Actually it is easy!
import { Injectable, Inject } from '@angular/core';
import { LoginService } from '../common/login.service';
@Injectable()
export class PermissionService {
constructor(@Inject(LoginService) private login_service: LoginService) { }
hasCategoryDeletePerm(){
const u = this.login_service.getUser()
return u.is_staff;
}
}
When you are lucky enough to have enough users that you decide to roll another cloud instance for your django app, logging becomes a little bit tough because in your architecture now you would be needing a load balancer which will be proxying request from one instance to another instance based on requirement. Previously we had log in one machine to log monitoring was easier, when someone reported a error we went to that instance and looked for errors, but now as we have multiple instance we have to go to all the instance, regardless of security risks, i would say it is a lot of work. So I think it would be wise to have a centralized log aggregating service.
For log management and monitoring we are using Elastic Logstash and Kibana popularly known as ELK stack. For this blog we will be logging pretty much all the request and its corresponding responses so that debugging process gets handy for us. To serve this purpose we will leverage django middlewares and python-logstash.
First of all let’s configure our settings.py for logging:
LOGGING = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'standard': {
'format': '%(asctime)s [%(levelname)s] %(name)s: %(message)s'
},
'logstash': {
'()': 'proj_name.formatter.SadafNoorLogstashFormatter',
},
},
'handlers': {
'default': {
'level':'DEBUG',
'class':'logging.handlers.RotatingFileHandler',
'filename': '/var/log/proj_name/django.log',
'maxBytes': 1024*1024*5, # 5 MB
'backupCount': 5,
'formatter':'standard',
},
'logstash': {
'level': 'DEBUG',
'class': 'logstash.TCPLogstashHandler',
'host': 'ec2*****.compute.amazonaws.com',
'port': 5959, # Default value: 5959
'version': 1, # Version of logstash event schema. Default value: 0 (for backward compatibility of the library)
'message_type': 'logstash', # 'type' field in logstash message. Default value: 'logstash'.
'fqdn': False, # Fully qualified domain name. Default value: false.
#'tags': ['tag1', 'tag2'], # list of tags. Default: None.
'formatter': 'logstash',
},
'request_handler': {
'level':'DEBUG',
'class':'logging.handlers.RotatingFileHandler',
'filename': '/var/log/proj_name/django.log',
'maxBytes': 1024*1024*5, # 5 MB
'backupCount': 5,
'formatter': 'standard',
},
},
'loggers': {
'sadaf_logger': {
'handlers': ['default', 'logstash'],
'level': 'DEBUG',
'propagate': True
},
}
}
As you can see we are using a custom logging format. We can leave this configuration and by default LogstashFormatterVersion1 is the logging format that will work just fine. But I chose to define my own logging format because my requirement is different, I am running behind a proxy server, I want to log who has done that and from which IP. So roughly my Log Formatter looks like following:
from logstash.formatter import LogstashFormatterVersion1
from django.utils.deprecation import MiddlewareMixin
class SadafNoorLogstashFormatter(LogstashFormatterVersion1):
def __init__(self,*kargs, **kwargs):
print(*kargs, **kwargs)
super().__init__(*kargs, **kwargs)
def format(self, record,sent_request=None):
print(record)
print(sent_request, "old req")
caddr = "unknown"
#print(record.request.META)
if 'HTTP_X_FORWARDED_FOR' in record.request.META:
caddr = record.request.META['HTTP_X_FORWARDED_FOR'] #.split(",")[0].strip()
# print(record.request.POST,record.request.GET, record.request.user)
message = {
'@timestamp': self.format_timestamp(record.created),
'@version': '1',
'message': record.getMessage(),
'host': self.host,
'client': caddr,
'username': str(record.request.user),
'path': record.pathname,
'tags': self.tags,
'type': self.message_type,
#'request': self.record
# Extra Fields
'level': record.levelname,
'logger_name': record.name,
}
# Add extra fields
# print(type(self.get_extra_fields(record)['request']))
message.update(self.get_extra_fields(record))
# If exception, add debug info
if record.exc_info:
message.update(self.get_debug_fields(record))
return self.serialize(message)
As our requirement is to log every request our middleware may look like following:
import logging
request_logger = logging.getLogger('sadaf_logger')
from datetime import datetime
from django.utils.deprecation import MiddlewareMixin
class LoggingMiddleware(MiddlewareMixin):
"""
Provides full logging of requests and responses
"""
_initial_http_body = None
def __init__(self, get_response):
self.get_response = get_response
def process_request(self, request):
self._initial_http_body = request.body # this requires because for some reasons there is no way to access request.body in the 'process_response' method.
