All posts by Marcia Villalba

AWS Weekly Roundup — Claude 3 Sonnet support in Bedrock, new instances, and more — March 11, 2024

Post Syndicated from Marcia Villalba original

Last Friday was International Women’s Day (IWD), and I want to take a moment to appreciate the amazing ladies in the cloud computing space that are breaking the glass ceiling by reaching technical leadership positions and inspiring others to go and build, as our CTO Werner Vogels says.Now go build

Last week’s launches
Here are some launches that got my attention during the previous week.

Amazon Bedrock – Now supports Anthropic’s Claude 3 Sonnet foundational model. Claude 3 Sonnet is two times faster and has the same level of intelligence as Anthropic’s highest-performing models, Claude 2 and Claude 2.1. My favorite characteristic is that Sonnet is better at producing JSON outputs, making it simpler for developers to build applications. It also offers vision capabilities. You can learn more about this foundation model (FM) in the post that Channy wrote early last week.

AWS re:Post – Launched last week! AWS re:Post Live is a weekly Twitch livestream show that provides a way for the community to reach out to experts, ask questions, and improve their skills. The show livestreams every Monday at 11 AM PT.

Amazon CloudWatchNow streams daily metrics on CloudWatch metric streams. You can use metric streams to send a stream of near real-time metrics to a destination of your choice.

Amazon Elastic Compute Cloud (Amazon EC2)Announced the general availability of new metal instances, C7gd, M7gd, and R7gd. These instances have up to 3.8 TB of local NVMe-based SSD block-level storage and are built on top of the AWS Nitro System.

AWS WAFNow supports configurable evaluation time windows for request aggregation with rate-based rules. Previously, AWS WAF was fixed to a 5-minute window when aggregating and evaluating the rules. Now you can select windows of 1, 2, 5 or 10 minutes, depending on your application use case.

AWS Partners – Last week, we announced the AWS Generative AI Competency Partners. This new specialization features AWS Partners that have shown technical proficiency and a track record of successful projects with generative artificial intelligence (AI) powered by AWS.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Some other updates and news that you may have missed:

One of the articles that caught my attention recently compares different design approaches for building serverless microservices. This article, written by Luca Mezzalira and Matt Diamond, compares the three most common designs for serverless workloads and explains the benefits and challenges of using one over the other.

And if you are interested in the serverless space, you shouldn’t miss the Serverless Office Hours, which airs live every Tuesday at 10 AM PT. Join the AWS Serverless Developer Advocates for a weekly chat on the latest from the serverless space.

Serverless office hours

The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in several languages. Check out the ones in FrenchGermanItalian, and Spanish.

AWS Open Source News and Updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open source projects, posts, events, and more.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS Summit season is about to start. The first ones are Paris (April 3), Amsterdam (April 9), and London (April 24). AWS Summits are free events that you can attend in person and learn about the latest in AWS technology.

GOTO x AWS EDA Day London 2024 – On May 14, AWS partners with GOTO bring to you the event-driven architecture (EDA) day conference. At this conference, you will get to meet experts in the EDA space and listen to very interesting talks from customers, experts, and AWS.

GOTO EDA Day 2022

You can browse all upcoming in-person and virtual events here.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Weekly Roundup—Amazon Route53, Amazon EventBridge, Amazon SageMaker, and more – January 15, 2024

Post Syndicated from Marcia Villalba original

We are in January, the start of a new year, and I imagine many of you have made a new year resolution to learn something new. If you want to learn something new and get a free Amazon Web Services (AWS) Learning Badge, check out the new Events and Workflows Learning Path. This learning path will teach you everything you need to know about AWS Step Functions, Amazon EventBridge, event-driven architectures, and serverless, and when you finish the learning path, you can take an assessment. If you pass the assessment, you get an AWS Learning Badge, credited by Credly, that you can share in your résumé and social media profiles.

Events and workflows learning path badge

Last Week’s Launches
Here are some launches that got my attention during the previous week.

Amazon Route 53 – Now you can enable Route 53 Resolver DNS Firewall to filter DNS traffic based on the query type contained in the question section of the DNS query format. In addition, Route 53 now supports geoproximity routing as an additional routing policy for DNS records. Expand and reduce the geographic area from which traffic is routed to a resource by changing the record’s bias value. This is really helpful for industries that need to deliver highly responsive digital experiences.

Amazon CloudWatch LogsCloudWatch Logs now support creating account-level subscription filters. This capability allows you to forward all the logs groups from an account to other services like Amazon OpenSearch Service or Amazon Kinesis Data Firehose.

Amazon Elastic Container Service (Amazon ECS) Amazon ECS now integrates with Amazon Elastic Block Store (Amazon EBS), allowing you to provision and attach EBS volumes to Amazon ECS tasks running on both AWS Fargate and Amazon Elastic Cloud Compute (Amazon EC2). Read the blog post Channy wrote where he shows this feature in action.

Amazon EventBridgeEventBridge now supports AWS AppSync as a target of EventBridge buses. This enables you to stream real-time updates from your backend applications to your front-end clients. For example, you can get notifications in your mobile application from an order you did when the order status changes on the backend.

Amazon SageMakerSageMaker now supports M7i, C7i, and R7i instances for machine learning (ML) inference. These instances are powered by custom 4th generation Intel Xeon scalable processors and deliver up to 15 percent better price performance than their previous generations.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates and news that you may have missed:

If you are a serverless enthusiast, this week, the AWS Compute Blog published the Serverless ICYMI (in case you missed it) quarterly recap for the last quarter of 2023. This post compiles the announcements made during the months of October, November, and December, with all the relevant content that was produced by AWS Developer Advocates during that time. In addition to that blog post, you can learn about ServerlessVideo, a new demo application that we launched at AWS re:Invent 2023.


This week there were also a couple of really interesting blog posts that explain how to solve very common challenges that customers face. The first one is the blog post in the AWS Security Blog that explains how to customize access tokens in Amazon Cognito user pools. And the second one is from the AWS Database Blog, which explains how to effectively sort data with Amazon DynamoDB.

The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in several languages. Check out the ones in FrenchGermanItalian, and Spanish.

AWS open source newsletter – This is a newsletter curated by my colleague Ricardo to bring you the latest open source projects, posts, events, and more.

For our customers in Turkey, on January 1, 2024, AWS Turkey Pazarlama Teknoloji ve Danışmanlık Hizmetleri Limited Şirketi (AWS Turkey) replaced AWS EMEA SARL (AWS Europe) as the contracting party and service provider to customers in Türkiye. This enables AWS customers in Türkiye to transact in their local currency (Turkish Lira) and with a local bank. For more information on AWS Turkey, visit the FAQ page.

Upcoming AWS Events
The beginning of the year is the season of AWS re:Invent recaps, which are happening all around the globe during the next two months. You can check the recaps page to find the one closest to you.

You can browse all upcoming AWS led in-person and virtual events, as well as developer-focused events such as AWS DevDay.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Easily deploy SaaS products with new Quick Launch in AWS Marketplace

Post Syndicated from Marcia Villalba original

Today we are excited to announce the general availability of SaaS Quick Launch, a new feature in AWS Marketplace that makes it easy and secure to deploy SaaS products.

Before SaaS Quick Launch, configuring and launching third-party SaaS products could be time-consuming and costly, especially in certain categories like security and monitoring. Some products require hours of engineering time to manually set up permissions policies and cloud infrastructure. Manual multistep configuration processes also introduce risks when buyers rely on unvetted deployment templates and instructions from third-party resources.

SaaS Quick Launch helps buyers make the deployment process easy, fast, and secure by offering step-by-step instructions and resource deployment using preconfigured AWS CloudFormation templates. The software vendor and AWS validate these templates to ensure that the configuration adheres to the latest AWS security standards.

Getting started with SaaS Quick Launch
It’s easy to find which SaaS products have Quick Launch enabled when you are browsing in AWS Marketplace. Products that have this feature configured have a Quick Launch tag in their description.

Quick Launch tag in AWS Marketplace

After completing the purchase process for a Quick Launch–enabled product, you will see a button to set up your account. That button will take you to the Configure and launch page, where you can complete the registration to set up your SaaS account, deploy any required AWS resources, and launch the SaaS product.

Step 1 - set permissions

The first step ensures that your account has the required AWS permissions to configure the software.

Step 1 - set permissions

The second step involves configuring the vendor account, either to sign in to an existing account or to create a new account on the vendor website. After signing in, the vendor site may pass essential keys and parameters that are needed in the next step to configure the integration.

Step 2 - Log into the vendor account

The third step allows you to configure the software and AWS integration. In this step, the vendor provides one or more CloudFormation templates that provision the required AWS resources to configure and use the product.

Step 3 - Configure your software and AWS integration

The final step is to launch the software once everything is configured.

Step 6 - Launch your software

Sellers can enable this feature in their SaaS product. If you are a seller and want to learn how to set this up in your product, check the Seller Guide for detailed instructions.

To learn more about SaaS in AWS Marketplace, visit the service page and view all the available SaaS products currently in AWS Marketplace.


AWS Lambda functions now scale 12 times faster when handling high-volume requests

Post Syndicated from Marcia Villalba original

Now AWS Lambda scales up to 12 times faster. Each synchronously invoked Lambda function now scales by 1,000 concurrent executions every 10 seconds until the aggregate concurrency across all functions reaches the account’s concurrency limit. In addition, each function within an account now scales independently from each other, no matter how the functions are invoked. These improvements come at no additional cost, and you don’t need to do any configuration in your existing functions.

Building scalable and high-performing applications can be challenging with traditional architectures, often requiring over-provisioning of compute resources or complex caching solutions for peak demands and unpredictable traffic. Many developers choose Lambda because it scales on-demand when applications face unpredictable traffic.

Before this update, Lambda functions could initially scale at the account level by 500–3,000 concurrent executions (depending on the Region) in the first minute, followed by 500 concurrent executions every minute until the account’s concurrency limit is reached. Because this scaling limit was shared between all the functions in the same account and Region, if one function experienced an influx of traffic, it could affect the throughput of other functions in the same account. This increased engineering efforts to monitor a few functions that could burst beyond the account limits, causing a noisy neighbor scenario and reducing the overall concurrency of other functions in the same account.

Now, with these scaling improvements, customers with highly variable traffic can reach concurrency targets faster than before. For instance, a news site publishing a breaking news story or an online store running a flash sale would experience a significant influx of visitors. Thanks to these improvements, they can now scale 12 times faster than before.

In addition, customers that use services such as Amazon Athena and Amazon Redshift with scalar Lambda-based UDFs to perform data enrichment or data transformations will see benefits from these improvements. These services rely on batching data and passing it in chunks to Lambda, simultaneously invoking multiple parallel functions. The enhanced concurrency scaling behavior ensures Lambda can rapidly scale and service level agreement (SLA) requirements are met.

How does this work in practice?
The following graph shows a function receiving requests and processing them every 10 seconds. The account concurrency limit is set to 7,000 concurrent requests and is shared between all the functions in the same account. Each function scaling-up rate is fixed to 1,000 concurrent executions every 10 seconds. This rate is independent from other functions in the same account, making it easier for you to predict how this function will scale and throttle the requests if needed.

  • 09:00:00 – The function has been running for a while, and there are already 1,000 concurrent executions that are being processed.
  • 09:00:10 – Ten seconds later, there is a new burst of 1,000 new requests. This function can process them with no problem because the function can scale up to 1,000 concurrent executions every 10 seconds.
  • 09:00:20 – The same happens here: a thousand new requests.
  • 09:00:30 – The function now receives 1,500 new requests. Because the maximum scale-up capacity for a function is 1,000 requests per 10 seconds, 500 of those requests will get throttled.
  • 09:01:00 – At this time, the function is already processing 4,500 concurrent requests. But there is a burst of 3,000 new requests. Lambda processes 1,000 of the new requests and throttles 2,000 because the function can scale up to 1,000 requests every 10 seconds.
  • 09:01:10 – After 10 seconds, there is another burst of 2,000 requests, and the function can now process 1,000 more requests. However, the remaining 1,000 requests get throttled because the function can scale to 1,000 requests every 10 seconds.
  • 09:01:20 – Now the function is processing 6,500 concurrent requests, and there are 1,000 incoming requests. The first 500 of those requests get processed, but the other 500 get throttled because the function reached the account concurrency limit of 7,000 requests. It’s important to remember that you can raise the account concurrency limit by creating a support ticket in the AWS Management Console.

