The Infoq Podcast

Duncan Macgregor speaks with Wes Reisz about the work being done on the experimental Graal Compiler. He talks about the use cases and where the new JIT compiler excels really well (compared to C2). In addition, Duncan talks about the relationship of Graal to Truffle. The two then discuss a language Duncan works on at OracleLabs (TruffleRuby) that is being implemented on the stack. Finally, the podcast wraps with a discussion of Project Loom and its relationship to TruffleRuby and Graal. Why listen to this podcast: - Graal is a replacement for the JVM’s C2 JIT compiler. It was tracked with JEP 295 (Ahead-of-Time Compilation) and included in Java 9. As of Java 10, Graal is experimental for the Linux x64 platform. - Graal is written in Java and excels at implementing code that takes a functional approach to solving problems (such as Scala). It can also offer improvements / optimizations for other languages (including other non-traditional JVM languages such as C and Ruby). - Truffle is a language implementatio

Today on The InfoQ Podcast, Wes talks with Rod Johnson. Rod is famously responsible for the creation of the Spring Framework. The two talk about the early years of the framework and provides some of the history of its creation. After discussing Spring, Wes and Rod discuss languages he’s been involved with since Java (these include Scala and TypeScript). He talks a bit about what he liked (and didn’t like) about each. Finally, the two wrap by discussing Atomist and how they’re trying to change the idea of software delivery from a statically defined pipeline (located in individual repositories) to an event hub that drives a series of actions for software delivery. He describes this as creating an API for your software. Why listen to this podcast: - The initial origins of the Spring Framework really came about through a process of trying to write a really great book about J2EE in 2002. It was through that process that Rod Johnson found he felt there was a better way and ultimately lead to the creation of the

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Katharine discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Katharine is the Co-Founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynotes at QCon.ai. Why listen to this podcast: - Ethical machine learning is about practices and strategies for creating more ethical machine learning models. There are many highly publicized/documented examples of machine learning gone awry that show the importance of the need to address ethical machine learning. - Some of the first steps to prevent bias in machine learning is awareness. You should take time to identify your team goals and establish fairness criteria that should be revisited over time. This fairness criteria then can be used to establish the minimum fairness criteria allowed in production. - Laws like GDPR in the EU an

Today on The InfoQ Podcast, Wes Reisz speaks with Grady Booch. Grady is well known as the co-creator of UML, an original member of the design patterns movement, and now work he’s doing around Artificial Intelligence. On the podcast today, the two discuss what today’s reality is for AI. Grady answers questions like what does an AI mean to the practice of writing software and around how he seems it impact delivering software. In addition, Grady talks about AI surges (and winters) of over the years, the importance of ethics in software, and host of other related questions. Why listen to this podcast: - There have been prior ages of AI that has lead to immediate winters of where reality set in. It stands to reason, there will be a version of an AI winter that follows today’s excitement around deep learning. - AIs are beginning to look at the code for testing edge cases in software and do things such as looking over your shoulder and identifying patterns in the code that you write. - AIs will remove tedium

Today on The InfoQ Podcast, Wes talks with Joe Beda. Joe is one of the co-creators of Kubernetes. What started in the fall of 2013 with Craig McLuckie, Joe Beda, and Brendan Burns working on cloud infrastructure has become the default orchestrator for cloud native architectures. Today on the show, the two discuss the recent purchase of Heptio by VMWare, the Kubernetes Privilege Escalation Flaw (and the response to it), Kubernetes Enhancement Proposals, the CNCF/organization of Kubernetes, and some of the future hopes for the platform. Why listen to this podcast: - Heptio, the company Joe and Craig McLuckie co-founded, viewed themselves as not a Kubernetes company, but more of a cloud native company. Joining VMWare allowed the company to continue a mission of helping people decouple “moving to cloud/taking advantage of cloud” patterns (regardless of where you’re running). - Re:Invent 2017 when EKS was announced was a watershed moment for Kubernetes. It marked a time where enough customers were asking for

