terça-feira, 21 de março de 2017

Apache Mesos, Overview and Architecture

Apache Mesos is a cluster manager, or distributed kernel system and use the same principle than linux kernel.

It abstract CPU, memory, storage and other physical and virtual resources, like fault tolerance and elastic distribution.

The Mesos kernel run in all machines providing applications ( Hadoop, Spark, Kafka, Elasticsearch) with APIs to manage resource and scheduling to datacenter or cloud.

It has fault tolerance of master and agents using zookeeper.

Native suport container with Docker and others images AppC(Organisation for the App Container specification, including the schema and associated tooling)

Support isolation of CPU, memory, disk, ports, GPU.
HTTP APIs to develop new distributed applications, to operate the cluster and for monitoring.


Mesos consists of a master daemon that manages agent daemons running on each cluster node and mesos frameworks that perform tasks on those agents.

The master allows the sharing of resources (CPU, RAM, ...) in structures and decides how many resources to offer each structure according to a given organizational policy, such as fair sharing or strict priority.

To support a diverse set of policies, the master employs a modular architecture that facilitates the addition of new allocation modules through a plug-in mechanism. 

A framework running on top of the Mesos consists of two components:
  •  a scheduler that registers with the master to be offered as a resource
  •  an executor process that is launched on agent nodes to perform the structure tasks.

The master determines how much feature is made available.
The scheduler determines which resource will be available

The figure below shows an example of how a structure is scheduled to perform a task. 

  1. Agent 1 reports to the master that it has 4 CPUs and 4GB of free memory. The master then invokes the allocation policy module and talks to the framework 1 that can offer its resources because they are free.
  2. The scheduler framework warns the master that it has two tasks to run in the agent and needs 2CPU and 1GB of memory and in the other task it needs 1 CPU and 2 GB of Memory.

Scheduling algorithm (Multilevel queue scheduling)

This algorithm can be used in situations where processes are divided into different groups.
Example: the division between foreground processes and background processes.
These two types of processes have different response times and requirements so you can have a different scheduling.
It is very useful for shared memory problems.


Mesos provides mechanisms to reserve resources in specific Slaves.
Two types of Reservation:
  • Static Reservation
  • Dinamic Reservation (Default) 


  • Isolate a task from other running tasks.
  • Container tasks for running in resource-limited time environment.
  • Control individual task resources (eg CPU, memory) programmatically.
  • Run the software on a pre-packaged file system image, allowing it to run in different environments.

Types of containerizers

Mesos manages to work with different types of container besides Docker, but by default Less uses its own container
Container Type supported:
  • Composing
  • Docker
  • Mesos Composing containerizer
Is the possibility of working with Docker and Mesos Container at the same time.
You can launch an image of Docker as a Task, or as an Executor.

Mesos  container 

This container allows tasks to be performed by an isolated container array provided by the Mesos

Allows mesos to control Tasks at runtime without relying on other containers.
You can have control of OS operations like cgroups / namespace
Promises to have the latest container technologies
Enables control of Disk Usage Limit
Insulation can be customized by task
High-Availability Mode

If the Master becomes unavailable, existing tasks will continue to run, but new features can not be. Allocated and new tasks can not be launched.
To reduce the possibility of this occurring, Mesos uses multiples, one active and several backups in case of failure.
Whoever coordinates the election of the new master is the Zookeper.

Mesos also use Apache Zookeeper, part of Hadoop, to synchronize distributed processes to ensure all clients receive consistent data and assure fault tolerance.

Nodes Discovery -> Is done by Zookeeper

When a network partition occurs and disconects a component (master, agent, or schedule) from the ZooKeeper, the master detects and induces a timeout.

Observability Metrics

The information reported by the mesos includes details about availability of resources, use of resources, registered frameworks,
Active agents and tasks state.
It is possible to create automated alerts and put different metrics in a dashboard.

Mesos provides two types of metrics:

Counter -> Accompanying the growth and the reduction of events

Gauges -> Represents some values of instantant magnitude

When you start a task, you can create a volume that exists outside the BOX of the task and persist even after the task is executed or completed.
Persistent Volumes

Mesos provides a mechanism to create a persistent volume of disk resources.
When the task finishes, its capabilities - including the persistent volume - can be offered back to the structure so that the structure can start the same task again, start a recovery task, or start a new task that consumes the previous task output as Your entry.
Persistent volumes allow services such as HDFS and Cassandra to store their data within the Mesos. 

