AWS Lambda is a serverless computing platform that runs code in response to events and automatically manages the underlying compute resources. A web service called EC2 offers scalable, secure compute capacity in the cloud. For developers, it is intended to make web-scale cloud computing simpler. In this article, we’ll contrast the two services in terms of cost, functionality, and usability.

 

What is AWS Lambda?

You can run code using AWS Lambda, a cloud-based service, without provisioning or managing servers. You may quickly develop a function with Lambda and upload your code to the cloud. Your code will then be executed on AWS Lambda’s high-availability computational infrastructure, and it will scale automatically to accommodate demand.

Lambda is a perfect solution for cases where you need to run code in response to events or process data in real-time. For example, you could use Lambda to process log files from Amazon S3 in real-time, analyze clickstream data from Amazon Kinesis Streams, or receive updates from an Amazon DynamoDB table.

Lambda is also very cost-effective. You only pay for the compute time you consume – there is no charge when your code is not running.If you are an individual interested in AWS, our AWS Training will definitely enhance your career.

How Does It Works?

In order to understand how AWS Lambda works, one must first understand what it is. AWS Lambda is a cloud-based service that allows users to run code without provisioning or managing servers. Essentially, it is a way to execute code in the cloud without having to worry about the underlying infrastructure. Lambda functions can be triggered by events from other AWS services, such as S3 or DynamoDB. These functions can be written in Node.js, Python, Java, and C#.

Once you have written your Lambda function, you simply upload it to AWS and specify the resources that it needs, such as memory and disk space. When someone makes a request that triggers your function, AWS Lambda will automatically run the code and return the results.

What is AWS EC2?

Resizable compute capacity is offered by the cloud computing service AWS EC2. For developers, it is intended to make web-scale cloud computing simpler. There are several different instance types available in AWS EC2, each with unique compute, memory, and storage capabilities.

Multiple Availability Zones allow for the creation of AWS EC2 instances and offer isolation from outages in other Availability Zones. Data centres can be found in one or more of the Availability Zones.Each Availability Zone is comprised of one or more data centers. Amazon EC2 provides customers with control over the placement of their instances, allowing them to choose which data center their instances launch in.

Amazon EC2 makes it easy to scale your compute resources up or down as your needs change. You can add or remove capacity within minutes, allowing you to easily adjust to changing demands.

How Does AWS Ec2 Works?

Renting virtual machines to run their own applications is possible using the cloud computing service AWS Ec2. To start and administer their virtual machines, customers can access Ec2’s web-based interface. Amazon’s own data centers, other AWS regions, and even on-premises data centers are just a few of the places from which Ec2 instances can be started.

Ec2 instances are created to offer a high level of scalability and flexibility. Users have the option of choosing the instance type that best meets their requirements and launching several instances to boost capacity or redundancy. Additionally, Ec2 provides a range of storage choices, such as S3 buckets, EBS volumes, and local storage.

A Comparison Of AWS Lambda and EC2:

High Availability:

One of Lambda’s key selling qualities is its high availability, yet there are some unnoticed drawbacks. Lambda functions spin up a virtual machine and begin executing after around 100 milliseconds of being called on a “cold start,” or a function start after not calling the function for a long time. Sure, it sounds wonderful for a non-customer facing project. However, 100 milliseconds for startup alone suddenly become excessive when a user application requires a latency of less than 50 milliseconds each call from beginning to end. An EC2 server, on the other hand, will remain online for however long is necessary and accept connections for however long and quickly it is set to do so.

Configuration ability:

As we had to deploy programmes across numerous accounts with multiple permissions, configuration used to be a problem. We ultimately used a number of cumbersome configuration files. Before instance variables, there was no way to automatically configure a Lambda. Thankfully, this flaw has been fixed as of November 2016 with the introduction of expression-specific environment variables and support from AWS’s command line interface. On the other hand EC2 is fully configurable manually or with Beanstalk.

Security:

In contrast to EC2 instances, Lambda expressions are by design more robust to DDoS attacks thanks to explicit timeouts, rate-based blacklisting, and the ability to spin up and down containers as needed. To withstand high loads, EC2 instances need extra services such as elastic IP services, load balancers, and AWS Web Application Firewall.

Scaling:

Because Lambda scales on demand, users only pay for what they really use or need rather than an unused baseline.

Unpredictable performance:

The unpredictability of Lambda’s performance is a drawback. Despite being reused, containers need time to warm up before each new instance. First requests are frequently slow, particularly when using Java. Node.js is designed to start up quickly but execute more slowly. For instance, if a user’s Lambda has certain libraries and a new 128 MB low memory Java instance is built, the initial call may take 30 seconds or longer. Instances are kept static in between requests. When an instance sits idle for a while, it takes longer for it to reactivate. Memory improvement can shorten wakeup and warm-up times. However, access to outside data sources can be the main issue.

