Tag Archives: messaging

The value of using an Email Service Provider (ESP) for end customer communications

Post Syndicated from Heidi Gloudemans original https://aws.amazon.com/blogs/messaging-and-targeting/the-value-of-using-an-email-service-provider/

In the world of pervasive consumer chat and messaging applications, email has retained its place as a ubiquitous channel for end customer communications. The Radicati Group reports that email usage will continue to grow from an estimate of 3.9 billion users in 2019 to 4.4 billion in 2023.1 Email is a welcome (and preferred) conduit for many consumers to stay connected to their favorite brands.2 Marketers and developers have found that email has the highest ROI of any end customer communications channel because of its low barrier to entry, affordability and ability to target specific recipients.

However, the very strength of email may also be its biggest weakness. The ubiquity of email as a communication channel means that many consumer mailboxes are inundated with potentially malicious or unwanted messages. Brands must then take action to ensure that their marketing and transactional messages are received by the end customer in a timely manner. While personalized content is one of the fastest growing parts of facilitating deeper customer engagement, organizations should first understand the value of partnering with a mature and trusted email service provider (ESP.)

Many organizations have built internal competencies around business email. Some businesses use an on-premises email server instance or subscribe to a managed email cloud offering (like Amazon WorkMail), while others use email servers on cloud-hosted infrastructure like Amazon EC2. While the fundamentals of email transport is shared between business and marketing email platforms, marketing and transactional email has unique parameters that require additional consideration. ESPs have built specialized expertise in delivering email at scale.

Security and Scale

ESPs have the ability to send email on behalf of an organization’s domain or subdomains. ESPs facilitate mail delivery securely through both domain-key identified mail (DKIM) records and sender policy framework (SPF) records. SPF validates for the mail received that the IP address is part of an accepted senders list. The second authentication protocol used is DKIM which provides a signature to verify each mail using an encrypted key pairing to validate the domain purported is the actual domain sending the message. The combination of both security methods is the first step in authenticating a message from an ESP to the mailbox provider or recipient domain. An additional optional authentication reporting mechanism is Domain Message Reporting and Conformance (DMARC), which can report back any attempts of non-authenticated parties to spoof sending domains.

In addition to security, the best ESPs can also deliver mail at a very large scale. Scale is not just the compute power to deliver hundreds of millions of email a day. It also includes the ability to sustain significant sending volumes of email over time while receiving end-point delivery messages. However, in addition to ESP infrastructure, there are other insights that ESPs can provide around deliverability of a message.

Deliverability

ESPs typically give mail senders the ability to manage their reputation assigned by major internet service providers (ISPs) that include Google Mail, Outlook.com, etc. Reputation is assigned to sending IP addresses and domains with a combination of influencing factors. Evaluation criteria includes, but is not limited to, the following:

  • Type of message and content – Marketing vs. Transactional
  • Valid email addresses and hard bounce rates
  • Feedback from end users (ie. Spam complaints and unsubscribes)
  • Open rates and engagement from end users
  • Time of delivery and cadence of delivery
  • Major blacklist listings, like Spamhaus

Reputation impacts deliverability, which impacts whether a message ends up in an end customer’s mailbox. Transactional messages and marketing messages are typically treated differently by ISPs. Transactional messages (like order confirmations, password resets, etc.) need to be sent and received within a short period of time after a transaction and has less strict rules when it comes to content and scheduling. Marketing messages must adhere to specific regulatory rules per country.

A poor deliverability designation may throttle or even block your message’s ability to reach its destination, instead ending up in the junk folder or blocked altogether. Top ESPs monitor and provide feedback, ensuring mail senders have the ability to take action. However, ESPs also have a responsibility to keep bad actors off their platforms and will block bad senders utilizing them as an ESP resource.

Most ESPs give mail senders the flexibility to manage their sender reputation through both deployment options and delivery statistics. Common deployment options include shared and dedicated IP addresses and pools. Shared IP pools comprise of many individual senders all using the same IP space. The IP reputation is shared across the pool and can both positively and negatively influence senders in the pool based on the majority of traffic. Dedicated IPs are managed directly by a single customer, and can even be dedicated to specific functions. For example a set of IPs can be dedicated to transaction emails vs. pure marketing communications. Many customers prefer dedicated environments because of the ability directly influence deliverability and prevent external influence on their sending reputation.

