AWS Lambda is a serverless compute service. It covers all other AWS services with business logic or can be used to create other backend services that operate with scale, performance, and security. As a serverless service, Lambda can be used to automate many processes in response to many events, including table updates in Amazon DynamoDB, HTTP requests through Amazon API Gateway, state transitions in AWS Set Functions, and modifications to objects in Amazon S3.
Lambda leverages Amazon’s highly available compute infrastructure, capable of performing all of the administration of the underlying platform, such as server and operating system maintenance, auto-scaling, patching, capacity provisioning, code monitoring, and logging. All you need is to upload your code to Lambda while including configuration guidelines on when to invoke; Lambda will ensure your system requirements of when to run code while maintaining high availability. You can create as simple as automation tasks to full-fledged microservice applications with Lambda.
Advantages of AWS Lambda
Amazon’s serverless computing service allows organizations and IT teams to trade operational complexity for agility and better pricing without compromising scale or reliability. Lambda offers many benefits to an organization, including the following:
1. No Server To Manage
Lambda runs your code on AWS’ distributed Availability Zones (AZs) in a single Region while seamlessly deploying code and looking after all the infrastructure’s administration, maintenance, and patches. In addition to this, it also offers built-in logging and monitoring, including integration with other AWS services such as AWS CloudTrail, CloudWatch Logs, and Amazon CloudWatch.
2. Bring Innovation
Since Lambda takes over the infrastructure management jobs, it frees up DevOps and engineers and enables them to focus on innovation and development of business logic to drive business value.
3. Continuous Scaling
Lambda runs the code on highly available and fault-tolerant AWS infrastructure. It also precisely manages the scaling of Lambda functions by running event-triggered code in parallel and processing each event individually.
4. Millisecond Metering
AWS keeps the Lambda pricing model on a millisecond basis. With Lambda, the bill is generated for every 1 millisecond (ms) the code runs and the number of times the code is triggered. Therefore, the bill is charged for consistent throughout or execution duration instead of the server unit.
Lambda@Edge is a feature of Amazon CloudFront. It allows you to run code closer to users of your application. Doing this improves performance and reduces latency of the application and ensures a better user experience. With Lambda@Edge, you don’t have to provision or manage infrastructure in multiple locations around the world.
6. Lambda Function URLs
Lambda Function URLs is a newly released feature that aims to help developers directly configure an HTTPS endpoint and CORS headers for a Lambda function without provisioning other services. It is dual stack-enabled, supporting IPv4 and IPv6. Now, developers will not need to go and link their function to an API gateway endpoint and configure all the settings as the function URL will be available as default.
7. AWS Step Function
AWS Step Function is a low-code, visual workflow that allows developers to focus on higher-value business logic. It allows the management of long-running workloads and supports high-volume event processing workloads. It could be combined with multiple AWS Lambda functions to design responsive serverless applications and microservices.
Supported Languages And Runtimes of Lambda
AWS is known to deliver simplicity and stability on its platform. One such example is that they have provided support for almost every popular programming language and its versions. Some programming languages and runtimes are left as well. However, here is a full list of languages currently supported by AWS Lambda:
- Node.js 8.10
- Node.js 10.x (normally the latest LTS version from the 10.x series)
- Node.js 12.x (normally the latest LTS version from the 12.x series)
- Python 2.7
- Python 3.6
- Python 3.7
- Python 3.8
- Ruby 2.5
- Java 8
- Java 11
- Go 1.x (latest release)
- C# — .NET Core 1.0
- C# — .NET Core 2.1
- PowerShell Core 6.0
AWS maintains all these runtimes and provides them in an Amazon Linux or Amazon Linux 2 environment. To help you write your Lambda function and integrate them easily with other AWS services, AWS provides an SDK for each supported language.
Limitations Of AWS Lambda
Though AWS Lambda has many advantages to serve, it still has some limitations, which you should know before using it in production. Let’s have a look at them as well:
1. Cold Start Time
You may experience a small latency between the event and when the function runs with Lambda. If the function hasn’t been used in the last 15 minutes, latency may be as high as 5-10 seconds. This factor makes Lambda less reliable for latency-critical applications.
