Summer series- Serverless

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

Wesley Reisz talks to Sid Anand, a data architect at cybersecurity company Agari, about building cloud-native data pipelines. The focus of their discussion is around a solution Agari uses that is built from Amazon Kinesis Streams, serverless functions, and auto scaling groups.

Sid Anand is an architect at Agari, and a former technical architect at eBay, Netflix, and LinkedIn.  He has 15 years of data infrastructure experience at scale, is a PMC for Apache Airflow, and is also a program committee chair for QCon San Francisco and QCon London.

Why listen to this podcast

 - Real-time data pipeline processing is very latency sensitive
 - Micro-batching allows much smaller amounts of data to be processed
 - Use the appropriate data store (or stores) to support the use of the dataIngesting data quickly into a clean database with minimal indexes can be fast
 - Communicate using a messaging system that supports schema evolution

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Sid Anand on Building Agari’s Cloud-native Data Pipelines with AWS Kinesis and Serverless

In this podcast, Wes talks to Guy Podjarny (Founder/CEO Synk). The two discuss the space between open source software and third-party dependencies, including a discussion of the Equifax hack (and what we can learn from it), the role of serverless architectures today (and what it means to application surface area), and then finally they wrap with security hygiene best practices with OSS and serverless.

Why listen to this podcast:

 - The majority of security vulnerabilities that exist in applications today comes from vulnerable third-party libraries, rather than the application’s own code.
 - An application shouldn’t permit total leak of all data because of a single vulnerability - defence in depth is important.
 - Equifax couldn’t have failed more spectacularly in the way they handled it.
 - The Equifax hack serves as a wake-up call to pay attention to vulnerabilities in dependencies.
 - If your build system breaks the build when a dependency vulnerability is found automatically, it will be applied sooner.

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Guy Podjarny on OSS Security, Serverless, and the Equifax Hack

On this week’s podcast, Wes Reisz talks with Chris Swan. Chris is the CTO for the global delivery organisation at DXC Technology. Chris is well versed in DevOps, Infrastructure, Culture, and what it means to put all these together. Today’s topics include both DevOps and NoOps, and what Chris calls LessOps, what Operations means in a world of Serverless, where he sees Configuration Management, Provisioning, Monitoring and Logging heading. The podcast then wraps talking about where he sees validating code in a serverless deployment, such as canaries and blue-green deployments.

Why listen to this podcast:

* Serverless still requires  ops - even if the ops aren’t focused on the technology
* Even with minimal functions, the amount of configuration may exceed it by a factor of three
* Disruptive services often move the decimal point
* ML is the ability to make the inferences and AI is the ability to make decisions based on those inferences

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Chris Swan on DevOps and NoOps, plus Operations and Code Validation in a Serverless Environment

The Serverless Framework is quickly becoming one of the more popular frameworks used in managing serverless deployments. David Wells, an engineer working on the framework, talks with Wes Reisz about serverless adoption and the use of the open source Serverless Framework. 

On this week’s podcast, the two dive into what it looks like to use the tool, the development experience, why a developer might want to consider a tool like the serverless framework, and finally wraps up with what the tool offers in areas like CI/CD, canaries, and blue/green deployment. 

Why listen to this podcast:

 - Serverless allows you to focus on the core business functionality and less on the infrastructure required to run your systems.
 - Serverless Framework allows you to simplify the amount of configuration you need for each cloud provider (for example, you can automate much of the configuration required for CloudFormation with AWS)
 - Serverless Framework is an open source CLI tool that supports all major cloud providers and several on-prem solutions for managing serverless functions.
 - The serverless space has room to grow in offering a local development space. Much of the workflow today involves frequent deploy and scoping the deployment for different stages.
 - Serverless Framework is open source and invites contributions from the community.

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Serverless and the Serverless Framework with David Wells

On this podcast, Pam Selle (an engineer for IOPipe who builds tooling for serverless observability) talks about the case for serverless and the challenges for developing observability solutions. Some of the things discussed on the podcast include tips for creating boundaries between serverless and non-serverless resources and how to think of distributed tracing in serverless environments.

Why listen to this podcast:

- Coca Cola was able to see a productivity gain of 29% by adopting serverless (as measured by the amount of time spent on business productivity applications).
- Tooling for serverless is often a challenge because resources are ephemeral. To address the ephemeral nature of serverless, you need to think about what information you will need to log ahead of time.
- Monitoring should focus on events important to the business.
- Build barriers between serverless and flat scaling non-serverless resources to prevent issues. Queues are an example of ways to protect flat scaling resources.
- In-memory caches are a handy way to help serverless functions scale when fronting databases.
- There are limitations with tracing and profiling on serverless. Several external products are available to help.
- Serverless (and Microservices) are not for every solution. If you are choosing between two things, and one of them lets you ship and the other does not choose the thing that lets you ship.

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Pam Selle on Serverless Observability

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Serverless and the Serverless Framework with David Wells

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