def process_response(self, request, response):
"""
Adding request and response logging
"""
# print(response.content, "xxxx")
if request.path.startswith('/') and \
(request.method == "POST" and
request.META.get('CONTENT_TYPE') == 'application/json'
or request.method == "GET"):
status_code = getattr(response, 'status_code', None)
print(status_code)
if status_code:
if status_code >= 400:
log_lvl = logging.ERROR
else:
log_lvl = logging.INFO
#request_logger.log(logging.DEBUG,)
request_logger.log(log_lvl,
"GET: {}"
""
.format(
request.GET,
),
extra ={
'request': request,
'request_method': request.method,
'request_url': request.build_absolute_uri(),
'request_body': self._initial_http_body.decode("utf-8"),
'response_body':response.content,
'status': response.status_code
}
#extra={
#'tags': {
# 'url': request.build_absolute_uri()
#}
#}
)
# print(request.POST,"fff")
print("hot")
return response
So pretty much you are done. Go login to your Kibana dashboard, make index pattern that you are interest and see your log:
AWS has its own continuous integration tool known as CodeDeploy, using a simple command you would be able to deploy on multiple servers when you want to change something on code base.
If code deploy client is not installed at your instance, you would need to do that:
sudo yum install -y ruby wget cd /opt wget https://aws-codedeploy-ap-south-1.s3.amazonaws.com/latest/install chmod +x ./install sudo ./install auto
You have to create Code Deploy application with Deployment type to Inplace deployment, and deployment Configuration set to CodeDeployDefault.OneAtATime.
Give it a name under Ec2 configuration and Amazon ec2 instance, say the name is Code deploy instance. Now you have to add the same tag to all your code deploy instances.
Now that we are done with installation, we would need to setup IAM rules:
First create an IAM group called CodeDeployGroup. This group needs AmazonS3FullAccess and AWSCodeDeployFullAccess permissions. Create a user and add it to this group. This user only needs programmatic access.Save key and key id to somewhere safe.
Create role that has Trusted entities and Policies are ec2.amazonaws.com and AWSCodeDeployRole AmazonS3FullAccess, respectively.
Edit trust relationship to following:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": [
"ec2.amazonaws.com",
"codedeploy.ap-south-1.amazonaws.com"
]
},
"Action": "sts:AssumeRole"
}
]
}
Create new s3 bucket with previously created IAM rules.
My codebase structure is something like following:
- src - <django project> - scripts install_dependencies start_server stop_server appspec.yml codedeploy_deploy.py deploy.sh
appspec.yml is the file that contains our hooks and configuration for code deploy.