Statistics that may prove to be leading indicators of deliverability problems include:

  • Mailbox placement (Inbox vs. Junk folder)
  • Detailed email deliverability statistics (bounce rates, etc.)
  • Blacklist monitoring

However, the most important factor impacting mailbox placement is having explicit permission from the end user, with agreed upon content and frequency. End users should know when they are opting into email communications with senders. It is not recommended to use purchased or rented lists, and doing so is typically in violation of the use policy of most ESPs. To stay compliant, it is important to keep your contact preferences up to date and quickly react to unsubscribes and complaints.

Email Analytics

In addition to measuring your sender reputation, metrics are also an important part of measuring the success of your email campaign. Part of calculating the ROI of your outbound email effort is understanding how many emails were opened and whether the end user completed any calls to action (CTAs) in the mail, like clicking a link.

Email analytics from your ESP should include the end-to-end lifecycle of the mail, including:

  • Deliverability rates
  • Delivery rates per ISP
  • Unique Open rates
  • Unique Click through rates
  • Unsubscribes
  • Complaints

Ultimately, in combination with web commerce integration, these metrics can help you determine conversion rates of your campaign to a paying customer.

Conclusion

Connecting to end customers and driving engagement is the goal of every brand. But to make each connection count, brands must ensure that their messages are being delivered through purpose-built infrastructure. Email Service Providers (ESPs) should be part of every organization’s successful email marketing strategy.

Amazon SES has been the trustworthy, flexible and affordable email service provider for developers and marketers since 2012. Learn more here: https://aws.amazon.com/ses/

1https://www.statista.com/statistics/255080/number-of-e-mail-users-worldwide/

2https://www.statista.com/statistics/984615/consumer-brand-communications-channels/

 

About the Author
Heidi Gloudemans is a Senior PMM on Amazon SES and Amazon Pinpoint

Integrating B2B using event notifications with Amazon SNS

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/integrating-b2b-using-event-notifications-with-amazon-sns/

This post is courtesy of Murat Balkan, AWS Solutions Architect

Event notification patterns are popular among B2B integrations. Their scalable and decoupled structure helps implement complex integration scenarios in a variety of enterprises.

This post introduces a generic serverless architecture that applies to external integrations that use event notifications with Amazon SNS and Event Fork Pipelines. Some business scenarios involving B2B integrations include:

  • Inventory information sourcing to customers
  • Catalog sourcing to suppliers or partners
  • Real-time event sourcing to customers (for example, in an online auction)

External integration use cases vary, but a fundamental fact unifies them: External integration is difficult because target systems are not under your control. For example, your IT capabilities may differ. You may not have an internal development team and might rely on tools that can read data from a specific source type.

Alternatively, you may have a large development team and therefore have more data-processing needs and capabilities. These systems may replicate the information from the source, run complex machine learning algorithms against historical data, and must act upon real-time data.

Overview of event notification

Event notification is the sharing of state changes that occur in an application or domain with other applications or domains. You can relate events to any domain object such as orders, products, shipments, or financial transactions. The owner of these entities publishes changes to subscribers. The subscribers subscribe to different events and receive notifications accordingly when a new event is available.

After receiving an event, the integrating party application must determine what to do with the event. The application can store the event, enrich it with additional information, relay it to another party, or ignore it.

To ensure scalability, the publishing application must write or publish to a durable and scalable destination, for you to read from there. These destinations can be message queues (such as Amazon MQ and Amazon SQS) or data streams (such as Amazon Kinesis and Apache Kafka, and the managed AWS version Amazon MSK). To choose between streams and queues, evaluate the traffic characteristics and business use cases. However, the main principles are the same for both.

While creating your B2B external integration architecture, consider different needs, and introduce a mechanism to subscribe to only events of interest. In the example of an online auction site, those that perform active and automated bidding might be interested in real-time bidding events. Others, such as shippers, are only interested in tomorrow’s auction inventory. For the latter, an InventoryItemCreated event can be enough.

Events reflect the nature of a business environment, which can be unpredictable. If a worldwide event affects the markets, event counts might soar dramatically. A marketing event can also cause order events to rise. You need a scalable infrastructure to support your architecture. Serverless is a perfect fit for these kinds of scenarios, and this post’s architecture leverages several AWS serverless components.

In this architecture, you interact with a self-subscription application that exposes a REST API. To start interacting with the system, you also select one or more integration channels for receiving the events. You may prefer SFTP, while others prefer webhooks or multiple channels at the same time. Your IT and development capabilities play an essential role in this selection.

