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

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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Show Notes

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

Ben Sigelman is the CEO of Lightstep and the author of the Dapper paper that spawned distributed tracing discussions in the software industry. On the podcast today, Ben discusses with Wes observability, and his thoughts on logging, metrics, and tracing. The two discuss detection and refinement as the real problem when it comes to diagnosing and troubleshooting incidents with data. The podcast is full of useful tips on building and implementing an effective observability strategy.

Why listen to this podcast:

 - If you’re getting woke up for an alert, it should actually be an emergency. When that happens, things to think about include: when did this happen, how quickly is it changing, how did it change, and what things in my entire system are correlated with that change.
 - A reality that seems to be happening in our industry is that we’re coupling the move to microservices with a move to allowing teams to fully self-determine technology stacks. This is dangerous because we’re not at the stage where all languages/tools/frameworks are equivalent.
 - While a service mesh offers a great potential for integrations at layer 7 many people have unrealistic expectations on how much observability will be enabled by a service mesh. The service mesh does a great job of showing you the communication between the services, but often the details get lost in the work that’s being done inside the service. Service owners need to still do much more work to instrument applications.
 - Too many people focus on the 3 Pillars of Observability. While logs, metrics, and tracing are important, observability strategy ought to be more focused on the core workflows and needs around detection and refinement.
 - Logging about individual transactions is better done with tracing. It’s unaffordable at scale to do otherwise.
 - Just like logging, metrics about individual transactions are less valuable. Application level metrics such as how long a queue is are metrics that are truly useful.
 - The problem with metrics are the only tools you have in a metrics system to explain the variations that you’re seeing is grouping by tags. The tags you want to group by have high cardinality, so you can’t group them. You end up in a catch 22.
 - Tracing is about taking traces and doing something useful with them. If you look at hundreds or thousands of tracing, you can answer really important questions about what’s changing in terms of workloads and dependencies about a system with evidence.
 - When it comes to serverless, tracing is more important than ever because everything is so ephemeral. Node is one of the most popular serverless languages/frameworks and, unfortunately, also one of the hardest of all to trace.
 - The most important thing is to make sure that you choose something portable for the actual instrumentation piece of a distributed tracing system. You don’t want to go back and rip out the instrumentation because you want to switch vendors. This is becoming conventional wisdom.

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2PPIdeE
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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Ben Sigelman, Co-Creator of Dapper & OpenTracing API, on 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.

Ben Sigelman, Co-Creator of Dapper & OpenTracing API, on Observability

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