version: 0.0
os: linux
files:
- source: src
destination: /home/centos/proj_name
hooks:
BeforeInstall:
- location: scripts/install_dependencies
timeout: 300
runas: root
ApplicationStop:
- location: scripts/stop_server
timeout: 300
runas: root
ApplicationStart:
- location: scripts/start_server
timeout: 300
runas: root
for django scripts/install_dependencies may look like following:
sudo yum install -y gcc openssl-devel bzip2-devel wget
sudo yum install -y make git
cd /opt
command -v python3.6 || {
wget https://www.python.org/ftp/python/3.6.3/Python-3.6.3.tgz
tar xzf Python-3.6.3.tgz
cd Python-3.6.3
sudo ./configure --enable-optimizations
sudo make altinstall
}
sudo yum install -y mysql-devel
for scripts/start_server I have following:
cd /home/centos/evaly pip3.6 install -r requirements.txt nohup uwsgi --http :80 --module evaly.wsgi > /dev/null 2>&1 &
for scripts/stop_server I have following:
pkill uwsgi
I have borrowed a python script from bitbucket team which looks like following:
# Copyright 2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file
# except in compliance with the License. A copy of the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is distributed on an "AS IS"
# BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under the License.
"""
A BitBucket Builds template for deploying an application revision to AWS CodeDeploy
narshiva@amazon.com
v1.0.0
"""
from __future__ import print_function
import os
import sys
from time import strftime, sleep
import boto3
from botocore.exceptions import ClientError
VERSION_LABEL = strftime("%Y%m%d%H%M%S")
BUCKET_KEY = os.getenv('APPLICATION_NAME') + '/' + VERSION_LABEL + \
'-bitbucket_builds.zip'
def upload_to_s3(artifact):
"""
Uploads an artifact to Amazon S3
"""
try:
client = boto3.client('s3')
except ClientError as err:
print("Failed to create boto3 client.\n" + str(err))
return False
try:
client.put_object(
Body=open(artifact, 'rb'),
Bucket=os.getenv('S3_BUCKET'),
Key=BUCKET_KEY
)
except ClientError as err:
print("Failed to upload artifact to S3.\n" + str(err))
return False
except IOError as err:
print("Failed to access artifact.zip in this directory.\n" + str(err))
return False
return True
def deploy_new_revision():
"""
Deploy a new application revision to AWS CodeDeploy Deployment Group
"""
try:
client = boto3.client('codedeploy')
except ClientError as err:
print("Failed to create boto3 client.\n" + str(err))
return False
try:
response = client.create_deployment(
applicationName=str(os.getenv('APPLICATION_NAME')),
deploymentGroupName=str(os.getenv('DEPLOYMENT_GROUP_NAME')),
revision={
'revisionType': 'S3',
's3Location': {
'bucket': os.getenv('S3_BUCKET'),
'key': BUCKET_KEY,
'bundleType': 'zip'
}
},
deploymentConfigName=str(os.getenv('DEPLOYMENT_CONFIG')),
description='New deployment from BitBucket',
ignoreApplicationStopFailures=True
)
except ClientError as err:
print("Failed to deploy application revision.\n" + str(err))
return False
"""
Wait for deployment to complete
"""
while 1:
try:
deploymentResponse = client.get_deployment(
deploymentId=str(response['deploymentId'])
)
deploymentStatus=deploymentResponse['deploymentInfo']['status']
if deploymentStatus == 'Succeeded':
print ("Deployment Succeeded")
return True
elif (deploymentStatus == 'Failed') or (deploymentStatus == 'Stopped') :
print ("Deployment Failed")
return False
elif (deploymentStatus == 'InProgress') or (deploymentStatus == 'Queued') or (deploymentStatus == 'Created'):
continue
except ClientError as err:
print("Failed to deploy application revision.\n" + str(err))
return False
return True
def main():
if not upload_to_s3('/Users/sadafnoor/Projects/evaly/artifact.zip'):
sys.exit(1)
if not deploy_new_revision():
sys.exit(1)
if __name__ == "__main__":
main()
I have written a script to zip up my source code so that the script can upload it to s3 and eventually all my ec2 instances will be downloading that zip from s3.
export APPLICATION_NAME="CodeDeployApplicationName" export AWS_ACCESS_KEY_ID="IAMUserKeyId" export AWS_DEFAULT_REGION="ap-south-1" export AWS_SECRET_ACCESS_KEY="IAMUserSecretKey" export DEPLOYMENT_CONFIG="CodeDeployDefault.OneAtATime" export DEPLOYMENT_GROUP_NAME="CodeDeployDeploymentGroup" export S3_BUCKET="S3BucketName" zip -r ../artifact.zip src/* appspec.yml scripts/* python codedeploy_deploy.py
Although I am big fan of django rest framework but sometime i feel it is gruesome to deal with nested serializers (Maybe I am doing something wrong, feel free to suggest me your favourite trick.)