After you determine the integration channels, optionally select types of events channels. The self-subscription application knows all possible event types. As part of its development process, the application provisions them each time a new event appears.

The architecture’s notification channels are as follows:

  • Submission of read-time updates using webhook integration
  • Direct S3 access or SFTP integration
  • Access to Kinesis directly from other AWS accounts

Main data flow

The data flow begins when the publisher applications publish all of their events to a single Amazon SNS topic. Amazon SNS follows the publish and subscribe pattern to fan out a published message to all subscribers of that message topic.

It is worth mentioning Amazon’s new serverless service offering for event-based integrations, Amazon EventBridge. Amazon EventBridge is an event bus that makes it easy to connect applications together using data from your own applications, Software-as-a-Service (SaaS) applications, and AWS services. Amazon EventBridge comes with a powerful rules engine which allows you to put the business logic onto the bus. It can manipulate the payload of the messages and deliver specific payloads to specific consumer applications. Native event integration capabilities with AWS services make it a good candidate for event-based integrations.

For this architecture, I used SNS because AWS offers a quick deployment option through Event Fork Pipelines, a collection of open-source event handling pipelines, based on the AWS Serverless Application Model (AWS SAM).

You can deploy Event Fork Pipelines directly from the AWS Serverless Application Repository into your AWS account. The proposed SNS based architecture also allows the use of custom message payloads in any JSON format, including raw.

SNS has a powerful feature called subscription filter policies. These policies serve as intercepting filters and pass only the desired types of messages to subscribers. Because SNS performs the filtering, you don’t have to implement filtering logic, which decreases their complexity. The policies look for specific attributes and their values in the message. You can use the message attribute Event_Type for filtering.

After filtering the events, route them to the previously selected notification channels. The events land in a queue at each channel before the channel’s logic processes. SNS has built-in integration with SQS, a powerful serverless queueing service. SQS holds your events and acts as a buffer. Every delivery channel’s characteristics and handlers are different. You need a different SQS queue per delivery channel type.

Lambda functions subscribed to the webhook queue handle the webhook notification mechanism. You can convert the polled events to external HTTPS calls against your web servers. Internet delivery over the HTTP protocol is always slower than internal message propagation.

To keep up with the constant flow of events and increase message throughput, webhooks are sent in parallel. SQS provides different features for handling errors that might occur on the subscribing side. For example, the visibility timeout mechanism causes messages to be available after a specified time period, which serves as an auto-retry mechanism for consumed but not properly processed messages. You can also reject functional errors, which cause SQS to send you to dead-letter queues (DLQ) for further troubleshooting.

Amazon S3 handles the file-based notification mechanism. In this integration, a Lambda function polls a dedicated queue that integrates with S3. This function forwards the events to Amazon Kinesis Data Firehose. Kinesis Data Firehose acts as a buffer to consolidate individual messages into bigger files. SQS provides up to 10 messages in a single batch.

After reaching the Kinesis Data Firehose batch size or batch interval, Kinesis Data Firehose delivers the files to an S3 bucket. You can share this bucket with accounts using cross-account access. If you rely on SFTP for file transfers, AWS Transfer for SFTP can expose the objects over SFTP.

Kinesis Data Firehose also lets you define Lambda functions for the transformation of data before your Lambda function writes it to S3. You can use this part of the process to cleanse, filter, or enrich your data. For direct system integration, SQS cross-account access is always an option if you have an account. For more information, see Basic Examples of Amazon SQS Policies.

Example architecture that uses different event types for different delivery channels

Figure 1: Example architecture that uses different event types for different delivery channels

Self-subscription application

A self-subscription application collects channel and event type selections. You can use a single-page application that interacts with a REST API that Amazon API Gateway hosts. API Gateway uses AWS Lambda for backend processing and Amazon DynamoDB for user profile persistence. After collecting integration channel selections and optionally event type filters for these channels, the self-subscription application also orchestrates cloud provisioning tasks.

As subscriptions occur, the self-subscription application’s backend Lambda function triggers AWS CloudFormation to update the subscriptions, subscription filters, and other notification infrastructure components. A different AWS CloudFormation stack manages every integrating party.

Because the whole architecture is serverless, you can use AWS SAM during your provisioning and let AWS SAM interact with AWS CloudFormation. AWS SAM aims to simplify infrastructure as code practices for serverless resources. AWS SAM also allows you to inject application resources from AWS Serverless Application Repository.

