In a cloud infrastructure environment, failures happen regularly. The servers can fail, the network can fail, and software bugs can crash your software unexpectedly.

The amount of failures that can occur in cloud infrastructure is one reason why storage is often separated from application logic. A developer can launch multiple instances of their application, with each instance providing a “stateless” environment for serving API requests.

When the application needs to save state, it can make a call out to a managed cloud infrastructure product. Managed cloud databases provide a reliable place to manage application state. Managed object storage systems like Amazon S3 provide a reliable place to store files.

The pattern of relying on remote cloud services does not work so well for on-prem and hybrid cloud environments. In these environments, companies are managing their own data centers and their own storage devices. As companies with on-prem infrastructure adopt Kubernetes, there is a need for ways to manage on-prem storage through Kubernetes.

Saad Ali is a senior engineer at Google, where he works on Kubernetes. He is also a part of the Kubernetes Storage Special Interest Group. Saad joins the show talk about how Kubernetes interacts with storage, and how to manage stateful workloads on Kubernetes. We discuss the basics of Kubernetes storage, including persistent volumes and the container storage interface.

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When a user makes a request to product like The New York Times, that request hits an API gateway. An API gateway is the entry point for an external request. An API gateway serves several purposes: authentication, security, routing, load balancing, and logging.

API gateways have grown in popularity as applications have become more distributed, and companies offer a wider variety of services. If an API is public, and anyone can access it, you might need to apply rate limiting so that users cannot spam the API. If the API is private, the user needs to be authenticated before the request is fulfilled.

Kong is a company that builds infrastructure for API management. The Kong API gateway is a widely used open source project, and Kong is a company built around supporting and building on top of the API gateway.

Marco Palladino is the co-founder and CTO of Kong. He joins the show to tell the story of starting Kong eight years ago, and how the API gateway product evolved out of an API marketplace. Marco also discusses the architecture of Kong and his vision for how the product will develop in the future–including the Kong service mesh.

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An instruction set defines a low level programming language for moving information throughout a computer. In the early 1970’s, the prevalent instruction set language used a large vocabulary of different instructions. One justification for a large instruction set was that it would give a programmer more freedom to express the logic of their programs.

Many of these instructions were rarely used. Think of your favorite programming language (or your favorite human language). What percentage of words in the vocabulary do you need to communicate effectively? We sometimes call these language features “syntactic sugar”. They add expressivity to a language, but may not improve functionality or efficiency.

These extra language features can have a cost.

Dave Patterson and John Hennessy created the RISC architecture: Reduced Instruction Set Compiler architecture. RISC proposed reducing the size of the instruction set so that the important instructions could be optimized for. Programs would become more efficient, easier to analyze, and easier to debug.

Dave Patterson’s first paper on RISC was rejected. He continued to research the architecture and advocate for it. Eventually RISC became widely accepted, and Dave won a Turing Award together with John Hennessy.

Dave joins the show to talk about his work on RISC and his continued work in computer science research to the present. He is involved in the Berkeley RISELab and works at Google on the Tensor Processing Unit.

Machine learning is an ocean of new scientific breakthroughs and applications that will change our lives. It was inspiring to hear Dave talk about the changing nature of computing, from cloud computing to security to hardware design.

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Open source software powers everything we do on the Internet. Google runs on Linux servers. Content sites are served by WordPress. Our data is queued in Kafka clusters and stored in MongoDB instances.

The success of an open source project often leads to the creator of that open source software becoming wealthy. An open source project can be monetized through enterprise add-ons, or consultation, or simplified hosting. The creators of open source software know their domains so well, that they are usually well-suited to operate this kind of open source business.

Open source business model success stories include Elastic (ElasticSearch), Cloudera (Hadoop), and Red Hat.

The rise in usage of cloud providers has changed the viability of some open source business models. AWS can monetize almost any open source project more profitably than the creators. This is because AWS has established distribution channels.

If I already run my application on AWS, and I am looking for someone to provide me with a hosted version of a database, I will probably choose the hosted database that AWS provides.

The Commons Clause is a license that open source projects can use to protect their code from being profited from. Redis, an open source in-memory object storage system, recently licensed their code with the Commons Clause with the goal of improving the business of Redis Labs, a company built by the creators of the Redis project.

