Serverless Applications – The Next Step in the Evolution of SaaS
Software-as-a-Service has been transformed potentially with serverless computing. Serverless SaaS is a boon in a continually shifting landscape of customers and load profiles that quintessentially virtualizes runtime & operational specifics through a third-party provider. With serverless SaaS, developers can focus on their business logic and attain faster time-to-market.
What is Serverless?
Serverless is an application design and deployment paradigm that is event-driven and incorporates scalable cloud services as computing resources from a third-party provider.
With serverless computing, developers can drop in code, create backend applications & event handling routines, and process data without concerns about servers, virtual machines (VMs), or the underlying compute resources which are maintained by the provider.
Serverless architectures offer significantly reduced complexity and engineering lead time. Service providers follow a pay-as-you-go model that accounts for the actual amount of resources consumed by the customers’ applications without associating costs to idle, down-time.
The best-known vendor host for serverless currently is AWS Lambda.
This serverless compute service runs your code in response to events and automatically manages the underlying compute resources for you. You can implement custom logic on AWS Lambda to extend other AWS services or create back-end services that operate at AWS scale, performance, and security.
AWS Lambda functions over HTTPS with the definition of custom REST API and endpoint using an API Gateway and then mapping individual methods to the specific inbuilt functions.
The user can send an HTTPS request to the API endpoint, where the gateway service invokes the corresponding function synchronously. API Gateway also inserts a layer between your app logic and users for throttling, data protection, and response caching.
Key Features of Serverless
Completely Automated Administration
With Lambda, no updates for the underlying OS are required when a patch is released, and neither is resizing or adding new servers based on dynamic usage patterns. AWS Lambda seamlessly deploys your code, does the administration, maintenance, and security patches, and provides built-in logging and monitoring through Amazon CloudWatch.
Serverless handles dynamic customer load, usage patterns, and workload by scaling up/down within seconds.
AWS Lambda performs selective code invocation and auto-scaling to support the rate of incoming requests. This ensures consistent performance during high-frequency events.
Serverless workflows simplify the orchestration of many tasks and help developers integrate different services without the need to code interactions, route requests, manage throttling/metering. With AWS Lambda, developers don’t have to orient themselves with new languages, tools, or frameworks. Customers can use any third-party libraries, package any code as a Lambda Layer, and manage multi-function sharing.
Flexible resource model
The customer can choose the amount of memory their functions need to be allocated. AWS Lambda allocates proportional CPU power, network bandwidth, and disk I/O.
Built-in fault tolerance
A serverless model maintains the compute capacity across multiple Availability Zones in each region to help protect your code against individual machine or data center facility failures. AWS Lambda provides built-in fault tolerance that banishes maintenance windows or scheduled downtimes.
How Serverless addresses Single Points of Failure (SPOF)
When it comes to software, there are multiple tasks like installation, configuration, upgrades, updates, and performance tuning that may lead to SPOF that can affect the entire system’s performance.
Here’s how it can be addressed by serverless.
Building a global infrastructure around the service provider’s segmented and isolated regions and Availability Zones ensures high availability. This promotes low-latency, high-throughput, and highly redundant networking.
AWS Availability Zones help design and operate applications and databases that automatically failover between Availability Zones without interruption while being fault-tolerant and scalable in case of service failures.
Serverless SaaS providers offer the resources and scale to identify, address, isolate, and prevent software glitches. Services like AWS Lambda offer several features to help support your data resiliency and backup needs.
- Versioning function’s code and configuration
- Function scaling based on the increasing request load
- Reserved concurrency for functions to scale based on additional requests
- Retries for asynchronous invocations and their subsets
- Dead-letter queue to receive events for troubleshooting or reprocessing failed events
The AWS Lambda Security Model promotes shared responsibility between the SaaS provider and the customer. This helps tailor security strategies to the specific risks of cloud-native serverless applications.
Security of code, storage, and accessibility of sensitive data, and application identity and access management (IAM) are customer responsibilities.
Lambda ensures security in two ways:
Security of the cloud – The service providers protect the infrastructure that runs their services on their cloud and also additional security services. Customer applications are verified for adequate security by Third-party auditors.
Security in the cloud – The customers are responsible for the sensitivity of their data and compliance.
Mechanisms for Managed Access to a REST API in API Gateway
- Resource-based policies
- Standard IAM role-based policies
- IAM tags for access control
- Endpoint Policies for Interface VPC Endpoints
- Lambda authorizers control access to REST API methods using bearer token authentication and headers, paths, query strings, stage variables, or context variables request parameters
- Amazon Cognito user pool
- Cross-origin resource sharing (CORS)
- Client-side SSL certificates
- API Web Application Firewall
- API Keys based on usage plans
Development Approach for Serverless Applications
Primarily, managed services are used for development in serverless computing where. Concurrent requests can be auto-scaled without the need for computing resources at the developer’s end.
Run Only Code
Developers do not have to work on provisioning, configuration, patch creation, and instances. They can write a function in their preferred programming language and post it to a serverless platform. The cloud service provider manages the infrastructure and the software and maps the function to an API endpoint that transparently scales function instances on demand.
Some of the features that help developers are:
- Testing for code style, unit tests, integration tests, UI tests must be done during development
- Automating artifacts-building, test case execution and returning results
- A deployment trigger to rerun tests
- Previous versions and database migrations rollback
- Debugging and monitoring tools
- Plugins to extend capabilities
- Docker containers for infra provisioning
In serverless SaaS like AWS Lambda, the user can create a function on the third-party console, invoke it, and view logs, metrics, and trace data. The user must create an account and Identity and Access Management (IAM) user with administrator permissions. Next, the user must create a password for console access, and access keys to use command-line tools.
Functions can be written in the Lambda console or with an IDE toolkit, command-line tools, or SDKs. The Lambda console provides a code editor for non-compiled languages that lets developers modify and test code quickly. The CLI gives you direct access to the API for advanced configuration and automation use cases.
The cloud vendor provides an environment for the production to run serverless. The cloud provider API runs your current system and the integration testing infrastructure on the cloud.
Replicating the serverless environment to check how the code deployed will perform is difficult. Testers need to maintain a balance between production resources and platform resource limits.
The best way forward with debugging is by using dedicated tools and logs for serverless applications like these from AWS.
- Lambda logs
- API Gateway logs
- Cloudformation logs
- CloudTrail logs
Distributed tracing, inbuilt logging, and monitoring capabilities of the SaaS provider influence debugging.
Wrapping it up
Organizations should consider investing in serverless for end-to-end application development, storage, messaging, data analytics, and services across databases.
Some serverless cloud services provide scalability and cost savings but create additional complexities like runtime constraints of vendor lock-in. Thus, a full-stack approach is recommended while opting for a serverless architecture.