Hello AWS Step Functions

Photo by Jukan Tateisi

Hello AWS step functions

At oRo, we start a project with Serverless architecture. With this opportunity, we have a chance to research the efficient design that helps the project that is scalable and flexible.

The first thing that we take a look is AWS Lambda. AWS Lambda is a great choice for solving many problems and situations. It is also cost-effective, but It has some limitations.

What are the limitations of AWS Lambda?

Technical Limitations

There are some limitation of AWS Lambda

• Container image code package size is 10 GB (maximum uncompressed image size, including all layers)
• The default deployment package size is 50 MB.
• The maximum execution timeout for a function is 900 seconds (15 minutes).
• Requests limitations by Lambda:
• Request and response (synchronous calls) body payload size can be up to to 6 MB.
• Event request (asynchronous calls) body can be up to 256 KB.

About the detail, you can check in the official document of AWS: https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html

There is no way to share data between executions

As each function execution is independent, there is no way to share data between lambda executions.

Latency is not guaranteed

The latency of a lambda function execution is not guaranteed because of cold starts.

So before executing the code, some setup process needs to happen. This waiting time is called the cold-start.

Not suitable for long-running tasks.

There is a hard limit of 15 minutes of execution per call for each lambda.

So if you are planning to get some long-running tasks done using AWS lambda, then think twice! Because the function will die after 15 minutes.

What are AWS Step Functions?

AWS Step Functions is a visual workflow service that helps developers use AWS services to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines.

AWS Step Function Benefits:

1. Build and deploy rapidly

Workflow Studio offers a straightforward drag-and-drop user interface that makes getting started quickly. Step Functions allow you to quickly connect services, systems, or people by using low-code, event-driven workflows to describe complex business logic.

2. Write less integration code

Build robust business workflows, data pipelines, or apps using AWS resources from more than 200 services, such as Lambda, ECS, Fargate, Batch, DynamoDB, SNS, SQS, SageMaker, EventBridge, or EMR.

3. Build fault-tolerant and stateful workflows

Step Functions keeps track of managing state, checkpoints, and restarts so that your workflows proceed as planned. Based on your predefined business logic, automatic error and exception handling are provided through built-in try/catch, retry, and rollback capabilities.

4. Designed for reliability and scale

Depending on your particular use case, you can choose between the Standard or Express workflow types that Step Functions offers. Long-running workloads are managed using standard workflows. Workloads for high-volume event processing are supported by Express Workflows.

5. Parallelism

Declarative parallelism is possible for the work. A state of a step machine may invoke different states simultaneously. The workflow will proceed more quickly as a result.

6. High Execution Time

If some of the tasks in the workflow require a lot of time (exceeding 10 minutes), they can be executed on ECS, EC2, or as an Activity hosted outside of AWS because Step Functions have a maximum execution time of one year.

How Step Functions works

When working with AWS Step Functions, there are some concept that we should know and keep in mind:

• Do some work in your state machine (a Task state)
• Make a choice between branches of execution (a Choice state)
• Stop an execution with a failure or success (a Fail or Succeed state)
• Pass its input to its output, or inject some fixed data into the workflow (a Pass state)
• Provide a delay for a certain amount of time, or until a specified date and time (a Wait state)
• Begin parallel branches of execution (a Parallel state)
• Dynamically iterate steps (a Map state)

I think the information above that is enough to get a practice with AWS Step Functions. In the next article, We will follow the AWS workshop here: https://www.image-processing.serverlessworkshops.io/ to build a real AWS Serverless Project with AWS Step Functions.