2. Timeout Limit
As already mentioned above, the max timeout limit of a Lambda Function is 15 minutes. But, there are many cases when a lambda function runs longer than the desired timeout value. This has been a limitation of Lambda function, but with Step Function now it could be resolved easily. If you are trying to execute many things in one function, consider using Step Function to break the functions into smaller pieces and orchestrate them for desired results.
3. Not Always Cost-Effective
With Lambda, you pay for the used function runtime and maybe for any associated charges such as network traffic. It could prove a significant cost saving for certain usage patterns like to perform cron jobs or other on-demand tasks. However, in scenarios where the load for application increases, the AWS Lambda cost increases proportionally and might be higher than the cost of similar infrastructure on AWS EC2.
4. Limited Number Of Supported Runtimes
Although AWS is trying to support every runtime on the platform, they have also made it available for you to add custom runtimes according to your needs. But creating them can be a lot of work. So, if your preferred language is not currently supported on Lambda, using AWS EC2 might be a better option.
Security Of The Lambda Function
One shouldn’t confuse the security requirements of Lambda. AWS follows the shared responsibility model for its cloud computing services. Lambda comes under it, hence, AWS manages the security of infrastructure, foundation services, and the operating system. You as a customer have to ensure robust security of the code, data being used by Lambda, and IAM policies essential to design and define to access the Lambda service.
The best practices that could be followed to secure the AWS Lambda function include:
- The use of fine-grained permissions for the IAM execution role allows only needed actions on required resources.
- Establish one role for one Lambda.
- Always follows the global standards for writing your Lambda code to prevent it from traditional application-level attacks such as SQL injection, cross-site scripting, and others.
- Make use of API Gateways to expose your function as it ensures DDoS protection and integration with authentication services such as AWS Cognito.
You can learn more about the importance of security of serverless architecture and the best practices in detail and redesign your security posture to be unaffected by cyber attacks.
Common AWS Lambda Use Cases
Given Lambda architecture, organizations have found several use cases over traditional cloud computing setups for applications where:
- Individual tasks run for a short time;
- Each task is generally self-contained;
- There is a large difference between the lowest and highest levels in the workload of the application.
- Some of the most common use cases for AWS Lambda that fit these criteria are:
1. Scalable APIs
When you develop APIs with the help of AWS Lambda, one execution of a Lambda function can serve a single HTTP request. It is also possible to route different API requests to different Lambda functions via Amazon API Gateway. Lambda is capable of automatically scaling individual functions according to the demand for them, so different parts of your API can scale differently based on current usage levels. The process delivers cost-effective and flexible API setups.
2. Data Processing
Lambda functions are optimized for event-based data processing. You can easily integrate AWS Lambda with data sources like Amazon DynamoDB and can trigger a Lambda function for specific kinds of data events. For instance, Lambda can be employed to do some work every time an item in DynamoDB is created or updated. It makes Lambda a good fit for notifications, counters, and analytics.
Amazon S3, Kinesis Data Streams, SNS, SQS, and CloudWatch are some other services where Lambda can be used to build a variety of real-time data processing systems. The ideal usage could include, real-time file processing, real-time stream processing, replacing cron jobs, ETL, and processing of AWS Events.
Cost Of Running Lambda-Based Applications
Lambda registers a request each time it starts executing in response to an invoke call or event notification, including a test, invoked from the console. As mentioned above, it creates a bill for the total number of requests across all your functions. It counts the duration from when the code begins executing until it gets back or otherwise terminates, rounded up to the nearest 1ms. The calculated bill includes the amount of memory you allocate to your function.
Since AWS Lambda participates in Compute Savings Plans, it proposes a flexible pricing model comprising low prices on AWS serverless services like AWS EC2, AWS Fargate, and AWS Lambda usage in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a 1 or 3-year term. Adopting Compute Savings Plans has enabled organizations to save up to 17% on AWS Lambda. However, AWS Lambda allows saving through three plans: Duration, Provisioned Concurrency, and Duration (Provisioned Concurrency).