Kevin Wang joins the show to discuss everything open source–from business models to security vulnerabilities to licensing. Kevin is the CEO at FOSSA, a system for managing open source licensing and security. Kevin was involved in the creation of the Commons Clause and has written about it in detail.

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Matt Klein has worked for three rapidly growing Internet companies. At AWS, he worked on EC2, the compute-as-a-service product that powers a large percentage of the Internet. At Twitter, he helped scale the infrastructure in the chaotic days before Twitter’s IPO. Today he works at Lyft, building systems to allow for ride sharing infrastructure to work more safely and reliably.

Hypergrowth Internet companies are faced with quickly growing demands on their software. The demands on the software expose problems with the core infrastructure. Simultaneously, the company tries to ramp up its hiring process. More engineers get hired, and the institutional knowledge within the company starts to weaken. Documentation gets out of date. Senior engineers burn out and leave the company.

When a company starts growing quickly, communications can break down. A hypergrowth company can suffer from a lack of “human scalability”. Matt Klein has observed these challenges at AWS, Twitter, and Lyft. In his article “The Human Scalability of ‘DevOps’”, he explains why these problems manifest and what can be done to alleviate them.

In a previous show, Matt discussed the engineering challenges at Lyft that led him to create Envoy, a service proxy. This episode covers some broad technical topics–DevOps, site reliability engineering, platform engineering–but the episode is mostly about how a hypergrowth company can manage culture, hiring, and engineering organization.

Matt is a very fun guest to have because he questions some of the strange practices that have been widely adopted by successful companies. Internet companies are a very new phenomenon, and the management tactics that they have adopted are not well proven–so it is great to have someone like Matt provide a fresh perspective on ways that companies can scale their technology and their organization more effectively.

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Google Search allows humans to find and access information across the web. A human enters an unstructured query into the search box, the search engine provides several links as a result, and the human clicks on one of those links. That link brings up a web page, which is a set of unstructured data. Humans can read and understand news articles, videos, and Wikipedia pages.

Google Search solves the problem of organizing and distributing all of the unstructured data across the web, for humans to consume. Diffbot is a company with a goal of solving a related, but distinctly different problem: how to derive structure from the unstructured web, understand relationships within that structure, and allow machines to utilize those relationships through APIs.

Mike Tung is the founder of Diffbot. He joins the show to talk about the last decade that he has spent building artificial intelligence applications, from his research at Stanford to a mature, widely used product in Diffbot. I have built a few applications with Diffbot, and I encourage anyone who is a tinkerer or prototype builder to play around with it. It’s an API for accessing web pages as structured data.

Diffbot crawls the entire web, parsing websites, using NLP and NLU to comprehend those pages, and using probabilistic estimations to draw relationships between entities. It’s an ambitious product, and Mike has been working on it for a long time. I enjoyed our conversation.

We recently launched a new podcast: Fintech Daily! Fintech Daily is about payments, cryptocurrencies, trading, and the intersection between finance and technology. You can find it on fintechdaily.co or Apple and Google podcasts. We are looking for other hosts who want to participate. If you are interested in becoming a host, send us an email: [email protected]

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Google Search is a highly interactive JavaScript application. As you enter a query, results are being automatically suggested to you before you even finish typing. When you press enter, some of your search results may be widgets that represent the weather, the price of a stock, a recipe for green bean soup, or a language translation for a phrase. These complex frontend components are loading dynamically–the Google Search application cannot prefetch every single possible widget that you might ask for–but the results do load very quickly.

Google has many other examples of advanced JavaScript engineering. The company is mostly known for backend engineering inventions like MapReduce, TensorFlow, Dremel, and Spanner. To turn these backend tools into user facing products, Google develops its own JavaScript frameworks and infrastructure to deliver information from the backend to the frontend.

“Backend” and “frontend” are not precise terms. At Google, there are so many layers of infrastructure between a user and the data center. If you are an engineer working on a service at Google, you probably have several “frontends” and “backends” on either side of you.

