Tag Archives: AWS Lambda

Field Notes: Customizing the AWS Control Tower Account Factory with AWS Service Catalog

Post Syndicated from Remek Hetman original https://aws.amazon.com/blogs/architecture/field-notes-customizing-the-aws-control-tower-account-factory-with-aws-service-catalog/

Many AWS customers who are managing hundreds or thousands of accounts know how complex and time consuming this process can be. To reduce the burden and simplify the process of creating new accounts, last year AWS released a new service, AWS Control Tower.

AWS Control Tower helps you automate the process of setting up a multi-account AWS environment (AWS Landing Zone) that is secure, well-architected, and ready to use. This Landing Zone is created following best practices established through AWS’ experience working with thousands of enterprises as they move to the cloud. This includes the configuration of AWS Organizations, centralized logging, federated access, mandatory guardrails, and networking.

Those elements are a good starting point to cover the initial configuration of the new account. For some organizations, the next step is to baseline a newly vended account to align it with the company policies and compliance requirements. This means create or deploy necessary roles, policies, governance controls, security groups and so on.

In this blog post, I describe a solution that helps achieve consistent governance and compliance requirements across accounts created by AWS Control Tower. The solution uses the AWS Service Catalog as the repository of products that will be deployed into the new accounts.

Prerequisites and assumptions

Before I get into how it works, let’s first review a few key AWS Service Catalog concepts:

  • An AWS Service Catalog product is a blueprint for building your AWS resources that you want to make available for deployment on AWS along with the configuration information.
  • A portfolio is a collection of products, together with the configuration information.
  • A provisioned product is an AWS CloudFormation stack.
  • Constraints control the way users can deploy a product. With launch constraints, you can specify a role that the AWS Service Catalog can assume to launch a product.
  • Review AWS Service Catalog reference blueprints for a quick way to set up and configure AWS Service Catalog portfolios and products.

You need an AWS Service Catalog portfolio with products that you plan to deploy to the accounts. The portfolio has to be created in the AWS Control Tower primary account. If you don’t have a portfolio, the starting point would be to review these resources:

Solution Overview

This solution supports the following scenarios:

  • Deployment of products to the newly vended AWS Control Tower account (Figure 1)
  • Update and deployment of products to existing accounts (Figure 2)
Figure 1 Deployment to new account

Figure 1 – Deployment to new account

The architecture diagram in figure 1 shows the process of deploying products to the new account.

  1. AWS Control Tower creates a new account
  2. Once an account is created successfully, Amazon CloudWatch Events trigger an AWS Lambda function
  3. The Lambda function pulls the configuration from an Amazon S3 bucket and:
    • Validates configuration
    • Grants Lambda role access to portfolio(s)
    • Creates StackSet constrains for products
  4. Lambda calls the AWS Step Function
  5. The AWS Step Function orchestrates deployment of the AWS Service Catalog products and monitors progress
Figure 2 Update or deployment to an existing account

Figure 2 – Update or deployment to an existing account

The architecture diagram in figure 2 shows process of updating product or deploying product to the existing account.

  1. User uploads update configuration to an Amazon S3 Bucket
  2. Update triggers AWS Lambda Function
  3. Lambda function reads uploaded configuration and:
    • Validates configuration
    • Grants Lambda role access to portfolio(s)
    • Creates StackSet constrains for products
  4. Calls AWS Step Function
  5. AWS Step Function orchestrate update/deployment of AWS Service Catalog products and monitoring progress

Deployment and configuration

Before proceeding with the deployment, you must install Python3.

Then, follow these steps:

  1. Download the solution from GitHub
  2. Go to folder src and run the following command:                                                                                    “pip3 install –r requirements.txt –t .”
  3.  Zip content of the src folder to “control-tower-account-factory-solution.zip” file
  4. Upload zip file to your Amazon S3 bucket created in the AWS Region where you want to deploy the solution
  5. Launch the AWS CloudFormation template

AWS CloudFormation Template Parameters:

  • ConfigurationBucketName – name of the Amazon S3 bucket from where AWS Lambda function should pull the configuration file. You can provide the name of an existing bucket.
  • CreateConfigurationBucket – set to true if you want to create a bucket specified in previous parameter. If a bucket exists, set value to false
  • ConfigurationFileName – name of the configuration file for a new account. Default value: config.yml
  • UpdateFileName – name of the configuration file for updates. Default value: update.yml
  • SourceCodeBucketName – name of the bucket where you uploaded the zipped Lambda code
  • SourceCodePackageName – name of the zipped file contain Lambda. If the file was uploaded to folder, include the name of folder(s) as well. Example: my_folder/control-tower-account-factory-solution.zip
  • BaselineFunctionName –name of the AWS Lambda function. Default value: control-tower- account-factory-lambda
  • BaselinetLambdaRoleName –name of the AWS Lambda function IAM role. Default value: control-tower- account-factory-lambda-role
  • StateMachineName  – name of the AWS Step Function. Default value: control-tower- account-factory-state-machine
  • StateMachineRoleName – name of the AWS Step Function IAM role. Default value: control-tower- account-factory-state-machine-role
  • TrackingTableName – name of the DynamoDB table to track all deployment and updates. Default value: control-tower-account-factory-tracking-table
  • MaxIterations –maximum iteration of the AWS Step Function before the reports time out. Default value: 30. It can be overwrite in configuration file. For more information see Configuration section.
  • TopicName – (optional) name of the SNS Topic that will be used for sending notification. Default value: control-tower- account-factory-account-notification
  • NotificationEmail – (optional) the email address where to send the notification. If this isn’t provided, the SNS topic won’t be created.

Configuration Files

The configuration files have to be in the YAML format, you can use the following schemas for new or existing accounts:

Configuration schema for new account:

The configuration can apply to all new accounts or can be divided based on the organization unit associated with the new account. Also, you can mix deployments where some products are always deployed and some only to specific organization units.

Example configuration file

Configuration schema for existing accounts

This configuration can be used to update provisioned products or deploy products into existing accounts. You can specify a target location either as account(s), organization unit(s) or both. If you specified an organization unit as a target, the product(s) will be updated/deployed to all accounts associated with organization unit.

If the product doesn’t exist in the account, the Lambda function attempts to deploy it. You can manage this by setting the parameter ‘deployifnotexist’ to true. If omitted or set to false, Lambda won’t provision the product to an existing account.

Example configuration file

All products deployed by the AWS Service Catalog are provisioned under their own name that has to be unique across all provisioned products. In this example, products are provisioned under the following naming convention:

<account id where product is provision>-<”provision_name” from configuration file>

Example:  123456789-my-product

In the configuration file you can specify dependencies between products. Dependencies need to be provided as the list of provisioned product name not a product name. If provided, deployment of the product will be suspended until all dependencies successfully deployed.

The Step Function is running in the loop with interval of 1 minute and checking if dependencies were deployed. In the event of an error in the dependency naming or configuration, the step function iterates only until it reaches the maximum iterations defined in the configuration file. If the maximum iterations are reached, the step function reports time out and interrupts the products’ deployment.

Important: if you updating a product by adding a new AWS Region, you cannot specify the dependency or run updates in existing regions the same time. In this scenario you should break the update as follows:

  • Upload configuration to create dependencies in the new region
  • Upload configuration to create products only in the new region

You can find different examples of configuration files in GitHub.

Note: The name of the configuration file and Amazon S3 location must match the values provided in the AWS CloudFormation template during solution deployment.

AWS Lambda functions deployment considerations

When deploying product is a Lambda function, you need to consider two requirements:

  1. The source code of the Lambda function needs to be in the Amazon S3 bucket created in the same AWS region where you are planning to deploy a Lambda function
  2. The destination account needs to have permission to the source Amazon S3 bucket

To accommodate both requirements, the approach is to create an Amazon S3 bucket under the AWS Control Tower primary account. There is one Amazon S3 bucket in each Region where the functions will be deployed.

Each deployment bucket should have attached the following policy:

{
    "Version": "2008-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": "*"
            },
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::<S3 bucket name>/*",
            "Condition": {
                "StringEquals": {
                    "aws:PrincipalOrgID": "<AWS Organizations Id>"
                },
                "StringLike": {
                    "aws:PrincipalArn": [
                        "arn:aws:iam::*:role/AWSControlTowerExecution",
        "arn:aws:iam::*:role/< name of the Lambda IAM role – default: control-tower-account-factory-lambda-role>"
                    ]
                }
            }
        }
    ]
}

This allows the deployment role to access Lambda’s source code from any account in your AWS Organizations.

Conclusion

In this blog post, I’ve outlined a solution to help you drive consistent governance and compliance requirements across accounts vended though AWS Control Tower. This solution provides enterprises with mechanisms to manage all deployments from a centralized account. Also, this reduces the need for maintaining multiple separate CI/CD pipelines.  Therefore you can simplify and reduce deployment time in a multi-account environment.

This solution allows you keep provisioned products up to date by updating existing products or bringing old accounts to the same compliance level as the new accounts.

Since this solution supports deployment to existing accounts and can be run without AWS Control Tower, it can be used for deployment of any AWS Service Catalog product either in single or multiple account environments. This solution then becomes an integral part of CI/CD pipeline.

For more information, review the AWS Control Tower documentation and AWS Service Catalog documentation. Also, review the links listed in the “Prerequisites and assumptions” section of this post.

Field Notes provides hands-on technical guidance from AWS Solutions Architects, consultants, and technical account managers, based on their experiences in the field solving real-world business problems for customers.

Building event-driven architectures with Amazon SNS FIFO

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/building-event-driven-architectures-with-amazon-sns-fifo/

This post is courtesy of Christian Mueller, Principal Solutions Architect.

Developers increasingly adopt event-driven architectures to decouple their distributed applications. Often, these events must be propagated in a strictly ordered manner to all subscribed applications. Using Amazon SNS FIFO topics and Amazon SQS FIFO queues, you can address use cases that require end-to-end message ordering, deduplication, filtering, and encryption.

In this blog post, I introduce a sample event-driven architecture. I walk through an implementation based on Amazon SNS FIFO topics and Amazon SQS FIFO queues.

Common requirements in event-driven-architectures

In event-driven architectures, data consistency is a common business requirement. This is often translated into technical requirements such as zero message loss and strict message ordering. For example, if you update your domain object rapidly, you want to be sure that all events are received by each subscriber in exactly the order they occurred. This way, the current domain object state is what each subscriber received as the latest update event. Similarly, all update events should be received after the initial create event.

Before Amazon SNS FIFO, architects had to design applications to check if messages are received out of order before processing.

Comparing SNS and SNS FIFO

Another common challenge is preventing message duplicates when sending events to the messaging service. If an event publisher receives an error, such as a network timeout, the publisher does not know if the messaging service could receive and successfully process the message or not.

The client may retry, as this is the default behavior for some HTTP response codes in AWS SDKs. This can cause duplicate messages.

Before Amazon SNS FIFO, developers had to design receivers to be idempotent. In some cases, where the event cannot be idempotent, this requires the receiver to be implemented in an idempotent way. Often, this is done by adding a key-value store like Amazon DynamoDB or Amazon ElastiCache for Redis to the service. Using this approach, the receiver can track if the event has been seen before.

Exactly once processing and message deduplication

Exploring the recruiting agency example

This sample application models a recruitment agency with a job listings website. The application is composed of multiple services. I explain 3 of them in more detail.

Sample application architecture

A custom service, the anti-corruption service, receives a change data capture (CDC) event stream of changes from a relational database. This service translates the low-level technical database events into meaningful business events for the domain services for easy consumption. These business events are sent to the SNS FIFO “JobEvents.fifo“ topic. Here, interested services subscribe to these events and process them asynchronously.

In this domain, the analytics service is interested in all events. It has an SQS FIFO “AnalyticsJobEvents.fifo” queue subscribed to the SNS FIFO “JobEvents.fifo“ topic. It uses SQS FIFO as event source for AWS Lambda, which processes and stores these events in Amazon S3. S3 is object storage service with high scalability, data availability, durability, security, and performance. This allows you to use services like Amazon EMR, AWS Glue or Amazon Athena to get insights into your data to extract value.

The inventory service owns an SQS FIFO “InventoryJobEvents.fifo” queue, which is subscribed to the SNS FIFO “JobEvents.fifo“ topic. It is only interested in “JobCreated” and “JobDeleted” events, as it only tracks which jobs are currently available and stores this information in a DynamoDB table. Therefore, it uses an SNS filter policy to only receive these events, instead of receiving all events.

This sample application focuses on the SNS FIFO capabilities, so I do not explore other services subscribed to the SNS FIFO topic. This sample follows the SQS best practices and SNS redrive policy recommendations and configures dead-letter queues (DLQ). This is useful in case SNS cannot deliver an event to the subscribed SQS queue. It also helps if the function fails to process an event from the corresponding SQS FIFO queue multiple times. As a requirement in both cases, the attached SQS DLQ must be an SQS FIFO queue.

Deploying the application

To deploy the application using infrastructure as code, it uses the AWS Serverless Application Model (SAM). SAM provides shorthand syntax to express functions, APIs, databases, and event source mappings. It is expanded into AWS CloudFormation syntax during deployment.

To get started, clone the “event-driven-architecture-with-sns-fifo” repository, from here. Alternatively, download the repository as a ZIP file from here and extract it to a directory of your choice.

As a prerequisite, you must have SAM CLI, Python 3, and PIP installed. You must also have the AWS CLI configured properly.

Navigate to the root directory of this project and build the application with SAM. SAM downloads required dependencies and stores them locally. Execute the following commands in your terminal:

git clone https://github.com/aws-samples/event-driven-architecture-with-amazon-sns-fifo.git
cd event-driven-architecture-with-amazon-sns-fifo
sam build

You see the following output:

Deployment output

Now, deploy the application:

sam deploy --guided

Provide arguments for the deployments, such as the stack name and preferred AWS Region:

SAM guided deployment

After a successful deployment, you see the following output:

Successful deployment message

Learning more about the implementation

I explore the three services forming this sample application, and how they use the features of SNS FIFO.

Anti-corruption service

The anti-corruption service owns the SNS FIFO “JobEvents.fifo” topic, where it publishes business events related to job postings. It uses an SNS FIFO topic, as end-to-end ordering per job ID is required. SNS FIFO is configured not to perform content-based deduplication, as I require a unique message deduplication ID for each event for deduplication. The corresponding definition in the SAM template looks like this:

  JobEventsTopic:
    Type: AWS::SNS::Topic
    Properties:
      TopicName: JobEvents.fifo
      FifoTopic: true
      ContentBasedDeduplication: false

For simplicity, the anti-corruption function in the sample application doesn’t consume an external database CDC stream. It uses Amazon CloudWatch Events as an event source to trigger the function every minute.

I provide the SNS FIFO topic Amazon Resource Name (ARN) as an environment variable in the function. This makes this function more portable to deploy in different environments and stages. The function’s AWS Identity and Access Management (IAM) policy grants permissions to publish messages to only this SNS topic:

  AntiCorruptionFunction:
    Type: AWS::Serverless::
    Properties:
      CodeUri: anti-corruption-service/
      Handler: app.lambda_handler
      Runtime: python3.7
      MemorySize: 256
      Environment:
        Variables:
          TOPIC_ARN: !Ref JobEventsTopic
      Policies:
        - SNSPublishMessagePolicy
            TopicName: !GetAtt JobEventsTopic.TopicName
      Events:
        Trigger:
          Type: 
          Properties:
            Schedule: 'rate(1 minute)'

The anti-corruption function uses features in the SNS publish API, which allows you to define a “MessageDeduplicationId” and a “MessageGroupId”. The “MessageDeduplicationId” is used to filter out duplicate messages, which are sent to SNS FIFO within in 5-minute deduplication interval. The “MessageGroupId” is required, as SNS FIFO processes all job events for the same message group in a strictly ordered manner, isolated from other message groups processed through the same topic.

Another important aspect in this implementation is the use of “MessageAttributes”. We define a message attribute with the name “eventType” and values like “JobCreated”, “JobSalaryUpdated”, and “JobDeleted”. This allows subscribers to define SNS filter policies to only receive certain events they are interested in:

import boto3
from datetime import datetime
import json
import os
import random
import uuid

TOPIC_ARN = os.environ['TOPIC_ARN']

sns = boto3.client('sns')

def lambda_handler(event, context):
    jobId = str(random.randrange(0, 1000))

    send_job_created_event(jobId)
    send_job_updated_event(jobId)
    send_job_deleted_event(jobId)
    return

def send_job_created_event(jobId):
    messageId = str(uuid.uuid4())

    response = sns.publish(
        TopicArn=TOPIC_ARN,
        Subject=f'Job {jobId} created',
        MessageDeduplicationId=messageId,
        MessageGroupId=f'JOB-{jobId}',
        Message={...},
        MessageAttributes = {
            'eventType': {
                'DataType': 'String',
                'StringValue': 'JobCreated'
            }
        }
    )
    print('sent message and received response: {}'.format(response))
    return

def send_job_updated_event(jobId):
    messageId = str(uuid.uuid4())

    response = sns.publish(...)
    print('sent message and received response: {}'.format(response))
    return

def send_job_deleted_event(jobId):
    messageId = str(uuid.uuid4())

    response = sns.publish(...)
    print('sent message and received response: {}'.format(response))
    return

Analytics service

The analytics service owns an SQS FIFO “AnalyticsJobEvents.fifo” queue which is subscribed to the SNS FIFO “JobEvents.fifo” topic. Following best practices, I define redrive policies for the SQS FIFO queue and the SNS FIFO subscription in the template:

  AnalyticsJobEventsQueue:
    Type: AWS::SQS::Queue
    Properties:
      QueueName: AnalyticsJobEvents.fifo
      FifoQueue: true
      RedrivePolicy:
        deadLetterTargetArn: !GetAtt AnalyticsJobEventsQueueDLQ.Arn
        maxReceiveCount: 3

  AnalyticsJobEventsQueueToJobEventsTopicSubscription:
    Type: AWS::SNS::Subscription
    Properties:
      Endpoint: !GetAtt AnalyticsJobEventsQueue.Arn
      Protocol: sqs
      RawMessageDelivery: true
      TopicArn: !Ref JobEventsTopic
      RedrivePolicy: !Sub '{"deadLetterTargetArn": "${AnalyticsJobEventsSubscriptionDLQ.Arn}"}'

The analytics function uses SQS FIFO as an event source for Lambda. The S3 bucket name is an environment variable for the function, which increases the code portability across environments and stages. The IAM policy for this function only grants permissions write objects to this S3 bucket:

  AnalyticsFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: analytics-service/
      Handler: app.lambda_handler
      Runtime: python3.7
      MemorySize: 256
      Environment:
        Variables:
          BUCKET_NAME: !Ref AnalyticsBucket
      Policies:
        - S3WritePolicy:
            BucketName: !Ref AnalyticsBucket
      Events:
        Trigger:
          Type: SQS
          Properties:
            Queue: !GetAtt AnalyticsJobEventsQueue.Arn
            BatchSize: 10

View the function implementation at the GitHub repo.

Inventory service

The inventory service also owns an SQS FIFO “InventoryJobEvents.fifo” queue which is subscribed to the SNS FIFO “JobEvents.fifo” topic. It uses redrive policies for the SQS FIFO queue and the SNS FIFO subscription as well. This service is only interested in certain events, so uses an SNS filter policy to specify these events:

  InventoryJobEventsQueue:
    Type: AWS::SQS::Queue
    Properties:
      QueueName: InventoryJobEvents.fifo
      FifoQueue: true
      RedrivePolicy:
        deadLetterTargetArn: !GetAtt InventoryJobEventsQueueDLQ.Arn
        maxReceiveCount: 3

  InventoryJobEventsQueueToJobEventsTopicSubscription:
    Type: AWS::SNS::Subscription
    Properties:
      Endpoint: !GetAtt InventoryJobEventsQueue.Arn
      Protocol: sqs
      RawMessageDelivery: true
      TopicArn: !Ref JobEventsTopic
      FilterPolicy: '{"eventType":["JobCreated", "JobDeleted"]}'
      RedrivePolicy: !Sub '{"deadLetterTargetArn": "${InventoryJobEventsQueueSubscriptionDLQ.Arn}"}'

The inventory function also uses SQS FIFO as event source for Lambda. The DynamoDB table name is set as an environment variable, so the function can look up the name during initialization. The IAM policy grants read/write permissions for only this table:

  InventoryFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: inventory-service/
      Handler: app.lambda_handler
      Runtime: python3.7
      MemorySize: 256
      Environment:
        Variables:
          TABLE_NAME: !Ref InventoryTable
      Policies:
        - DynamoDBCrudPolicy:
            TableName: !Ref InventoryTable
      Events:
        Trigger:
          Type: SQS
          Properties:
            Queue: !GetAtt InventoryJobEventsQueue.Arn
            BatchSize: 10

View the function implementation at the GitHub repo.

Conclusion

Amazon SNS FIFO topics can simplify the design of event-driven architectures and reduce custom code in building such applications.

By using the native integration with Amazon SQS FIFO queues, you can also build architectures that fan out to thousands of subscribers. This pattern helps achieve data consistency, deduplication, filtering, and encryption in near real time, using managed services.

For information on regional availability and service quotas, see SNS endpoints and quotas and SQS endpoints and quotas. For more information on the FIFO functionality, see SNS FIFO and SQS FIFO in their Developer Guides.

Architecting a Data Lake for Higher Education Student Analytics

Post Syndicated from Craig Jordan original https://aws.amazon.com/blogs/architecture/architecting-data-lake-for-higher-education-student-analytics/

One of the keys to identifying timely and impactful actions is having enough raw material to work with. However, this up-to-date information typically lives in the databases that sit behind several different applications. One of the first steps to finding data-driven insights is gathering that information into a single store that an analyst can use without interfering with those applications.

