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WHEN PROGRESSIVE WEB APPS ARE NEEDED
Introduction
PWA apps look and feel like native mobile apps, built with web technologies. They allow websites to be stored on devices. The app shows up as an icon in the form of a website app. The basic methodology here is to combine native app experience with the browser feature.PWA on a Real Device
A PWA is a website that can be downloaded onto the computer or mobile device. It actually pulls in updates in the background each time a user runs it. So, as and when a web application is updated, it not only gives the app site access to new features, but does so without explicitly performing any kind of update.Key Features of PWA
Responsive
PWA apps are built responsive by nature; they're designed to be adaptable to all types of devices with different screen sizes. So, the app will be used on many screen sizes and its contents will be available at all viewport sizes.Installable
Installation of the PWA application is very easy. On a desktop computer or a mobile device, the app is added to the home screen although installation is not necessary. The service worker is set up behind the scenes, the first time a visitor sees the website.Connectivity-Independent
Applications built with progressive web standards can operate offline and on low-quality networks. They also keep a user active in the app even when the user is not connected. The app stores items offline and manages network requests to get the items from the local cache, in a flexible manner, with the help of service workers.Cross Platform & App-Like
A PWA is created to tie together the app and website user experience. Additionally, users can take advantage of these services without going into the app store. Heavy-lifting duties like downloading and data storage are not necessary for PWA installation. PWAs work on all browsers and systems. Prior to installing them, users have to be able to use them in any browser.Load Time & Secure
PWA apps have faster load time. In comparison to the conventional mobile web, the progressive web app reduces page load time by 88 percent, or about 400 milliseconds. Native apps require a lot of security measures, but PWAs are inherently secure because they run on HTTPS and are encrypted with an SSL certificate. This in turn, adds an extra layer of security.Use Cases of PWA
- Better user experience
- Increased user engagement
- Increased security and ability to use offline
- Increased organic search traffic
- PWAs typically cost less to develop and maintain than native apps
When Do We Require a PWA?
Usage of Application in Multi-Devices
Whenever there is a need to use applications on both mobile and desktop devices, progressive web apps is the way to go. PWAs are becoming increasingly popular because they are lightweight and load quickly. Additionally, using PWAs, web apps can be viewed on mobile devices. By doing so, user gets a native mobile application feel and look along with a browser feature.Speed and Reliability
If speed is the main concern, PWA is the answer because PWA is significantly faster than a native mobile application. According to statistics, PWA has a faster load time and a lower demand on devices. In other words, when the app must be consistently high-quality and lightning-fast despite no network connection or limited bandwidth, a PWA is the best option.Responsiveness
When the user plans to install or use applications across different devices, it is always better to use PWAs. They are responsive to most devices and make the UI appealing on any device.Security
PWAs are secure by nature since the technology that powers the app requires it to be served over the HTTPS protocol in order to work. It is delivered through TLS, which provides major benefits for both users and developers.Platform Independent
Whenever an application is built for cross-platform usage, with a single technology, PWA is the way to go! It is available on all platforms and simplifies the development process for developers.Advantages of Using PWA
- Lightweight and Easy to install in devices
- Provides offline support
- Safe and Secure to use
- Faster than native mobile applications
- Helps to boost Search Engine Optimization
- Targets Cross platform
Disadvantages of PWA
- Cannot access the various device features
- Consumes more battery
- No access to app stores
- UI and UX Limitations
- If user does not use the app for a long time, the cache is deleted
- Push notification features are not possible in iOS devices
Conclusion
The importance of PWAs would definitely be felt to a large extent in the future. There are many PWA features currently under development and the PWA community is growing by the day. One of the main reasons as to why users are more likely to choose PWAs over native apps is because PWAs encourage them to interact more. Further, the low costs and the ease of implementation play a huge role in influencing the spread of this technology.References
Progressive web apps (PWAs) | MDN What is a PWA and why should you think about it?Agile Delivery Process
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Technologies
Jira Integration with GitHub
OBJECTIVE:
To configure github with jira through pytest to update results on jira tickets, When Merging a Pull Request the github workflow will be executed, After the workflow execution the status of the jira tickets will be updated as per the result of github workflow execution through pytestWhat is Jira?
Jira is a web application used as tracking tools for tasks like epic, story, bugs and so on. Jira is available as an open source and paid version.Why do we use Jira ?
It is used for various kinds of projects like business projects, software projects and service projects Applications like Github, slack, jenkins, zendesk etc can be integrated with jira. By using jira a ticket can be created for each type of task to monitor application development, Here we integrate Github with jira through the pytest framework.What is Pytest ?
Pytest is an automation testing framework in python which is used for testing software applications.Why do we use Pytest ?
Pytest is a python framework, using pytest we can create TDD, BDD as well as Hybrid testing framework used for automation testing like UI, REST API and it is flexible for supporting different actions. Here we are going to execute the test cases that are triggered from the github actions workflow and update the particular jira tickets based on the workflow execution results.What is Rest API ?
REST is abbreviated as (Representational State Transfer) it is an architectural style for interacting between the client and the server, the client sends the request and a response is received from the server in of JSON, XML, or HTML, but json is most commonly used response type because it is readable for both human and machine. Here we interact with the Jira through Rest API, the API Endpoints we are using to interact with Jira is given belowEXECUTION FLOW OF GITHUB FOR JIRA THROUGH PYTEST
To update the jira tickets through pytest, we need to know about the Github workflow execution, Jira Rest API endpoints, pytest configurationsThings we need to know for execution:
- To create a github workflow file to execute pytest test cases when a PR is merged
- To configure pytest test cases with jira API endpoints to send the workflow results
JIRA REST API ENDPOINTS
Prerequisites for Jira API Integration:
Steps to create API Token:
- STEP 1: Login to Jira with the registered Email ID Use this link https://id.atlassian.com/login to login into the jira
- STEP 2: Click on your jira Profile and click on manage accounts in the popup
- STEP 3: Click on Security tab and click create and manage API token
- STEP 4: Click on create API Token button:
- STEP 5: Provide a label for the token and click create, a new API Token will be generated Copy the token and save the token in a separate file because you cant able to get the same token again
Encoding the API Token:
Encoding the Api token can be done in the terminal, to create a base64 encoded token use the following command In LINUX/ mac-os:GET Transition ID API:
- GET:
Transition Status | Transition ID |
---|---|
To-Do | 11 |
In-Progress | 21 |
Done | 31 |
Issue 1 | 2 |
Issue 2 | 3 |
Update Transition Status API:
Post :
https://<JIRA_DOMAIN>.atlassian.net/rest/api/2/issue/<TICKET-ID>/transitions This API Endpoint is used to update the transition status of the jira ticket. Ticket ID is passed in the path param and the Transition ID is passed in the body of the request, The status of the ticket will be updated with respect to the transition id, Curl for the following API endpoint is mentioned below, The Transition ID can be obtained by using the Get transition ID API, which is mentioned aboveAdd Attachments API:
Post:
https://<JIRA_DOMAIN>.atlassian.net/rest/api/2/issue/<TICKET-ID>/attachments This API endpoint is used to add the attachment in a JIRA ticket according to the given Ticket ID and passed file. Use the below curl commandSearch API:
GET:
https://<jira_domain>.atlassian.net/rest/api/2/search This API Endpoint is used to get the ticket information using the Jira Query Language (JQL) syntax, the JQL should be passed as an parameter to this API, By using this API we can get the information of all/any of the ticket, An example for JQL to get the ticket information using PR Link which is mentioned in the Github info paragraph field of a jira ticket Example for JQL:CONFIGURING GITHUB WITH JIRA:
There are two ways in configuring github with jira, one is by providing the PR link in a separate field of jira ticket and the other way is by configuring github app in jira1. Configuring jira with PR link:
- We can able to identify the ticket information by providing the PR link in a Jira ticket
- PR Link should be provided in the custom field of Jira ticket
- After placing the PR link in custom field, we need to use the Jira Search API endpoint through Jira Query Language(JQL) syntax
2. Steps to configure PR link in Jira Ticket on custom field:
- Go to Project Board > Project settings > Issue types
- Select the Paragraph field type > Enter the field name and description
- Click Save changes
3. Configure github app with jira:
- To configure Github with jira,Login into Jira, go to Apps➡ Manage Your apps
- Select Github for jira ➡ click connect github organization
- CLICK Install github for jira on new organization
- On clicking Install github for jira on new organization select the github organization in which you want to install jira
- Select repository which you want to configure and click install
- Now you can see your git repository that have been configured in the github for jira tab
UPDATING EXECUTION RESULTS TO JIRA TICKET USING PYTEST:
- All the test cases and the reports report generation for all the test cases are done using pytest
- After the Workflow execution, the build status and PR link will be added as comments and the reports will be added as attachments to the jira ticket, this is done by the pytest fixture. The pytest fixture is used to execute the conditions before and after the execution of test case, the yield keyword is used to execute the conditions after the execution of all test cases
- The teardown module() method calls the Jira API Endpoints for Adding Comments and Attachments.
Technologies
Build APIs in Python Using FastAPI Framework
FastAPI could be a modern, high-performance, web framework for building APIs with Python language. Good artificial language frameworks make it easy to provide quality products faster. Great frameworks even make the entire development experience enjoyable. FastAPI could be a new Python web framework that’s powerful and enjoyable to use.
FastAPI is an ASGI web framework. What this implies is that different requests don’t necessarily sit up for the others before them to end up doing their tasks. Additional requests can do their task completed in no particular order. On the other hand, the WSGI frameworks process requests in a sequential manner.ASGI:
ASGI is structured as one, asynchronous callable. It takes a scope, which could be a dict containing details about the precise connection, sends an asynchronous callable that lets the appliance send event messages to the client, and receives an asynchronous callable that lets the application receive event messages from the client.Does FastAPI need Uvicorn?
The main thing needed to run a FastAPI application in an exceedingly remote server machine is an ASGI server program like Uvicorn.Using WSGIMiddleware:
Need to import WSGIMiddleware.Then wrap the WSGI (e.g. Flask) app with the middleware. Then mount that beneath a path.FastAPI Different from other Frameworks:
Building a CRUD Application with FastAPI
Setup:
Start by creating a brand new folder to carry your project called "sql_app".Difference between Database Models & Pydantic Models:
FastAPI suggests calling Pydantic models schemas to assist make the excellence clear. Appropriately, let’s put all our database models into a python models.py file and every one of our Pydantic models into a schemas.py file. In doing this, we’ll also have to update database.py and main.py.Models.py:
database.py:
schema.py:
FastAPI interactive documentation
A feature that I like about API is its interactive documentation. FastAPI is based on OpenAPI, which is a set of rules that defines how to describe, create and visualize APIs. OpenAPI needs software, which is Swagger, which is the one that allows us to show the API documented. To access this interactive documentation you simply need to go to “/docs”.Structuring of FastAPI:
Models:
It is for your database models, by doing this you'll import the identical database session or object from v1 and v2.Schemas:
It is for Pydantic's Settings Management which is extremely useful, you will be ready to use the identical variables without redeclaring it, to work out how it should somewhat be useful for taking a glance at our documentation for Settings and Environment Variables.Settings.py:
It is for Pydantic's Settings Management which is extremely useful, you'll be able to use the identical variables without redeclaring it, to determine how it may well be useful for you take a look at our documentation for Settings and Environment VariablesViews:
This is optional if you're visiting render your frontend with Jinja, you'll have something near MVC pattern.Core views
- v1_views.py
- v2_views.py
Tests:
It is good to own your tests inside your backend folder.APIs:
Create them independently by APIRouter, rather than gathering all of your APIs inside one file.Logging
It could be a means of tracking events that happen when some software runs. The software’s developer adds logging calls to their code to point out that certain events have occurred. an occasion is described by a descriptive message which might optionally contain variable data (ex. data that's potentially different for every occurrence of the event). Events even have an importance that the developer ascribes to the event; the importance also can be called the amount or severity.Conclusion
Modern Python Frameworks and Async capabilities are evolving to support robust implementation of web applications and API endpoints. FAST API is definitely one strong contender. In this blog, we had a quick look at a simple implementation of the FAST API and code structure. Many tech giants like Microsoft, Uber, and Netflix are beginning to adopt. This will result in growing developer maturity and stability of the framework.Reference Link:
https://fastapi.tiangolo.com/ https://www.netguru.com/blog/python-flask-versus-fastapiTechnologies
How to use Apache spark with Python?
Apache Spark is based on the Scala programming language. The Apache Spark community created PySpark to help Python work with Spark. You can use PySpark to work with RDDs in the Python programming language as well. This can be done using a library called Py4j.
Apache spark:
Apache spark is an open-source analytics and distributed data processing system for large amounts of data (large-scale datasets). It employs an in-memory caching and an accelerated query execution for quick analytic queries against any size of data. It is faster because it distributes large tasks across multiple nodes and uses RAM to cache and process data instead of using a file system. Data scientists and developers use it to quickly perform ETL jobs on large amounts of data from IoT devices, sensors, and other sources. Spark also has a Python DataFrame API that can read a JSON file into a DataFrame and infer the schema automatically. Spark provides development APIs for Python, Java, Scala, and R. It shares most of its features with PySpark, including Spark SQL, DataFrame, Streaming, MLlib, and Spark Core. We will be looking at PySpark.Spark Python:
Python is well known for its simple syntax and is a high-level language that is simple to learn. Despite its simple syntax, it is also extremely productive. Programmers can do much more with it. Since it provides an easier interface, you don't have to worry about visualizations or Data Science libraries with Python API. The core components of R can be easily ported to Python as well. It is most certainly the preferred programming language for implementing Machine Learning algorithms.PySpark :
Spark is implemented in Scala which runs on JVM. PySpark is a Python-based wrapper on top of the Scala API. PySpark is a Python interface to Apache Spark. It is a Spark Python API that helps you connect Resilient Distributed Datasets (RDDs) to Apache Spark and Python. It not only allows you to write Spark applications using python but also provides the PySpark shell for interactively analyzing your data in a distributed environment.PySpark features:
-
- Spark SQL brings native SQL support to Spark and simplifies the process of querying data stored in RDDs (Spark's distributed datasets) as well as external sources. Spark SQL makes it easy to blend RDDs and relational tables. By combining these powerful abstractions, developers can easily mix SQL commands querying external data with complex analytics, all within a single application.
-
- DataFrame A DataFrame is a distributed data collection organized into named columns. It is conceptually equivalent to relational tables with advanced optimization techniques. DataFrame can be built from a variety of sources, including Hive tables, Structured Data files, external databases, and existing RDDs. This API was created with inspiration from DataFrame in R Programming and Pandas in Python for modern Big Data and data science applications.
-
- Streaming is a Spark API extension that allows data engineers and data scientists to process real-time data from a variety of sources like Kafka and Amazon Kinesis. This processed data can then be distributed to file systems, databases, and live dashboards. Streaming is a fault-tolerant, scalable streaming processing system. It supports both batch and streaming workloads natively.
-
- Machine Learning Library (MLlib) is a scalable machine learning library made up of widely used learning tools and algorithms, such as dimensionality reduction, collaborative filtering, classification, regression, and clustering. With other Spark components like Spark SQL, Spark streaming, and DataFrames, Spark MLLib works without any issues.
-
- Spark Core is a general execution engine of Spark and is the foundation upon which all other functionality is built. It offers an RDD (Resilient Distributed Dataset) and supports in-memory computing.