Example of a function scaling

In the case of having more than one function in your account, the functions scale independently until the total account concurrency limit is reached. After that, all new invocations will be throttled.

These scaling improvements will be enabled by default for all functions. Starting on November 26 through mid-December, AWS is gradually rolling out these scaling improvements to all AWS Regions except China and GovCloud Regions.

If you want to learn more about Lambda’s new scaling behavior, read the Lambda scaling behavior documentation page.


External endpoints and testing of task states now available in AWS Step Functions

Post Syndicated from Marcia Villalba original

Now AWS Step Functions HTTPS endpoints let you integrate third-party APIs and external services to your workflows. HTTPS endpoints provide a simpler way of making calls to external APIs and integrating with existing SaaS providers, like Stripe for handling payments, GitHub for code collaboration and repository management, and Salesforce for sales and marketing insights. Before this launch, customers needed to use an AWS Lambda function to call the external endpoint, handling authentication and errors directly from the code.

Also, we are announcing a new capability to test your task states individually without the need to deploy or execute the state machine.

AWS Step Functions is a visual workflow service that makes it easy for developers to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines. Step Functions integrates with over 220 AWS services and provides features that help developers build, such as built-in error handling, real-time and auditable workflow execution history, and large-scale parallel processing.

HTTPS endpoints
HTTPS endpoints are a new resource for your task states that allow you to connect to third-party HTTP targets outside AWS. Step Functions invokes the HTTP endpoint, deliver a request body, headers, and parameters, and get a response from the third-party services. You can use any preferred HTTP method, such as GET or POST.

HTTPS endpoints use Amazon EventBridge connections to manage the authentication credentials for the target. This defines the authorization type used, which can be a basic authentication with a username and password, an API key, or OAuth. EventBridge connections use AWS Secrets Manager to store the secret. This keeps the secrets out of the state machine, reducing the risks of accidentally exposing your secrets in logs or in the state machine definition.

Getting started with HTTPS endpoints
To get started with HTTPS endpoints, first you need to create an EventBridge connection. Then you need to create a new AWS Identity and Access Management (IAM) role and give permissions so your state machine can access the connection resource, get the secret from Secrets Manager, and get permissions to invoke an HTTP endpoint.

Here are the policies that you need to include in your state machine execution role:

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": [
            "Resource": "arn:aws:secretsmanager:*:*:secret:events!connection/*"
    "Version": "2012-10-17",
    "Statement": [
            "Sid": "RetrieveConnectionCredentials",
            "Effect": "Allow",
            "Action": [
            "Resource": [
    "Version": "2012-10-17",
    "Statement": [
            "Sid": "InvokeHTTPEndpoint",
            "Effect": "Allow",
            "Action": [
            "Resource": [

After you have everything ready, you can create your state machine. In your state machine, add a new task state to call a third-party API. You can configure the API endpoint to point to the third-party URL you need, set the correct HTTP method, pick the connection Amazon Resource Name (ARN) for the connection you created previously as the authentication for that endpoint, and provide a request body if needed. In addition, all these parameters can be set dynamically at runtime from the state JSON input.

Call a third party API

Now, making external requests with Step Functions is easy, and you can take advantage of all the configurations that Step Functions provides to handle errors, such as retries for transient errors or momentary service unavailability, and redrive for errors that require longer investigation or resolution time.

Test state
To accelerate feedback cycles, we are also announcing a new capability to test individual states. This new feature allows you to test states independently from the execution of your workflow. This is particularly useful for testing endpoints configuration. You can change the input and test the different scenarios without the need to deploy your workflow or execute the whole state machine. This new feature is available in all task, choice, and pass states.

You will see the testing capability in the Step Functions Workflow Studio when you select a task.

Test state button

When you choose the Test state, you will be redirected to a different view where you can test the task state. You can test that the state machine role has the right permissions, the endpoint you want to call is correctly configured, and verify that the data manipulations work as expected.

How to test a state

Now, with all the features that Step Functions provides, it’s never been easier to build state machines that can solve a wide variety of problems, like payment flows, workflows with manual inputs, and integration to legacy systems. Using Step Functions HTTPS endpoints, you can directly integrate with popular payment platforms while ensuring that your users’ credit cards are only charged once and errors are handled automatically. In addition, you can test this new integration even before you deploy the state machine using the new test state feature.

These new features are available in all AWS Regions except Asia Pacific (Hyderabad), Asia Pacific (Melbourne), AWS Israel (Tel Aviv), China, and GovCloud Regions.

To get started you can try the “Generate Invoices using Stripe” sample project from Step Functions in the AWS Managment Console or check out the AWS Step Functions Developer Guide to learn more.


Build generative AI apps using AWS Step Functions and Amazon Bedrock

Post Syndicated from Marcia Villalba original

Today we are announcing two new optimized integrations for AWS Step Functions with Amazon Bedrock. Step Functions is a visual workflow service that helps developers build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines.

In September, we made available Amazon Bedrock, the easiest way to build and scale generative artificial intelligence (AI) applications with foundation models (FMs). Bedrock offers a choice of foundation models from leading providers like AI21 Labs, Anthropic, Cohere, Stability AI, and Amazon, along with a broad set of capabilities that customers need to build generative AI applications, while maintaining privacy and security. You can use Amazon Bedrock from the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs.

The new Step Functions optimized integrations with Amazon Bedrock allow you to orchestrate tasks to build generative AI applications using Amazon Bedrock, as well as to integrate with over 220 AWS services. With Step Functions, you can visually develop, inspect, and audit your workflows. Previously, you needed to invoke an AWS Lambda function to use Amazon Bedrock from your workflows, adding more code to maintain them and increasing the costs of your applications.

Step Functions provides two new optimized API actions for Amazon Bedrock:

  • InvokeModel – This integration allows you to invoke a model and run the inferences with the input provided in the parameters. Use this API action to run inferences for text, image, and embedding models.
  • CreateModelCustomizationJob – This integration creates a fine-tuning job to customize a base model. In the parameters, you specify the foundation model and the location of the training data. When the job is completed, your custom model is ready to be used. This is an asynchronous API, and this integration allows Step Functions to run a job and wait for it to complete before proceeding to the next state. This means that the state machine execution will pause while the create model customization job is running and will resume automatically when the task is complete.

Optimized connectors

The InvokeModel API action accepts requests and responses that are up to 25 MB. However, Step Functions has a 256 kB limit on state payload input and output. In order to support larger payloads with this integration, you can define an Amazon Simple Storage Service (Amazon S3) bucket where the InvokeModel API reads data from and writes the result to. These configurations can be provided in the parameters section of the API action configuration parameters section.

How to get started with Amazon Bedrock and AWS Step Functions
Before getting started, ensure that you create the state machine in a Region where Amazon Bedrock is available. For this example, use US East (N. Virginia), us-east-1.

From the AWS Management Console, create a new state machine. Search for “bedrock,” and the two available API actions will appear. Drag the InvokeModel to the state machine.

Using the invoke model connector

You can now configure that state in the menu on the right. First, you can define which foundation model you want to use. Pick a model from the list, or get the model dynamically from the input.

Then you need to configure the model parameters. You can enter the inference parameters in the text box or load the parameters from Amazon S3.

Configuration for the API Action

If you keep scrolling in the API action configuration, you can specify additional configuration options for the API, such as the S3 destination bucket. When this field is specified, the API action stores the API response in the specified bucket instead of returning it to the state output. Here, you can also specify the content type for the requests and responses.

Additional configuration for the connector

When you finish configuring your state machine, you can create and run it. When the state machine runs, you can visualize the execution details, select the Amazon Bedrock state, and check its inputs and outputs.

Executing the state machine

Using Step Functions, you can build state machines as extensively as you need, combining different services to solve many problems. For example, you can use Step Functions with Amazon Bedrock to create applications using prompt chaining. This is a technique for building complex generative AI applications by passing multiple smaller and simpler prompts to the FM instead of a very long and detailed prompt. To build a prompt chain, you can create a state machine that calls Amazon Bedrock multiple times to get an inference for each of the smaller prompts. You can use the parallel state to run all these tasks in parallel and then use an AWS Lambda function that unifies the responses of the parallel tasks into one response and generates a result.

Available now
AWS Step Functions optimized integrations for Amazon Bedrock are limited to the AWS Regions where Amazon Bedrock is available.

You can get started with Step Functions and Amazon Bedrock by trying out a sample project from the Step Functions console.


New Amazon CloudWatch log class for infrequent access logs at a reduced price

Post Syndicated from Marcia Villalba original

Amazon CloudWatch Logs announces today a new log class called Infrequent Access. This new log class offers a tailored set of capabilities at a lower cost for infrequently accessed logs, enabling customers to consolidate all their logs in one place in a cost-effective manner.

As customers’ applications continue to scale and grow, so does the volume of logs generated. To limit the increase of logging costs, many customers are forced to make hard trade-offs. For example, some customers limit the logs generated by their applications, which can hinder the visibility of the application, or choose a different solution for different log types, which adds complexity and inefficiencies in managing different logging solutions. For instance, customers may send logs needed for real-time analytics and alerting to CloudWatch Logs and send more detailed logs needed for debugging and troubleshooting to a lower-cost solution that doesn’t have as many features as CloudWatch. In the end, these workarounds can impact the observability of the application, because customers need to navigate across multiple solutions to see their logs.

The Infrequent Access log class allows you to build a holistic observability solution using CloudWatch by centralizing all your logs in one place to ingest, query, and store your logs in a cost-efficient way. Infrequent Access is 50 percent lower per GB ingestion price than Standard log class. It provides a tailored set of capabilities for customers that don’t need advanced features like Live Tail, metric extraction, alarming, or data protection that the Standard log class provides. With Infrequent Access, you can still get the benefits of fully managed ingestion, storage, and the ability to deep dive using CloudWatch Logs Insights.

The following table shows a side-by-side comparison of the features that the new Infrequent Access and the Standard log classes have.

Feature Infrequent Access log class Standard log class
Fully managed ingestion and storage Available Available
Cross-account Available Available
Encryption with KMS Available Available
Logs Insights Available Available
Subscription filters / Export to S3 Not available Available
GetLogEvents / FilterLogEvents Not available Available
Contributor, Container, and Lambda Insights Not available Available
Metric filter and alerting Not available Available
Data protection Not available Available
Embedded metric format (EMF) Not available Available
Live Tail Not available Available

When to use the new Infrequent Access log class
Use the Infrequent Access log class when you have a new workload that doesn’t require advanced features provided by the Standard log class. One important consideration is that when you create a log group with a specific log class, you cannot change that log group log class afterward.

The Infrequent Access log class is suitable for debug logs or web server logs because they are quite verbose and rarely require any of the advanced functionality that the Standard log class provides.

Another good workload for the Infrequent Access log class is an Internet of Things (IoT) fleet sending detailed logs that are only accessed for after the fact forensic analysis after the event. In addition, the Infrequent Access log class is a good choice for workloads where logs need to be stored for compliance because they will be queried infrequently.

Getting started
To get started using the new Infrequent Access log class, create a new log group in the CloudWatch Logs console and select the new Infrequent Access log class. You can create logs groups with the new Infrequent Access log class not only from the AWS Management Console but also from the AWS Command Line Interface (AWS CLI), AWS CloudFormation, AWS Cloud Development Kit (AWS CDK), and AWS SDKs.

Create log group

Once you have the new log group created, you can start using it in your workloads. For this example, I will configure a web application to send debug logs to this log group. After a while of the web application executes for a while, you can go back to the log group, where you see a new log stream.

View log group

When you select a log stream, you will be directed to CloudWatch Logs Insights.

Log insights

Using the same familiar CloudWatch Logs Insights experience you get with Standard Class, you can create queries and search those logs to find relevant information, and you can analyze all the logs quickly in one place.

Available now
The new Infrequent Access log class is now available in all AWS Regions except the China and GovCloud Regions. You can start using it and enjoy a more cost-effective way to collect, store, and analyze your logs in a fully managed experience.

To learn more about the new log class, you can check the CloudWatch Logs user guide dedicated page for the Infrequent Access log class.


