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#1040
Jan 15, 2019 · 1 hr
Notebooks at Netflix with Matthew Seal
Netflix has petabytes of data and thousands of workloads running across that data every day. These workloads generate movie recommendations for users, create dashboards for data analysts to study, and reshape data in ETL jobs, to make it more accessible across the organization. Over the last ten years, data engineering has become a key component of what makes Netflix successful. There are many different engineering roles who interact with the data infrastructure–including data analyst, machine learning scientist, analytics engineer, and software engineer. Data engineering at Netflix has come a long way from the days of Hadoop MapReduce jobs running nightly, and generating reports of the most popular movies. As data engineering and data science has grown, the tooling has expanded. The people in different data roles at Netflix might use Apache Spark, Presto, Python, Scala, SQL, and many other applications to study data–but in recent years, there is one tool that has stood out for its ability to be distinctly useful: Jupyter Notebooks. A Jupyter Notebook lets users create and share documents that contain live code, visualizations, documentation, and many other types of components. In some ways, it is like a shareable IDE, that allows other people to see how you are working with your code and why you are making certain decisions. It is also a tool for building interactive, user-friendly applications–you can embed videos and images in a Jupyter notebook. A Jupyter Notebook stores both the code and the results together in one place. By combining code with results in one document, you can have context around why a certain result came out the way it did. Matthew Seal is a senior software engineer at Netflix, where he builds infrastructure and internal tools around Jupyter Notebooks. He joins the show to explain what problems Jupyter Notebooks solve for Netflix, and why they have quickly grown in popularity within the company. Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
#1039
Jan 14, 2019 · 54 min
Kubernetes in China with Dan Kohn
Chinese Internet companies operate at a massive scale. WeChat has over a billion users and is widely used as the primary means of payment by urban Chinese consumers. Alibaba ships 12 million packages per day, which is four times the amount of Amazon. JD.com, a Chinese ecommerce company, has perhaps the largest production Kubernetes installation in the world. China’s rapid adoption of Internet services, combined with a large population and a growing middle class has led to the creation of Internet giants on par with the social networks, ecommerce sites, and ridesharing startups of the United States. Last November, I attended the first KubeCon China and saw firsthand how the Chinese Internet companies are using open source software to scale their infrastructure. Despite the differences between the US and China, the culture of technologists at KubeCon felt familiar. In some ways, it was just like any other Kubernetes conference that I have attended: large numbers of engineers trying to find the cutting edge of technology, and learning how to solve the problems they are facing back at the office. There were presentations on scaling databases and service meshes and machine learning on Kubernetes. Outside of these presentation halls, there were tables where you could pick up a translation device so that Chinese-only and English-only presentations could be understood by the other nationality. Dan Kohn joins the show to talk about Chinese Internet companies and how they are adopting Kubernetes. Dan is the executive director of the Cloud Native Computing Foundation, an organization within the Linux Foundation that organizes KubeCon. Before joining the CNCF, Dan worked as an entrepreneur, engineer, and executive at several technology companies.
#1037
Jan 11, 2019 · 1 hr 2 min
AWS Analysis with Corey Quinn
Amazon Web Services changed how software engineers work. Before AWS, it was common for startups to purchase their own physical servers. AWS made server resources as accessible as an API request, and has gone on to create higher-level abstractions for building applications. For the first few years of AWS, the abstractions were familiar. S3 provided distributed, reliable object storage. Elastic MapReduce provided a managed Hadoop system. Kinesis provided a scalable queue. Amazon provided developers with managed alternatives to complicated open source software. More recently, AWS has started to release products that are unlike anything else. A perfect example is AWS Lambda, the first function-as-a-service platform. Other newer AWS products include Ground Station, a service for processing satellite data and AWS DeepRacer, a miniature race car for developers to build and test machine learning algorithms on. As AWS has grown into new categories, the blog announcements of new services and features have started coming so frequently that it is hard to keep track of it all. Corey Quinn is the author of “Last Week in AWS”, a popular newsletter about what is changing across Amazon Web Services. Corey joins the show to give his perspective on the growing, shifting behemoth that is Amazon Web Services–as well as the other major cloud providers that have risen to prominence. He’s also the host of the Screaming in the Cloud podcast, which you should check out if you like this episode.
