Get Started With AWS Lambda In 12 Steps
For those getting started using Amazon's popular function-based serverless platform, here's what to do.
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In Amazon Web Services (AWS) one of the most popular services is Lambda. To define it, Lambda is a serverless computing software that automatically uploads any code you upload to it as functions. With AWS Lambda, developers are freed from writing too many codes, spin up servers, configure them or incur charges for them to run. Lambda makes it possible to get the work done with minimum effort and cost. It also enables the user to write easy functions and then wire those functions to a request or an event after which Lambda carries out the function whenever the request/event happens. Moreover, the user incurs charges only when their code runs.
As all AWS services are integrated with one other, Lambda can make use of several other AWS services including DynamoDB, Alexa, S3 and the like to use events in these services to activate the user’s operations. Thus Lambda is an effective way to test Amazon cloud and develop things that everyone needs.
A user can get started on Lambda in just 12 steps.
Step 1: Create an AWS Account
The first step is to create an AWS account. Many will already have an account as they can use the same amazon account they use for shopping. Once the user has the account, they can rent computers and also deploy anything they prefer.
Step 2: Plan the Architecture
It is better not to immediately start writing codes using the examples available in Amazon. Ideally, the user should envision the whole process: the way data is created, analyzed, and distributed within the application.
Step 3: Select an Amazon Datastore
An important step, this pertains to data storage. This is critical for all applications. Once the user determines the flow of the data, they should make a decision regarding its storage. This becomes difficult because there are a plethora of options available in Amazon. S3 storage is sufficient for basic blocks of bits; for more structured data, Aurora, DynamoDB, and other sophisticated options are available in Amazon for analysis and organization of data.
The decision should be made keeping in mind the data structure and the code that is used for analyzing the same. For instance, Aurora is an SQL-capable database that functions like PostgreSQL/MySQL; the user can take SQL code and directly apply it to reap benefits of its advanced queries and existing tabular structure.
Step 4: Prepare an Event List
Lambda functions are prompted by events such as the start of a new file or when a user visits the webpage. This event list will help to understand Lamda application and will also illustrate what it does to data and the time.
Nearly all of Amazon’s services can create Lambda functions. For instance, it can be as simple as calls to a web address.
Step 5: Look at Other AWS Services
There is a number of services offered by AWS that can assist the user. So, it is more viable for the user to explore whether they can skip code writing by utilizing Amazon Run API which would enable the user to finish the work faster. AWS has services for analytics, security, machine learning, game development, and a few other areas. Lambda can be seen as a linking together of all the AWS options. With an Amazon service, it is possible to substitute the various stops along the pipeline.
Step 6: Select a Programming Language
After the user chooses a programming language, then they should download the development tools and get familiar with the examples. One important thing to remember is that Lambda is unlike any other development programs like Python or Java. With Lambda, the user will be writing only a single function that acts to a narrow range of parameters. The user will not enjoy the same liberty as with other platforms. Lambda runtime will assume the user’s code to receive a small packet of data, make decisions and then proceed further.
Step 7: Establish a Development Environment
The user should select the right environment for the programming language that they choose. With popular tools and models, Amazon tries to appeal to developers even though there might be overlaps. Amazon also provides a good web-based editor for any functions which means that the user will not have to do much locally.
Step 8: Write Down a Function
Write a single function. This function will enable the user to others to invoke other functions like libraries; this is not much different from writing a large app.
Step 9: Deploy the Application
It is possible that the user’s Lambda app is deployed before itself, particularly if they are using cloud development tools. Amazon provides for numerous levels of production and staging which enables the user to set up a superior testing workflow and development.
Step 10: Become Aware of The Limits
User can think of their software as the ultimate authority when they are writing software for their computer. They should keep in mind that their machine is controlled by their software. The lambda code they write is like a guest which runs according to AWS Lambda. Thus, the function they write should be aware of its place or risk termination at the hands of AWS.
There are more than a few numbers of limits placed for most of the simple tasks that fit well in the Lambda model. For instance, a user’s function is given only 300 seconds to answer or it gets shut down. It can only utilize 3008 MB memory, 1024 file descriptors, and 1024 threads.
Most of these might not apply to all users alike. However, it is best to be versed in the limit rather than make assumptions. Lambda is certain to scale a user’s application to help with demands, but it will not scale it in every workable way.
Step 11: Debug the Application
The easiest way of debugging can happen in a user’s development environment on their local machine. It can be through Eclipse, command line or Visual Studio. It is possible to tackle most of the easy bugs here.
Lambda’s built-in logging functions can carry out the more complex debugging required. Lambda’s logs are an easy and effective way to monitor the data flow and the way the user’s code reacts to events.
Step 12: Practice Economy
Lambda might seem economical in the beginning. However, once the user exceeds the first million requests and the first 400,000 GB seconds of free compute time, they have to pay a small fraction of a cent. It is difficult to predict exactly how much the application will empty cost in the future.
Users can benefit from making easy architectural decisions regarding how frequently they generate events. For instance, if they are developing sensor networks that phone in the outcome to AWS cloud, they can enable them to call in every second or an hour. While it might seem appealing to offer feedback in nearly real-time and good service, it should always be in the back of their mind that the first option also uses 3,600 times invocations by the second. So before making a decision, they should think of the tradeoffs, the needs, gauge the computation and then make the decision regarding the costs that a market can bear.
Time to Rest
A user can relax after their code is installed and running. Unlike many web apps which require constant watch and housekeeping, Lambda automates most of the tasks, including checking for full desks/overburdened servers
However, users should still periodically monitor to ensure their code is running properly and is functional. After it runs properly for over a week, they can get a stable mark of how much running it would incur. If the cost is not feasible, then it is time for them to explore other options for making the work their code does simple so that they can reduce the cost. know more about AWS.
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