Triggering AWS Lambda function from a cross-account Amazon Managed Streaming for Apache Kafka

Post Syndicated from Marcia Villalba original

This post is written by Subham Rakshit, Senior Specialist Solutions Architect, and Ismail Makhlouf, Senior Specialist Solutions Architect.

Many organizations use a multi-account strategy for stream processing applications. This involves decomposing the overall architecture into a single producer account and many consumer accounts. Within AWS, in the producer account, you can use Amazon Managed Streaming for Apache Kafka (Amazon MSK), and in their consumer accounts have AWS Lambda functions for event consumption. This blog post explains how you can trigger Lambda functions from a cross-account Amazon MSK cluster.

The Lambda event sourcing mapping (ESM) for Amazon MSK continuously polls for new events from the Amazon MSK cluster, aggregates them into batches, and then triggers the target Lambda function. The ESM for Amazon MSK functions as a serverless set of Kafka consumers that ensures that each event is processed at least once. Additionally, events are processed in the same order they are received within each Kafka partition. In addition, the ESM batches the stream of data and filters the events based on configured logic.


Amazon MSK supports two different deployment types: provisioned and serverless. Triggering a Lambda function from a cross-account Amazon MSK cluster is only supported with a provisioned cluster deployed within the same Region. To facilitate this functionality, Amazon MSK uses multi-VPC private connectivity, powered by AWS PrivateLink, which simplifies connecting Kafka consumers hosted in different AWS accounts to an Amazon MSK cluster.

The following diagram illustrates the architecture of this example:

Architecture Diagram

The architecture is divided in two parts: the producer and the consumer.

In the producer account, you have the Amazon MSK cluster with multi-VPC connectivity enabled. Multi-VPC connectivity is only available for authenticated Amazon MSK clusters. Cluster policies are required to grant permissions to other AWS accounts, allowing them to establish private connectivity to the Amazon MSK cluster. You can delegate permissions to relevant roles or users. When combined with AWS Identity and Access Management (IAM) client authentication, cluster policies offer fine-grained control over Kafka data plane permissions for connecting applications.

In the consumer account, you have the Lambda ESM for Amazon MSK and the managed VPC connection deployed within the same VPC. The managed VPC connection allows private connectivity from the consumer application VPC to the Amazon MSK cluster. The Lambda ESM for Amazon MSK connects to the cross-account Amazon MSK cluster via IAM authentication. It also supports SASL/SCRAM, and mutual TLS (mTLS) authenticated clusters. The ESM receives the event from the Kafka topic and invokes the Lambda function to process it.

Deploying the example application

To set up the Lambda function trigger from a cross-account Amazon MSK cluster as the event source, follow these steps. The AWS CloudFormation templates for deploying the example are accessible in the GitHub repository.

As a part of this example, some sample data is published using the Kafka console producer and Lambda processes these events and writes to Amazon S3.


For this example, you need two AWS accounts. This post uses the following naming conventions:

  • Producer (for example, account No: 1111 1111 1111): Account that hosts the Amazon MSK cluster and Kafka client instance.
  • Consumer (for example, account No: 2222 2222 2222): Account that hosts the Lambda function and consumes events from Amazon MSK.

To get started:

  1. Clone the repository locally:
    git clone
  2. Set up the producer account: you must configure the VPC networking, deploy the Amazon MSK cluster, and a Kafka client instance to publish data. To do this, deploy the CloudFormation template producer-account.yaml from the AWS console and take note of the MSKClusterARN from the CloudFormation outputs tab.
  3. Set up the consumer account: To set up the consumer account, you need the Lambda function, IAM role used by the Lambda function, and S3 bucket receiving the data. For this, deploy the CloudFormation template consumer-account.yaml from the AWS console with the input parameter MSKAccountId, that is the producer AWS account ID (for example, account Id: 1111 1111 1111). Note the LambdaRoleArn from the CloudFormation outputs tab.

Setting up multi-VPC connectivity in the Amazon MSK cluster

Once the accounts are created, you must enable connectivity between them. By enabling multi-VPC private connectivity in the Amazon MSK cluster, you set up the network connection to allow the cross-account consumers to connect to the cluster.

  1. In the producer account, navigate to the Amazon MSK console.
  2. Choose producer-cluster, and go to the Properties tab.
  3. Scroll to Networking settings, choose Edit, and select Turn on multi-VPC connectivity. This takes some time, then appears as follows.Networking settings
  4. Add the necessary cluster policy to allow cross-account consumers to connect to Amazon MSK. In the producer account, deploy the CloudFormation template producer-msk-cluster-policy.yaml from the AWS console with the following input parameters:
    • MSKClusterArnAmazon Resource Name (ARN) of the Amazon MSK cluster in producer account. Find this information in the CloudFormation output of producer-account.yaml.
    • LambdaRoleArn – ARN of the IAM role attached to the Lambda function in the consumer account. Find this information in the CloudFormation output of consumer-account.yaml.
    • LambdaAccountId – Consumer AWS account ID (for example, account Id: 2222 2222 2222).

Creating a Kafka topic in Amazon MSK and publishing events

In the producer account, navigate to the Amazon MSK console. Choose the Amazon MSK cluster named producer-cluster. Choose View client information to show the bootstrap server.

Client information

The CloudFormation template also deploys a Kafka client instance to create topics and publish events.

To access the client, go to the Amazon EC2 console and choose the instance producer-KafkaClientInstance1. Connect to EC2 instance with Session Manager:

sudo su - ec2-user
#Set MSK Broker IAM endpoint
export BS=<<Provide IAM bootstrap address here>>

You must use the single-VPC Private endpoint for the Amazon MSK cluster and not the multi-VPC private endpoint, as you are going to publish events from a Kafka console producer from the producer account.

Run these scripts to create the customer topic and publish sample events in the topic:


Creating a managed VPC connection in the consumer account

To establish a connection to the Amazon MSK cluster in the producer account, you must create a managed VPC connection in the consumer account. Lambda communicates with cross-account Amazon MSK through this managed VPC connection.

For detailed setup steps, read the Amazon MSK managed VPC connection documentation.

Configuring the Lambda ESM for Amazon MSK

The final step is to set up the Lambda ESM for Amazon MSK. Setting up the ESM enables you to connect to the Amazon MSK cluster in the producer account via the managed VPC endpoint. This allows you to trigger the Lambda function to process the data produced from the Kafka topic:

  1. In the consumer account, go to the Lambda console.
  2. Open the Lambda function msk-lambda-cross-account-iam.
  3. Go to the Configuration tab, select Triggers, and choose Add Trigger.
  4. For Trigger configuration, select Amazon MSK.

Lambda trigger

To configure this trigger:

  1. Select the shared Amazon MSK cluster. This automatically defaults to the IAM authentication that is used to connect to the cluster.
    MSK Lambda trigger
  2. By default, the Active trigger check box is enabled. This ensures that the trigger is in the active state after creation. For the other values:
    1. Keep the Batch size default to 100.
    2. Change the Starting Position to Trim horizon.
    3. Set the Topic name as customer.
    4. Set the Consumer Group ID as msk-lambda-iam.

Trigger configuration

Scroll to the bottom and choose Add. This starts creating the Amazon MSK trigger, which takes several minutes. After creation, the state of the trigger shows as Enabled.

Verifying the output on the consumer side

The Lambda function receives the events and writes them in an S3 bucket.

To validate that the function is working, go to the consumer account and navigate to the S3 console. Search for the cross-account-lambda-consumer-data-<<REGION>>-<<AWS Account Id>> bucket. In the bucket, you see the customer-data-<<datetime>>.csv files.

S3 bucket objects

Cleaning up

You must empty and delete the S3 bucket, managed VPC connection, and the Lambda ESM for Amazon MSK manually from the consumer account. Next, delete the CloudFormation stacks from the AWS console from both the producer and consumer accounts to remove all other resources created as a part of the example.


With Lambda and Amazon MSK, you can now build a decentralized application distributed across multiple AWS accounts. This post shows how you can set up Amazon MSK as an event source for cross-account Lambda functions and also walks you through the configuration required in both producer and consumer accounts.

For further reading on AWS Lambda with Amazon MSK as an event source, visit the documentation.

For more serverless learning resources, visit Serverless Land.

The serverless attendee’s guide to AWS re:Invent 2023

Post Syndicated from Marcia Villalba original

AWS re:Invent 2023 is fast approaching, bringing together tens of thousands of Builders in Las Vegas in November. However, even if you can’t attend in person, you can catch up with sessions on-demand.

Breakout sessions are lecture-style 60-minute informative sessions presented by AWS experts, customers, or partners. These sessions cover beginner (100 level) topics to advanced and expert (300–400 level) topics. The sessions are recorded and uploaded a few days after to the AWS Events YouTube channel.

This post shares the “must watch” breakout sessions related to serverless architectures and services.

Sessions related to serverless architecture


SVS401 | Best practices for serverless developers
Provides architectural best practices, optimizations, and useful shortcuts that experts use to build secure, high-scale, and high-performance serverless applications.

Chris Munns, Startup Tech Leader, AWS
Julian Wood, Principal Developer Advocate, AWS

SVS305 | Refactoring to serverless
Shows how you can refactor your application to serverless with real-life examples.

Gregor Hohpe, Senior Principal Evangelist, AWS
Sindhu Pillai, Senior Solutions Architect, AWS

SVS308 | Building low-latency, event-driven applications
Explores building serverless web applications for low-latency and event-driven support. Marvel Snap share how they achieve low-latency in their games using serverless technology.

Marcia Villalba, Principal Developer Advocate, AWS
Brenna Moore, Second Dinner

SVS309 | Improve productivity by shifting more responsibility to developers
Learn about approaches to accelerate serverless development with faster feedback cycles, exploring best practices and tools. Watch a live demo featuring an improved developer experience for building serverless applications while complying with enterprise governance requirements.

Heeki Park, Principal Solutions Architect, AWS
Sam Dengler, Capital One

GBL203-ES | Building serverless-first applications with MAPFRE
This session is delivered in Spanish. Learn what modern, serverless-first applications are and how to implement them with services such as AWS Lambda or AWS Fargate. Find out how MAPFRE have adopted and implemented a serverless strategy.

Jesus Bernal, Senior Solutions Architect, AWS
Iñigo Lacave, MAPFRE
Mat Jovanovic, MAPFRE

Sessions related to AWS Lambda


BOA311 | Unlocking serverless web applications with AWS Lambda Web Adapter
Learn about the AWS Lambda Web Adapter and how it integrates with familiar frameworks and tools. Find out how to migrate existing web applications to serverless or create new applications using AWS Lambda.

Betty Zheng, Senior Developer Advocate, AWS
Harold Sun, Senior Solutions Architect, AWS

OPN305 | The pragmatic serverless Python developer
Covers an opinionated approach to setting up a serverless Python project, including testing, profiling, deployments, and operations. Learn about many open source tools, including Powertools for AWS Lambda—a toolkit that can help you implement serverless best practices and increase developer velocity.

Heitor Lessa, Principal Solutions Architect, AWS
Ran Isenberg, CyberArk

XNT301 | Build production-ready serverless .NET apps with AWS Lambda
Explores development and architectural best practices when building serverless applications with .NET and AWS Lambda, including when to run ASP.NET on Lambda, code structure, and using native AOT to massively increase performance.

James Eastham, Senior Cloud Architect, AWS
Craig Bossie, Solutions Architect, AWS

COM306 | “Rustifying” serverless: Boost AWS Lambda performance with Rust
Discover how to deploy Rust functions using AWS SAM and cargo-lambda, facilitating a smooth development process from your local machine. Explore how to integrate Rust into Python Lambda functions effortlessly using tools like PyO3 and maturin, along with the AWS SDK for Rust. Uncover how Rust can optimize Lambda functions, including the development of Lambda extensions, all without requiring a complete rewrite of your existing code base.

Efi Merdler-Kravitz, Cloudex

COM305 | Demystifying and mitigating AWS Lambda cold starts
Examines the Lambda initialization process at a low level, using benchmarks comparing common architectural patterns, and then benchmarking various RAM configurations and payload sizes. Next, measure and discuss common mistakes that can increase initialization latency, explore and understand proactive initialization, and learn several strategies you can use to thaw your AWS Lambda cold starts.