Setting up PySpark on Linux(Ubuntu)
Follow the steps below to setup and try pyspark: Please note that python version 3.7 or above is required. Create a new directory. Navigate to the directory.PySpark shell
Pyspark comes with an interactive shell. It helps us to test, learn and analyze data in the command line. Launch pyspark shell with command line ‘pyspark’. It launches the pyspark shell and gives you a prompt to interact with Spark in the Python language. To exit from spark shell use exit()Create pyspark Dataframe:
Like in pandas, here also we can create dataframe manually by using these two methods toDF() and createDataFrame(), and also from JSON, CSV, TXT, XML formats by reading from S3, Azure Blob file systems e.t.c. First, create columns and datasRDD dataframe:
An existing RDD is an easy way to manually create a PySpark DataFrame. First, let's create a Spark RDD from a List collection by calling the parallelize() function from the SparkContext. This rdd object is required for all of the following examples. A spark session is an entry point for the spark to access components. To create a Dataframe using toDF() method, we have to build a spark session and then pass the data as an argument to parallelize. Finally, we use “toDF(columns)” to specify column names as in the below code snippets.Kafka and PySpark:
We are going to use pyspark to produce a stream dataframe to Kafka and consume the stream dataframe. We need kafka and pyspark for the same. We have already setup pyspark in our system, Now we are going to setup kafka in the our system. If you have already setup kafka you can skip this, otherwise you can setup kafka by following these steps: Set up Kafka using Docker compose: Docker Compose is used to run multiple containers as a single service and it works on all environments. Docker Compose files are written YAML files. Now, create docker-compose YAML file named docker-compose.yml for Kfka. Enter the following and save the file. It will run everything for you via Docker.Produce CSV data to kafka topic, Consume using PySpark :
Produce CSV data to kafka topic :
For that we need a CSV. Download or create your own CSV file Install the kafka-python package in a virtual environment. kafka-Python is a python client for the Apache Kafka distributed stream processing system. With pythonic interfaces, kafka-python is intended to operate similarly to the official java client. In the below code we have configured Kafka producer and created an object with it. In config we have to give info like bootstrap server and value_serializer. serializer instructs on how to turn the key and value objects the user provides with their ProducerRecord into bytes.What is schema/StructType in spark ?
It defines the structure of the DataFrame. We can define it using StructType, which is a collection of StructFields that define the column name, DataType, column nullability, and metadata. Below code will write the data frame stream on consoleConclusion :
One of the popular tools for working with Big data is Spark. It has the PySpark API for Python users.This article covers the basics of data frames, how to install PySpark on Linux, what spark and PySpark features are, and how to manually generate data frames using the toDF() and createDataFrame() functions in the PySpark shell. Due to its functional similarities to pandas and SQL, PySpark is simple to learn and use. Additionally, we look at setting up Kafka, putting data into Kafka, and using PySpark to read data streams from Kafka. I hope you use this information and put it to use in your work.Reference Link:
Apache Spark: https://spark.apache.org/docs/latest/api/python/getting_started/install.html Pyspark : https://sparkbyexamples.com/pyspark-tutorial/ Kafka: https://sparkbyexamples.com/spark/spark-streaming-with-kafka/Technologies
Resemblance and Explanation of Golang vs Python
Everyone has been looking for the best programming language to use when creating software and also there has recently been a battle between Golang and Python.
Golang
Golang is a procedural, compiled, and statically typed programming language(syntax similar to C). It was developed in 2007 by Ken Thompson, Robert Griesemer, and Rob Pike at Google but launched in 2009 as an open-source programming language. This language is designed for networking and infrastructure-related applications. While it is similar to C, it adds a variety of next-gen features such as Garbage collection, Structural typing, and Memory management. Go is much faster than many other programming languages. Kubernetes, Docker, and Prometheus are written in it. (developed using this language or written in it)Features of Golang
Simplicity
The developers of the Go language focus on credibility, readability, and maintainability by incorporating only the essential attributes of the language. So we can avoid any kind of language complications resulting from the addition of complex traits.Robust standard Library
It has a strong set of library packages, making it simple to compose our code.Web application building
This language has garnered as a web application building language owing to its easy constructs and more agile execution speed.Concurrency
- Go deals with Goroutines and channels.
- Concurrency effectively makes use of the multiprocessor architecture.
- Concurrency also helps huge programs scale more consistently.
- Some notable examples of projects written in Go are Docker, Hugo, Kubernetes, and Dropbox.
Speed of Compilation
- Go offers a much more powerful speed of completion and compilation than several other popular programming languages.
- Go is readily parsable without a symbol table.
Testing support
- The "go test" command in Go allows users to test their code written in '*_test.go' files.
Pros:
- Ease to use - Go’s core resembles C/C++, so experienced programmers can pick up the basics fast, and Simple syntax is easy to understand and learn
- Cross-platform development opportunities - Go can be used with various platforms like UNIX, Linux, Windows, and Other Operating systems, and Mobile devices also.
- Faster compilation and execution - Go is a compiler-based language so it completely reads the code and executes due to this it executes faster than c,c++, and java.
- Concurrent - Run various processes together and effectively
Cons:
- Still developing - Still in development
- Absence of GUI Library - There is no native support
- Poor Error handling - The built-in errors in Go don't have stack traces and don't support the usual try/catch handling techniques.
- Lack of frameworks - Minimal amount of frameworks
- No OOPS Support
Output:
- package main - Every Go program begins with code inside the package main
- Import “fmt” - its an I/O function.
- func main - This function always needs to be placed in the main package.{} Here we can write our code/logic.
- fmt.Println - Print function, print the text on the screen.
Why Go?
- It's a statically strongly typed programming language with a great way to handle errors.
- It allows using static linking to combine all dependency libraries and modules into one single binary file based on the type of the OS and architecture.
- This language performs more efficiently because of its CPU scalability and concurrency model.
- This language offers support for multiple libraries and tools, so it does not require any 3rd party libraries.
Python
Python is a universal, high quality and very popular programming language. Python was introduced and developed by Guido van Rossum in the year of 1991. Python is used in machine learning applications, data science, web development, and all modern software technologies. Python has an easy-to-learn syntax that improves readability and reduces program maintenance costs. Python code is interpreted when it is converted to machine language at run time. It is the most widely used programming language because of its tightly typed and dynamic characteristics. Python was originally used for trivial projects and is known as a "scripting language". Instagram, Google, and Spotify use Python and its frameworks.Features of Python
- Free and open source
- Easy to code
- Object-oriented programming
- GUI Programming support
- Extensible and portable
-
-
- Python is an extensible language.
- We can formulate some Python code into the C or C++ language.
- Furthermore, we can compile that code in the C or C++ language.
- Python is also a very portable language.
- If we have Python code for Windows and want to run it on platforms such as Unix, Linux, and Mac, we do not need to change it. This code is platform-independent.
-
- Interpreted and High-level language
-
-
- Python is a high-end language.
- When we formulate programs in Python, there is no need to remember the system architecture, nor do we need to manage the memory.
- Like in other programming languages, there is no requirement to compile Python code, making it easy to debug our code.
- Python's source code is converted to an instantaneous form known as bytecode. Python is classified as an interpreted language because Python code is executed line by line.
-
Pros:
- Simple syntax: Easy to read and understand
- Larger Community support: Python community is vast
- Dynamically typed: The variable type is not required to be declared.
- Auto memory management: Memory allocation and deallocation methods in Python are automatic because the Python developers created a garbage collector for Python so that the user does not have to manually collect garbage.
- Embeddable: Python can be used in embedded systems
- Vast library support: Lots of Libraries are available. For Example, TensorFlow, Opencv, Apache spark, Requests and Pytorch etc.,
Cons:
- Slow speed
- Not Memory Efficient
- Weak in mobile computing
- Runtime errors
- Poor database access
Why Python?
Python is platform-independent; it runs on (Windows, Mac, Linux, Raspberry Pi, etc.). Python has a simple syntax that is related to that of the English language. Python's syntax allows programmers to write programmes with fewer lines than in other programming languages. Python is an interpreter-based language. As a result, prototyping can be completed quickly. Python can be processed as procedural, object-oriented, or functional. Frameworks for Web Development: Django, Flask, Fastapi, Bottle, etc.Comparison of Go vs Python:
Case studies:
Concurrency:
Output:
Exception Handling :
Output :
Go vs Python: Which is Better?
When it comes to productivity, Golang is the best language to learn to become a more productive programmer. The syntax is restricted, and the libraries are much lighter because there is less code to write; tasks can be completed in fewer lines of code. Python consists of a large number of packages and libraries. Python has the advantage in terms of versatility due solely to the number of libraries and syntax options. However, flexibility comes at a cost, and that cost is productivity. Which language is more productive in this Python vs Golang battle? The winner is Golang, which is designed to be more productive, easier to debug, and, most importantly, easier to read. Python is without a doubt the most popular choice for developers looking to create a machine learning model. The reason for this is that Python is the most popular language for machine learning and is the home of TensorFlow, a deep learning framework built on Python. Learning a programming language like Python, which almost resembles pseudo-code, is an added benefit that makes learning easier. On the other hand, Golang is super fast, and effortless to write and comes along with Go doc, which creates documentation automatically, making the life of the programmer easier.Conclusion
Python and Golang are winners in their respective areas depending on the specific capabilities and underlying design principles of the language1.Maturity
It's difficult to make conclusions about Go vs Python because comparing a mature language to a young one doesn't seem fair. Python may be the winner here2.In ML and Data science Usage
Python is the leading language not only for machine learning and data analysis but also for web development. Golang has only been around for a decade, and it has yet to establish a robust ecosystem or community.3.Performance
The main advantage of Go is speed. However, Python is slow when it comes to code execution.4.Microservices and Future Readiness
When it comes to microservices, APIs, and other fast-loading features, Golang is better than Python. Go is equipped to be a future-ready web development framework with a lot of adoption around the world of containers.Referral Link:
Python - https://docs.python.org/3/ Go - https://go.dev/doc/Technologies
Flask vs FastAPI – A Comparison Guide to Assist You Make a Better Decision
What is Flask?
Flask is a micro web framework written in python. Armin Ronacher came up with the idea. Flask is built on the WSGI (Web Server Gateway Interface) Werkzeug toolkit (For the implementation of Request and Response) and the Jinja2 (template engine). WSGI is a standard for web application development. It is used to build small-scale web applications and Rest API Flask's framework is more explicit than Django's framework, and it is also easier to learn because it requires less basic code to construct a simple web application.In this real-world top Companies are using Flask
What makes Flask special?
- Lightweight Extensible Framework.
- Integrated unit test support.
- Provided development server and debugger.
- Uses Jinja templating.
- Restful request handling.
When should you use a Flask?
- Flask is old and it has Good Community support
- For Developing web applications and creating a quick Prototype.
Flask Web Application Development
- Creation of a virtual environment
- The venv environment is being restarted.
- Database.
- Login and register for several users
- Mode Debug
- Creating a User Profile Page
- Creating an avatar
- Handling errors
Build a sample webpage using Flask, it will return the string
Pros
- Flask has a built-in development server, integrated support, and other features.
- Flask Provides integrated support for unit tests.
- Flask Uses Jinja2 templates.
- Flask is just a collection of libraries and modules that helps developers to write their applications free without worrying about low-level details like Protocols and Thread Management.
- Because of its simplicity, Flask is particularly beginner-friendly, allowing developers to learn easier. It also allows developers to construct apps quickly and easily.
Cons
- Flask makes use of Modules, which is third-party participation that might lead to security breaches. Modules are the intermediary between the framework and the developer.
- Flask does not create automatic documentation; They need several extensions like Flasgger and Flask RESTX and also require additional setup.
- Flask has a single source, which suggests that it will handle each request one by one, therefore regardless of how many multiple requests there are, it will still take them in turns, which takes extra time.
What is FastAPI:
FastAPI is built in ASGI (Asynchronous Server Gateway Interface) and pydantic, Starlette. This framework is used for building a web application and RestAPI. In FastAPI we need uvicorn to run the server. There is no built-in development server So the ASGI server Uvicorn is required to run the FastAPI application. The best thing that we Highlight in the FastAPI is documentation. It will generate documentation and create Swagger UI. which helps the developers to test endpoints effectively. In fast API, Also includes data validation and returns an Explanation of the error when the user enters the invalid data. It implements all of the OpenAI requirements and Swagger for these specifications. As a developer we will concentrate on developing logic; the rest is handled by the FastAPI.When should you use a FastAPI?
- Has Good speed and performance when compared with Flask.
- Decreasing bugs and Errors in code.
- It Generates Automatic documentation
- Built-in data validation.
What makes FastAPI special?
- Fast Development
- Fewer Bugs
- High and Fast Performance
- Automatic swagger UI
- Data validation
Pros
- FastAPI is considered as one of the fastest Framework in python, It has an native async support and provides a simple and easy-to-use dependency injection framework. Another advantage to consider is built-in data validation and Have Interactive API documentation support .
- Dependency Injection support
- Fast API is based on standards such as JSON Schema (a tool for validating the structure of JSON data), OAuth 2.0 (an industry standard protocol for authorization), and OpenAPI (an open application programming interface).
Cons
- In FastAPI there is insufficient Security and also it will support OAuth.
- Because FastAPI is relatively new, the community is small compared to other frameworks, and regardless of its detailed documentation, there are very few external educational materials.
Difference between Flask and FastAPI:
Both offer the same features but the implementation is different. The main difference between Flask and FastAPI is that Flask is built in WSGI (Web Server Gateway Interface) and FastAPI is built in ASGI(Asynchronous Gateway Interface). So, the FastAPI will support concurrency and asynchronous codes. In FastAPI there is automatic documentation for Swagger UI(docs and redocs), But in Flask we need to add some extensions like Flasgger or Flask RESTX and some dependencies setup. Unlike Flask, FastAPI provides data validation for defining a specific data type and it will raise an Error if the user enters an invalid data type.Performance:
FastAPI uses an Async library which is helpful to write concurrent code. Async is greatly helpful for doing tasks that involve something like fetching data from API and querying from a database, reading the content of the file and FastAPI has ASGI whereas Flask is a WSGI ApplicationData Validation:
There is no data validation in Flask. So Flask allows any kind of data type. In Flask the data validation will be handled by developers. But In FastAPI there is inbuilt data validation (pydantic). So it will raise an error when it gets an invalid data type from the user. This is useful for developers to interact with the API endpoints.Documentation:
Flask doesn’t have any inbuilt documentation for swagger UI. We need to add some extensions like Flassger and Flask-RESTEX and some dependency setups. But In FastAPI it generates an automatic swagger UI when the API is created. For accessing the auto-generated swagger UI hit the endpoint with /docs or /redoc. It will show all the endpoints in your application.HTTP METHODS:
Flask | FastAPI |
---|---|
@app.route("/get", methods= ['GET']) | @app.get('/get', tags=['sample']) |
Production Server
At some point, you’ll want to deploy your application and show it to the world.- Flask
- FastAPI
Asynchronous Tasks
- Flask
Installations
Example:
FastAPI:
In FastAPI there is a default AsyncIO. So we can simply add the async keyword before the function.FastAPI was Built with Primary concerns
- Speed and Developer Experience
- Open Standards.
- FastAPI can connect Starlette, Pydantic, OpenAPI, and JSON Schema.
- FastAPI uses Pydantic for data validation and Starlette for tooling making it twice as fast as Flask and equivalent to high-speed web APIs written in Node or Go.
- Starlette + Uvicorn supports async requests, while Flask does not.
- Data validation, serialization and deserialization (for API development), and automatic documentation are all included (via JSON Schema and OpenAPI).
Which Framework is Best for AI/ML
Both Flask and FastAPI are the popular Framework for developing Machine learning and web applications. But most data scientists and Machine learning developers prefer Flask. Flask is the primary choice of Machine learning developers for writing the API’s . A few disadvantages of using Flask is time consuming for running the big applications. The major disadvantage in flask is to add more dependencies by adding plugins and the other one is lack of Async support whereas FastAPI supports Async by default . FastAPI is used for the creation of ML instances and applications. In the machine learning community Flask is one of the popular frameworks.Flask is perfect for ML engineers who want to create web models. FastAPI, on the other hand, is the best bet for a framework that provides both speed and scalability.Migrating Flask TO FastAPI :
The Application object in Flask and FastAPI is
Simple Example for Migrate Flask to FastAPI:
- Flask Application
1.To Migrate the Flask to FastAPI we need to install and import libraries.
2. URL Parameters (/basic_api/employees/)
The Request Methods in Flask and FastAPI is
Query Parameters:
Like URL Parameters, Query parameters are also used for managing the state(For sorting or Filtering).-
Flask
-
FastAPI
Run the server in Flask And FastAPI
Main (Flask)And Finally the FastAPI Application looks like :
- FastAPI Application
When Should you choose FastAPI instead of Flask and Django?
- Native Async Support:The FastAPI web framework was created using the ASGI web server. Native asynchronous support eliminates inference latency.
- Improved latency: As a high-performance framework, the framework's total latency is lower when compared to Flask and Django.
- Production-ready:With the FastAPI web framework's auto validation and short defaults, developers may easily design web apps without rewriting the code.
- High Performance: Developers have access to the key functionalities of Starlette and Pydantic because they are compatible. Needless to say, Pydantic is one of the quickest libraries, and as a result, overall speed improves, making FastAPI the preferred library for web development.