For years, reporting environments have relied on a data warehouse stored in a single, separate relational database management system (RDBMS). But now, due to the growing use of Software as a service (SaaS) applications and NoSQL database options, data may be stored outside the data center and in formats other than tables of rows and columns. It’s increasingly difficult to access the data these applications maintain, and a data warehouse may not be flexible enough to house the gathered information.

For these reasons, reporting teams are building data lakes, and those responsible for using data analytics at universities and colleges are no different. However, it can be challenging to know exactly how to start building this expanded data repository so it can be ready to use quickly and still expandable as future requirements are uncovered. Helping higher education institutions address these challenges is the topic of this post.

About Maryville University

Maryville University is a nationally recognized private institution located in St. Louis, Missouri, and was recently named the second fastest growing private university by The Chronicle of Higher Education. Even with its enrollment growth, the university is committed to a highly personalized education for each student, which requires reliable data that is readily available to multiple departments. University leaders want to offer the right help at the right time to students who may be having difficulty completing the first semester of their course of study. To get started, the data experts in the Office of Strategic Information and members of the IT Department needed to create a data environment to identify students needing assistance.

Critical data sources

Like most universities, Maryville’s student-related data centers around two significant sources: the student information system (SIS), which houses student profiles, course completion, and financial aid information; and the learning management system (LMS) in which students review course materials, complete assignments, and engage in online discussions with faculty and fellow students.

The first of these, the SIS, stores its data in an on-premises relational database, and for several years, a significant subset of its contents had been incorporated into the university’s data warehouse. The LMS, however, contains data that the team had not tried to bring into their data warehouse. Moreover, that data is managed by a SaaS application from Instructure, called “Canvas,” and is not directly accessible for traditional extract, transform, and load (ETL) processing. The team recognized they needed a new approach and began down the path of creating a data lake in AWS to support their analysis goals.

Getting started on the data lake

The first step the team took in building their data lake made use of an open source solution that Harvard’s IT department developed. The solution, comprised of AWS Lambda functions and Amazon Simple Storage Service (S3) buckets, is deployed using AWS CloudFormation. It enables any university that uses Canvas for their LMS to implement a solution that moves LMS data into an S3 data lake on a daily basis. The following diagram illustrates this portion of Maryville’s data lake architecture:

The data lake for the Learning Management System data

Diagram 1: The data lake for the Learning Management System data

The AWS Lambda functions invoke the LMS REST API on a daily schedule resulting in Maryville’s data, which has been previously unloaded and compressed by Canvas, to be securely stored into S3 objects. AWS Glue tables are defined to provide access to these S3 objects. Amazon Simple Notification Service (SNS) informs stakeholders the status of the data loads.

Expanding the data lake

The next step was deciding how to copy the SIS data into S3. The team decided to use the AWS Database Migration Service (DMS) to create daily snapshots of more than 2,500 tables from this database. DMS uses a source endpoint for secure access to the on-premises database instance over VPN. A target endpoint determines the specific S3 bucket into which the data should be written. A migration task defines which tables to copy from the source database along with other migration options. Finally, a replication instance, a fully managed virtual machine, runs the migration task to copy the data. With this configuration in place, the data lake architecture for SIS data looks like this:

Diagram 2: Migrating data from the Student Information System

Diagram 2: Migrating data from the Student Information System

Handling sensitive data

In building a data lake you have several options for handling sensitive data including:

  • Leaving it behind in the source system and avoid copying it through the data replication process
  • Copying it into the data lake, but taking precautions to ensure that access to it is limited to authorized staff
  • Copying it into the data lake, but applying processes to eliminate, mask, or otherwise obfuscate the data before it is made accessible to analysts and data scientists

The Maryville team decided to take the first of these approaches. Building the data lake gave them a natural opportunity to assess where this data was stored in the source system and then make changes to the source database itself to limit the number of highly sensitive data fields.

Validating the data lake

With these steps completed, the team turned to the final task, which was to validate the data lake. For this process they chose to make use of Amazon Athena, AWS Glue, and Amazon Redshift. AWS Glue provided multiple capabilities including metadata extraction, ETL, and data orchestration. Metadata extraction, completed by Glue crawlers, quickly converted the information that DMS wrote to S3 into metadata defined in the Glue data catalog. This enabled the data in S3 to be accessed using standard SQL statements interactively in Athena. Without the added cost and complexity of a database, Maryville’s data analyst was able to confirm that the data loads were completing successfully. He was also able to resolve specific issues encountered on particular tables. The SQL queries, written in Athena, could later be converted to ETL jobs in AWS Glue, where they could be triggered on a schedule to create additional data in S3. Athena and Glue enabled the ETL that was needed to transform the raw data delivered to S3 into prepared datasets necessary for existing dashboards.

Once curated datasets were created and stored in S3, the data was loaded into an AWS Redshift data warehouse, which supported direct access by tools outside of AWS using ODBC/JDBC drivers. This capability enabled Maryville’s team to further validate the data by attaching the data in Redshift to existing dashboards that were running in Maryville’s own data center. Redshift’s stored procedure language allowed the team to port some key ETL logic so that the engineering of these datasets could follow a process similar to approaches used in Maryville’s on-premises data warehouse environment.

Conclusion

The overall data lake/data warehouse architecture that the Maryville team constructed currently looks like this:

The complete architecture

Diagram 3: The complete architecture

Through this approach, Maryville’s two-person team has moved key data into position for use in a variety of workloads. The data in S3 is now readily accessible for ad hoc interactive SQL workloads in Athena, ETL jobs in Glue, and ultimately for machine learning workloads running in EC2, Lambda or Amazon Sagemaker. In addition, the S3 storage layer is easy to expand without interrupting prior workloads. At the time of this writing, the Maryville team is both beginning to use this environment for machine learning models described earlier as well as adding other data sources into the S3 layer.

Acknowledgements

The solution described in this post resulted from the collaborative effort of Christine McQuie, Data Engineer, and Josh Tepen, Cloud Engineer, at Maryville University, with guidance from Travis Berkley and Craig Jordan, AWS Solutions Architects.

New – Use AWS PrivateLink to Access AWS Lambda Over Private AWS Network

Post Syndicated from Harunobu Kameda original https://aws.amazon.com/blogs/aws/new-use-aws-privatelink-to-access-aws-lambda-over-private-aws-network/

AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. You simply upload your code and Lambda does all the work to execute and scale your code for high availability. Many AWS customers today use this serverless computing platform to significantly improve their productivity while developing and operating applications.

Today, I am happy to announce that AWS Lambda now supports AWS PrivateLink which lets you invoke Lambda functions securely from inside your virtual private cloud (VPC) or on-premises data centers without exposing traffic to the public Internet.

Until now, in order to call Lambda functions, a VPC required an Internet Gateway, network address translation (NAT) gateway, and/or public IP address. With this update, PrivateLink routes the call through the AWS private network, eliminating the need for Internet access. Additionally, you can now call the Lambda API directly from your on-premises data centers by connecting to a VPC using AWS Direct Connect or AWS VPN Connections.

Some customers wanted to manage and call Lambda functions from a VPC that doesn’t have internet access due to internal IT governance requirements. With this update, you will be able to use Lambda. Also, customer who have maintained NAT Gateway to access Lambda from a VPC, can use a VPC endpoint instead of the NAT Gateway thus saving the cost of NAT Gateway. Security is further improved because you no longer need to allow Internet access to your VPC to call Lambda functions, and network architecture becomes more simple, and easily manageable. Previously, in the case of VPC-enabled Lambda function calling another Lambda function, such a call had to go through a NAT GW but now customer’s can use a VPC endpoint instead.

How to Get Started With AWS PrivateLink

AWS PrivateLink uses an elastic network interface called the “Interface VPC endpoint” to act as an entry point for traffic targeting AWS services. Interface endpoints limit all network traffic to AWS internal network and provide secure access to your services. The Interface VPC endpoint is a redundant, highly available VPC component that has a private IP address and is scaled horizontally.

Getting Started Using the AWS Management Console

To get started, you can use the AWS Management Console, AWS CLI, or AWS CloudFormation. In this first example, I’ll show the Management Console.

First, you access the VPC management console, and click “Endpoints.”

Click “Create Endpoint” button.

Type “lambda” in the search bar, and you’ll see Service Name. Select it, and choose the VPC where you want to create the interface endpoint.

After that, you are prompted to specify subnets where you may want to create endpoints.

If you want, you can set your own DNS name to the endpoint with Amazon Route53 private hosted zones when you enable “Enable DNS name” option. With this option enabled, any request for Lambda functions in your public subnet can not invoke Lambda via your Internet Gateway, and communications has to go through via VPC endpoints in Private subnet.

Next, specify “Security Group” for protocols, port, and source/target IP address control.

Then, set the policy to control who has access to the VPC endpoint. By default, “Full Access” is selected, but we always recommend you first grant access only to the minimum necessary principal; you can modify this later.

Following is a sample you can customize to create your “Policy.” With this sample, only the IAM user “MyUser” can invoke a Lambda function of “my-function.”

{
    "Statement": [
        {
            "Principal": "arn:aws:iam::123412341234:user/MyUser",
            "Action": [
                "lambda:InvokeFunction"
            ],
            "Effect": "Allow",
            "Resource": [
               "arn:aws:lambda:us-east-2:123456789012:function:my-function:1”
            ]
        }
    ]
}

Now, it’s time for the final step. Click the “Create endpoint” button. You’ll see the success dialog shown below.

Now you can invoke Lambda functions with the endpoint DNS name. You can also invoke Lambda functions from another VPC connected to the original VPC via VPC peering, AWS Transit Gateway, or you can even do so from another AWS account.

Getting Started Using the AWS Command Line Interface (CLI)

Using AWS CLI is more precise and easy if you already have the AWS CLI environment. 

aws ec2 create-vpc-endpoint --vpc-id vpc-ec43eb89 \
        --vpc-endpoint-type Interface --service-name lambda.<region code>.amazonaws.com \
        --subnet-id subnet-abababab --security-group-id sg-1a2b3c4d

Available Today

AWS PrivateLink support by AWS Lambda is now available in all AWS Regions except for Africa (Cape Town) and Europe (Milan). Supporting those regions are on our roadmap, and is coming soon. Standard AWS PrivateLink pricing apply to Lambda interface endpoints. You will be billed each hour the interface endpoint is provisioned in each Availability Zone, and for the data processed through the interface endpoint. No additional fee is required for AWS Lambda. See the AWS PrivateLink pricing page, and documentation for more detail.

– Kame;

 

Optimizing the cost of serverless web applications

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/optimizing-the-cost-of-serverless-web-applications/

Web application backends are one of the most frequent types of serverless use-case for customers. The pay-for-value model can make it cost-efficient to build web applications using serverless tools.

While serverless cost is generally correlated with level of usage, there are architectural decisions that impact cost efficiency. The impact of these choices is more significant as your traffic grows, so it’s important to consider the cost-effectiveness of different designs and patterns.

This blog post reviews some common areas in web applications where you may be able to optimize cost. It uses the Happy Path web application as a reference example, which you can read about in the introductory blog post.

Serverless web applications generally use a combination of the services in the following diagram. I cover each of these areas to highlight common areas for cost optimization.

Serverless architecture by AWS service

The API management layer: Selecting the right API type

Most serverless web applications use an API between the frontend client and the backend architecture. Amazon API Gateway is a common choice since it is a fully managed service that scales automatically. There are three types of API offered by the service – REST APIs, WebSocket APIs, and the more recent HTTP APIs.

HTTP APIs offer many of the features in the REST APIs service, but the cost is often around 70% less. It supports Lambda service integration, JWT authorization, CORS, and custom domain names. It also has a simpler deployment model than REST APIs. This feature set tends to work well for web applications, many of which mainly use these capabilities. Additionally, HTTP APIs will gain feature parity with REST APIs over time.

The Happy Path application is designed for 100,000 monthly active users. It uses HTTP APIs, and you can inspect the backend/template.yaml to see how to define these in the AWS Serverless Application Model (AWS SAM). If you have existing AWS SAM templates that are using REST APIs, in many cases you can change these easily:

REST to HTTP API

Content distribution layer: Optimizing assets

Amazon CloudFront is a content delivery network (CDN). It enables you to distribute content globally across 216 Points of Presence without deploying or managing any infrastructure. It reduces latency for users who are geographically dispersed and can also reduce load on other parts of your service.

A typical web application uses CDNs in a couple of different ways. First, there is the distribution of the application itself. For single-page application frameworks like React or Vue.js, the build processes create static assets that are ideal for serving over a CDN.

However, these builds may not be optimized and can be larger than necessary. Many frameworks offer optimization plugins, and the JavaScript community frequently uses Webpack to bundle modules and shrink deployment packages. Similarly, any media assets used in the application build should be optimized. You can use tools like Lighthouse to analyze your web apps to find images that can be resized or compressed.

Optimizing images

The second common CDN use-case for web apps is for user-generated content (UGC). Many apps allow users to upload images, which are then shared with other users. A typical photo from a 12-megapixel smartphone is 3–9 MB in size. This high resolution is not necessary when photos are rendered within web apps. Displaying the high-resolution asset results in slower download performance and higher data transfer costs.

The Happy Path application uses a Resizer Lambda function to optimize these uploaded assets. This process creates two different optimized images depending upon which component loads the asset.

Image sizes in front-end applications

The upload S3 bucket shows the original size of the upload from the smartphone:

The distribution S3 bucket contains the two optimized images at different sizes:

Optimized images in the distribution S3 bucket

The distribution file sizes are 98–99% smaller. For a busy web application, using optimized image assets can make a significant difference to data transfer and CloudFront costs.

Additionally, you can convert to highly optimized file formats such as WebP to reduce file size even further. Not all browsers support this format, but you can use CSS on the frontend to fall back to other types if needed:

<img src="myImage.webp" onerror="this.onerror=null; this.src='myImage.jpg'">

The data layer

AWS offers many different database and storage options that can be useful for web applications. Billing models vary by service and Region. By understanding the data access and storage requirements of your app, you can make informed decisions about the right service to use.

Generally, it’s more cost-effective to store binary data in S3 than a database. First, when the data is uploaded, you can upload directly to S3 with presigned URLs instead of proxying data via API Gateway or another service.

If you are using Amazon DynamoDB, it’s best practice to store larger items in S3 and include a reference token in a table item. Part of DynamoDB pricing is based on read capacity units (RCUs). For binary items such as images, it is usually more cost-efficient to use S3 for storage.

Many web developers who are new to serverless are familiar with using a relational database, so choose Amazon RDS for their database needs. Depending upon your use-case and data access patterns, it may be more cost effective to use DynamoDB instead. RDS is not a serverless service so there are monthly charges for the underlying compute instance. DynamoDB pricing is based upon usage and storage, so for many web apps may be a lower-cost choice.

Integration layer

This layer includes services like Amazon SQS, Amazon SNS, and Amazon EventBridge, which are essential for decoupling serverless applications. Each of these have a request-based pricing component, where 64 KB of a payload is billed as one request. For example, a single SQS message with a 256 KB payload is billed as four requests. There are two optimization methods common for web applications.

1. Combine messages

Many messages sent to these services are much smaller than 64 KB. In some applications, the publishing service can combine multiple messages to reduce the total number of publish actions to SNS. Additionally, by either eliminating unused attributes in the message or compressing the message, you can store more data in a single request.

For example, a publishing service may be able to combine multiple messages together in a single publish action to an SNS topic:

  • Before optimization, a publishing service sends 100,000,000 1KB-messages to an SNS topic. This is charged as 100 million messages for a total cost of $50.00.
  • After optimization, the publishing service combines messages to send 1,562,500 64KB-messages to an SNS topic. This is charged as 1,562,500 messages for a total cost of $0.78.

2. Filter messages

In many applications, not every message is useful for a consuming service. For example, an SNS topic may publish to a Lambda function, which checks the content and discards the message based on some criteria. In this case, it’s more cost effective to use the native filtering capabilities of SNS. The service can filter messages and only invoke the Lambda function if the criteria is met. This lowers the compute cost by only invoking Lambda when necessary.

For example, an SNS topic receives messages about customer orders and forwards these to a Lambda function subscriber. The function is only interested in canceled orders and discards all other messages:

  • Before optimization, the SNS topic sends all messages to a Lambda function. It evaluates the message for the presence of an order canceled attribute. On average, only 25% of the messages are processed further. While SNS does not charge for delivery to Lambda functions, you are charged each time the Lambda service is invoked, for 100% of the messages.
  • After optimization, using an SNS subscription filter policy, the SNS subscription filters for canceled orders and only forwards matching messages. Since the Lambda function is only invoked for 25% of the messages, this may reduce the total compute cost by up to 75%.

3. Choose a different messaging service

For complex filtering options based upon matching patterns, you can use EventBridge. The service can filter messages based upon prefix matching, numeric matching, and other patterns, combining several rules into a single filter. You can create branching logic within the EventBridge rule to invoke downstream targets.

EventBridge offers a broader range of targets than SNS destinations. In cases where you publish from an SNS topic to a Lambda function to invoke an EventBridge target, you could use EventBridge instead and eliminate the Lambda invocation. For example, instead of routing from SNS to Lambda to AWS Step Functions, instead create an EventBridge rule that routes events directly to a state machine.

Business logic layer

Step Functions allows you to orchestrate complex workflows in serverless applications while eliminating common boilerplate code. The Standard Workflow service charges per state transition. Express Workflows were introduced in December 2019, with pricing based on requests and duration, instead of transitions.

For workloads that are processing large numbers of events in shorter durations, Express Workflows can be more cost-effective. This is designed for high-volume event workloads, such as streaming data processing or IoT data ingestion. For these cases, compare the cost of the two workflow types to see if you can reduce cost by switching across.

Lambda is the on-demand compute layer in serverless applications, which is billed by requests and GB-seconds. GB-seconds is calculated by multiplying duration in seconds by memory allocated to the function. For a function with a 1-second duration, invoked 1 million times, here is how memory allocation affects the total cost in the US East (N. Virginia) Region:

Memory (MB)GB/SCompute costTotal cost
128125,000$ 2.08$ 2.28
512500,000$ 8.34$ 8.54
10241,000,000$ 16.67$ 16.87
15361,500,000$ 25.01$ 25.21
20482,000,000$ 33.34$ 33.54
30082,937,500$ 48.97$ 49.17

There are many ways to optimize Lambda functions, but one of the most important choices is memory allocation. You can choose between 128 MB and 3008 MB, but this also impacts the amount of virtual CPU as memory increases. Since total cost is a combination of memory and duration, choosing more memory can often reduce duration and lower overall cost.

Instead of manually setting the memory for a Lambda function and running executions to compare duration, you can use the AWS Lambda Power Tuning tool. This uses Step Functions to run your function against varying memory configurations. It can produce a visualization to find the optimal memory setting, based upon cost or execution time.

Optimizing costs with the AWS Lambda Power Tuning tool

Conclusion

Web application backends are one of the most popular workload types for serverless applications. The pay-per-value model works well for this type of workload. As traffic grows, it’s important to consider the design choices and service configurations used to optimize your cost.

Serverless web applications generally use a common range of services, which you can logically split into different layers. This post examines each layer and suggests common cost optimizations helpful for web app developers.

To learn more about building web apps with serverless, see the Happy Path series. For more serverless learning resources, visit https://serverlessland.com.

Unlocking Data from Existing Systems with a Serverless API Facade

Post Syndicated from Santiago Freitas original https://aws.amazon.com/blogs/architecture/unlocking-data-from-existing-systems-with-serverless-api-facade/

In today’s modern world, it’s not enough to produce a good product; it’s critical that your products and services are well integrated into the surrounding business ecosystem. Companies lose market share when valuable data about their products or services are locked inside their systems. Business partners and internal teams use data from multiple sources to enhance their customers’ experience.

This blog post explains an architecture pattern for providing access to data and functionalities from existing systems in a consistent way using well-defined APIs. It then covers what the API Facade architecture pattern looks like when implemented on AWS using serverless for API management and mediation layer.

Background

Modern applications are often developed with an application programming interface (API)-first approach. This significantly eases integrations with internal and third-party applications by exposing data and functionalities via well-documented APIs.

On the other hand, applications built several years ago have multiple interfaces and data formats which creates a challenge for integrating their data and functionalities into new applications. Those existing applications store vast amounts of historical data. Integrating their data to build new customer experiences can be very valuable.

Figure 1: Existing applications use a broad range of integration methods and data formats

API Facade pattern

When building modern APIs for existing systems, you can use an architecture pattern called API Facade. This pattern creates a layer that exposes well-structured and well-documented APIs northbound, and it integrates southbound with the required interfaces and protocols that existing applications use. This pattern is about creating a facade, which creates a consistent view from the perspective of the API consumer—usually an application developer, and ultimately another application.

In addition to providing a simple interface for complex existing systems, an API Facade allows you to protect future compatibility of your solution. This is because if the underlying systems are modified or replaced, the facade layer will remain the same. From the API consumer perspective, nothing will have changed.

The API facade consists of two layers: 1) API management layer; and 2) mediation layer.

Figure 2: Conceptual representation of API facade pattern.

Figure 2: Conceptual representation of API facade pattern.

The API management layer exposes a set of well-designed, well-documented APIs with associated URLs, request parameters and responses, a list of supported headers and query parameters, and possible error codes and descriptions. A developer portal is used to help API consumers discover which APIs are available, browse the API documentation, and register for—and immediately receive—an API key to build applications. The APIs exposed by this layer can be used by external as well as internal consumers and enables them to build applications faster.

The mediation layer is responsible for integration between API and underlying systems. It transforms API requests into formats acceptable for different systems and then process and transform underlying systems’ responses into response and data formats the API has promised to return to the API consumers. This layer can perform tasks ranging from simple data manipulations, such as converting a response from XML to JSON, to much more complex operations where an application-specific client is required to run in order to connect to existing systems.