AJ Stuyvenberg, Datadog

Sessions related to event-driven architecture


API302 | Building next gen applications with event driven architecture
Learn about common integration patterns and discover how you can use AWS messaging services to connect microservices and coordinate data flow using minimal custom code. Learn and plan for idempotency, handling duplicating events and building resiliency into your architectures.

Eric Johnson, Principal Developer Advocate, AWS

API303 | Navigating the journey of serverless event-driven architecture
Learn about the journey businesses undertake when adopting EDAs, from initial design and implementation to ongoing operation and maintenance. The session highlights the many benefits EDAs can offer organizations and focuses on areas of EDA that are challenging and often overlooked. Through a combination of patterns, best practices, and practical tips, this session provides a comprehensive overview of the opportunities and challenges of implementing EDAs and helps you understand how you can use them to drive business success.

David Boyne, Senior Developer Advocate, AWS

API309 | Advanced integration patterns and trade-offs for loosely coupled apps
In this session, learn about common design trade-offs for distributed systems, how to navigate them with design patterns, and how to embed those patterns in your cloud automation.

Dirk Fröhner, Principal Solutions Architect, AWS
Gregor Hohpe, Senior Principal Evangelist, AWS

SVS205 | Getting started building serverless event-driven applications
Learn about the process of prototyping a solution from concept to a fully featured application that uses Amazon API Gateway, AWS Lambda, Amazon EventBridge, AWS Step Functions, Amazon DynamoDB, AWS Application Composer, and more. Learn why serverless is a great tool set for experimenting with new ideas and how the extensibility and modularity of serverless applications allow you to start small and quickly make your idea a reality.

Emily Shea, Head of Application Integration Go-to-Market, AWS
Naren Gakka, Solutions Architect, AWS

API206 | Bringing workloads together with event-driven architecture
Attend this session to learn the steps to bring your existing container workloads closer together using event-driven architecture with minimal code changes and a high degree of reusability. Using a real-life business example, this session walks through a demo to highlight the power of this approach.

Dhiraj Mahapatro, Principal Solutions Architect, AWS
Nicholas Stumpos, JPMorgan Chase & Co

COM301 | Advanced event-driven patterns with Amazon EventBridge
Gain an understanding of the characteristics of EventBridge and how it plays a pivotal role in serverless architectures. Learn the primary elements of event-driven architecture and some of the best practices. With real-world use cases, explore how the features of EventBridge support implementing advanced architectural patterns in serverless.

Sheen Brisals, The LEGO Group

Sessions related to serverless APIs


SVS301 | Building APIs: Choosing the best API solution and strategy for your workloads
Learn about access patterns and how to evaluate the best API technology for your applications. The session considers the features and benefits of Amazon API Gateway, AWS AppSync, Amazon VPC Lattice, and other options.

Josh Kahn, Tech Leader Serverless, AWS
Arthi Jaganathan, Principal Solutions Architect, AWS

SVS323 | I didn’t know Amazon API Gateway did that
This session provides an introduction to Amazon API Gateway and the problems it solves. Learn about the moving parts of API Gateway and how it works, including common and not-so-common use cases. Discover why you should use API Gateway and what it can do.

Eric Johnson, Principal Developer Advocate, AWS

FWM201 | What’s new with AWS AppSync for enterprise API developers
Join this session to learn about all the exciting new AWS AppSync features released this year that make it even more seamless for API developers to realize the benefits of GraphQL for application development.

Michael Liendo, Senior Developer Advocate, AWS
Brice Pellé, Principal Product Manager, AWS

FWM204 | Implement real-time event patterns with WebSockets and AWS AppSync
Learn how the PGA Tour uses AWS AppSync to deliver real-time event updates to their app users; review new features, like enhanced filtering options and native integration with Amazon EventBridge; and provide a sneak peek at what’s coming next.

Ryan Yanchuleff, Senior Solutions Architect, AWS
Bill Fine, Senior Product Manager, AWS
David Provan, PGA Tour

Sessions related to AWS Step Functions


API401 | Advanced workflow patterns and business processes with AWS Step Functions
Learn about architectural best practices and repeatable patterns for building workflows and cost optimizations, and discover handy cheat codes that you can use to build secure, high-scale, high-performance serverless applications

Ben Smith, Principal Developer Advocate, AWS

BOA304 | Using AI and serverless to automate video production
Learn how to use Step Functions to build workflows using AI services and how to use Amazon EventBridge real-time events.

Marcia Villalba, Principal Developer Advocate, AWS

SVS204 | Building Serverlesspresso: Creating event-driven architectures
This session explores the design decisions that were made when building Serverlesspresso, how new features influenced the development process, and lessons learned when creating a production-ready application using this approach. Explore useful patterns and options for extensibility that helped in the design of a robust, scalable solution that costs about one dollar per day to operate. This session includes examples you can apply to your serverless applications and complex architectural challenges for larger applications.

James Beswick, Senior Manager Developer Advocacy, AWS

API310 | Scale interactive data analysis with Step Functions Distributed Map
Learn how to build a data processing or other automation once and readily scale it to thousands of parallel processes with serverless technologies. Explore how this approach simplifies development and error handling while improving speed and lowering cost. Hear from an AWS customer that refactored an existing machine learning application to use Distributed Map and the lessons they learned along the way.

Adam Wagner, Principal Solutions Architect, AWS
Roberto Iturralde, Vertex Pharmaceuticals

Sessions related to handling data using serverless services and serverless databases


SVS307 | Scaling your serverless data processing with Amazon Kinesis and Kafka
Explore how to build scalable data processing applications using AWS Lambda. Learn practical insights into integrating Lambda with Amazon Kinesis and Apache Kafka using their event-driven models for real-time data streaming and processing.

Julian Wood, Principal Developer Advocate, AWS

DAT410 | Advanced data modeling with Amazon DynamoDB
This session shows you advanced techniques to get the most out of DynamoDB. Learn how to “think in DynamoDB” by learning the DynamoDB foundations and principles for data modeling. Learn practical strategies and DynamoDB features to handle difficult use cases in your application.

Alex De Brie – Independent consultant

COM308 | Serverless data streaming: Amazon Kinesis Data Streams and AWS Lambda
Explore the intricacies of creating scalable, production-ready data streaming architectures using Kinesis Data Streams and Lambda. Delve into tips and best practices essential to navigating the challenges and pitfalls inherent to distributed systems that arise along the way, and observe how AWS services work and interact.

Anahit Pogosova, Solita

Additional resources

If you are attending the event, there are many chalk talks, workshops, and other sessions to visit. See ServerlessLand for a full list of all the serverless sessions and also the Serverless Hero, Danielle Heberling’s Serverless re:Invent attendee guide for her top picks.

Visit us in the AWS Village in the Expo Hall where you can find the Serverless and Containers booth and enjoy a free cup of coffee at Serverlesspresso.

For more serverless learning resources, visit Serverless Land.

AWS Weekly Roundup—Reserve GPU capacity for short ML workloads, Finch is GA, and more—November 6, 2023

Post Syndicated from Marcia Villalba original

The year is coming to an end, and there are only 50 days until Christmas and 21 days to AWS re:Invent! If you are in Las Vegas, come and say hi to me. I will be around the Serverlesspresso booth most of the time.

Last week’s launches
Here are some launches that got my attention during the previous week.

Amazon EC2 – Amazon EC2 announced Capacity Blocks for ML. This means that you can now reserve GPU compute capacity for your short-duration ML workloads. Learn more about this launch on the feature page and announcement blog post.

Finch – Finch is now generally available. Finch is an open source tool for local container development on macOS (using Intel or Apple Silicon). It provides a command line developer tool for building, running, and publishing Linux containers on macOS. Learn more about Finch in this blog post written by Phil Estes or on the Finch website.

AWS X-Ray – AWS X-Ray now supports W3C format trace IDs for distributed tracing. AWS X-Ray supports trace IDs generated through OpenTelemetry or any other framework that conforms to the W3C Trace Context specification.

Amazon Translate Amazon Translate introduces a brevity customization to reduce translation output length. This is a new feature that you can enable in your real-time translations where you need a shorter translation to meet caption size limits. This translation is not literal, but it will preserve the underlying message.

AWS IAM IAM increased the actions last accessed to 60 more services. This functionality is very useful when fine-tuning the permissions of the roles, identifying unused permissions, and granting the least amount of permissions that your roles need.

AWS IAM Access AnalyzerIAM Access Analyzer policy generator expanded support to identify over 200 AWS services to help you create fine-grained policies based on your AWS CloudTrail access activity.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Some other news and blog posts that you may have missed:

AWS Compute BlogDaniel Wirjo and Justin Plock wrote a very interesting article about how you can send and receive webhooks on AWS using different AWS serverless services. This is a good read if you are working with webhooks on your application, as it not only shows you how to build these solutions but also what considerations you should have when building them.

AWS Storage Blog Bimal Gajjar and Andrew Peace wrote a very useful blog post about how to handle event ordering and duplicate events with Amazon S3 Event Notifications. This is a common challenge for many customers.

Amazon Science BlogDavid Fan wrote an article about how to build better foundation models for video representation. This article is based on a paper that Prime Video presented at a conference about this topic.

The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in several languages. Check out the ones in FrenchGermanItalian, and Spanish.

AWS open-source news and updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open source projects, posts, events, and more.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: Ecuador (November 7), Mexico (November 11), Montevideo (November 14), Central Asia (Kazakhstan, Uzbekistan, Kyrgyzstan, and Mongolia on November 17–18), and Guatemala (November 18).

AWS re:Invent (November 27–December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the highlights for generative artificial intelligence (AI).

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Marcia

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Building resilient serverless applications using chaos engineering

Post Syndicated from Marcia Villalba original

This post is written by Suranjan Choudhury (Head of TME and ITeS SA) and Anil Sharma (Sr PSA, Migration) 

Chaos engineering is the process of stressing an application in testing or production environments by creating disruptive events, such as outages, observing how the system responds, and implementing improvements. Chaos engineering helps you create the real-world conditions needed to uncover hidden issues and performance bottlenecks that are challenging to find in distributed applications.

You can build resilient distributed serverless applications using AWS Lambda and test Lambda functions in real world operating conditions using chaos engineering.  This blog shows an approach to inject chaos in Lambda functions, making no change to the Lambda function code. This blog uses the AWS Fault Injection Simulator (FIS) service to create experiments that inject disruptions for Lambda based serverless applications.

AWS FIS is a managed service that performs fault injection experiments on your AWS workloads. AWS FIS is used to set up and run fault experiments that simulate real-world conditions to discover application issues that are difficult to find otherwise. You can improve application resilience and performance using results from FIS experiments.

The sample code in this blog introduces random faults to existing Lambda functions, like an increase in response times (latency) or random failures. You can observe application behavior under introduced chaos and make improvements to the application.

Approaches to inject chaos in Lambda functions

AWS FIS currently does not support injecting faults in Lambda functions. However, there are two main approaches to inject chaos in Lambda functions: using external libraries or using Lambda layers.

Developers have created libraries to introduce failure conditions to Lambda functions, such as chaos_lambda and failure-Lambda. These libraries allow developers to inject elements of chaos into Python and Node.js Lambda functions. To inject chaos using these libraries, developers must decorate the existing Lambda function’s code. Decorator functions wrap the existing Lambda function, adding chaos at runtime. This approach requires developers to change the existing Lambda functions.

You can also use Lambda layers to inject chaos, requiring no change to the function code, as the fault injection is separated. Since the Lambda layer is deployed separately, you can independently change the element of chaos, like latency in response or failure of the Lambda function. This blog post discusses this approach.

Injecting chaos in Lambda functions using Lambda layers

A Lambda layer is a .zip file archive that contains supplementary code or data. Layers usually contain library dependencies, a custom runtime, or configuration files. This blog creates an FIS experiment that uses Lambda layers to inject disruptions in existing Lambda functions for Java, Node.js, and Python runtimes.

The Lambda layer contains the fault injection code. It is invoked prior to invocation of the Lambda function and injects random latency or errors. Injecting random latency simulates real world unpredictable conditions. The Java, Node.js, and Python chaos injection layers provided are generic and reusable. You can use them to inject chaos in your Lambda functions.