AWS provides a set of serverless applications via AWS Serverless Application Repository to cover common integration scenarios for event-driven architectures. These off-the-shelf applications speed up the development and delivery of common event-driven mechanisms such as Command Query Responsibility Segregation (CQRS), Event Replay, and Event Storage or Backup. You can reference Event Fork   applications within AWS SAM templates to use in your applications.

While designing the provisioning pipeline of the proposed architecture, reuse two of the Event Fork Pipeline applications: Event Replay Pipeline (fork-event-replay-pipeline) and Event Storage and Backup Pipeline (fork-event-storage-backup-pipeline). Your webhooks use case is a custom Event Fork Pipeline application.

Because a single AWS SAM template contains these applications, you can deploy and manage the subscription filters and Event Fork Pipelines as a single self-subscription application stack for each integrating party.

CI/CD Pipeline that deploys the serverless application via AWS SAM

Figure 2: CI/CD Pipeline that deploys the serverless application via AWS SAM

In this architecture, Lambda converts the user input into an AWS SAM template and puts it into an S3 bucket. This PUT action triggers an AWS CodePipeline pipeline. The pipeline’s build phase downloads, packages, and deploys the provided AWS SAM template. You can also enhance the pipeline with features such as notifications, manual approvals, or external integrations.

Conclusion

Architectures such as this help you share your business or data events with suppliers, partners, and customers while minimizing integration time and streamlining your business processes. You can try out existing Event Fork Pipelines that was published by AWS, create custom pipelines for your internal use or share them with other AWS users in the AWS Serverless Application Repository.

Migrating from IBM MQ to Amazon MQ using a phased approach

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/migrating-from-ibm-mq-to-amazon-mq-using-a-phased-approach/

This post is contributed by Mithun Mallick, Solutions Architect and Christian Mueller, Solutions Architect

Message-oriented middleware (MOM), or message brokers, are the backbone that integrates business critical applications in many industries. MOMs are used to integrate systems like inventory management, payment systems, and CRM systems. They are also used to orchestrate order-processing workflows across multiple systems, or integrate modern web applications with legacy backend applications.

Some of the most commonly used MOMs in the market are IBM MQ, TIBCO EMS, Rabbit MQ, and Apache ActiveMQ. These systems are often costly to maintain and can have high licensing costs.

Our customers often tell us they want to migrate their applications to the cloud but are hindered by the heavy-lift involved in migrating their MOM systems. They are concerned that migrating their MOM is difficult, and that they must migrate all interconnected systems in one step.

In this post, we show how to build a bridge solution from on-premises IBM MQ to Amazon MQ, to migrate your applications in a step-by-step manner. Amazon MQ is a managed message broker service from AWS that makes it easy to set up and operate message brokers in the cloud.

We explain the steps to move the producers (senders) and consumers (receivers) in phases from your on-premises to the cloud. This process uses Amazon MQ as the message broker, and decommissions IBM MQ once all producers/consumers have been successfully migrated.

You can migrate applications without disrupting existing business systems, and take advantage of the agility, flexibility, and reliability of the cloud. The same pattern can be used if you are using another MOM system, like TIBCO EMS or Rabbit MQ (which supports JMS/NMS, AMQP, or MQTT).

In this post, we show how the phased migration approach helps you mitigate the risk involved a ‘big bang’ cutover. The goal of the solution is to enable an incremental migration of your on-premises applications and MOM to the AWS Cloud. It also replaces your message broker with Amazon MQ. The solution is shown in the diagram below:

Migrating from IBM MQ to Amazon MQ

You can follow the step-by-step instructions in the GitHub’s README file for your own migration.

Initial state and pre-requisites

In the initial state, you are running producers and consumers connected to an on-premises IBM MQ broker.

On-premises IBM MQ broker.

The first step is to facilitate a bridge from IBM MQ to Amazon MQ. Given that IBM MQ is running on-premises, the bridge solution must either exchange messages over the internet by setting up an AWS VPN tunnel, or by using AWS Direct Connect to establish connectivity with the AWS Cloud.

To ensure confidentiality and security for your message exchange, we recommend using AWS VPN. If you have low latency requirements to forward your messages, using AWS Direct Connect is the recommended way.

To complete the hands-on portion of this blog, you must have access to an IBM MQ broker. If you don’t have access, you can provision an IBM MQ broker, running on a Docker container in AWS Fargate.