- Learn Simple:This is a minimalist framework so it is easy to understand and learn.
Flask Or FastAPI: Which is better
S.No | Flask | FastAPI |
---|---|---|
1. | Flask is a micro web framework to develop small-scale web applications and Rest API. Flask is depends on WSGI toolkit (werkzeug, Jinja2) | FastAPI is considered one of the Fastest frameworks compared to the flask. FastAPI is built in Pydantic and starlette. |
2. | Flask is built in Web Server Gateway Interface(WSGI) | FastAPI is built in Asynchronous Server Gateway Interface(ASGI) |
3. | It does not have any inbuilt documentation such as swagger UI and needs to add some extensions like Flasgger or Flask RESTX. | In FastAPI it has inbuilt documentation like (docs and redocs). |
4. | There is no Inbuilt data validation in Flask, we need to define the data type in requests. | In FastApI there is an Inbuilt data validation that will raise the error if the user provides an invalid data type. |
5. | Flask is more flexible than Other frameworks. | FastAPI is flexible in code standards and it does not restrict the code layouts. |
Conclusion:
After learning about both Flask and FastAPI. Both are used to create a Web application and Rest API. But FastAPI is better when compared with Flask. Because FastAPI has a native ASGI support (Asynchronous Server Gateway Interface). So it is Faster and High in performance. Also, it has inbuilt documentation(Swagger UI) and data validation. FastAPI has High and Fast performance, Efficiency and it's easy to understand and learn. When compared to Flask, FastAPI has less community support but it reaches a lot in a short period of time.Reference link:
Flask: https://flask.palletsprojects.com/en/2.2.x/
FastAPI: https://fastapi.tiangolo.com/
Case Studies
11 essentials devops metrics to boost productivity
The technology landscape is always evolving, whether it is through new infrastructure, or a new CO tool coming out to help you manage your fleet better
—Mike Kail
How does DevOps work?
DevOps is one of the most important concepts in modern software development. It's a collaboration method that encourages communication and cooperation between developers, operations staff, and testers. DevOps helps to speed up the process of creating and deploying software by automating many of the manual tasks while enhancing the problem-solving aspect all on its own. Cloud computing being centralized offers standard strategies for deployment, testing, and dynamic integration of the produced collaboration. It’s a survival skill of adapting according to the ever-changing and demanding market requirements.TIP
DevOps helps you manage things effectively so that teams can spend more time on research, development, and betterment of the product.
Here are 11 essential DevOps metrics to increase productivity in organizations:
Frequency of deployment
It is vital to promote and sustain an ambitious edge by providing updates, new functions, and enhancements to the product's quality and technological efficiency. Increased delivery intensity enables greater adaptability and compliance with changing client obligations. The objective should be to enable smaller deployments as frequently as possible. Software testing and deployment are significantly more comfortable with smaller deployments.TIP
Organizations can use platforms such as Jenkins to automate the deployment sequence from staging to production. Continuous deployment ensures that the code is automatically sent to the production environment after passing all of the test cases in the QA environment.
Time required for deployment
This indicator indicates how long it will take to accomplish a deployment. While deployment time may look trivial at first glance, it is one of the DevOps indicators that indicates possible difficulties. If deployment takes hours, for example, there must be an issue. As a result, concentrating on smaller but more regular deployments is beneficial.Size of the deployment
This measure is used to monitor the number of feature requests, and bug patches sent to production. The number of individual task items varies significantly depending on their size. Additionally, you can keep track of the number of milestones and other parameters for deploymentEnhance Customer satisfaction
A positive customer experience is important to the longevity of a product. Increased sales volumes are the outcome of happy customers and excellent customer service. As a result, customer tickets represent customer satisfaction, which then reflects the DevOps process quality. The fewer the numbers, the higher the quality of service.Minimize defect escape rate
Are you aware of the number of software defects detected in production versus QA? To ship code rapidly, you must have confidence in your ability to spot software defects before they reach production. Your defect escape rate is a good DevOps statistic for monitoring the frequency with which those defects make their way into production.Understanding cost breakups
While the cloud is an excellent approach to reducing infrastructure expenses, certain unplanned failures and incidents can be rather costly. As a result, you should prioritize collecting and decreasing unnecessary costs. DevOps plays a major role here. Understanding your spending sources might assist you in determining which behaviors are the most expensive.Reduce frequent deployment failures
We hope this never occurs, but how frequently do your releases result in outages or other severe issues for your users? While you never want to undo a failed deployment, you should always plan for the possibility. If you are experiencing troubles with failed deployments, monitor this indicator over time.Time required for detection
While minimizing or even eliminating failed changes is the optimal strategy, recognizing errors as they occur is crucial. The time required to discover the fault will affect the appropriateness of existing response actions. Protracted detection times may impose limits on the entire operation. Establishing effective application monitoring enables a more complete picture of "detection time."Error Levels
It is vital to monitor the application's error rate. They serve as a measure not only of quality difficulties but also of continuing efficiency and uptime issues. For excellent software to exist, the best methods for handling exceptions are necessary.TIP
Track down and record new exceptions thrown in your code that occur as a result of a deployment.
Application Utilization & Traffic
You may wish to verify that the quantity of transactions or users logging into your system seems to be normal post-deployment. If there is a sudden lack of traffic or a big increase in traffic, something may be amiss. Numerous monitoring technologies are available to provide this data.Performance of the application
Before launching, check for performance concerns, unknown defects, and other issues. Additionally, you should see changes in the overall output of the program both during and after deployment. To detect changes in the usage of particular queries, web server operations, and other requirements following a release utilize monitoring tools that accurately reflect the changes.Case Studies
Prometheus vs Influxdb : Monitoring Tool Comparison
When it comes to data storage, there are few alternatives that can compete with the venerable Prometheus, such as InfluxDB. But what if you need more than just collected data? What if you need real-time insights into your systems?
When it comes to data storage, there are few alternatives that can compete with the venerable Prometheus, such as InfluxDB. But what if you need more than just collected data? What if you need real-time insights into your systems? Another powerful platform for real-time data analytics and storage is InfluxDB. Let's compare how they fare with one another.
Prometheus is a memory-efficient, quick, and simple infrastructure monitoring system. InfluxDB, on the other hand, is a distributed time-series database used to gather information from various system nodes. In this article, we are going to compare Prometheus and InfluxDB. Both systems have their strengths and weaknesses, but they are both effective monitoring tools. If you are looking for a system that can monitor your database servers, then Prometheus is a good option. If you are looking for a system that can monitor your entire infrastructure, then InfluxDB is a better choice.What exactly is Prometheus?
Prometheus is a time-series database and monitoring tool that is open source. Prometheus gives its users sophisticated query languages, storage, and visualization tools. It also includes several client libraries for easy interaction. Prometheus can also work with various systems (for example, Docker, StatsD, MySQL, Consul, etc.)TIPS
Prometheus can be great for monitoring as long as the environment does not exceed 1000 nodes. Prometheus + Grafana = best ecosystem
What is InfluxDB?
By InfluxData, Inc., a database management system called InfluxDB was created. InfluxDB is open-source and cost-free to use. The InfluxDB Enterprise version is installed on a server inside a corporate network and comes with maintenance contracts and unique access controls for business customers. A web-based user interface for data ingestion and visualization is also included in the new InfluxDB 2.0 version, which operates as a cloud service that is fully customizable.TIPS
When it comes to storing monitoring metrics, InfluxDB excels (e.g. performance data). If you need to store different sorts of data, InfluxDB is not the best option (like plain text, data relations, etc.)
Let's see how these differ from one another
Features | Prometheus | InfluxDB |
---|---|---|
Data Gathering | Prometheus is a system that operates on the principle of pull. The metrics are published by an application at a certain endpoint and Prometheus retrieves them on a regular basis. | The system InfluxDB is based on is a push-based system. It requires an application to push data into InfluxDB on a regular basis. |
Storage | Prometheus and InfluxDB both follow the key/value datastores. However, these are executed very differently on the two systems. Each metric in Prometheus is kept in its own file and is stored in indices that use LevelDB. Metrics recording and monitoring based on those are the major uses of Prometheus. | Both the indices and the metric values are stored in monolithic databases by InfluxDB. Compared to Prometheus, InfluxDB often uses more disc space. The best database for event logging is InfluxDB. So we have a choice based on the requirements. |
Extensibility and Plug-ins | Prometheus’ key benefit is its widespread community support, which stems from its CNCF-accredited project status. Many apps, particularly cloud-native applications, already support Prometheus. | While InfluxDB has a lot of integrations, it doesn’t possess as many as Prometheus. |
Case Studies | Prometheus was designed for monitoring, specifically distributed, cloud-native monitoring. It shines in this category, with several beneficial integrations with current products. | While InfluxDB can support monitoring, it is not as well known as Prometheus for this purpose. As a result, you may have to develop your own integrations. If you want to do more than a mere monitoring tool, InfluxDB is a fantastic solution for storing time-series data, such as data from sensor networks or data used in real-time analytics. |
Query language | Prometheus uses PromQL, a language that is much easier and has no connection to conventional SQL syntax. Let's say we want a number for CPU load that is greater than 0.5. In that case, we can simply enter CPU load>0.5 in the Prometheus command prompt. | For its querying purposes, InfluxDB uses a standard SQL syntax known as InfluxQL. For instance, we could write select * from tbl where CPU load>0.5 in the Prometheus cell. This seems simple to an associate with a background in SQL, but Prometheus is also not a challenging experience. |
Community | Prometheus is an open-source project with a huge community of users that can rapidly resolve your queries. Having a big network of support is an added benefit since there is a high probability that the challenges one is having might previously have been encountered by someone in the community. | InfluxDB, despite its popularity, needs to improve on community support in comparison to Prometheus. |
Scaling | When the load rises, the monitoring Prometheus servers require scaling as well.This is due to the fact that the Prometheus server is independent. Thus, the Prometheus server works great for the simpler load. | Since the commercial section of Influx DB is distributed, there will be many interconnected nodes. As a result, as the server scales up, we don’t have to worry about scaling nodes. Thus, Influxdb nodes might be considered redundant while handling complicated loads. |
TIPS
InfluxDB performs exceptionally well at storing monitoring metrics (e.g., performance data). Compared to Prometheus, InfluxDB utilizes more disc space and has a monolithic data storage strategy. It performs well for recording occurrences.
Conclusion
As a result, you can consider the factors discussed in this article while choosing between Prometheus and InfluxDB as monitoring systems for time series data, depending on your business case. When it regards monitoring services for time series data, both platforms are extremely well-liked by enterprises. Some claim that PromQL is new and InfluxQL is similar to SQL and will thus be better, but the reality is different. PromQL is considerably more user-friendly for querying, so go for it. Prometheus has a lot more functionality and integrations, so you can choose it. InfluxDB is a better option if you're looking for something specifically for IoT, sensors, and other analytics.Relevant Topics:
https://prometheus.io/ https://github.com/influxdata/influxdb https://v2.docs.influxdata.com/v2.0/ https://www.influxdata.com/blog/multiple-data-center-replication-influxdb/ https://logz.io/blog/prometheus-monitoring/Success Stories
Success Stories
How to work with offshore teams and manage remote offshore teams successfully?
Introduction
Working with offshore teams is a common practice in the IT industry. Web and mobile app development are among the most common tasks that are outsourced to offshore development teams. Outsourcing to an offshore team allows you to hire expert professionals for a fraction of the price that you would have to pay if you would hire full-time in-house employees. Along with salary of the employee, other expenses like health insurance, insurance contribution, and bonuses can also be saved. You are not required to spend money on office space, IT infrastructure, and utilities such as electricity. Moreover, a business can easily decrease or increase the size of the team according to its requirements. Thus, businesses enjoy both flexibility and scalability by outsourcing their projects to offshore teamsOverview of Offshore Teams
An offshore team usually implies a certain number of specialists who work for you remotely. They can be located at any place and the communication is done through phone calls, messengers like slack or video calls through zoom, meet. Hiring remote developers can be of great help financially. It helps to save cost. Such practice was usually associated with small companies that had a lack of financing. However, today the situation is different and even huge companies turn offshore as it has a number of undeniable benefitsHere are 5 reasons why businesses hire offshore teams:
- Controlled Costs
- Drastically Improved Efficiency
- Focus on Overlooked Areas
- Access to a Global Talent
- Flexibility
Handling the Common Challenges in Offshore Team Management
Here are the most common challenges that clients come across while handing their offshore teamsCommunication Challenges
There are two things that can make or break the whole project: time-zone difference and language skills of an offshore vendor. The time zone difference creates difficulty in communicating with an outsourced team. So the calls are postponed which in turn lowers productivity and causes delay in project delivery How to overcome the challenge - There might be few hours overlap in time zone. This overlap can be used effectively for various activities like feedback, checking project progress and for communicating with the remote team When all the expectations are set up front, it becomes much easier to achieve them, as well as monitor the development process. The outsourced team has to clearly understand what is expected from them and what the requirements for the future product areLack of Control
For some managers, it is very crucial to be in charge of each step on the development timeline– So working with an independent vendor is not a comfortable experience. Not being able to ensure the project progresses according to plan is one of the challenges that come with outsourcing How to overcome the challenge - To know how to manage a remote development team, a business manager can send a trusted employee to work at the dedicated office and oversee project development. Asking for a personal account manager who keeps tabs on the product’s progress is another way to get more control over the developmentIneffective Project Management
what’s happening at each stage of development, are the red signs that something is wrong. If you came across these offshore team challenges, it can either mean that the vendor is incompetent or aren't following the methods that you use in your work How to overcome the challenges – The outsourced team should understand clearly and follow the development methodology a project needs. Make sure that the outsourcing company does have access to the technologies or tools needed for the completion of the projectHow to Get Maximum Productivity from an Offshore Team
Daily Meetups
Communication is the key to development success. That goes for any kind of group effort, and doubly so when the people are geographically disparate. When your team is far away from you, it should be ensured that you are updated about the progress of work. For updates about the progress of work, project management models like Scrum, Kanban or Agile can be used. To make the most out of it, use one of the project management models (Scrum, Kanban, Agile, etc.) while working with the outsourcing agency. By ensuring that your people all talk to each other daily, you’re keeping information flowing. Everyone knows what’s being worked on, and what their part isSprint Planning
Despite the daily stand-up, everyone is going to be largely working on their own tasks, and there’s a real danger of people’s work getting in each other’s way. The planning meeting is when those tasks are explained and handed out. If you’re following Agile (which you should), then you’ll have a scrum master and a product owner both participating to ensure that everyone comes out of the meeting knowing exactly what to do and why. As for the length of the sprint, that’s up to youDiscuss Your Project Goals
Just assigning a project to an offshore team without letting them know the goal behind it can land you in trouble. The developed product may not be as you expected it to be. Offshore developers require full product vision before starting working on the project. They should be given complete details like why the product is required, what functions will be carried out by the product, what all specifications is required in the product, and when it is expected to beMake Use of Agile Methodology
As software development is a process that requires a high level of interaction and iteration, the adoption of agile methodology for offshore teams helps in the development and delivery of a high-quality software product on time In some cases a sprint may be just one weeks’ time or it may be one or two months. Based on priority, features can be allocated to different sprints. At the beginning of each sprint, you and your offshore team can have a discussion regarding the features to be developed and create a detailed plan. Whenever required face to face communication can be initiated with your remote team.Communicate Frequently and Use Simple Language
When working with your remote team, you can communicate very easily and frequently by initiating face-to-face talk whenever you require. With offshore teams, you should communicate even more frequently so that all the things are clear and there is no confusion despite the large distance between your in-house and offshore teamsCommunicating with Offshore Teams
Here are some highly recommendable communication tools that help you when you are working with remote teamsJIRA
This includes everything from planning till analytics. It allows to set clear and actionable goals. Their progress can be easily tracked. Jira is customizable and it is capable of working equally good for all of the Agile methodologiesConfluence
Another product from the makers of Jira, Confluence is one of the best collaborative document systems and it is more like Google Docs. On the surface, it’s similar to Google Docs. Multiple people can share a document, view and edit it simultaneously. Changes can be both suggested as well as accepted. The software supports templates for different documents. Above all, Confluence can interface perfectly with Jira. The software supports user-definable templates for different documents, labeling, and cross-document notes. On top of all that, Confluence interfaces perfectly with Jira. Taken together, the tools make a very powerful collaboration systemBitbucket
Bitbucket Cloud is a Git-based code hosting and collaboration tool, built for teams. Bitbucket's best-in-class Jira and Trello integrations are designed to bring the entire software team together to execute a project. It helps you to track, preview, and confidently promote your deploymentsGitHub
GitHub hosts your source code projects in a variety of different programming languages and keeps track of the various changes made to every iteration. It lets you and others work together on projects from anywhereTeamViewer
TeamViewer, is an all-in-one solution for remote support, remote access, and online meetings which allows you to assist customers remotely, work with colleagues from a distance and also stay connected with your own devices or assist friends and family members. It even supports mobile devices, a must for mobile app developmentSlack or Skype
Both slack and Skype have a chat room and supports the private messaging feature, with VoIP and video calls. They feature robust chat room and private messaging functionality, along with VoIP and even video calls. Use these tools correctly, and you’ll eliminate all of the communication problems endemic to offshoringSuccess Stories
What Are The Best Practices For Microservice Orchestration and Multicluster management
Introduction
Container bundles up the OS and microservice runtime environment such as source code, dependencies, system libraries, etc. There are many tools available in the market for configuring the containers. Some of them are Kubernetes (including AKS, EKS and GKE) and ECS. Multicluster management is for managing many k8s clusters in an environment. For this, we have tools like rancher and kubesphere. In this article, Kubernetes deployment through Istio and Rancher multicluster management is covered. Istio is a service mesh which provides a language independent and transparent way for easy automation of application network functions. Istio’s features help to monitor, connect and secure services. Rancher is a complex stack for teams that adopt containers. It combines everything the organization needs to adopt and run in production. As it was built on Kubernetes it allows DevOps teams to test, deploy and manage the application in a lightweight frameworkOverview of Kubernetes Deployment through Istio
Kubernetes also known as K8s is system which helps to automat e the process of deployment and containerized applications management Istio extends Kubernetes with Envoy service proxy for establishing a programmable and application aware network. With Kubernetes and olden workloads, Istio makes universal traffic management, security and telemetry to deploymentHow system Works
Sample workflow for Istio
Architecture Diagram for Rancher
What are the best features of Istio and Rancher
- Service Mesh
-
It has ways Ways to control data sharing between different parts of an application
-
Secure communication between service to service
-
Load Balancing is automatic for http
-
Control in traffic behaviour
-
TLS encryption, authorization and authentication tools are available to protect data and services
-
Observability - Monitoring, Logging, Tracing
Features of Rancher
-
The users can deploy an entire multi-container clustered application from the application catalog with a single click of a button
-
Managing of deployed applications by automatically upgrading to newer versions
-
It contains the distribution of container orchestration like Docker swarm, Mesos and Kubernetes
-
Infrastructure services include networking, storage, load balancer, and security services
-
Users interact with ranchers using a command-line tool called rancher-compose. Users interact with ranchers using a command-line tool called rancher-compose. User can deploy multiple services and containers on Rancher infrastructure depending on Docker compose templates. The rancher-compose tool also supports the docker-compose.yml file format
-
Interactive UI for managing tasks and maintaining clusters
Agile Delivery Process
10decoders has a very strong focus on process. We help our clients to capture the requirements in clear process flows and screen design. Understand how our process driven culture helps customer to grow their business
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How To Effectively Use Istio For Enterprise Governance and Monitoring
Introduction
The client offers deep and contextual application-layer visibility to remove the blind spots within distributed and cloud-native application environments, in a completely frictionless manner while being agnostic to the platform, cloud, environment, and workload type. The solution allows many people like cloud application practitioners, security leaders, and application owners to have a visibility which would help them to address compliance, and security controls for microservices and other distributed applications
Overview of Challenges faced by Client
- The client has run their pre-production application in cloud infrastructure and it costs high
- The client engineering team spent most of their time deploying their changes on cloud infrastructure for evaluation
- The client team faced more challenges in deploying their application in cloud environments and that cost 45% of their monthly budget allocation.