The Chaos Injection Lambda Layers

Java Lambda Layer for Chaos Injection

Java Lambda Layer for Chaos Injection

The chaos injection layer for Java Lambda functions uses the JAVA_TOOL_OPTIONS environment variable. This environment variable allows specifying the initialization of tools, specifically the launching of native or Java programming language agents. The JAVA_TOOL_OPTIONS has a javaagent parameter that points to the chaos injection layer. This layer uses Java’s premain method and the Byte Buddy library for modifying the Lambda function’s Java class during runtime.

When the Lambda function is invoked, the JVM uses the class specified with the javaagent parameter and invokes its premain method before the Lambda function’s handler invocation. The Java premain method injects chaos before Lambda runs.

The FIS experiment adds the layer association and the JAVA_TOOL_OPTIONS environment variable to the Lambda function.

Python and Node.js Lambda Layer for Chaos Injection

Python and Node.js Lambda Layer for Chaos Injection

When injecting chaos in Python and Node.js functions, the Lambda function’s handler is replaced with a function in the respective layers by the FIS aws:ssm:start-automation-execution action. The automation, which is an SSM document, saves the original Lambda function’s handler to in AWS Systems Manager Parameter Store, so that the changes can be rolled back once the experiment is finished.

The layer function contains the logic to inject chaos. At runtime, the layer function is invoked, injecting chaos in the Lambda function. The layer function in turn invokes the Lambda function’s original handler, so that the functionality is fulfilled.

The result in all runtimes (Java, Python, or Node.js), is invocation of the original Lambda function with latency or failure injected. The observed changes are random latency or failure injected by the layer.

Once the experiment is completed, an SSM document is provided. This rolls back the layer’s association to the Lambda function and removes the environment variable, in the case of the Java runtime.

Sample FIS experiments using SSM and Lambda layers

In the sample code provided, Lambda layers are provided for Python, Node.js and Java runtimes along with sample Lambda functions for each runtime.

The sample deploys the Lambda layers and the Lambda functions, FIS experiment template, AWS Identity and Access Management (IAM) roles needed to run the experiment, and the AWS Systems Manger (SSM) Documents. AWS CloudFormation template is provided for deployment.

Step 1: Complete the prerequisites

  • To deploy the sample code, clone the repository locally:
    git clone
  • Complete the prerequisites documented here.

Step 2: Deploy using AWS CloudFormation

The CloudFormation template provided along with this blog deploys sample code. Execute

When this is complete, it returns the StackId that CloudFormation created:

Step 3: Run the chaos injection experiment

By default, the experiment is configured to inject chaos in the Java sample Lambda function. To change it to Python or Node.js Lambda functions, edit the experiment template and configure it to inject chaos using steps from here.

Step 4: Start the experiment

From the FIS Console, choose Start experiment.

 Start experiment

Wait until the experiment state changes to “Completed”.

Step 5: Run your test

At this stage, you can inject chaos into your Lambda function. Run the Lambda functions and observe their behavior.

1. Invoke the Lambda function using the command below:

aws lambda invoke --function-name NodeChaosInjectionExampleFn out --log-type Tail --query 'LogResult' --output text | base64 -d

2. The CLI commands output displays the logs created by the Lambda layers showing latency introduced in this invocation.

In this example, the output shows that the Lambda layer injected 1799ms of random latency to the function.

The experiment injects random latency or failure in the Lambda function. Running the Lambda function again results in a different latency or failure. At this stage, you can test the application, and observe its behavior under conditions that may occur in the real world, like an increase in latency or Lambda function’s failure.

Step 6: Roll back the experiment

To roll back the experiment, run the SSM document for rollback. This rolls back the Lambda function to the state before chaos injection. Run this command:

aws ssm start-automation-execution \
--document-name “InjectLambdaChaos-Rollback” \
--document-version “\$DEFAULT” \
--parameters \
”]}’ \
--region eu-west-2

Cleaning up

To avoid incurring future charges, clean up the resources created by the CloudFormation template by running the following CLI command. Update the stack name to the one you provided when creating the stack.

aws cloudformation delete-stack --stack-name myChaosStack

Using FIS Experiments results

You can use FIS experiment results to validate expected system behavior. An example of expected behavior is: “If application latency increases by 10%, there is less than a 1% increase in sign in failures.” After the experiment is completed, evaluate whether the application resiliency aligns with your business and technical expectations.


This blog explains an approach for testing reliability and resilience in Lambda functions using chaos engineering. This approach allows you to inject chaos in Lambda functions without changing the Lambda function code, with clear segregation of chaos injection and business logic. It provides a way for developers to focus on building business functionality using Lambda functions.

The Lambda layers that inject chaos can be developed and managed separately. This approach uses AWS FIS to run experiments that inject chaos using Lambda layers and test serverless application’s performance and resiliency. Using the insights from the FIS experiment, you can find, fix, or document risks that surface in the application while testing.

For more serverless learning resources, visit Serverless Land.

How Vercel Shipped Cron Jobs in 2 Months Using Amazon EventBridge Scheduler

Post Syndicated from Marcia Villalba original

Vercel implemented Cron Jobs using Amazon EventBridge Scheduler, enabling their customers to create, manage, and run scheduled tasks at scale. The adoption of this feature was rapid, reaching over 7 million weekly cron invocations within a few months of release. This article shows how they did it and how they handle the massive scale they’re experiencing.

Vercel builds a front-end cloud that makes it easier for engineers to deploy and run their front-end applications. With more than 100 million deployments in Vercel in the last two years, Vercel helps users take advantage of best-in-class AWS infrastructure with zero configuration by relying heavily on serverless technology. Vercel provides a lot of features that help developers host their front-end applications. However, until the beginning of this year, they hadn’t built Cron Jobs yet.

A cron job is a scheduled task that automates running specific commands or scripts at predetermined intervals or fixed times. It enables users to set up regular, repetitive actions, such as backups, sending notification emails to customers, or processing payments when a subscription needs to be renewed. Cron jobs are widely used in computing environments to improve efficiency and automate routine operations, and they were a commonly requested feature from Vercel’s customers.

In December 2022, Vercel hosted an internal hackathon to foster innovation. That’s where Vincent Voyer and Andreas Schneider joined forces to build a prototype cron job feature for the Vercel platform. They formed a team of five people and worked on the feature for a week. The team worked on different tasks, from building a user interface to display the cron jobs to creating the backend implementation of the feature.

Amazon EventBridge Scheduler
When the hackathon team started thinking about solving the cron job problem, their first idea was to use Amazon EventBridge rules that run on a schedule. However, they realized quickly that this feature has a limit of 300 rules per account per AWS Region, which wasn’t enough for their intended use. Luckily, one of the team members had read the announcement of Amazon EventBridge Scheduler in the AWS Compute blog and they thought this would be a perfect tool for their problem.

By using EventBridge Scheduler, they could schedule one-time or recurrently millions of tasks across over 270 AWS services without provisioning or managing the underlying infrastructure.

How cron jobs work

For creating a new cron job in Vercel, a customer needs to define the frequency in which this task will run and the API they want to invoke. Vercel, in the backend, uses EventBridge Scheduler and creates a new schedule when a new cron job is created.

To call the endpoint, the team used an AWS Lambda function that receives the path that needs to be invoked as input parameters.

How cron jobs works

When the time comes for the cron job to run, EventBridge Scheduler invokes the function, which then calls the customer website endpoint that was configured.

By the end of the week, Vincent and his team had a working prototype version of the cron jobs feature, and they won a prize at the hackathon.

Building Vercel Cron Jobs
After working for one week on this prototype in December, the hackathon ended, and Vincent and his team returned to their regular jobs. In early January 2023, Vicent and the Vercel team decided to take the project and turn it into a real product.

During the hackathon, the team built the fundamental parts of the feature, but there were some details that they needed to polish to make it production ready. Vincent and Andreas worked on the feature, and in less than two months, on February 22, 2023, they announced Vercel Cron Jobs to the public. The announcement tweet got over 400 thousand views, and the community loved the launch.

Tweet from Vercel announcing cron jobs

The adoption of this feature was very rapid. Within a few months of launching Cron Jobs, Vercel reached over 7 million cron invocations per week, and they expect the adoption to continue growing.

Cron jobs adoption

How Vercel Cron Jobs Handles Scale
With this pace of adoption, scaling this feature is crucial for Vercel. In order to scale the amount of cron invocations at this pace, they had to make some business and architectural decisions.

From the business perspective, they defined limits for their free-tier customers. Free-tier customers can create a maximum of two cron jobs in their account, and they can only have hourly schedules. This means that free customers cannot run a cron job every 30 minutes; instead, they can do it at most every hour. Only customers on Vercel paid tiers can take advantage of EventBridge Scheduler minute granularity for scheduling tasks.

Also, for free customers, minute precision isn’t guaranteed. To achieve this, Vincent took advantage of the time window configuration from EventBridge Scheduler. The flexible time window configuration allows you to start a schedule within a window of time. This means that the scheduled tasks are dispersed across the time window to reduce the impact of multiple requests on downstream services. This is very useful if, for example, many customers want to run their jobs at midnight. By using the flexible time window, the load can spread across a set window of time.

From the architectural perspective, Vercel took advantage of hosting the APIs and owning the functions that the cron jobs invoke.

Validating the calls

This means that when the Lambda function is started by EventBridge Scheduler, the function ends its run without waiting for a response from the API. Then Vercel validates if the cron job ran by checking if the API and Vercel function ran correctly from its observability mechanisms. In this way, the function duration is very short, less than 400 milliseconds. This allows Vercel to run a lot of functions per second without affecting their concurrency limits.

Lambda invocations and duration dashboard

What Was The Impact?
Vercel’s implementation of Cron Jobs is an excellent example of what serverless technologies enable. In two months, with two people working full time, they were able to launch a feature that their community needed and enthusiastically adopted. This feature shows the completeness of Vercel’s platform and is an important feature to convince their customers to move to a paid account.

If you want to get started with EventBridge Scheduler, see Serverless Land patterns for EventBridge Scheduler, where you’ll find a broad range of examples to help you.


AWS Weekly Roundup – AWS AppSync, AWS CodePipeline, Events and More – August 21, 2023

Post Syndicated from Marcia Villalba original

In a few days, I will board a plane towards the south. My tour around Latin America starts. But I won’t be alone in this adventure, you can find some other News Blog authors, like Jeff or Seb, speaking at AWS Community Days and local events in Peru, Argentina, Chile, and Uruguay. If you see us, come and say hi. We would love to meet you.

Latam Community in reInvent 2022

Last Week’s Launches
Here are some launches that got my attention during the previous week.

AWS AppSync now supports JavaScript for all resolvers in GraphQL APIs – Last year, we announced that AppSync now supports JavaScript pipeline resolvers. And starting last week, developers can use JavaScript to write unit resolvers, pipeline resolvers, and AppSync functions that are run on the AppSync Javascript runtime.

AWS CodePipeline now supports GitLabNow you can use your source repository to build, test, and deploy code changes using AWS CodePipeline, in addition to other providers like AWS CodeCommit, Bitbucket,, and GitHub Enterprise Server.

Amazon CloudWatch Agent adds support for OpenTelemetry traces and AWS X-Ray With the new version of the agent you are now able to collect metrics, logs, and traces with a single agent, not only for CloudWatch but also for OpenTelemetry and AWS X-Ray. Simplifying the installation, configuration, and management of telemetry collection.

New instance types: Amazon EC2 M7a and Amazon EC2 Hpc7a – The new Amazon EC2 M7a is a general purpose instance type powered by 4th Gen AMD EPYC processor. In the announcement blog, you can find all the specifics for this instance type. The new Amazon EC2 Hpc7a instances are also powered by 4th Gen AMD EPYC processors. These instance types are optimized for high performance computing and Channy Yun wrote a blog post describing the different characteristics of the Amazon EC2 Hpc7a instance type.

AWS DeepRacer Educator PlaybooksLast week we introduced the AWS DeepRacer educator playblooks, these are a tool for educators to integrate foundational machine learning (ML) curriculum and labs into their classrooms. Educators can use these playbooks to easily upskill students in the basics of ML with autonomous vehicles.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates and news that you might have missed:

Guide for using AWS Lambda to process Apache Kafka StreamsJulian Wood just published the most complete guide you can find on how to use Lambda with Apache Kafka. If you are an Amazon Kinesis user, don’t worry. We’ve got you covered with this video series where you will find similar topics.

