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In this week's podcast QCon chair Wesley Reisz talks to Heather Fleming, who is the VP of product and program management at GILT, where she is responsible for not only the customer-facing website, but also back office things from distribution to order processing.

Why listen to this podcast:

- GILT treats every person as an individual, with a skillset that is outside their responsibilities.
- You should be able to be your authentic self wherever you are.
- Google found creating a psychologically safe work environment was key to high performing teams.
- You can kill a great team by taking away their autonomy and empowerment.
- Great engineers that want to be managers are fearful of losing their skill, and great engineers that don't want to be managers are put in those roles and are terrible managers.

Notes and links can be found on InfoQ: http://bit.ly/1U2Wgq9

1m 49s - One of the things that makes the culture at GILT so special is the "ingredients framework" that considers every person an individual, with a skillset that is outside what their title might say and what their role and responsibilities might be.
3m 29s - One of the team ingredients is called "motivator" - the ability to make sure that the team understands what they're working on, and more importantly the "why" and how it's driving business or customer value.
4m 35s - The relationship you have with your co-workers should be deeper than many of us are allowing them to be, because they are like your second family.

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Attend Heather Fleming's session at QCon New York 2016, Jun 13-17: http://bit.ly/1U2f3og

Show notes

GILT VP Heather Fleming on Unlocking the "Secret Sauce" of Great Teams

In this week's podcast, QCon chair Wesley Reisz talks to Kolton Andrus. Andrus is the founder of Gremlin Inc. He was a Chaos Engineer at Netflix, focused on the resilience of the Edge services. He designed and built FIT: Netflix’s failure injection service. Prior, he improved the performance and reliability of the Amazon Retail website. 

Why listen to this podcast:

- Gremlin, Kolton Andrus' new start-up, is focused on providing failure testing as a service. Version 1, currently in closed beta, is focused on infrastructure failures.
- Lineage-driven Fault Injection (LDFI) allowed Netflix to dramatically reduce the number of tests they needed to run in order to explore a problem space.
- You generally want to run failure tests in production, but you can't start there. Start in developemnt and build up.
- Having failure testing at an application level, as Netflix does, so you can have request level fault injection for a specific user or a specific device. 
- Being able to trace infrastructure with something like Dapper or Zipkin offers tremendous value. At Netflix, the failure injection system is integrated into the tracing system, which meant that when they caused a failure they could see all the points in the system that it touched.

More on this:
 
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Show Notes

Kolton Andrus on Lessons Learnt From Failure Testing at Amazon and Netflix and New Venture Gremlin

In this week’s podcast, Thomas Betts talks with Haley Tucker, a Senior Software Engineer on the Playback Features team at Netflix. While at QCon San Francisco 2016, Tucker told some production war stories about trying to deliver content to 65 million members.

Why listen to this podcast:

 - Distributed systems fail regularly, often due to unexpected reasons
 - Data canaries can identify invalid metadata before it can enter and corrupt the production environment
 - ChAP, the Chaos Automation Platform, can test failure conditions alongside the success conditions
 - Fallbacks are an important component of system stability, but the fallbacks must be fast and light to not cause secondary failures
 - Distributed systems are fundamentally social systems, and require a blameless culture to be successful

Notes and links can be found on: http://bit.ly/2hqzQ6K

2m:05s - The Video Metadata Service aggregates several sources into a consistent API consumed by other Netflix services.

2m:43s - Several checks and validations were in place within the video metadata service, but it is impossible to predict every way consumers will be using the data.

3m:30s - The access pattern used by the playback service was different than that used in the checks, and led to unexpected results in production.

3m:58s - Now, the services consuming the data are also responsible for testing and verifying the data before it rolls out to production. The Video Metadata Service can orchestrate the testing process.

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Haley Tucker on Responding to Failures in Playback Features at Netflix

In this week’s podcast, Thomas Betts talks with Chris Richardson, a developer, architect, Java Champion and author of POJOs in Action. Before his workshop on Microservices w/ Spring Boot and Docker at QCon San Francisco 2016, Richardson took time to discuss his ideas on how to use DDD and CQRS concepts as a guide for implementing a robust microservices architecture.

Why listen to this podcast:

 - "Microservice architecture" is a better term than "microservices".   The latter suggests that a single microservice is somehow interesting
 - The concepts discussed in Domain-Driven Design are an excellent guide for how to implement a microservices architecture
 - Bounded Contexts correspond well to individual microservices
 - Event sourcing and CQRS provide patterns for how to implement loosely coupled services
 - When converting a monolith to microservices, avoid a big bang rewrite, in favor of an iterative approach

Notes and links can be found on: http://bit.ly/2hZ8TM1

11m:51s - Microservices must be loosely coupled, usually creating a model with one database per service.
12m:45s - There is a business requirement to maintain data consistency across services, and using an event driven architecture is a good way to achieve that.
13m:38s - Event sourcing is specific technique for persisting domain objects as a series of events.
14m:11s - Just as transactions don’t like to be split across microservices, queries cannot simply join across multiple data sources. CQRS provides a solution that accommodates querying via microservices and materialized views.