The Mesos Replicated Log

Mesos provides a library that allows you to create fault-tolerant replicated logs;
This library is known as the replicated log.
The Mesos master uses this library to store cluster state in a replicated and durable way;
The library is also available for use by frameworks to store the replicated structure state or to implement the common pattern of "replicated state machine".
Replicated Log is often used to allow applications to manage the replicated state in a strong consistency. 

Mesos  Frameworks:

  • Vamp is a deployment and workflow tool for container orchestration systems, including Mesos/Marathon. It brings canary releasing, A/B testing, auto scaling and self healing through a web UI, CLI and REST API.
  • Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
  • Marathon is a private PaaS built on Mesos. It automatically handles hardware or software failures and ensures that an app is “always on”.
  • Spark is a fast and general-purpose cluster computing system which makes parallel jobs easy to write.
  • Chronos is a distributed job scheduler that supports complex job topologies. It can be used as a more fault-tolerant replacement for Cron.

Mesos offers many of the features that you would expect from a cluster manager, such as:

  • Scalability to over 10,000 nodes
  • Resource isolation for tasks through Linux Containers
  • Efficient CPU and memory-aware resource scheduling
  • Highly-available master through Apache ZooKeeper
  • Web UI for monitoring cluster state

sexta-feira, 3 de fevereiro de 2017

JSON Web Token, Security for applications

JSON Web Token called JWT, is an open standard RFC 7519 that defines a compact and self-contained way for securely transmitting information between parties as a JSON object. 
Each information can be verified and trusted because it is digitally signed. 
JWTs can be signed using a secret (with the HMAC algorithm) or a public/private key pair using RSA.

Some importants concepts:
  • Compact: JWTs can be sent through a URL, POST parameter, or inside an HTTP header. 
  • Self-contained: The payload contains all the required information about the user, avoiding the need to query the database more than once.

When should you use JSON Web Tokens:
  • Authentication: This is the most common scenario for using JWT. Once the user is logged in, each subsequent request will include the JWT, allowing the user to access routes, services, and resources that are permitted with that token. 
  • Information Exchange: JSON Web Tokens are a good way of securely transmitting information between parties, because as they can be signed, for example using public/private key pairs, you can be sure that the senders are who they say they are. 

JWT Structure:

A complete JWT is represented something like this:


This token could be sliced in 3 parts:




Each part is separated by “.” 
<base64url-encoded header>.<base64url-encoded claims>.<base64url-encoded signature>

Here one simple sample of the flow to authentication of User to access a API Server.

terça-feira, 3 de janeiro de 2017

Automating Infrastructure on Premise or Cloud with Ansible

Ansible Tasks are idempotent. Without a lot of extra coding, bash scripts are usually not safety run again and again. Ansible uses "Facts", which is system and environment information it gathers ("context") before running Tasks.

Design Principles

  • Have a dead simple setup process and a minimal learning curve
  • Manage machines very quickly and in parallel
  • Avoid custom-agents and additional open ports, be agentless by leveraging the existing SSH daemon
  • Describe infrastructure in a language that is both machine and human friendly
  • Focus on security and easy auditability/review/rewriting of content
  • Manage new remote machines instantly, without bootstrapping any software
  • Allow module development in any dynamic language, not just Python
  • Be usable as non-root
  • Be the easiest IT automation system to use, ever.

Ansible by default manages machines over the SSH protocol.

Once Ansible is installed, it will not add a database, and there will be no daemons to start or keep running. You only need to install it on one machine (which could easily be a laptop) and it can manage an entire fleet of remote machines from that central point. When Ansible manages remote machines, it does not leave software installed or running on them, so there’s no real question about how to upgrade Ansible when moving to a new version.

Playbooks could be considered the main concept in Ansible.

Playbooks are Ansible’s configuration, deployment, and orchestration language. They can describe a policy you want your remote systems to enforce, or a set of steps in a general IT process.

At a basic level, playbooks can be used to manage configurations of and deployments to remote machines. At a more advanced level, they can sequence multi-tier rollouts involving rolling updates, and can delegate actions to other hosts, interacting with monitoring servers and load balancers along the way.

Playbooks are designed to be human-readable and are developed in a basic text language.