- Client teams spend more time on deployment and testing in cloud infrastructure will extend the delivery time of the application
How Current system Works
Enterprises today deploy perimeter-centric solutions, such as network firewalls, web application firewalls, and/or API Gateways. Others like container firewalls, network-layer micro-segmentation, or manual application testing are also tried. Some other solutions concentrate on one type of workload (e.g. containers) or are focused on data-in-use or data-at-rest and do little to secure against run-time attacks embedded deep within the application-layer componentsHow we proposed system architecture
The client ideally needs an Infrastructure with different topologies of system types templated as a solution.Generic engine for generation and regeneration of infrastructures need to be utilized. Following are some of the key considerations- The solution proposed is to create an environment like cloud infrastructure in local machines
- Writing tests framework to make the client engineering team use for their Unit Testing
- We are using MetalLB for implementing network Load Balancer in K8 local infrastructure
- Implementation of microservices to simplify the deployment and improve the performance of the application. By using testing frameworks to deliver the flawless application in a production environment
- Containerize the microservice components to achieve the CI/CD process with the K8 cluster to reduce the time spent for on deployment
- Provide scripts to automate the process of testing and deliver the application with zero bugs
What are the Benefits of the proposed system?
-
- DevOps process will reduce the deployment and testing time
- The product has been cleverly architected to ensure zero latency to the application, while still providing all the security features and benefits
- Provides a vulnerability assessment of the application components and provides recommendations on how to make them more secure
- Reduced the cloud Infrastructure cost up to 45%
Case Studies
Cloud Migration : Lift & Shift Strategy
Introduction
Lift-and-shift is the process of migrating a workload from on-premise to Cloud with little or no modification. A lift-and-shift is a common route for enterprises to move to the cloud and can be a transitionary state to a more cloud-native approach
There are also some workloads that simply can’t be refactored because they’re third-party software or it’s not a business priority to do a total rewrite. Simply shifting to the cloud is the end of the line for these workloads
Applications are expertly “lifted” from the present environment and “shifted” just as it is to new hosting premises, which means in the cloud. There are often no severe alterations to make in the data flow, application architecture, or authentication mechanisms
It allows your business to modernize its IT infrastructure for improved performance and resiliency at a fraction of the cost of other methods
Overview of Market Share
In recent days there is great growth in Cloud computing market. Companies are trying out various cloud models with right balance of flexibility and functionality The cloud migrations hosts the application and data in an effective environment based on various factors. This is the key role of cloud migration Many companies migrate their on-site data and application from their data center to cloud infrastructure with the benefits of redundancy, elasticity, self-service provisioning and flexible pay per use model. These factors are further expected to drive tremendous growth in the global cloud migration services market during the forecast period 2020-2027. Academic writers at Buyessayfriend continue to play a vital role in the evolving landscape of cloud migration services. Also, they are always ready to help students write a paper. According to the report, the global cloud migration services market generated $88.46 billion in 2019 and is estimated to reach $515.83 billion by 2027, witnessing a CAGR of 24.8% from 2020 to 2027 The growth of the market is attributed to an increase in cloud computation among small and medium enterprises around the globeWhat are the best features to have?
- Workloads that demand specialized hardware, say, for example, graphical cards or HPC, can be directly moved to specialized VMs in the cloud, which will provide similar capabilities
- A lift and shift allows you to migrate your on-premises identity services components such as Active Directory to the cloud along with the application
- Security and compliance management in a lift and shift cloud migration is relatively simple as you can translate the requirements to controls that should be implemented against compute, storage, and network resources
- The lift and shift approach uses the same architecture constructs even after the migration to the cloud takes place. That means there are no significant changes required in terms of the business processes associated with the application as well as monitoring and management interfaces
- It is the fastest way to shift work systems and applications on the public cloud because there isn’t a need for code tweaks or optimization right away
- Considered the most cost-effective model, the lift and shift help save migration costs as there isn’t any need for configuration or code tweaks. In the long run, these savings could give way to extra spending if workload costs are not optimized
- With minimal planning required, the lift and shift model needs the least amount of resources and strategy
- Posing the least risk, the lift and shift model is a safe option as compared to refactoring applications especially in the scenario where you don’t have code updating resources
Cloud Migration Steps: Ensuring a Smooth Transition
- First, choose which platform that you wish to migrate to
- Examine all the connections in and out of the application and its data
- If you are lifting and shifting more than one application, then you may need to consider automating multiple migrations
- You should consider containerization to replicate the existing software configurations. This will also allow you to test configurations in the cloud before moving to production
- Back up the databases from the existing system as well as supporting files. When the new database is ready, restore the backups
- Once migrated, test the application
- Check that all the current data compliance and regulatory requirements are running in the new cloud deployment. Run your normal validation tests against the newly migrated application
- Don’t be tempted to introduce new features during the migration. This can lead to many hours of additional testing to make sure you have not created new bugs
- Retire your old systems once testing is complete
Technical Stack
Google Cloud and Azure are new but still they take advantage of the experience and framework of tech giants namely Microsoft and Google AWS is a public cloud, flexible and ready to meet the needs of both big and small applications. Azure’s strong values are perfect integrations with Microsoft 365 ecosystem and focus on enterprise market To help you make an informed choice, we’ve prepared a table that compares the most significant features of AWS, Azure, and GCPSuccess Stories
How to build a video streaming app like netflix?
Introduction
The way people communicate all over the world has changed now!!! Live streaming is the live broadcasting of video content over internet. This has caused a major change in the way we communicate. Live streaming is becoming inevitable in the digital world where all sorts of organizations like education, business, entertainment, even family & friends meeting are flourishing because of thisOverview of Market Share
There is a great demand for live streaming. This has caused the live streaming market to grow. Covid - 19 is also one of the important growth factor. According to Global market insights the video chat market will grow by over 15% CAGRHow live streaming works?
A streaming server has to be already created and running. A broadcaster can initiate a stream by registering with a stream name. The users who want to be an audience can access the stream with the same stream name. When a stream is initiated, the below process happens to make the video available at the receiving endLive streaming undergoes the below steps
- Compression
- Encoding
- Segmentation
- Content Delivery Network (CDN) distribution
- CDN caching
- Decoding
- Video playback
Features to have
User Sign up & Sign in
It can be a simple registration with an email or phone number and password. It’s a good way to offer sign-ups or ins via Facebook, Twitter, or Google as it saves the users’ time. A password reset feature via email or text message is neededUser Profile
It is better to decide on what kind of personal information will be in the user profiles like profile picture, full name, subscriptions, etc., This can be viewed by friends & subscribersLive Streaming
Allows the user to record and broadcast a live stream to members who have subscribed to his/her channel or the publicChat
Chat is an essential part of any communication application. Chat combined with live streaming will be very useful for the audience to give their feedback. Third-party tools like Firebase or Twilio helps to integrate chat into video chatting. Can include Emojis to make the chat interestingRecord
Feature to record the videos and can have a user gallery to store and organize the recorded videos on user profileHow to develop a live streaming app using WebRTC?
Backend development
Create a live streaming application by means of WebRTC technology For a live stream to happen, the live video has to be sent to a server so that it can distribute the live stream to the audience or subscribers. So, a media server should be running somewhere that you can access There are many open source WebRTC media servers available. One such is Ant Media Server that can live stream & supports ultra-low latency (0.5 seconds) adaptive streaming and records live videos in several formats like HLS, MP Set up a media server You can download ant media server & use its trial version license Broadcast live stream Provide a stream name for the video stream and start recording. This will be passed to the ant media server View live stream The subscriber can use the same steam name to join the stream and view the live videoUI/UX design
Next comes the good & attractive user interface & user experience. It is better to have simple navigation as it will be convenient to understand. The user has to grasp the need for features & their performanceTech stack
- Cloudflare
- Amazon CloudFront
- WebRTC
- RTMP
- Swift
- Kotlin
- Java
- Swift
- Kotlin
- Java
Video Section
Case Studies
Case Studies
WHEN PROGRESSIVE WEB APPS ARE NEEDED
Introduction
PWA apps look and feel like native mobile apps, built with web technologies. They allow websites to be stored on devices. The app shows up as an icon in the form of a website app. The basic methodology here is to combine native app experience with the browser feature.PWA on a Real Device
A PWA is a website that can be downloaded onto the computer or mobile device. It actually pulls in updates in the background each time a user runs it. So, as and when a web application is updated, it not only gives the app site access to new features, but does so without explicitly performing any kind of update.Key Features of PWA
Responsive
PWA apps are built responsive by nature; they're designed to be adaptable to all types of devices with different screen sizes. So, the app will be used on many screen sizes and its contents will be available at all viewport sizes.Installable
Installation of the PWA application is very easy. On a desktop computer or a mobile device, the app is added to the home screen although installation is not necessary. The service worker is set up behind the scenes, the first time a visitor sees the website.Connectivity-Independent
Applications built with progressive web standards can operate offline and on low-quality networks. They also keep a user active in the app even when the user is not connected. The app stores items offline and manages network requests to get the items from the local cache, in a flexible manner, with the help of service workers.Cross Platform & App-Like
A PWA is created to tie together the app and website user experience. Additionally, users can take advantage of these services without going into the app store. Heavy-lifting duties like downloading and data storage are not necessary for PWA installation. PWAs work on all browsers and systems. Prior to installing them, users have to be able to use them in any browser.Load Time & Secure
PWA apps have faster load time. In comparison to the conventional mobile web, the progressive web app reduces page load time by 88 percent, or about 400 milliseconds. Native apps require a lot of security measures, but PWAs are inherently secure because they run on HTTPS and are encrypted with an SSL certificate. This in turn, adds an extra layer of security.Use Cases of PWA
- Better user experience
- Increased user engagement
- Increased security and ability to use offline
- Increased organic search traffic
- PWAs typically cost less to develop and maintain than native apps
When Do We Require a PWA?
Usage of Application in Multi-Devices
Whenever there is a need to use applications on both mobile and desktop devices, progressive web apps is the way to go. PWAs are becoming increasingly popular because they are lightweight and load quickly. Additionally, using PWAs, web apps can be viewed on mobile devices. By doing so, user gets a native mobile application feel and look along with a browser feature.Speed and Reliability
If speed is the main concern, PWA is the answer because PWA is significantly faster than a native mobile application. According to statistics, PWA has a faster load time and a lower demand on devices. In other words, when the app must be consistently high-quality and lightning-fast despite no network connection or limited bandwidth, a PWA is the best option.Responsiveness
When the user plans to install or use applications across different devices, it is always better to use PWAs. They are responsive to most devices and make the UI appealing on any device.Security
PWAs are secure by nature since the technology that powers the app requires it to be served over the HTTPS protocol in order to work. It is delivered through TLS, which provides major benefits for both users and developers.Platform Independent
Whenever an application is built for cross-platform usage, with a single technology, PWA is the way to go! It is available on all platforms and simplifies the development process for developers.Advantages of Using PWA
- Lightweight and Easy to install in devices
- Provides offline support
- Safe and Secure to use
- Faster than native mobile applications
- Helps to boost Search Engine Optimization
- Targets Cross platform
Disadvantages of PWA
- Cannot access the various device features
- Consumes more battery
- No access to app stores
- UI and UX Limitations
- If user does not use the app for a long time, the cache is deleted
- Push notification features are not possible in iOS devices
Conclusion
The importance of PWAs would definitely be felt to a large extent in the future. There are many PWA features currently under development and the PWA community is growing by the day. One of the main reasons as to why users are more likely to choose PWAs over native apps is because PWAs encourage them to interact more. Further, the low costs and the ease of implementation play a huge role in influencing the spread of this technology.References
Progressive web apps (PWAs) | MDN What is a PWA and why should you think about it?Case Studies
11 essentials devops metrics to boost productivity
The technology landscape is always evolving, whether it is through new infrastructure, or a new CO tool coming out to help you manage your fleet better
—Mike Kail
How does DevOps work?
DevOps is one of the most important concepts in modern software development. It's a collaboration method that encourages communication and cooperation between developers, operations staff, and testers. DevOps helps to speed up the process of creating and deploying software by automating many of the manual tasks while enhancing the problem-solving aspect all on its own. Cloud computing being centralized offers standard strategies for deployment, testing, and dynamic integration of the produced collaboration. It’s a survival skill of adapting according to the ever-changing and demanding market requirements.TIP
DevOps helps you manage things effectively so that teams can spend more time on research, development, and betterment of the product.
