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Showing posts from November, 2017

Getting Started with Dyno Queues

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Dyno-Queue is an interesting queue solution on top of Dynomite. Dyno-queue uses dynomite java driver a.k.a Dyno. Dyno Queue gets all benefits from dynomite like Strong Consistency, High Availability, Stability, High Throughput and Low latency and extend to queue semantics.

I highly recommend you read Netflix post about dyno-queue.



Running Multi-Nodes Akka Cluster on Docker

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Akka a great actor framework for the JVM.  Akka is high performance with high throughput and low latency, resilient by Design.  It's possible to use Akka with Java however I always used and recommend you use with Scala.

Create actors systems is pretty easy however creating multiple nodes on Akka cluster locally could be boring and error-prone. So I want to show how easy is to create an Akka cluster using Scala and Docker(Engine & Docker-Compose). We will create a very simple actor system, we will just log events on the cluster however you can use this as a base to do more complex code.


The power of Scala Type Classes

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Scala language it was very inspired by Haskell. Type classes in another sample of this and many inspirations from Haskell.

There are always several ways and styles of thinking to address problems. Type classes is another way of thinking and dealing with problems. For some problems, you might just use Pattern Matcher -- But if you have to add a type? Right, I could stick with good old OO since Scala is hybrid right? --  But what if you need to add one operator? Type Classes can help you out.

Running Dynomite on AWS with Docker in multi-host network Overlay

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Dynomite is a kick-ass project. Basically, allow you to have strong consistency on top of NoSQL Databases. I've been using dynomite for a while in production(AWS) and I can say the core is rock solid and it just works.

Lots of developers use Windows or Mac for instance and dynomite is built in C and it's really meant for Linux(Like all good things).  So some time ago I made 2 simple projects to get started quickly with dynomite.  Basically, the project creates a simple dynomite 3 node cluster and let you run on your local machine with docker.

There are 2 projects - One to create a dynomite cluster with Redis -- The other with Facebook's RocksDB(Experimental). So you can use it on your local machine to Debug and it works just fine. So why not go 1 step further and run Dynomite in AWS using docker? There are cool benefits if you do this approach.

DevOps Engineering is about Workflow

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DevOps Engineering is about Workflow

Currently, I work doing a bunch of different things(like most of Brazilians). I do Architecture, DevOps Engineering, Software Engineering, Agile Coaching and a bit of management. There different types of companies and cultures but basically or you are more specialized or more cross-functional I think in general Brazil is similar like Europe and more cross-functional and the US is more specialized but really depends on the company. I also run a small team doing: Dynomite / DM, Serverless Remediation, Stress Test and Chaos Platform and Telemetry / Observability. My teams need to provide solutions(engineering)but also Stability.

Building Effective Microservices - 80% OFF until 30th November

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Want to learn how to build microservices using Java 8, NetflixOSS Stack(Eureka, RxNetty, Feign, Hystrix, Ribbon) using Kubernetes(Minikube). 

That's the real deal you read it right. Get my videos series: Building Effective Microservices 80% until 30th November 2017.

Extending Redis with Redis-Modules

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Redis is a great K/V store written in C. Redis can accomplish a lot for a single thread process. There are many applications for redis like for instance:  - Frontend Database
- Real-time Counters
- Ad Serving
- Message Queue
- Geo and TimeSeries DB
- Session State
- Cache
That's all great but let's say I want more, how can we customize redis? There are some options like a Lua Script, Fork Redis, Tak to @Antirez or Create your own NoSQL database based on redis or not. Redis provided another solution - Redis added support for external modules in 2016.

Dynamic Configurations with Annotations and NetflixOSS Archaius 2

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NetflixOSS Archius 2 is a great Dynamic Configuration solution for microservices. Archaius is based on Apache Commons Configurations project.

Using Archaius we can load configurations from several sources such as OS env vars or any Database like Oracle or even from Zookeeper. If there is a missing configuration source you can add it pretty easy and load your configs. 
Archaius can be used in any java project no matter if is a microservice or not. 
Archaius also support dynamic configuration refresh via callbacks -- In short, this means you can reload your configs without re-deploy or downtime in your microservice -- which is really great.
Archaius has many nice features. Archaius also is very modular and easy to extend there are some nice community extensions available as well. 


Getting started with NetflixOSS Governator

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NetflixOSS Governator is a set of Google Guice extensions to create REST services using Jersey.

Using Governator we can easily configure servers like Jetty and Tomcat in order to build microservices. We also can use set of guice modules to integrated with Archaius and Eureka-Client.

Governator is not opinionated, it's similar to Spring Boot in comparison. However, Governator is configured to work with Guice and not Spring framework.

Governator it's cool because you can define pretty much everything using java code and annotations in a declarative fashion. All code is configured in Guice so we can take benefit of Ioc and Dependency injection and end up creating solution more testable by nature.

DevOps Monolith

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DevOps Monolith

The term Monolith it's often used in legacy systems or system poorly designed and poorly architected.  Microservices are the facto stand architecture when we talk about software. There are lots of companies also doing DevOps Engineering and DevOps per se its related to microservices. Microservices required some infrastructure work like for instance: Provisioning: Install OS system packages, files, scripts. Telemetry: Dashboards and alerts for you microservice eco-system. Testing: Stress Testing, Chaos Testing, Load Testing, etc... Canary: Automated canary analysis, deploy, score and rollback.

Infrastructure work could be more or less depending on your level of abstractions for instance if you are working with bare metal or IaaS you will definitely have more things to get done. However, if you are doing cloud-native microservices using kubernetes or any FaaS stack you might have less work but always well have some infrastructure work for someone to do.