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Trendy purposes more and more require advanced and long-running coordination between companies, comparable to multi-step cost processing, AI agent orchestration, or approval processes awaiting human choices. Constructing these historically required vital effort to implement state administration, deal with failures, and combine a number of infrastructure companies.
Beginning right this moment, you should utilize AWS Lambda sturdy features to construct dependable multi-step purposes immediately throughout the acquainted AWS Lambda expertise. Sturdy features are common Lambda features with the identical occasion handler and integrations you already know. You write sequential code in your most popular programming language, and sturdy features observe progress, robotically retry on failures, and droop execution for as much as one 12 months at outlined factors, with out paying for idle compute throughout waits.
AWS Lambda sturdy features use a checkpoint and replay mechanism, often called sturdy execution, to ship these capabilities. After enabling a perform for sturdy execution, you add the brand new open supply sturdy execution SDK to your perform code. You then use SDK primitives like “steps” so as to add automated checkpointing and retries to your online business logic and “waits” to effectively droop execution with out compute prices. When execution terminates unexpectedly, Lambda resumes from the final checkpoint, replaying your occasion handler from the start whereas skipping accomplished operations.
Getting began with AWS Lambda sturdy features
Let me stroll you thru methods to use sturdy features.
First, I create a brand new Lambda perform within the console and choose Writer from scratch. Within the Sturdy execution part, I choose Allow. Observe that, sturdy perform setting can solely be set throughout perform creation and at the moment can’t be modified for present Lambda features.
After I create my Lambda sturdy perform, I can get began with the offered code.
Lambda sturdy features introduces two core primitives that deal with state administration and restoration:
- Steps—The
context.step()methodology provides automated retries and checkpointing to your online business logic. After a step is accomplished, it will likely be skipped throughout replay. - Wait—The
context.wait()methodology pauses execution for a specified period, terminating the perform, suspending and resuming execution with out compute prices.
Moreover, Lambda sturdy features supplies different operations for extra advanced patterns: create_callback() creates a callback that you should utilize to await outcomes for exterior occasions like API responses or human approvals, wait_for_condition() pauses till a selected situation is met like polling a REST API for course of completion, and parallel() or map() operations for superior concurrency use circumstances.
Constructing a production-ready order processing workflow
Now let’s increase the default instance to construct a production-ready order processing workflow. This demonstrates methods to use callbacks for exterior approvals, deal with errors correctly, and configure retry methods. I maintain the code deliberately concise to concentrate on these core ideas. In a full implementation, you can improve the validation step with Amazon Bedrock so as to add AI-powered order evaluation.
Right here’s how the order processing workflow works:
- First,
validate_order()checks order information to make sure all required fields are current. - Subsequent,
send_for_approval()sends the order for exterior human approval and waits for a callback response, suspending execution with out compute prices. - Then,
process_order()completes order processing. - All through the workflow, try-catch error dealing with distinguishes between terminal errors that cease execution instantly and recoverable errors inside steps that set off automated retries.
Right here’s the whole order processing workflow with step definitions and the principle handler:
import random
from aws_durable_execution_sdk_python import (
DurableContext,
StepContext,
durable_execution,
durable_step,
)
from aws_durable_execution_sdk_python.config import (
Length,
StepConfig,
CallbackConfig,
)
from aws_durable_execution_sdk_python.retries import (
RetryStrategyConfig,
create_retry_strategy,
)
@durable_step
def validate_order(step_context: StepContext, order_id: str) -> dict:
"""Validates order information utilizing AI."""
step_context.logger.information(f"Validating order: {order_id}")
# In manufacturing: calls Amazon Bedrock to validate order completeness and accuracy
return {"order_id": order_id, "standing": "validated"}
@durable_step
def send_for_approval(step_context: StepContext, callback_id: str, order_id: str) -> dict:
"""Sends order for approval utilizing the offered callback token."""
step_context.logger.information(f"Sending order {order_id} for approval with callback_id: {callback_id}")
# In manufacturing: ship callback_id to exterior approval system
# The exterior system will name Lambda SendDurableExecutionCallbackSuccess or
# SendDurableExecutionCallbackFailure APIs with this callback_id when approval is full
return {
"order_id": order_id,
"callback_id": callback_id,
"standing": "sent_for_approval"
}
@durable_step
def process_order(step_context: StepContext, order_id: str) -> dict:
"""Processes the order with retry logic for transient failures."""