Playbooks are expressed in YAML format and have a syntax, which intentionally tries to not be a programming language or script, but rather a model of a configuration or a process.

In my example, set two virtual machines with Vagrant, where the first I put Ansible installed and the second I applied some configurations.

Configure multi-machine like this in my previous post

Vagrantfile to multi-machine:

 Vagrant.configure(2) do |config|  
  config.vm.box = "ubuntu/trusty64"  
  config.vm.define "machine1" do |node1|  
    node1.vm.network "private_network", ip: ""  
    node1.vm.hostname = "machine1"  
    node1.vm.provider "virtualbox" do |v|  
     v.memory = 1024  
     v.cpus = 1  
  config.vm.define "machine2" do |node2|  
    node2.vm.network "private_network", ip: ""  
    node2.vm.hostname = "machine2"  
    node2.vm.provider "virtualbox" do |v|  
     v.memory = 1024  
     v.cpus = 1  

On machine1 install Ansible with these commands below:

#vagrant ssh machine1

If ask for password put “vagrant"

Commands to Install Ansible:

  1.  sudo apt-get install software-properties-common
  2.  sudo apt-add-repository ppa:ansible/ansible
  3.  sudo apt-get update
  4.  sudo apt-get install ansible

Edit /etc/ansible/hosts  and add IPs ( ,

To check if everything ok run this command: 

ansible all -m ping -s -k -u vagrant

Result should be:
machine2 | SUCCESS => {
    "changed": false,
    "ping": "pong"


First Playbook is to install java and tomcat in second machine.

playbook-tomcat.yml :

 - hosts: machine2  
   http_port: 80  
   max_clients: 200  
  remote_user: vagrant  
   - name: updates a server  
    apt: update_cache=yes  
   - name: upgrade a server  
    apt: upgrade=full  
   - name: install java   
    apt: name=default-jdk state=latest  
   - name: install tomcat  
    apt: name=tomcat7 state=latest  
   - name: make sure apache is running  
    service: name=tomcat7 state=started  

ansible-playbook playbook-tomcat.yml -sudo -u vagrant --ask-pass

terça-feira, 15 de novembro de 2016

Spring Cloud Netflix Zuul - Edge Server/Api Gateway/Gateway Service

Zuul is the front door for all requests from devices and web sites to the backend of the Netflix streaming application. As an edge service application, Zuul is built to enable dynamic routing, monitoring, resiliency and security. 

Routing in an integral part of a microservice architecture. For example, may be mapped to your web application, /api/users is mapped to the user service and /api/shop is mapped to the shop service. Zuul is a JVM based router and server side load balancer by Netflix.

The volume and diversity of Netflix API traffic sometimes results in production issues arising quickly and without warning. We need a system that allows us to rapidly change behavior in order to react to these situations.

Zuul uses a range of different types of filters that enables us to quickly and nimbly apply functionality to our edge service. These filters help us perform the following functions: 

  • Authentication and Security: identifying authentication requirements for each resource.
  • Insights and Monitoring: tracking meaningful data and statistics.
  • Dynamic Routing: dynamically routing requests to different backend..
  • Stress Testing: gradually increasing the traffic.
  • Load Shedding: allocating capacity for each type of request and dropping requests.
  • Static Response handling: building some responses directly.
  • Multiregion Resiliency: routing requests across AWS regions.

Zuul contains multiple components:
  • zuul-core: library which contains the core functionality of compiling and executing Filters
  • zuul-simple-webapp: webapp which shows a simple example of how to build an application with zuul-core
  • zuul-netflix: library which adds other NetflixOSS components to Zuul - using Ribbon for routing requests, for example.
  • zuul-netflix-webapp: webapp which packages zuul-core and zuul-netflix together into an easy to use package
Zuul gives us a lot of insight, flexibility, and resiliency, in part by making use of other Netflix OSS components:
  • Hystrix is used to wrap calls to our origins, which allows us to shed and prioritize traffic when issues occur
  • Ribbon is our client for all outbound requests from Zuul, which provides detailed information into network performance and errors, as well as handles software load balancing for even load distribution
  • Turbine aggregates fine­grained metrics in real­time so that we can quickly observe and react to problems
  • Archaius handles configuration and gives the ability to dynamically change properties

I talk about these components here:

We can create a filter to route a specific customer or device to a separate API cluster for debugging. Prior to using Zuul, we were using Hadoop to query through billions of logged requests to find the several thousand requests we were interested in.