Dependency on S3:

Developers who dealt with the S3 outage at the end of February 2017 are aware of the hazards involved with placing all your eggs in one AWS basket. AWS Lambda, which depends on S3 for code storage, became unexpectedly unavailable, affecting codebases used by API functions, Amazon Echo skills, and other third-party applications. Of course, a number of other apps were also impacted, including EC2 instances that utilized S3 for fresh launches, demonstrating the hidden dependency between the two services. Any application that accepts connections on a need-to-know basis and hosts functions in Lambda may eventually find that the startup time is too much of a bottleneck. To get around this, it is significantly preferable to have an EC2 server that continuously checks for connections.

Pricing :

The price of Lambda (Serverless Architecture) is another another valuable selling feature (cost as per use). Cost analysis gets somewhat challenging because of how differently Lambda’s price and EC2’s pricing are both constructed. While an on-demand EC2 server with the same statistics (at 2 nano servers with 0.5 GB of memory) costs $0.0059 per hour, a Lambda function with 0.5 GB of memory running for 1 hour costs $0.030024 (or, more likely, numerous executions of the same function adding up to an hour of uptime). This appears to be clearly in the EC2 server’s favor at first inspection. However, that server is made to be active and listening for connections round-the-clock while perhaps just carrying out a few calculations.is a Lambda expression that runs continuously for an hour. Therefore, the cost of EC2 will increase by 24 once again (24 * $0.0059). Consider another method of calculation, such as –

While EC2 and Elastic Beanstalk have auto-scaling functionality in AWS. For failover behaviour, one should usually always run at least two instances. Without savings for reserved instances, running two “nano” instances as a minimum for failover will cost the user $0.0059 * 24 * 30.5 = $4.31 for the VM and $0.05 * 8 GB = $0.40. A single instance costs $4.71, while two are $9.42. But in order for the Auto-Scaling to function, the user also needs a Load-Balancer setting of at least $0.0225 * 24 * 30.5 = $16.47. (ignoring LCU charges). Multiple services may share the load balancer. For this calculation, I simply divided it arbitrarily by 10, arriving at the result that using Elastic Beanstalk or EC2 will cost you $9.62 + $1.65 = $11.27 for one microservice.

We can now check how it compares to Lambda. User pays per call and per gigabyte second with Lambda. The call fees, which are $0.20 per 1 million requests, are not important to me. In a month, there are 1 million inquiries, or 0.4 requests every second. One would also be responsible for the Load Balancer LCU fees if they had higher loads. The cost of Lambda is $0.00001667 per GB-second. Both EC2 and Elastic Beanstalk will use some of the nanos 512 MB of RAM for the container and operating system. I’m just going to presume that 256 MB are genuinely functional. This would be 2 * 256 MB/1024 MB * 60 * 60 * 24 * 30.5 = 1317600 GB-seconds if there were two instances. You would have to pay 1317600 * $0.00001667, or $21.96 for this quantity of GB-seconds.While this may seem more expensive, keep in mind that because your traffic is probably not distributed evenly, more instances would likely be required, raising the expenses. Users of Lambda only pay for what they really use.

Function size:

AWS advises using Lambda for smaller tasks to acquire a feel for it, and EC2 for functions that are substantially larger. People may conclude from this that Lambda is a terrible development tool, however this is untrue. When utilized correctly and with appropriate expectations, AWS Lambda may be a potent combination of power and economy.

Other limits:

As was already mentioned, connection pooling is not directly supported, which can cause issues with access to databases or other systems in general. Frameworks may not be appropriate for use with Lambda if you use them to speed up development.

There was a restriction in that there was no mechanism to control how many Lambda function instances could run concurrently in order to avoid using up all available database connections or exceeding use caps on third-party APIs. This restriction is now lifted. These days, Lambdas can operate inside a VPC. Although I’m not certain of a formal technique to restrict the amount of active instances at once,

Since each Lambda requires an IP address and if someone utilizes a VPC, they can restrict the subnet of available IPs, which indirectly limits the number of Lambda instances.

Conclusion:

In conclusion,AWS Lambda and Amazon EC2 are both great options for cloud computing. They both have their own advantages and disadvantages, so it is important to choose the right one for your needs. If you need more flexibility and control, then EC2 is the better option. However, if you want a more hands-off approach and don’t mind sacrificing some control, then Lambda is the way to go.