Suppose we have two models, ASerializer is based on A model, BSerializer is based on `B` model. A and B models are related, say B has a foreign key to A. So while creating B it is mandatory to define A but A serializer is full of so much data that I don’t want to have that unnecessary overhead at my BSerializer, but when creating B I must have it. Here how I solved it:
For the sake of brevity let’s say A is our Category, and B is Product. Every Product has a Category, so Product has a foreign key of Category, but I am not making it visible at ProductSerializer given that category has a lot of unnecessary information that is not necessary.
from django.shortcuts import get_object_or_404
class ProductSerializer(serializers.ModelSerializer):
def to_internal_value(self, data):
if data.get('category'):
self.fields['category'] = serializers.PrimaryKeyRelatedField(
queryset=Category.objects.all())
cat_slug = data['category']['slug']
cat = get_object_or_404(Category, slug=cat_slug)
data['category']= cat.id
return super().to_internal_value(data)
I am using following Django (2.0.1), djangorestframework (3.7.7), djangorestframework-jwt (1.11.0) on top of python 3.6.3. By default djangorestframework-jwt does not include users in django’s usual requst.user. If you are using code>djangorestframework, chances are you have a huge code base at API which is leveraging this, not to mention at your permission_classes at viewsets. Since you are convinced about the fact that jwts are the best tools for your project, no wonder that you would love to migrate from your old token to new jwt tokens. To make the migration steps easier, we will write a middleware that will set request.user for us.
from django.utils.functional import SimpleLazyObject
from rest_framework_jwt.serializers import VerifyJSONWebTokenSerializer
from rest_framework.exceptions import ValidationError
#from rest_framework.request from Request
class AuthenticationMiddlewareJWT(object):
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
request.user = SimpleLazyObject(lambda: self.__class__.get_jwt_user(request))
if not request.user.is_authenticated:
token = request.META.get('HTTP_AUTHORIZATION', " ").split(' ')[1]
print(token)
data = {'token': token}
try:
valid_data = VerifyJSONWebTokenSerializer().validate(data)
user = valid_data['user']
request.user = user
except ValidationError as v:
print("validation error", v)
return self.get_response(request)
And you need to register your middleware in settings:
MIDDLEWARE = [
#...
'path.to.AuthenticationMiddlewareJWT',
]
To prepare our html to upload images we would need to add input field to our html.
<input type="file" class="form-control" name="logo" required (change)="handleFileInput($event.target.files)" >
When file is changed it is going to call a function. We would need to define that function at our component.
image_to_upload: File = null;
handleFileInput(files: FileList) {
this.image_to_upload = files.item(0);
}
create(image_data) {
this.image_service.uploadImage(this.image_to_upload, image_data);
}
Then at our service we would need to make the post request.
