Continuous Integration and Delivery Pipeline for Python | Quick Guide

Overview of Continuous Delivery

CI concentrates on automating the software delivery process so that teams can quickly and confidently deploy their code to production at any point. Click to explore about, Continuous Delivery Pipeline with Jenkins

Prerequisites are mentioned below

• Kubernetes
• Kubectl
• Shared Persistent Storage - Option are GlusterFS, Ceph FS, AWS EBS, AzureDisk etc.
• Python Application Source Code
• Dockerfile
• Container-Registry

Kubernetes is an open source platform that automates container operations, and Minikube is best for testing kubernetes in a local environment.

Kubectl is command line interface to manage kubernetes cluster either remotely or locally. Shared Persistent Storage is permanent storage that we attach to the kubernetes container. We will be using cephfs as a persistent data store for kubernetes container applications.

Application Source Code is the source code that we want to run inside a kubernetes container. Dockerfile contains all the actions that are performed to build python application. The Registry is an online image store for container images. Below mentioned options are few most popular registries.

• Private Docker Hub
• AWS ECR
• Docker Store
• Google Container Registry

Building Dockerfile for Python applications

FROM python: 2.7
MAINTAINER XenonStack

# Creating Application Source Code Directory
RUN mkdir - p / usr / src / app

# Setting Home Directory
for containers
WORKDIR / usr / src / app

# Installing python dependencies
COPY requirements.txt / usr / src / app /
 RUN pip install--no - cache - dir - r requirements.txt

# Copying src code to Container
COPY. / usr / src / app

# Application Environment variables
ENV APP_ENV development

# Exposing Ports
EXPOSE 5035

# Setting Persistent data
VOLUME["/app-data"]

# Running Python Application
CMD["python", "wsgi.py"]

Building Python Docker Image

$ docker build - t < name of your python application > : < version of application > .

Publishing Container Image

• Adding Container Registry to Docker Daemon

$ docker version
Client:
 Version: 17.03 .1 - ce
API version: 1.27
Go version: go1 .7 .5
Git commit: c6d412e
Built: Mon Mar 27 17: 14: 09 2017
OS / Arch: linux / amd64(Ubuntu 16.04)

$ sudo nano / etc / docker / daemon.json

{
&quot;insecure-registries&quot;: [&quot;<name of="" private="" registry="" your="">&quot;]
}

$ sudo systemctl daemon - reload
$ sudo service docker restart

$ docker info

Insecure Registries:
<your container="" name="" registry="">
127.0.0.0/8

• Pushing container Images to Registry

#!/bin/bash
pip install --upgrade --user awscli

mkdir -p ~/.aws && chmod 755 ~/.aws

cat << EOF > ~/.aws/credentials
[default]
aws_access_key_id = XXXXXX
aws_secret_access_key = XXXXXX
EOF

cat << EOF > ~/.aws/config
[default]
output = json
region = XXXXX
EOF

chmod 600 ~/.aws/credentials

ecr-login=$(aws ecr get-login --region XXXXX)
$ecr-login

$ docker tag &lt; name of your application &gt; : &lt; version of your application &gt; &lt; aws ecr repository link &gt; /<name application="" of="" your="">:<version application="" of="" your="">

$ docker push &lt; aws ecr repository link &gt; /<name application="" of="" your="">:<version application="" of="" your="">

Configure Persistent Volume (optional)

• Persistent Volume

-- -
apiVersion: v1
kind: PersistentVolume
metadata:
 name: app - disk1
namespace: < namespace of Kubernetes >
 spec:
 capacity:
 storage: 50 Gi
accessModes:
 -ReadWriteMany
cephfs:
 monitors:
 -"172.16.0.34:6789"
user: admin
secretRef:
 name: ceph - secret
readOnly: false

• Persistent Volume Claim

-- -
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
 name: appclaim1
namespace: < namespace of Kubernetes >
 spec:
 accessModes:
 -ReadWriteMany
resources:
 requests:
 storage: 10 Gi
 

• Adding Claims to Kubernetes

$ kubectl create - f persistent - volume.yml
$ kubectl create - f persistent - volume - claim.yml

Creating Deployment Files for Kubernetes

• Deployment File

apiVersion: extensions / v1beta1
kind: Deployment
metadata:
 name: < name of application >
 namespace: < namespace of Kubernetes >
 spec:
 replicas: 1
template:
 metadata:
 labels:
 k8s - app: < name of application >
 spec:
 containers:
 -name: < name of application >
 image: < image name > : < version tag >
 imagePullPolicy: "IfNotPresent"
ports:
 -containerPort: 5035
volumeMounts:
 -mountPath: /app-data
name: < name of application >
 volumes:
 -name: < name of application >
 persistentVolumeClaim:
 claimName: appclaim1
 

• Service File

apiVersion: v1
kind: Service
metadata:
 labels:
 k8s - app: < name of application >
 name: < name of application >
 namespace: < namespace of Kubernetes >
 spec:
 type: NodePort
ports:
 -port: 5035
selector:
 k8s - app: < name of application >
 

Running Python Application on Kubernetes

$ kubectl create - f < name of application > .deployment.yml
$ kubectl create - f < name of application > .service.yml

Verifying Dockerfile for Python Applications

$ kubectl get po--namespace = < namespace of kubernetes > | grep < application name >

Testing Dockerfile for Python Applications

$ kubectl get svc--namespace = < namespace of kubernetes > | grep < application name >

• http://<kubernetes master ip address >: <application service port number>
• http://<cluster ip address >: <application port number>

Troubleshooting Dockerfile

• Check Status of Pods.
• Check Logs of Pods/Containers.
• Check Service Port Status.
• Check requirements/dependencies of application.

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How can XenonStack Help You?

• Single Click Deployment
• Continuous Integration and Continuous Deployment
• Support Microservices and Serverless Computing - Docker and Kubernetes
• Deploy On-Premises, Public or Hybrid Cloud

• Learn more about "Azure DevOps Pipeline for Laravel Php"
• Read more about "Continuous Delivery for Scala Applications"