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Chris Richardson on Domain-Driven Microservices Design

Wesley Reisz talks to Daniel Bryant on moving from monoliths to micro-services, covering bounded contexts, when to break up micro-services, event storming, practices like observability and tracing, and more.

Why listen to this podcast:

- Migrating a monolith to micro-services is best done by breaking off a valuable but not critical part first.
 - Designing a greenfield application as micro-services requires a strong understanding of the domain.
 - When a request enters the system, it needs to be tagged with a correlation id that flows down to all fan-out service requests.
 - Observability and metrics are essential parts to include when moving micro-services to production.
 - A service mesh allows you to scale services and permit binary transports without losing observability.

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Daniel Bryant on Microservices and Domain Driven Design

Wesley Reisz talks to Martijn Verburg, co-founder of the London Java Community and CEO of jClarity, about the JCP EC “no” vote on the Java Platform Module System (JPMS), which is due to be shipped as part of Java 9.  The talk about what JPMS offers, how it works, what the no vote means and what happens next.

Why listen to this podcast:

- Jigsaw isn’t dead
- The “no” vote was based on the submission being a bit early, and without expert group consensus that it should be submitted
- Since the vote started, several amendments have been made which addressed some of the concerns listed by those who voted “no”
- Daily calls with the expert group and interested parties will work to resolve the outstanding issues promptly
- A resubmission is due within 30 days with a future vote expected to go through

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Martijn Verburg on the JCP EC “No” Vote for the Java Modules

Werner Schuster talks to Martin Hadley, data scientist at University of Oxford. They discuss the state of the R language, the rich R ecosystem that covers development (RStudio), notebooks for publication (R Notebooks, RPubs), writing web apps (Shiny), and the pros/cons of the different data frames implementations.

Why listen to this podcast:

 - R is the tool for working with rectangular data
 - Modern data frame implementations are Tibble and data.table (for large amounts of data)
 - RMarkdown and R Notebooks allow to explore data and then publish it the results and (interactive) visualization
 - Use Shinyapps to publish server side R applications
 - Tidyverse is the place to look for modern R packages

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Martin Hadley on R and the modern R ecosystem

Wesley Reisz talks with Sam Newman about microservices.  They explore the current state of the art with regards the architectural style and corresponding tooling and deployment platforms. They then discuss how microservices increase the surface area of where sensitive information can be read or manipulated, but also have the potential to create systems that are more secure.

Why listen to this podcast:

 - Different organisations have different risk appetites for new technology, so what may be appropriate for one organisation may not be appropriate technology choices for another. 
 - If you are deploying micro services then you need to know why you are doing it and what benefits you expect to get from deploying them.
 - Micro services are defined by their independently deployable units rather than their size. 
 - Using a cryptographic token that is verifiable off line is a common pattern for passing authentication contexts around to different services.
 - Serverless architectures redeuce the need to monitor server patching but does not diminish the need for monitoring application runtime or library dependencies from security patching.

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Security Considerations and the State of Microservices with Sam Newman

Sander Mak and Wes Reisz discuss the Java module system and how adoption is going. Topics discussed on this podcast include Java modularity steps / migrations, green field projects, some of the concerns that caused the EC to initially vote no on Java 9, and a new tool for building custom JREs called JLink. Additionally, as Java 10 was recently released a short bit at the end was added to discuss some of the latest news with Java.

Why listen to this podcast:

• People quickly moved to Java 8 because of features like Streams and Lambdas. Java 9 has a different story around modularity and application architecture. Adoption is slower and more intentional.
• Migrating large codebases to use modularity is hard. Many of the projects using modules are greenfield, and those large codebases that are moving now are most often using the classpath.
• Jlink is a new command line tool released with Java 9. It allows developers to create their own lightweight, customized JRE for a module-based Java application.
• Java version scheme has dropped the 1.* prefix. Future releases of the JDK will have the version number and follow the form *.0.1 (i.e. 9.0.1)
• While the module system will likely show it’s benefit mostly for new development, many 3rd party libraries are moving to adopt modularity and removing their dependencies on JDK internal APIs. It’s improving the experience for teams adopting modularity.
• There are no known open JEPS regarding the enhancement of the Java module system.
• Java 10 has been released. The release features changes to the freely available Java versions, local variable type inference (var), experimental GRAAL JIT compiler, application class data sharing, improved container support/awareness, and others.

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Sander Mak on the Java Module System

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 therefore has no impact on the idea of clean architecture.
* There is room for “creature comforts” in a code base that makes for more livable, convenient code.
* “We have no ethics that are defined [in software].” If we don’t find a way to police it ourselves, governments will. We have to come up with a code of ethics.

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Uncle Bob Martin on Clean Software, Craftsperson, Origins of SOLID, DDD, & Software Ethics

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.

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Vaughn Vernon on Developing a Domain Driven Design first Actor-Based Microservices Framework

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 activations.

 - From here the idea is to mix image embeddings with word embeddings.  The embeddings, whether for words or images, are just a vector that represents a thing.  There are many approaches to getting vectors for words, but the one that started it is word2vec.

 - For both image embeddings and word embeddings you can typically use pre-trained models, meaning that you only need to train the final step of bringing the two models together.

 - Before deploying to production it is important that you validate the model against biases such as sexism, typically using outside people to a carry out a through audit.

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