Serverless computing is a method of providing backend services on an as-used basis. A Serverless provider allows users to write and deploy code without the hassle of worrying about the underlying infrastructure. A company that gets backend services from a serverless vendor is charged based on their computation and do not have to reserve and pay for a fixed amount of bandwidth or number of servers, as the service is auto-scaling. Note that although called serverless, physical servers are still used but developers do not need to be aware of them.
In the early days of the web, anyone who wanted to build a web application had to own the physical hardware required to run a server, which is a cumbersome and expensive undertaking.
Then came the cloud, where fixed numbers of servers or amounts of server space could be rented remotely. Developers and companies who rent these fixed units of server space generally over-purchase to ensure that a spike in traffic or activity wouldn’t exceed their monthly limits and break their applications. This meant that much of the server space that was paid for usually went to waste. Cloud vendors have introduced auto-scaling models to address the issue, but even with auto-scaling an unwanted spike in activity, such as a DDoS Attack, could end up being very expensive.
Serverless computing allows developers to purchase backend services on a flexible ‘pay-as-you-go’ basis, meaning that developers only have to pay for the services they use. This is like switching from a cell phone data plan with a monthly fixed limit, to one that only charges for each byte of data that actually gets used.
The term ‘serverless’ is somewhat misleading, as there are still servers providing these backend services, but all of the server space and infrastructure concerns are handled by the vendor. Serverless means that the developers can do their work without having to worry about servers at all.
Application development is generally split into two realms: the frontend and the backend. The frontend is the part of the application that users see and interact with, such as the visual layout. The backend is the part that the user doesn’t see; this includes the server where the application’s files live and the database where user data and business logic is persisted.
For example, let’s imagine a website that sells concert tickets. When a user types a request into the browser window, the browser sends a request to the backend server, which responds with the website data. The user will then see the frontend of the website, which will include text, images, and form fields for the user to fill out. The user can then interact with one of the form fields on the frontend to search for their favorite musical act. When the user clicks on ‘submit’, this will trigger another request to the backend. The backend code checks its database to see if a performer with this name exists, and if so, when they will be playing next, and how many tickets are available. The backend will then pass that data back to the frontend, and the frontend will display the results in a way that makes sense to the user. Similarly, when the user creates an account and enters financial information to buy the tickets, another back-and-forth communication between the frontend and backend will occur.
Most serverless providers offer database and storage services to their customers, and many also have Function-as-a-Service (FaaS) platforms, like Cloudflare Workers. These platforms can execute pieces of code on the edge without storing any data.
#web-development #security #aws #web-service #serverless
In the past few years, especially after Amazon Web Services (AWS) introduced its Lambda platform, serverless architecture became the business realm’s buzzword. The increasing popularity of serverless applications saw market leaders like Netflix, Airbnb, Nike, etc., adopting the serverless architecture to handle their backend functions better. Moreover, serverless architecture’s market size is expected to reach a whopping $9.17 billion by the year 2023.
Why use serverless computing?
As a business it is best to approach a professional mobile app development company to build apps that are deployed on various servers; nevertheless, businesses should understand that the benefits of the serverless applications lie in the possibility it promises ideal business implementations and not in the hype created by cloud vendors. With the serverless architecture, the developers can easily code arbitrary codes on-demand without worrying about the underlying hardware.
But as is the case with all game-changing trends, many businesses opt for serverless applications just for the sake of being up-to-date with their peers without thinking about the actual need of their business.
The serverless applications work well with stateless use cases, the cases which execute cleanly and give the next operation in a sequence. On the other hand, the serverless architecture is not fit for predictable applications where there is a lot of reading and writing in the backend system.
Another benefit of working with the serverless software architecture is that the third-party service provider will charge based on the total number of requests. As the number of requests increases, the charge is bound to increase, but then it will cost significantly less than a dedicated IT infrastructure.
Defining serverless software architecture
In serverless software architecture, the application logic is implemented in an environment where operating systems, servers, or virtual machines are not visible. Although where the application logic is executed is running on any operating system which uses physical servers. But the difference here is that managing the infrastructure is the soul of the service provider and the mobile app developer focuses only on writing the codes.
There are two different approaches when it comes to serverless applications. They are
Backend as a service (BaaS)
Function as a service (FaaS)
Moreover, other examples of third-party services are Autho, AWS Cognito (authentication as a service), Amazon Kinesis, Keen IO (analytics as a service), and many more.
FaaS serverless architecture is majorly used with microservices architecture as it renders everything to the organization. AWS Lambda, Google Cloud functions, etc., are some of the examples of FaaS implementation.
Pros of Serverless applications
There are specific ways in which serverless applications can redefine the way business is done in the modern age and has some distinct advantages over the traditional could platforms. Here are a few –
🔹 Highly Scalable
The flexible nature of the serverless architecture makes it ideal for scaling the applications. The serverless application’s benefit is that it allows the vendor to run each of the functions in separate containers, allowing optimizing them automatically and effectively. Moreover, unlike in the traditional cloud, one doesn’t need to purchase a certain number of resources in serverless applications and can be as flexible as possible.
