DEV Community: Proofdock

Chaos Middleware: where Spring Boot meets Chaos Engineering

Marcin Kaszubski — Mon, 12 Oct 2020 05:55:28 +0000

Chaos engineering tools activate features like stopping virtual machines or terminating pods in your Kubernetes cluster. The Cloud Bug Study states that neglected error handling - buggy code, timeouts, etc. - accounts for the vast majority of system malfunction.

Chaos Middleware allows you to inject turbulent conditions into your application. It lets you delay or raise errors when handling requests, and by applying additional conditions, you can decrease the blast radius of your attack.

The following sections will guide you through: i) the installation and configuration process of Chaos Middleware in your Spring Boot application and ii) simulation of delays and application faults.

Installation & configuration

1. Install the Chaos Middleware by including following dependency in your spring boot application pom.xml.

<dependency>
  <groupId>io.proofdock</groupId>
  <artifactId>spring-boot-starter-chaos-middleware</artifactId>
  <version>0.1.4</version>
</dependency>

2. Add an application name to the application.properties.

chaos.middleware.application-name=webshop-order

3. Start your application.

Simulate delays

The delay attack introduces delays to the request/response cycle. This type of the attack enables you to verify the behavior of your system in case of timeouts or slow responses.

Trigger delays by setting the x-proofdock-attack request header. The Chaos Middleware detects any value inside this header and injects appropriate turbulent conditions.

To simulate delay, set the x-proofdock-attack header's value to

{"actions":[{"name":"delay", "value":"5"}], "target":{  "application":"webshop-order"}}

name - set delay attack
value - delay response by 5 seconds
target - target application name

Let's attack an application using Postman.

Simulate faults

The fault attack injects an exception into your code. This type of the attack enables you to verify the behavior of your application in case of an unexpected event.

Trigger faults by setting the x-proofdock-attack request header.

{"actions":[{"name":"fault", "value":"java.lang.Exception"}], "target":{  "application":"webshop-order"}}

name - set fault attack
value - thow exception of the type java.lang.Excpetion
target - the targeted application name

Let's attack an application using Postman.

Chaos Panel for Azure DevOps users

A more convenient way to control such attacks is the usage of the Chaos Panel provided by Proofdock's Chaos Platform. The Chaos Platform extends the Azure DevOps with chaos engineering capabilities. The Chaos Panel allows you to control application attacks via a graphical user interface.

The Chaos Panel in Azure DevOps

Check out the docs, if you are interested in the Chaos Platform.

Thank you

We hope you enjoyed reading this article and got a new perspective on resilience.

We are Proofdock, a software tech company located in Germany, created to help engineers build more resilient and reliable software products. Check out the Chaos Platform for Microsoft Azure and explore your system.

Article photo by Raychel Sanner on Unsplash

Introduce Chaos Middleware for Python Flask: Simulate disruptions to improve resiliency

Bugra Derre — Mon, 28 Sep 2020 05:44:24 +0000

Resiliency is key to software nowadays. It contributes to your application's reliability and plays a major role in the competitive advantage of your business. Resilient applications smartly exploit the strengths of software patterns that handle timeouts, delays and faulty service responses.

Great authors and presenters like Uwe Friedrichsen and Donald Firesmith share their ideas and knowledge about resilient software design. Such articles describe software patterns that overcome disruptions and improve application resiliency in a handsome way.

However, deploying a resilience pattern requires you as a developer to verify your implementation. When touching the ground of verification, things appear to get difficult.

Chaos engineering tools advertise features like stopping a virtual machine, or to terminate pods in your Kubernetes cluster. The "blast radius" gets high here. Higher than a developer expects when exploring a resilience design pattern on Friday afternoon at 2PM.

That is where the Chaos Middleware package goes into action. The Cloud Bug Study states, that neglected error handling - buggy code, timeouts, and others - leads to a great portion of a system's unavailability.

The Chaos Middleware allows you to throw your application at turbulent conditions. It lets you simulate delayed responses or raise errors. Activate disruptions when needed and narrow them down on specific URL paths. The Chaos Middleware gives you the opportunity to minimize the "blast radius" and verify your application's resilience, even on a Friday afternoon.

The next sections will guide you through i) the installation and configuration process in your Flask application ii) and the simulation of delays and application faults. Simulations are enabled by HTTP header requests or by the Chaos Platform control panel.