uploadImage(fileToUpload, image_data) {
const endpoint = 'http://localhost:8000/image/';
const formData: FormData = new FormData();
for(const k in image_data){
formData.append(k, image_data[k]);
}
formData.append('image', fileToUpload, fileToUpload.name);
console.log(formData);
return this.http
.post(endpoint, formData).toPromise().then(
(res) => {
console.log(res);
}
);
}
Which will be sufficient for any API that accepts POST request:
{
image:
}
If we want to implement it using django, it would look like following:
Django model:
class Image(models.Model):
image = models.FileField(storage = MyStorage(location="media/shop"))
Django Rest Framework serializers and viewsets:
class ImageSerializer(serializers.ModelSerializer):
class Meta:
model = Image
fields = ('id','logo',)
class ImageViewSet(viewsets.ModelViewSet):
queryset = Image.objects.all()
serializer_class = ImageSerializer
router = routers.DefaultRouter()
router.register(r'image', ImageViewSet)
urlpatterns = router.urls
If we are lucky enough to get high amount of traffic at our website, next thing we start to think about is performance. The throughput of a website loading depends on the speed we are being able to deliver the contents of the website, to our users from our storages. In vanilla django, all assets including css, js, files and images are being stored locally in a predefined or preconfigured folder. To enhance performance we may have to decide to use a third party storage service that alleviate the headache of caching, zoning, replicating and to build the infrastructure of a Content Delivery Network. Ideally we would like to have a pluggable solution, something that allows us to switch storages from this to that, based on configuration. django-storages is one of the cool libraries from django community that helps to maintain 3rd party storage services like aws s3, google cloud, ftp, dropbox and so on. Amazon Webservice is one of the trusted service that offers a large range of services, s3 is one of the cool services from AWS that helps us to store static assets. boto3 is a python library being distributed by amazon to interact with amazon s3.
First thing first, to be able to store files on s3 we would need permission. In AWS world, all sorts of permissions are being managed using Identity Access Management (IAM).
i) In amazon console, you will be able to find IAM under Security, Identity & Compliance. Go there.
ii) We would need to add user with programmatic access.
iii) We would need to add new group.
iv) We would need to set policy for the group. Amazon provides bunch of predefined policies. For our use case, we can choose AmazonS3FullAccess
v) We have to store User, Access key ID and the Secret access key.
In s3 we can organize our contents into multiple buckets. We can use several buckets for a single Django project, sometime it is more efficient to use more but for now we will use only one. We will need to create bucket.
Now we need to install:
pip install boto3 pip install django-storages
We will need to add storages inside our INSTALLED_APPS of settings.py along with other configuration files of
django-storage
INSTALLED_APPS = [
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'storages',
]
AWS_ACCESS_KEY_ID = '#######'
AWS_SECRET_ACCESS_KEY = '#####'
AWS_STORAGE_BUCKET_NAME = '####bucket-name'
AWS_S3_CUSTOM_DOMAIN = '%s.s3.amazonaws.com' % AWS_STORAGE_BUCKET_NAME
#AWS_S3_OBJECT_PARAMETERS = {
# 'CacheControl': 'max-age=86400',
#}
AWS_LOCATION = 'static'
STATICFILES_DIRS = [
os.path.join(BASE_DIR, 'mysite/static'),
]
STATIC_URL = 'https://%s/%s/' % (AWS_S3_CUSTOM_DOMAIN, AWS_LOCATION)
STATICFILES_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'
When we are using django even when we don’t write any html, css or js files for our projects, it already has few because many of classes that we will be using at our views, its parent class may have static template files, base html files, css, js files. These static assets are being stored in our python library folder. To move then from library folder to s3 we will need to use following command:
python manage.py collectstatic
Thing to notice here is that, previously static referred to localhost:port but now it is being referred to s3 link.
{% static 'img/logo.png' %}
We may like to have some custom configuration for file storage, say we may like to put media files in a separate directory, we may not like it to be overwritten by another user. In that case we can define a child class of S3Boto3Storage and change the value of DEFAULT_FILE_STORAGE.
#storage_backends.py
from storages.backends.s3boto3 import S3Boto3Storage
class MyStorage(S3Boto3Storage):
location = 'media'
file_overwrite = False
DEFAULT_FILE_STORAGE = 'mysite.storage_backends.MediaStorage'
Now all our file related fields like models.FileField(), models.ImageField() will be uploading file in our s3 bucket inside the directory ‘media’.