Using AWS Lambda with Kafka guide

The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in several languages. Check out the ones in FrenchGermanItalian, and Spanish.

AWS Open-Source News and Updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open source projects, posts, events, and more.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

Join AWS Hybrid Cloud & Edge Day to learn how to deploy your applications in the everywhere cloud

AWS Global SummitsAWS Summits – The 2023 AWS Summits season is almost ending with the last two in-person events in Mexico City (August 30) and Johannesburg (September 26).

AWS re:Invent reInvent(November 27–December 1) – But don’t worry because re:Invent season is coming closer. Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Registration is now open.

AWS Community Days AWS Community Day– Join a community-led conference run by AWS user group leaders in your region:Taiwan (August 26), Aotearoa (September 6), Lebanon (September 9), Munich (September 14), Argentina (September 16), Spain (September 23), and Chile (September 30). Check all the upcoming AWS Community Days here.

CDK Day (September 29) – A community-led fully virtual event with tracks in English and in Spanish about CDK and related projects. Learn more in the website.

That’s all for this week. Check back next Monday for another Week in Review!

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

— Marcia

Automatically delete schedules upon completion with Amazon EventBridge Scheduler

Post Syndicated from Marcia Villalba original

Amazon EventBridge Scheduler now supports configuring automatic deletion of schedules after completion. Now you can configure one-time and recurring schedules with an end date to be automatically deleted upon completion to avoid managing individual schedules.

Amazon EventBridge Scheduler allows you to create, run, and manage schedules on scale. Using EventBridge Scheduler, you can schedule millions of tasks to invoke over 270 AWS services and over 6,000 API operations, such as AWS Lambda, AWS Step Functions, and Amazon SNS.

By default, EventBridge Scheduler allows customers to have 1 million schedules per account, which can be increased as needed. However, completed schedules are counted towards the account quota limits. In addition, completed schedules are visible when listing schedules, and require customers to remove them. Some customers have created their own patterns to automatically remove completed schedules and since the EventBridge Scheduler announcement last November, this was one of the most requested features by customers.

Deleting after completion

When you configure automatic deletion for a schedule, EventBridge Scheduler deletes the schedule shortly after its last target invocation. You can set up automatic deletion when you create the schedule, or you can update the schedule settings at any point before its last invocation.

You can configure this setting in one-time and recurring schedules.

  • One-time schedules: your schedule is deleted after the schedule has invoked its target once.
  • Recurring schedules: set with rate or cron expressions, your schedule is deleted after the last invocation.

If all retries are exhausted because of failure for a schedule configured with automatic deletion, the schedule is deleted shortly after the last unsuccessful attempt.

Console action after schedule completition

With this new capability, you can save time, resources, and operational costs when managing your schedules.

Setting up schedules to delete after completion

You can create schedules that are automatically deleted after completion from the AWS Management Console, AWS SDK, or AWS CLI in all AWS Regions where EventBridge Scheduler is available.

For example, imagine that you are a developer on a platform that allows end users to receive notifications when a task is due. You are already using EventBridge Scheduler to implement this feature. For every task that your users create in your application, your code creates a new schedule in EventBridge Scheduler. You can now configure all these schedules to be deleted automatically after completion. And shortly after the schedules run, they are removed from your EventBridge Scheduler, allowing you to scale your system and keep on creating schedules, making it easier to manage your active schedules and quota limits.

Let see how you can implement this example with the new capability of EventBridge Scheduler. When a user creates a new task with a reminder, a function is triggered from your application. That function creates a one-time schedule in EventBridge Scheduler.

Create a schedule diagram

This example shows how you can create a new one-time schedule that is automatically deleted after completion using the AWS CLI and has SNS as a target. Make sure that you update the AWS CLI to the latest version. Then you can create a new schedule with the parameter action-after-completion ‘DELETE’.

$ aws scheduler create-schedule --name SendEmailOnce \
--schedule-expression ”at(2023-08-02T17:35:00)",\
--schedule-expression-timezone "Europe/Helsinki" \
--flexible-time-window "{\"Mode\": \"OFF\"}" \
--target "{\"Arn\": \"arn:aws:sns:us-east-1:xxx:test-send-email\", \"RoleArn\": \" arn:aws:iam::xxxx:role/sam_scheduler_role\" }" \
--action-after-completion 'DELETE'

This command creates a one-time schedule with the name SendEmailOnce, that runs at a specific date, defined in the schedule-expression, and in a specific time zone, defined in the schedule-expression-timezone. This schedule is not using the flexible time window feature. Next, you must define the target for this schedule. This one sends a message to an SNS topic.

You can validate that your schedule is created correctly from the AWS CLI with the get-schedule command.

$ aws scheduler get-schedule --name SendEmailOnce
    "ActionAfterCompletion": "DELETE",
    "Arn": "arn:aws:scheduler:us-east-1:905614108351:schedule/default/SendEmailOnce",
    "CreationDate": 1690874334.83,
    "FlexibleTimeWindow": {
        "Mode": "OFF"
    "GroupName": "default",
    "LastModificationDate": 1690874334.83,
    "Name": "SendEmailOnce3",
    "ScheduleExpression": "at(2023-08-02T17:35:00)",
    "ScheduleExpressionTimezone": "Europe/Helsinki",
    "State": "ENABLED",
    "Target": {
        "Arn": "arn:aws:sns:us-east-1:xxxx:test-send-email",
        "RetryPolicy": {
            "MaximumEventAgeInSeconds": 86400,
            "MaximumRetryAttempts": 185
        "RoleArn": "arn:aws:iam::XXXX:role/scheduler_role"

In addition, you can see the details of the schedule from the AWS Management Console.

Schedule details

Now when the date of the notification arrives, EventBridge Scheduler invokes the target configured in the schedule, in this case SNS, and emails a notification to the customer.

Schedule starts

Shortly after this schedule is completed, if you list the schedules, you see that the schedule was deleted and it is no longer listed.

$ aws scheduler list-schedules
"Schedules": [

Benefits of automation

Traditionally, many problems that EventBridge Scheduler solves were addressed using batch processes and pull-based models.

Some organizations are using EventBridge Scheduler to replace their pull-based models for a more dynamic push-based model. Before implementing Scheduler, they were relying on the customer to ask for the data when they need it. Now, with EventBridge Scheduler, they are creating schedules to report back to their customers at critical times of their journey.

For example, an airline can use EventBridge Scheduler to create one-time schedules 24 hours, 4 hours, and 2 hours before the flight, to keep their passengers up to date with the flight status. Customers receive a notification with the link for the online check-in, the check-in counter number, baggage pick up information, and any flight changes that occur. In this way, passengers are always up to date on their flight status and they can take immediate action. This dynamic model not only helps to improve the customer experience but also improves the operational efficiency for the airline.

Other organizations use EventBridge Scheduler to replace batch operations, as you can configure a schedule that starts a batch process at the time of the day you need. Also, you can take advantage of EventBridge Scheduler time zones and run the processes at the time that make sense for your end customer.

For example, consider an international financial institution that must send customers a statement of their account at the end of the day. You can use EventBridge Scheduler to set up a recurrent schedule for each of your customers that sends a report at the end of the day of your customers’ time zone. In this way, you can improve the customer experience as now the system is personalized for their settings, and also reduce operational overhead, as the processing operations are distributed throughout the day.

In addition, EventBridge Scheduler solves many new use cases for customers. For example, if you are a financial institution that handles payments, you can create a one-time schedule for every large transaction that needs a confirmation. If the transaction is not confirmed when the schedule runs, you can cancel the transaction. This decreases the risk of handling transactions, improves the customer experience, and also improves the automation of your processes by making them real time.

Another use case is to handle credit card expiration dates. You can create a one-time schedule that emails the customer to update their credit card information one month before the expiration date. This solution removes operational overhead compared to the traditional implementation of using servers and batch processes.


In the preceding use cases listed, automation and task scheduling improve the end user experience, remove undifferentiated heavy lifting, and benefit from using the new capability of removing schedules after their completion.

This blog post introduces the new capability from Amazon EventBridge Scheduler that automatically deletes the completed schedules. This feature simplifies the use of EventBridge Scheduler, reduces the operational overhead of managing schedules at scale, and allows you to scale even further.

To get started with EventBridge Scheduler, visit Serverless Land patterns where you can find over 20 patterns using this service.

How GoDaddy Implemented a Multi-Region Event-Driven Platform at Scale

Post Syndicated from Marcia Villalba original

GoDaddy, a leading global provider of domain registration and web hosting services, has served over 84 million domains and 22 million customers since its establishment in 1997. Among its various internal systems, the Customer Signal Platform provides tooling to capture, analyze, and act on customer and product data to drive better business outcomes. With this platform, GoDaddy can track user visits and interactions on its website and use meaningful event data to improve its customer experience and overall business performance.

Nowadays, the Customer Signal Platform processes 400 million events every day. As GoDaddy expands its integrations, it aims to increase this number to 2 billion events per day in the near future.

When building the Customer Signal Platform, GoDaddy had three main requirements for the system architecture:

  1. Minimize their operational load.
  2. Scale automatically as traffic changes.
  3. Provide high availability and ensure that all the customer signals are captured.

Amazon EventBridge Event Bus
After evaluating many options against their requirements, GoDaddy decided to implement the customer signal platform using Amazon EventBridge Event Bus. EventBridge Event Bus is a serverless event bus that helps you receive, filter, transform, route, and deliver events. Because EventBridge is serverless, it requires minimal configuration to get started and scales automatically—GoDaddy’s first two requirements were checked.

To comply with the third requirement, the solution needed to provide business continuity and ensure that no event is lost from the moment the client produces it until it gets to the platform to be analyzed. EventBridge Event Bus comes with many features that helped GoDaddy build their application with this requirement in mind.

The main feature that GoDaddy took advantage of was global endpoints. EventBridge global endpoints provide a reliable and simple way to improve the business continuity of event-driven applications. This new feature, added in 2022, allows customers to build a multi-Region event-driven application.

EventBridge Global Endpoints
Global endpoints allow you to configure a managed DNS endpoint in EventBridge, to which your applications will send events. Then you need to configure two custom event buses in two distinct AWS Regions. One is the primary Region, and the other is the failover, or secondary Region. The failover of events is decided based on the health indicated by an Amazon Route 53 health check. When the health check is healthy, the events are routed from the global endpoint to the custom event bus in the primary Region. And if the health check is unhealthy, then the global endpoint will send the events to the event bus in the secondary Region.

Healthcheck status

The simplest configuration for global endpoints is the active/archive configuration. This configuration provides business continuity and simplicity at the same time. The active/archive configuration defines two different Regions. The primary Region is where the application is deployed and all the business processes are happening. The archive Region is where only a custom bus is deployed and all the events are archived.

In addition, there is a bidirectional replication rule between the buses in separate Regions. In the normal case, when there are no errors, whenever an event arrives at the custom bus in the primary Region, the event is automatically replicated to the archive custom bus in the secondary Region.

In the case of failover, the global endpoint redirects the events to the secondary Region, where they get archived for processing at another time.

Active/ Archive configuration

GoDaddy Implementation of Global Endpoints
GoDaddy was looking for a solution that minimized their operations load while still providing business continuity, and that is why they adopted global endpoints and the active/archive configuration. In this way, they could have the event processing logic in their primary Region and have a secondary Region in case of any issues.

In their configuration, events are archived in the secondary Region for 30 days, after which the events expire. In the case of a failover, because they don’t need to process the events in real time, they collect them in the archive. If the issue is resolved within 24 hours, the retention period for the replication rule, the events are sent automatically to the primary Region. If the issue is solved in more than 24 hours the events need to be replayed to the primary Region.

The following image shows what their current solution looks like. They are working with two Regions. US West (Oregon) is their primary Region and is the location of the data lake, which is the primary consumer of the events. US East (N. Virginia) is the secondary Region. Events are being produced in different clients; from the clients, they are sent to Amazon API Gateway. GoDaddy deployed two API Gateways in their two Regions. The events are sent to the API Gateway with the smallest latency from the client. To do that, they use latency-based routing provided by Amazon Route 53. Then events are sent to an AWS Lambda function that validates the events and forwards them to the EventBridge global endpoint at the DNS level.