API Facade pattern on AWS serverless platform

To build the API management and the mediation layer, you can leverage services from the AWS serverless platform.

Amazon API Gateway allows you to build the API management. With API Gateway you can create RESTful APIs and WebSocket APIs. It supports integration with the mediation layer running on containers on Amazon Elastic Container Service (ECS) or Amazon Elastic Kubernetes Service (EKS), and also integration with serverless compute using AWS Lambda. API Gateway allows you to make your APIs available on the Internet for your business partners and third-party developers or keep them private. Private APIs hosted within your VPC can be accessed by resources inside your VPC, or those connected to your VPC via AWS Direct Connect or Site-to-Site VPN. This allows you to leverage API Gateway for building the API management of the API facade pattern for internal and external API consumers.

When it comes to building the Mediation layer, AWS Lambda is a great choice as it runs your mediation code without requiring you to provision or manage servers. AWS Lambda hosts the code that ingests the request coming from the API management layer, processes it, and makes the required format and protocols transformations. It can connect to the existing systems, and then return the response to the API management layer to send it back to the system which originated the request. AWS Lambda functions run outside your VPC or they can be configured to access systems in your VPC or those running in your own data centers connected to AWS via Direct Connect or Site-to-Site VPN.

However, some of the most complex mediations may require a custom client or have the need to maintain a persistent connection to the backend system. In those cases, using containers, and specifically AWS Fargate, would be more suitable. AWS Fargate is a serverless compute engine for containers with support for Amazon ECS and Amazon EKS. Containers running on AWS Fargate can access systems in your VPC or those running in your own data centers via Direct Connect or Site-to-Site VPN.

When building the API Facade pattern using AWS Serverless, you can focus most of your resources writing the API definition and mediation logic instead of managing infrastructure. This makes it easier for the teams who own the existing applications that need to expose data and functionality to own the API management and mediation layer implementations. A team that runs an existing application usually knows the best way to integrate with it. This team is also better equipped to handle changes to the mediation layer, which may be required as a result of changes to the existing application. Those teams will then publish the API information into a developer portal, which could be made available as a central API repository provided by a company’s tools team.

The following figure shows the API Facade pattern built on AWS Serverless using API Gateway for the API management layer and AWS Lambda and Fargate for the mediation layer. It functions as a facade for the existing systems running on-premises connected to AWS via Direct Connect and Site-to-Site VPN. The APIs are also exposed to external consumers via a public API endpoint as well as to internal consumers within a VPC. API Gateway supports multiple mechanisms for controlling and managing access to your API.

Figure 3: API Facade pattern built on AWS Serverless

Figure 3: API Facade pattern built on AWS Serverless

To provide an example of a practical implementation of this pattern we can look into UK Open Banking. The Open Banking standard set the API specifications for delivering account information and payment initiation services banks such as HSBC had to implement. HSBC internal landscape is hugely varied and they needed to harness the power of multiple disparate on-premises systems while providing uniform API to the outside world. HSBC shared how they met the requirements on this re:Invent 2019 session.

Conclusion

You can build differentiated customer experiences and bring services to market faster when you integrate your products and services into the surrounding business ecosystem. Your systems can participate in a business ecosystem more effectively when they expose their data and capabilities via well-established APIs. The API Facade pattern enables existing systems that don’t offer well-established APIs natively to participate on this well-integrated business ecosystem. By building the API Facade pattern on the AWS serverless platform, you can focus on defining the APIs and the mediation layer code instead of spending resources on managing the infrastructure required to implement this pattern. This allows you to implement this pattern faster.

Event-driven refresh of SPICE datasets in Amazon QuickSight

Post Syndicated from Dylan Qu original https://aws.amazon.com/blogs/big-data/event-driven-refresh-of-spice-datasets-in-amazon-quicksight/

Businesses are increasingly harnessing data to improve their business outcomes. To enable this transformation to a data-driven business, customers are bringing together data from structured and unstructured sources into a data lake. Then they use business intelligence (BI) tools, such as Amazon QuickSight, to unlock insights from this data.

To provide fast access to datasets, QuickSight provides a fully managed calculation engine called SPICE—the Super-fast, Parallel, In-Memory Calculation Engine. At the time of writing, SPICE enables you to cache up to 250 million rows or 500 GB of data per dataset.

To extract value from the data quickly, you need access to new data as soon as it’s available. In this post, we describe how to achieve this by refreshing SPICE datasets as part of your extract, transform, and load (ETL) pipelines.

Solution architecture

In this post, you automate the refresh of SPICE datasets by implementing the following architecture.

This architecture consists of two parts: an example ETL job and a decoupled event-driven process to refresh SPICE.

For the ETL job, you use Amazon Simple Storage Service (Amazon S3) as your primary data store. Data lands in an S3 bucket, which we refer to as the raw zone. An Amazon S3 trigger configured on this bucket triggers an AWS Lambda function, which starts an AWS Glue ETL job. This job processes the raw data and outputs processed data into another S3 bucket, which we refer to as the processed zone.

This sample ETL job converts the data to Apache Parquet format and stores it in the processed S3 bucket. You can modify the ETL job to achieve other objectives, like more granular partitioning, compression, or enriching of the data. The Glue Data Catalog stores the metadata and QuickSight datasets are created using Amazon Athena data sources.

To trigger the SPICE dataset refresh, after the ETL job finishes, an Amazon EventBridge rule triggers a Lambda function that initiates the refresh.

In summary, this pipeline transforms your data and updates QuickSight SPICE datasets upon completion.

Deploying the automated data pipeline using AWS CloudFormation

Before deploying the AWS CloudFormation template, make sure you have signed up for QuickSight in one of the 11 supported Regions:

  • US East (Ohio)
  • US East (N. Virginia)
  • US West (Oregon)
  • Asia Pacific (Mumbai)
  • Asia Pacific (Seoul)
  • Asia Pacific (Singapore)
  • Asia Pacific (Sydney)
  • Asia Pacific (Tokyo)
  • EU (Frankfurt)
  • EU (Ireland)
  • EU (London)

This post works with both Standard and Enterprise editions of QuickSight. Enterprise Edition provides richer features and higher limits compared to Standard Edition.

  1. After you sign up for QuickSight, you can use CloudFormation templates to create all the necessary resources by choosing Launch stack:
  2. Enter a stack name; for example, SpiceRefreshBlog.
  3. Acknowledge the AWS Identity and Access Management (IAM) resource creation.
  4. Choose Create stack.

The CloudFormation template creates the following resources in your AWS account:

  • Three S3 buckets to store the following:
    • AWS Glue ETL job script
    • Raw data
    • Processed data
  • Three Lambda functions to do the following:
    • Create the ETL job
    • Initiate the ETL job upon upload of new data in the raw zone
    • Initiate the SPICE dataset refresh when the ETL job is complete
  • An AWS Glue database
  • Two AWS Glue tables to store the following:
    • Raw data
    • Processed data
  • An ETL job to convert the raw data from CSV into Apache Parquet format
  • Four IAM roles: One each for the Lambda functions and one for the ETL job
  • An EventBridge rule that triggers on an AWS Glue job state change event with a state of Succeeded and invokes a Lambda function that performs the SPICE dataset refresh

Importing the dataset

For this post, you use the taxi Trip Record Data dataset publicly available from the NYC Taxi & Limousine Commission Trip Record Data dataset. You upload monthly data in CSV format to the raw zone S3 bucket.

This data is available in Amazon S3 through Open Data on AWS, a service designed to let you spend more time on data analysis rather than data acquisition.

You start by copying the For Hire Vehicle (FHV) data for March 2020. Because the data is already available in Amazon S3 through Open Data, run the following command to copy the data into the raw zone. Make sure you replace <raw bucket name> with the name of the raw bucket created by the CloudFormation template:

aws s3 cp "s3://nyc-tlc/trip data/fhv_tripdata_2020-03.csv" s3://<raw bucket name>

After you copy the data into the raw zone, the Amazon S3 event trigger invokes the Lambda function that triggers the ETL job. You can see the job status on the AWS Glue console by choosing Jobs in the navigation pane. The process takes about 2 minutes.

When the job is complete, check that you can see the Parquet files in the processed zone S3 bucket.

Creating a QuickSight analysis of the data

To visualize the taxi data, we create a QuickSight analysis.

First, you need to give QuickSight the necessary permissions to access the processed zone S3 bucket. For instructions, see I Can’t Connect to Amazon S3.

Then complete the following steps to create an analysis of the taxi data:

  1. On the QuickSight console, choose Datasets.
  2. Choose New dataset.
  3. Choose Athena and provide a name for the data source (such as Athena).
  4. Choose Create data source.
  5. For Database, choose the name of the taxi AWS Glue database (starting with taxigluedatabase).
  6. For Tables, select processed_taxi_data as the table to visualize.
  7. Choose Select.
  8. Ensure Import to SPICE for quicker analytics is selected and choose Visualize.

After the data is imported into SPICE, you can create visuals to display the data. For example, the following screenshot shows a key performance indicator (KPI) of the number of taxi journeys aggregated at the month level and the number of journeys over time.

We use this dashboard to visualize the dataset again after we refresh SPICE with more data.

Automating the SPICE refresh

To refresh the SPICE dataset when the ETL job is complete, the CloudFormation template we deployed created an EventBridge rule that triggers a Lambda function each time an AWS Glue ETL job successfully completes. The following screenshot shows the code for the event pattern.

We need to configure the Lambda function with the ETL job name and the ID of the SPICE dataset we created in QuickSight.

  1. Locate the ETL job name on the AWS Glue console, named TaxiTransformationGlueJob-<unique id>.
  2. To find the SPICE dataset ID, run the following command using the AWS Command Line Interface (AWS CLI):
    aws quicksight list-data-sets --aws-account-id <your AWS account id> 

    The following screenshot shows the output with the dataset ID.

  3. On the Lambda console, open the Lambda function named SpiceRefreshBlog-QuicksightUpdateLambda-<unique id>.
  4. Update line 9 of the code to replace ReplaceWithGlueJobName with the AWS Glue job name and ReplaceWithYourDatasetID with the dataset ID.

Once a Glue job succeeds, this Lambda function is triggered. The EventBridge event that triggers the Lambda contains the name of the job. You can access this from the event as follows, as seen on line 25 of the function:

succeededJob = event[‘detail’][‘jobName’]

The Lambda function looks up the job name in the data_set_map dictionary. If the dictionary contains the job name, the dataset ID is accessed and the function calls the QuickSight Create Ingestion API to refresh the SPICE datasets.

You can extend the data_set_map dictionary to include additional job names and associated SPICE dataset IDs to be refreshed. If using this approach at scale, you might choose to move this configuration information to an Amazon DynamoDB table.

  1. Save the Lambda function by choosing Deploy.

Testing the automated refresh

Now that you have configured the Lambda function, we can test the ETL end-to-end process and make the next month’s data available for analysis.

To add the FHV data for April, run the following AWS CLI command:

aws s3 cp "s3://nyc-tlc/trip data/fhv_tripdata_2020-04.csv" s3://<raw bucket name>

As before, this upload to the raw zone triggers the Lambda function that starts the ETL job. You can to see the progress of the job on the AWS Glue console.

When the job is complete, navigate to QuickSight and open the taxi analysis (or, if you still have it open, refresh the window).

You can now see that both months’ data is available for analysis. This step might take 1–2 minutes to load.

To see the status of each SPICE refresh, navigate back to the dataset on the QuickSight console and choose View History.

The following screenshot shows the status of previous refreshes and the number of rows that have been ingested into SPICE.

Now that you have tested the end-to-end process, you can try copying more FHV data to the raw zone and see the data within your QuickSight analysis.

Cleaning up

To clean up the resources you created by following along with this post, complete the following steps:

  1. Delete the QuickSight analysis you created.
  2. Delete the QuickSight dataset that you created.
  3. Delete the QuickSight data source:
    1. Choose New dataset.
    2. Select the data source and choose Delete data source.
  4. On the Amazon S3 console, delete the contents of the raw and processed S3 buckets.
  5. On the AWS CloudFormation console, select the stack SpiceRefreshBlog and choose Delete.

Conclusion

Using an event-based architecture to automate the refresh of your SPICE datasets makes sure that your business analysts are always viewing the latest available data. This reduction in time to analysis can help your business unlock insights quicker without having to wait for a manual or scheduled process. Additionally, by only refreshing SPICE when new data is available, the underlying data storage resources are used efficiently, so you only pay for what you need!

Get started with QuickSight today!


About the Authors

Rob Craig is a Senior Solutions Architect with AWS. He supports customers in the UK with their cloud journey, providing them with architectural advice and guidance to help them achieve their business outcomes.

 

 

 

 

Dylan Qu is an AWS solutions architect responsible for providing architectural guidance across the full AWS stack with a focus on Data Analytics, AI/ML and DevOps.

ICYMI: Serverless Q3 2020

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/icymi-serverless-q3-2020/

Welcome to the 11th edition of the AWS Serverless ICYMI (in case you missed it) quarterly recap. Every quarter, we share all of the most recent product launches, feature enhancements, blog posts, webinars, Twitch live streams, and other interesting things that you might have missed!

Q3 Calendar

In case you missed our last ICYMI, checkout what happened last quarter here.

AWS Lambda

MSK trigger in Lambda

In August, we launched support for using Amazon Managed Streaming for Apache Kafka (Amazon MSK) as an event source for Lambda functions. Lambda has existing support for processing streams from Kinesis and DynamoDB. Now you can process data streams from Amazon MSK and easily integrate with downstream serverless workflows. This integration allows you to process batches of records, one per partition at a time, and scale concurrency by increasing the number of partitions in a topic.

We also announced support for Java 8 (Corretto) in Lambda, and you can now use Amazon Linux 2 for custom runtimes. Amazon Linux 2 is the latest generation of Amazon Linux and provides an application environment with access to the latest innovations in the Linux ecosystem.

Amazon API Gateway

API integrations

API Gateway continued to launch new features for HTTP APIs, including new integrations for five AWS services. HTTP APIs can now route requests to AWS AppConfig, Amazon EventBridge, Amazon Kinesis Data Streams, Amazon SQS, and AWS Step Functions. This makes it easy to create webhooks for business logic hosted in these services. The service also expanded the authorization capabilities, adding Lambda and IAM authorizers, and enabled wildcards in custom domain names. Over time, we will continue to improve and migrate features from REST APIs to HTTP APIs.

In September, we launched mutual TLS for both regional REST APIs and HTTP APIs. This is a new method for client-to-server authentication to enhance the security of your API. It can protect your data from exploits such as client spoofing or man-in-the-middle. This enforces two-way TLS (or mTLS) which enables certificate-based authentication both ways from client-to-server and server-to-client.

Enhanced observability variables now make it easier to troubleshoot each phase of an API request. Each phase from AWS WAF through to integration adds latency to a request, returns a status code, or raises an error. Developers can use these variables to identify the cause of latency within the API request. You can configure these variables in AWS SAM templates – see the demo application to see how you can use these variables in your own application.

AWS Step Functions

X-Ray tracing in Step Functions

We added X-Ray tracing support for Step Functions workflows, giving you full visibility across state machine executions, making it easier to analyze and debug distributed applications. Using the service map view, you can visually identify errors in resources and view error rates across workflow executions. You can then drill into the root cause of an error. You can enable X-Ray in existing workflows by a single-click in the console. Additionally, you can now also visualize Step Functions workflows directly in the Lambda console. To see this new feature, open the Step Functions state machines page in the Lambda console.

Step Functions also increased the payload size to 256 KB and added support for string manipulation, new comparison operators, and improved output processing. These updates were made to the Amazon States Languages (ASL), which is a JSON-based language for defining state machines. The new operators include comparison operators, detecting the existence of a field, wildcarding, and comparing two input fields.

AWS Serverless Application Model (AWS SAM)

AWS SAM goes GA

AWS SAM is an open source framework for building serverless applications that converts a shorthand syntax into CloudFormation resources.

In July, the AWS SAM CLI became generally available (GA). This tool operates on SAM templates and provides developers with local tooling for building serverless applications. The AWS SAM CLI offers a rich set of tools that enable developers to build serverless applications quickly.

AWS X-Ray

X-Ray Insights

X-Ray launched a public preview of X-Ray Insights, which can help produce actionable insights for anomalies within your applications. Designed to make it easier to analyze and debug distributed applications, it can proactively identify issues caused by increases in faults. Using the incident timeline, you can visualize when the issue started and how it developed. The service identifies a probable root cause along with any anomalous services. There is no additional instrumentation needed to use X-Ray Insights – you can enable this feature within X-Ray Groups.

Amazon Kinesis

In July, Kinesis announced support for data delivery to generic HTTP endpoints, and service providers like Datadog, New Relic, MongoDB, and Splunk. Use the Amazon Kinesis console to configure your data producers to send data to Amazon Kinesis Data Firehose and specify one of these new delivery targets. Additionally, Amazon Kinesis Data Firehose is now available in the Europe (Milan) and Africa (Cape Town) AWS Regions.

Serverless Posts

Our team is always working to build and write content to help our customers better understand all our serverless offerings. Here is a list of the latest posts published to the AWS Compute Blog this quarter.

July

August

September

Tech Talks & Events

We hold several AWS Online Tech Talks covering serverless tech talks throughout the year, so look out for them in the Serverless section of the AWS Online Tech Talks page. We also regularly deliver talks at conferences and events around the globe, regularly join in on podcasts, and record short videos you can find to learn in quick byte sized chunks.

Here are some from Q3:

Learning Paths

Ask Around Me

Learn How to Build and Deploy a Web App Backend that Supports Authentication, Geohashing, and Real-Time Messaging

Ask Around Me is an example web app that shows how to build authenticaton, geohashing and real-time messaging into your serverless applications. This learning path includes videos and learning resources to help walk you through the application.

Build a Serverless Web App for a Theme Park

This five-video learning path walks you through the Innovator Island workshop, and provides learning resources for building realtime serverless web applications.

Live streams

July

August

September

There are also a number of other helpful video series covering serverless available on the Serverless Land YouTube channel.

New AWS Serverless Heroes

Serverless Heroes Q3 2020

We’re pleased to welcome Angela Timofte, Luca Bianchi, Matthieu Napoli, Peter Hanssens, Sheen Brisals, and Tom McLaughlin to the growing list of AWS Serverless Heroes.

The AWS Hero program is a selection of worldwide experts that have been recognized for their positive impact within the community. They share helpful knowledge and organize events and user groups. They’re also contributors to numerous open-source projects in and around serverless technologies.

New! The Serverless Land website

Serverless Land

To help developers find serverless learning resources, we have curated a list of serverless blogs, videos, events and training programs at a new site, Serverless Land. This is regularly updated with new information – you can subscribe to the RSS feed for automatic updates, follow the LinkedIn page or subscribe to the YouTube channel.

Still looking for more?

The Serverless landing page has lots of information. The Lambda resources page contains case studies, webinars, whitepapers, customer stories, reference architectures, and even more Getting Started tutorials.

You can also follow all of us on Twitter to see the latest news, follow conversations, and interact with the team.

Nielsen: Processing 55TB of Data Per Day with AWS Lambda

Post Syndicated from Boaz Ziniman original https://aws.amazon.com/blogs/architecture/nielsen-processing-55tb-of-data-per-day-with-aws-lambda/

Earlier this year, I went into the studio with Opher Dubrovsky from Nielsen Marketing Cloud (a data management platform) to record an episode of This is My Architecture about Big Data architecture.

In preparation for the recording and during my initial conversations with Opher, I realized that there is an amazing story here that can help a lot of developers and architects who are building, or thinking of building, serverless architecture.

Many of these builders aren’t sure if serverless can withstand the load they need, how the costs is going to look like, or how complicated and different it is from what they are familiar with, and they have countless more questions. Nielsen’s story is a great example of how you can take this technology, which I like so much, to the extreme.

Serverless Big Data?

There are many use cases for serverless architecture, such as web systems, use of IoT, connection to microservices via API, automation, and more. Using serverless for data processing is one of the earliest architectures, and serverless Big Data, whether it is for processing large-volume data streams or file processing, is a very common practice.

What surprised me about Nielsen’s story was the size and complexity of the solution. Nielsen’s system, called “DataOut,” is processing 250 billion events-per-day, which translates to 55 terabytes (TB) of data. The system can automatically scale up and down, thanks to the capabilities of serverless architecture, processing from 1TB to 6TB of data per hour, and costing “only” $1,000 per day. (If this sounds like a lot to you, think again about how much computing power it takes to process this amount of data and how much workforce time the system saves.)

What did we learn?

More and more organizations (small and large) are adopting serverless architecture-based solutions for their core systems. The technology has the ability to scale up (and down), reach a very large scale, and provide flexibility at a reasonable price. This is what makes this architecture, even in the world of Big Data, something worth considering when planning a new system or improving an existing one.

True, there are still challenges to be solved and knowledge to be gained as more and more developers use these solutions, but Nielsen’s story is just one of a series of solutions built for better scalability and mostly simpler to operate and maintain.

Spend 10 minutes with Opher and me in the below video to learn how Nielsen built a data processing machine without machines (kind of).

For more content like this, subscribe to our YouTube channels This is My Architecture, This is My Code, and This is My Model, or visit the This is My Architecture on AWS, which has search functionality and the ability to filter by industry, language, and service.

Building Extensions for AWS Lambda – In preview

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/building-extensions-for-aws-lambda-in-preview/

AWS Lambda is announcing a preview of Lambda Extensions, a new way to easily integrate Lambda with your favorite monitoring, observability, security, and governance tools. Extensions enable tools to integrate deeply into the Lambda execution environment to control and participate in Lambda’s lifecycle. This simplified experience makes it easier for you to use your preferred tools across your application portfolio today.