Here are 11 essential DevOps metrics to increase productivity in organizations:
Frequency of deployment
It is vital to promote and sustain an ambitious edge by providing updates, new functions, and enhancements to the product's quality and technological efficiency. Increased delivery intensity enables greater adaptability and compliance with changing client obligations. The objective should be to enable smaller deployments as frequently as possible. Software testing and deployment are significantly more comfortable with smaller deployments.TIP
Organizations can use platforms such as Jenkins to automate the deployment sequence from staging to production. Continuous deployment ensures that the code is automatically sent to the production environment after passing all of the test cases in the QA environment.
Time required for deployment
This indicator indicates how long it will take to accomplish a deployment. While deployment time may look trivial at first glance, it is one of the DevOps indicators that indicates possible difficulties. If deployment takes hours, for example, there must be an issue. As a result, concentrating on smaller but more regular deployments is beneficial.Size of the deployment
This measure is used to monitor the number of feature requests, and bug patches sent to production. The number of individual task items varies significantly depending on their size. Additionally, you can keep track of the number of milestones and other parameters for deploymentEnhance Customer satisfaction
A positive customer experience is important to the longevity of a product. Increased sales volumes are the outcome of happy customers and excellent customer service. As a result, customer tickets represent customer satisfaction, which then reflects the DevOps process quality. The fewer the numbers, the higher the quality of service.Minimize defect escape rate
Are you aware of the number of software defects detected in production versus QA? To ship code rapidly, you must have confidence in your ability to spot software defects before they reach production. Your defect escape rate is a good DevOps statistic for monitoring the frequency with which those defects make their way into production.Understanding cost breakups
While the cloud is an excellent approach to reducing infrastructure expenses, certain unplanned failures and incidents can be rather costly. As a result, you should prioritize collecting and decreasing unnecessary costs. DevOps plays a major role here. Understanding your spending sources might assist you in determining which behaviors are the most expensive.Reduce frequent deployment failures
We hope this never occurs, but how frequently do your releases result in outages or other severe issues for your users? While you never want to undo a failed deployment, you should always plan for the possibility. If you are experiencing troubles with failed deployments, monitor this indicator over time.Time required for detection
While minimizing or even eliminating failed changes is the optimal strategy, recognizing errors as they occur is crucial. The time required to discover the fault will affect the appropriateness of existing response actions. Protracted detection times may impose limits on the entire operation. Establishing effective application monitoring enables a more complete picture of "detection time."Error Levels
It is vital to monitor the application's error rate. They serve as a measure not only of quality difficulties but also of continuing efficiency and uptime issues. For excellent software to exist, the best methods for handling exceptions are necessary.TIP
Track down and record new exceptions thrown in your code that occur as a result of a deployment.
Application Utilization & Traffic
You may wish to verify that the quantity of transactions or users logging into your system seems to be normal post-deployment. If there is a sudden lack of traffic or a big increase in traffic, something may be amiss. Numerous monitoring technologies are available to provide this data.Performance of the application
Before launching, check for performance concerns, unknown defects, and other issues. Additionally, you should see changes in the overall output of the program both during and after deployment. To detect changes in the usage of particular queries, web server operations, and other requirements following a release utilize monitoring tools that accurately reflect the changes.Case Studies
Prometheus vs Influxdb : Monitoring Tool Comparison
When it comes to data storage, there are few alternatives that can compete with the venerable Prometheus, such as InfluxDB. But what if you need more than just collected data? What if you need real-time insights into your systems?
When it comes to data storage, there are few alternatives that can compete with the venerable Prometheus, such as InfluxDB. But what if you need more than just collected data? What if you need real-time insights into your systems? Another powerful platform for real-time data analytics and storage is InfluxDB. Let's compare how they fare with one another.
Prometheus is a memory-efficient, quick, and simple infrastructure monitoring system. InfluxDB, on the other hand, is a distributed time-series database used to gather information from various system nodes. In this article, we are going to compare Prometheus and InfluxDB. Both systems have their strengths and weaknesses, but they are both effective monitoring tools. If you are looking for a system that can monitor your database servers, then Prometheus is a good option. If you are looking for a system that can monitor your entire infrastructure, then InfluxDB is a better choice.What exactly is Prometheus?
Prometheus is a time-series database and monitoring tool that is open source. Prometheus gives its users sophisticated query languages, storage, and visualization tools. It also includes several client libraries for easy interaction. Prometheus can also work with various systems (for example, Docker, StatsD, MySQL, Consul, etc.)TIPS
Prometheus can be great for monitoring as long as the environment does not exceed 1000 nodes. Prometheus + Grafana = best ecosystem
What is InfluxDB?
By InfluxData, Inc., a database management system called InfluxDB was created. InfluxDB is open-source and cost-free to use. The InfluxDB Enterprise version is installed on a server inside a corporate network and comes with maintenance contracts and unique access controls for business customers. A web-based user interface for data ingestion and visualization is also included in the new InfluxDB 2.0 version, which operates as a cloud service that is fully customizable.TIPS
When it comes to storing monitoring metrics, InfluxDB excels (e.g. performance data). If you need to store different sorts of data, InfluxDB is not the best option (like plain text, data relations, etc.)
Let's see how these differ from one another
Features | Prometheus | InfluxDB |
---|---|---|
Data Gathering | Prometheus is a system that operates on the principle of pull. The metrics are published by an application at a certain endpoint and Prometheus retrieves them on a regular basis. | The system InfluxDB is based on is a push-based system. It requires an application to push data into InfluxDB on a regular basis. |
Storage | Prometheus and InfluxDB both follow the key/value datastores. However, these are executed very differently on the two systems. Each metric in Prometheus is kept in its own file and is stored in indices that use LevelDB. Metrics recording and monitoring based on those are the major uses of Prometheus. | Both the indices and the metric values are stored in monolithic databases by InfluxDB. Compared to Prometheus, InfluxDB often uses more disc space. The best database for event logging is InfluxDB. So we have a choice based on the requirements. |
Extensibility and Plug-ins | Prometheus’ key benefit is its widespread community support, which stems from its CNCF-accredited project status. Many apps, particularly cloud-native applications, already support Prometheus. | While InfluxDB has a lot of integrations, it doesn’t possess as many as Prometheus. |
Case Studies | Prometheus was designed for monitoring, specifically distributed, cloud-native monitoring. It shines in this category, with several beneficial integrations with current products. | While InfluxDB can support monitoring, it is not as well known as Prometheus for this purpose. As a result, you may have to develop your own integrations. If you want to do more than a mere monitoring tool, InfluxDB is a fantastic solution for storing time-series data, such as data from sensor networks or data used in real-time analytics. |
Query language | Prometheus uses PromQL, a language that is much easier and has no connection to conventional SQL syntax. Let's say we want a number for CPU load that is greater than 0.5. In that case, we can simply enter CPU load>0.5 in the Prometheus command prompt. | For its querying purposes, InfluxDB uses a standard SQL syntax known as InfluxQL. For instance, we could write select * from tbl where CPU load>0.5 in the Prometheus cell. This seems simple to an associate with a background in SQL, but Prometheus is also not a challenging experience. |
Community | Prometheus is an open-source project with a huge community of users that can rapidly resolve your queries. Having a big network of support is an added benefit since there is a high probability that the challenges one is having might previously have been encountered by someone in the community. | InfluxDB, despite its popularity, needs to improve on community support in comparison to Prometheus. |
Scaling | When the load rises, the monitoring Prometheus servers require scaling as well.This is due to the fact that the Prometheus server is independent. Thus, the Prometheus server works great for the simpler load. | Since the commercial section of Influx DB is distributed, there will be many interconnected nodes. As a result, as the server scales up, we don’t have to worry about scaling nodes. Thus, Influxdb nodes might be considered redundant while handling complicated loads. |
TIPS
InfluxDB performs exceptionally well at storing monitoring metrics (e.g., performance data). Compared to Prometheus, InfluxDB utilizes more disc space and has a monolithic data storage strategy. It performs well for recording occurrences.
Conclusion
As a result, you can consider the factors discussed in this article while choosing between Prometheus and InfluxDB as monitoring systems for time series data, depending on your business case. When it regards monitoring services for time series data, both platforms are extremely well-liked by enterprises. Some claim that PromQL is new and InfluxQL is similar to SQL and will thus be better, but the reality is different. PromQL is considerably more user-friendly for querying, so go for it. Prometheus has a lot more functionality and integrations, so you can choose it. InfluxDB is a better option if you're looking for something specifically for IoT, sensors, and other analytics.Relevant Topics:
https://prometheus.io/ https://github.com/influxdata/influxdb https://v2.docs.influxdata.com/v2.0/ https://www.influxdata.com/blog/multiple-data-center-replication-influxdb/ https://logz.io/blog/prometheus-monitoring/Agile Delivery Process
10decoders has a very strong focus on process. We help our clients to capture the requirements in clear process flows and screen design. Understand how our process driven culture helps customer to grow their business
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8 Proven Ways to Reduce Your AWS EC2 Costs
Here are 8 Proven Ways to minimize your EC2 costs:
Decide on EC2, ECS, Fargate Or Serverless Archictecture
Instances that can fulfill your applications' and workloads' needs. You can do this by evaluating your computing demands. Memory, network, SSD storage, CPU architecture, and CPU count are all factors to consider. Once you have this information, you should seek an instance that offers the greatest performance for the amount you are willing to pay. It is not hard to discover low-cost cloud instances based on your requirements. We can use serverless architecture if the REST service or deployment does not rely on the existence of running machines and can be an event driven architecture. We can also set up ECS or Fargate machines with the right size, memory, and storage to scale up or down depending on your needs.TIPS
You can save licensing cost with predefined or bulk license management.
Leverage reserved EC2 instances
Reserved Instances are a way to buy EC2 machines for a long term and reduce overall pricing through an agreed discount. Since a reserved instance is a pre-paid model, Amazon offers a 75 percent drop on the hourly pricing per instance. As a result, the entry-level instance will cost less. The availability of the reserved instance model is likewise higher than that of the on-demand instance. Why? In a nutshell, it's because it's prepaid. As a result, it is pre-booked, allowing Amazon to schedule the time required. Finally, users can sign up for a one-year or three-year commitment to use the EC2 reserved instance.Leverage GPU Instances
CPUs and GPUs have a significant impact on both cost and performance. You would choose which type is most suitable for your requirements. For example, if you wish to do machine learning activities on a cloud, you should use modern GPU instances such as the G3 or P3 series. Even though GPUs have a higher cost per hour, GPU instances can dramatically accelerate training time and result in cost savings (as compared to CPUs).Spot Instances for stateless and non-production workloads
Spot instances can save a lot of money for stateless and non-production workloads. You can save up to 90% off the on-demand pricing and lower your AWS EC2 expenses. The fact that Spot Instances could be taken before the instance is used and that they are susceptible to change should be noted.Leverage Tags & Setup Availability times
Understanding the NFR Non-functional requirements of a business can help determine the hours our EC2 machines need to run. On this basis, we can schedule the machine's startup and shutdown times and prevent unnecessary running costs and downtime for the devices. You can save money on EC2 by prioritizing some EC2 instances over others. For Example, You could restrict your search to only production, non-production, and other Instances. The AWS dashboard and the AWS API are both used to find and optimize instances using tags. Security and compliance are other possible uses for Tags.Auto-Scaling
The Amazon Web Services (AWS) Auto Scaling mechanism ensures that the appropriate number of Amazon EC2 instances are running to meet the demand of a specific application. Auto Scaling changes compute performance dynamically based on a predetermined schedule or the current load measurements, increasing or decreasing the number of instances as necessary. To modify capacity to actual demands, you can use different types of scaling options that Amazon offers. By dynamically reducing the capacity, you can easily save money and prevent waste. Configure Auto Scaling with precision to maximize cost savings. You can over-provision capacity if you use Auto Scaling for applications that are too big or include too many instances.EC2 Instances of appropriate size
Right-sizing is adopting an EC2 instance type that is a suitable match for your application or workloads to prevent underutilized resources. To identify the kind of instance necessary, evaluate the number of CPU and memory resources utilized by a certain application. After that, you can choose the instance type and number of instances that are most suited to your needs. By choosing your size wisely, you can gain also the most of your reserved instance purchases. Once you've determined the best configuration for your instance, you can save even more money by signing up for a specific term and obtaining reserved instances. However, it may be difficult to determine the right size when dealing with unpredictable workloads, and reserved instances are often wasted.Orphaned Snapshots should be detected and eliminated
As per standard rules, any associated EBS volumes are automatically erased when an EC2 instance is terminated. Any snapshots that are still on S3 and are billing. These expenses might be more than you anticipate. Most backups are incremental, while the first snapshot captures the whole drive. Additionally, over time, incremental snapshots may need more data storage than the first one. Although S3 is less costly than EBS volumes, you'll need a strategy for deleting EBS volume snapshots when an EBS volume is destroyed. Over time, this might result in considerable storage cost savings.TIPS
Always plan to set up budgets and consume resources within the budget. Custom alerts can also help us realize if we used 50%, 75% or 90% of our limit
Conclusion
The Amazon EC2 service is a great way to get some computing power without having to manage a server. However, you can't leave an instance running all day and night without paying for it. For one thing, it's not free! Common problems with EC2 costs include tracking reserved instances with unused hours, underutilized and idle EC2 instances, and migration of EC2 instances from a previous generation. Oversizing and inefficiency of the system bring their own set of challenges. Finally, there are numerous ways to lower your EC2 costs. By following the tips in this article, you can resolve the above challenges, save money and improve your efficiency.The cost of an EC2 instance is based on the instance configuration associated with data processing needs. Successfully minimizing EC2 expenses is reliant on the balance between the cloud computing needs to process corporate data and the quantity of the data that is being processed. To reduce your EC2 costs, get in touch with us. Making the best choice for your tools will be easier for you with our expertise.Related Blog
https://segment.com/blog/spotting-a-million-dollars-in-your-aws-account/ https://cloudcheckr.com/cloud-cost-management/aws-cost-issues-quick-fix/ https://www.apptio.com/blog/decoding-your-hidden-aws-ec2-costs/Case Studies
Top 5 Java Development companies in Chennai
1. Hakuna Matata Tech Solutions
Hakuna Matata Tech solution develops applications by using latest digital technologies to come-up with Client specific solutions which can transform enterprises from their traditional processes impacting their efficiency and productivity to have a rapid growthEmployees Review
- “Good place to start working”
- “Excellent work culture and platform for learning”
- “Good place to learn and grow”
- Media
- Healthcare
- Manufacturing
- Retail
- Construction
2. 10Decoders Consultancy Services Private Limited
10decoders is a cloud engineering company with solid experience architecting and building highly scalable and highly available systems on the cloud. 10decoders helps startups and businesses to scale their remote teams with the right people 10decoders has a vast client base and experience working on silicon valley startups, healthcare giants & Fin-tech companies in the USA & Canada. 10decoders specialize in AgriTech and RegulatoryTech product implementations also Started as a small company with 5 members in 2015, 10decoders has grown into a team of 80 members with capabilities across web, mobile, and cloud engineeringEmployees Review
- “Great place to explore, challenge and strengthen your skills. An actively growing company, you'd love to be a part of!”