As the organizations don’t need to spend hundreds and thousands of dollars on hardware, they don’t need to pay anything to the engineers to maintain the hardware. The serverless application’s pricing model is execution based as the organization is charged according to the executions they have made.
The company that uses the serverless applications is allotted a specific amount of time, and the pricing of the execution depends on the memory required. Different types of costs like presence detection, access authorization, image processing, etc., associated with a physical or virtual server is completely eliminated with the serverless applications.
🔹 Focuses on user experience
As the companies don’t always think about maintaining the servers, it allows them to focus on more productive things like developing and improving customer service features. A recent survey says that about 56% of the users are either using or planning to use the serverless applications in the coming six months.
Moreover, as the companies would save money with serverless apps as they don’t have to maintain any hardware system, it can be then utilized to enhance the level of customer service and features of the apps.
🔹 Ease of migration
It is easy to get started with serverless applications by porting individual features and operate them as on-demand events. For example, in a CMS, a video plugin requires transcoding video for different formats and bitrates. If the organization wished to do this with a WordPress server, it might not be a good fit as it would require resources dedicated to serving pages rather than encoding the video.
Moreover, the benefits of serverless applications can be used optimally to handle metadata encoding and creation. Similarly, serverless apps can be used in other plugins that are often prone to critical vulnerabilities.
Cons of serverless applications
Despite having some clear benefits, serverless applications are not specific for every single use case. We have listed the top things that an organization should keep in mind while opting for serverless applications.
🔹 Complete dependence on third-party vendor
In the realm of serverless applications, the third-party vendor is the king, and the organizations have no options but to play according to their rules. For example, if an application is set in Lambda, it is not easy to port it into Azure. The same is the case for coding languages. In present times, only Python developers and Node.js developers have the luxury to choose between existing serverless options.
Therefore, if you are planning to consider serverless applications for your next project, make sure that your vendor has everything needed to complete the project.
🔹 Challenges in debugging with traditional tools
It isn’t easy to perform debugging, especially for large enterprise applications that include various individual functions. Serverless applications use traditional tools and thus provide no option to attach a debugger in the public cloud. The organization can either do the debugging process locally or use logging for the same purpose. In addition to this, the DevOps tools in the serverless application do not support the idea of quickly deploying small bits of codes into running applications.
#serverless-application #serverless #serverless-computing #serverless-architeture #serverless-application-prosand-cons
In this Serverless Saturday video, we’ll be going over “What is Serverless Computing?”
In the next video, we’ll be going over how to create your first serverless function on AWS Lambda!
Serverless on AWS: https://aws.amazon.com/serverless/
Serverless on Azure: https://azure.microsoft.com/en-us/solutions/serverless/
Serverless on GCP: https://cloud.google.com/serverless
#serverless computing #lamda #aws #computing #serverless saturday
The abundance of terms makes you feel confused and wonder how to differentiate one concept from another. The statistics show that people aren’t aware of these terms. Only one of the four customers may thoroughly explain the difference between cloud computing vs serverless.
This article will define the terms and briefly explain the peculiarities of each one.
Let’s start with defining the term cloud computing. Generally speaking, this phrase is used when we talk about the data centers available to many users all over the Internet. Basically, it saves the space of your computer’s hard drive as it stores the data over the net.
The funny thing is that this term can be explained in various ways by different people. Some describe it as a bought-in computer service, while others view it as IT outsourcing. Still, we all use it on a regular basis even though the majority of us don’t think about it.
For instance, when you prepare a document over the net, or write web-based emails, or type a query into Google. All these cases are examples of cloud computing and show that it is inevitable nowadays. Moreover, it has plenty of advantages as it helps to lower IT costs, scale more easily, and improve agility.
There are three main types of cloud computing:
Well, as you can see, this term isn’t as complicated as it seems. But what is serverless, and how does it work?
#cloud #cloud computing #serverless #serverless computing
Serverless M (or Serverless Modular) is a plugin for the serverless framework. This plugins helps you in managing multiple serverless projects with a single serverless.yml file. This plugin gives you a super charged CLI options that you can use to create new features, build them in a single file and deploy them all in parallel
Currently this plugin is tested for the below stack only
Make sure you have the serverless CLI installed
# Install serverless globally $ npm install serverless -g
To start the serverless modular project locally you can either start with es5 or es6 templates or add it as a plugin
# Step 1. Download the template $ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es6 --path myModularService # Step 2. Change directory $ cd myModularService # Step 3. Create a package.json file $ npm init # Step 3. Install dependencies $ npm i serverless-modular serverless-webpack webpack --save-dev
# Step 1. Download the template $ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es5 --path myModularService # Step 2. Change directory $ cd myModularService # Step 3. Create a package.json file $ npm init # Step 3. Install dependencies $ npm i serverless-modular --save-dev
If you dont want to use the templates above you can just add in your existing project
plugins: - serverless-modular
Now you are all done to start building your serverless modular functions
The serverless CLI can be accessed by
# Serverless Modular CLI $ serverless modular # shorthand $ sls m
Serverless Modular CLI is based on 4 main commands
sls m init
sls m feature
sls m function
sls m build
sls m deploy
sls m init
The serverless init command helps in creating a basic
.gitignore that is useful for serverless modular.
