Create Virtual Machines.
Let us create the required number of virtual machines for setting up cluster using the preferred operating system. Here, I am going with Ubuntu-18.04.3. I have planned to setup a cluster using single control plane(master) and three worker nodes.
Each node should be equipped with at least 2GB memory, 20GB disk space and 2vCPUs. To make the disk space usage optimal in VMware, enable thin provisioning while creating virtual disk.
Let us customise the virtual machines with the preferred configuration and start booting through ISO. Once the virtual machines are created successfully, go ahead with the below steps to configure a Kubernetes cluster. Kubernetes online training helps you to learn more techniques and skills.
Based on your networking solution, configure network settings in the virtual machines. Ensure that all the machines are connected to each other.
Setup meaningful hostname in all the nodes if necessary.
sudo hostnamectl set-hostname
Reboot the machine to make the change effective.
Enable ssh on the machines
If ssh is not configured, install openssh-server on the virtual machines and enable connectivity between them.
sudo apt-get install openssh-server -y
Disable swap on the virtual machines.
As a super user, disable swap on all the machines. Execute the below command to disable swap on the machines.
In order to disable swap permanently , comment out swap entry in /etc/fstabfile.
This can be verified using the following command.
root@host1:~# free -h total used free shared buff/cache availableMem: 7.8G 990M 6.0G 13M 797M 6.6GSwap: 2.0G 0B 2.0G
Note: This has to be done on all the machines.
Install necessary Packages
Let us install curl and
apt-transport-https in all the machines.
sudo apt-get update && sudo apt-get install -y apt-transport-https curl
Obtain the Key for the kubernetes repository and add it to your local key-manager by executing the below command.
root@host1:~# curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -OK
After adding the above key, execute the below command to add the kubernetes repo to your local system.
cat <<EOF | sudo tee /etc/apt/sources.list.d/kubernetes.listdeb https://apt.kubernetes.io/ kubernetes-xenial mainEOF
kubeadm, kubectl and kubelet installation
After adding the above install kubeadm, kubelet and kubectl in all the machines.
sudo apt-get updatesudo apt-get install -y kubelet kubeadm kubectl
After installing the above packages, let us hold them as it is in the machine by executing the following command.
root@host1:~# sudo apt-mark hold kubelet kubeadm kubectlkubelet set on hold.kubeadm set on hold.kubectl set on hold.
Install Container Runtime
In each node, container runtime (CRI) component should be installed to manage the containers. In this setup, I will install the container runtime
docker by executing the below command.
sudo apt-get install docker.io -y
Install Control plane
In the master node, execute kubeadm init command to deploy control plane components
kubeadm init --pod-network-cidr=192.168.2.0/16
When the above command execution is successful, it will yield a command to be executed on all the worker nodes to configure them with the master.
After configuring the master node successfully, configure the worker nodes by executing the join command displayed in master node.
kubeadm join x.x.x.x:6443 --token \ --discovery-token-ca-cert-hash
You can communicate with the cluster components using kubectl interface. In order to communicate, you need kubernetes cluster config file to be placed in the home directory of the user from where you want to access the cluster. Once the cluster is created, a file named admin.conf will be generated in /etc/kubernetes directory. This file has to be copied to the home directory of target user. Kubernetes online course helps you to learn more effectively.
Let us execute the below commands from the non-root user to access cluster from that respective user.
mkdir -p $HOME/.kubesudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/configsudo chown $(id -u):$(id -g) $HOME/.kube/config
After setting up the kubeconfig file , check the node status. All the machines will be in not ready state.
k8s@master:~$ kubectl get nodesNAME STATUS ROLES AGE VERSIONmaster NotReady master 5m41s v1.17.2host1 NotReady 3m2s v1.17.2host2 NotReady 2m58s v1.17.2host3 NotReady 2m54s v1.17.2
And you can observe that coredns pod is not started.