In this post I explain how Lambda extensions work, the changes to the Lambda lifecycle, and how to build an extension. To learn how to use extensions with your functions, see the companion blog post “Introducing AWS Lambda extensions”.

Extensions are built using the new Lambda Extensions API, which provides a way for tools to get greater control during function initialization, invocation, and shut down. This API builds on the existing Lambda Runtime API, which enables you to bring custom runtimes to Lambda.

You can use extensions from AWS, AWS Lambda Ready Partners, and open source projects for use-cases such as application performance monitoring, secrets management, configuration management, and vulnerability detection. You can also build your own extensions to integrate your own tooling using the Extensions API.

There are extensions available today for AppDynamics, Check Point, Datadog, Dynatrace, Epsagon, HashiCorp, Lumigo, New Relic, Thundra, Splunk, AWS AppConfig, and Amazon CloudWatch Lambda Insights. For more details on these, see “Introducing AWS Lambda extensions”.

The Lambda execution environment

Lambda functions run in a sandboxed environment called an execution environment. This isolates them from other functions and provides the resources, such as memory, specified in the function configuration.

Lambda automatically manages the lifecycle of compute resources so that you pay for value. Between function invocations, the Lambda service freezes the execution environment. It is thawed if the Lambda service needs the execution environment for subsequent invocations.

Previously, only the runtime process could influence the lifecycle of the execution environment. It would communicate with the Runtime API, which provides an HTTP API endpoint within the execution environment to communicate with the Lambda service.

Lambda and Runtime API

Lambda and Runtime API

The runtime uses the API to request invocation events from Lambda and deliver them to the function code. It then informs the Lambda service when it has completed processing an event. The Lambda service then freezes the execution environment.

The runtime process previously exposed two distinct phases in the lifecycle of the Lambda execution environment: Init and Invoke.

1. Init: During the Init phase, the Lambda service initializes the runtime, and then runs the function initialization code (the code outside the main handler). The Init phase happens either during the first invocation, or in advance if Provisioned Concurrency is enabled.

2. Invoke: During the invoke phase, the runtime requests an invocation event from the Lambda service via the Runtime API, and invokes the function handler. It then returns the function response to the Runtime API.

After the function runs, the Lambda service freezes the execution environment and maintains it for some time in anticipation of another function invocation.

If the Lambda function does not receive any invokes for a period of time, the Lambda service shuts down and removes the environment.

Previous Lambda lifecycle

Previous Lambda lifecycle

With the addition of the Extensions API, extensions can now influence, control, and participate in the lifecycle of the execution environment. They can use the Extensions API to influence when the Lambda service freezes the execution environment.

AWS Lambda execution environment with the Extensions API

AWS Lambda execution environment with the Extensions API

Extensions are initialized before the runtime and the function. They then continue to run in parallel with the function, get greater control during function invocation, and can run logic during shut down.

Extensions allow integrations with the Lambda service by introducing the following changes to the Lambda lifecycle:

  1. An updated Init phase. There are now three discrete Init tasks: extensions Init, runtime Init, and function Init. This creates an order where extensions and the runtime can perform setup tasks before the function code runs.
  2. Greater control during invocation. During the invoke phase, as before, the runtime requests the invocation event and invokes the function handler. In addition, extensions can now request lifecycle events from the Lambda service. They can run logic in response to these lifecycle events, and respond to the Lambda service when they are done. The Lambda service freezes the execution environment when it hears back from the runtime and all extensions. In this way, extensions can influence the freeze/thaw behavior.
  3. Shutdown phase: we are now exposing the shutdown phase to let extensions stop cleanly when the execution environment shuts down. The Lambda service sends a shut down event, which tells the runtime and extensions that the environment is about to be shut down.
New Lambda lifecycle with extensions

New Lambda lifecycle with extensions

Each Lambda lifecycle phase starts with an event from the Lambda service to the runtime and all registered extensions. The runtime and extensions signal that they have completed by requesting the Next invocation event from the Runtime and Extensions APIs. Lambda freezes the execution environment and all extensions when there are no pending events.

Lambda lifecycle for execution environment, runtime, extensions, and function.png

Lambda lifecycle for execution environment, runtime, extensions, and function.png

For more information on the lifecycle phases and the Extensions API, see the documentation.

How are extensions delivered and run?

You deploy extensions as Lambda layers, which are ZIP archives containing shared libraries or other dependencies.

To add a layer, use the AWS Management Console, AWS Command Line Interface (AWS CLI), or infrastructure as code tools such as AWS CloudFormation, the AWS Serverless Application Model (AWS SAM), and Terraform.

When the Lambda service starts the function execution environment, it extracts the extension files from the Lambda layer into the /opt directory. Lambda then looks for any extensions in the /opt/extensions directory and starts initializing them. Extensions need to be executable as binaries or scripts. As the function code directory is read-only, extensions cannot modify function code.

Extensions can run in either of two modes, internal and external.

  • Internal extensions run as part of the runtime process, in-process with your code. They are not separate processes. Internal extensions allow you to modify the startup of the runtime process using language-specific environment variables and wrapper scripts. You can use language-specific environment variables to add options and tools to the runtime for Java Correto 8 and 11, Node.js 10 and 12, and .NET Core 3.1. Wrapper scripts allow you to delegate the runtime startup to your script to customize the runtime startup behavior. You can use wrapper scripts with Node.js 10 and 12, Python 3.8, Ruby 2.7, Java 8 and 11, and .NET Core 3.1. For more information, see “Modifying-the-runtime-environment”.
  • External extensions allow you to run separate processes from the runtime but still within the same execution environment as the Lambda function. External extensions can start before the runtime process, and can continue after the runtime shuts down. External extensions work with Node.js 10 and 12, Python 3.7 and 3.8, Ruby 2.5 and 2.7, Java Corretto 8 and 11, .NET Core 3.1, and custom runtimes.

External extensions can be written in a different language to the function. We recommend implementing external extensions using a compiled language as a self-contained binary. This makes the extension compatible with all of the supported runtimes. If you use a non-compiled language, ensure that you include a compatible runtime in the extension.

Extensions run in the same execution environment as the function, so share resources such as CPU, memory, and disk storage with the function. They also share environment variables, in addition to permissions, using the same AWS Identity and Access Management (IAM) role as the function.

For more details on resources, security, and performance with extensions, see the companion blog post “Introducing AWS Lambda extensions”.

For example extensions and wrapper scripts to help you build your own extensions, see the GitHub repository.

Showing extensions in action

The demo shows how external extensions integrate deeply with functions and the Lambda runtime. The demo creates an example Lambda function with a single extension using either the AWS CLI, or AWS SAM.

The example shows how an external extension can start before the runtime, run during the Lambda function invocation, and shut down after the runtime shuts down.

To set up the example, visit the GitHub repo, and follow the instructions in the README.md file.

The example Lambda function uses the custom provided.al2 runtime based on Amazon Linux 2. Using the custom runtime helps illustrate in more detail how the Lambda service, Runtime API, and the function communicate. The extension is delivered using a Lambda layer.

The runtime, function, and extension, log their status events to Amazon CloudWatch Logs. The extension initializes as a separate process and waits to receive the function invocation event from the Extensions API. It then sleeps for 5 seconds before calling the API again to register to receive the next event. The extension sleep simulates the processing of a parallel process. This could, for example, collect telemetry data to send to an external observability service.

When the Lambda function is invoked, the extension, runtime and function perform the following steps. I walk through the steps using the log output.

1. The Lambda service adds the configured extension Lambda layer. It then searches the /opt/extensions folder, and finds an extension called extension1.sh. The extension executable launches before the runtime initializes. It registers with the Extensions API to receive INVOKE and SHUTDOWN events using the following API call.

curl -sS -LD "$HEADERS" -XPOST "http://${AWS_LAMBDA_RUNTIME_API}/2020-01-01/extension/register" --header "Lambda-Extension-Name: ${LAMBDA_EXTENSION_NAME}" -d "{ \"events\": [\"INVOKE\", \"SHUTDOWN\"]}" > $TMPFILE
Extension discovery, registration, and start

Extension discovery, registration, and start

2. The Lambda custom provided.al2 runtime initializes from the bootstrap file.

Runtime initialization

Runtime initialization

3. The runtime calls the Runtime API to get the next event using the following API call. The HTTP request is blocked until the event is received.

curl -sS -LD "$HEADERS" -X GET "http://${AWS_LAMBDA_RUNTIME_API}/2018-06-01/runtime/invocation/next" > $TMPFILE &

The extension calls the Extensions API and waits for the next event. The HTTP request is again blocked until one is received.

curl -sS -L -XGET "http://${AWS_LAMBDA_RUNTIME_API}/2020-01-01/extension/event/next" --header "Lambda-Extension-Identifier: ${EXTENSION_ID}" > $TMPFILE &
Runtime and extension call APIs to get the next event

Runtime and extension call APIs to get the next event

4. The Lambda service receives an invocation event. It sends the event payload to the runtime using the Runtime API. It sends an event to the extension informing it about the invocation, using the Extensions API.

Runtime and extension receive event

Runtime and extension receive event

5. The runtime invokes the function handler. The function receives the event payload.

Runtime invokes handler

Runtime invokes handler

6. The function runs the handler code. The Lambda runtime receives back the function response and sends it back to the Runtime API with the following API call.

curl -sS -X POST "http://${AWS_LAMBDA_RUNTIME_API}/2018-06-01/runtime/invocation/$REQUEST_ID/response" -d "$RESPONSE" > $TMPFILE
Runtime receives function response and sends to Runtime API

Runtime receives function response and sends to Runtime API

7. The Lambda runtime then waits for the next invocation event (warm start).

Runtime waits for next event

Runtime waits for next event

8. The extension continues processing for 5 seconds, simulating the processing of a companion process. The extension finishes, and uses the Extensions API to register again to wait for the next event.

Extension processing

Extension processing

9. The function invocation report is logged.

Function invocation report

Function invocation report

10. When Lambda is about to shut down the execution environment, it sends the Runtime API a shut down event.

Lambda runtime shut down event

Lambda runtime shut down event

11. Lambda then sends a shut down event to the extensions. The extension finishes processing and then shuts down after the runtime.

Lambda extension shut down event

Lambda extension shut down event

The demo shows the steps the runtime, function, and extensions take during the Lambda lifecycle.

An external extension registers and starts before the runtime. When Lambda receives an invocation event, it sends it to the runtime. It then sends an event to the extension informing it about the invocation. The runtime invokes the function handler, and the extension does its own processing of the event. The extension continues processing after the function invocation completes. When Lambda is about to shut down the execution environment, it sends a shut down event to the runtime. It then sends one to the extension, so it can finish processing.

To see a sequence diagram of this flow, see the Extensions API documentation.

Pricing

Extensions share the same billing model as Lambda functions. When using Lambda functions with extensions, you pay for requests served and the combined compute time used to run your code and all extensions, in 100 ms increments. To learn more about the billing for extensions, visit the Lambda FAQs page.

Conclusion

Lambda extensions enable you to extend Lambda’s execution environment to more easily integrate with your favorite tools for monitoring, observability, security, and governance.

Extensions can run additional code; before, during, and after a function invocation. There are extensions available today from AWS Lambda Ready Partners. These cover use-cases such as application performance monitoring, secrets management, configuration management, and vulnerability detection. Extensions make it easier to use your existing tools with your serverless applications. For more information on the available extensions, see the companion post “Introducing Lambda Extensions – In preview“.

You can also build your own extensions to integrate your own tooling using the new Extensions API. For example extensions and wrapper scripts, see the GitHub repository.

Extensions are now available in preview in the following Regions: us-east-1, us-east-2, us-west-1, us-west-2, ca-central-1, eu-west-1, eu-west-2, eu-west-3, eu-central-1, eu-north-1, eu-south-1, sa-east-1, me-south-1, ap-northeast-1, ap-northeast-2, ap-northeast-3, ap-southeast-1, ap-southeast-2, ap-south-1, and ap-east-1.

For more serverless learning resources, visit https://serverlessland.com.

Introducing AWS Lambda Extensions – In preview

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/introducing-aws-lambda-extensions-in-preview/

AWS Lambda is announcing a preview of Lambda Extensions, a new way to easily integrate Lambda with your favorite monitoring, observability, security, and governance tools. In this post I explain how Lambda extensions work, how you can begin using them, and the extensions from AWS Lambda Ready Partners that are available today.

Extensions help solve a common request from customers to make it easier to integrate their existing tools with Lambda. Previously, customers told us that integrating Lambda with their preferred tools required additional operational and configuration tasks. In addition, tools such as log agents, which are long-running processes, could not easily run on Lambda.

Extensions are a new way for tools to integrate deeply into the Lambda environment. There is no complex installation or configuration, and this simplified experience makes it easier for you to use your preferred tools across your application portfolio today. You can use extensions for use-cases such as:

  • capturing diagnostic information before, during, and after function invocation
  • automatically instrumenting your code without needing code changes
  • fetching configuration settings or secrets before the function invocation
  • detecting and alerting on function activity through hardened security agents, which can run as separate processes from the function

You can use extensions from AWS, AWS Lambda Ready Partners, and open source projects. There are extensions available today for AppDynamics, Check Point, Datadog, Dynatrace, Epsagon, HashiCorp, Lumigo, New Relic, Thundra, Splunk SignalFX, AWS AppConfig, and Amazon CloudWatch Lambda Insights.

You can learn how to build your own extensions, in the companion post “Building Extensions for AWS Lambda – In preview“.

Overview

Lambda Extensions is designed to be the easiest way to plug in the tools you use today without complex installation or configuration management. You deploy extensions as Lambda layers, with the AWS Management Console and AWS Command Line Interface (AWS CLI). You can also use infrastructure as code tools such as AWS CloudFormation, the AWS Serverless Application Model (AWS SAM), Serverless Framework, and Terraform. You can use Stackery to automate the integration of extensions from Epsagon, New Relic, Lumigo, and Thundra.

There are two components to the Lambda Extensions capability: the Extensions API and extensions themselves. Extensions are built using the new Lambda Extensions API which provides a way for tools to get greater control during function initialization, invocation, and shut down. This API builds on the existing Lambda Runtime API, which enables you to bring custom runtimes to Lambda.

AWS Lambda execution environment with the Extensions API

AWS Lambda execution environment with the Extensions API

Most customers will use extensions without needing to know about the capabilities of the Extensions API that enables them. You can just consume capabilities of an extension by configuring the options in your Lambda functions. Developers who build extensions use the Extensions API to register for function and execution environment lifecycle events.

Extensions can run in either of two modes – internal and external.

  • Internal extensions run as part of the runtime process, in-process with your code. They allow you to modify the startup of the runtime process using language-specific environment variables and wrapper scripts. Internal extensions enable use cases such as automatically instrumenting code.
  • External extensions allow you to run separate processes from the runtime but still within the same execution environment as the Lambda function. External extensions can start before the runtime process, and can continue after the runtime shuts down. External extensions enable use cases such as fetching secrets before the invocation, or sending telemetry to a custom destination outside of the function invocation. These extensions run as companion processes to Lambda functions.

For more information on the Extensions API and the changes to the Lambda lifecycle, see “Building Extensions for AWS Lambda – In preview

AWS Lambda Ready Partners extensions available at launch

Today, you can use extensions with the following AWS and AWS Lambda Ready Partner’s tools, and there are more to come:

  • AppDynamics provides end-to-end transaction tracing for AWS Lambda. With the AppDynamics extension, it is no longer mandatory for developers to include the AppDynamics tracer as a dependency in their function code, making tracing transactions across hybrid architectures even simpler.
  • The Datadog extension brings comprehensive, real-time visibility to your serverless applications. Combined with Datadog’s existing AWS integration, you get metrics, traces, and logs to help you monitor, detect, and resolve issues at any scale. The Datadog extension makes it easier than ever to get telemetry from your serverless workloads.
  • The Dynatrace extension makes it even easier to bring AWS Lambda metrics and traces into the Dynatrace platform for intelligent observability and automatic root cause detection. Get comprehensive, end-to-end observability with the flip of a switch and no code changes.
  • Epsagon helps you monitor, troubleshoot, and lower the cost for your Lambda functions. Epsagon’s extension reduces the overhead of sending traces to the Epsagon service, with minimal performance impact to your function.
  • HashiCorp Vault allows you to secure, store, and tightly control access to your application’s secrets and sensitive data. With the Vault extension, you can now authenticate and securely retrieve dynamic secrets before your Lambda function invokes.
  • Lumigo provides a monitoring and observability platform for serverless and microservices applications. The Lumigo extension enables the new Lumigo Lambda Profiler to see a breakdown of function resources, including CPU, memory, and network metrics. Receive actionable insights to reduce Lambda runtime duration and cost, fix bottlenecks, and increase efficiency.
  • Check Point CloudGuard provides full lifecycle security for serverless applications. The CloudGuard extension enables Function Self Protection data aggregation as an out-of-process extension, providing detection and alerting on application layer attacks.
  • New Relic provides a unified observability experience for your entire software stack. The New Relic extension uses a simpler companion process to report function telemetry data. This also requires fewer AWS permissions to add New Relic to your application.
  • Thundra provides an application debugging, observability and security platform for serverless, container and virtual machine (VM) workloads. The Thundra extension adds asynchronous telemetry reporting functionality to the Thundra agents, getting rid of network latency.
  • Splunk offers an enterprise-grade cloud monitoring solution for real-time full-stack visibility at scale. The Splunk extension provides a simplified runtime-independent interface to collect high-resolution observability data with minimal overhead. Monitor, manage, and optimize the performance and cost of your serverless applications with Splunk Observability solutions.
  • AWS AppConfig helps you manage, store, and safely deploy application configurations to your hosts at runtime. The AWS AppConfig extension integrates Lambda and AWS AppConfig seamlessly. Lambda functions have simple access to external configuration settings quickly and easily. Developers can now dynamically change their Lambda function’s configuration safely using robust validation features.
  • Amazon CloudWatch Lambda Insights enables you to efficiently monitor, troubleshoot, and optimize Lambda functions. The Lambda Insights extension simplifies the collection, visualization, and investigation of detailed compute performance metrics, errors, and logs. You can more easily isolate and correlate performance problems to optimize your Lambda environments.

You can also build and use your own extensions to integrate your organization’s tooling. For instance, the Cloud Foundations team at Square has built their own extension. They say:

The Cloud Foundations team at Square works to make the cloud accessible and secure. We partnered with the Security Infrastructure team, who builds infrastructure to secure Square’s sensitive data, to enable serverless applications at Square,​ and ​provide mTLS identities to Lambda​.

Since beginning work on Lambda, we have focused on creating a streamlined developer experience. Teams adopting Lambda need to learn a lot about AWS, and we see extensions as a way to abstract away common use cases. For our initial exploration, we wanted to make accessing secrets easy, as with our current tools each Lambda function usually pulls 3-5 secrets.

The extension we built and open source fetches secrets on cold starts, before the Lambda function is invoked. Each function includes a configuration file that specifies which secrets to pull. We knew this configuration was key, as Lambda functions should only be doing work they need to do. The secrets are cached in the local /tmp directory, which the function reads when it needs the secret data. This makes Lambda functions not only faster, but reduces the amount of code for accessing secrets.

Showing extensions in action with AWS AppConfig

This demo shows an example of using the AWS AppConfig with a Lambda function. AWS AppConfig is a capability of AWS Systems Manager to create, manage, and quickly deploy application configurations. It lets you dynamically deploy external configuration without having to redeploy your applications. As AWS AppConfig has robust validation features, all configuration changes can be tested safely before rolling out to your applications.

AWS AppConfig has an available extension which gives Lambda functions access to external configuration settings quickly and easily. The extension runs a separate local process to retrieve and cache configuration data from the AWS AppConfig service. The function code can then fetch configuration data faster using a local call rather than over the network.

To set up the example, visit the GitHub repo and follow the instructions in the README.md file.

The example creates an AWS AppConfig application, environment, and configuration profile. It stores a loglevel value, initially set to normal.

AWS AppConfig application, environment, and configuration profile

AWS AppConfig application, environment, and configuration profile

An AWS AppConfig deployment runs to roll out the initial configuration.

AWS AppConfig deployment

AWS AppConfig deployment

The example contains two Lambda functions that include the AWS AppConfig extension. For a list of the layers that have the AppConfig extension, see the blog post “AWS AppConfig Lambda Extension”.

As extensions share the same permissions as Lambda functions, the functions have execution roles that allow access to retrieve the AWS AppConfig configuration.

Lambda function add layer

Lambda function add layer

The functions use the extension to retrieve the loglevel value from AWS AppConfig, returning the value as a response. In a production application, this value could be used within function code to determine what level of information to send to CloudWatch Logs. For example, to troubleshoot an application issue, you can change the loglevel value centrally. Subsequent function invocations for both functions use the updated value.

Both Lambda functions are configured with an environment variable that specifies which AWS AppConfig configuration profile and value to use.

Lambda environment variable specifying AWS AppConfig profile

Lambda environment variable specifying AWS AppConfig profile

The functions also return whether the invocation is a cold start.

Running the functions with a test payload returns the loglevel value normal. The first invocation is a cold start.

{
  "event": {
    "hello": "world"
  },
  "ColdStart": true,
  "LogLevel": "normal"
}

Subsequent invocations return the same value with ColdStart set to false.

{
  "event": {
    "hello": "world"
  },
  "ColdStart": false,
  "LogLevel": "normal"
}

Create a new AWS Config hosted configuration profile version setting the loglevel value to verbose. Run a new AWS AppConfig deployment to update the value. The extension for both functions retrieves the new value. The function configuration itself is not changed.

Running another test invocation for both functions returns the updated value still without a cold start.

{
  "event": {
    "hello": "world"
  },
  "ColdStart": false,
  "LogLevel": "verbose"
}

AWS AppConfig has worked seamlessly with Lambda to update a dynamic external configuration setting for multiple Lambda functions without having to redeploy the function configuration.