step_context.logger.information(f"Processing order: {order_id}")
# Simulate flaky API that generally fails
if random.random() > 0.4:
step_context.logger.information("Processing failed, will retry")
increase Exception("Processing failed")
return {
"order_id": order_id,
"standing": "processed",
"timestamp": "2025-11-27T10:00:00Z",
}
@durable_execution
def lambda_handler(occasion: dict, context: DurableContext) -> dict:
strive:
order_id = occasion.get("order_id")
# Step 1: Validate the order
validated = context.step(validate_order(order_id))
if validated["status"] != "validated":
increase Exception("Validation failed") # Terminal error - stops execution
context.logger.information(f"Order validated: {validated}")
# Step 2: Create callback
callback = context.create_callback(
title="awaiting-approval",
config=CallbackConfig(timeout=Length.from_minutes(3))
)
context.logger.information(f"Created callback with id: {callback.callback_id}")
# Step 3: Ship for approval with the callback_id
approval_request = context.step(send_for_approval(callback.callback_id, order_id))
context.logger.information(f"Approval request despatched: {approval_request}")
# Step 4: Look ahead to the callback outcome
# This blocks till exterior system calls SendDurableExecutionCallbackSuccess or SendDurableExecutionCallbackFailure
approval_result = callback.outcome()
context.logger.information(f"Approval acquired: {approval_result}")
# Step 5: Course of the order with customized retry technique
retry_config = RetryStrategyConfig(max_attempts=3, backoff_rate=2.0)
processed = context.step(
process_order(order_id),
config=StepConfig(retry_strategy=create_retry_strategy(retry_config)),
)
if processed["status"] != "processed":
increase Exception("Processing failed") # Terminal error
context.logger.information(f"Order efficiently processed: {processed}")
return processed
besides Exception as error:
context.logger.error(f"Error processing order: {error}")
increase error # Re-raise to fail the execution
This code demonstrates a number of vital ideas:
- Error dealing with—The try-catch block handles terminal errors. When an unhandled exception is thrown outdoors of a step (just like the validation test), it terminates the execution instantly. That is helpful when there’s no level in retrying, comparable to invalid order information.
- Step retries—Contained in the
process_orderstep, exceptions set off automated retries primarily based on the default (step 1) or configuredRetryStrategy(step 5). This handles transient failures like short-term API unavailability. - Logging—I take advantage of
context.loggerfor the principle handler andstep_context.loggerinside steps. The context logger suppresses duplicate logs throughout replay.
Now I create a check occasion with order_id and invoke the perform asynchronously to begin the order workflow. I navigate to the Take a look at tab and fill within the optionally available Sturdy execution title to establish this execution. Observe that, sturdy features supplies built-in idempotency. If I invoke the perform twice with the identical execution title, the second invocation returns the present execution outcome as a substitute of making a replica.
I can monitor the execution by navigating to the Sturdy executions tab within the Lambda console:
Right here I can see every step’s standing and timing. The execution exhibits CallbackStarted adopted by InvocationCompleted, which signifies the perform has terminated and execution is suspended to keep away from idle prices whereas ready for the approval callback.
I can now full the callback immediately from the console by selecting Ship success or Ship failure, or programmatically utilizing the Lambda API.
I select Ship success.
After the callback completes, the execution resumes and processes the order. If the process_order step fails because of the simulated flaky API, it robotically retries primarily based on the configured technique. As soon as all retries succeed, the execution completes efficiently.
Monitoring executions with Amazon EventBridge
You may as well monitor sturdy perform executions utilizing Amazon EventBridge. Lambda robotically sends execution standing change occasions to the default occasion bus, permitting you to construct downstream workflows, ship notifications, or combine with different AWS companies.
To obtain these occasions, create an EventBridge rule on the default occasion bus with this sample:
{
"supply": ["aws.lambda"],
"detail-type": ["Durable Execution Status Change"]
}
Issues to know
Listed below are key factors to notice:
- Availability—Lambda sturdy features are actually out there in US East (Ohio) AWS Area. For the newest Area availability, go to the AWS Capabilities by Area web page.
- Programming language assist—At launch, AWS Lambda sturdy features helps JavaScript/TypeScript (Node.js 22/24) and Python (3.13/3.14). We suggest bundling the sturdy execution SDK along with your perform code utilizing your most popular package deal supervisor. The SDKs are fast-moving, so you’ll be able to simply replace dependencies as new options grow to be out there.
- Utilizing Lambda variations—When deploying sturdy features to manufacturing, use Lambda variations to make sure replay all the time occurs on the identical code model. In case you replace your perform code whereas an execution is suspended, replay will use the model that began the execution, stopping inconsistencies from code modifications throughout long-running workflows.
- Testing your sturdy features—You possibly can check sturdy features domestically with out AWS credentials utilizing the separate testing SDK with pytest integration and the AWS Serverless Utility Mannequin (AWS SAM) command line interface (CLI) for extra advanced integration testing.
- Open supply SDKs—The sturdy execution SDKs are open supply for JavaScript/TypeScript and Python. You possibly can overview the supply code, contribute enhancements, and keep up to date with the newest options.
- Pricing—To be taught extra on AWS Lambda sturdy features pricing, discuss with the AWS Lambda pricing web page.
Get began with AWS Lambda sturdy features by visiting the AWS Lambda console. To be taught extra, discuss with AWS Lambda sturdy features documentation web page.
Completely happy constructing!
— Donnie