We have an automated process that uses dynamic Archaius configurations within a Zuul filter to steadily increase the traffic routed to a small cluster of origin servers. As the instances receive more traffic, we measure their performance characteristics and capacity.

Spring Cloud has created an embedded Zuul proxy to ease the development of a very common use case where a UI application wants to proxy calls to one or more back end services. This feature is useful for a user interface to proxy to the backend services it requires, avoiding the need to manage CORS and authentication concerns independently for all the backends.

To enable it, annotate a Spring Boot main class with @EnableZuulProxy, and this forwards local calls to the appropriate service. By convention, a service with the ID "users", will receive requests from the proxy located at /users.
The proxy uses Ribbon to locate an instance to forward to via discovery, and all requests are executed in a hystrix command, so failures will show up in Hystrix metrics, and once the circuit is open the proxy will not try to contact the service.

Zuul Request Lifecycle

In this picture is possible check that, before access the original server, Zuul provide some functionality to add in requests , or after requests (response), like filter, routing, aggregation, error treatment etc.

In my sample, I implemented filter/routing with Zuul.

I have 2 components in this sample, service and Zuul.

Service will provide some operations :

@SpringBootApplicationpublic class BookApplication {

@RequestMapping(value = "/available")
public String available() {
return "Spring in Action";

@RequestMapping(value = "/checked-out")
public String checkedOut() {
return "Spring Boot in Action";

public static void main(String[] args) {
class, args);

Zuul service:

public class GatewayApplication {
  public static void main(String[] args) {
class, args);
public SimpleFilter simpleFilter() {
return new SimpleFilter();

public class SimpleFilter extends ZuulFilter {
  private static Logger log = LoggerFactory.getLogger(SimpleFilter.class);
public String filterType() {
return "pre";
public int filterOrder() {
return 1;
public boolean shouldFilter() {
return true;
public Object run() {
ctx = RequestContext.getCurrentContext();
    HttpServletRequest request = ctx.getRequest();
    log.info(String.format("%s request to %s", request.getMethod(), request.getRequestURL().toString()));
    return null;

With Zuul and book service working together we have possibility access books operation available and checked-out across  http://localhost:8080/books


terça-feira, 18 de outubro de 2016

Managing your Database with Liquibase and Gradle

One of the major system development problems has always been how and when we will update the database.

When happen some database change in the development environment, always some questions appear:
  • Scripts for DB changes were created?
  • Where to save the DB changes?
  • When should we apply these changes?
  • These changes have already been implemented?
  • How do we track and manage database changes?
  • Who did these changes?

Liquibase is the library that can help address these issues.

Liquibase is an independent library open source database used to track, manage, and apply changes to the database.

Liquibase works better because it understands what the changes are. For example, a database comparison program would simply see the “person” table on integration has a “firstname” and a “lastname” column, but on live, the “person” table has a “name” column. It would report that you need to drop the “name” column and add a “firstname” and a “lastname” column. While this would leave you with the correct schema, you would lose everyone’s name in the process. With Liquibase, you would have a changeset that says “rename ‘name’ to ‘lastname’ and add a ‘firstname’ column” or, even better, “split the name column on a space and place the values in new ‘firstname’ and ‘lastname’ columns, then drop the ‘name’ column.” Knowing why they are different allows changes to production databases without the fear of losing valuable data.

In this post I will show how you can use the powerful tool Liquibase together with Gradle to automate these tasks, from there will be easy to put Liquibase to work with your continuos integration tools.

Some important concepts:

Changelog file

  • Is the file that contains references of all scripts that should be applied to the database in any environment.

ChangeSet Files:

  • Are all the files recorded in a Changelog
  • Changesets files can be written primarily in XML, YAML, JSON, SQL
    •  I chose for this example SQL.

Some Advices:

  • IDs cannot be repeated, otherwise they will not run
  • Scripts should be smalls
  • Should be added script Rollback whenever possible
  • Must be added new scripts on the changelog.xml
  • Everything that was executed is registered on table DATABASECHANGELOG






I created tasks for every environment I have and where liquibase should run the script.

With Gradle I need only choose a task as it is below:
  • To execute:
    • gradle task dev update
    • gradle task qa update
    • gradle task prod update

After that you can check in your database, that these scripts 001 and 002 were applied.