The only function configuration required is to add the layer which contains the AWS AppConfig extension.

Pricing

Extensions share the same billing model as Lambda functions. When using Lambda functions with extensions, you pay for requests served and the combined compute time used to run your code and all extensions, in 100 ms increments. To learn more about the billing for extensions, visit the Lambda FAQs page.

Resources, security, and performance with extensions

Extensions run in the same execution environment as the function code. Therefore, they share resources with the function, such as CPU, memory, disk storage, and environment variables. They also share permissions, using the same AWS Identity and Access Management (IAM) role as the function.

You can configure up to 10 extensions per function, using up to five layers at a time. Multiple extensions can be included in a single layer.

The size of the extensions counts towards the deployment package limit. This cannot exceed the unzipped deployment package size limit of 250 MB.

External extensions are initialized before the runtime is started so can increase the delay before the function is invoked. Today, the function invocation response is returned after all extensions have completed. An extension that takes time to complete can increase the delay before the function response is returned. If an extension performs compute-intensive operations, function execution duration may increase. To measure the additional time the extension runs after the function invocation, use the new PostRuntimeExtensionsDuration CloudWatch metric to measure the extra time the extension takes after the function execution. To understand the impact of a specific extension, you can use the Duration and MaxMemoryUsed CloudWatch metrics, and run different versions of your function with and without the extension. Adding more memory to a function also proportionally increases CPU and network throughput.

The function and all extensions must complete within the function’s configured timeout setting which applies to the entire invoke phase.

Conclusion

Lambda extensions enable you to extend the Lambda service to more easily integrate with your favorite tools for monitoring, observability, security, and governance.

Today, you can install a number of available extensions from AWS Lambda Ready Partners. These cover use-cases such as application performance monitoring, secrets management, configuration management, and vulnerability detection. Extensions make it easier to use your existing tools with your serverless applications.

You can also build extensions to integrate your own tooling using the new Extensions API. For more information, see the companion post “Building Extensions for AWS Lambda – In preview“.

Extensions are now available in preview in the following Regions: us-east-1, us-east-2, us-west-1, us-west-2, ca-central-1, eu-west-1, eu-west-2, eu-west-3, eu-central-1, eu-north-1, eu-south-1, sa-east-1, me-south-1, ap-northeast-1, ap-northeast-2, ap-northeast-3, ap-southeast-1, ap-southeast-2, ap-south-1, and ap-east-1.

For more serverless learning resources, visit https://serverlessland.com.

The serverless LAMP stack part 6: From MVC to serverless microservices

Post Syndicated from Benjamin Smith original https://aws.amazon.com/blogs/compute/the-serverless-lamp-stack-part-6-from-mvc-to-serverless-microservices/

In this post, you learn how to build serverless PHP applications using microservices.

I show how to move from using a single Lambda function as scalable web host with an MVC framework, to a decoupled microservice model. The accompanying code examples for this blog post can be found in this GitHub repository.

The MVC architectural pattern

A traditional LAMP stack often implements the Model-View-Controller (MVC) architecture. This is a well-established way of separating application logic into three parts: the model, the view, and the controller.

  • Model: This part is responsible for managing the data of the application. Its role is to retrieve raw information from the database or receive user input from the controller.
  • View: This component focuses on the display. Data received from the model is presented to the user. Any response from the user is also recognized and sent to the controller component.
  • Controller: This part is responsible for the application logic. It responds to the user input and performs interactions on the data model objects.

The MVC principal of decoupling data, logic, and presentation layers means that changes in one layer have minimal impact on the others. This speeds the development process and makes it easier to update layouts, change business rules, and add new features. Components are more adaptable for reuse and refactoring, and allow for a degree of simultaneous development.

The serverless LAMP stack

The serverless LAMP stack

The preceding serverless LAMP stack architecture is first discussed in this post. A web application is split in to two components. A single AWS Lambda function contains the application’s MVC framework. Each response is synchronously returned via Amazon API Gateway. This architecture addresses the scalability challenge that is often seen in traditional LAMP stack applications. It scales automatically with a managed infrastructure and a pay-per-use billing model. However, the serverless paradigm makes it possible to apply the MVC principles of decoupling and reusability to an even greater degree.

The “Lambda-lith”

The preceding architecture represents a serverless monolith or “Lambda-lith”. A single Lambda function contains the entire business logic within an MVC framework. This implementation can be used to “lift and shift” from a legacy MVC to a serverless application. Simple applications often start this way too, but as the application grows more complex over time new challenges can occur.

 

day1-day100

Lambda Day 1 to day 100

A Lambda-lith is often maintained in a single repository that contains the entire application logic. This is sometimes referred to as a mono-repo.

Lamba-lith monorepo

Lamba-lith monorepo

A mono-repo makes it harder to separate responsibility of ownership between development teams. Consequently, projects in a mono-repo are prone to depend on each other, creating tight coupling. The tightly coupled code base with all of its interconnected modules be challenging to maintain a regular release cadence. Any small fix can require updates to other parts of the code base, making maintenance challenging without fracturing the whole application. Onboarding can be slow as new developers take time to learn and understand the code base and all of the interdependencies.

By applying the following principles, Lambda-lith MVC applications can be refactored into decoupled serverless microservices.

Divide into independent Lambda functions with finite business logic

The following example illustrates a Lambda-lith with all business and routing logic stored in a single Lambda function. Every request is routed to this function from API Gateway. The function code base contains a `router.php` file to direct requests to the correct model, view, or controller.

This is similar to a traditional LAMP stack implementation in which a web server such as Apache or NGINX routes all requests to a single index.php function. However, it’s often more practical to split applications into multiple functions or services.

Lambda as a web server

In the following example, this Lambda function is split into multiple functions based on each CRUD operation. The internal routing logic is now decoupled from the business logic. The API Gateway service uses rules to route requests to the correct Lambda function. This allows each function to scale independently and updates can be made to one function without impacting another.

Routing decoupled from business logic

Build micro-perimeters to enforce strict verification of every person or service.

Traditional MVC applications often use a castle-and-moat security model. This provides security by placing a perimeter around the entire application to protect it from malicious actors. This perimeter guards the application or network by verifying requests and user identities at the point of entry or exit.

This is typically achieved with firewalls, proxy servers, honeypots, and other intrusion prevention tools. It assumes that activity inside the perimeter is safe. However, a network vulnerability may provide access to everything inside.

Microservice-based applications allow developers to apply a “zero trust” security model. This enables developers to build micro-perimeters around each resource. This is sometimes referred to as the principle of least privilege. It ensures that each request, service, or user can access only the data or resource that is necessary for its legitimate purpose. Even with a vulnerability, the blast radius is limited only to the service within that micro-perimeter.

Castle-and-moat vs zero trust security model

Use AWS Identity and Access Management (IAM) resource policies and execution roles to decouple business logic from security posture. Lambda resource policies define the events and services that are authorized to invoke the function. Lambda execution roles place constraints the resource or service the Lambda function has access to. When defining resource policies and execution roles, start with a minimum set of permissions and grant additional permissions as necessary.

Create building blocks based on common functionality

Each component is a single building block that makes up an application together with other blocks. These blocks form microservices that deliver a set of capabilities on a specific domain. This makes is easy to change, upgrade, and replace with no impact on the remaining microservice components. This creates natural ownership boundaries to help organize repositories.

Development teams can then easily be assigned ownership to individual microservice repositories. Use the AWS Serverless Application Model (AWS SAM) to organize microservices into multiple code repositories, as explained in this blog post.

Use messages to connect and communicate between microservices.

In traditional MVC applications, one part of the application uses method calls to communicate with the other parts. With serverless microservices, the code base is spread across short-lived stateless functions and services. Communication between these services is achieved using asynchronous messages or synchronous HTTP requests.

Synchronous communication

In this method, a service calls an API and waits for a response from the receiving service before proceeding. Use API Gateway to create a front door to your backend microservices. API Gateway is a fully managed service for creating and managing RESTful and WebSocket APIs.

Using API Gateway to transport data addresses common concerns such as authorization, API tokens, access control and rate limiting from your code, and helps to reduce code complexity. API Gateway can also be used for synchronous internal microservice communications where the services have clear separation, strict authentication requirements, or have been deployed across accounts.

The following architecture demonstrates an application that is deployed across two accounts. The Booking microservice, invokes a loyalty booking function via API Gateway that exists in the Loyalty points account.

Synchronous internal microservice communications

Asynchronous communication

In this pattern, a service sends a message without waiting for a response, and one or more services process the message asynchronously. Here, the services involved do not directly communicate with each other. Instead, services publish messages to a broker such as Amazon Simple Queue Service (SQS) or Amazon EventBridge. Other services can choose to subscribe to the topic in the broker that they care about. This enables further decoupling of business logic from data transportation and reduces your code complexity.

Use services instead of code, where possible

A service-first mindset is an important part of serverless application development. Each line of code you write may limit your project’s responsiveness to change and adds cognitive overhead for new developers. Using an appropriate AWS service for each domain (messaging, storage, orchestration) helps to build faster. Embracing this mind-set allows developers to focus on solving those unique challenges that add the most value to their customers.

By applying these principles to refactor an MVC Lambda-lith, I build the following CRUD API microservice. This application can be deployed from this GitHub repository. It uses an AWS Serverless Application Model (AWS SAM) template to define an HTTP API, 5 Lambda functions, an Amazon DynamoDB table and all the IAM roles required.

All routing logic and authentication is managed by Amazon API Gateway. Each Lambda function has limited scope and minimal business logic. It uses a lightweight custom-built PHP runtime, explained in this post. Each Lambda function uses the AWS PHP SDK to interact with the DynamoDB table. This architecture is suitable as a serverless microservice for a website backend.

A serverless API microservice with PHP

Conclusion

In this post, I show how to move from using a single Lambda function as a scalable web host with an MVC framework, to a decoupled microservice model. I explain the principles that can be applied to help transition an MCV application into a collection of microservices and show the benefits of doing so. I provide code examples for a serverless PHP CRUD microservice with a deployable AWS SAM template.

PHP development teams can transition from Lambda-lith MVC applications to a decoupled microservice model. This allows them to focus on shipping code to delight their customers without managing infrastructure.

Find more resources for building serverless PHP applications at ServerlessLand.com.

Building resilient serverless patterns by combining messaging services

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/building-resilient-no-code-serverless-patterns-by-combining-messaging-services/

In “Choosing between messaging services for serverless applications”, I explain the features and differences between the core AWS messaging services. Amazon SQS, Amazon SNS, and Amazon EventBridge provide queues, publish/subscribe, and event bus functionality for your applications. Individually, these are robust, scalable services that are fundamental building blocks of serverless architectures.

However, you can also combine these services to solve specific challenges in distributed architectures. By doing this, you can use specific features of each service to build sophisticated patterns with little code. These combinations can make your applications more resilient and scalable, and reduce the amount of custom logic and architecture in your workload.

In this blog post, I highlight several important patterns for serverless developers. I also show how you use and deploy these integrations with the AWS Serverless Application Model (AWS SAM).

Examples in this post refer to code that can be downloaded from this GitHub repo. The README.md file explains how to deploy and run each example.

SNS to SQS: Adding resilience and throttling to message throughput

SNS has a robust retry policy that results in up to 100,010 delivery attempts over 23 days. If a downstream service is unavailable, it may be overwhelmed by retries when it comes back online. You can solve this issue by adding an SQS queue.

Adding an SQS queue between the SNS topic and its subscriber has two benefits. First, it adds resilience to message delivery, since the messages are durably stored in a queue. Second, it throttles the rate of messages to the consumer, helping smooth out traffic bursts caused by the service catching up with missed messages.

To build this in an AWS SAM template, you first define the two resources, and the SNS subscription:

  MySqsQueue:
    Type: AWS::SQS::Queue

  MySnsTopic:
    Type: AWS::SNS::Topic
    Properties:
      Subscription:
        - Protocol: sqs
          Endpoint: !GetAtt MySqsQueue.Arn

Finally, you provide permission to the SNS topic to publish to the queue, using the AWS::SQS::QueuePolicy resource:

  SnsToSqsPolicy:
    Type: AWS::SQS::QueuePolicy
    Properties:
      PolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Sid: "Allow SNS publish to SQS"
            Effect: Allow
            Principal: "*"
            Resource: !GetAtt MySqsQueue.Arn
            Action: SQS:SendMessage
            Condition:
              ArnEquals:
                aws:SourceArn: !Ref MySnsTopic
      Queues:
        - Ref: MySqsQueue

To test this, you can publish a message to the SNS topic and then inspect the SQS queue length using the AWS CLI:

aws sns publish --topic-arn "arn:aws:sns:us-east-1:123456789012:sns-sqs-MySnsTopic-ABC123ABC" --message "Test message"
aws sqs get-queue-attributes --queue-url "https://sqs.us-east-1.amazonaws.com/123456789012/sns-sqs-MySqsQueue- ABC123ABC " --attribute-names ApproximateNumberOfMessages

This results in the following output:

CLI output

Another usage of this pattern is when you want to filter messages in architectures using an SQS queue. By placing the SNS topic in front of the queue, you can use the message filtering capabilities of SNS. This ensures that only the messages you need are published to the queue. To use message filtering in AWS SAM, use the AWS:SNS:Subcription resource:

  QueueSubcription:
    Type: 'AWS::SNS::Subscription'
    Properties:
      TopicArn: !Ref MySnsTopic
      Endpoint: !GetAtt MySqsQueue.Arn
      Protocol: sqs
      FilterPolicy:
        type:
        - orders
        - payments 
      RawMessageDelivery: 'true'

EventBridge to SNS: combining features of both services

Both SNS and EventBridge have different characteristics in terms of targets, and integration with broader features. This table compares the major differences between the two services:

Amazon SNSAmazon EventBridge
Number of targets10 million (soft)5
Limits100,000 topics. 12,500,000 subscriptions per topic.100 event buses. 300 rules per event bus.
Input transformationNoYes – see details.
Message filteringYes – see details.Yes, including IP address matching – see details.
FormatRaw or JSONJSON
Receive events from AWS CloudTrailNoYes
TargetsHTTP(S), SMS, SNS Mobile Push, Email/Email-JSON, SQS, Lambda functions15 targets including AWS LambdaAmazon SQSAmazon SNSAWS Step FunctionsAmazon Kinesis Data StreamsAmazon Kinesis Data Firehose.
SaaS integrationNoYes – see integration partners.
Schema Registry integrationNoYes – see details.
Dead-letter queues supportedYesNo
Public visibilityCan create public topicsCannot create public buses
Cross-RegionYou can subscribe your AWS Lambda functions to an Amazon SNS topic in any Region.Targets must be same Region. You can publish across Region to another event bus.

In this pattern, you configure an SNS topic as a target of an EventBridge rule:

SNS topic as a target for an EventBridge rule

In the AWS SAM template, you declare the resources in the preceding diagram as follows:

Resources:
  MySnsTopic:
    Type: AWS::SNS::Topic

  EventRule: 
    Type: AWS::Events::Rule
    Properties: 
      Description: "EventRule"
      EventPattern: 
        account: 
          - !Sub '${AWS::AccountId}'
        source:
          - "demo.cli"
      Targets: 
        - Arn: !Ref MySnsTopic
          Id: "SNStopic"

The default bus already exists in every AWS account, so there is no need to declare it. For the event bus to publish matching events to the SNS topic, you define permissions using the AWS::SNS::TopicPolicy resource:

  EventBridgeToToSnsPolicy:
    Type: AWS::SNS::TopicPolicy
    Properties: 
      PolicyDocument:
        Statement:
        - Effect: Allow
          Principal:
            Service: events.amazonaws.com
          Action: sns:Publish
          Resource: !Ref MySnsTopic
      Topics: 
        - !Ref MySnsTopic       

EventBridge has a limit of five targets per rule. In cases where you must send events to hundreds or thousands of targets, publishing to SNS first and then subscribing those targets to the topic works around this limit. Both services have different targets, and this pattern allows you to deliver EventBridge events to SMS, HTTP(s), email and SNS mobile push.

You can transform and filter the message using these services, often without needing an AWS Lambda function. SNS does not support input transformation but you can do this in an EventBridge rule. Message filtering is possible in both services but EventBridge provides richer content filtering capabilities.

AWS CloudTrail can log and monitor activity across services in your AWS account. It can be a useful source for events, allowing you to respond dynamically to objects in Amazon S3 or react to changes in your environment, for example. This natively integrates with EventBridge, allowing you to ingest events at scale from dozens of services.

Using EventBridge enables you to source events from outside your AWS account, offering integrations with a list of software as a service (SaaS) providers. This capability allows you to receive events from your accounts with SaaS providers like Zendesk, PagerDuty, and Auth0. These events are delivered to a partner event bus in your account, and can then be filtered and routed to an SNS topic.

Additionally, this pattern allows you to deliver events to Lambda functions in other AWS accounts and in other AWS Regions. You can invoke Lambda from SNS topics in other Regions and accounts. It’s also possible to make SNS topics publicly read-only, making them extensible endpoints that other third parties can consume from. SNS has comprehensive access control, which you can incorporate into this pattern.

Cross-account publishing

EventBridge to SQS: Building fault-tolerant microservices

EventBridge can route events to targets such as microservices. In the case of downstream failures, the service retries events for up to 24 hours. For workloads where you need a longer period of time to store and retry messages, you can deliver the events to an SQS queue in each microservice. This durably stores those events until the downstream service recovers. Additionally, this pattern protects the microservice from large bursts of traffic by throttling the delivery of messages.

Fault-tolerant microservices architecture

The resources declared in the AWS SAM template are similar to the previous examples, but it uses the AWS::SQS::QueuePolicy resource to grant the appropriate permission to EventBridge:

  EventBridgeToToSqsPolicy:
    Type: AWS::SQS::QueuePolicy
    Properties:
      PolicyDocument:
        Statement:
        - Effect: Allow
          Principal:
            Service: events.amazonaws.com
          Action: SQS:SendMessage
          Resource:  !GetAtt MySqsQueue.Arn
      Queues:
        - Ref: MySqsQueue

Conclusion

You can combine these services in your architectures to implement patterns that solve complex challenges, often with little code required. This blog post shows three examples that implement message throttling and queueing, integrating SNS and EventBridge, and building fault tolerant microservices.

To learn more building decoupled architectures, see this Learning Path series on EventBridge. For more serverless learning resources, visit https://serverlessland.com.

Unified serverless streaming ETL architecture with Amazon Kinesis Data Analytics

Post Syndicated from Ram Vittal original https://aws.amazon.com/blogs/big-data/unified-serverless-streaming-etl-architecture-with-amazon-kinesis-data-analytics/

Businesses across the world are seeing a massive influx of data at an enormous pace through multiple channels. With the advent of cloud computing, many companies are realizing the benefits of getting their data into the cloud to gain meaningful insights and save costs on data processing and storage. As businesses embark on their journey towards cloud solutions, they often come across challenges involving building serverless, streaming, real-time ETL (extract, transform, load) architecture that enables them to extract events from multiple streaming sources, correlate those streaming events, perform enrichments, run streaming analytics, and build data lakes from streaming events.

In this post, we discuss the concept of unified streaming ETL architecture using a generic serverless streaming architecture with Amazon Kinesis Data Analytics at the heart of the architecture for event correlation and enrichments. This solution can address a variety of streaming use cases with various input sources and output destinations. We then walk through a specific implementation of the generic serverless unified streaming architecture that you can deploy into your own AWS account for experimenting and evolving this architecture to address your business challenges.

Overview of solution

As data sources grow in volume, variety, and velocity, the management of data and event correlation become more challenging. Most of the challenges stem from data silos, in which different teams and applications manage data and events using their own tools and processes.

Modern businesses need a single, unified view of the data environment to get meaningful insights through streaming multi-joins, such as the correlation of sensory events and time-series data. Event correlation plays a vital role in automatically reducing noise and allowing the team to focus on those issues that really matter to the business objectives.

To realize this outcome, the solution proposes creating a three-stage architecture:

  • Ingestion
  • Processing
  • Analysis and visualization

The source can be a varied set of inputs comprising structured datasets like databases or raw data feeds like sensor data that can be ingested as single or multiple parallel streams. The solution envisions multiple hybrid data sources as well. After it’s ingested, the data is divided into single or multiple data streams depending on the use case and passed through a preprocessor (via an AWS Lambda function). This highly customizable processor transforms and cleanses data to be processed through analytics application. Furthermore, the architecture allows you to enrich data or validate it against standard sets of reference data, for example validating against postal codes for address data received from the source to verify its accuracy. After the data is processed, it’s sent to various sink platforms depending on your preferences, which could range from storage solutions to visualization solutions, or even stored as a dataset in a high-performance database.

The solution is designed with flexibility as a key tenant to address multiple, real-world use cases. The following diagram illustrates the solution architecture.

The architecture has the following workflow:

  1. We use AWS Database Migration Service (AWS DMS) to push records from the data source into AWS in real time or batch. For our use case, we use AWS DMS to fetch records from an on-premises relational database.
  2. AWS DMS writes records to Amazon Kinesis Data Streams. The data is split into multiple streams as necessitated through the channels.
  3. A Lambda function picks up the data stream records and preprocesses them (adding the record type). This is an optional step, depending on your use case.
  4. Processed records are sent to the Kinesis Data Analytics application for querying and correlating in-application streams, taking into account Amazon Simple Storage Service (Amazon S3) reference data for enrichment.

Solution walkthrough

For this post, we demonstrate an implementation of the unified streaming ETL architecture using Amazon RDS for MySQL as the data source and Amazon DynamoDB as the target. We use a simple order service data model that comprises orders, items, and products, where an order can have multiple items and the product is linked to an item in a reference relationship that provides detail about the item, such as description and price.

