Add new challenge

challenges/ansible_minikube_docker.md

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+## Ansible, Minikube and Docker
+
+* Write a simple program in any language you want that outputs "I'm on %HOSTNAME%" (HOSTNAME should be the actual host name on which the app is running)
+* Write a Dockerfile which will run your app
+* Create the YAML files required for deploying the pods
+* Write and run an Ansible playbook which will install Docker, Minikube and kubectl and then create a deployment in minikube with your app running.

challenges/ci_for_open_source_project.md

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+## CI for Open Source Project
+
+1. Choose an open source project from Github and fork it
+2. Create a CI pipeline/workflow for the project you forked
+3. The CI pipeline/workflow will include anything that is relevant to the project you forked. For example:
+  * If it's a Python project, you will run PEP8
+  * If the project has unit tests directory, you will run these unit tests as part of the CI
+4. In a separate file, describe what is running as part of the CI and why you chose to include it. You can also describe any thoughts, dilemmas, challenge you had
+
+### Bonus
+
+Containerize the app of the project you forked using any containerization technology you want.

challenges/cloud_slack_bot.md

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+## Cloud Slack Bot
+
+Create a slack bot to manage cloud instances. You can choose whatever cloud provider you want (e.g. Openstack, AWS, GCP, Azure)
+You should provide:
+
+  * Instructions on how to use it
+  * Source code of the slack bot
+  * A running slack bot account or a deployment script so we can test it
+
+The bot should be able to support:
+
+  * Creating new instances
+  * Removing existing instances
+  * Starting an instance
+  * Stopping an instance
+  * Displaying the status of an instance
+  * List all available instances
+
+The bot should also be able to show help message.

challenges/elk_kibana_aws.md

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+# Elasticsearch, Kibana and AWS
+
+Your task is to build an elasticsearch cluster along with Kibana dashboard on one of the following clouds:
+
+* AWS
+* OpenStack
+* Azure
+* GCP
+
+You have to describe in details (preferably with some drawings) how you are going to set it up.
+Please describe in detail:
+
+- How you scale it up or down
+- How you quickly (less 20 minutes) provision the cluster
+- How you apply security policy for access control
+- How you transfer the logs from the app to ELK
+- How you deal with multi apps running in different regions
+
+## Solution
+
+One Possible solution can be found [here](solutions/elk_kibana_aws.md)

challenges/flask_container_ci/README.md

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+Your mission, should you choose to accept it, involves fixing the app in this directory, containerize it and set up a CI for it.
+Please read carefully all the instructions.
+
+## Installation
+
+1. Create a virtual environment with `python3 -m venv challenge_venv`
+2. Activate it with `source challenge_venv/bin/activate`
+3. Install requirements.txt `pip install -r requirements.txt`
+
+## Run the app
+
+1. Run `export FLASK_APP=app/app.py`
+1. To run the app execute `flask run`. If it doesn't works, fix it
+
+
+## Containers
+
+Using Docker or Podman, containerize the flask app so users can run the following two commands:
+
+```
+docker build -t app:latest /path/to/Dockerfile
+docker run -d -p 5000:5000 app
+```
+
+1. You can use any image base you would like
+2. Containrize only what you need for running the application, nothing else.
+
+## CI
+
+Great, now that we have a working app and also can run it in a container, let's set up a CI for it so it won't break again in the future
+
+1. The CI should run the tests in the app directory
+2. There should be some kind of test for the Dockerfile you wrote
+2. Add additional unit test (or another level of tests)

challenges/flask_container_ci/app/app.py

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+#!/usr/bin/env python
+# coding=utf-8
+
+from flask import Flask
+app = Flask(__name__)
+
+@app.route('/')
+def hello_world():
+    return 'Hello, World!'