Once the connectivity is established, you must set up an Amazon MQ broker from the AWS Management Console, or using an AWS CloudFormation template. There is a template provided as part of this post.

Step – 1

Begin by deploying some consumer applications on AWS. These consumers can be new applications or additional instances of applications already running on-premises. You configure these consumer applications to receive messages from Amazon MQ. At this stage, message producers are still on-premises sending messages to the IBM MQ broker.

Next, bridge from IBM MQ to Amazon MQ using a proxy pattern. The proxy pattern is technology-agnostic, and you implement the pattern using Apache Camel to build a JMS bridge. Apache Camel is an open source integration framework for implementing Enterprise Integration Patterns. Apache Camel includes JMS components that easily connect with IBM MQ and Amazon MQ.

In this step, you build an Apache Camel route to consume messages from IBM MQ, and forward to Amazon MQ. Here is an example from the camel-context.xml file, which defines the configuration:

<route id="ibmMQ-to-amazonMQ">
<description>Camel Route from IBM MQ to Amazon MQ</description>
<from uri="ibmMQ:queue:DEV.QUEUE.2?concurrentConsumers=5"/>
<inOnly uri="amazonMQ:queue:DEV.QUEUE.2?preserveMessageQos=true"/>
</route>

This Apache Camel route defines how messages from the producer applications connected to IBM MQ move to Amazon MQ. In this example, there is one sample route but you may have many routes in your production use-case.

Apache Camel is deployed as a Docker container running on AWS Fargate as compute engine for Amazon Elastic Container Service (ECS). ECS is a container orchestration framework that manages the deployment of the containers, and runs them in a highly scalable manner.

AWS Fargate eliminates the heavy lifting of scaling the underlying virtual machines for your ECS cluster. By defining the desired capacity of AWS Fargate tasks, it introduces self-healing capabilities to the JMS bridge. AWS Fargate tracks the number of healthy tasks, and creates new tasks automatically if an old one is no longer available.

Now the JMS bridge and the on-premises consumers are listening on the same queue and waiting for messages. Messages sent to IBM MQ are consumed by on-premises consumers as well as the JMS bridge.  The JMS bridge forwards messages to Amazon MQ to be consumed by the applications already migrated to the cloud. You can now validate that messages are processed by applications on AWS and on-premises.

Now phase 1 of the migration is validated. You can continue to move more consumers as you get more comfortable with the availability and scalability of the bridge solution. The goal of this phase is to reach a state where messages are still produced on-premises, with some consumers running on AWS.

Migrate IBM MQ to Amazon MQ Step 1

Step – 2

Several consumer applications are now migrated to AWS. The goal now is to move the producers to AWS. Start the migration by running a few producer applications on AWS and connect them to Amazon MQ. The Apache Camel bridge is also updated to facilitate bidirectional flow of messages.

The following configuration code shows the route, which moves messages from Amazon MQ to IBM MQ:

<route id="amazonMQ-to-ibmMQ">
<description>Camel Route from Amazon MQ to IBM MQ</description>
<from uri="amazonMQ:queue:DEV.QUEUE.2?concurrentConsumers=5"/>
<inOnly uri="ibmMQ:queue:DEV.QUEUE.2?preserveMessageQos=true"/>
</route>

At this point, messages originating from the producers on AWS have consumers on both on-premises and on AWS. You can validate the processing of messages on AWS as well as on-premises. The state can be seen in the picture as below:

MIgrate IBM MQ to AMazon MQ Step 2

Move more producer applications to AWS as you validate test results, and are comfortable with the bridge solution. Step 2 of the migration is validated once you have confirmed the results of the testing on AWS.

Step – 3

To make this JMS bridge resilient, it must scale in and out automatically, based on your current load. You can configure this by using Amazon CloudWatch metrics and CloudWatch alarms. These alarms can trigger scaling activities to scale in or out, with a fixed number of instances or a percentage-based scaling.

You can also scale out your AWS system based on the utilization of your on-premises broker by defining custom CloudWatch metrics. For example, by running a CRON script on the on-premises broker machine to periodically report the metrics such as queue depth.

Migrate IBM MQ to Amazon MQ Step 3

At this point, this has shown the advantages that the cloud offers for running services efficiently with high availability and resiliency. The automatic scaling capabilities of Amazon ECS generate additional instances of Apache Camel containers as load increases and the queues are filling up. It also scales it in as the load decreases.