Today on the InfoQ Podcast, Wes speaks with ThirdLove’s Megan Cartwright. Megan is the Director of Data Science for the personalized bra company. In the podcast, Megan first discusses why their customers need a more personal experience and how their using technology to help. She focuses quite a bit of time in the podcast discussing how the team got to an early MVP and then how they did the same for getting to an early machine learning MVP for product recommendations. In this later part, she discusses decisions they made on what data to use, how to get the solution into production quickly, how to update/train new models, and where they needed help. It’s a real early stage startup story of a lean team leveraging machine learning to get to a practical recommendations solution in a very short timeframe. Why listen to this podcast: - The experience for women selecting bras is poor experience characterized by awkward fitting experiences and an often uncomfortable product that may not even fit correctly. ThirdLov

Lynn Langit is a consulting cloud architect who holds recognitions from all three major cloud vendors on her contributions to their respective communities. On today’s podcast, Wes talks with Lynn about a concept she calls 25% time and a project it led her to become involved within genomic research. 25% time is her own method of learning while collaborating with someone else for a greater good. A recent project leads her to become involved with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia. Through cloud adoption and some lean startup practices, they were able to drop the run time for a machine learning algorithm against a genomic dataset from 500 hours to 10 minutes. Why listen to this podcast: - 25% time is a way to learn, study, or collaborate with someone else for a greater good. It’s unbilled time in the service of offers. Using the idea of 25% time along with some personal events that occurred in her life, Lynn became involved with genomic researchers in Austral

In this podcast Charles Humble and Wes Reisz talk about autonomous vehicles, GDPR, quantum computing, microservices, AR/VR and more. * Waymo vehicles are now allowed to be on the road in California running fully autonomous; they seem to be a long way ahead in terms of the number of autonomous miles they’ve driven, but there are something like 60 other companies in California approved to test autonomous vehicles. * It seems reasonable to assume that considerably more regulation around privacy will appear over the next few years, as governments and regulators grapple with not only social media but also who owns the data from technology like AR glasses or self-driving cars. * We’ve seen a huge amount of interest in the ethical implications of technology this year, with Uber getting into some regulatory trouble, and Facebook being co-opted by foreign governments for nefarious purposes. As software becomes more and more pervasive in people's lives the ethical impact of what we all do becomes more and more profo

On this week’s podcast, Wes Reisz talks with Brian Goetz. Brian is the Java Language Architect at Oracle. The two start with a discussion on what the six-month cadence has meant to the teams developing Java. Then move to a review of the features in Java 9 through 12. Finally, the two discuss the longer-term side projects (such as Amber, Loom, and Valhalla) and their role in the larger release process for the JDK. * The JVM’s sixth-month cadence changed the way the JDK is delivered and planned. While it definitely provides more rapid delivery at expected intervals, the release train approach turned out to also improve flexibility and efficiency. * Oracle JDK and OpenJDK are almost identical. Most of the JDK distributions are forks from OpenJDK with different bug fixes and backports applied. So the difference between the distributions now is largely which bug fixes are picked up. * Local Variable inference (which was released as part of Java 10) illustrated the tension on making changes to the language. Many p

This week on the InfoQ Podcast, Wes Reisz talks to Tanya Reilly (Principal Engineer at Squarespace and previously a staff SRE at Google). Tanya discusses her research into how the fire code evolved in New York and draws on some of the parallels she sees in software. Along the way, she discusses what it means to be an SRE, what effective aspects of the role might look like, and her opinions on what we as an industry should be doing to prevent disasters. This podcast features discussion on paved roads, prevention, testing, firefighting (in software), and reliability questions to ask throughout the software lifecycle. Why listen to this podcast: - Teams increasingly are responsible for the entire software lifecycle. When this happens, they think about the software differently because they know their the ones that will get paged if it fails. This idea is at the core of the “You Build It, You Run It” philosophy in DevOps. - The role of SRE is to define how to do things in a really reliable way. The focus is to