- “There are so many great things about working at 10Decoders. It provides great opportunities to develop my technical skills. An overall, work is good in its way, the client and co-workers are well supported. Excellent place to start your career with. Has multiple domains to gain knowledge on”
- “Friendly Staff and Friendly co-workers, best work to improve ourselves and learn new technologies”
Technologies we Work On
Front End: React.js, Angular Back End: Java, Python, Node.js Framework: Django, Flask, FastAPI, Spring / Spring Boot, Express Database: MongoDB, DynamoDB, MySQL, MS SQL Infrastructure: Azure, AWS, Google Cloud, Digital OceanIndustries From Where Our Clients Belong
- FinTech
- Healthcare & MedTech
- Agriculture
3. Siam Computing
Siam computing is one of the top software development companies in Chennai. They have professional services for developing and improving software solutions. The developers make sure that they effectively use the latest technology and the latest digital strategies and technology are integratedEmployees Review
- “One of the best companies I have worked for”
- “Best Place to develop your skills”
- “Web development – The best place to develop your skills”
Industries From Where Their Clients Belong
- Real Estate
- Marketing and Advertising
- Education
- Information Technology
- Financial & Payments
4. Zencode Technologies
Zencode offers a wide range of business solutions to its customers. From mobile application development to artificial intelligence and data analytics, they cover everything. Their main aim is to provide top-notch services to the customers to fulfill their varying business needs. Over the years, they have offered out customized business solutions to a huge number of industries which include Finance, Engineering, E-commerce, Logistics, and HealthcareEmployees Review
- “Working in Zencode will build your confidence as you are encouraged at every step in your work”
- “Good work culture and environment. The company is striving towards innovation and latest technology, providing opportunities for employees to learn and grow professionally”
Industries From Where Their Clients Belong
- Hospitality & Leisure
- Business Services
- Financial Services
5. Agriya
Agriya is a software development company with more than 150 employees spread across two development centers in India. Their head office is located in Chennai. Agirya is listed in top 10 software companies in Chennai due its top-quality work. The company was established in 2000Employees Review
- “Peaceful environment to work”
- “Perfect company to kick-start your career”
- “Great concern to learn and work with new technologies”
Industries From Where Their Clients Belong
- Information Technology
- Art, Entertainment & Music
- Business Services
- Advertising & Marketing
- Retail
Case Studies
Cloud Migration : Lift & Shift Strategy
Introduction
Lift-and-shift is the process of migrating a workload from on-premise to Cloud with little or no modification. A lift-and-shift is a common route for enterprises to move to the cloud and can be a transitionary state to a more cloud-native approach
There are also some workloads that simply can’t be refactored because they’re third-party software or it’s not a business priority to do a total rewrite. Simply shifting to the cloud is the end of the line for these workloads
Applications are expertly “lifted” from the present environment and “shifted” just as it is to new hosting premises, which means in the cloud. There are often no severe alterations to make in the data flow, application architecture, or authentication mechanisms
It allows your business to modernize its IT infrastructure for improved performance and resiliency at a fraction of the cost of other methods
Overview of Market Share
In recent days there is great growth in Cloud computing market. Companies are trying out various cloud models with right balance of flexibility and functionality The cloud migrations hosts the application and data in an effective environment based on various factors. This is the key role of cloud migration Many companies migrate their on-site data and application from their data center to cloud infrastructure with the benefits of redundancy, elasticity, self-service provisioning and flexible pay per use model. These factors are further expected to drive tremendous growth in the global cloud migration services market during the forecast period 2020-2027. Academic writers at Buyessayfriend continue to play a vital role in the evolving landscape of cloud migration services. Also, they are always ready to help students write a paper. According to the report, the global cloud migration services market generated $88.46 billion in 2019 and is estimated to reach $515.83 billion by 2027, witnessing a CAGR of 24.8% from 2020 to 2027 The growth of the market is attributed to an increase in cloud computation among small and medium enterprises around the globeWhat are the best features to have?
- Workloads that demand specialized hardware, say, for example, graphical cards or HPC, can be directly moved to specialized VMs in the cloud, which will provide similar capabilities
- A lift and shift allows you to migrate your on-premises identity services components such as Active Directory to the cloud along with the application
- Security and compliance management in a lift and shift cloud migration is relatively simple as you can translate the requirements to controls that should be implemented against compute, storage, and network resources
- The lift and shift approach uses the same architecture constructs even after the migration to the cloud takes place. That means there are no significant changes required in terms of the business processes associated with the application as well as monitoring and management interfaces
- It is the fastest way to shift work systems and applications on the public cloud because there isn’t a need for code tweaks or optimization right away
- Considered the most cost-effective model, the lift and shift help save migration costs as there isn’t any need for configuration or code tweaks. In the long run, these savings could give way to extra spending if workload costs are not optimized
- With minimal planning required, the lift and shift model needs the least amount of resources and strategy
- Posing the least risk, the lift and shift model is a safe option as compared to refactoring applications especially in the scenario where you don’t have code updating resources
Cloud Migration Steps: Ensuring a Smooth Transition
- First, choose which platform that you wish to migrate to
- Examine all the connections in and out of the application and its data
- If you are lifting and shifting more than one application, then you may need to consider automating multiple migrations
- You should consider containerization to replicate the existing software configurations. This will also allow you to test configurations in the cloud before moving to production
- Back up the databases from the existing system as well as supporting files. When the new database is ready, restore the backups
- Once migrated, test the application
- Check that all the current data compliance and regulatory requirements are running in the new cloud deployment. Run your normal validation tests against the newly migrated application
- Don’t be tempted to introduce new features during the migration. This can lead to many hours of additional testing to make sure you have not created new bugs
- Retire your old systems once testing is complete
Technical Stack
Google Cloud and Azure are new but still they take advantage of the experience and framework of tech giants namely Microsoft and Google AWS is a public cloud, flexible and ready to meet the needs of both big and small applications. Azure’s strong values are perfect integrations with Microsoft 365 ecosystem and focus on enterprise market To help you make an informed choice, we’ve prepared a table that compares the most significant features of AWS, Azure, and GCPTechnologies
Case Studies
WHEN PROGRESSIVE WEB APPS ARE NEEDED
Introduction
PWA apps look and feel like native mobile apps, built with web technologies. They allow websites to be stored on devices. The app shows up as an icon in the form of a website app. The basic methodology here is to combine native app experience with the browser feature.PWA on a Real Device
A PWA is a website that can be downloaded onto the computer or mobile device. It actually pulls in updates in the background each time a user runs it. So, as and when a web application is updated, it not only gives the app site access to new features, but does so without explicitly performing any kind of update.Key Features of PWA
Responsive
PWA apps are built responsive by nature; they're designed to be adaptable to all types of devices with different screen sizes. So, the app will be used on many screen sizes and its contents will be available at all viewport sizes.Installable
Installation of the PWA application is very easy. On a desktop computer or a mobile device, the app is added to the home screen although installation is not necessary. The service worker is set up behind the scenes, the first time a visitor sees the website.Connectivity-Independent
Applications built with progressive web standards can operate offline and on low-quality networks. They also keep a user active in the app even when the user is not connected. The app stores items offline and manages network requests to get the items from the local cache, in a flexible manner, with the help of service workers.Cross Platform & App-Like
A PWA is created to tie together the app and website user experience. Additionally, users can take advantage of these services without going into the app store. Heavy-lifting duties like downloading and data storage are not necessary for PWA installation. PWAs work on all browsers and systems. Prior to installing them, users have to be able to use them in any browser.Load Time & Secure
PWA apps have faster load time. In comparison to the conventional mobile web, the progressive web app reduces page load time by 88 percent, or about 400 milliseconds. Native apps require a lot of security measures, but PWAs are inherently secure because they run on HTTPS and are encrypted with an SSL certificate. This in turn, adds an extra layer of security.Use Cases of PWA
- Better user experience
- Increased user engagement
- Increased security and ability to use offline
- Increased organic search traffic
- PWAs typically cost less to develop and maintain than native apps
When Do We Require a PWA?
Usage of Application in Multi-Devices
Whenever there is a need to use applications on both mobile and desktop devices, progressive web apps is the way to go. PWAs are becoming increasingly popular because they are lightweight and load quickly. Additionally, using PWAs, web apps can be viewed on mobile devices. By doing so, user gets a native mobile application feel and look along with a browser feature.Speed and Reliability
If speed is the main concern, PWA is the answer because PWA is significantly faster than a native mobile application. According to statistics, PWA has a faster load time and a lower demand on devices. In other words, when the app must be consistently high-quality and lightning-fast despite no network connection or limited bandwidth, a PWA is the best option.Responsiveness
When the user plans to install or use applications across different devices, it is always better to use PWAs. They are responsive to most devices and make the UI appealing on any device.Security
PWAs are secure by nature since the technology that powers the app requires it to be served over the HTTPS protocol in order to work. It is delivered through TLS, which provides major benefits for both users and developers.Platform Independent
Whenever an application is built for cross-platform usage, with a single technology, PWA is the way to go! It is available on all platforms and simplifies the development process for developers.Advantages of Using PWA
- Lightweight and Easy to install in devices
- Provides offline support
- Safe and Secure to use
- Faster than native mobile applications
- Helps to boost Search Engine Optimization
- Targets Cross platform
Disadvantages of PWA
- Cannot access the various device features
- Consumes more battery
- No access to app stores
- UI and UX Limitations
- If user does not use the app for a long time, the cache is deleted
- Push notification features are not possible in iOS devices
Conclusion
The importance of PWAs would definitely be felt to a large extent in the future. There are many PWA features currently under development and the PWA community is growing by the day. One of the main reasons as to why users are more likely to choose PWAs over native apps is because PWAs encourage them to interact more. Further, the low costs and the ease of implementation play a huge role in influencing the spread of this technology.References
Progressive web apps (PWAs) | MDN What is a PWA and why should you think about it?Technologies
Jira Integration with GitHub
OBJECTIVE:
To configure github with jira through pytest to update results on jira tickets, When Merging a Pull Request the github workflow will be executed, After the workflow execution the status of the jira tickets will be updated as per the result of github workflow execution through pytestWhat is Jira?
Jira is a web application used as tracking tools for tasks like epic, story, bugs and so on. Jira is available as an open source and paid version.Why do we use Jira ?
It is used for various kinds of projects like business projects, software projects and service projects Applications like Github, slack, jenkins, zendesk etc can be integrated with jira. By using jira a ticket can be created for each type of task to monitor application development, Here we integrate Github with jira through the pytest framework.What is Pytest ?
Pytest is an automation testing framework in python which is used for testing software applications.Why do we use Pytest ?
Pytest is a python framework, using pytest we can create TDD, BDD as well as Hybrid testing framework used for automation testing like UI, REST API and it is flexible for supporting different actions. Here we are going to execute the test cases that are triggered from the github actions workflow and update the particular jira tickets based on the workflow execution results.What is Rest API ?
REST is abbreviated as (Representational State Transfer) it is an architectural style for interacting between the client and the server, the client sends the request and a response is received from the server in of JSON, XML, or HTML, but json is most commonly used response type because it is readable for both human and machine. Here we interact with the Jira through Rest API, the API Endpoints we are using to interact with Jira is given belowEXECUTION FLOW OF GITHUB FOR JIRA THROUGH PYTEST
To update the jira tickets through pytest, we need to know about the Github workflow execution, Jira Rest API endpoints, pytest configurationsThings we need to know for execution:
- To create a github workflow file to execute pytest test cases when a PR is merged
- To configure pytest test cases with jira API endpoints to send the workflow results
JIRA REST API ENDPOINTS
Prerequisites for Jira API Integration:
Steps to create API Token:
- STEP 1: Login to Jira with the registered Email ID Use this link https://id.atlassian.com/login to login into the jira
- STEP 2: Click on your jira Profile and click on manage accounts in the popup
- STEP 3: Click on Security tab and click create and manage API token
- STEP 4: Click on create API Token button:
- STEP 5: Provide a label for the token and click create, a new API Token will be generated Copy the token and save the token in a separate file because you cant able to get the same token again
Encoding the API Token:
Encoding the Api token can be done in the terminal, to create a base64 encoded token use the following command In LINUX/ mac-os:GET Transition ID API:
- GET:
Transition Status | Transition ID |
---|---|
To-Do | 11 |
In-Progress | 21 |
Done | 31 |
Issue 1 | 2 |
Issue 2 | 3 |
Update Transition Status API:
Post :
https://<JIRA_DOMAIN>.atlassian.net/rest/api/2/issue/<TICKET-ID>/transitions This API Endpoint is used to update the transition status of the jira ticket. Ticket ID is passed in the path param and the Transition ID is passed in the body of the request, The status of the ticket will be updated with respect to the transition id, Curl for the following API endpoint is mentioned below, The Transition ID can be obtained by using the Get transition ID API, which is mentioned aboveAdd Attachments API:
Post:
https://<JIRA_DOMAIN>.atlassian.net/rest/api/2/issue/<TICKET-ID>/attachments This API endpoint is used to add the attachment in a JIRA ticket according to the given Ticket ID and passed file. Use the below curl commandSearch API:
GET:
https://<jira_domain>.atlassian.net/rest/api/2/search This API Endpoint is used to get the ticket information using the Jira Query Language (JQL) syntax, the JQL should be passed as an parameter to this API, By using this API we can get the information of all/any of the ticket, An example for JQL to get the ticket information using PR Link which is mentioned in the Github info paragraph field of a jira ticket Example for JQL:CONFIGURING GITHUB WITH JIRA:
There are two ways in configuring github with jira, one is by providing the PR link in a separate field of jira ticket and the other way is by configuring github app in jira1. Configuring jira with PR link:
- We can able to identify the ticket information by providing the PR link in a Jira ticket
- PR Link should be provided in the custom field of Jira ticket
- After placing the PR link in custom field, we need to use the Jira Search API endpoint through Jira Query Language(JQL) syntax
2. Steps to configure PR link in Jira Ticket on custom field:
- Go to Project Board > Project settings > Issue types
- Select the Paragraph field type > Enter the field name and description
- Click Save changes
3. Configure github app with jira:
- To configure Github with jira,Login into Jira, go to Apps➡ Manage Your apps
- Select Github for jira ➡ click connect github organization
- CLICK Install github for jira on new organization
- On clicking Install github for jira on new organization select the github organization in which you want to install jira
- Select repository which you want to configure and click install
- Now you can see your git repository that have been configured in the github for jira tab
UPDATING EXECUTION RESULTS TO JIRA TICKET USING PYTEST:
- All the test cases and the reports report generation for all the test cases are done using pytest
- After the Workflow execution, the build status and PR link will be added as comments and the reports will be added as attachments to the jira ticket, this is done by the pytest fixture. The pytest fixture is used to execute the conditions before and after the execution of test case, the yield keyword is used to execute the conditions after the execution of all test cases
- The teardown module() method calls the Jira API Endpoints for Adding Comments and Attachments.
Agile Delivery Process
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Technologies
Build APIs in Python Using FastAPI Framework
FastAPI could be a modern, high-performance, web framework for building APIs with Python language. Good artificial language frameworks make it easy to provide quality products faster. Great frameworks even make the entire development experience enjoyable. FastAPI could be a new Python web framework that’s powerful and enjoyable to use.
FastAPI is an ASGI web framework. What this implies is that different requests don’t necessarily sit up for the others before them to end up doing their tasks. Additional requests can do their task completed in no particular order. On the other hand, the WSGI frameworks process requests in a sequential manner.ASGI:
ASGI is structured as one, asynchronous callable. It takes a scope, which could be a dict containing details about the precise connection, sends an asynchronous callable that lets the appliance send event messages to the client, and receives an asynchronous callable that lets the application receive event messages from the client.Does FastAPI need Uvicorn?
The main thing needed to run a FastAPI application in an exceedingly remote server machine is an ASGI server program like Uvicorn.Using WSGIMiddleware:
Need to import WSGIMiddleware.Then wrap the WSGI (e.g. Flask) app with the middleware. Then mount that beneath a path.FastAPI Different from other Frameworks:
Building a CRUD Application with FastAPI
Setup:
Start by creating a brand new folder to carry your project called "sql_app".Difference between Database Models & Pydantic Models:
FastAPI suggests calling Pydantic models schemas to assist make the excellence clear. Appropriately, let’s put all our database models into a python models.py file and every one of our Pydantic models into a schemas.py file. In doing this, we’ll also have to update database.py and main.py.Models.py:
database.py:
schema.py:
FastAPI interactive documentation
A feature that I like about API is its interactive documentation. FastAPI is based on OpenAPI, which is a set of rules that defines how to describe, create and visualize APIs. OpenAPI needs software, which is Swagger, which is the one that allows us to show the API documented. To access this interactive documentation you simply need to go to “/docs”.Structuring of FastAPI:
Models:
It is for your database models, by doing this you'll import the identical database session or object from v1 and v2.Schemas:
It is for Pydantic's Settings Management which is extremely useful, you will be ready to use the identical variables without redeclaring it, to work out how it should somewhat be useful for taking a glance at our documentation for Settings and Environment Variables.Settings.py:
It is for Pydantic's Settings Management which is extremely useful, you'll be able to use the identical variables without redeclaring it, to determine how it may well be useful for you take a look at our documentation for Settings and Environment VariablesViews:
This is optional if you're visiting render your frontend with Jinja, you'll have something near MVC pattern.Core views
- v1_views.py
- v2_views.py
Tests:
It is good to own your tests inside your backend folder.APIs:
Create them independently by APIRouter, rather than gathering all of your APIs inside one file.Logging
It could be a means of tracking events that happen when some software runs. The software’s developer adds logging calls to their code to point out that certain events have occurred. an occasion is described by a descriptive message which might optionally contain variable data (ex. data that's potentially different for every occurrence of the event). Events even have an importance that the developer ascribes to the event; the importance also can be called the amount or severity.Conclusion
Modern Python Frameworks and Async capabilities are evolving to support robust implementation of web applications and API endpoints. FAST API is definitely one strong contender. In this blog, we had a quick look at a simple implementation of the FAST API and code structure. Many tech giants like Microsoft, Uber, and Netflix are beginning to adopt. This will result in growing developer maturity and stability of the framework.Reference Link:
https://fastapi.tiangolo.com/ https://www.netguru.com/blog/python-flask-versus-fastapiTechnologies
How to use Apache spark with Python?