You have now established a stable and scalable bridge solution. The next step is to move all the remaining producer/consumer applications to the AWS Cloud. If you have applications that cannot move to the cloud, such as mainframe applications, these can remained connected through your on-premises IBM broker. All other applications can be migrated.

Step – 4

All producers and consumer applications have now been moved to AWS. All the messages that are sent to Amazon MQ broker are processed directly by the consumers running on AWS. The Apache Camel route to move messages from Amazon MQ to IBM MQ and vice versa is disabled.

Migrate IBM MQ to Amazon MQ Step 4

Step – 5

The final goal is to move all application from on-premises to the AWS Cloud. Once all applications are migrated, you can decommission the Apache Camel bridge solution. All the resources deployed in the Apache Camel bridge solution are deleted, along with the automatic scaling and Amazon CloudWatch alarm configuration.

All producers and consumers are now migrated to running on AWS with Amazon MQ as their message broker.

Migrate IBM MQ to Amazon MQ Step 5

Conclusion

In this blog, we showed how to migrate on-premises applications that are dependent on commercial message brokers to the AWS Cloud. The approach relies on a bridge solution, which is based on the proxy pattern, and is technology-independent.

The bridge provides a low risk migration of the applications in phases so that you can validate the migration and avoid any disruption to your business.

For more information on migrating to Amazon MQ and using Apache Camel, please see Migrating from RabbitMQ to Amazon MQ and Integrating Amazon MQ with other AWS services via Apache Camel.

Application integration patterns for microservices: Fan-out strategies

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/application-integration-patterns-for-microservices-fan-out-strategies/

This post is courtesy of Dirk Fröhner

The first blog in this series introduced asynchronous messaging for building loosely coupled systems that can scale, operate, and evolve individually. It considered messaging as a communications model for microservices architectures. This post covers concrete architectural considerations, focusing on the messaging architecture.

Wild Rydes

Wild Rydes is a fictional technology start-up. You may have heard of it – it disrupts individual transportation by replacing traditional taxis with unicorns. We use the Wild Rydes storyline in several hands-on AWS workshops. It illustrates concepts such as serverless development, event-driven design, API management, and messaging in microservices.

This blog post explores the decision-making process in building the Wild Rydes workshop, with a goal of helping you apply these concepts to your applications.

In the workshop, a customer requests a ‘unicorn’ ride using the Wild Rydes customer application. Registered unicorn drivers can use the application to manage their rides. Unicorn drivers submit a ride completion message after they have successfully delivered a customer to their destination.

Wild Rydes app image

Submit a ride completion

API exposed by the unicorn management service

At Wild Rydes, end-user clients are implemented as mobile applications and communicate via REST APIs (also known as hypermedia APIs) with the backend services.

For this use case, the application interacts with the API exposed by the unicorn management service. It uses the submit-ride-completion resource that it discovered from the API’s home document to send the relevant details of a ride to the backend. In response, the backend persists these details, creates a new completed-ride resource. This returns the respective status code, the location, and a representation of the new resource to the client. The API details are shown below.

Request from client to submit the details of a completed ride:

POST /<submit-ride-completion-resource-path> HTTP/1.1
Content-Type: application/json;charset=UTF-8
...

{
    "from": "...",
    "to": "...",
    "duration": "...",
    "distance": "...",
    "customer": "...",
    "fare": "..."
}

Response from the unicorn management service:

HTTP/1.1 201 Created
Date: Sat, 31 Aug 2019 12:00:00 GMT
Location: <url-of-newly-created-completed-ride-resource>
Content-Location: <url-of-newly-created-completed-ride-resource>
Content-Type: application/json;charset=UTF-8
...

{
    "links": {
        "self": {
            "href": "https://..."
        }
    },
    <completed-ride-resource-representation-properties>
}

Schematic architecture for the use case

The schematic architecture for the use case is shown in diagram 1 below:

Diagram 1: Mutliple microservices need information about ride completion

Diagram 1: Multiple microservices need information about ride completion

There are other microservices in Wild Rydes that are also interested in a new completed ride. The examples from the diagram are:

  • Customer notification service: customers should receive a notification in the app about their latest completed ride.
  • Customer accounting service: After all, Wild Rydes is a business, so this service is responsible for collecting the fare from the customer.
  • Customer loyalty service: Everybody wants to collect miles and would like to receive benefits for being a loyal customer.
  • Data lake ingestion service: Wild Rydes is a data-driven company and they want to ingest all data generated from any process into their data lake for arbitrary analytics.
  • Extraordinary rides service: This special service is interested in rides with fares or distances above certain thresholds for preparing insights for business managers.