On today’s podcast, Wes Reisz talks with Jason Maude of Starling Bank. Starling Bank is a relatively new startup in the United Kingdom working in the banking sector. The two discuss the architecture, technology choices, and design processes used at Starling. In addition, Maude goes into some of the realities of building in the cloud, working with regulators, and proven robustness with practices like chaos testing. Why listen to this podcast: - Starling Bank was created because the government lowered the barrier to entry for banking startups in reaction to previous industry bailouts. - The system is composed of around 19 applications hosted on AWS and running Java and backed by a PostgreSQL database. - These applications are not monolithic but are focused around common functionality (such as a Card or Payment Service). - Java was chosen primarily because of its maturity and long term viability/reliability in the market. - The heart of Starling is every action the system takes happens at least once and a

Martin Fowler chats about the work he’s done over the last couple of years on the rewrite of the original Refactorings book. He discusses how this thought process has changed and how that’s affected the new edition of the book. In addition to discussing Refactors, Martin and Wes discuss his thoughts on evolutionary architecture, team structures, and how the idea of refactors can be applied in larger architecture contexts. Why listen to this podcast: - Refactoring is the idea of trying to identify the sequence of small steps that allows you to make a big change. That core idea hasn’t changed. - Several new refactorings in the book deal with the idea of transforming data structures into other data structures, Combine Functions into Transform for example. - Several of the refactorings were removed or not added to the book in favor of adding them to a web edition of the book. - A lot of the early refactorings are like cleaning the dirt off the glass of a window. You just need them to be able to see where

In June of this year, Consul 1.2 was released. The release expanded Consul’s capability around service segmentation (controlling who and how services connect East and West). On this week’s podcast, Wes and Mitchell discuss Consul in detail. The two discuss Consul’s design decisions around focusing on user space networking, layer 4 routing, Go, Windows’ performance characteristics, the roadmap for eBPF on Linux, and an interesting feature that Consul implements called Network Tomography. The show wraps with Mitchell’s discussion on some of the research that Hashicorp is doing around machine learning and security with Consul. Why listen to this podcast: - Consul is first and foremost a centralized service registry that provides discovery. While it has a key-value store, it is Consul’s least important feature. With the June release (1.2), Consul entered more into the space of a service mesh with the focus on service segmentation (controlling how you connect and who can connect). - Hashicorp attempts to limit t

On the podcast this week Charles Humble talks to Camille Fournier about running a platform team, how her current role differs from the CTO role she had a Rent the Runway, the skills developers need to acquire as they move from engineering to management positions, tends like Holacracy, and her book "The Manager's Path" Why listen to this podcast: - When looking for platform engineers Camille looks for people who understand what it takes to build and run distributed systems - network, availability, data - and customer empathy. - The team needs to be focussed on taking the time do build robust software for operational excellence. - The technical skills were different at Rent the Runway - these would tend to be more full-stack engineers who worked in a more iterative way. - Much of what we do at work is really about human relationships. One thing about relationships is that they tend to be better when you have one on one conversations with people on our regular basis. A lot of the value of one on one meet

On this week’s podcast, Wes Reisz talks to Emmanuel Ameisen, head of AI for Insight Data Science, about building a semantic search system for images using convolution neural networks and word embeddings, how you can build on the work done by companies like Google, and then explores where the gaps are and where you need to train your own models. The podcast wraps up with a discussion around how you get something like this into production. Why listen to this podcast: - A common use case is the ability to search for similar things - I want to find another pair of sunglasses like these, or I want a cat that looks like this picture, or even a tool like Google’s Smart Reply, can all be considered broadly the domain of semantic search. - For image classification you generally want a convolutional neural network. You typically use a model pre-trained with a public data set like Imagenet pre-trained to generate embeddings, using the pre-trained model up to the penultimate layer, and storing the value of the acti