#1036
Jan 10, 2019 · 1 hr 6 min
Zeit: Serverless Cloud with Guillermo Rauch
Serverless computing is a technique for deploying applications without an addressable server. A serverless application is running on servers, but the developer does not have access to the server in the traditional sense. The developer is not dealing with IP addresses and configuring instances of their different services to be able to scale. Just as higher level languages like C abstracted away the necessity of a developer to work with assembly code, serverless computing gives a developer more leverage by letting them focus on business logic while a serverless platform takes care of deployment, uptime, autoscaling, and other aspects of cloud computing that are fundamental to every application. “Serverless” can several different things: backend-as-a-service products like Firebase, functions-as-a-service like AWS Lambda, and high-level APIs such as Twilio. Zeit is a deployment platform built for serverless development. In Zeit, users model a GitHub repository in terms of the functions within their application. Zeit deploys the code from those functions onto functions-as-a-service and allows you to run your code across all the major cloud providers. Guillermo Rauch is the founder of Zeit, and he joins the show to discuss his vision for the company and the platform as it looks today. Guillermo was previously on the show to discuss Socket.io, which he created.
#1035
Jan 9, 2019 · 50 min
Cloud Events with Doug Davis
Functions-as-a-service allow developers to run their code in a “serverless” environment. A developer can provide a function to a cloud provider and the code for that function will be scheduled onto a container and executed whenever an event triggers that function. An “event” can mean many different things. It is a signal that something has changed within your application. When you save a file to an Amazon S3 bucket, that creates an event. When a user signs up for your app, that can create an event. Functions-as-a-service are allowing people to build applications completely out of managed cloud infrastructure. Apps can be fully “serverless”, with managed databases, queueing systems, and APIs tied together by event-triggered functions. Today, there is not a consistent format for events across different applications and cloud providers. This makes it more difficult to stitch together events across these different environments. Ideally, events would be lightweight, easy to deserialize, and easy to interoperate with. The Cloud Events specification is a project within the Cloud Native Computing Foundation with the goal of creating a standard format for events. Doug Davis is the CTO for developer advocacy of containers at Microsoft. He joins the show to discuss how events and event-based programming works, and the need for a common format across cloud events.
#1033
Jan 8, 2019 · 1 hr 8 min
Multicloud with Ben Hindman
Most applications today are either deployed to on-premise environments or deployed to a single cloud provider. Developers who are deploying on-prem struggle to set up complicated open source tools like Kafka and Hadoop. Developers who are deploying to a cloud provider tend to stay within that specific cloud provider, because moving between different clouds and integrating services across clouds adds complexity. Ben Hindman started the Apache Mesos project when he was working in the Berkeley AMPLab. Mesos is a scheduler for resources in a distributed system, allowing compute and storage to be scheduled onto jobs that can use those resources. In his time at the AMPLab, Ben collaborated with Matei Zaharia, creator of Apache Spark. Ben founded Mesosphere based off of his work on Apache Mesos, and since 2013 he has been building a company to bring it to market. In the meantime, several market forces have influenced the enterprise market. Enterprise businesses built on virtual machines and on-prem hardware are trying to migrate to containers, Kubernetes, and Spark. Cloud providers like Google and Microsoft have risen to prominence in addition to Amazon’s continued growth, and enterprises are increasingly willing to adopt multiple clouds. I spoke with Ben Hindman at Kubecon North America. Today, the company that he co-founded works to provide tools for managing these changes in infrastructure. In our conversation, we talked about the necessary mindset shifts for taking a research project and turning it into a highly successful product. We also talked about the newer trends in infrastructure–why enterprises will want multicloud deployments and how serverless APIs and backends will make the lives of developers much easier.
#1032
Jan 7, 2019 · 57 min
Stateful Kubernetes with Saad Ali
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.
#1031
Jan 4, 2019 · 59 min
Kong API Platform with Marco Palladino
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.
#1029
Jan 3, 2019 · 52 min
Ubiquity6: Augmented Reality Platform with Ankit Kumar
Augmented reality glasses will let us walk through a world where the digital blends together with the physical. 3-D objects will be rendered and superimposed onto our field of vision, creating an environment for people to build applications we can hardly dream of today. These augmented reality glasses are probably three to five years away from being ready for consumer use. But developers are already building augmented reality applications for smartphones using Apple ARKit and Android ARCore. These augmented reality toolkits use powerful smartphone processors and computer vision to give developers simple primitives for placing and manipulating 3-D objects. Most of these AR applications are made for a single phone, and AR is useful for a single phone–for example, you could hold your phone up in front of an empty room, and see on your phone how it would look if you had an IKEA couch sitting in the middle of that room. But shared augmented reality experiences are much more exciting. Shared augmented reality can allow us to play a game of virtual basketball, both controlling the game that is synchronized between us. Shared AR would let me go to a restaurant, and create a virtual billboard in front of the restaurant that only you could see when you walked up to the restaurant and held your phone in front of you. Ubiquity6 is a company with the goal of enabling shared AR experiences. Ankit Kumar is the co-founder and CTO of Ubiquity6, and he joins the show to explain why building shared AR is a challenging technical problem. It requires building a digital model of the real world, and mapping that model to coordinates in space, so that users can reliably persist augmented reality objects that each other can see. We discuss computer vision, digital mapping, the increasing power of phone processors, and the potential of shared AR.