Based on this scenario, let’s review the integration options.

Integration options

Integration via database

The unicorn management service stores the details of a completed ride in a database. It could share the database with the other services directly, but that creates tight coupling. Sharing the database also restricts your flexibility to scale and evolve your services.

Integration via REST APIs

What about using REST APIs for the integration? The HTTP-based implementation of the REST architectural style uses the distributed architecture concepts of the web. However, what does this mean for the implementation?

Diagram 2: Using REST APIs to communicate to microservices

Diagram 2: Using REST APIs to communicate to microservices

As shown in diagram 2 above:

  • Effectively, all interested services on the right-hand side would have to expose an API resource. These would be called by the unicorn management service for each newly completed ride.
  • To enable elasticity behind a single resource URL, you may need a load balancer in front of each interested service.
  • The unicorn management service would have to know about all these interested services and their respective APIs. Hopefully, each service uses a streamlined API resource.
  • Lastly, the unicorn management service must store, retry, and track all request attempts in case an interested service is not available. This ensures durability so we don’t lose any of these notifications.

One approach is to manage a recipient list in the unicorn management service. This adds additional complexity to the unicorn management service and coupling on both sides. Although there are self-registration and discovery approaches, managing a recipient list is not the core use case of the unicorn management service.

Diagram 3: Using a separate service to manage the fan-out to other services

Diagram 3: Using a separate service to manage the fan-out to other services

A better approach would be to externalize the recipient list into a separate Request Distribution Service, as diagram 3 shows. This decouples both sides, but binds each side to the new service. Still, the unicorn management service is still responsible for the delivery of the ride data to all the recipients. Again, this heavy lifting is not the main task of this service.

Diagram 4: Filtering information for extraordinary rides

Diagram 4: Filtering information for extraordinary rides

In diagram 4, the information filtering for the Extraordinary Rides Service is self-managed. This means that there is code on one side to either not send or to discard irrelevant ride data.

For this use case, integration via REST APIs potentially adds coupling to the services. And it adds heavy lifting to the services that is beyond their actual domain.

Integration via messaging

A third option could use messaging for the integration.

Publish-subscribe pattern

Both Amazon SNS and Amazon EventBridge can be used to implement the publish-subscribe pattern.  In this use case, we recommend Amazon SNS, which scales to support high throughput and fan-out applications. Amazon EventBridge includes direct integrations with software as a service (SaaS) applications and other AWS services. It’s ideal for publish-subscribe use cases involving these types of integrations.

Diagram 5: Using Amazon SNS to implement a publish-subscribe pattern

Diagram 5: Using Amazon SNS to implement a publish-subscribe pattern

Diagram 5 shows an SNS topic called Ride Completion Topic. The unicorn management service can now send the details about a completed ride into that topic. All interested services on the right-hand side can subscribe to this topic.

Using a message topic to publish the details of a completed ride frees us from managing the recipient list, as well as making ensuring reliable delivery of the messages. It also decouples both sides as much as possible. Services on the right-hand side can autonomously subscribe to the topic. The Unicorn Management Service does not know anything about the topic’s subscribers.

Message filter pattern

Looking at the Extraordinary Rides Service, the message filter functionality of Amazon SNS can autonomously and individually discard irrelevant messages. The Extraordinary Rides Service can specify the threshold values for the fare and distance.

Diagram 6: Filtering extraordinary rides using Amazon SNS

Diagram 6: Filtering extraordinary rides using Amazon SNS

Topic-queue-chaining pattern

Consider the publish-subscribe channel between the Unicorn Management Service, and the subscribing services on the right-hand side.

One of the consuming services may go offline for maintenance. Or the code that processes messages from the ride completion topic could run into an exception. These are two examples where a subscriber service could potentially miss topic messages.

A good pattern to apply here is topic-queue-chaining. That means that you add a queue, in our case an SQS queue, between the ride completion topic and each of the subscriber services. As messages are buffered persistently in an SQS queue, it prevents lost messages if a subscriber process run into problems for many hours or days.

Diagram 7: Chaining topics and queues to buffer messages persistently

Diagram 7: Chaining topics and queues to buffer messages persistently

Queues as buffering load balancers

An SQS queue in front of each subscriber service also acts as a buffering load balancer.

