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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
By default, every DynamoDB table buils an index of items based on the primary-key attribute. Apart form that, it’s possible to create other indexes based on different attributes of the same items.
Two types of indexes are supported: local and global. The main differences are:
Local indexes can vary only the sort-key, maintaining the same partition-key as the base table, thus being useful only for different sorting patterns.
Global indexes support different attributes for both partition-key and sort-key.
In general, developers should avoid using Local indexes and prefer Global ones. Local indexes limit data volume to 10 Gb for any given partition-key value, which defeats the high scalability purpose of DynamoDB. Global indexes, on the other hand, impose no restrictions.
When setting up an index, DynamoDB will allow three data projection options. A data projection is a definition of which item attributes are projected – or copied – into the index:
Keeping the number of indexes to a minimum is recommended. Instead of creating too many indexes, consider using one table and one index (or very few indexes) for all querying requirements. This is possible by writing additional items to support alternative access patterns[^1].
Beware that indexes will consume storage space and write resources from the base table. When an item is inserted or modified in a table, DynamoDB needs to update the associated index(es). Updating an item in a table with five indexes, for example, will consume six write requests: one for the base table and five for the indexes.
Although projecting all attributes in indexes can be convenient, it might not be the smartest decision in all cases. Projected attributes consume storage space. Depending on the size of a table, projections can become very expensive.
Consider cases when items are rarely accessed or when just a few attributes are needed frequently. In those scenarios, it might be better to project only the key-attributes, or a minimum set of frequently used attributes.
Whenever is needed, it is always possible to retrieve additional attributes from the base table. This will consume an extra read requet, but could be way cheaper than doubling storage permanently on a project-all index.
[^1] Refer to the Access Pattern Strategies page
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