We implement a streaming serverless data pipeline that ingests orders and items as they are recorded in the source system into Kinesis Data Streams via AWS DMS. We build a Kinesis Data Analytics application that correlates orders and items along with reference product information and creates a unified and enriched record. Kinesis Data Analytics outputs output this unified and enriched data to Kinesis Data Streams. A Lambda function consumer processes the data stream and writes the unified and enriched data to DynamoDB.

To launch this solution in your AWS account, use the GitHub repo.

Prerequisites

Before you get started, make sure you have the following prerequisites:

Setting up AWS resources in your account

To set up your resources for this walkthrough, complete the following steps:

  1. Set up the AWS CDK for Java on your local workstation. For instructions, see Getting Started with the AWS CDK.
  2. Install Maven binaries for Java if you don’t have Maven installed already.
  3. If this is the first installation of the AWS CDK, make sure to run cdk bootstrap.
  4. Clone the following GitHub repo.
  5. Navigate to the project root folder and run the following commands to build and deploy:
    1. mvn compile
    2. cdk deploy UnifiedStreamETLCommonStack UnifiedStreamETLDataStack UnifiedStreamETLProcessStack

Setting up the orders data model for CDC

In this next step, you set up the orders data model for change data capture (CDC).

  1. On the Amazon Relational Database Service (Amazon RDS) console, choose Databases.
  2. Choose your database and make sure that you can connect to it securely for testing using bastion host or other mechanisms (not detailed in scope of this post).
  3. Start MySQL Workbench and connect to your database using your DB endpoint and credentials.
  4. To create the data model in your Amazon RDS for MySQL database, run orderdb-setup.sql.
  5. On the AWS DMS console, test the connections to your source and target endpoints.
  6. Choose Database migration tasks.
  7. Choose your AWS DMS task and choose Table statistics.
  8. To update your table statistics, restart the migration task (with full load) for replication.
  9. From your MySQL Workbench session, run orders-data-setup.sql to create orders and items.
  10. Verify that CDC is working by checking the Table statistics

Setting up your Kinesis Data Analytics application

To set up your Kinesis Data Analytics application, complete the following steps:

  1. Upload the product reference products.json to your S3 bucket with the logical ID prefix unifiedBucketId (which was previously created by cdk deploy).

You can now create a Kinesis Data Analytics application and map the resources to the data fields.

  1. On the Amazon Kinesis console, choose Analytics Application.
  2. Choose Create application.
  3. For Runtime, choose SQL.
  4. Connect the streaming data created using the AWS CDK as a unified order stream.
  5. Choose Discover schema and wait for it to discover the schema for the unified order stream. If discovery fails, update the records on the source Amazon RDS tables and send streaming CDC records.
  6. Save and move to the next step.
  7. Connect the reference S3 bucket you created with the AWS CDK and uploaded with the reference data.
  8. Input the following:
    1. “products.json” on the path to the S3 object
    2. Products on the in-application reference table name
  9. Discover the schema, then save and close.
  10. Choose SQL Editor and start the Kinesis Data Analytics application.
  11. Edit the schema for SOURCE_SQL_STREAM_001 and map the data resources as follows:
Column NameColumn TypeRow Path
orderIdINTEGER$.data.orderId
itemIdINTEGER$.data.orderId
itemQuantityINTEGER$.data.itemQuantity
itemAmountREAL$.data.itemAmount
itemStatusVARCHAR$.data.itemStatus
COL_timestampVARCHAR$.metadata.timestamp
recordTypeVARCHAR$.metadata.table-name
operationVARCHAR$.metadata.operation
partitionkeytypeVARCHAR$.metadata.partition-key-type
schemanameVARCHAR$.metadata.schema-name
tablenameVARCHAR$.metadata.table-name
transactionidBIGINT$.metadata.transaction-id
orderAmountDOUBLE$.data.orderAmount
orderStatusVARCHAR$.data.orderStatus
orderDateTimeTIMESTAMP$.data.orderDateTime
shipToNameVARCHAR$.data.shipToName
shipToAddressVARCHAR$.data.shipToAddress
shipToCityVARCHAR$.data.shipToCity
shipToStateVARCHAR$.data.shipToState
shipToZipVARCHAR$.data.shipToZip

 

  1. Choose Save schema and update stream samples.

When it’s complete, verify for 1 minute that nothing is in the error stream. If an error occurs, check that you defined the schema correctly.

  1. On your Kinesis Data Analytics application, choose your application and choose Real-time analytics.
  2. Go to the SQL results and run kda-orders-setup.sql to create in-application streams.
  3. From the application, choose Connect to destination.
  4. For Kinesis data stream, choose unifiedOrderEnrichedStream.
  5. For In-application stream, choose ORDER_ITEM_ENRICHED_STREAM.
  6. Choose Save and Continue.

Testing the unified streaming ETL architecture

You’re now ready to test your architecture.

  1. Navigate to your Kinesis Data Analytics application.
  2. Choose your app and choose Real-time analytics.
  3. Go to the SQL results and choose Real-time analytics.
  4. Choose the in-application stream ORDER_ITEM_ENRCIHED_STREAM to see the results of the real-time join of records from the order and order item streaming Kinesis events.
  5. On the Lambda console, search for UnifiedStreamETLProcess.
  6. Choose the function and choose Monitoring, Recent invocations.
  7. Verify the Lambda function run results.
  8. On the DynamoDB console, choose the OrderEnriched table.
  9. Verify the unified and enriched records that combine order, item, and product records.

The following screenshot shows the OrderEnriched table.

Operational aspects

When you’re ready to operationalize this architecture for your workloads, you need to consider several aspects:

  • Monitoring metrics for Kinesis Data Streams: GetRecords.IteratorAgeMilliseconds, ReadProvisionedThroughputExceeded, and WriteProvisionedThroughputExceeded
  • Monitoring metrics available for the Lambda function, including but not limited to Duration, IteratorAge, Error count and success rate (%), Concurrent executions, and Throttles
  • Monitoring metrics for Kinesis Data Analytics (millisBehindLatest)
  • Monitoring DynamoDB provisioned read and write capacity units
  • Using the DynamoDB automatic scaling feature to automatically manage throughput

We used the solution architecture with the following configuration settings to evaluate the operational performance:

  • Kinesis OrdersStream with two shards and Kinesis OrdersEnrichedStream with two shards
  • The Lambda function code does asynchronous processing with Kinesis OrdersEnrichedStream records in concurrent batches of five, with batch size as 500
  • DynamoDB provisioned WCU is 3000, RCU is 300

We observed the following results:

  • 100,000 order items are enriched with order event data and product reference data and persisted to DynamoDB
  • An average of 900 milliseconds latency from the time of event ingestion to the Kinesis pipeline to when the record landed in DynamoDB

The following screenshot shows the visualizations of these metrics.

Cleaning up

To avoid incurring future charges, delete the resources you created as part of this post (the AWS CDK provisioned AWS CloudFormation stacks).

Conclusion

In this post, we designed a unified streaming architecture that extracts events from multiple streaming sources, correlates and performs enrichments on events, and persists those events to destinations. We then reviewed a use case and walked through the code for ingesting, correlating, and consuming real-time streaming data with Amazon Kinesis, using Amazon RDS for MySQL as the source and DynamoDB as the target.

Managing an ETL pipeline through Kinesis Data Analytics provides a cost-effective unified solution to real-time and batch database migrations using common technical knowledge skills like SQL querying.


About the Authors

Ram Vittal is an enterprise solutions architect at AWS. His current focus is to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes. In his spare time, he enjoys tennis, photography, and movies.

 

 

 

 

Akash Bhatia is a Sr. solutions architect at AWS. His current focus is helping customers achieve their business outcomes through architecting and implementing innovative and resilient solutions at scale.

 

 

Using AWS Lambda as a consumer for Amazon Kinesis

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/using-aws-lambda-as-a-consumer-for-amazon-kinesis/

This post is courtesy of Prateek Mehrotra, Software Development Engineer.

AWS Lambda integrates natively with Amazon Kinesis as a consumer to process data ingested through a data stream. The polling, checkpointing, and error handling complexities are abstracted when you use this native integration. This allows the Lambda function code to focus on business logic processing.

This blog post describes how to operate and optimize this integration at high throughput with low system overhead time and processing latencies.

To learn more about Kinesis concepts and terminology, visit the documentation page.

Overview

You can attach a Lambda function to a Kinesis stream to process data. Multiple Lambda functions can consume from a single Kinesis stream for different kinds of processing independently. These can be used alongside other consumers such as Amazon Kinesis Data Firehose.

If a Kinesis stream has ‘n’ shards, then at least ‘n’ concurrency is required for a consuming Lambda function to process data without any induced delay. Less than ‘n’ available concurrency results in elevated iterator age in the Kinesis stream and elevated iterator age in the Lambda consumer. In a multi-consumer paradigm, if the Kinesis iterator age spikes then at least one of the stream consumers also reports a corresponding iterator age spike.

Stream poller

When the parallelization factor is greater than 1 for a Lambda consumer, the record processor polls up-to ‘parallelization-factor’ partition keys at a time while processing from a single shard. To learn more, read about handling traffic with a parallelization factor.

Kinesis shard level metrics

When using Kinesis streams, it’s best practice to enable enhanced shard level metrics. These metrics can help in detecting if the data distribution is happening uniformly within the shards of the stream, or not.

In a single-source, multiple-consumer use case, enhanced shard level metrics can help identify the cause of elevated iterator age. This could be due to a single shard receiving data too quickly, or at least one of the consumers failing to process the data.

To learn more about Kinesis monitoring, visit the documentation page. If per-partition processing is not a requirement, distribute data uniformly across shards. To learn more about Kinesis partition keys, visit the documentation page.

Processing delay caused by consumer misconfiguration

Kinesis reports an iterator age metric. If this value spikes, data processing from the stream is delayed. The metric value is set by the earliest record read from the stream measured over the specified time period.

This delay slows the data processing of the pipeline. This happens when a single shard is receiving data faster than the consumer can process it or the consumer is failing to complete processing due to errors.

Graph of records iterator age

In a single-source, multiple-consumer use case, at least one of the consumers shows a corresponding iterator age spike. If there are multiple Lambda consumers of the same data stream, then each Lambda consumer will report its own iterator age metric. This helps identify the problematic consumer for further analysis.

Tuning the configuration to optimize for iterator age

There are several tuning options available when the iterator age is increasing for the consumer Lambda function.

1. Increase the batch size

If the Lambda function operates at a low maximum duration, a single invocation may process less than a maximum batch size. Increase the batch size (up to a maximum of 10,000) to read more records from a shard in a single batch. This can help normalize the iterator age.

2. Change the parallelization factor

Increasing the parallelization factor in the Lambda function allows concurrent invocations to read a single shard. Multiple batches of records are created in the shard based on partition keys, resulting in faster data consumption.

Iterator age can spike when the batch size is set to 10,000 and the parallelization factor is set to 10. This can happen when data is produced faster than the consumer can process it, backing up the per-shard/per-partition queues. To mitigate this, subdivide the partition into multiple keys. This helps distribute the data for that partition key more evenly across shards.

Partition keys

3. Reduce the batch window

If data is distributed unequally across shards, or there is low write volume from producers, the Lambda poller may wait for an entire batch. You can reduce this wait time by reducing the batch window, which results in faster processing.

To learn more about Lambda poller batch window for Kinesis, visit the documentation page.

4. De-scale the Kinesis stream if overprovisioned

If the Kinesis stream metrics indicate that the stream is over-provisioned, de-scaling the stream helps increase data compaction within shards. This results in better throughput per Lambda invocation.

After reducing stream size, reduce the Lambda concurrency to maintain a 1:1 ratio of shard count to Lambda concurrency mapping. As load increases, increase the parallelization factor the keep the shard size constant. With this increase, the Lambda concurrency should be at least shard count * parallelization factor.

To learn more, read about handling traffic with a parallelization factor.

5. Enable enhanced fan-out for consumers

Enhanced fan-out allows developers to scale up the number of stream consumers by offering each stream consumer its own read throughput.

To learn more about Kinesis enhanced fan-out, visit the documentation page.

Conclusion

This blog post shows some of the best practices when using Lambda with Kinesis. It covers operational levers for high-throughput, low latency, single source data processing pipelines.

The enhanced Amazon Kinesis shard level metrics help monitor the maximum overhead processing delay per shard. When correlated with the Lambda consumer’s iterator age metrics, this shows each consumer’s performance. The effective combination of batch size, parallelization factor, batch window, and partition key can lead to more efficient stream processing.

To learn more about Amazon Kinesis, visit the Getting Started page.

Choosing between messaging services for serverless applications

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/choosing-between-messaging-services-for-serverless-applications/

Most serverless application architectures use a combination of different AWS services, microservices, and AWS Lambda functions. Messaging services are important in allowing distributed applications to communicate with each other, and are fundamental to most production serverless workloads.

Messaging services can improve the resilience, availability, and scalability of applications, when used appropriately. They can also enable your applications to communicate beyond your workload or even the AWS Cloud, and provide extensibility for future service features and versions.

In this blog post, I compare the primary messaging services offered by AWS and how you can use these in your serverless application architectures. I also show how you use and deploy these integrations with the AWS Serverless Application Model (AWS SAM).

Examples in this post refer to code that can be downloaded from this GitHub repository. The README.md file explains how to deploy and run each example.

Overview

Three of the most useful messaging patterns for serverless developers are queues, publish/subscribe, and event buses. In AWS, these are provided by Amazon SQS, Amazon SNS, and Amazon EventBridge respectively. All of these services are fully managed and highly available, so there is no infrastructure to manage. All three integrate with Lambda, allowing you to publish messages via the AWS SDK and invoke functions as targets. Each of these services has an important role to play in serverless architectures.

SNS enables you to send messages reliably between parts of your infrastructure. It uses a robust retry mechanism for when downstream targets are unavailable. When the delivery policy is exhausted, it can optionally send those messages to a dead-letter queue for further processing. SNS uses topics to logically separate messages into channels, and your Lambda functions interact with these topics.

SQS provides queues for your serverless applications. You can use a queue to send, store, and receive messages between different services in your workload. Queues are an important mechanism for providing fault tolerance in distributed systems, and help decouple different parts of your application. SQS scales elastically, and there is no limit to the number of messages per queue. The service durably persists messages until they are processed by a downstream consumer.

EventBridge is a serverless event bus service, simplifying routing events between AWS services, software as a service (SaaS) providers, and your own applications. It logically separates routing using event buses, and you implement the routing logic using rules. You can filter and transform incoming messages at the service level, and route events to multiple targets, including Lambda functions.

Integrating an SQS queue with AWS SAM

The first example shows an AWS SAM template defining a serverless application with two Lambda functions and an SQS queue:

Producer-consumer example

You can declare an SQS queue in an AWS SAM template with the AWS::SQS::Queue resource:

  MySqsQueue:
    Type: AWS::SQS::Queue

To publish to the queue, the publisher function must have permission to send messages. Using an AWS SAM policy template, you can apply policy that enables send messaging to one specific queue:

      Policies:
        - SQSSendMessagePolicy:
            QueueName: !GetAtt MySqsQueue.QueueName

The AWS SAM template passes the queue name into the Lambda function as an environment variable. The function uses the sendMessage method of the AWS.SQS class to publish the message:

const AWS = require('aws-sdk')
AWS.config.region = process.env.AWS_REGION 
const sqs = new AWS.SQS({apiVersion: '2012-11-05'})

// The Lambda handler
exports.handler = async (event) => {
  // Params object for SQS
  const params = {
    MessageBody: `Message at ${Date()}`,
    QueueUrl: process.env.SQSqueueName
  }
  
  // Send to SQS
  const result = await sqs.sendMessage(params).promise()
  console.log(result)
}

When the SQS queue receives the message, it publishes to the consuming Lambda function. To configure this integration in AWS SAM, the consumer function is granted the SQSPollerPolicy policy. The function’s event source is set to receive messages from the queue in batches of 10:

  QueueConsumerFunction:
    Type: AWS::Serverless::Function 
    Properties:
      CodeUri: code/
      Handler: consumer.handler
      Runtime: nodejs12.x
      Timeout: 3
      MemorySize: 128
      Policies:  
        - SQSPollerPolicy:
            QueueName: !GetAtt MySqsQueue.QueueName
      Events:
        MySQSEvent:
          Type: SQS
          Properties:
            Queue: !GetAtt MySqsQueue.Arn
            BatchSize: 10

The payload for the consumer function is the message from SQS. This is an array of messages up to the batch size, containing a body attribute with the publishing function’s MessageBody. You can see this in the CloudWatch log for the function:

CloudWatch log result

Integrating an SNS topic with AWS SAM

The second example shows an AWS SAM template defining a serverless application with three Lambda functions and an SNS topic:

SNS fanout to Lambda functions

You declare an SNS topic and the subscribing Lambda functions with the AWS::SNS:Topic resource:

  MySnsTopic:
    Type: AWS::SNS::Topic
    Properties:
      Subscription:
        - Protocol: lambda
          Endpoint: !GetAtt TopicConsumerFunction1.Arn    
        - Protocol: lambda
          Endpoint: !GetAtt TopicConsumerFunction2.Arn

You provide the SNS service with permission to invoke the Lambda functions but defining an AWS::Lambda::Permission for each:

  TopicConsumerFunction1Permission:
    Type: 'AWS::Lambda::Permission'
    Properties:
      Action: 'lambda:InvokeFunction'
      FunctionName: !Ref TopicConsumerFunction1
      Principal: sns.amazonaws.com

The SNSPublishMessagePolicy policy template grants permission to the publishing function to send messages to the topic. In the function, the publish method of the AWS.SNS class handles publishing:

const AWS = require('aws-sdk')
AWS.config.region = process.env.AWS_REGION 
const sns = new AWS.SNS({apiVersion: '2012-11-05'})

// The Lambda handler
exports.handler = async (event) => {
  // Params object for SNS
  const params = {
    Message: `Message at ${Date()}`,
    Subject: 'New message from publisher',
    TopicArn: process.env.SNStopic
  }
  
  // Send to SQS
  const result = await sns.publish(params).promise()
  console.log(result)
}

The payload for the consumer functions is the message from SNS. This is an array of messages, containing subject and message attributes from the publishing function. You can see this in the CloudWatch log for the function:

CloudWatch log result

Differences between SQS and SNS configurations

SQS queues and SNS topics offer different functionality, though both can publish to downstream Lambda functions.

An SQS message is stored on the queue for up to 14 days until it is successfully processed by a subscriber. SNS does not retain messages so if there are no subscribers for a topic, the message is discarded.

SNS topics may broadcast to multiple targets. This behavior is called fan-out. It can be used to parallelize work across Lambda functions or send messages to multiple environments (such as test or development). An SNS topic can have up to 12,500,000 subscribers, providing highly scalable fan-out capabilities. The targets may include HTTP/S endpoints, SMS text messaging, SNS mobile push, email, SQS, and Lambda functions.

In AWS SAM templates, you can retrieve properties such as ARNs and names of queues and topics, using the following intrinsic functions:

Amazon SQSAmazon SNS
Channel typeQueueTopic
Get ARN!GetAtt MySqsQueue.Arn!Ref MySnsTopic
Get name!GetAtt MySqsQueue.QueueName!GetAtt MySnsTopic.TopicName

Integrating with EventBridge in AWS SAM

The third example shows the AWS SAM template defining a serverless application with two Lambda functions and an EventBridge rule:

EventBridge integration with AWS SAM

The default event bus already exists in every AWS account. You declare a rule that filters events in the event bus using the AWS::Events::Rule resource:

  EventRule: 
    Type: AWS::Events::Rule
    Properties: 
      Description: "EventRule"
      EventPattern: 
        source: 
          - "demo.event"
        detail: 
          state: 
            - "new"
      State: "ENABLED"
      Targets: 
        - Arn: !GetAtt EventConsumerFunction.Arn
          Id: "ConsumerTarget"

The rule describes an event pattern specifying matching JSON attributes. Events that match this pattern are routed to the list of targets. You provide the EventBridge service with permission to invoke the Lambda functions in the target list:

  PermissionForEventsToInvokeLambda: 
    Type: AWS::Lambda::Permission
    Properties: 
      FunctionName: 
        Ref: "EventConsumerFunction"
      Action: "lambda:InvokeFunction"
      Principal: "events.amazonaws.com"
      SourceArn: !GetAtt EventRule.Arn

The AWS SAM template uses an IAM policy statement to grant permission to the publishing function to put events on the event bus:

  EventPublisherFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: code/
      Handler: publisher.handler
      Timeout: 3
      Runtime: nodejs12.x
      Policies:
        - Statement:
          - Effect: Allow
            Resource: '*'
            Action:
              - events:PutEvents      

The publishing function then uses the putEvents method of the AWS.EventBridge class, which returns after the events have been durably stored in EventBridge:

const AWS = require('aws-sdk')
AWS.config.update({region: 'us-east-1'})
const eventbridge = new AWS.EventBridge()

exports.handler = async (event) => {
  const params = {
    Entries: [ 
      {
        Detail: JSON.stringify({
          "message": "Hello from publisher",
          "state": "new"
        }),
        DetailType: 'Message',
        EventBusName: 'default',
        Source: 'demo.event',
        Time: new Date 
      }
    ]
  }
  const result = await eventbridge.putEvents(params).promise()
  console.log(result)
}

The payload for the consumer function is the message from EventBridge. This is an array of messages, containing subject and message attributes from the publishing function. You can see this in the CloudWatch log for the function:

CloudWatch log result

Comparing SNS with EventBridge

SNS and EventBridge have many similarities. Both can be used to decouple publishers and subscribers, filter messages or events, and provide fan-in or fan-out capabilities. However, there are differences in the list of targets and features for each service, and your choice of service depends on the needs of your use-case.