On the table DATABASECHANGELOG is possible check some records like these:

This sample above complete in my GitHub 


quinta-feira, 13 de outubro de 2016

Library for configuration management from Netflix

Archaius is the Netflix client side configuration library. It is the library used by all of the Netflix OSS components for configuration. 

It is an extension of the Apache Commons Configuration project. It allows updates to configuration by either polling a source for changes or for a source to push changes to the client. Archaius uses Dynamic Property classes as handles to properties.

Some Archaius features: 
  • Dynamic, Typed Properties
  • High throughput and Thread Safe Configuration operations
  • A polling framework that allows obtaining property changes of a Configuration Source
  • A Callback mechanism that gets invoked on effective
  • A JMX MBean that can be accessed via JConsole to inspect and invoke operations on properties
  • Out of the box, Composite Configurations for applications
  • Implementations of dynamic configuration sources for URLs, JDBC and Amazon DynamoDB
  • Scala dynamic property wrappers

In my sample I will show how use Dynamic Properties and how is could be used Archaius with configuration with many environments.

About ArchaiusSandBox class:
  • Set default configuration with property archaius.properties 
  • installConfig is a method that apply config, with polling to each 1 second 
  • In set environment we will get value from "environment" variable, we expected in this case DEV or TEST
  • In the main method, we call the configuration to get "myprop"
    • it is possible put default configuration to property, I defined "NOT FOUND" but is possible put any value.
    • waiting 10 seconds to put new configuration and call again 

Resources folder. There are 3 properties:
  • archaius.properties is the default configuration with  reference to configuration available per environment
  • archaius-dev.properties configuration specific to DEV
  • archaius-test configuration specific to TEST

In runtime is possible change property, to work in this sample, should be added on archaius.properties (within 10 seconds) the property myprop=TEST-VALUE.

After 10 seconds put in main method it should printed the new value.

In this post I used some archaius functionalities, there are many others available. But with these few codes is possible create configuration to many environments with possibility to change in runtime.


segunda-feira, 26 de setembro de 2016

Spring Cloud Netflix - Load Balancer with Ribbon/Feign

Spring Cloud Netflix to provide client-side load balancing in calls to another microservice.
The idea in this post is show some concepts about load balancing, Ribbon and Feign, and step by step who work Ribbon and Feign.

Load Balancing 

 Load Balancing automatically distributes incoming application traffic between two or more computers. It enables you to achieve fault tolerance in your applications, seamlessly providing the required amount of load balancing capacity needed to route application traffic. Load balancing aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any single resource. Using multiple components with load balancing instead of a single component may increase reliability and availability through redundancy.
Ribbon  - Load Balancer without Eureka 

Ribbon is a client side load balancer, which gives you a lot of control over the behavior of HTTP and TCP clients. Ribbon's Client component offers a good set of configuration options such as connection timeouts, retries, retry algorithm (exponential, bounded back off) etc. Ribbon comes built in with a pluggable and customizable Load Balancing component. 
Some of the load balancing strategies offered are listed below:

  • Simple Round Robin LB
  • Weighted Response Time LB
  • Zone Aware Round Robin LB
  • Random LB

Client-Side  Load Balancing

An approach to load balancing is to deliver a list of server IPs to the client, and then to have client randomly select the IP from the list on each connection. It has been claimed that client-side random load balancing tends to provide better load distribution than round-robin DNS. With this approach, the method of delivery of list of IPs to the client can vary, and may be implemented as a DNS list (delivered to all the clients without any round-robin), or via hardcoding it to the list. If a "smart client" is used, detecting that randomly selected server is down and connecting randomly again, it also provides fault tolerance.
Ribbon provides the following features:

  • Load balancing
  • Fault tolerance
  • Multiple protocol (HTTP, TCP, UDP) support in an asynchronous and reactive model
  • Caching and batching
A central concept in Ribbon is that of the named client. Each load balancer is part of an ensemble of components that work together to contact a remote server on demand, and the ensemble has a name that you give it as an application developer.