challenges/flask_container_ci/app/config.py

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+#!/usr/bin/env python
+# coding=utf-8
+
+import os
+
+basedir = os.path.abspath(os.path.dirname(__file__))
+
+SECRET_KEY = 'shhh'
+CSRF_ENABLED = True
+
+SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, 'app.db')
+

challenges/flask_container_ci/app/tests.py

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+#!/usr/bin/env python
+# coding=utf-8
+
+import os
+import unittest
+
+from config import basedir
+from app import app 
+from app import db
+
+class TestCase(unittest.TestCase):
+
+    def setUp(self):
+        app.config['TESTING'] = True
+        app.config['WTF_CSRF_ENABLED'] = False
+        app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'test.db')
+        self.app = app.test_client()
+        db.create_all()
+
+    def tearDown(self):
+        db.session.remove()
+        db.drop_all()
+
+if __name__ == '__main__':
+    unittest.main()

challenges/jenkins/scripts/jobs_with_string.groovy

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+def jobs = Jenkins.instance.items.findAll { job -> job.name =~ /"REMOVE_ME"/ }
+    
+jobs.each { job ->
+    println job.name
+    //job.delete()
+}

challenges/jenkins/scripts/old_builds.groovy

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+def removeOldBuilds(buildDirectory, days = 14) {
+
+  def wp = new File("${buildDirectory}")
+  def currentTime = new Date()
+  def backTime = currentTime - days
+
+  wp.list().each { fileName ->
+      folder = new File("${buildDirectory}/${fileName}")
+      if (folder.isDirectory()) {
+          def timeStamp = new Date(folder.lastModified())
+          if (timeStamp.before(backTime)) {
+            folder.delete()
+          }
+      }
+  }
+}

challenges/jenkins_pipelines.md

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+## Jenkins Pipelines
+
+Write/Create the following Jenkins pipelines:
+
+* A pipeline which will run unit tests upon git push to a certain repository
+* A pipeline which will do to the following:
+
+  * Provision an instance (can also be a container)
+  * Configure the instance as Apache web server
+  * Deploy a web application on the provisioned instance

challenges/jenkins_scripts.md

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+## Jenkins Scripts
+
+Write the following scripts:
+
+* Remove all the jobs which include the string "REMOVE_ME" in their name
+* Remove builds older than 14 days
+
+### Answer
+
+* [Remove jobs which include specific string](jenkins/scripts/jobs_with_string.groovy)
+* [Remove builds older than 14 days](jenkins/scripts/old_builds.groovy)

challenges/solutions/elk_kibana_aws.md

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+# Elasticsearch, Kibana and AWS - Solution
+
+This one out of many possible solutions. This solution is relying heavily on AWS.
+
+* Create a VPC with subnet so we can place Elasticsearch node(s) in internal environment only.
+  If required, we will also setup NAT for public access. 
+
+* Create an IAM role for the access to the cluster. Also, create a separate role for admin access.
+
+* To provision the solution quickly, we will use the elasticsearch service directly from AWS for production deployment.
+  This way we also cover multiple AZs. As for authentication, we either use Amazon cognito or the organization LDAP server.
+
+* To transfer data, we will have to install logstash agent on the instances. The agent will be responsible
+  for pushing the data to elasticsearch.
+
+* For monitoring we will use:
+
+    * Cloud watch to monitor cluster resource utilization
+    * Cloud metrics dashboard
+
+* If access required from multiple regions we will transfer all the data to S3 which will allow us to view the data
+  from different regions and consolidate it in one dashboard

challenges/write_dockerfile_run_container.md

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+# Write a Dockerfile and run a container
+
+Your task is as follows:
+
+1. Create a Docker image:
+  * Use centos or ubuntu as the base image
+  * Install apache web server
+  * Deploy any web application you want
+  * Add https support (using HAProxy as reverse-proxy)))
+2. Once you wrote the Dockerfile and created an image, run the container and test the application. Describe how did you test it and provide output
+3. Describe one or more weakness of your Dockerfile. Is it ready to be used in production?