NAMESPACE NAME READY STATUS RESTARTS AGEkube-system coredns-6955765f44-9nlw5 0/1 Pending 0 4m33skube-system coredns-6955765f44-wjxj2 0/1 Pending 0 4m33skube-system etcd-master 1/1 Running 0 4m45skube-system kube-apiserver-master 1/1 Running 0 4m45skube-system kube-controller-manager-master 1/1 Running 0 4m45skube-system kube-proxy-bzcbw 1/1 Running 0 2m6skube-system kube-proxy-clmpz 1/1 Running 0 2m14skube-system kube-proxy-crx5v 1/1 Running 0 4m32skube-system kube-proxy-xcmlv 1/1 Running 0 2m10skube-system kube-scheduler-master 1/1 Running 0 4m45s
This will be resolved when you deploy network CNI plugin in the cluster. Here, I will deploy calico by executing the following command in the master node. online kubernetes course for more.
kubectl apply -f https://docs.projectcalico.org/v3.8/manifests/calico.yaml
In next few minutes, your cluster will be created successfully. Check the node status and
ensure the successful creation.
k8s@master:~$ kubectl get nodesNAME STATUS ROLES AGE VERSIONmaster Ready master 50m v1.17.2host1 Ready 47m v1.17.2host2 Ready 47m v1.17.2host3 Ready 47m v1.17.2
You can check the cluster state by executing the following command.
k8s@master:~$ kubectl get pods --all-namespacesNAMESPACE NAME READY STATUS RESTARTS AGEdefault abc1-b95b76d84-2qmhw 1/1 Running 0 2m41skube-system calico-kube-controllers-5c45f5bd9f-r9rxj 1/1 Running 0 4m59skube-system calico-node-bd4tx 1/1 Running 0 5mkube-system calico-node-lxk75 1/1 Running 0 5mkube-system calico-node-zmnn4 1/1 Running 0 5mkube-system calico-node-zzvhk 1/1 Running 0 5mkube-system coredns-6955765f44-9nlw5 1/1 Running 0 10mkube-system coredns-6955765f44-wjxj2 1/1 Running 0 10mkube-system etcd-master 1/1 Running 0 10mkube-system kube-apiserver-master 1/1 Running 0 10mkube-system kube-controller-manager-master 1/1 Running 0 10mkube-system kube-proxy-bzcbw 1/1 Running 0 8m19skube-system kube-proxy-clmpz 1/1 Running 0 8m27skube-system kube-proxy-crx5v 1/1 Running 0 10mkube-system kube-proxy-xcmlv 1/1 Running 0 8m23skube-system kube-scheduler-master 1/1 Running 0 10m
Now, the kubernetes cluster has been created successfully. You can verify this by setting up a deployment/pod.
k8s@master:~$ kubectl create deploy nginx --image=nginxdeployment.apps/nginx created
You can check the pod status by executing the below command.
k8s@master:~$ kubectl get podsNAME READY STATUS RESTARTS AGEnginx-86c57db685-rpzm2 1/1 Running 0 70s
Kubernetes cluster can be teared down by executing the below single command.
sudo kubeadm reset
Thus, a cluster can be deleted.
#kubernetes training #kubernetes course #kubernetes online course #cka training #kubernetes certification training
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu" >>> LastName = "Jordan" >>> FirstName, LastName = LastName, FirstName >>> print(FirstName, LastName) ('Jordan', 'kalebu')
#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development
Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips
Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…
You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.
class AnyClass: def __init__(): print("Init called on its own") obj = AnyClass()
The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.
The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.
Init called on its own
Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,
class AnyClass: def __init__(self, var): self.some_var = var def __add__(self, other_obj): print("Calling the add method") return self.some_var + other_obj.some_var obj1 = AnyClass(5) obj2 = AnyClass(6) obj1 + obj2
#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=str(“Hello python world”)****#str**
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
#single (') Quoted String
# Double (") Quoted String
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type