AWS Lambda is a powerful serverless computing platform.
In a nutshell, the service allows users to run code without having to meddle with management technicalities, server shenanigans, or any of that update or upgrade nonsense. It automatically manages all resources linked to the code.
You may also enjoy: Get Started With AWS Lambda In 12 Steps
The amount of administrative work is close to zero as well. This is to say you’re only responsible for your code.
That may not seem like much, but this apparent simplicity can be deceiving. There are many advanced methods for using Lambda creatively as a customizable and cost-effective tool.
So let’s get down to business: here’s how to ensure you’re using resources effectively in AWS Lambda.
Examining AWS Lambda at Face Value
Lambda is a great base of operations for a variety of computer use cases.
This dependable workhorse enables you to transition to a serverless infrastructure. Many people use it to deploy rapid and scalable projects.
Let’s get familiar with the way Lambda harnesses resources required for your code.
First of all, the platform performs all tasks related to resource administration.
Specifically, it tackles:
As you can see, Amazon’s tool covers a range of operational and administrative areas for you. All you have to do is upload a code language that Lambda supports. You’re not permitted to compute instances, alter OS, or customize language runtime.
But, if you want to make the most of the platform, there are some more bases to cover. Not having to worry about moving parts doesn’t mean neglecting them altogether is a good idea.
Diving Deeper With Events
A “run it and then forget about it” mentality doesn’t cut it.
Namely, you want to “wake up” your solution every now and then to deliver specific functionalities. You can do it via event timers for setting desired tasks in motion.
In other words, you have an opportunity to run your code based on external events. They can take the form of data/object changes that occur in the Amazon S3 bucket or Dynamo DB table. You can invoke code only in these concrete instances that warrant processing.
Some other trigger anchors to consider are:
- Notifications from Amazon SNS
- Messages transmitted through Amazon Kinesis Data Streams
- Data synchronization events in Amazon Cognito
- API call logs originating from AWS CloudTrail
- Other custom events for apps
It’s also possible to employ API Gateway and initiate code as a response to HTTP requests. Similarly, some people decide to utilize API calls they create via AWS SDKs.
Oh, and don’t overlook any logs generated by services. Carry out their auditing and tracking whenever needed for your online project.
Finally, note that you may craft serverless apps in order to establish data-processing prompts. You essentially create new functions which are tiled to specific events. After that, you can deploy them using the appropriate tool (such as AWS CodeBuild).
The Art of Fine-Tuning
Lambda functions are self-sufficient, unlike Amazon EC2 containers.
The latter are subject to heavy scripting, but EC2 only does runtime execution. It’s up to you to do everything else and maintain containers over the whole lifecycle.
Though it inherited some traits from containers, Lambda has a few noticeable differences.
Apart from self-sufficiency, the functions are stateless. That means one can scale them nice and easy. What’s more, users are able to add multiple functions to the same source.
On the other hand, Execution Context reuse gives you a chance to boost the performance of the function.
This technique works as follows. You locally reference external configurations and dependencies. Next, you refrain from re-initializing variables and objects upon every invocation.
Instead, you take advantage of static initialization and construction, as well as global/static singletons and variables.
Navigating AWS Lambda Runtime Environment
Lastly, it helps to develop a deeper understanding of the Lambda runtime environment.
It contains a range of libraries such as AWS SKD. Periodic updates roll in and supply you with the latest goodies. To remain in control of which components you use, integrate your dependencies with the deployment package.
Secondly, you need to be aware of certain limitations:
- Disk space restricted to 512 MB
- Default deployment package size is 50 MB
- Function execution timeout is 15 minutes
- Memory ranges from 128 to 3008 MB
- Maximum request & response call body payload size is 6 MB
- Request body can go up to 128 KB
- Concurrent executions are given a region to 1,000
- Functional and layer storage limited to 75 GB
The good news is there are some workaround maneuvers to explore.
For example, you can extend the default deployment package size by pulling the package from S3. The bar is much higher in that case compared to directly uploading to Lambda. To push other boundaries, you can also try issuing a request to the Support Center Console.
Just keep one thing in mind. Larger package sizes impede the cold start times of Lambda functions. So, don’t venture too far beyond default limits.
In fact, consider tying the deployment package size to runtime bare necessaries. By keeping things lightweight, you shorten the time it takes to download and unpack the package. Another thing that helps is sticking to simpler frameworks that load quicker.
Getting the Most Bang for Your Buck
Using AWS Lambda to its full potential gives you a nice edge, but make sure you’re proactive in your approach.
You can start by building an automated, event-driven infrastructure. Then, learn to configure and execute basic functions. Take the time to understand what Lambda can and cannot do and test the limits of the computing and storage resources at your disposal.
Likewise, don’t hesitate to play around with event triggers to see what works for you. Assess ongoing events to promptly react to the changes in the environment. If you keep your resources well-managed, Lambda will run like a dream.
For more helpful content, make sure to check out our serverless resources for other insights.
from DZone Cloud Zone