Malte Ubl is a senior staff engineer at Google. He’s heavily involved in Google’s JavaScript infrastructure, and has written about managing large JavaScript applications in detail. He also works on AMP, an open-source project for delivering web pages in a fast, performant format. He joins the show to describe Google’s history with JavaScript frameworks, the process of building frontends and middleware to deliver JavaScript applications, and the engineering behind AMP. There are criticisms of AMP, but some of them misunderstand how the AMP technology actually works.  AMP allows pages to be cached, prefetched, and served to a user more quickly. AMP does not necessarily centralize pages around being served from Google Search. A good example of AMP speeding up pages outside of Google is reddit.

We recently launched a new podcast: Fintech Daily! Fintech Daily is about payments, cryptocurrencies, trading, and the intersection between finance and technology. You can find it on fintechdaily.co or Apple and Google podcasts. We are looking for other hosts who want to participate. If you are interested in becoming a host, send us an email: [email protected]

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Google Brain is an engineering team focused on deep learning research and applications. One growing area of interest within Google Brain is that of generative models. A generative model uses neural networks and a large data set to create new data similar to the ones that the network has seen before.

One approach to making use of generative models is GANs: generative adversarial networks. GANs can use a generative model (which creates new examples) together with a discriminator model (which can classify examples).

As an example, let’s take the task of generating new pictures of cats. We want an artificial cat picture generator. First, we train a discriminator by feeding it billions of example pictures of cats. We now have a model that can tell what a cat is. Next, we make a model that generates completely random images. We feed those randomly generated images to the discriminator. The discriminator outputs a “loss” for these random images. Loss is a metric we can use to represent how far off a given image is from being something that the discriminator would recognize as a cat. Finally, you can feed this “loss” back into the generative model, so that the generative model will adjust its weights in a way that will reduce loss. Over time, the generator gets better and better at reducing loss, until the discriminator starts believing that some of these semi-random images are actually cats.

Generative model systems have produced useful applications, such as object detection, image editing, and text-to-image generation. Today’s guest Doug Eck works on the Magenta team at Google Brain. Magenta uses applications of deep learning to produce tools and experiments around music, art, and creativity.

In a previous show, Doug described his vision for humans and computers to work together on creative tasks such as music. Today, we dive into some of the core machine learning building blocks that make machine creativity possible.

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Airbnb began in 2008 as a monolithic Rails application serving the simple purpose of listing homes for rental. Over time, the number of listings increased dramatically, as did the number of people who were renting.

With that scale, the Rails app had to be broken into different services, and entire teams were built out to focus on challenges such as pricing, application infrastructure, and search. Surabhi Gupta joined in 2013 to work on the search team, and has worked on different teams at Airbnb over time.

Today she is a director of engineering leading the Homes business for Airbnb, which includes Growth, Search, Hosts, Pricing, and Business Travel. Surabhi has helped scale Airbnb through a hypergrowth period, and joins the show to share those experiences. One distinct area that we spent time on was Airbnb’s search engine. Surabhi formerly worked at Google, and she described how the engineering problem of a search engine for homes differs from a general purpose search engine like Google.

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GraphQL allows developers to communicate with all of their different data backends through a consistent query interface. A GraphQL query can be translated into queries to MySQL, MongoDB, ElasticSearch, or whatever kind of API or backend is needed to fulfill the GraphQL query. GraphQL users need to set up a GraphQL server to fulfill this query federation.

Prisma is a tool for automatically generating a GraphQL API and serving GraphQL queries. The developer defines a data model and deploys with Prisma. Prisma generates the necessary GraphQL infrastructure to serve queries from the developer’s database. This can allow the developer to get up and running faster than they would setting up GraphQL infrastructure and defining the middleware query layer by hand.

Prisma is an open source project, but it is also a company. The opportunities to build a business around a GraphQL infrastructure layer are numerous.

In recent episodes, we have explored the complexities of the “data platform.” From newer companies like Uber to older companies like Procter and Gamble, engineers are struggling to find and access their data sources. Data engineers and data scientists spend months configuring their infrastructure to connect to BI tools and run distributed queries.

GraphQL could simplify data platforms by providing a unified, standardized layer. At this layer, you could also offer caching, virtual data sets, and crowdsourced queries from across the company.

Soren Bramer Schmidt is the CTO and co-founder of Prisma, and he joins the show to discuss why GraphQL has become so popular, how Prisma works, and the opportunities to build developer tooling around GraphQL.

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