#1028
Jan 2, 2019 · 53 min
Crossplane: Multicloud Control Plane with Bassam Tabbara
Cloud providers created the ability for developers to easily deploy their applications to servers on data centers. In the early days of the cloud, most of the code that a developer wrote for their application could run on any cloud provider, whether it was Amazon, Google, or Microsoft. These cloud providers were giving developers the same Linux server that they would expect from an on-premise deployment. Early cloud applications such as Netflix, Airbnb, and Uber took advantage of this cloud infrastructure to quickly scale their businesses. In the process, these companies had to figure out how to manage open source distributed systems tools such as Hadoop and Kafka. Cloud servers were easy to create, but orchestrating them together to build distributed systems was still very hard. As the cloud providers matured, they developed higher level systems that solved many of the painful infrastructure problems. Managed databases, autoscaling queueing systems, machine learning APIs, and hundreds of other tools. Examples include Amazon Kinesis and Google BigQuery. These tools are invaluable because they allow a developer to quickly build applications on top of durable, resilient cloud infrastructure. With all of these managed services, developers are spending less time on infrastructure and more time on business logic. But managed services also lead to a new infrastructure problem—how do you manage resources across multiple clouds? A bucket storage system like Amazon S3 has different APIs than Google Cloud Storage. Google Cloud PubSub has different APIs than Amazon Kinesis. Since different clouds have different APIs, developers have trouble connecting cloud resources together, and it has become difficult to migrate your entire application from one cloud provider to another. Crossplane is an open source control plane for managing resources across multiple clouds. Crossplane’s goal is to provide a single API surface for interfacing with all the parts of your application, regardless of what cloud they are on. Crossplane is a project that was started by Upbound, a company with the goal of making multicloud software development easier. Bassam Tabbara is the CEO of Upbound, and he joins the show to talk about multi cloud deployments, Kubernetes federation, and his strategy for building a multi cloud API.
#1027
Dec 28, 2018 · 57 min
Word2Vec with Adrian Colyer Holiday Repeat
Originally posted on 13 September 2017. Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers. Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a human. In recent shows, we have explored how to train machine learning models to understand images and video. Today, we explore words. You might be thinking–”isn’t a word easy to understand? Can’t you just take the dictionary definition?” A dictionary definition does not capture the richness of a word. Dictionaries do not give you a way to measure similarity between one word and all other words in a given language. Word2vec is a system for defining words in terms of the words that appear close to that word. For example, the sentence “Howard is sitting in a Starbucks cafe drinking a cup of coffee” gives an obvious indication that the words “cafe,” “cup,” and “coffee” are all related. With enough sentences like that, we can start to understand the entire language. Adrian Colyer is a venture capitalist with Accel, and blogs about technical topics such as word2vec. We talked about word2vec specifically, and the deep learning space more generally. We also explored how the rapidly improving tools around deep learning are changing the venture investment landscape.
#1026
Dec 27, 2018 · 55 min
Self-Driving Deep Learning with Lex Fridman Holiday Repeat
Originally posted on 28 July 2017. Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a computer every single circumstance that a car might find itself in. The computer needs to perceive its surroundings, plan how to take action, execute control over the situation, and respond to changing circumstances inside and outside of the car. Lex Fridman has worked on autonomous vehicles with companies like Google and Tesla. He recently taught a class on deep learning for semi-autonomous vehicles at MIT, which is freely available online. There was so much ground to cover in this conversation. Most of the conversation was higher level. How do you even approach the problem? What is the hardware and software architecture of a car? I enjoyed talking to Lex, and if you want to hear more from him check out his podcast Take It Uneasy, which is about jiu jitsu, judo, wrestling, and learning.