EventBridge offers two newer capabilities that are not available in SNS. The first is software as a service (SaaS) integration. This enables you to authorize supported SaaS providers to send events directly from their EventBridge event bus to partner event buses in your account. This replaces the need for polling or webhook configuration, and creates a highly scalable way to ingest SaaS events directly into your AWS account.

The second feature is the Schema Registry, which makes it easier to discover and manage OpenAPI schemas for events. EventBridge can infer schemas based on events routed through an event bus by using schema discovery. This can be used to generate code bindings directly to your IDE for type-safe languages like Python, Java, and TypeScript. This can help accelerate development by automating the generation of classes and code directly from events.

This table compares the major features of both services:

Amazon SNSAmazon EventBridge
Number of targets10 million (soft)5
Availability SLA99.9%99.99%
Limits100,000 topics. 12,500,000 subscriptions per topic.100 event buses. 300 rules per event bus.
Publish throughputVaries by Region. Soft limits.Varies by Region. Soft limits.
Input transformationNoYes – see details.
Message filteringYes – see details.Yes, including IP address matching – see details.
Message size maximum256 KB256 KB
BillingPer 64 KB
FormatRaw or JSONJSON
Receive events from AWS CloudTrailNoYes
TargetsHTTP(S), SMS, SNS Mobile Push, Email/Email-JSON, SQS, Lambda functions.15 targets including AWS LambdaAmazon SQSAmazon SNSAWS Step FunctionsAmazon Kinesis Data StreamsAmazon Kinesis Data Firehose.
SaaS integrationNoYes – see integrations.
Schema Registry integrationNoYes – see details.
Dead-letter queues supportedYesNo
FIFO ordering availableNoNo
Public visibilityCan create public topicsCannot create public buses
Pricing$0.50/million requests + variable delivery cost + data transfer out cost. SMS varies.$1.00/million events. Free for AWS events. No charge for delivery.
Billable request size1 request = 64 KB1 event = 64 KB
AWS Free Tier eligibleYesNo
Cross-RegionYou can subscribe your AWS Lambda functions to an Amazon SNS topic in any Region.Targets must be in the same Region. You can publish across Regions to another event bus.
Retry policy
  • For SQS/Lambda, exponential backoff over 23 days.
  • For SMTP, SMS and Mobile push, exponential backoff over 6 hours.
At-least-once event delivery to targets, including retry with exponential backoff for up to 24 hours.

Conclusion

Messaging is an important part of serverless applications and AWS services provide queues, publish/subscribe, and event routing capabilities. This post reviews the main features of SNS, SQS, and EventBridge and how they provide different capabilities for your workloads.

I show three example applications that publish and consume events from the three services. I walk through AWS SAM syntax for deploying these resources in your applications. Finally, I compare differences between the services.

To learn more building decoupled architectures, see this Learning Path series on EventBridge. For more serverless learning resources, visit https://serverlessland.com.

Introducing IAM and Lambda authorizers for Amazon API Gateway HTTP APIs

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/introducing-iam-and-lambda-authorizers-for-amazon-api-gateway-http-apis/

Amazon API Gateway HTTP APIs enable you to create RESTful APIs with lower latency and lower cost than API Gateway REST APIs.

The API Gateway team is continuing work to improve and migrate popular REST API features to HTTP APIs. We are adding two of the most requested features, AWS Identity and Access Management (IAM) authorizers and AWS Lambda authorizers.

HTTP APIs already support JWT authorizers as a part of OpenID Connect (OIDC) and OAuth 2.0 frameworks. For more information, see “Simple HTTP API with JWT Authorizer.”

IAM authorization

AWS IAM roles and policies offer flexible, robust, and fully managed access controls, without writing any code. You can use IAM roles and policies to control who can create and manage your APIs, in addition to who can invoke them. IAM authorization for HTTP API routes is the best choice for internal or private APIs called by other AWS services like AWS Lambda.

IAM authorization for HTTP API APIs is similar to that for REST APIs. IAM access is determined by identity policies, which are attached to IAM users, groups, or roles. These policies define what identity can access which HTTP APIs routes. See “AWS Services That Work with IAM.”

Lambda authorization

A Lambda authorizer is a Lambda function which API Gateway calls for an authorization check when a client makes a request to an HTTP API route. You can use Lambda authorizers to implement custom authorization schemes to comply with your security requirements.

New authorizer features

HTTP API Lambda authorizers have some new features compared to REST APIs. There is a new payload and response format, including a simple Boolean authorization option.

New payload versions and response format

Lambda authorizers for HTTP APIs introduce a new payload format, version 2.0. If you need compatibility to use the same Lambda authorizers for both REST and HTTP APIs, you can continue to use version 1.0.

The payload format version also determines the request format and response structure that you must send to and return from your Lambda authorizer function. The version 2.0 payload context now allows non-string values. With version 1.0, your Lambda authorizer must return an IAM policy that allows or denies access to your API route. This is the same existing functionality as REST APIs. You can use standard IAM policy syntax in the policy. For examples of IAM policies, see “Control access for invoking an API.”

If you choose the new 2.0 format version when configuring the authorizer, you can now return either a Boolean value, or an IAM policy. The Boolean value enables simple responses from the authorizer without having to construct an IAM policy, and is in the format:

{
  "isAuthorized": true/false,
  "context": {
    "exampleKey": "exampleValue"
  }
}

The context object is optional. You can pass context properties on to Lambda integrations or access logs by using $context.authorizer.property. To learn more, see “Customizing HTTP API access logs.”

Caching authorizer responses

You can enable caching for a Lambda authorizer for up to one hour. To enable caching, your authorizer must have at least one identity source. API Gateway calls the Lambda authorizer function only when all of the specified identity sources are present. API Gateway uses the identity sources as the cache key. If a client specifies the same identity source parameters within the cache TTL, API Gateway uses the cached authorizer result. The Lambda authorizer function is not invoked.

Caching is enabled at the API Gateway level per authorizer. It is important to understand the effect of caching, particularly with simple responses and multiple routes. When using a simple response, the authorizer fully allows or denies all API requests that match the cached identity source values.

For example, you have two different routes using the same Lambda authorizer with a simple response. Both routes have different access requirements. The first route allows access to GET /list-users with an Authorization header with the value SecretTokenUsers. The second route denies access using the same header to GET /list-admins.

The Lambda authorizer has a single identity source, $request.header.Authorization, with the following code:

$request.header.Authorization.
exports.handler = async(event, context) => {
    let response = {
        "isAuthorized": false,
        "context": {
            "AuthInfo": "defaultdeny"
        }
    };
    if ((event.routeKey === "GET /list-users") && (event.headers.Authorization === "SecretTokenUsers")) {
        response = {
            "isAuthorized": true,
            "context": {
                "AuthInfo": "true-users"
            }
        };
    }
    if ((event.routeKey === "GET /list-admins") && (event.headers.authorization === "SecretTokenUsers")) {
        response = {
            "isAuthorized": false,
            "context": {
                "AuthInfo": "false-admins",
            }
        };
    }
    return response;
};

As both routes share the same identity source parameter, a cache result from successfully accessing /list-users with the Authorization header could allow access to /list-admins which is not intended. To cache responses differently per route, add $context.routeKey as an additional identity source. This creates a cache key that is unique for each route.

If more granular permissions are required, disable simple responses and return an IAM policy instead.

Testing Lambda authorizers

You have an existing Lambda function behind an HTTP API and want to add a Lambda authorizer using the new Boolean simple response. Create a new Lambda authorizer function with the following code.

exports.handler = async(event, context) => {
    let response = {
        "isAuthorized": false,
        "context": {
            "AuthInfo": "defaultdeny"
        }
    };
    if (event.headers.Authorization === "secretToken") {
        response = {
            "isAuthorized": true,
            "context": {
                "AuthInfo": "Customer1"
            }
        };
    }
    return response;
};

The authorizer returns true if a header called Authorization has the value secretToken.

To create an authorizer, browse to the API Gateway console. Navigate to your HTTP API, choose Authorization under Develop, select the Attach authorizers to routes tab, and choose Create and attach an authorizer.

Create and attach HTTP API authorizer

Create and attach HTTP API authorizer

Create the Lambda authorizer, pointing to your Lambda authorizer function. Select Payload format version 2.0 with a Simple response.

Create Lambda simple authorizer settings

Create Lambda simple authorizer settings

Enable caching and add two identity sources, $request.header.Authorization and $context.routeKey, to ensure that your cache key is unique when adding multiple routes.

Add caching and identity sources to Lambda authorizer

Add caching and identity sources to Lambda authorizer

Choose Create and attach. The route is now using a Lambda authorizer.

HTTP API route includes Lambda authorizer

HTTP API route includes Lambda authorizer

The following examples to test the API authentication use Postman but you can use any HTTP client.

Send a GET request to the HTTP APIs URL without specifying any authorization header.

Postman unauthorized GET request

Postman unauthorized GET request

API Gateway returns a 401 Unauthorized response, as expected. The required $request.header.Authorization identity source is not provided, so the Lambda authorizer is not called.

Enter a valid Authorization header key, but an invalid value.

Postman Forbidden GET request

Postman Forbidden GET request

API Gateway returns a 403 Forbidden response as the request is now passed to the Lambda authorizer, which has evaluated the value, and returned "isAuthorized": false.

Supply a valid Authorization header key and value.

Postman successful authorized GET request

Postman successful authorized GET request

API Gateway authorizes the request using the Lambda authorizer and sends the request to the Lambda function integration which returns a successful 200 response.

For more Lambda authorizer code examples see “Custom Authorizer Blueprints for AWS Lambda.”

AWS CloudFormation support

Lambda authorizers for HTTP APIs are configured as AWS::ApiGatewayV2::Authorizer CloudFormation resources. Today, they are imported into AWS Serverless Application Model (AWS SAM) applications as native CloudFormation resources.

LambdaAuthorizer:
    Type: 'AWS::ApiGatewayV2::Authorizer'
    Properties:
    Name: LambdaAuthorizer
    ApiId: !Ref HttpApi
    AuthorizerType: REQUEST
    AuthorizerUri: arn:aws:apigateway:{region}:lambda:path/2015-03-31/functions/arn:aws:lambda: {region}:{account id}:function:{Function name}/invocations
    IdentitySource:
        - $request.header.Authorization
    AuthorizerPayloadFormatVersion: 2.0

Conclusion

IAM and Lambda authorizers are two of the most requested features for Amazon API Gateway HTTP APIs. You can now use IAM authorization in a similar way to API Gateway REST APIs. Lambda authorizers for HTTP APIs offer the option of a simpler Boolean response with the new version 2.0 payload and response format. You configure identity sources to specify the location of data that’s required to authorize a request, which are also used as the cache key.

These authorizers are generally available in all AWS Regions where API Gateway is available. To learn more about options for protecting your APIs, you can read the documentation. For more information about Amazon API Gateway, visit the product page.

For the latest blogs, videos, and training for AWS Serverless, see https://serverlessland.com/.

Introducing queued purchases for Savings Plans

Post Syndicated from Roshni Pary original https://aws.amazon.com/blogs/compute/introducing-queued-purchases-for-savings-plans/

This blog post is contributed by Idan Maizlits, Sr. Product Manager, Savings Plans

AWS now provides the ability for you to queue purchases of Savings Plans by specifying a time, up to 3 years in the future, to carry out those purchases. This blog reviews how you can queue purchases of Savings Plans.

In November 2019, AWS launched Savings Plans. This is a new flexible pricing model that allows you to save up to 72% on Amazon EC2, AWS Fargate, and AWS Lambda in exchange for making a commitment to a consistent amount of compute usage measured in dollars per hour (for example $10/hour) for a 1- or 3-year term. Savings Plans is the easiest way to save money on compute usage while providing you the flexibility to use the compute options that best fits your needs as they change.

Queueing Savings Plans allows you to plan ahead for future events. Say, you want to purchase a Savings Plan three months into the future to cover a new workload. Now, with the ability to queue plans in advance, you can easily schedule the purchase to be carried out at the exact time you expect your workload to go live. This helps you plan in advance by eliminating the need to make “just-in-time” purchases, and benefit from low prices on your future workloads from the get-go. With the ability to queue purchases, you can also enjoy uninterrupted Savings Plans coverage by scheduling renewals of your plans ahead of their expiry. This makes it even easier to save money on your overall AWS bill.

So how do queued purchases for Savings Plans work? Queued purchases are similar to regular purchases in all aspects but one – the start date. With a regular purchase, a plan goes active immediately whereas with a queued purchase, you select a date in the future for a plan to start. Up until the said future date, the Savings Plan remains in a queued state, and on the future date any upfront payments are charged and the plan goes active.

Now, let’s look at this in more detail with a couple of examples. I walk through three scenarios – a) queuing Savings Plans to cover future usage b) renewing expiring Savings Plans and c) deleting a queued Savings plan.

How do I queue a Savings Plan?

If you are planning ahead and would like to queue a Savings Plan to support future needs such as new workloads or expiring Reserved Instances, head to the Purchase Savings Plans page on the AWS Cost Management Console. Then, select the type of Savings Plan you would like to queue, including the term length, purchase commitment, and payment option.

Select the type of Savings Plan

Now, indicate the start date and time for this plan (this is the date/time at which your Savings Plan becomes active). The time you indicate is in UTC, but is also shown in your browser’s local time zone. If you are looking to replace an existing Reserved Instance, you can provide the start date and time to align with the expiration of your existing Reserved Instances. You can find the expiration time of your Reserved Instances on the EC2 Reserved Instances Console (this is in your local time zone, convert it to UTC when you queue a Savings Plan).

After you have selected the start time and date for the Savings Plan, click “Add to cart”. When you are ready to complete the purchase, click “Submit Order,” which completes the purchase.

Once you have submitted the order, the Savings Plans Inventory page lists the queued Savings Plan with a “Queued” status and that purchase will be carried out on the date and time provided.

How can I replace an expiring plan?

If you have already purchased a Savings Plan, queuing purchases allow you to renew that Savings Plan upon expiry for continuous coverage. All you have to do is head to the AWS Cost Management Console, go to the Savings Plans Inventory page, and select the Savings Plan you would like to renew. Then, click on Actions and select “Renew Savings Plan” as seen in the following image.

This action automatically queues a Savings Plan in the cart with the same configuration (as your original plan) to replace the expiring one. The start time for the plan automatically sets to one second after expiration of the old Savings Plan. All you have to do now is submit the order and you are good to go.

If you would like to renew multiple Savings Plans, select each one and click “Renew Savings Plan,” which adds them to the Cart. When you are done adding new Savings Plans, your cart lists all of the Savings Plans that you added to the order. When you are ready to submit the order, click “Submit order.

How can I delete a queued Savings Plan?

If you have queued Savings Plans that you no longer need to purchase, or need to modify, you can do so by visiting the console. Head to the AWS Cost Management Console, select the Savings Plans Inventory page, and then select the Savings Plan you would like to delete. By selecting the Savings Plan and clicking on Actions, as seen in the following image, you can delete the queued purchase if you need to make changes or if you no longer need the plan to be purchased. If you need the Savings Plan at a different commitment value, you can make a new queued purchase.

Conclusion

AWS Savings Plans allow you to save up to 72% of On-demand prices by committing to a 1- or 3- year term. Starting today, with the ability to queue purchases of Savings Plans, you can easily plan for your future needs or renew expiring Savings Plan ahead of time, all with just a few clicks. In this blog, I walked through various scenarios. As you can see, it’s even easier to save money with AWS Savings Plans by queuing your purchases to meet your future needs and continue benefiting from uninterrupted coverage.

Click here to learn more about queuing purchases of Savings Plans and visit the AWS Cost Management Console to get started.

Automating bucketing of streaming data using Amazon Athena and AWS Lambda

Post Syndicated from Ahmed Saef Zamzam original https://aws.amazon.com/blogs/big-data/automating-bucketing-of-streaming-data-using-amazon-athena-and-aws-lambda/

In today’s world, data plays a vital role in helping businesses understand and improve their processes and services to reduce cost. You can use several tools to gain insights from your data, such as Amazon Kinesis Data Analytics or open-source frameworks like Structured Streaming and Apache Flink to analyze the data in real time. Alternatively, you can batch analyze the data by ingesting it into a centralized storage known as a data lake. Data lakes allow you to import any amount of data that can come in real time or batch. With Amazon Simple Storage Service (Amazon S3), you can cost-effectively build and scale a data lake of any size in a secure environment where data is protected by 99.999999999% (11 9s) of durability.

After the data lands in your data lake, you can start processing this data using any Big Data processing tool of your choice. Amazon Athena is a fully managed interactive query service that enables you to analyze data stored in an Amazon S3-based data lake using standard SQL. You can also integrate Athena with Amazon QuickSight for easy visualization of the data.

When working with Athena, you can employ a few best practices to reduce cost and improve performance. Converting to columnar formats, partitioning, and bucketing your data are some of the best practices outlined in Top 10 Performance Tuning Tips for Amazon Athena. Bucketing is a technique that groups data based on specific columns together within a single partition. These columns are known as bucket keys. By grouping related data together into a single bucket (a file within a partition), you significantly reduce the amount of data scanned by Athena, thus improving query performance and reducing cost. For example, imagine collecting and storing clickstream data. If you frequently filter or aggregate by user ID, then within a single partition it’s better to store all rows for the same user together. If user data isn’t stored together, then Athena has to scan multiple files to retrieve the user’s records. This leads to more files being scanned, and therefore, an increase in query runtime and cost.

Like partitioning, columns that are frequently used to filter the data are good candidates for bucketing. However, unlike partitioning, with bucketing it’s better to use columns with high cardinality as a bucketing key. For example, Year and Month columns are good candidates for partition keys, whereas userID and sensorID are good examples of bucket keys. By doing this, you make sure that all buckets have a similar number of rows. For more information, see Bucketing vs Partitioning.

For real-time data (such as data coming from sensors or clickstream data), streaming tools like Amazon Kinesis Data Firehose can convert the data to columnar formats and partition it while writing to Amazon S3. With Kafka, you can do the same thing with connectors. But what about bucketing? This post shows how to continuously bucket streaming data using AWS Lambda and Athena.

Overview of solution

The following diagram shows the high-level architecture of the solution.

The architecture includes the following steps:

  1. We use the Amazon Kinesis Data Generator (KDG) to simulate streaming data. Data is then written into Kinesis Data Firehose; a fully managed service that enables you to load streaming data to an Amazon S3-based data lake.
  2. Kinesis Data Firehose partitions the data by hour and writes new JSON files into the current partition in a /raw Each new partition looks like /raw/dt=<YYYY-MM-dd-HH>. Every hour, a new partition is created.
  3. Two Lambda functions are triggered on an hourly basis based on Amazon CloudWatch Events.
    • Function 1 (LoadPartition) runs every hour to load new /raw partitions to Athena SourceTable, which points to the /raw prefix.
    • Function 2 (Bucketing) runs the Athena CREATE TABLE AS SELECT (CTAS) query.
  4. The CTAS query copies the previous hour’s data from /raw to /curated and buckets the data while doing so. It loads the new data as a new partition to TargetTable, which points to the /curated prefix.

Overview of walkthrough

In this post, we cover the following high-level steps:

  1. Install and configure the KDG.
  2. Create a Kinesis Data Firehose delivery stream.
  3. Create the database and tables in Athena.
  4. Create the Lambda functions and schedule them.
  5. Test the solution.
  6. Create view that the combines data from both tables.
  7. Clean up.

Installing and configuring the KDG

First, we need to install and configure the KDG in our AWS account. To do this, we use the following AWS CloudFormation template.

For more information about installing the KDG, see the KDG Guide in GitHub.

To configure the KDG, complete the following steps:

  1. On the AWS CloudFormation console, locate the stack you just created.
  2. On the Outputs tab, record the value for KinesisDataGeneratorUrl.
  3. Log in to the KDG main page using the credentials created when you deployed the CloudFormation template.
  4. In the Record template section, enter the following template. Each record has three fields: sensorID, currentTemperature, and status.
    {
        "sensorId": {{random.number(4000)}},
        "currentTemperature": {{random.number(
            {
                "min":10,
                "max":50
            }
        )}},
        "status": "{{random.arrayElement(
            ["OK","FAIL","WARN"]
        )}}"
    }
    

  5. Choose Test template.

The result should look like the following screenshot.

We don’t start sending data now; we do this after creating all other resources.

Creating a Kinesis Data Firehose delivery stream

Next, we create the Kinesis Data Firehose delivery stream that is used to load the data to the S3 bucket.

  1. On the Amazon Kinesis console, choose Kinesis Data Firehose.
  2. Choose Create delivery stream.
  3. For Delivery stream name, enter a name, such as AutoBucketingKDF.
  4. For Source, select Direct PUT or other sources.
  5. Leave all other settings at their default and choose Next.
  6. On Process Records page, leave everything at its default and choose Next.
  7. Choose Amazon S3 as the destination and choose your S3 bucket from the drop-down menu (or create a new one). For this post, I already have a bucket created.
  8. For S3 Prefix, enter the following prefix:
    raw/dt=!{timestamp:yyyy}-!{timestamp:MM}-!{timestamp:dd}-!{timestamp:HH}/

We use custom prefixes to tell Kinesis Data Firehose to create a new partition every hour. Each partition looks like this: dt=YYYY-MM-dd-HH. This partition-naming convention conforms to the Hive partition-naming convention, <PartitionKey>=<PartitionKey>. In this case, <PartitionKey> is dt and <PartitionValue> is YYYY-MM-dd-HH. By doing this, we implement a flat partitioning model instead of hierarchical (year=YYYY/month=MM/day=dd/hour=HH) partitions. This model can be much simpler for end-users to work with, and you can use a single column (dt) to filter the data. For more information on flat vs. hierarchal partitions, see Data Lake Storage Foundation on GitHub.

