipating what we now call the cloud. Go has become the language of the cloud, but more than that, Go has become the language of the open-source cloud, including Containerd, CoreDNS, Docker, Envoy, Etcd, Istio, Kubernetes, Prometheus, Terraform, and Vitess.

From our earliest days working on Go, we planned for Go to be open source. We knew that bootstrapping a new language and ecosystem was too large a project for one team or even one company to do alone. Go needed a thriving open-source community to curate and grow the ecosystem, to write books and tutorials, to teach courses to developers of all skill levels, and of course to find bugs and work on code improvements and new features. And of course we also wanted to share what we had created with everyone.

Open source at its best is about people working together to accomplish far more than any of them could have done alone. We are incredibly grateful to the thousands of people who have built up Go, its ecosystem, and its community with us over the past decade.

There are over a million Go developers worldwide, and companies all over the globe are looking to hire more. In fact, people often tell us that learning Go helped them get their first jobs in the tech industry. In the end, what we're most proud of about Go is not a well-designed feature or a clever bit of code but the positive impact Go has had in so many people's lives. We aimed to create a language that would help us be better developers, and we are thrilled that Go has helped so many others. Today we launched go.dev to be a hub for all Go developers to learn more and find ways to connect with each other.

As a thank you from us on the Go team at Google to Go contributors and developers worldwide for joining us on Go's journey, we are distributing a commemorative 10th anniversary pin at this month's Go Developer Network meetups. Renee French, who created the Go gopher for the release back in 2009, designed this special pin and also painted the mission control gopher scene at the top of this post. We thank Renee for giving Go so much of her time and a mascot that continues to delight and inspire a decade on.

As #GoTurns10, we hope everyone will take a moment to celebrate the Go community and all we have achieved together. On behalf of the entire Go team at Google, thank you to everyone who has joined us over the past decade. Let's make the next one even more incredible!



By Russ Cox, for the Go team

103 attendees here this week received diversity scholarships! This is the largest investment in diversity for ANY conference EVER — thanks to this incredible committee, @googlecloud, @Azure, @awscloud & @twistlockteam 👏🏼 [LIVE from #KubeCon + #CloudNativeCon] pic.twitter.com/GtxNCjb1g5

— CNCF (@CloudNativeFdn) December 6, 2017

Year after year, we hear from conference attendees that it's not just the content they came for, it's the connections. Meeting new people, getting new perspectives, making new friends (and sometimes hiring them!) is a big part of KubeCon Life. We want to make sure that the Kubecon community is welcoming to people from diverse backgrounds but just being welcoming is not enough: we have to actually do the work to help people get through the door.

The easiest way to help people get through the door is through diversity scholarships. One of the biggest blockers to full participation in our community is just having the resources to get to the room where it happens, and a diversity scholarship—not just a ticket, but travel assistance too—helps increase participation.

Dope. @googlecloud went swagless this Kubecon and sending the funds to the Kubecon diversity scholarship for Barcelona.

Pretty cool instead of getting plastic.

— Amy Codes 🌻 (@TheAmyCode) December 12, 2018

1: Going Swagless

This Kubecon we want you to take away the really important things from the conference: new knowledge and new connections... not just another pen or plastic doodad. (Although to be fair, we will also have plenty of stickers... stickers aren't swag, they're an essential part of Kubecon!)

Google prides itself on being a data-driven company, so when we need to decide where we can spend our dollars to make the most impact and do the most good for the Kubecon community, we turn to the data. We know there is an issue from the CNCF KubeCon report in Seattle 2018 reporting in 11% women (and that’s not even a complete diversity metric). Now looking at the things conference attendees have told us they value about Kubecon, we put together this handy chart to help us guide our decision-making:

	Travel + Conf Ticket Scholarship	Branded Pen
Face to face learning	✅	❌
Career development	✅	❌
OSS community building	✅	❌
Writing tools	❌	✅

We also need to consider externalities when we make our decisions—and going #swagless and dedicating those resources to improving the conversation and community at Kubecon has some positive externalities: less plastic (and lighter luggage going home) is better for the planet, too!

If our work to support diversity and inclusion at Kubecon has inspired you and you want to know what your org can do to participate, there is plenty of room in the #swagless tent for everyone—redirect your swag budget to D&I efforts. Shoutouts to conference organizers like SpringOne that went totally swagless this year!

2: Diversity Lunch + Hack

Pair-Programming table #Diversity lunch #KubeCon 👍👍👍 very great moment ! pic.twitter.com/21rNrqUg5i

— Gaëlle Acas 🐳 (@Gaelleacas) May 22, 2019

Our commitment to a welcoming environment and a diverse community doesn't stop at getting people in the door: we also need to work on inclusion. Our diversity lunch and hack is a place where people can:

• Build their skills through pair programming
• Get installation help
• Do deep-dives on k8s topics
• Connect with others in the community

Our diversity lunch isn't just talking about diversity: it's about working towards diversity through skill-building and creating stronger community bonds. Register here!

We welcomed 220 friends and allies in Barcelona and expect to continue the sold-out streak in San Diego (get your ticket now)!

3: Redirecting Even More

But wait, there's more! We're not just going #swagless, we're also redirecting all the hands-on workshop registration fees ($50) from Anthos Day, Anthos&GKE Lab, OSS: Agones, Knative, and Kubeflow to the diversity scholarship fund. You can build a stronger, more diverse community while you build your skills—a total twofer. (And our workshops are also walking the walk of inclusion by being accessible themselves: if you need support to attend a workshop, whether financial or physical, send us a note.

4: Hiring

Also, one of the best things any company can do to drive D&I is to hire people who will help your company become more diverse, whether as a consultant to help you build your program, or as a team member who will help you bring a wider perspective to your product! Come meet a Googler at any of the activities we are doing during the week to discuss jobs at Google Cloud: g.co/Kubecon.

By: Paris Pittman, Google Open Source

“I was able to make my first contribution yesterday, and today it was merged. I'm so excited about my first steps in open source", a participant said about the First Summit for Open Source Contributors, which took place this September in Guadalajara, México.

How do you involve others in open source? How can we make this space more inclusive for groups with low representation in the field?


(1) Aggregate data from Tech Crunch: https://techcrunch.com/2019/06/17/the-future-of-diversity-and-inclusion-in-tech/
(2) Data from the last Apache Software Foundation Committer Survey, applied in 2016, 765 respondents (13% of committers)

Security begins with secure infrastructure. To have higher confidence in the security and integrity of the infrastructure, we need to anchor our trust at the foundation—in a special-purpose chip.

By Royal Hansen‎, Vice President, Google and Dominic Rizzo, OpenTitan Lead, Google Cloud

GSoC Experience Series

InterMine is an open source data warehouse for biological data. In 2017, we decided at short-ish notice to participate in a call from Open Genome Informatics for Google Summer of Code (GSoC) mentoring organisations. InterMine had never participated in a program like this before, and we weren’t entirely sure if the time investment was actually going to be worth it. We nervously said “no more than two projects”, but we had so many great applications, we ended up taking on five brilliant students.

We are excited to welcome 29 open source organizations to mentor students as part of Google Code-in 2019. The contest, now in its tenth year, offers students ages 13-17 from around the world, an opportunity to learn and practice their coding skills while contributing to open source projects—all virtually!