#1025
Dec 26, 2018 · 45 min
Technology Utopia with Michael Solana Holiday Repeat
Originally posted on 1 May 2018. Technology is pushing us rapidly toward a future that is impossible to forecast. We try to imagine what that future might look like, and we can’t help having our predictions shaped by the media we have consumed. 1984, Terminator, Gattaca, Ex Machina, Black Mirror–all of these stories present a dystopian future. But if you look around the world, the most successful technologists are mostly guided by a sense of optimism. Technologists themselves are mostly idealistic–they see the future through a utopian lens. Popular media largely tells a different story: that we are headed for a dystopian world. Why is there such a gulf in the level of idealism between technologists and the media? Mike Solana found himself asking that question on a regular basis during his work at Founder’s Fund, where he is a vice president. Founder’s Fund has a bias toward funding difficult, cutting-edge technology like gene editing, robotics, and nuclear energy. This technology that Mike was seeing made him excited about the future–which led to his creation of the podcast “Anatomy of Next.” “Anatomy of Next” has explored biology, robotics, nuclear energy, superintelligence, and the nature of reality. Soon the podcast will be exploring how our civilization will explore and settle the solar system–specifically Mars. I’ve listened through the entire first season of the show twice and enjoyed it so much because Mike explores questions that are on the border of philosophy and technology–questions about the nature of reality, and what makes us human–and nobody can give perfect answers to these questions. But Mike interviews top experts on the show, which provides us with a framework. Guests on “Anatomy of Next” include Nick Bostrom (the author of Superintelligence), George Church (a pioneer in gene editing), and Palmer Luckey (the founder of VR company Oculus). Mike joins the show to talk about why he started “Anatomy of Next,” and his own perspective on the future.
#1024
Dec 25, 2018 · 1 hr 8 min
Google Early Days with John Looney Holiday Repeat
Originally posted on 16 June 2017. John Looney spent more than 10 years at Google. He started with infrastructure, and was part of the team that migrated Google File System to Colossus, the successor to GFS. Imagine migrating every piece of data on Google from one distributed file system to another. In this episode, John sheds light on the engineering culture that has made Google so successful. He has very entertaining stories about clusterops and site-reliability engineering. Google’s success in engineering is due to extremely high standards, and a culture of intellectual honesty. With the volume of data and throughput that Google responds to, 1-in-a-million events are likely to occur. There isn’t room for sloppy practices. John now works at Intercom, where he is adjusting to the modern world of Google infrastructure for everyone. This conversation made me feel quite grateful to be an engineer in a time where everything is so much cheaper, so much easier, and so much more performant than it was in the days when Google first built everything from scratch. I had a great time talking to John, and hope he comes back on the show again in the future because it felt like we were just scratching the surface of his experience.
#1023
Dec 24, 2018 · 54 min
Service Proxying with Matt Klein Holiday Repeat
Originally posted on 14 February 2017. Most tech companies are moving toward a highly distributed microservices architecture. In this architecture, services are decoupled from each other and communicate with a common service language, often JSON over HTTP. This provides some standardization, but these companies are finding that more standardization would come in handy. At the ridesharing company Lyft, every internal service runs a tool called Envoy. Envoy is a service proxy. Whenever a service sends or receives a request, that request goes through Envoy before meeting its destination. Matt Klein started Envoy, and he joins the show to explain why it is useful to have this layer of standardization between services. He also gives some historical context for why Envoy was so helpful to Lyft.
#1022
Dec 21, 2018 · 54 min
Rockset Data Platform with Venkat Venkataramani
At Facebook, Venkat Venkataramani saw how large volumes of data were changing software infrastructure. Applications such as logging servers and advertising were creating fast moving, semi-structured data. The user base was growing, the traffic was growing, and the volume of data was growing. And the popular methods for managing this data were insufficient for the applications that developers wanted to build on top. In previous episodes about data platforms, we have covered similar difficulties as experienced by Uber and Doordash. Incoming data is often in JSON, which is hard to query. The data is transformed to a file format like Parquet, which requires an ETL job. Once it is in a Parquet file on disk in a data lake, the access time is slow. To query the data efficiently, it must be loaded into a data warehouse, which loads the data into memory, often in a columnar format that is easy to aggregate. Imagine being a developer at Facebook, Uber, or Doordash, and trying to build a simple dashboard, or a machine learning application on top of this data platform. Where do you find the right data? How do you know it is up to date? And what if you don’t know the shape of your queries ahead of time, and you haven’t defined indexes over your data? The access speed will be too slow to do exploratory analysis. There are so many steps in this process, and each of these steps creates friction for application developers that want to build on top of “big data”. Since even Facebook was having trouble managing this problem of the data platform, Venkat figured there was an opportunity to build a company around solving the data platform for other software companies. Venkat is the CEO of Rockset, a data system that is built to make it easy for developers to build data-driven apps. In Rockset, data can be ingested from data streams, data lakes, and databases. Rockset creates multiple indexes and schemas across the data. Because there are multiple models for querying, Rockset can analyze an incoming query and create an intelligent query plan for serving it. Venkat joins the show to discuss his time working on data at Facebook, the untapped opportunities of using that data, and the architecture of Rockset.