  1. For S3 error prefix, enter the following code:
    myFirehoseFailures/!{firehose:error-output-type}/

  2. On the Settings page, leave everything at its default.
  3. Choose Create delivery stream.

Creating an Athena database and tables

In this solution, the Athena database has two tables: SourceTable and TargetTable. Both tables have identical schemas and will have the same data eventually. However, each table points to a different S3 location. Moreover, because data is stored in different formats, Athena uses a different SerDe for each table to parse the data. SourceTable uses JSON SerDe and TargetTable uses Parquet SerDe. One other difference is that SourceTable’s data isn’t bucketed, whereas TargetTable’s data is bucketed.

In this step, we create both tables and the database that groups them.

  1. On the Athena console, create a new database by running the following statement:
    CREATE DATABASE mydatabase

  2. Choose the database that was created and run the following query to create SourceTable. Replace <s3_bucket_name> with the bucket name you used when creating the Kinesis Data Firehose delivery stream.
    CREATE EXTERNAL TABLE mydatabase.SourceTable(
      sensorid string, 
      currenttemperature int, 
      status string)
    PARTITIONED BY ( 
      dt string)
    ROW FORMAT SERDE 
      'org.openx.data.jsonserde.JsonSerDe' 
    STORED AS INPUTFORMAT 
      'org.apache.hadoop.mapred.TextInputFormat' 
    OUTPUTFORMAT 
      'org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat'
    LOCATION
      's3://<s3_bucket_name>/raw/'
    

  3. Run the following CTAS statement to create TargetTable:
    CREATE TABLE TargetTable
    WITH (
          format = 'PARQUET', 
          external_location = 's3://<s3_bucket_name>/curated/', 
          partitioned_by = ARRAY['dt'], 
          bucketed_by = ARRAY['sensorID'], 
          bucket_count = 3) 
    AS SELECT *
    FROM SourceTable

SourceTable doesn’t have any data yet. However, the preceding query creates the table definition in the Data Catalog. We configured this data to be bucketed by sensorID (bucketing key) with a bucket count of 3. Ideally, the number of buckets should be so that the files are of optimal size.

Creating Lambda functions

The solution has two Lambda functions: LoadPartiton and Bucketing. We use an AWS Serverless Application Model (AWS SAM) template to create, deploy, and schedule both functions.

Follow the instructions in the GitHub repo to deploy the template. When deploying the template, it asks you for some parameters. You can use the default parameters, but you have to change S3BucketName and AthenaResultLocation. For more information, see Parameter Details in the GitHub repo.

LoadPartition function

The LoadPartiton function is scheduled to run the first minute of every hour. Every time Kinesis Data Firehose creates a new partition in the /raw folder, this function loads the new partition to the SourceTable. This is crucial because the second function (Bucketing) reads this partition the following hour to copy the data to /curated.

Bucketing function

The Bucketing function is scheduled to run the first minute of every hour. It copies the last hour’s data from SourceTable to TargetTable. It does so by creating a tempTable using a CTAS query. This tempTable points to the new date-hour folder under /curated; this folder is then added as a single partition to TargetTable.

To implement this, the function runs three queries sequentially. The queries use two parameters:

  • <s3_bucket_name> – Defined by an AWS SAM parameter and should be the same bucket used throughout this solution
  • <last_hour_partition> – Is calculated by the function depending on which hour it’s running

The function first creates TempTable as the result of a SELECT statement from SourceTable. It stores the results in a new folder under /curated. The results are bucketed and stored in Parquet format. See the following code:

CREATE TABLE TempTable
    WITH (
      format = 'PARQUET', 
      external_location = 's3://<s3_bucket_name>/curated/dt=<last_hour_partition>/', 
      bucketed_by = ARRAY['sensorID'], 
      bucket_count = 3) 
    AS SELECT *
    FROM SourceTable
    WHERE dt='<last_hour_partiton>';

We create a new subfolder in /curated, which is new partition for TargetTable. So, after the TempTable creation is complete, we load the new partition to TargetTable:

ALTER TABLE TargetTable
                ADD IF NOT EXISTS
                PARTITION ('<last_hour_partiton>');

Finally, we delete tempTable from the Data Catalog:

DROP TABLE TempTable

Testing the solution

Now that we have created all resources, it’s time to test the solution. We start by generating data from the KDG and waiting for an hour to start querying data in TargetTable (the bucketed table).

  1. Log in to the KDG. You should find the template you created earlier. For the configuration, choose the following:
    1. The Region used.
    2. For the delivery stream, choose the Kinesis Data Firehose you created earlier.
    3. For records/sec, enter 3000.
  2. Choose Send data.

The KDG starts sending simulated data to Kinesis Data Firehose. After 1 minute, a new partition should be created in Amazon S3.

The Lambda function that loads the partition to SourceTable runs on the first minute of the hour. If you started sending data after the first minute, this partition is missed because the next run loads the next hour’s partition, not this one. To mitigate this, run MSCK REPAIR TABLE SourceTable only for the first hour.

  1. To benchmark the performance between both tables, wait for an hour so that the data is available for querying in TargetTable.
  2. When the data is available, choose one sensorID and run the following query on SourceTable and TargetTable.
    SELECT sensorID, avg(currenttemperature) as AverageTempreture 
    FROM <TableName>
    WHERE dt='<YYYY-MM-dd-HH>' AND sensorID ='<sensorID_selected>'
    GROUP BY 1

The following screenshot shows the query results for SourceTable. It shows the runtime in seconds and amount of data scanned.

The following screenshot shows the query results for TargetTable.

If you look at these results, you don’t see a huge difference in runtime for this specific query and dataset; for other datasets, this difference should be more significant. However, from a data scanning perspective, after bucketing the data, we reduced the data scanned by approximately 98%. Therefore, for this specific use case, bucketing the data lead to a 98% reduction in Athena costs because you’re charged based on the amount of data scanned by each query.

Querying the current hour’s data

Data for the current hour isn’t available immediately in TargetTable. It’s available for querying after the first minute of the following hour. To query this data immediately, we have to create a view that UNIONS the previous hour’s data from TargetTable with the current hour’s data from SourceTable. If data is required for analysis after an hour of its arrival, then you don’t need to create this view.

To create this view, run the following query in Athena:

CREATE OR REPLACE VIEW combined AS

SELECT *, "$path" AS file
FROM SourceTable
WHERE dt >= date_format(date_trunc('hour', (current_timestamp)), '%Y-%m-%d-%H')

UNION ALL 

SELECT *, "$path" AS file
FROM TargetTable
WHERE dt < date_format(date_trunc('hour', (current_timestamp)), '%Y-%m-%d-%H')

Cleaning up

Delete the resources you created if you no longer need them.

  1. Delete the Kinesis Data Firehose delivery stream.
  2. In Athena, run the following statements
    1. DROP DATABASE mydatabase
    2. DROP TABLE SourceTable
    3. DROP TABLE TargetTable
  3. Delete the AWS SAM template to delete the Lambda functions.
  4. Delete the CloudFormation stack for the KDG. For more information, see Deleting a stack on the AWS CloudFormation console.

Conclusion

Bucketing is a powerful technique and can significantly improve performance and reduce Athena costs. In this post, we saw how to continuously bucket streaming data using Lambda and Athena. We used a simulated dataset generated by Kinesis Data Generator. The same solution can apply to any production data, with the following changes:

  • DDL statements
  • Functions used can work with data that is partitioned by hour with the partition key ‘dt’ and partition value <YYYY-MM-dd-HH>. If your data is partitioned in a different way, edit the Lambda functions accordingly.
  • Frequency of Lambda triggers.

About the Author

Ahmed Zamzam is a Solutions Architect with Amazon Web Services. He supports SMB customers in the UK in their digital transformation and their cloud journey to AWS, and specializes in Data Analytics. Outside of work, he loves traveling, hiking, and cycling.

 

 

 

 

Field Notes: Monitoring the Java Virtual Machine Garbage Collection on AWS Lambda

Post Syndicated from Steffen Grunwald original https://aws.amazon.com/blogs/architecture/field-notes-monitoring-the-java-virtual-machine-garbage-collection-on-aws-lambda/

When you want to optimize your Java application on AWS Lambda for performance and cost the general steps are: Build, measure, then optimize! To accomplish this, you need a solid monitoring mechanism. Amazon CloudWatch and AWS X-Ray are well suited for this task since they already provide lots of data about your AWS Lambda function. This includes overall memory consumption, initialization time, and duration of your invocations. To examine the Java Virtual Machine (JVM) memory you require garbage collection logs from your functions. Instances of an AWS Lambda function have a short lifecycle compared to a long-running Java application server. It can be challenging to process the logs from tens or hundreds of these instances.

In this post, you learn how to emit and collect data to monitor the JVM garbage collector activity. Having this data, you can visualize out-of-memory situations of your applications in a Kibana dashboard like in the following screenshot. You gain actionable insights into your application’s memory consumption on AWS Lambda for troubleshooting and optimization.

The lifecycle of a JVM application on AWS Lambda

Let’s first revisit the lifecycle of the AWS Lambda Java runtime and its JVM:

  1. A Lambda function is invoked.
  2. AWS Lambda launches an execution context. This is a temporary runtime environment based on the configuration settings you provide, like permissions, memory size, and environment variables.
  3. AWS Lambda creates a new log stream in Amazon CloudWatch Logs for each instance of the execution context.
  4. The execution context initializes the JVM and your handler’s code.

You typically see the initialization of a fresh execution context when a Lambda function is invoked for the first time, after it has been updated, or it scales up in response to more incoming events.

AWS Lambda maintains the execution context for some time in anticipation of another Lambda function invocation. In effect, the service freezes the execution context after a Lambda function completes. It thaws the execution context when the Lambda function is invoked again if AWS Lambda chooses to reuse it.

During invocations, the JVM also maintains garbage collection as usual. Outside of invocations, the JVM and its maintenance processes like garbage collection are also frozen.

Garbage collection and indicators for your application’s health

The purpose of JVM garbage collection is to clean up objects in the JVM heap, which is the space for an application’s objects. It finds objects which are unreachable and deletes them. This frees heap space for other objects.

You can make the JVM log garbage collection activities to get insights into the health of your application. One example for this is the free heap after each garbage collection. If this metric keeps shrinking, it is an indicator for a memory leak – eventually turning into an OutOfMemoryError. If there is not enough of free heap, the JVM might be too busy with garbage collection instead of running your application code. Otherwise, a heap that is too big does indicate that there’s potential to decrease the memory configuration of your AWS Lambda function. This keeps garbage collection pauses low and provides a consistent response time.

The garbage collection logging can be configured via an environment variable as part of the AWS Lambda function configuration. The environment variable JAVA_TOOL_OPTIONS is considered by both the Java 8 and 11 JVMs. You use it to pass options that you would usually add to the command line when launching the JVM. The options to configure garbage collection logging and the output is specific to the Java version.

Java 11 uses the Unified Logging System (JEP 158 and JEP 271) which has been introduced in Java 9. Logging can be configured with the environment variable:

JAVA_TOOL_OPTIONS=-Xlog:gc+metaspace,gc+heap,gc:stdout:time,tags

The Serial Garbage Collector will output the logs:

[<TIMESTAMP>][gc] GC(4) Pause Full (Allocation Failure) 9M->9M(11M) 3.941ms (D)
[<TIMESTAMP>][gc,heap] GC(3) DefNew: 3063K->234K(3072K) (A)
[<TIMESTAMP>][gc,heap] GC(3) Tenured: 6313K->9127K(9152K) (B)
[<TIMESTAMP>][gc,metaspace] GC(3) Metaspace: 762K->762K(52428K) (C)
[<TIMESTAMP>][gc] GC(3) Pause Young (Allocation Failure) 9M->9M(21M) 23.559ms (D)

Prior to Java 9, including Java 8, you configure the garbage collection logging as follows:

JAVA_TOOL_OPTIONS=-XX:+PrintGCDetails -XX:+PrintGCDateStamps

The Serial garbage collector output in Java 8 is structured differently:

<TIMESTAMP>: [GC (Allocation Failure)
    <TIMESTAMP>: [DefNew: 131042K->131042K(131072K), 0.0000216 secs] (A)
    <TIMESTAMP>: [Tenured: 235683K->291057K(291076K), 0.2213687 secs] (B)
    366725K->365266K(422148K), (D)
    [Metaspace: 3943K->3943K(1056768K)], (C)
    0.2215370 secs]
    [Times: user=0.04 sys=0.02, real=0.22 secs]
<TIMESTAMP>: [Full GC (Allocation Failure)
    <TIMESTAMP>: [Tenured: 297661K->36658K(297664K), 0.0434012 secs] (B)
    431575K->36658K(431616K), (D)
    [Metaspace: 3943K->3943K(1056768K)], 0.0434680 secs] (C)
    [Times: user=0.02 sys=0.00, real=0.05 secs]

Independent of the Java version, the garbage collection activities are logged to standard out (stdout) or standard error (stderr). Logs appear in the AWS Lambda function’s log stream of Amazon CloudWatch Logs. The log contains the size of memory used for:

  • A: the young generation
  • B: the old generation
  • C: the metaspace
  • D: the entire heap

The notation is before-gc -> after-gc (committed heap). Read the JVM Garbage Collection Tuning Guide for more details.

Visualizing the logs in Amazon Elasticsearch Service

It is hard to fully understand the garbage collection log by just reading it in Amazon CloudWatch Logs. You must visualize it to gain more insight. This section describes the solution to achieve this.

Solution Overview

Java Solution Overview

Amazon CloudWatch Logs have a feature to stream CloudWatch Logs data to Amazon Elasticsearch Service via an AWS Lambda function. The AWS Lambda function for log transformation is subscribed to the log group of your application’s AWS Lambda function. The subscription filters for a pattern that matches the one of the garbage collection log entries. The log transformation function processes the log messages and puts it to a search cluster. To make the data easy to digest for the search cluster, you add code to transform and convert the messages to JSON. Having the data in a search cluster, you can visualize it with Kibana dashboards.

Get Started

To start, launch the solution architecture described above as a prepackaged application from the AWS Serverless Application Repository. It contains all resources ready to visualize the garbage collection logs for your Java 11 AWS Lambda functions in a Kibana dashboard. The search cluster consists of a single t2.small.elasticsearch instance with 10GB of EBS storage. It is protected with Amazon Cognito User Pools so you only need to add your user(s). The T2 instance types do not support encryption of data at rest.

Read the source code for the application in the aws-samples repository.

1. Spin up the application from the AWS Serverless Application Repository:

launch stack button

2. As soon as the application is deployed completely, the outputs of the AWS CloudFormation stack provide the links for the next steps. You will find two URLs in the AWS CloudFormation console called createUserUrl and kibanaUrl.

search stack

3. Use the createUserUrl link from the outputs, or navigate to the Amazon Cognito user pool in the console to create a new user in the pool.

a. Enter an email address as username and email. Enter a temporary password of your choice with at least 8 characters.

b. Leave the phone number empty and uncheck the checkbox to mark the phone number as verified.

c. If necessary, you can check the checkboxes to send an invitation to the new user or to make the user verify the email address.

d. Choose Create user.

create user dialog of Amazon Cognito User Pools

4. Access the Kibana dashboard with the kibanaUrl link from the AWS CloudFormation stack outputs, or navigate to the Kibana link displayed in the Amazon Elasticsearch Service console.

a. In Kibana, choose the Dashboard icon in the left menu bar

b. Open the Lambda GC Activity dashboard.

You can test that new events appear by using the Kibana Developer Console:

POST gc-logs-2020.09.03/_doc
{
  "@timestamp": "2020-09-03T15:12:34.567+0000",
  "@gc_type": "Pause Young",
  "@gc_cause": "Allocation Failure",
  "@heap_before_gc": "2",
  "@heap_after_gc": "1",
  "@heap_size_gc": "9",
  "@gc_duration": "5.432",
  "@owner": "123456789012",
  "@log_group": "/aws/lambda/myfunction",
  "@log_stream": "2020/09/03/[$LATEST]123456"
}

5. When you go to the Lambda GC Activity dashboard you can see the new event. You must select the right timeframe with the Show dates link.

Lambda GC activity

The dashboard consists of six tiles:

  • In the Filters you optionally select the log group and filter for a specific AWS Lambda function execution context by the name of its log stream.
  • In the GC Activity Count by Execution Context you see a heatmap of all filtered execution contexts by garbage collection activity count.
  • The GC Activity Metrics display a graph for the metrics for all filtered execution contexts.
  • The GC Activity Count shows the amount of garbage collection activities that are currently displayed.
  • The GC Duration show the sum of the duration of all displayed garbage collection activities.
  • The GC Activity Raw Data at the bottom displays the raw items as ingested into the search cluster for a further drill down.

Configure your AWS Lambda function for garbage collection logging

1. The application that you want to monitor needs to log garbage collection activities. Currently the solution supports logs from Java 11. Add the following environment variable to your AWS Lambda function to activate the logging.

JAVA_TOOL_OPTIONS=-Xlog:gc:stderr:time,tags

The environment variables must reflect this parameter like the following screenshot:

environment variables

2. Go to the streamLogs function in the AWS Lambda console that has been created by the stack, and subscribe it to the log group of the function you want to monitor.

streamlogs function

3. Select Add Trigger.

4. Select CloudWatch Logs as Trigger Configuration.

5. Input a Filter name of your choice.

6. Input "[gc" (including quotes) as the Filter pattern to match all garbage collection log entries.

7. Select the Log Group of the function you want to monitor. The following screenshot subscribes to the logs of the application’s function resize-lambda-ResizeFn-[...].

add trigger

8. Select Add.

9. Execute the AWS Lambda function you want to monitor.

10. Refresh the dashboard in Amazon Elasticsearch Service and see the datapoint added manually before appearing in the graph.

Troubleshooting examples

Let’s look at an example function and draw some useful insights from the Java garbage collection log. The following diagrams show the Sample Amazon S3 function code for Java from the AWS Lambda documentation running in a Java 11 function with 512 MB of memory.

  • An S3 event from a new uploaded image triggers this function.
  • The function loads the image from S3, resizes it, and puts the resized version to S3.
  • The file size of the example image is close to 2.8MB.
  • The application is called 100 times with a pause of 1 second.

Memory leak

For the demonstration of a memory leak, the function has been changed to keep all source images in memory as a class variable. Hence the memory of the function keeps growing when processing more images:

GC activity metrics

In the diagram, the heap size drops to zero at timestamp 12:34:00. The Amazon CloudWatch Logs of the function reveal an error before the next call to your code in the same AWS Lambda execution context with a fresh JVM:

Java heap space: java.lang.OutOfMemoryError
java.lang.OutOfMemoryError: Java heap space
 at java.desktop/java.awt.image.DataBufferByte.<init>(Unknown Source)
[...]

The JVM crashed and was restarted because of the error. You leverage primarily the Amazon CloudWatch Logs of your function to detect errors. The garbage collection log and its visualization provide additional information for root cause analysis:

Did the JVM run out of memory because a single image to resize was too large?

Or was the memory issue growing over time?

The latter could be an indication that you have a memory leak in your code.

The Heap size is too small

For the demonstration of a heap that was chosen too small, the memory leak from the preceding image has been resolved, but the function was configured to 128MB of memory. From the baseline of the heap to the maximum heap size, there are only approximately 5 MB used.

GC activity metrics

This will result in a high management overhead of your JVM. You should experiment with a higher memory configuration to find the optimal performance also taking cost into account. Check out AWS Lambda power tuning open source tool to do this in an automated fashion.

Finetuning the initial heap size

If you review the development of the heap size at the start of an execution context, this indicates that the heap size is continuously increased. Each heap size change is an expensive operation consuming time of your function. Over time, the heap size is changed as well. The garbage collector logs 502 activities, which take almost 17 seconds overall.

GC activity metrics

This on-demand scaling is useful on a local workstation where the physical memory is shared with other applications. On AWS Lambda, the configured memory is dedicated to your function, so you can use it to its full extent.

You can do so by setting the minimum and maximum heap size to a fixed value by appending the -Xms and -Xmx parameters to the environment variable we introduced before.

The heap is not the only part of the JVM that consumes memory, so you must experiment with this setting and closely monitor the performance.

Start with the heap size that you observe to be working from the garbage collection log. If you set the heap size too large, your function will not initialize at all or break unexpectedly. Remember that the ability to tweak JVM parameters might change with future service features.

Let’s set 400 MB of the 512 MB memory and examine the results:

JAVA_TOOL_OPTIONS=-Xlog:gc:stderr:time,tags -Xms400m -Xmx400m

GC activity metrics

The preceding dashboard shows that the overall garbage collection duration was reduced by about 95%. The garbage collector had 80% fewer activities.

The garbage collection log entries displayed in the dashboard reveal that exclusively minor garbage collection (Pause Young) activities were triggered instead of major garbage collections (Pause Full). This is expected as the images are immediately discarded after the download, resize, upload operation. The effect on the overall function durations of 100 invocations, is a 5% decrease on average in this specific case.

Lambda duration

Cost estimation and clean up

Cost for the processing and transformation of your function’s Amazon CloudWatch Logs incurs when your function is called. This cost depends on your application and how often garbage collection activities are triggered. Read an estimate of the monthly cost for the search cluster. If you do not need the garbage collection monitoring anymore, delete the subscription filter from the log group of your AWS Lambda function(s). Also, delete the stack of the solution above in the AWS CloudFormation console to clean up resources.

Conclusion

In this post, we examined further sources of data to gain insights about the health of your Java application. We also demonstrated a pipeline to ingest, transform, and visualize this information continuously in a Kibana dashboard. As a next step, launch the application from the AWS Serverless Application Repository and subscribe it to your applications’ logs. Feel free to submit enhancements to the application in the aws-samples repository or provide feedback in the comments.

Field Notes provides hands-on technical guidance from AWS Solutions Architects, consultants, and technical account managers, based on their experiences in the field solving real-world business problems for customers.