Installation & configuration

Install the Chaos Middleware for the Flask application:

 cd /path/to/your/flask-application  # Enter your Flask application
 venv/bin/activate  # Active virtual environment
 pip install -U proofdock-chaos-middleware-flask

Configure the Flask application:

 from flask import Flask, jsonify
 from pdchaos.middleware.contrib.flask.flask_middleware import FlaskMiddleware

 app = Flask(__name__)
 app.config['CHAOS_MIDDLEWARE_APPLICATION_NAME'] = 'example-application-name'
 app.config['CHAOS_MIDDLEWARE_APPLICATION_ENV'] = 'example-environment'
 app.config['CHAOS_MIDDLEWARE_PROOFDOCK_API_TOKEN'] = 'eyJ0eXAi...05'

 middleware = FlaskMiddleware(app)

 # Your business logic here

 @app.route("/products")
 def hello():
     return "Hello, from the products"

 if __name__ == "__main__":
     app.run()

Start your application

Simulate delays

Trigger delays by setting the x-proofdock-attack request header. The Chaos Middleware detects any value inside this header.

Set the header's value to {"actions":[{"name":"delay","value":"15"}]}. The value describes the simulation name and its value in seconds. The described simulation delays the response by 15 seconds.

For the simulation, we use the Postman client to visualize the HTTP requests.

A delay simulation

Simulate faults

Raise exceptions by setting the x-proofdock-attack request header.

Set the header's value to {"actions":[{"name":"fault","value":"Exception"}]}. The value describes the simulation name and its value as a fully qualified name of the exception to be raised. The simulation will raise the Exception.

A fault simulation

A control panel for Azure DevOps users

A more appealing way to control such attacks is the usage of the control panel provided by Proofdock's Chaos Platform. The Chaos Platform extends the Azure DevOps with chaos engineering capabilities. The control panel allows you to control application attacks via a graphical user interface and lets your application cache the configured attack. Check out the docs when you are interested in the Chaos Platform.

The control panel in Azure DevOps

Thank you

We hope you had fun and learned some new aspects on resilience.

We are Proofdock, a software tech company located in Germany helping engineers build more resilient and reliable software products. Check out the Chaos Platform for Microsoft Azure and explore your system.

References

Check out the source code at GitHub
Read the docs
Install the Chaos Platform

Chaos Middleware: Simulate disruptions in Django applications to improve resiliency

Bugra Derre — Tue, 08 Sep 2020 05:38:03 +0000

However, deploying a resilience pattern requires you as a developer to verify your implementation. When touching the ground of verification, things appear to get difficult.

The next sections will guide you through i) the installation and configuration process in your Django application ii) and the simulation of delays and application faults. Simulations are enabled by HTTP header requests or by the Chaos Platform control center.

Installation & configuration

Install the Chaos Middleware for the Django application:

 cd /path/to/your/django-application  # Enter your Django application
 venv/bin/activate  # Active virtual environment
 pip install -U proofdock-chaos-middleware-django

Configure the Django application:

 # Activate the ``Chaos Middleware`` component by adding 
 # it to the MIDDLEWARE list in your Django settings
 MIDDLEWARE = [
   '..'
   'pdchaos.middleware.contrib.django.django_middleware.DjangoMiddleware',
   '..'
 ]

 # Set the Chaos Middleware settings
 CHAOS_MIDDLEWARE = {
   'CHAOS_MIDDLEWARE_APPLICATION_NAME': 'example-application-name',
   'CHAOS_MIDDLEWARE_APPLICATION_ENV': 'example-environment'
 }

Start your application

Simulate delays

Trigger delays by setting the x-proofdock-attack request header. The Chaos Middleware detects any value inside this header.

For the simulation, we use the Postman client to visualize the HTTP requests.

A delay simulation

Simulate faults

Raise exceptions by setting the x-proofdock-attack request header.

A fault simulation

A control center for Azure DevOps users

A more appealing way to control such attacks is the usage of the control center provided by Proofdock's Chaos Platform. The Chaos Platform extends the Azure DevOps with chaos engineering capabilities. The control center allows you to control application attacks via a graphical user interface and lets your application cache the configured attack. Check out the docs when you are interested in the Chaos Platform.

The control center in Azure DevOps

Thank you

We hope you had fun and learned some new aspects on resilience.

References

Check out the source code at GitHub
Read the docs
Install the Chaos Platform