Apache Spark is based on the Scala programming language. The Apache Spark community created PySpark to help Python work with Spark. You can use PySpark to work with RDDs in the Python programming language as well. This can be done using a library called Py4j.
Apache spark:
Apache spark is an open-source analytics and distributed data processing system for large amounts of data (large-scale datasets). It employs an in-memory caching and an accelerated query execution for quick analytic queries against any size of data. It is faster because it distributes large tasks across multiple nodes and uses RAM to cache and process data instead of using a file system. Data scientists and developers use it to quickly perform ETL jobs on large amounts of data from IoT devices, sensors, and other sources. Spark also has a Python DataFrame API that can read a JSON file into a DataFrame and infer the schema automatically. Spark provides development APIs for Python, Java, Scala, and R. It shares most of its features with PySpark, including Spark SQL, DataFrame, Streaming, MLlib, and Spark Core. We will be looking at PySpark.Spark Python:
Python is well known for its simple syntax and is a high-level language that is simple to learn. Despite its simple syntax, it is also extremely productive. Programmers can do much more with it. Since it provides an easier interface, you don't have to worry about visualizations or Data Science libraries with Python API. The core components of R can be easily ported to Python as well. It is most certainly the preferred programming language for implementing Machine Learning algorithms.PySpark :
Spark is implemented in Scala which runs on JVM. PySpark is a Python-based wrapper on top of the Scala API. PySpark is a Python interface to Apache Spark. It is a Spark Python API that helps you connect Resilient Distributed Datasets (RDDs) to Apache Spark and Python. It not only allows you to write Spark applications using python but also provides the PySpark shell for interactively analyzing your data in a distributed environment.PySpark features:
-
- Spark SQL brings native SQL support to Spark and simplifies the process of querying data stored in RDDs (Spark's distributed datasets) as well as external sources. Spark SQL makes it easy to blend RDDs and relational tables. By combining these powerful abstractions, developers can easily mix SQL commands querying external data with complex analytics, all within a single application.
-
- DataFrame A DataFrame is a distributed data collection organized into named columns. It is conceptually equivalent to relational tables with advanced optimization techniques. DataFrame can be built from a variety of sources, including Hive tables, Structured Data files, external databases, and existing RDDs. This API was created with inspiration from DataFrame in R Programming and Pandas in Python for modern Big Data and data science applications.
-
- Streaming is a Spark API extension that allows data engineers and data scientists to process real-time data from a variety of sources like Kafka and Amazon Kinesis. This processed data can then be distributed to file systems, databases, and live dashboards. Streaming is a fault-tolerant, scalable streaming processing system. It supports both batch and streaming workloads natively.
-
- Machine Learning Library (MLlib) is a scalable machine learning library made up of widely used learning tools and algorithms, such as dimensionality reduction, collaborative filtering, classification, regression, and clustering. With other Spark components like Spark SQL, Spark streaming, and DataFrames, Spark MLLib works without any issues.
-
- Spark Core is a general execution engine of Spark and is the foundation upon which all other functionality is built. It offers an RDD (Resilient Distributed Dataset) and supports in-memory computing.
Setting up PySpark on Linux(Ubuntu)
Follow the steps below to setup and try pyspark: Please note that python version 3.7 or above is required. Create a new directory. Navigate to the directory.PySpark shell
Pyspark comes with an interactive shell. It helps us to test, learn and analyze data in the command line. Launch pyspark shell with command line ‘pyspark’. It launches the pyspark shell and gives you a prompt to interact with Spark in the Python language. To exit from spark shell use exit()Create pyspark Dataframe:
Like in pandas, here also we can create dataframe manually by using these two methods toDF() and createDataFrame(), and also from JSON, CSV, TXT, XML formats by reading from S3, Azure Blob file systems e.t.c. First, create columns and datasRDD dataframe:
An existing RDD is an easy way to manually create a PySpark DataFrame. First, let's create a Spark RDD from a List collection by calling the parallelize() function from the SparkContext. This rdd object is required for all of the following examples. A spark session is an entry point for the spark to access components. To create a Dataframe using toDF() method, we have to build a spark session and then pass the data as an argument to parallelize. Finally, we use “toDF(columns)” to specify column names as in the below code snippets.Kafka and PySpark:
We are going to use pyspark to produce a stream dataframe to Kafka and consume the stream dataframe. We need kafka and pyspark for the same. We have already setup pyspark in our system, Now we are going to setup kafka in the our system. If you have already setup kafka you can skip this, otherwise you can setup kafka by following these steps: Set up Kafka using Docker compose: Docker Compose is used to run multiple containers as a single service and it works on all environments. Docker Compose files are written YAML files. Now, create docker-compose YAML file named docker-compose.yml for Kfka. Enter the following and save the file. It will run everything for you via Docker.Produce CSV data to kafka topic, Consume using PySpark :
Produce CSV data to kafka topic :
For that we need a CSV. Download or create your own CSV file Install the kafka-python package in a virtual environment. kafka-Python is a python client for the Apache Kafka distributed stream processing system. With pythonic interfaces, kafka-python is intended to operate similarly to the official java client. In the below code we have configured Kafka producer and created an object with it. In config we have to give info like bootstrap server and value_serializer. serializer instructs on how to turn the key and value objects the user provides with their ProducerRecord into bytes.What is schema/StructType in spark ?
It defines the structure of the DataFrame. We can define it using StructType, which is a collection of StructFields that define the column name, DataType, column nullability, and metadata. Below code will write the data frame stream on consoleConclusion :
One of the popular tools for working with Big data is Spark. It has the PySpark API for Python users.This article covers the basics of data frames, how to install PySpark on Linux, what spark and PySpark features are, and how to manually generate data frames using the toDF() and createDataFrame() functions in the PySpark shell. Due to its functional similarities to pandas and SQL, PySpark is simple to learn and use. Additionally, we look at setting up Kafka, putting data into Kafka, and using PySpark to read data streams from Kafka. I hope you use this information and put it to use in your work.Reference Link:
Apache Spark: https://spark.apache.org/docs/latest/api/python/getting_started/install.html Pyspark : https://sparkbyexamples.com/pyspark-tutorial/ Kafka: https://sparkbyexamples.com/spark/spark-streaming-with-kafka/Technologies
Resemblance and Explanation of Golang vs Python
Everyone has been looking for the best programming language to use when creating software and also there has recently been a battle between Golang and Python.
Golang
Golang is a procedural, compiled, and statically typed programming language(syntax similar to C). It was developed in 2007 by Ken Thompson, Robert Griesemer, and Rob Pike at Google but launched in 2009 as an open-source programming language. This language is designed for networking and infrastructure-related applications. While it is similar to C, it adds a variety of next-gen features such as Garbage collection, Structural typing, and Memory management. Go is much faster than many other programming languages. Kubernetes, Docker, and Prometheus are written in it. (developed using this language or written in it)Features of Golang
Simplicity
The developers of the Go language focus on credibility, readability, and maintainability by incorporating only the essential attributes of the language. So we can avoid any kind of language complications resulting from the addition of complex traits.Robust standard Library
It has a strong set of library packages, making it simple to compose our code.Web application building
This language has garnered as a web application building language owing to its easy constructs and more agile execution speed.Concurrency
- Go deals with Goroutines and channels.
- Concurrency effectively makes use of the multiprocessor architecture.
- Concurrency also helps huge programs scale more consistently.
- Some notable examples of projects written in Go are Docker, Hugo, Kubernetes, and Dropbox.
Speed of Compilation
- Go offers a much more powerful speed of completion and compilation than several other popular programming languages.
- Go is readily parsable without a symbol table.
Testing support
- The "go test" command in Go allows users to test their code written in '*_test.go' files.
Pros:
- Ease to use - Go’s core resembles C/C++, so experienced programmers can pick up the basics fast, and Simple syntax is easy to understand and learn
- Cross-platform development opportunities - Go can be used with various platforms like UNIX, Linux, Windows, and Other Operating systems, and Mobile devices also.
- Faster compilation and execution - Go is a compiler-based language so it completely reads the code and executes due to this it executes faster than c,c++, and java.
- Concurrent - Run various processes together and effectively
Cons:
- Still developing - Still in development
- Absence of GUI Library - There is no native support
- Poor Error handling - The built-in errors in Go don't have stack traces and don't support the usual try/catch handling techniques.
- Lack of frameworks - Minimal amount of frameworks
- No OOPS Support
Output:
- package main - Every Go program begins with code inside the package main
- Import “fmt” - its an I/O function.
- func main - This function always needs to be placed in the main package.{} Here we can write our code/logic.
- fmt.Println - Print function, print the text on the screen.
Why Go?
- It's a statically strongly typed programming language with a great way to handle errors.
- It allows using static linking to combine all dependency libraries and modules into one single binary file based on the type of the OS and architecture.
- This language performs more efficiently because of its CPU scalability and concurrency model.
- This language offers support for multiple libraries and tools, so it does not require any 3rd party libraries.
Python
Python is a universal, high quality and very popular programming language. Python was introduced and developed by Guido van Rossum in the year of 1991. Python is used in machine learning applications, data science, web development, and all modern software technologies. Python has an easy-to-learn syntax that improves readability and reduces program maintenance costs. Python code is interpreted when it is converted to machine language at run time. It is the most widely used programming language because of its tightly typed and dynamic characteristics. Python was originally used for trivial projects and is known as a "scripting language". Instagram, Google, and Spotify use Python and its frameworks.Features of Python
- Free and open source
- Easy to code
- Object-oriented programming
- GUI Programming support
- Extensible and portable
-
-
- Python is an extensible language.
- We can formulate some Python code into the C or C++ language.
- Furthermore, we can compile that code in the C or C++ language.
- Python is also a very portable language.
- If we have Python code for Windows and want to run it on platforms such as Unix, Linux, and Mac, we do not need to change it. This code is platform-independent.
-
- Interpreted and High-level language
-
-
- Python is a high-end language.
- When we formulate programs in Python, there is no need to remember the system architecture, nor do we need to manage the memory.
- Like in other programming languages, there is no requirement to compile Python code, making it easy to debug our code.
- Python's source code is converted to an instantaneous form known as bytecode. Python is classified as an interpreted language because Python code is executed line by line.
-
Pros:
- Simple syntax: Easy to read and understand
- Larger Community support: Python community is vast
- Dynamically typed: The variable type is not required to be declared.
- Auto memory management: Memory allocation and deallocation methods in Python are automatic because the Python developers created a garbage collector for Python so that the user does not have to manually collect garbage.
- Embeddable: Python can be used in embedded systems
- Vast library support: Lots of Libraries are available. For Example, TensorFlow, Opencv, Apache spark, Requests and Pytorch etc.,
Cons:
- Slow speed
- Not Memory Efficient
- Weak in mobile computing
- Runtime errors
- Poor database access
Why Python?
Python is platform-independent; it runs on (Windows, Mac, Linux, Raspberry Pi, etc.). Python has a simple syntax that is related to that of the English language. Python's syntax allows programmers to write programmes with fewer lines than in other programming languages. Python is an interpreter-based language. As a result, prototyping can be completed quickly. Python can be processed as procedural, object-oriented, or functional. Frameworks for Web Development: Django, Flask, Fastapi, Bottle, etc.Comparison of Go vs Python:
Case studies:
Concurrency:
Output:
Exception Handling :
Output :
Go vs Python: Which is Better?
When it comes to productivity, Golang is the best language to learn to become a more productive programmer. The syntax is restricted, and the libraries are much lighter because there is less code to write; tasks can be completed in fewer lines of code. Python consists of a large number of packages and libraries. Python has the advantage in terms of versatility due solely to the number of libraries and syntax options. However, flexibility comes at a cost, and that cost is productivity. Which language is more productive in this Python vs Golang battle? The winner is Golang, which is designed to be more productive, easier to debug, and, most importantly, easier to read. Python is without a doubt the most popular choice for developers looking to create a machine learning model. The reason for this is that Python is the most popular language for machine learning and is the home of TensorFlow, a deep learning framework built on Python. Learning a programming language like Python, which almost resembles pseudo-code, is an added benefit that makes learning easier. On the other hand, Golang is super fast, and effortless to write and comes along with Go doc, which creates documentation automatically, making the life of the programmer easier.Conclusion
Python and Golang are winners in their respective areas depending on the specific capabilities and underlying design principles of the language1.Maturity
It's difficult to make conclusions about Go vs Python because comparing a mature language to a young one doesn't seem fair. Python may be the winner here2.In ML and Data science Usage
Python is the leading language not only for machine learning and data analysis but also for web development. Golang has only been around for a decade, and it has yet to establish a robust ecosystem or community.3.Performance
The main advantage of Go is speed. However, Python is slow when it comes to code execution.4.Microservices and Future Readiness
When it comes to microservices, APIs, and other fast-loading features, Golang is better than Python. Go is equipped to be a future-ready web development framework with a lot of adoption around the world of containers.Referral Link:
Python - https://docs.python.org/3/ Go - https://go.dev/doc/Technologies
Flask vs FastAPI – A Comparison Guide to Assist You Make a Better Decision
What is Flask?
Flask is a micro web framework written in python. Armin Ronacher came up with the idea. Flask is built on the WSGI (Web Server Gateway Interface) Werkzeug toolkit (For the implementation of Request and Response) and the Jinja2 (template engine). WSGI is a standard for web application development. It is used to build small-scale web applications and Rest API Flask's framework is more explicit than Django's framework, and it is also easier to learn because it requires less basic code to construct a simple web application.In this real-world top Companies are using Flask
What makes Flask special?
- Lightweight Extensible Framework.
- Integrated unit test support.
- Provided development server and debugger.
- Uses Jinja templating.
- Restful request handling.
When should you use a Flask?
- Flask is old and it has Good Community support
- For Developing web applications and creating a quick Prototype.
Flask Web Application Development
- Creation of a virtual environment
- The venv environment is being restarted.
- Database.
- Login and register for several users
- Mode Debug
- Creating a User Profile Page
- Creating an avatar
- Handling errors
Build a sample webpage using Flask, it will return the string
Pros
- Flask has a built-in development server, integrated support, and other features.
- Flask Provides integrated support for unit tests.
- Flask Uses Jinja2 templates.
- Flask is just a collection of libraries and modules that helps developers to write their applications free without worrying about low-level details like Protocols and Thread Management.
- Because of its simplicity, Flask is particularly beginner-friendly, allowing developers to learn easier. It also allows developers to construct apps quickly and easily.
Cons
- Flask makes use of Modules, which is third-party participation that might lead to security breaches. Modules are the intermediary between the framework and the developer.
- Flask does not create automatic documentation; They need several extensions like Flasgger and Flask RESTX and also require additional setup.
- Flask has a single source, which suggests that it will handle each request one by one, therefore regardless of how many multiple requests there are, it will still take them in turns, which takes extra time.
What is FastAPI:
FastAPI is built in ASGI (Asynchronous Server Gateway Interface) and pydantic, Starlette. This framework is used for building a web application and RestAPI. In FastAPI we need uvicorn to run the server. There is no built-in development server So the ASGI server Uvicorn is required to run the FastAPI application. The best thing that we Highlight in the FastAPI is documentation. It will generate documentation and create Swagger UI. which helps the developers to test endpoints effectively. In fast API, Also includes data validation and returns an Explanation of the error when the user enters the invalid data. It implements all of the OpenAI requirements and Swagger for these specifications. As a developer we will concentrate on developing logic; the rest is handled by the FastAPI.When should you use a FastAPI?
- Has Good speed and performance when compared with Flask.
- Decreasing bugs and Errors in code.
- It Generates Automatic documentation
- Built-in data validation.
What makes FastAPI special?
- Fast Development
- Fewer Bugs
- High and Fast Performance
- Automatic swagger UI
- Data validation
Pros
- FastAPI is considered as one of the fastest Framework in python, It has an native async support and provides a simple and easy-to-use dependency injection framework. Another advantage to consider is built-in data validation and Have Interactive API documentation support .
