Observability is a crucial aspect of operating Microservices at scale today. 

Today on the InfoQ podcast, Wes Reisz speaks with Chronosphere’s CEO Martin Mao about how he thinks about observability. Specifically, the two discuss Chronosphere’s strategy for implementing a successful observability program. Starting with alerting, Martin discusses how metrics (usually things like RED metrics or Google’s Four Golden Signals) are tools to aggregate counts and let operators know when things are moving towards an incident. In stage two of this approach, operators begin to isolate and triage what’s happening in an effort to provide a quick system restoration. Finally, Martin talks about root cause analysis (RCA) in the final stage as a way of preventing what happened from happening again. Martin uses this three stage approach (and the questions that should be asked in each of these stages) as a way of focusing on what’s important (or reducing things like Mean Time to Recovery) in a modern cloud native architecture.

Observability is the ability to understand the state of a system by observing its outputs, on today’s podcast we talk about a strategy for implementing a meaning observability program.

Read a transcript of this interview: https://bit.ly/3AZYpkD

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InfoQ Podcast

Transcript

Martin Mao on Observability, focusing on Alerting, Triage, & RCA

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

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Martin Mao on Observability, focusing on Alerting, Triage, & RCA

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