GoDaddy architecture

The global endpoint is configured with the active/archive setup, and the failover is configured to be triggered via a Route 53 health check that monitors an Amazon CloudWatch alarm. That alarm observes the IngestionToInvocationStartLatency metric in the primary Region.

IngestionToInvocationStartLatency is a service-level metric that exposes the time to process events from the point at which they are ingested by EventBridge to the point the first invocation of a target in the configured rules is made. This metric is measured across all the rules in your bus and provides an indication of the health of the EventBridge service. Any extended periods of high latency over 30 seconds indicate a service disruption.

When the system is in the normal state, the events are forwarded from the global endpoint to the custom ingress event bus in the primary Region. That custom event bus has replication enabled; this means that all the events that arrive at the bus get replicated automatically in the secondary Region custom ingress event bus.

All the events received by the ingress event bus are sent to the enrichment function. This function performs basic validation and authentication, and it enriches the event data to make sure that all the events from different clients are standard.

From there, the events are forwarded to the data platform event bus to be sent to the different consumer targets. The main target is their data lake solution, which analyzes all the events.

What Was the Impact?
For GoDaddy, business continuity is important, and their customer signals are not getting lost due to any issue with their platform. This makes them confident that they can expand their customer signal platforms from 400 million events per day to 2 billion events per day without introducing any additional operations overhead.

Now, they can confidently process hundreds of millions of events per day to their system, and they can keep on growing. The following image shows the number of events ingested by global endpoints in a normal day.

Events ingested

While GoDaddy’s use of the active/archive pattern enables them to ensure they never lose any events, they’re already starting to see certain use cases where they want to minimize any delays in processing their events, even when service disruptions occur. Because they’re already replicating their events to a secondary Region, they can deploy their most critical consumers to both Regions and enable an active/active configuration for their mission-critical systems. Active/active configuration allows you to process parallel events in both the primary and secondary Regions, simplifying the processing of events even during disruptions and enabling business continuity.

The vision when building the Customer Signal Platform was to align with GoDaddy’s high bar for reliability, scalability, and maintainability and, at the same time, keep the platform self-service so that developers can focus on business needs. This led GoDaddy to choose Amazon EventBridge global endpoints and serverless technologies to build this solution.

GoDaddy Customer Signal Platform is an excellent example of what serverless technologies enable. By leveraging the cloud to handle as much of the undifferentiated heavy lifting as possible, GoDaddy has reduced the operational complexity of setting up an event bus for a multi-Region strategy, implemented failover mechanisms in the case of Regional distruptions, and ensured that events are not lost by enabling replication. Global endpoints active/archive configuration improves the availability of customer applications with the least amount of configuration changes.

If you want to get started with EventBridge global endpoints, you can check out this talk on event-driven applications. For a working demo on how to use EventBridge global endpoints for failover events, check out this Serverless Land repository.


AWS Week in Review – Automate DLQ Redrive for SQS, Lambda Supports Ruby 3.2, and More – June 12, 2023

Post Syndicated from Marcia Villalba original

Today I’m boarding a plane for Madrid. I will attend the AWS Summit Madrid this Thursday, and I will take Serverlesspresso with me. Serverlesspresso is a demo that we take to events, in where you can learn how to build event-driven architectures with serverless. If you are visiting an AWS Summit, most probably you will find one of our booths.

Serverlesspresso at Madrid

Last Week’s Launches
Here are some launches that got my attention during the previous week.

Amazon SQS – Customers were very excited when we announced the DLQ redrive for Amazon SQS as that feature helped them to easily redirect the failed messages. This week we added support for AWS SDK and CLI for this feature, allowing you to redrive the messages on the DLQ automatically, making it even easier to use this feature. You can read Seb’s blog post about this new feature to learn how to get started.

AWS Lambda – AWS Lambda now supports Ruby 3.2. Ruby 3.2 has many new improvements, for example, passing anonymous arguments to functions or having endless methods. Check out this blog post that goes in depth into each of the new features.

Amazon Fraud DetectorAmazon Fraud Detector supports event orchestration with Amazon EventBridge. This is a very important feature because now you can act on the different events that Fraud Detector emits, for example, send notifications to different stakeholders.

AWS Glue – This week, AWS Glue made two important announcements. First, it announced the general availability of AWS Glue for Ray, a new data integration engine option for AWS Glue. Ray is a popular new open-source compute framework that helps developers to scale their Python workloads. In addition, AWS Glue announced AWS Glue Data Quality, a new capability that automatically measures and monitors data lake and data pipeline quality.

Amazon Elastic Container Registry (Amazon ECR)AWS Signer and Amazon ECR announced a new feature that allows you to sign and verify container images. You can use Signer to validate that only container images you have approved are deployed in your Amazon Elastic Kubernetes Service (Amazon EKS) clusters.

Amazon QuickSightAmazon QuickSight now supports APIs to automate asset deployment, so you can replicate the same QuickSight assets in multiple Regions and account easily. You can read more on how to use those APIs in this blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates and news that you may have missed:

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

  • AWS Silicon Innovation Day (June 21) – A one-day virtual event that focuses on AWS Silicon and how you can take advantage of AWS’s unique offerings. Learn more and register here.
  • AWS Global Summits – There are many summits going on right now around the world: Toronto (June 14), Madrid (June 15), and Milano (June 22).
  • AWS Community Day – Join a community-led conference run by AWS user group leaders in your region: Chicago (June 15), Manila (June 29–30), Chile (July 1), and Munich (September 14).
  • CDK Day CDK Day is happening again this year on September 29. The call for papers for this event is open, and this year we are also accepting talks in Spanish. Submit your talk here.

That’s all for this week. Check back next Monday for another Week in Review!

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

— Marcia

Learn How to Modernize Your Applications at AWS Serverless Innovation Day

Post Syndicated from Marcia Villalba original

Join us on Wednesday, May 17, for AWS Serverless Innovation Day, a free full-day virtual event. You will learn about AWS Serverless technologies and event-driven architectures from customers, experts, and leaders.

AWS Serverless Innovation Day is an event to empower builders and technical decision-makers with different AWS Serverless technologies, including AWS Lambda, Amazon Elastic Container Service (Amazon ECS) with AWS Fargate, Amazon EventBridge, and AWS Step Functions. The talks of the day will cover three key topics: event-driven architectures, serverless containers, and serverless functions, and how they can be utilized to build and modernize applications. Application modernization is a priority for organizations this year, and serverless helps to increase the software delivery speed and reduce the total cost of ownership.

AWS Serverless Innovation Day

Eric Johnson and Jessica Deen will be the hosts for the event. Holly Mesrobian, VP of Serverless Compute at AWS, will deliver the welcome keynote and share AWS’s vision for Serverless. The day ends with closing remarks from James Beswick and Usman Khalid, Events and Workflows Director at AWS.

The event is split into three groups of talks: event-driven architecture, serverless containers, and Lambda-based applications. Each group kicks off with a fireside chat between AWS customers and an AWS leader. You can learn how organizations, such as Capital One, PostNL, Pentasoft, Delta Air Lines, and Smartsheets, are using AWS Serverless technologies to solve their most challenging problems and continue to innovate.

During the day, all the sessions include demos and use cases, where you can learn the best practices and how to build applications. If you cannot attend all day, here are some of my favorite sessions to watch:

  • Building with serverless workflows at scaleBen Smith will show you how to unleash the power of AWS Step Functions.
  • Event design and event-first development – In this session, David Boyne will show you a robust approach to event design with Amazon EventBridge.
  • Best practices for AWS Lambda – You will learn from Julian Wood how to get the most out of your functions.
  • Optimizing for cost using Amazon ECSScott Coulton will show you how to reduce operational overhead from the control plane with Amazon ECS.

There is no up-front registration required to join the AWS Serverless Innovation Day, but if you want to be notified before the event starts, get in-depth news, articles, and event updates, and get a notification when the on-demand videos are available, you can register on the event page. The event will be streamed on Twitch, LinkedIn Live, YouTube, and Twitter.

See you there.


Debugging SnapStart-enabled Lambda functions made easy with AWS X-Ray

Post Syndicated from Marcia Villalba original

This post is written by Rahul Popat (Senior Solutions Architect) and Aneel Murari (Senior Solutions Architect) 

Today, AWS X-Ray is announcing support for SnapStart-enabled AWS Lambda functions. Lambda SnapStart is a performance optimization that significantly improves the cold startup times for your functions. Announced at AWS re:Invent 2022, this feature delivers up to 10 times faster function startup times for latency-sensitive Java applications at no extra cost, and with minimal or no code changes.

X-Ray is a distributed tracing system that provides an end-to-end view of how an application is performing. X-Ray collects data about requests that your application serves and provides tools you can use to gain insight into opportunities for optimizations. Now you can use X-Ray to gain insights into the performance improvements of your SnapStart-enabled Lambda function.

With today’s feature launch, by turning on X-Ray tracing for SnapStart-enabled Lambda functions, you see separate subsegments corresponding to the Restore and Invoke phases for your Lambda function’s execution.

How does Lambda SnapStart work?

With SnapStart, the function’s initialization is done ahead of time when you publish a function version. Lambda takes an encrypted snapshot of the initialized execution environment and persists the snapshot in a tiered cache for low latency access.

When the function is first invoked or scaled, Lambda restores the cached execution environment from the persisted snapshot instead of initializing anew. This results in reduced startup times.

X-Ray tracing before this feature launch

Using an example of a Hello World application written in Java, a Lambda function is configured with SnapStart and fronted by Amazon API Gateway:

Before today’s launch, X-Ray was not supported for SnapStart-enabled Lambda functions. So if you had enabled X-Ray tracing for API Gateway, the X-Ray trace for the sample application would look like:

The trace only shows the overall duration of the Lambda service call. You do not have insight into your function’s execution or the breakdown of different phases of Lambda function lifecycle.

Next, enable X-Ray for your Lambda function and see how you can view a breakdown of your function’s total execution duration.

Prerequisites for enabling X-Ray for SnapStart-enabled Lambda function

SnapStart is only supported for Lambda functions with Java 11 and newly launched Java 17 managed runtimes. You can only enable SnapStart for the published versions of your Lambda function. Once you’ve enabled SnapStart, Lambda publishes all subsequent versions with snapshots. You may also create a Lambda function alias, which points to the published version of your Lambda function.

Make sure that the Lambda function’s execution role has appropriate permissions to write to X-Ray.

Enabling AWS X-Ray for your Lambda function with SnapStart

You can enable X-Ray tracing for your Lambda function using AWS Management Console, AWS Command Line Interface (AWS CLI), AWS Serverless Application Model (AWS SAM), AWS CloudFormation template, or via AWS Cloud Deployment Kit (CDK).

This blog shows how you can achieve this via AWS Management Console and AWS SAM. For more information on enabling SnapStart and X-Ray using other methods, refer to AWS Lambda Developer Guide.

Enabling SnapStart and X-Ray via AWS Management Console

To enable SnapStart and X-Ray for Lambda function via the AWS Management Console:

  1. Navigate to your Lambda Function.
  2. On the Configuration tab, choose Edit and change the SnapStart attribute value from None to PublishedVersions.
  3. Choose Save.

To enable X-Ray via the AWS Management Console:

  1. Navigate to your Lambda Function.
  2. ­On the Configuration tab, scroll down to the Monitoring and operations tools card and choose Edit.
  3. Under AWS X-Ray, enable Active tracing.
  4. Choose Save

To publish a new version of Lambda function via the AWS Management Console:

  1. Navigate to your Lambda Function.
  2. On the Version tab, choose Publish new version.
  3. Verify that PublishedVersions is shown below SnapStart.
  4. Choose Publish.

To create an alias for a published version of your Lambda function via the AWS Management Console:

  1. Navigate to your Lambda Function.
  2. On the Aliases tab, choose Create alias.
  3. Provide a Name for an alias and select a Version of your Lambda function to point the alias to.
  4. Choose Save.

Enabling SnapStart and X-Ray via AWS SAM

To enable SnapStart and X-Ray for Lambda function via AWS SAM:

    1. Enable Lambda function versions and create an alias by adding a AutoPublishAlias property in template.yaml file. AWS SAM automatically publishes a new version for each new deployment and automatically assigns the alias to the newly published version.
          type: AWS::Serverless::Function
            AutoPublishAlias: live
    2. Enable SnapStart on Lambda function by adding the SnapStart property in template.yaml file.
          type: AWS::Serverless::Function 
             ApplyOn: PublishedVersions
    3. Enable X-Ray for Lambda function by adding the Tracing property in template.yaml file.
          type: AWS::Serverless::Function
            Tracing: Active 

You can find the complete AWS SAM template for the preceding example in this GitHub repository.