#1021
Dec 20, 2018 · 1 hr 1 min
Modern Front End: React, GraphQL, VR, WebAssembly with Adam Conrad
Ten years ago, there was a distinction between “backend” and “frontend” developers. A backend developer would be managing the business logic and database transactions using Ruby on Rails or Java. A frontend developer would be responsible for implementing designs and arranging buttons using raw HTML and JavaScript. Today, developers can build entire applications in JavaScript. Developers who spent their early career developing frontend JavaScript skills are finding themselves with a surprising amount of power. With NodeJS providing a backend framework and React, Vue, or Angular on the frontend, a single JavaScript developer can write all the code for a whole application—hence the rise of the “full stack developer”. At the same time, the cloud infrastructure is becoming easier to use. Backend-as-a-service simplifies the frustrations of deploying your application, and standing up a database. GraphQL improves the relationship between the frontend and the backend. And futuristic technologies like WebAssembly and web virtual reality are promising to make a JavaScript engineer’s life even more interesting. Adam Conrad is an engineer and a writer for Software Engineering Daily. In recent articles, he has documented the changing nature of the frontend, including JavaScript engines, virtual reality, and how mature corporations are using React and GraphQL. He joins the show to share his perspective on what is changing in the frontend—and how full stack JavaScript engineers can position themselves for future success in a quickly changing market.
#1020
Dec 19, 2018 · 54 min
Linkerd Service Mesh with William Morgan
Software products are distributed across more and more servers as they grow. With the proliferation of cloud providers like AWS, these large infrastructure deployments have become much easier to create. With the maturity of Kubernetes, these distributed applications are more reliable. Developers and operators can use a service mesh to manage the interactions between services across this distributed application. A service mesh is a layer across a distributed microservices application that consists of service proxy sidecars running alongside each service in a cluster, along with a central control plane for communicating with those sidecar proxies. A service mesh has many uses. Every request and response within the application gets routed through the service proxy, which can improve observability, traffic control to different instances, and circuit breaking in case of an instance failure. The central control plane can be used manage network policy throughout the whole system. We have done shows about each of the different components of a service mesh system, including different types of service proxies, as well as the service meshes built on top of these proxies. Linkerd, which is made by the startup Buoyant, was the first service mesh product to come to market, and it has the most production use, with customers like Expedia and Monzo bank. Istio is a more recent service mesh which uses the Envoy service proxy. Istio came out of Google and is also supported by IBM—setting up a classic competition between a startup and the large incumbents. William Morgan is the CEO of Buoyant, and he joins the show to talk about the use cases and adoption of service mesh. He also talks about the business landscape of the service mesh category, and how to compete with giant cloud providers.
#1019
Dec 18, 2018 · 1 hr 5 min
Market Strategy with Herb Cunitz
Market strategy defines how a company is positioning itself to be successful. This strategy encompasses engineering, sales, marketing, recruiting, and everything else within a company. Herb Cunitz has led teams at Hortonworks, VMware, SpringSource, and several other companies over his 30 year career in software. After working as president of Hortonworks, Herb started AccelG2M. AccelG2M works with software companies to define their go-to-market strategy. Software companies require a great deal of long-term strategic thinking. Engineering, sales, marketing, and leadership must work together to build a plan that will allow the company to reach an exit: either an acquisition or an IPO. Executives at a software company must create a clear strategy and communicate it to the employees throughout the organization. The strategy must be implemented, meeting deadlines and hitting milestones. New team members must be recruited, and unsuccessful workers must be let go. In today’s show, Herb provides some invaluable strategic wisdom for anyone working in software–whether you are an engineer, salesperson, or investor.
#1017
Dec 17, 2018 · 57 min
Mattermost: Self-Hosted Slack Alternative with Corey Hulen
Software companies today rely on group chat applications. The world of startups and small businesses is dominated by Slack. But for some large enterprises, regulatory constraints prevent them from using Slack. Slack is a web application that is hosted in the cloud, and regulated industries such as banking often need to run their applications on their own on-prem infrastructure. Mattermost is an open source alternative to Slack that can be self-hosted. This means that all of the networking complexities and scalability challenges that are controlled in the cloud by Slack need to be handled by open source code rather than managed services running in the cloud. Because it is open source, Mattermost can also be redesigned and customized. Uber designed their own custom version of Mattermost called uChat. Corey Hulen is a co-founder and the CTO of Mattermost. He joins the show to discuss the motivation for building Mattermost and the engineering challenges of building an open source chat system. For more episodes about building chat systems, we’ve done several shows about Slack, covering the engineering, security, and chat system.

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