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

More on this:
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

In this podcast, Daniel Bryant sat down with Greg Law, CTO at Undo. Topics discussed included: the challenges with debugging modern software systems, the need for “hyper-observability” and the benefit of being able to record and replay exact application execution; and the challenges with implementing the capture of nondeterministic system data in Undo’s LiveRecorder product for JVM-based languages that are Just-In-Time (JIT) compiled.

Why listen to this podcast:

 - Understanding modern software systems can be very challenging, especially when the system is not doing what is expected. When debugging an issue, being able to observe a system and look at logging output is valuable, but it doesn’t always provide all of the information a developer needs. Instead we may need “hyper observability”; the ability to “zoom into” bugs and replay an exact execution.
 - Being able to record all nondeterministic stimuli to an application -- such as user input, network traffic, interprocess signals, and threading operations -- allows for the replay of an exact execution of an application for debugging purposes. Execution can be paused, rewound, and replayed, and additional logging data can be added ad hoc.
 - Undo’s LiveRecorder allows for the capture of this nondeterministic data, and this can be exported and shared among development teams. The UndoDB debugger, which is based on the GNU Project Debugger, supports the loading of this data and the execution and debugging in forwards and reverse execution of the application. There is also support for other debuggers, such as that included within IntelliJ IDEA.
 - Advanced techniques like multi-process correlation reveal the order in which processes and threads alter data structures in shared memory, and thread fuzzing randomizes thread execution to reveal race conditions and other multi-threading defects.
 - The challenges of using this type of technology when debugging (micro)service-based application lies within the user experience i.e. how should the multiple process debugging experience be presented to a developer?
Live Recorder currently supports C/C++, Go, Rust, Ada applications on Linux x86 and x86_64, with Java support available in alpha. Supporting the capture and replay of data associated with JVM language execution, which contain extra abstractions and are often Just-In-Time (JIT) compiled, presented extra challenges.

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/37XLUa0
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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Greg Law on Debugging, Record & Replay of Data, and Hyper-Observability

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.

Greg Law on Debugging, Record & Replay of Data, and Hyper-Observability

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