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Jonas Bonér, CTO of LightBend and creator Akka, discusses using Akka when developing distributed systems. He talks about the Actor Model, and how every Microservice needs to be viewed as a system to be successful. 

Why listen to this podcast:

 - Akka is JVM-based framework design for developing distributed systems leveraging the Actor Model - an approach for writing concurrent systems that treat actors as universal primitives and the most successful model with abstraction has been streaming
 - Circuit breakers in Akka are a backup and retry policy; they protect you by capturing failure data and allow you to roll back 
 - Every Microservice needs to be viewed as a system, it needs to have multiple parts that run on different machines in order to function and be fully resilient - thus is a Microsystem
 - Two different trends have emerged when it comes to hardware and environments: one is the trend toward Multi-core, the is a movement toward virtualized environments and the cloud
 - Saga pattern of managing long running transactions in a distributed system fits very well with messaging style architectures

Notes and links can be found on: http://bit.ly/2kwB2eB

Akka
The Actor Model
When Akka and the Actor Model is the perfect choice
Circuit breakers patterns in distributed systems
Two trends toward Multi-core
Reactive Manifesto
Event Driven vs. Message Driven
Reactive Programming and Streams
Microliths to Microsystems
What do you have to get right before you start trying to deploy a distributed systems?
Working with ML / AI at Lightbend to understand tracing through distributed system
Saga Pattern

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Show Notes

Jonas Bonér on the Actor Model, Akka, Reactive Programming, Microservices and Distributed Systems

In this interview NGINX's Chris Stetson describes some of the biggest pain points associated with microservices development and describes the various patterns and models - including the Circuit Breaker Pattern - covered in his new book, "Microservices Reference Architecture."  Chris also talks about the fundamental differences between microservices and SOA and how NGINX has extended the 12-Factor App to encompass microservices.  

Chris Stetson, Chief Architect of Microservices Engineering at NGINX, has architected applications for twenty years, including the first versions of Sirius Satellite Radio, Visa.com, Intel.com, Microsoft.com, and Lexus.com. He has built large monolithic systems, service-oriented architecture (SOA) applications, and most recently microservice-based systems. Most recently, Chris has authored The Microservices Reference Architecture eBook, a practical guide that helps solve many of the challenges of building microservices-based applications.

Chris Stetson on the Microservices Reference Architecture

Rossen Stoyanchev talks to Wesley Reisz about blocking and non-blocking architectures, upcoming changes in Spring including Spring WebFlux, the reactive web stack in Spring framework 5, due this summer.  He also discusses the differences between rxJava and Reactor.

Why listen to this podcast:

 - Spring Framework 5 is due to be released June 25 2017
 - Spring Web Flux provides a web programming model designed for asynchronous APIs
 - Back-pressure is important in a server environment; less so within a UI environment
 - It’s possible to use a Spring Web Flux client within a Spring MVC applciation
 - Managing sets of thread pools is more complicated than having a scalable asynchronous system

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Rossen Stoyanchev on Reactive Programming with Spring 5 and Spring WebFlux

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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http://bit.ly/2q20esc

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

Wesley Reisz talks to Sachin Kulkarni, Director of Engineering at Facebook, about the engineering challenges for Facebook live, and how it compares to the video upload platform at Facebook.

Why listen to this podcast:

 - Facebook Infrastructure powers the board family of apps including the Facebook app, Messenger and Instagram.  It is largely a C++ shop.  There is some Java and Python, and the business logic is all done in PHP.  The iOS apps are written in Objective C and the Android apps are in Java.

 - The video infra team at Facebook builds the video infrastructure across the whole company.  Projects include a distributed video encoding platform which results in low latency video encoding, video upload and ingest.

 - Facebook Live does encoding on both the client and the server.  The trade-off between encoding on the client side and the server side is mostly around the quality of the video vs. latency and reliability.

 - Facebook gets around 10x speed-up by encoding data in parallel compared to serial.  

 - They also have an AI-based encoding system which resulted in 20% smaller files than raw H.264.

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Sachin Kulkarni Describes the Architecture Behind Facebook Live

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 people wanted the change, but many others felt it would enable people to write bad code. Oracle had to balance the two views when making the change.
* The number of Java versions allow finer grain decision making on what is appropriate for an application. With the adoption of containers, applications are bundled with an exact JDK version rather than having to use one from a systems level. The different versions give developers more options.
* Incubating features are new libraries added to the JDK. They were offered starting with Java 9 as a way for people to test and offer feedback more rapidly. With Java 12, preview features will be released. Preview features are similar but are core platform and language features.
* Shenandoah and ZGC are both low latency garbage collectors. They originally came from different sources. While both garbage collectors are similar, each has different performance characteristics under different workloads. The two garbage collectors represent options available to JVM developers.
* Most non-trivial JDK features take more than six months to develop. Longer term side projects like Amber, Loom, Valhalla are where these features are developed prior to being released with a version of the JDK. The projects range from language enhancements to concurrency work.

Java Language Architect Brian Goetz on Java and the JDK

Software engineers, architects and team leads have found inspiration to drive change and innovation in their team by listening to the weekly InfoQ Podcast. They have received essential information that helped them validate their software development map. We have achieved that by interviewing some of the top CTOs, engineers and technology directors from companies like Uber, Netflix and more. Over 1,200,000 downloads in the last 3 years.

Java Language Architect Brian Goetz on Java and the JDK

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