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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Quick scan our curated show notes on InfoQ http://bit.ly/2kwB2eB

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Show Notes

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

The three pillars of observability are logs, metrics, and tracing. Most teams are able to handle logs and metrics, while proper tracing can still be a challenge. On this podcast, we talk with Yuri Shkuro, the creator of Jaeger, author of the book Mastering Distributed Tracing, and a software engineer at Uber, about how the Jaeger tracing backend implements the OpenTracing API to handle distributed tracing.
 
Why listen to the podcast:

 - Jaeger is an open-source tracing backend, developed at Uber. It also has a collection of libraries that implement the OpenTracing API.
 - At a high level, Jaeger is very similar to Zipkin, but Jaeger has features not available in Zipkin, including adaptive sampling and advanced visualization tools in the UI.
 - Tracing is less expensive than logging because data is sampled. It also gives you a complete view of the system. You can see a macro view of the transaction, and how it interacted with dozens of microservices, while still being able to drill down into the details of one service.
 - If you have only a handful of services, you can probably get away with logging and metrics, but once the complexity increases to dozens, hundreds, or thousands of microservices, you must have tracing.
 - Tracing does not work with a black box approach to the application. You can't simply use a service mesh then add a tracing framework. You need correlation between a single request and all the subsequent requests that it generates. A service mesh still relies on the underlying components handling that correlation.

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2ZlvMyR
You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

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Yuri Shkuro on Tracing Distributed Systems Using Jaeger

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Yuri Shkuro on Tracing Distributed Systems Using Jaeger

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