Now we may have different types of storages, some of them will be storing documents, some of them will be publicly accessible, some of them will be classified. Their directory could be different and so on so forth.
class MyPrivateFileStorage(S3Boto3Storage):
location = 'classified'
default_acl = 'private'
file_overwrite = False
custom_domain = False
If we want to use any other storages that is not defined in DEFAULT_FILE_STORAGE in settings.py. We would need to define it at the field of our model models.FileField(storage=PrivateMediaStorage()).
Recently I was looking for more flexible way to ship our code in production, docker and kubernetes are the sweetheart of the devops engineers. Docker is something that let us containerise our app in an image and then we let that image to run on production. I know that we all have some experience with Virtual Machines, it is easy to get confused about the difference that will stop you from appreciating what docker does. A virtual machine works separately on top of the hypervisor of your computer on the other hand docker creates another layer of abstraction on top of your OS. It lets you share the similarities the images that you already have on your OS, and on top of that it adds another layer that has the differences. Now we can run multiple linux image on top of one machine without costing double. So it optimises, it is intelligent and it saves us. When we are running a cluster of n nodes, the complexity exponentially, what if something broke somewhere in a docker container in a cluster of n nodes? How can we ensure that which docker container should be run on which node? How do we move that docker container to another node because that node is going to be turned off for maintenance? We need a manager, who takes care of them, don’t we? Kubernetes comes along takes the responsibility.
First of all lets setup kubernetes.
cat <<EOF > /etc/yum.repos.d/kubernetes.repo
[kubernetes]
name=Kubernetes
baseurl=https://packages.cloud.google.com/yum/repos/kubernetes-el7-x86_64
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://packages.cloud.google.com/yum/doc/yum-key.gpg https://packages.cloud.google.com/yum/doc/rpm-package-key.gpg
exclude=kube*
EOFWe would turn the selinux off as the documentation says so.
setenforce 0
sed -i 's/^SELINUX=enforcing$/SELINUX=permissive/' /etc/selinux/config
Now we would be installing docker, kubeadm, and in more abstract kubernetes.
yum install -y docker kubelet kubeadm kubectl --disableexcludes=kubernetes
We would need to ensure that these are the service that should be the first thing it should be doing when the computer turns on:
systemctl enable kubelet && systemctl start kubelet systemctl enable docker && systemctl start docker
Now that we have our kubernetes and docker running on master and slave node. We would need to change one or two things in configuration for a safe initial launching on master.
vi /var/lib/kubelet/kubeadm-flags.env
KUBELET_KUBEADM_ARGS=--cgroup-driver=systemd #--network-plugin=cni
We are initializing the node on master:
kubeadm init
for future token creation
sudo kubeadm token create --print-join-command
It should generate a token for you which you would need to copy and paste on your slave node, but before that you would be need to put configuration file in proper directory with proper permission.
mkdir -p $HOME/.kube sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config sudo chown $(id -u):$(id -g) $HOME/.kube/config
on slave server
kubeadm join 10.0.15.10:6443 --token vzau5v.vjiqyxq26lzsf28e --discovery-token-ca-cert-hash sha256:e6d046ba34ee03e7d55e1f5ac6d2de09fd6d7e6959d16782ef0778794b94c61e
if you are getting something similar:
I0706 07:18:56.609843 1084 kernel_validator.go:96] Validating kernel config
[WARNING RequiredIPVSKernelModulesAvailable]: the IPVS proxier will not be used, because the following required kernel modules are not loaded: [ip_vs_sh ip_vs ip_vs_rr ip_vs_wrr] or no builtin kernel ipvs support: map[ip_vs_wrr:{} ip_vs_sh:{} nf_conntrack_ipv4:{} ip_vs:{} ip_vs_rr:{}]
you can solve this problem with following methods:
1. Run 'modprobe -- ' to load missing kernel modules;
2. Provide the missing builtin kernel ipvs support
Pulling images required for setting up a Kubernetes cluster
running following would help
for i in ip_vs ip_vs_rr ip_vs_wrr ip_vs_sh nf_conntrack_ipv4; do modprobe $i; done
Now if you run following in server, you would know that you have some nodes attached to your kubernetes cluster.
