The imperative need to have a functional service that executes a task as and when required cannot be emphasised enough! Whether it’s a service oriented architecture, an event driven architecture, microservices, distributed applications or even a simple use case such as transforming data through business logic to render it dynamic. A service that performs such functionality immediately without having to provision a server is invaluable in any business operation. AWS offers this function as a service(FaaS) known as AWS Lambda.
AWS Lambda is primarily a compute service that allows you to execute and run backend logic(code) without the need to spin up servers nor manage any infrastructural jobs like OS installation, patching, and batch jobs associated with infrastructure as a service offerings such as EC2. It extends this functionality to include logging and metrics that are recorded to cloudwatch(an AWS monitoring service) for intervals of time, 1 minute and provisions capacity on demand through autoscaling.
The utility of AWS lambda requires you to simply provide your business logic as code that will be run and executed. It supports programming languages such as Python, Node.js, Java, Ruby, .Net and Go. The great benefits of using these runtimes is that AWS is constantly improving their performance to better serve the needs of customers for their end users. One of this recent improvements is AWS Lambda SnapStarts that was recently launched at reinvent. This new improvement has been designed to optimized and boost the duration it takes to get a Java written lambda function up and running.
This performance optimisation can be better understood with an understanding of cold starts and warm starts in Lambda. Primarily, this refers to the initialisation period it takes to setup your environment. Any request(http) or event that invokes lambda creates an instance of a lambda function, which is run in a container environment known as Firecracker, and after a period of inactivity it will be automatically shutdown.
The level of performance you require can simply be tuned in lambda by optimising memory,CPU and network resources. Fortunately enough, Lambda offers libraries that can boost your functions performance. There also a number of optimization mechanisms such as AWS Lambda Powertools, AWS Lambda power tuning and AWS Lambda execution environment reuse. In addition to these awesome set of tools, it is recommended and advised that your code is optimised to ensure that it runs efficiently to avoid longer runtimes that reflect in costs and latency.
The pricing of lambda is based on how long your code runs, so making use of these libraries helps cut costs.Interestingly enough, this same observation can be made from allocating more memory capacity.
A wide variety of applications and backend services can be run on lambda by refactoring code to suit operability in lambda. It can run code for a duration of 15 minutes so you must ensure that your application code is designed to fit this purpose, especially because lambda is built to be invoked to perform a function and shut down to avoid wasting idle resources that gather extra costs.
AWS Lambda integrates seamlessly with other AWS services such as API Gateway, S3, SQS, SNS, DynamoDB , DynamoDB Streams and Amazon Eventbridge, especially in event driven architectures
Some use cases where AWS lambda comes in handy include:
i. Automating backups of EC2 instances.
ii. The removal of unattached volumes.
iii. Processing streaming data that is ingested and stored in Amazon kineses.
iv. Running a simple workflow that process data upon the upload of an object to S3 or when data is entered in DynamoDB.
v. Processing IoT data.
vi. Serving as a backend layer to process web and mobile application data when triggered.
vii. Creating lambda layers. A very expedient use of lambda functions is in the creation of a zip file that stores application files such as libraries, packages and other dependencies. This way, any required package or library needed for a lambda function to executes its logic can be accessed from a lambda layer.
The aforementioned are just a few cases where AWS lambda can be seen in action but the options are wide and it all depends on your use case. Hopefully this article has provided you with a general insight on AWS lambda. So the next time you’re considering a fully managed, scalable and serveless compute option for your application, remember to leverage AWS lambda!
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