Using X-Ray to gain insights into SnapStart-enabled Lambda function’s performance

To demonstrate X-Ray integration for your Lambda function with SnapStart, you can build, deploy, and test the sample Hello World application using AWS SAM CLI. To do this, follow the instructions in the README file of the GitHub project.

The build and deployment output with AWS SAM looks like this:

Once your application is deployed to your AWS account, note that SnapStart and X-Ray tracing is enabled for your Lambda function. You should also see an alias `live` created against the published version of your Lambda function.

You should also have an API deployed via API Gateway, which is pointing to the `live` alias of your Lambda function as the backend integration.

Now, invoke your API via `curl` command or any other HTTP client. Make sure to replace the url with your own API’s url.

$ curl --location --request GET https://{rest-api-id}.execute-api.{region}{stage}/hello

Navigate to Amazon CloudWatch and under the X-Ray service map, you see a visual representation of the trace data generated by your application.

Under Traces, you can see the individual traces, Response code, Response time, Duration, and other useful metrics.

Select a trace ID to see the breakdown of total Duration on your API call.

You can now see the complete trace for the Lambda function’s invocation with breakdown of time taken during each phase. You can see the Restore duration and actual Invocation duration separately.

Restore duration shown in the trace includes the time it takes for Lambda to restore a snapshot on the microVM, load the runtime (JVM), and run any afterRestore hooks if specified in your code. Note that, the process of restoring snapshots can include time spent on activities outside the microVM. This time is not reported in the Restore sub-segment, but is part of the AWS::Lambda segment in X-Ray traces.

This helps you better understand the latency of your Lambda function’s execution, and enables you to identify and troubleshoot the performance issues and errors.


This blog post shows how you can enable AWS X-Ray for your Lambda function enabled with SnapStart, and measure the end-to-end performance of such functions using X-Ray console. You can now see a complete breakdown of your Lambda function’s execution time. This includes Restore duration along with the Invocation duration, which can help you to understand your application’s startup times (cold starts), diagnose slowdowns, or troubleshoot any errors and timeouts.

To learn more about the Lambda SnapStart feature, visit the AWS Lambda Developer Guide.

For more serverless learning resources, visit Serverless Land.

AWS Week in Review – AWS Notifications, Serverless event, and More – May 8, 2023

Post Syndicated from Marcia Villalba original

At the end of this week, I’m flying to Seattle to take part in the AWS Serverless Innovation Day. Along with many customers and colleagues from AWS, we are going to be live on May 17 at a virtual free event. During the AWS Serverless Innovation Day we will share best practices related to building event-driven applications and using serverless functions and containers. Get a calendar reminder and check the full agenda at the event site.

Serverless innovation day

Last Week’s Launches
Here are some launches that got my attention during the previous week.

New Local Zones in Auckland – AWS Local Zones allow you to deliver applications that require single-digit millisecond latency or local data processing. Starting last week, AWS Local Zones is available in Auckland, New Zealand.

All AWS Local Zones

AWS Notifications Channy wrote an article explaining how you can view and configure notifications for your AWS account. In addition to the AWS Management Console notifications, the AWS Console Mobile Application now allows you to create and receive actionable push notifications when a resource requires your attention.

AWS SimSpace Weaver Last reInvent, we launched AWS SimSpace Weaver, a fully managed compute service that helps you deploy large spatial simulations in the cloud. Starting last week, AWS SimSpace Weaver allows you to save the state of the simulations at a specific point in time.

AWS Security Hub Added four new integration partners to help customers with their cloud security posture monitoring, and now it provides detailed tracking of finding changes with the finding history feature. This last feature provides an immutable trail of changes to get more visibility into the changes made to your findings.

AWS Compute Optimizer – AWS Compute Optimizer supports inferred workload type filtering on Amazon EC2 instance recommendations and automatically detects the applications that might run on your AWS resources. Now AWS Compute Optimizer supports filtering your rightsizing recommendation by tags and identifies and filters Microsoft SQL Server workloads as an inferred workload type.

AWS AppSyncNow AWS AppSync GraphQL APIs support Private API. With Private APIs, you can now create GraphQL APIs that can only be accessed from your Amazon Virtual Private Cloud (Amazon VPC).

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other updates and news that you may have missed:

  • Responsible AI in the Generative EraAmazon Science published a very interesting blog post this week about the special challenges raised by building a responsible generative AI and the different things builders of applications can do in order to solve these challenges.
  • Patterns for Building an API to Upload Files to Amazon S3 – Amazon S3 is one of the most used services by our customers, and applications often require a way for users to upload files. In this article, Thomas Moore shows different ways to do this in a secure way.
  • The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in your local languages. Check out the ones in FrenchGermanItalian, and Spanish.
  • AWS Open-Source News and Updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open-source projects, posts, events, and more.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

  • AWS Serverless Innovation DayJoin us on May 17 for a virtual and free event about AWS Serverless. We will have talks and fireside chats with customers related to AWS Lambda, Amazon ECS with Fargate, AWS Step Functions, and Amazon EventBridge.
  • AWS re:Inforce 2023You can now register for AWS re:Inforce, happening in Anaheim, California, on June 13–14.
  • AWS Global Summits – There are many summits going on right now around the world: Stockholm (May 11), Hong Kong (May 23), India (May 25), Amsterdam (June 1), London (June 7), Washington, DC (June 7–8), Toronto (June 14), Madrid (June 15), and Milano (June 22).
  • AWS Community Day – Join a community-led conference run by AWS user group leaders in your region: Warsaw (June 1), Chicago (June 15), Manila (June 29–30), and Munich (September 14).
  • AWS User Group Peru Conference – The local AWS User Group announced a one-day cloud event in Spanish and English in Lima on September 23. Seb, Jeff, and I will be attending the event from the AWS News blog team. Register today!

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

How CyberCRX cut ML processing time from 8 days to 56 minutes with AWS Step Functions Distributed Map

Post Syndicated from Marcia Villalba original

Last December, Sébastien Stormacq wrote about the availability of a distributed map state for AWS Step Functions, a new feature that allows you to orchestrate large-scale parallel workloads in the cloud. That’s when Charles Burton, a data systems engineer for a company called CyberGRX, found out about it and refactored his workflow, reducing the processing time for his machine learning (ML) processing job from 8 days to 56 minutes. Before, running the job required an engineer to constantly monitor it; now, it runs in less than an hour with no support needed. In addition, the new implementation with AWS Step Functions Distributed Map costs less than what it did originally.

What CyberGRX achieved with this solution is a perfect example of what serverless technologies embrace: letting the cloud do as much of the undifferentiated heavy lifting as possible so the engineers and data scientists have more time to focus on what’s important for the business. In this case, that means continuing to improve the model and the processes for one of the key offerings from CyberGRX, a cyber risk assessment of third parties using ML insights from its large and growing database.

What’s the business challenge?
CyberGRX shares third-party cyber risk (TPCRM) data with their customers. They predict, with high confidence, how a third-party company will respond to a risk assessment questionnaire. To do this, they have to run their predictive model on every company in their platform; they currently have predictive data on more than 225,000 companies. Whenever there’s a new company or the data changes for a company, they regenerate their predictive model by processing their entire dataset. Over time, CyberGRX data scientists improve the model or add new features to it, which also requires the model to be regenerated.

The challenge is running this job for 225,000 companies in a timely manner, with as few hands-on resources as possible. The job runs a set of operations for each company, and every company calculation is independent of other companies. This means that in the ideal case, every company can be processed at the same time. However, implementing such a massive parallelization is a challenging problem to solve.

First iteration
With that in mind, the company built their first iteration of the pipeline using Kubernetes and Argo Workflows, an open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes. These were tools they were familiar with, as they were already using them in their infrastructure.

But as soon as they tried to run the job for all the companies on the platform, they ran up against the limits of what their system could handle efficiently. Because the solution depended on a centralized controller, Argo Workflows, it was not robust, and the controller was scaled to its maximum capacity during this time. At that time, they only had 150,000 companies. And running the job with all of the companies took around 8 days, during which the system would crash and need to be restarted. It was very labor intensive, and it always required an engineer on call to monitor and troubleshoot the job.

The tipping point came when Charles joined the Analytics team at the beginning of 2022. One of his first tasks was to do a full model run on approximately 170,000 companies at that time. The model run lasted the whole week and ended at 2:00 AM on a Sunday. That’s when he decided their system needed to evolve.

Second iteration
With the pain of the last time he ran the model fresh in his mind, Charles thought through how he could rewrite the workflow. His first thought was to use AWS Lambda and SQS, but he realized that he needed an orchestrator in that solution. That’s why he chose Step Functions, a serverless service that helps you automate processes, orchestrate microservices, and create data and ML pipelines; plus, it scales as needed.

Charles got the new version of the workflow with Step Functions working in about 2 weeks. The first step he took was adapting his existing Docker image to run in Lambda using Lambda’s container image packaging format. Because the container already worked for his data processing tasks, this update was simple. He scheduled Lambda provisioned concurrency to make sure that all functions he needed were ready when he started the job. He also configured reserved concurrency to make sure that Lambda would be able to handle this maximum number of concurrent executions at a time. In order to support so many functions executing at the same time, he raised the concurrent execution quota for Lambda per account.

And to make sure that the steps were run in parallel, he used Step Functions and the map state. The map state allowed Charles to run a set of workflow steps for each item in a dataset. The iterations run in parallel. Because Step Functions map state offers 40 concurrent executions and CyberGRX needed more parallelization, they created a solution that launched multiple state machines in parallel; in this way, they were able to iterate fast across all the companies. Creating this complex solution, required a preprocessor that handled the heuristics of the concurrency of the system and split the input data across multiple state machines.

This second iteration was already better than the first one, as now it was able to finish the execution with no problems, and it could iterate over 200,000 companies in 90 minutes. However, the preprocessor was a very complex part of the system, and it was hitting the limits of the Lambda and Step Functions APIs due to the amount of parallelization.

Second iteration with AWS Step Functions

Third and final iteration
Then, during AWS re:Invent 2022, AWS announced a distributed map for Step Functions, a new type of map state that allows you to write Step Functions to coordinate large-scale parallel workloads. Using this new feature, you can easily iterate over millions of objects stored in Amazon Simple Storage Service (Amazon S3), and then the distributed map can launch up to 10,000 parallel sub-workflows to process the data.

When Charles read in the News Blog article about the 10,000 parallel workflow executions, he immediately thought about trying this new state. In a couple of weeks, Charles built the new iteration of the workflow.

Because the distributed map state split the input into different processors and handled the concurrency of the different executions, Charles was able to drop the complex preprocessor code.

The new process was the simplest that it’s ever been; now whenever they want to run the job, they just upload a file to Amazon S3 with the input data. This action triggers an Amazon EventBridge rule that targets the state machine with the distributed map. The state machine then executes with that file as an input and publishes the results to an Amazon Simple Notification Service (Amazon SNS) topic.

Final iteration with AWS Step Functions

What was the impact?
A few weeks after completing the third iteration, they had to run the job on all 227,000 companies in their platform. When the job finished, Charles’ team was blown away; the whole process took only 56 minutes to complete. They estimated that during those 56 minutes, the job ran more than 57 billion calculations.

Processing of the Distributed Map State

The following image shows an Amazon CloudWatch graph of the concurrent executions for one Lambda function during the time that the workflow was running. There are almost 10,000 functions running in parallel during this time.

Lambda concurrency CloudWatch graph

Simplifying and shortening the time to run the job opens a lot of possibilities for CyberGRX and the data science team. The benefits started right away the moment one of the data scientists wanted to run the job to test some improvements they had made for the model. They were able to run it independently without requiring an engineer to help them.

And, because the predictive model itself is one of the key offerings from CyberGRX, the company now has a more competitive product since the predictive analysis can be refined on a daily basis.

Learn more about using AWS Step Functions:

You can also check the Serverless Workflows Collection that we have available in Serverless Land for you to test and learn more about this new capability.