dynamodb-toolbox VS LocalStack

One of them was that it had originally been coded in JavaScript. Although Jeremy rewrote the source code in TypeScript in 2020, it didn't handle type inference, a feature that I eventually came to implement myself in the v0.4.

If you have in mind features that I missed, or would like to see some of the ones I mentioned prioritised, please comment this article and/or create an issue or open a discussion on the official repo with the v1 label 👍

For instance, here is an example of the same UpdateCommand with one of those wrappers, DynamoDB-Toolbox:

It's been mature enough for at least four years.

1. Not an issue for me. Connection reuse support in Lambda is quite good

2. NoSQL is a good skill to keep your in bucket anyhow. DynamoDB is a different approach, but much of the same tenets you'll find in other NoSQL databases still apply. Using tools like dynamodb-toolbox [1] help greatly with paradigm shifts into Dynamo.

3. True. Ask yourself how much this matters. How likely is it that you'll need to support another cloud provider for a single product? In 20 years I've seen a platform provider switch exactly once. And DynamoDB can be exported easily.

4. Nope. But there are things to learn about cold starts; how to structure code, where to initialize things, which things should be singletons, etc.

5. Depends on the situations and needs. The right tool for the right job, if you will. I've written GraphQL servers that run on lambda which serve 300k users daily. I've also done the same using Fargate/ECS et al. Much of the decisions revolve around complexity of execution and cost factors (e.g. the cost and complexity of running lambda's to process data often versus a Fargate service). You're getting into Software Architecture now.

6. Again, depends on the situation. You'll need to start thinking about what individual services/components/things are doing, what they need, and how they need to run. Gather that information, and then start cost comparisons using the pricing tools the provider has.

[1] https://github.com/jeremydaly/dynamodb-toolbox

Note that all these entities will belong to the same DynamoDB table. We define only one partition key and one sort key for this table: both of type string. Key is the values we provide for these keys. As you can imagine, this quickly can become a bit tangled mess. Therefore I recommend to express this 'schema' (e.g. of what types of keys we lay over our base table) in code. Later in this article I will show how this can be accomplished using the DynamoDB Toolbox framework.

I use single table design with app sync through this library: https://github.com/jeremydaly/dynamodb-toolbox

Serverless Cloud further reduces the cognitive load on developers by automatically applying best practices for APIs, data, storage, CDNs, and more. For example, Jeremy Daly, GM of Serverless Cloud, created the DynamoDB Toolbox to help developers implement the best practices of single table design for Amazon DynamoDB. Serverless Data bakes in those best practices for you and provides a simple interface to manage your data. Similarly, your API endpoint will automatically have a sufficient amount of computational resources by simply using the api interface of our SDK. Serverless Cloud continuously adapts the best configuration for your application, letting you focus on solving business problems, not infrastructure ones.

I like the idea, especially since I found libraries like https://github.com/jeremydaly/dynamodb-toolbox or https://github.com/sensedeep/dynamodb-onetable not elastic or up to date enough for me and reverted to raw AWS SDK. I look forward to AWS SDK v3 support in your typings!

Later you can use any S3 compatible storage because the code I write will still work, but for testing purposes on my local machine, I will use LocalStack:

Acesse o site da LocalStack e faça login.

To give this a slightly different spin:

--> "The best optimization is simply not spinning things up."

At least for local development and testing, as made possible by LocalStack (https://localstack.cloud), among other local testing solutions and emulators.

We've seen so many teams fall into the trap of "someone forgot to shut down dev resource X for a week and now we've racked up a $$$ bill on AWS".

What is everyone's strategy to avoid this kind of situation? Tools like `aws-nuke` (https://github.com/rebuy-de/aws-nuke) are awesome (!) to clean up unused resources, but frankly they should not be necessary in the first place.

(For others who hadn't heard of it: localstack is

LocalStack Website

O Localstack: https://localstack.cloud/, é um recurso que possibilita simular diversos recursos AWS (dynamoDB, s3, iam, cognito, ses), dentro da sua máquina, utilizando o docker.