- Dependency Injection support
- Fast API is based on standards such as JSON Schema (a tool for validating the structure of JSON data), OAuth 2.0 (an industry standard protocol for authorization), and OpenAPI (an open application programming interface).
Cons
- In FastAPI there is insufficient Security and also it will support OAuth.
- Because FastAPI is relatively new, the community is small compared to other frameworks, and regardless of its detailed documentation, there are very few external educational materials.
Difference between Flask and FastAPI:
Both offer the same features but the implementation is different. The main difference between Flask and FastAPI is that Flask is built in WSGI (Web Server Gateway Interface) and FastAPI is built in ASGI(Asynchronous Gateway Interface). So, the FastAPI will support concurrency and asynchronous codes. In FastAPI there is automatic documentation for Swagger UI(docs and redocs), But in Flask we need to add some extensions like Flasgger or Flask RESTX and some dependencies setup. Unlike Flask, FastAPI provides data validation for defining a specific data type and it will raise an Error if the user enters an invalid data type.Performance:
FastAPI uses an Async library which is helpful to write concurrent code. Async is greatly helpful for doing tasks that involve something like fetching data from API and querying from a database, reading the content of the file and FastAPI has ASGI whereas Flask is a WSGI ApplicationData Validation:
There is no data validation in Flask. So Flask allows any kind of data type. In Flask the data validation will be handled by developers. But In FastAPI there is inbuilt data validation (pydantic). So it will raise an error when it gets an invalid data type from the user. This is useful for developers to interact with the API endpoints.Documentation:
Flask doesn’t have any inbuilt documentation for swagger UI. We need to add some extensions like Flassger and Flask-RESTEX and some dependency setups. But In FastAPI it generates an automatic swagger UI when the API is created. For accessing the auto-generated swagger UI hit the endpoint with /docs or /redoc. It will show all the endpoints in your application.HTTP METHODS:
Flask | FastAPI |
---|---|
@app.route("/get", methods= ['GET']) | @app.get('/get', tags=['sample']) |
Production Server
At some point, you’ll want to deploy your application and show it to the world.- Flask
- FastAPI
Asynchronous Tasks
- Flask
Installations
Example:
FastAPI:
In FastAPI there is a default AsyncIO. So we can simply add the async keyword before the function.FastAPI was Built with Primary concerns
- Speed and Developer Experience
- Open Standards.
- FastAPI can connect Starlette, Pydantic, OpenAPI, and JSON Schema.
- FastAPI uses Pydantic for data validation and Starlette for tooling making it twice as fast as Flask and equivalent to high-speed web APIs written in Node or Go.
- Starlette + Uvicorn supports async requests, while Flask does not.
- Data validation, serialization and deserialization (for API development), and automatic documentation are all included (via JSON Schema and OpenAPI).
Which Framework is Best for AI/ML
Both Flask and FastAPI are the popular Framework for developing Machine learning and web applications. But most data scientists and Machine learning developers prefer Flask. Flask is the primary choice of Machine learning developers for writing the API’s . A few disadvantages of using Flask is time consuming for running the big applications. The major disadvantage in flask is to add more dependencies by adding plugins and the other one is lack of Async support whereas FastAPI supports Async by default . FastAPI is used for the creation of ML instances and applications. In the machine learning community Flask is one of the popular frameworks.Flask is perfect for ML engineers who want to create web models. FastAPI, on the other hand, is the best bet for a framework that provides both speed and scalability.Migrating Flask TO FastAPI :
The Application object in Flask and FastAPI is
Simple Example for Migrate Flask to FastAPI:
- Flask Application
1.To Migrate the Flask to FastAPI we need to install and import libraries.
2. URL Parameters (/basic_api/employees/)
The Request Methods in Flask and FastAPI is
Query Parameters:
Like URL Parameters, Query parameters are also used for managing the state(For sorting or Filtering).-
Flask
-
FastAPI
Run the server in Flask And FastAPI
Main (Flask)And Finally the FastAPI Application looks like :
- FastAPI Application
When Should you choose FastAPI instead of Flask and Django?
- Native Async Support:The FastAPI web framework was created using the ASGI web server. Native asynchronous support eliminates inference latency.
- Improved latency: As a high-performance framework, the framework's total latency is lower when compared to Flask and Django.
- Production-ready:With the FastAPI web framework's auto validation and short defaults, developers may easily design web apps without rewriting the code.
- High Performance: Developers have access to the key functionalities of Starlette and Pydantic because they are compatible. Needless to say, Pydantic is one of the quickest libraries, and as a result, overall speed improves, making FastAPI the preferred library for web development.
- Learn Simple:This is a minimalist framework so it is easy to understand and learn.
Flask Or FastAPI: Which is better
S.No | Flask | FastAPI |
---|---|---|
1. | Flask is a micro web framework to develop small-scale web applications and Rest API. Flask is depends on WSGI toolkit (werkzeug, Jinja2) | FastAPI is considered one of the Fastest frameworks compared to the flask. FastAPI is built in Pydantic and starlette. |
2. | Flask is built in Web Server Gateway Interface(WSGI) | FastAPI is built in Asynchronous Server Gateway Interface(ASGI) |
3. | It does not have any inbuilt documentation such as swagger UI and needs to add some extensions like Flasgger or Flask RESTX. | In FastAPI it has inbuilt documentation like (docs and redocs). |
4. | There is no Inbuilt data validation in Flask, we need to define the data type in requests. | In FastApI there is an Inbuilt data validation that will raise the error if the user provides an invalid data type. |
5. | Flask is more flexible than Other frameworks. | FastAPI is flexible in code standards and it does not restrict the code layouts. |
Conclusion:
After learning about both Flask and FastAPI. Both are used to create a Web application and Rest API. But FastAPI is better when compared with Flask. Because FastAPI has a native ASGI support (Asynchronous Server Gateway Interface). So it is Faster and High in performance. Also, it has inbuilt documentation(Swagger UI) and data validation. FastAPI has High and Fast performance, Efficiency and it's easy to understand and learn. When compared to Flask, FastAPI has less community support but it reaches a lot in a short period of time.Reference link:
Flask: https://flask.palletsprojects.com/en/2.2.x/
FastAPI: https://fastapi.tiangolo.com/
Technologies
Why and when choose custom Software development?
Introduction
Custom software development is the process of designing, developing, deploying, and maintaining software for a certain set of Users or a specific Organization. Any software will meet the generalized need of the end-users. The existing software may not address all the needs of the Organization. In such a case they move in for customization of the existing software. Customized solutions are developed to meet the needs of the UserOverview of market share
Custom Software Development Services Market is huge and is growing at a moderate speed with substantial growth rates over the last few years and is estimated that the market will grow significantly in the next few years. The Custom Software Development Services Market is driven by the growing requirements for customized software among Organizations. Moreover, organizations are always looking for reducing long-term costs. Custom software development is becoming popular among organizations that are largely looking for scaling up of their business operations. A holistic evaluation report of the market is provided by the Global custom software development services market. The report offers a comprehensive analysis of key segments, trends, drivers, restraints, competitive landscape, and factors that are playing a substantial role in the market.How custom software development process Works
The process followed in custom software development is same as SDLC. It starts with Planning and Analysis followed by Design, Development, Testing, and finally maintenance of the completed product. The main goal of planning and analysis is to collect as much data as possible. The design transforms the requirements into a detailed system design requirements document. It is like a blueprint for the solution which is used for developing the code. Developing code is the actual implementation phase, which is followed by rigorous testing. Testing is done until all issues are identified and resolved. Finally, the product is deployed into the live environment. And the product gets into the maintenance phase.Reason to choose custom software development
Generally, developing an application from scratch is a complex and time-consuming process. If there is not much time and a solution needs to be implemented as quickly as possible, then custom software development would be a better choice. The next factor to be considered is software development cost. Ready-to-use applications can save the budget if they provide the desired functions and match the standard requirements and do not need any customization. In case the ready-to-use application can’t meet the demands of all kinds and the development team needs to handle complex processes and comply with high security and industry regulations then a custom software development process would be the best option.What are the Benefits of the Custom Software development process
Some of the benefits of the custom software development processUniqueness
One of the important benefits of custom applications is uniqueness. Tailored solutions are built to fit the user’s specifications. A development team experienced in custom software development help to deliver a solution that will include the features requested.Flexibility & Scalability
Regular software cannot be manipulated and it will remain constant. It will become unsuitable to keep using it. But custom software can be scaled according to the needs of the company and easily integrated with business. So the user need not change according to the application but the application can be changed according to the user.Cost effectiveness
Readily available software might be less expensive but it might have some recurring costs which will make it less beneficial. They might also lack some critical functionality. In such cases developing a product from scratch might cost more. When existing software is customized, a huge sum of money need not be invested.Security
While customizing or developing software, the important feature that needs to be handled is security When an organization needs to support expensive security protocols, it might be an add-on cost to them. But with customized software, they can decide about the security technology to be used and choose one which is ideal for their business.Team Capabilities
Team experience and technical skills A software team with strong technical skills, in-depth knowledge of the latest technologies, and experience with multiple companies need to be considered for customizing software.Cost Structure
When a third party is hired for customizing software, it should be ensured that they give a clear picture of all the costs involved and do not keep the costs hidden.Communication Skills
The custom software development team should be strong in communication skills. Their strong communication skills will help them to understand the details of the unique requirements needed by the client. When they have a clear understanding, they can carefully design and develop software with accuracy.Why choose 10Decoders for custom software development?
- 10Decoders team has worked on customizing multiple types of applications for many clients.
- We have also tried and tested various methodologies for successful completion.
- Also, we work with highly secured and safe systems. So your data will be protected in our hands.
- Depending on the complexity of Customization our charges are reasonable. And we do not have any hidden costs.
- We have Engineers who are highly skilled in multiple technologies, who can readily work on customizing your needs.
Technologies
Voice Enabled Banking and Chatbots with Dialogflow
Introduction
Banking chatbots generate better results and superior customer experiences for the banking industry and other financial institutions. They help the customers in multiple ways like getting account balances, to apply for loan or credit card, transfer funds, pay bills, or to update the profile details. Regular customer interactions can be automated partially or fully using a banking chatbot which is available 24/7. A voice enabledWhat is Chatbot?
chatbot is a variation of a conversational AI solution. It leverages NLP combined with speech-to-text(self-developed or already existing platforms) and automates speech recognition to deliver resolution immediately. Voice Assistants can either be a complete voice-based model or as a multimodal chatbot supporting both text and voiceWhat is Dialogflow?
Dialogflow is a natural language understanding platform used to design and integrate a conversational user interface into mobile apps, web applications, devices, bots, interactive voice response systems, and related usesOverview of Market Share
The global chatbot market size was estimated at USD 430.9 million in 2020. The growth is expected to be driven by the increasing adoption of customer service activities among enterprises to reduce operating costs. A chatbot is an interactive application developed using either a set of rules or artificial intelligence technology. A chatbot is basically developed using AI technology or a set of rules. It is designed in such a way that it can interact with humans through text. To assists users in various sectors, it is integrated with other messaging services. Various innovative ideas are implemented in Machine Learning (ML) and Artificial Intelligence (AI) technologies which will enhance the features of chatbots, which, in turn, will create greater demand for chatbots. Since businesses are looking for ways to automate their sales and other services Chatbots are becoming popular. This helps the organizations to stick to the schedule at reduced cost.How do Chatbots work?
- 1A user sends a text/voice message to a device or an App
- The App/Device transfers the message to Dialogflow( via detecting API )
- The message is categorized and matched to a corresponding intent (Intents are defined manually by developers in Dialogflow)
- We define the following actions for each intent in the fulfillment (Webhook)
- When a certain intent is found by Dialogflow, the webhook will use external APIs to find a response in external databases
- The external databases send back the required information to the webhook
- Webhook sends a formatted response to the intent
- Intent generates actionable data according to different channels
- The actionable data go to output Apps/Devices
- The user gets a text/image/voice response
How to build your first Chatbots?
Agent: An agent is merely another term used to refer to the chatbot. While using Dialogflow, you will find that many people start off by asking you to ‘name the agent.’ This just means giving your chatbot a name, so even in this context, it's one and the same Intent – ‘Intents’ are how a chatbot understands Expressions Responses: This is the chatbot’s output that is aimed at satisfying the user’s intent Entities: ‘Entities’ are Dialogflow’s mechanism. It helps to identify and extract useful data from the natural language inputs given by user. Actions & Parameters are also Dialogflow mechanisms Actions & Parameters These too, are Dialogflow mechanisms. They serve as a method to identify/annotate the keywords/values in the training phrases by connecting them with Entities We will see how to create a chatbot in Dialogflow using the followingStep1: Login with DialogFlow Account
- Go to https://dialogflow.cloud.google.com
- Click ‘Go to console’ in the top right corner
- Login with a Gmail account
Step2: Create a new Agent
- Start off by clicking ‘Create Agent’ in the column menu to your left
- Give your Bot a name! We’re going to call ours a ‘Testing’
- Be sure to select your time zone and language as required
- Click ‘Create’
Step3: Create a new Intent
- Click “Intent” on the left side
- Add the Intent Name and Training Phrases
- If you have already created Entity, Please mark the entity for the corresponding questions. Here I have created one entity as “Cheque” and marked that keyword to that training phrase
- After that, we need to add the response in the Intent
- Click “Save” in Intent
Step4: Check Question
We are able to check the questions on the right side of the top corner and it will give the intent name, Entity name and answer alsoBest features
Some best features are given below- Self Service Customer Support Self Service via a voice bot is more scalable and customer-centric. Giving your customers a voice bot as the first mode of communication can help them resolve their queries faster and for major queries, the AI-enabled voice bot can transfer the call or the message to the right agent
- Zero Wait Time Calling any customer support center can be a nightmare for most people, basically, because of the wait time and redirections. Enabling FAQs on automating general queries on IVR, Alexa or Google Assistant can save a lot of time and the agent can take over or the call can be transferred to the agent only for critical issues
- 24/7 Availability Humans require rest, but machines do not. Even if your agent is not available, the voice bots can resolve the queries for your customers and take their details in case of urgent queries. And your agent can contact them at their earliest convenience
- Break from Monotonous Texts Provide a multimodal Intelligent Virtual assistant supporting both chat and voice, rather than just a text-based chatbot. Just a text-based chatbot requires a lot of patience, and time from the user’s end. And also sometimes it becomes difficult to understand voiceless messages as it lacks sentiments. AI-enabled voice bot is highly automated, intelligent, and customer-friendly; making it a need of the hour for brand-customer engagement platforms
- No human contact Pandemic made it really clear the need for an automated customer support system, as most customer support offices were closed down. Many businesses and banking institutions were seen adopting IVR support for resolving customer queries like Kotak, ICICI, etc
- Save Cost An automated AI-enabled voice bot increases your team’s productivity, by taking care of all repetitive queries. Your team can just focus on critical queries, thus saving a lot of time and money for your business
- Increased Productivity Using voice bots, your customers can handle multiple tasks simultaneously, and in one call. Customers can schedule appointments, organize and modify meetings, check balance, do transactions, get account details, set reminders, etc
Tech Stack and Team Capabilities
A company can use Dialogflow to create messaging bots that respond to customer queries in platforms like Alexa Voice Services (AVS), Google Assistant, Facebook Messenger, Slack, Twitter, Skype, Twilio, Telegram, and several other messaging integrations. Dialogflow can be integrated into WhatsApp, tooOther chatbot platforms
- Google Dialogflow
- Amazon Lex
- IBM Watson Assistant
- Facebook’s Wit.ai
- Microsoft Azure Bot Service
Programming Language support
Dialogflow supports the following programming languages c#, Go, Java, Node.js, PHP, Python, and Ruby Choosing NodeJS is clearly a straightforward choice because NodeJS is asynchronousPlatform case study with a link
You can browse the sample code about Dialogflow integration from Google at GitHub with the links belowLanguage | Links |
---|---|
C# | GoogleCloudPlatform/dotnet-docs-samples/ |
Go | GoogleCloudPlatform/golang-samples |
Java | googleapis/java-dialogflow |
Node.js | googleapis/nodejs-dialogflow |
PHP | GoogleCloudPlatform/php-docs-samples |
Python | googleapis/python-dialogflow |
Ruby | googleapis/google-cloud-ruby |