On this week’s podcast, Wes Reisz talks with Ben Kehoe of iRobot. Ben is a Cloud Robotics Research Scientist where he works on using the Internet to allow robots to do more and better things. AWS and, in particular, Lambda is a core part of cloud enabled robots. The two discuss iRobot’s cloud architecture. Some of the key lessons on the podcast include: thoughts on logging, deploying, unit/integration testing, service discovery, minimizing costs of service to service calls, and Conway’s Law. Why listen to this podcast: - The AWS Platform, including services such as Kinesis, Lambda, and IoT Gateway were key components in allowing iRobot to build out everything they needed for Internet-connected robots in 2015. - Cloud-enabled Roombas talk to the cloud via the IoT Gateway (which is MQQT) and are able to perform large file uploads using mutually authenticated certificates signed via an iRobot Certificate Authority. The entire system is event-driven with lambda being used to perform actions based on the event

Vaughn Vernon is thought-leader in the space of reactive software and Domain Driven Design (DDD). Vaughn has recently released a new open source project called vlingo. The platform is designed to support DDD at the framework and toolkit level. On today’s podcast, Vaughn discusses what the framework is all about, why he felt it was needed, and some of the design decisions made in developing the platform, including things like the architecture, actor model decisions, clustering algorithm, and how DDD is realized with the framework. Why listen to this podcast: - Vlingo is an open source system for building distributed, concurrent, event-driven, reactive microservices that supports (at the framework level) Domain Driven Design. - The platform is in the early stages. It runs on the JVM. There is a port to C#. All code is pushed up stream. - The platform uses the actor model and all messages are sent in a type-safe way. - Vlingo supports clustering and uses a bully algorithm to achieve consensus. More on thi

On today’s podcast, Justin Cormack discusses how the modern operating system is being decomposed with toolkits and libraries such as LinuxKit, eBPF, XDP, and what the kernel space service mesh Cilium is doing. Wes Reisz and Justin Cormack also discuss how Cilium differs from service meshes like an Istio, Linkerd2 (previously Conduit), or Envoy. Justin is a systems engineer at Docker. He previously was working with unikernels at Unikernel Systems in Cambridge before being acquired by Docker. (edited) *Key Takeaways:* * LinuxKit is an appliance way of thinking about your operating system and is gaining adoption. There are contributions now from Oracle, Cloudflare, Intel, etc. Docker has seen interesting use cases such as customers running LinuxKit on large cloud providers directly on bare metal (more on this coming soon). * The operating system of today is really unchanged since the Sun workstation of the 90’s. Yet everything else about software has really changed such as automation, build pipelines, and deliv

Probabilistic Programming has been discussed as a programming paradigm that uses statistical approaches to dealing with uncertainty in data as a first class construct. On today’s podcast, Wes talks with Mike Lee Williams of Cloudera’s Fast Forward Labs about Probabilistic Programming. The two discusses how Bayesian Inference works, how it’s used in Probabilistic Programming, production-level languages in the space, and some of the implementations/libraries that we’re seeing. Key Takeaways * Federated machine learning is an approach of developing models at an edge device and returning just the model to a centralized location. By taking the averages of the edge models, you can protect privacy and distribute processing of building models. *Probabilistic Programming is a family of programming languages that make statistical problems easier to describe and solve. *It is heavily influenced by Bayesian Inference or an approach to experimentation that turns what you know before the experiment and the results of the

Wes Reisz sits down and chats with Uncle Bob about The Clean Architecture, the origins of the Software Craftsperson Movement, Livable Code, and even ethics in software. Uncle Bob discusses his thoughts on how The Clean Architecture is affected by things like functional programming, services meshes, and microservices. Why listen to this podcast: * Michael Feathers wrote to Bob and said if you rearrange the order of the design principles, it spells SOLID. * Software Craftsperson should be used when you talking about software craftsmanship in a gender-neutral way to steer clear of anything exclusionary. * Clean Architecture is a way to develop software with low coupling and is independent of implementation details. * Clean Architecture and Domain Driven Design (DDD) are compatible terms. You would find the ubiquitous language and bounded context of DDD at the innermost circles of a clean architecture. * Services do not form an architecture. They form a deployment pattern that is a way of decoupling and therefo