.gitignore for serverless modular looks like this
#node_modules node_modules #sm main functions sm.functions.yml #serverless file generated by build src/**/serverless.yml #main serverless directories generated for sls deploy .serverless #feature serverless directories generated sls deploy src/**/.serverless #serverless logs file generated for main sls deploy .sm.log #serverless logs file generated for feature sls deploy src/**/.sm.log #Webpack config copied in each feature src/**/webpack.config.js
The feature command helps in building new features for your project
This command comes with three options
--name: Specify the name you want for your feature
--remove: set value to true if you want to remove the feature
|--basePath||-p||❎||string||same as name|
Creating a basic feature
# Creating a jedi feature $ sls m feature -n jedi
Creating a feature with different base path
# A feature with different base path $ sls m feature -n jedi -p tatooine
Deleting a feature
# Anakin is going to delete the jedi feature $ sls m feature -n jedi -r true
The function command helps in adding new function to a feature
This command comes with four options
--name: Specify the name you want for your function
--feature: Specify the name of the existing feature
|--path||-p||❎||string||same as name|
Creating a basic function
# Creating a cloak function for jedi feature $ sls m function -n cloak -f jedi
Creating a basic function with different path and method
# Creating a cloak function for jedi feature with custom path and HTTP method $ sls m function -n cloak -f jedi -p powers -m POST
The build command helps in building the project for local or global scope
This command comes with four options
--scope: Specify the scope of the build, use this with "--feature" tag
--feature: Specify the name of the existing feature you want to build
Saving build Config in serverless.yml
You can also save config in serverless.yml file
custom: smConfig: build: scope: local
all feature build (local scope)
# Building all local features $ sls m build
Single feature build (local scope)
# Building a single feature $ sls m build -f jedi -s local
All features build global scope
# Building all features with global scope $ sls m build -s global
The deploy command helps in deploying serverless projects to AWS (it uses
sls deploy command)
This command comes with four options
--sm-parallel: Specify if you want to deploy parallel (will only run in parallel when doing multiple deployments)
--sm-scope: Specify if you want to deploy local features or global
--sm-features: Specify the local features you want to deploy (comma separated if multiple)
Saving deploy Config in serverless.yml
You can also save config in serverless.yml file
custom: smConfig: deploy: scope: local parallel: true ignoreBuild: true
Deploy all features locally
# deploy all local features $ sls m deploy
Deploy all features globally
# deploy all global features $ sls m deploy --sm-scope global
Deploy single feature
# deploy all global features $ sls m deploy --sm-features jedi
Deploy Multiple features
# deploy all global features $ sls m deploy --sm-features jedi,sith,dark_side
Deploy Multiple features in sequence
# deploy all global features $ sls m deploy --sm-features jedi,sith,dark_side --sm-parallel false
Serverless Computing is the most promising trend for the future of Cloud Computing. As of 2020, all major cloud providers offer a wide variety of serverless services. Some of the FaaS offerings provided withing different cloud providers are AWS Lambda, Google Cloud Functions, Google Cloud Run, Azure Functions, and IBM Cloud Functions. If you want to use your current infrastructure, you could also use the open-source alternatives like OpenFaaS, IronFunctions, Apache OpenWhisk, Kubeless, Fission, OpenLambda, and Knative.
In a previous article, I iterated the most important autoscaling patterns used in major cloud services, along with their pros/cons. In this post, I will go through the process of predicting key performance characteristics and the cost of scale-per-request serverless platforms (like AWS Lambda, IBM Cloud Functions, Azure Functions, and Google Cloud Functions) with different workload intensities (in terms of requests per second) using a performance model. I will also include a link to a simulator that can generate more detailed insights at the end.
A performance model is “A model created to define the significant aspects of the way in which a proposed or actual system operates in terms of resources consumed, contention for resources, and delays introduced by processing or physical limitations” [source]. So using a performance model, you can “predict” how different characteristics of your service will change in different settings without needing to perform costly experiments for them.
The performance model we will be using today is from one of my recent papers called “Performance Modeling of Serverless Computing Platforms”. You can try an interactive version of my model to see what kind of information you can expect from it.
The input properties that need to be provided by the user to the performance model along with some default values.
The only system property you need to provide is the “idle expiration time” which is the amount of time the serverless platform will keep your function instance around after your last request before terminating it and freeing its resources (to know more about this, you are going to have to read my paper, especially the system description section). The good news is, this is a fixed value for all workloads which you don’t need to think about and is 10 minutes for AWS Lambda, Google Cloud Function, and IBM Cloud Functions and 20 minutes for Azure Functions.
The next thing you need is the cold/warm response time of your function. The only way you can get this value, for now, is by actually running your code on the platform and measuring the response times. Of course, there are tools that can help you with that, but I haven’t used them, so, I would be glad if you could tell me in the comments about how they were. Tools like the AWS Lambda Power Tuning can also tell you the response time for different memory settings, so you can check which one fits your QoS guarantees.
#serverless-computing #performance #serverless-architecture #serverless #serverless-apps