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
A Lambda function’s concurrency level is the number of invocations being served simultaneously at any given point in time. Lambda doesn’t limit the number of “requests per second/minute“, for example, as is common in API services. Developers can run as many requests per period of time as needed, providing that it doesn’t violates concurrency limits.
As stated below, concurrency is the total number of simultaneous requests in a given time. Below is a visual representation of this concept, to make it easier to understand.
Key takeaways from the diagram above:
Lambda concurrency limits will depend on the Region where the function is deployed. It will vary from 500 to 3,000.
New functions are limited to this default concurrency threshold set by Lambda. After an initial burst of traffic, Lambda can scale up every minute by an additional 500 microVMs1 (or instances of a function).
This scaling process continues until the concurrency limit is met. Developers can request a concurrency increase in the AWS Support Center2.
When Lambda is not able to cope with the amount of concurrent requests an application is experiencing, requesters will receive a throttling error (429 HTTP status code)3.
The concurrency limit discussed in the previous topic is shared across all functions in an AWS account. Developers might want to limit one or more functions, so that they don’t eat up all the concurrency capacity.
This can be done by setting the Reserved Concurrency parameter in the AWS Lambda configuration. For more information, please follow the AWS documentation about Reserving Concurrency for a Lambda Function.
AWS Lambda allows developers to anticipate how many instances of a function should be provisioned and warm to serve requests. By setting a minimal provisioned concurrency level, the performance of all requests are guaranteed to stay below double-digit milliseconds.
Using this feature can be beneficial for workloads that are time-sensitive, such as customer-facing endpoints. Nevermind, it is a step back in the serverless model and comes with several financial caveats.
Learn more about this feature and its caveats in its dedicated Knowledge Base page.
Reserved concurrency setting is recommended to be used whenever possible in all Lambda functions. Since it prevents Low & Slow DoS attacks4.
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