Monitoring platform for keeping systems up and running at all times.
Full stack visibility across the entire stack.
Detect and resolve any incident in record time.
Conform to industry best practices.
Dashbird continuously monitors and analyses your serverless applications to ensure reliability, cost and performance optimisation and alignment with the Well Architected Framework.
What defines a serverless system, main characteristics and how it operates
What are the types of serverless systems for computing, storage, queue processing, etc.
What are the challenges of serverless infrastructures and how to overcome them?
How systems can be reliable and the importance to cloud applications
What is a scalable system and how to handle increasing loads
Making systems easy to operate, manage and evolve
Learn the three basic concepts to build scalable and maintainable applications on serverless backends
The pros and cons of each architecture and insights to choose the best option for your projects
Battle-tested serverless patterns to make sure your cloud architecture is ready to production use
Strategies to compose functions into flexible, scalable and maintainable systems
Achieving loosely-coupled architectures with the asynchronous messaging pattern
Using message queues to manage task processing asynchronously
Asynchronous message and task processing with Pub/Sub
A software pattern to control workflows and state transitions on complex processes
The strategy and practical considerations about AWS physical infrastructure
How cloud resources are identified across the AWS stack
What makes up a Lambda function?
What is AWS Lambda and how it works
Suitable use cases and advantages of using AWS Lambda
How much AWS Lambda costs, pricing model structure and how to save money on Lambda workloads
Learn the main pros/cons of AWS Lambda, and how to solve the FaaS development challenges
Main aspects of the Lambda architecture that impact application development
Quick guide for Lambda applications in Nodejs, Python, Ruby, Java, Go, C# / .NET
Different ways of invoking a Lambda function and integrating to other services
Building fault-tolerant serverless functions with AWS Lambda
Understand how Lambda scales and deals with concurrency
How to use Provisioned Concurrency to reduce function latency and improve overall performance
What are Lambda Layers and how to use them
What are cold starts, why they happen and what to do about them
Understand the Lambda retry mechanism and how functions should be designed
Managing AWS Lambda versions and aliases
How to best allocate resources and improve Lambda performance
What is DynamoDB, how it works and the main concepts of its data model
How much DynamoDB costs and its different pricing models
Query and Scan operations and how to access data on DynamoDB
Alternative indexing methods for flexible data access patterns
How to organize information and leverage DynamoDB features for advanced ways of accessing data
Different models for throughput capacity allocation and optimization in DynamoDB
Comparing NoSQL databases: DynamoDB and Mongo
Comparing managed database services: DynamoDB vs. Mongo Atlas
How does an API gateway work and what are some of the most common usecases
Learn what are the benefits or drawbacks of using APIGateway
Picking the correct one API Gateway service provider can be difficult
Types of possible errors in an AWS Lambda function and how to handle them
Best practices for what to log in an AWS Lambda function
How to log objects and classes from the Lambda application code
Program a proactive alerting system to stay on top of the serverless stack
As we covered in the Lambda Programming Model1 page, Lambda functions run inside a microVM, which is launched with a particular runtime (in crude terms, a programming language environment).
Lambda allows developers to implement custom runtimes2, but also offer a list of execution environments available out-of-the-box. Implementing applications in these ‘default’ environments are the ones this guide is going to cover.
Developers can run Javascript code in AWS Lambda by using NodeJS environment.
At the time of writing this piece, the NodeJS versions supported were:
The underlying operating system depends on the version. Lambda uses Amazon Linux 2 for versions 10+ and Amazon Linux 1 for version 8.
By default, the main file in a NodeJS Lambda function is index.js. It should export a function with the name handler. The Lambda environment will passes two arguments to this function:
index.js
handler
event
context
The names of the index.js file and handler function can be customized in the AWS Console or through the Lambda CLI.
A hello-world NodeJS function in Lambda would be:
Example index.js file:
exports.handler = async function(event, context) { console.log('Event: ', event) console.log('Remaining time: ', context.getRemainingTimeInMillis()) console.log('Function name: ', context.functionName) return { 'logStreamName': context.logStreamName } }
Use the console to log errors, debugging or informational messages in the application:
console
exports.handler = async function(event, context) { console.info("Event:\n" + JSON.stringify(event, null, 2)) console.warn("A warning message goes here.") console.log("ENV variables:\n" + JSON.stringify(process.env, null, 2)) return { 'logStreamName': context.logStreamName } }
All logs and messages output by the Lambda function will be available in CloudWatch4.
We will soon add here content about Python, Ruby, Java, Go and C# / .NET.
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