We have been talking about Serverless a lot lately and there are a couple of reasons for that. Serverless computing will provide greater scalability, more flexibility, and quicker time to release at reduced cost and this is the reason everybody is after it. Is serverless architecture enough to drive all this we talked about just now? The answer is no, of course. Also, State management has been absolutely challenging in serverless computing but Cloudstate will make it possible for us, wondering how? Keep reading the article until and you will learn it. The Lightbend’s Cloudstate which was released in August last year(2019) has proved to be a game-changer here as it provides stateful serverless computing, Cloudstate brings powerful new distributed and durable state management primitives based on Akka to the Serverless paradigm. Serving of stateful functions powered by Akka Cluster. That was all for the introduction, let’s talk about Cloudstate in detail below.
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
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
By this point most enterprises, including those running on legacy infrastructures, are familiar with the benefits of serverless computing:
The benefits of agility and cost reduction are especially relevant in the current macroeconomic environment when customer behavior is changing, end-user needs are difficult to predict, and development teams are under pressure to do more with less.
So serverless is a no-brainer, right?
Not exactly. Serverless might be relatively painless for a new generation of cloud-native software companies that grew up in a world of APIs and microservices, but it creates headaches for the many organizations that still rely heavily on legacy infrastructure.
In particular, enterprises running mainframe CICS programs are likely to encounter frustrating stumbling blocks on the path to launching Functions as a Service (FaaS). This population includes global enterprises that depend on CICS applications to effectively manage high-volume transactional processing requirements – particularly in the banking, financial services, and insurance industries.
These organizations stand to achieve time and cost savings through a modern approach to managing legacy infrastructure, as opposed to launching serverless applications on a brittle foundation. Here are three of the biggest obstacles they face and how to overcome them.
Middleware that introduces complexity, technical debt, and latency. Many organizations looking to integrate CICS applications into a microservices or serverless architecture rely on middleware (e.g., an ESB or SOA) to access data from the underlying applications. This strategy introduces significant runtime performance challenges and creates what one bank’s chief architect referred to as a “lasagna architecture,” making DevOps impossible.
#serverless architecture #serverless functions #serverless benefits #mainframes #serverless api #serverless integration
Till now we know how to create a linear pipeline/linear graph. But in real life scenario we generally don’t have linear graphs to implement. The graphs can be complex. In Akka Streams computation graphs are written in a more graph-resembling DSL. It aims to make translating graph drawings (e.g. from notes taken from design discussions, or illustrations in protocol specifications) to and from code simpler.
Graphs are used to perform fan-in and fan-out operations. You can consider graph operations as junctions(multiple flows connected at a single point).
Fan-in : It takes multiple inputs and produces a single output.
Fan-out : It produces multiple outputs by taking a single input.
#akka #akka-streams #big data and fast data #scala #akka-streaming #graphs #graphs-in-akka #partialgraphs
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