Using Ribbon - Code:

First Service is hello-service 

This service provide some aleatory greeting when access http://localhost:8090/greeting

public class HelloApplication {

private static Logger log = LoggerFactory.getLogger(HelloApplication.class);

@RequestMapping(value = "/greeting")
public String greet() {
log.info("Access /greeting");

List<String> greetings = Arrays.asList("Hi there", "Greetings", "Salutations");
Random rand = new Random();

int randomNum = rand.nextInt(greetings.size());
return greetings.get(randomNum);

@RequestMapping(value = "/")
public String home() {
log.info("Access /");
return "Hi!";

public static void main(String[] args) {
SpringApplication.run(HelloApplication.class, args);


File: application.yml

    name: hello-service

  port: 8090

OBS: should create two more hello-service in Port 9092, 9999

Second Service is user-service 
This service call for hello-service, operation greeting, with ribbon configured, this service call hello-service based on algorithm round-robin.

@RibbonClient(name = "hello-service", configuration = HelloServiceConfiguration.class)
public class UserApplication {

RestTemplate restTemplate() {
return new RestTemplate();

RestTemplate restTemplate;

public String hi(@RequestParam(value = "name", defaultValue = "Rafael") String name) {
String greeting = this.restTemplate.getForObject("http://hello-service/greeting", String.class);
return String.format("%s, %s!", greeting, name);

public static void main(String[] args) {
SpringApplication.run(UserApplication.class, args);


public class HelloServiceConfiguration {
IClientConfig ribbonClientConfig;

public IPing ribbonPing(IClientConfig config) {
return new PingUrl();

public IRule ribbonRule(IClientConfig config) {
return new AvailabilityFilteringRule();

File: application.yml
In ribbon configuration are listed all services available.

    name: user-service

  port: 8888
      enabled: false
    listOfServers: localhost:8090,localhost:9092,localhost:9999

    ServerListRefreshInterval: 15000

When we call the service on URL http://localhost:8090/greeting , on console is possible see each service it is called, for example, in the fist call any host configured on list of servers could be used.

Feign - Load Balancer using Eureka 

Feign is a declarative web service client, or declarative REST client. It makes writing web service clients easier. To use Feign create an interface and annotate it. It has pluggable annotation support including Feign annotations and JAX-RS annotations. Feign also supports pluggable encoders and decoders. Spring Cloud adds support for Spring MVC annotations and for using the same HttpMessageConverters used by default in Spring Web. 
Spring Cloud integrates Ribbon and Eureka to provide a load balanced http client when using Feign.

I talk more about Eureka here.

Using Feign - Code:

Eureka server Project: http://localhost:8761

public class ApplicationEurekaServer {
    public static void main(String[] args) {
        new SpringApplicationBuilder(ApplicationEurekaServer.class)


File: application.yml

#Server Specifics
  port: 8761

      enabled: false

    name: eureka-server

#Eureka Specifics

    hostname: localhost
    registerWithEureka: false
    fetchRegistry: false
      defaultZone: http://${eureka.instance.hostname}:${server.port}/eureka/

Service is hello-service http://localhost:7111/greeting

public class HelloApplication {
DiscoveryClient client;

public String hello() {
ServiceInstance localInstance = client.getLocalServiceInstance();
return "Hello World: "+ localInstance.getServiceId()+":"+localInstance.getHost()+":"+localInstance.getPort();

public static void main(String[] args) {
SpringApplication.run(HelloApplication.class, args);


File: application.yml

    name: hello-service

  port: 7111
      enabled: true
      defaultZone: http://localhost:8761/eureka/  

Should be create other hello-service on Port 7112 http://localhost:7112/greeting

And the service that will consume these hello-service is user-service.


public class UserApplication {
HelloClient client;

public String hello() {
return client.hello();

public static void main(String[] args) {
SpringApplication.run(UserApplication.class, args);

interface HelloClient {
@RequestMapping(value = "/", method = GET)
String hello();


File: application.yml

    name: user-service

  port: 7211
      defaultZone: http://localhost:8761/eureka/
    leaseRenewalIntervalInSeconds: 10
      instanceId: ${vcap.application.instance_id:${spring.application.name}:${spring.application.instance_id:${server.port}}}

    enabled: true
    enabled: true
    sensitive: false

This service will be available on http://localhost:7211
user-service will ask to Eureka the hello-service address, and will receive some address that is registered.

If everything OK this service will be registered on Eureka:

When we call http://localhost:7211 on of these message will be showed:

Hello World: hello-service:

Hello World: hello-service: