Complete guide to AWS Step Functions

Learn AWS Step Functions, an orchestration service using state machines to manage workflow execution, with visual workflow design, Lambda integration via console, API Gateway, and CloudWatch events.

Explore the AWS Step Functions console to inspect state machines, executions, and CloudWatch metrics, and learn to model flows with choices, parallel, wait, and error handling.

Use AWS Step Functions for business-critical, complex, or long-running workflows to gain robust error handling, retries, and an auditable, visual design and runtime history; weigh its cost against Lambda invocations.

Explore the seven state types in AWS Step Functions, including task, pass, wait, choice, parallel, succeed, and fail, and learn how to configure transitions, timeouts, and terminal outcomes.

Discover how AWS Step Functions manage execution state by binding input, transforming data with states like subtract, invoking lambda functions, and using result paths to produce final output.

Learn how to handle errors in AWS Step Functions by configuring retries and catches on task and parallel states, using specific error types, backoff, and max attempts.

Learn about AWS Step Functions service limits, including hard limits like one million open executions and year-long execution time, 32 kB payloads, and using S3 or DynamoDB for larger data.

Monitor and debug AWS Step Functions using CloudWatch metrics and latency insights, set alarms for failure metrics, and review executions, inputs, outputs, and state paths to diagnose issues.

Explore express workflows, a cheaper, high-throughput cut-down version of step functions with five-minute limits, CloudWatch logging, and Lambda-backed executions, including pricing, limitations, and supported integrations.

Explore AWS PrivateLink for Step Functions by using VPC endpoints to access the service from inside a VPC without the public internet, keeping traffic fully within your network.

Learn to create and deploy state machines with the serverless framework using the Serverless Step Functions plugin, define state machines in service.yaml, and invoke them with a Lambda function.

Chain function calls in an aws step functions state machine by linking add and double tasks, passing x and y to produce 55, then 110, demonstrating sequential execution.

Configure retry in a Step Functions state using a custom error 'number is too big' with max attempts zero, and apply a catch-all retry for errors up to three times.

Learn how catch recovers from failed states in AWS step functions when the max number of retries is exceeded, transitioning to a default response (42).

Explore branching in a step function state machine by using a choice state to route input when numeric value exceeds 50, transitioning to the numbers state.

Create a parallel state in AWS Step Functions with multiple branches, nested parallel logic, and unique state names; observe outputs as an array and cancellation on failure.

Explore the wait state in AWS Step Functions, pausing a state machine for flexible durations with four options: seconds, UTC timestamp, input-driven seconds, or input-driven timestamp.

Learn how the pass state in AWS Step Functions modifies execution state, including adding a Z attribute, overriding X to 42, and using XPath to pick the first array element.

Learn how the map state enables dynamic parallelism by processing an input array with an iterator, per-item delays, and optional max concurrency, including error handling.

Create express workflows with the serverless step functions plugin and YAML, configure CloudWatch logging via ARN, and note limited metrics and no notifications.

Set up an API gateway trigger for a step function using the serverless framework to start a fire-and-forget execution via HTTP post, with the request body as input.

Trigger AWS Step Functions with CloudWatch events using serverless framework, using scheduled rules and S3 put object event patterns, and pass custom inputs to state machines.

Discover activities in AWS Step Functions, where a state machine publishes a task to a queue, waits for a poller to complete the work, and resumes on success or failure.

Create an activity ahead of time, poll it with the command line interface, and drive a state machine task to report success or failure using the task token.

Configure activities in serverless.yml with the serverless step functions plugin, using the pseudo parameters plugin to construct full ARNs, and deploy a state machine with multiple activities.

Learn how the SNS integration works with Step Functions to send static or dynamic messages to an SNS topic using state parameters and a serverless plugin for IAM role creation.

Demonstrates wiring a step functions state machine with a parallel state to send messages to SQS via task state, including static and dynamic messages, and parameterized queue URL and body.

Explore how DynamoDB integrates with AWS Step Functions by a state machine that puts, gets, and deletes items, including parameterized table names and verbose item formats.

Learn how AWS Step Functions callbacks pause a state machine with a task token until it receives success or failure, using Lambda, SQS, SNS, and nested workflows.

Configure a serverless Step Functions workflow that publishes messages to SQS and SNS, invokes Lambda, and uses task tokens to wait for callbacks and send task success with output 42.

Explore nested workflows in AWS Step Functions by starting a nested execution via fire-and-forget, synchronous, or callback with task tokens, using service integrations with SNS, SQS, and DynamoDB.

Learn how to configure nested AWS Step Functions workflows in serverless.yml, exploring fire-and-forget, synchronous, and callback with task token patterns using SQS and Lambda for async completion.

Learn blue-green deployments for AWS Step Functions by keeping running executions stable with fixed lambda versions and deploying changes using the serverless functions plugin.

Demonstrates blue-green deployments for AWS Step Functions using the Serverless framework and plugin, swapping Lambda function references with versions to run both old and new code safely.

Set top-level, task, and heartbeat timeouts to prevent stalls. Pass ARNs instead of large payloads, retry Lambda and other services, maintain pollers, and monitor failures with CloudWatch alarms.