sudo kubectl get nodes sudo kubectl describe nodes
if you had issues dealing with nodes:
kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml
For this demonstration I will be adding an existing python/django application on this cluster. So first of all I would be needing to dockerize that first.
at my Dockerfile
# set the base image FROM python:3.7 # File Author / Maintainer MAINTAINER Sadaf #add project files to the usr/src/app folder #set directoty where CMD will execute WORKDIR /usr/src/app ADD app_name ./app_name COPY /app_name/requirements.txt . # Get pip to download and install requirements: --no-cache-dir RUN pip install -r requirements.txt # Expose ports EXPOSE 8000 # default command to execute WORKDIR /usr/src/app/app_name RUN chmod +x app_name/gunicorn.sh CMD ./app_name/gunicorn.sh #ENTRYPOINT ["/bin/bash", "app_name/gunicorn.sh"]
Now that we have a dockerfile ready. Lets build that image:
sudo docker build -t app_name_api_server .
Time to run that image and expose it on port 8000
sudo docker run -p 8000:8000 -i -t app_name_api_server
If you like what you see on localhost:8000! Congratulations! Your app is working on docker. Now let’s push that image on docker hub. For me I have created a private repo on docker hub. To be able to push your image on docker hub you would be needing to add tags to that image first then you can push it.
sudo docker tag app_name_api_server sadaf2605/app_name_api_server sudo docker push sadaf2605/app_name_api_server
Now that you have your image pushed on docker hub. Now we will go back to our kubernetes master. As the image that we want to pull are on private image, no wonder we would need some sort of credential to pull it.
DOCKER_REGISTRY_SERVER=docker.io DOCKER_USER=Type your dockerhub username, same as when you `docker login` DOCKER_EMAIL=Type your dockerhub email, same as when you `docker login` DOCKER_PASSWORD=Type your dockerhub pw, same as when you `docker login` kubectl create secret docker-registry myregistrykey \ --docker-server=$DOCKER_REGISTRY_SERVER \ --docker-username=$DOCKER_USER \ --docker-password=$DOCKER_PASSWORD \ --docker-email=$DOCKER_EMAIL
Now lets define and yaml file that we are going to define our kubernetes deployments, app_name.yaml.
apiVersion: apps/v1
kind: Deployment
metadata:
name: app-name-api-server
spec:
selector:
matchLabels:
run: app-name-api-server
replicas: 1
template:
metadata:
labels:
run: app-name-api-server
spec:
containers:
- name: app-name-api-server
image: index.docker.io/sadaf2605/app_name_api_server:latest
imagePullPolicy: Always
ports:
- containerPort: 8000
#hostPort: 8000
env:
- name: DB_USERNAME
value: "user"
- name: DB_PASSWORD
value: "password"
- name: DB_NAME
value: "dbname"
- name: DB_HOST
value: "1.2.2.3"
imagePullSecrets:
- name: myregistrykey
hostNetwork: true
dnsPolicy: ClusterFirstWithHostNet
dnsPolicy: "None"
dnsConfig:
nameservers:
- 8.8.8.8
imagePullSecrets:
- name: myregistrykey
---
apiVersion: v1
kind: ConfigMap
metadata:
name: kube-dns
namespace: kube-system
data:
upstreamNameservers: |
["8.8.8.8"]
Now time to run a deployment using that configuration:
sudo kubectl apply -f app_name.yaml
Lets check if we have a deployment or not:
sudo kubectl get deployments
Lets check if any instance of our docker container is running or not.
sudo kubectl get pods
Now we would be creating a service that would let us access to these pods from outside of pods.
sudo kubectl expose deployment app-name-api-server --type=LoadBalancer --name=app-name-api-server
sudo kubeadm upgrade plan --feature-gates CoreDNS=true