Since every message is delivered to one of potentially many consumer processes, you can scale out the subscriber services, and the message load is distributed over the available consumer processes.

As messages are buffered in the queue, they are preserved during a scaling event, such as when you must wait until an additional consumer process becomes operational.

Lastly, these queue characteristics help flatten peak loads for your consumer processes, buffering messages until consumers are available. This allows you to process messages at a pace decoupled from the message source.

Conclusion

The Wild Rydes example shows how messaging can provide decoupling and greater flexibility for your microservices landscape.

In contrast to REST APIs, a messaging system takes care of message delivery outside of your service code. Using a publish-subscribe channel provides simple fan-out capability. And message filters allow for selective message reception without the effort of implementing that logic into your code.

With topic-queue-chaining pattern, you can add queue characteristics to a fan-out scenario so that you can easily scale out on the consumer side, and flatten peak loads.

For a deeper dive into queues and topics and how to use them in your microservices architecture, please use the following resources:

  1. AWS whitepaper: Implementing Microservices on AWS
  2. AWS blog: Implementing enterprise integration patterns with AWS messaging services: point-to-point channels
  3. AWS blog: Implementing enterprise integration patterns with AWS messaging services: publish-subscribe channels
  4. AWS blog: Building Scalable Applications and Microservices: Adding Messaging to Your Toolbox

Understanding asynchronous messaging for microservices

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/understanding-asynchronous-messaging-for-microservices/

This post is courtesy of Dirk Fröhner

One of the implications of applying the microservices architectural style is that much communication between components happens over the network. After all, your microservices landscape is a distributed system. To achieve the promises of microservices, such as being able to individually scale, operate, and evolve each service, this communication must happen in a loosely coupled and reliable manner.

A common way to loosely couple services is to expose an API following the REST architectural style. REST APIs are based on the architecture of the web and provide loose coupling between communicating parties. REST APIs offer a great way to decouple interfaces from concrete implementations, and to advise clients about what they can do next, by the use of links and link relations.

While REST APIs are common and useful in microservices design, REST APIs tend to be designed with synchronous communications, where a response is required. A request coming from an end-user client can trigger a complex communications path within your services landscape, which can effectively add coupling between the services at runtime. After all, this is why there are mitigation patterns like circuit-breaker in the first place. REST APIs can also add some heavy lifting to your infrastructure that we will discuss further below.

Asynchronous messaging

If loose-coupling is important, especially in a system that requires high resilience and has unpredictable scale, another option is asynchronous messaging.

Asynchronous messaging is a fundamental approach for integrating independent systems, or building up a set of loosely coupled systems that can operate, scale, and evolve independently and flexibly. As our colleague Tim Bray said, “If your application is cloud-native, or large-scale, or distributed, and doesn’t include a messaging component, that’s probably a bug.” In this blog post, we will outline some fundamental benefits of asynchronous messaging for the communications between microservices.

For a refresher on the fundamental messaging patterns and their implementations with Amazon SQS, Amazon SNS, and Amazon MQ, please read our previous blog posts

For a summary of the semantics of queues and topics:

  • A queue is like a buffer. You can put messages into a queue, and you can retrieve messages from a queue. Message queues operate so that any given message is only consumed by one receiver, although multiple receivers can be connected to the queue.
  • A topic is like a broadcasting station. You can publish messages to a topic, and anyone interested in these messages can subscribe to the topic. In this model, any message published to a topic is immediately received by all of the subscribers of the topic (unless you have applied the message filter pattern).

Use-case

Consider a typical scenario illustrated in the diagram below. An end-user client (EUC) addresses an API resource of one of our services, through Amazon API Gateway in this example. From there, the request can potentially follow a path across the microservices landscape to get completely processed.

To provide the final result, there will be potentially cascading subsequent requests sent between other microservices. This example illustrates the complexity involved in processing a single end user request.

End User Client accessing a service using an API

Diagram 1: End-User Client accessing a service using an API

End-user clients (EUCs) often communicate with services via REST APIs in a synchronous manner. However, the communication can also be designed using an asynchronous approach. For instance, if an EUC submits a request that takes some time to process, the respective API resource can respond with HTTP status 202 Accepted, and a link to a resource that provides the current processing status. Downstream, the communication between the service that receives that request, and other services that are involved in processing the request, can happen asynchronously using messaging services.

There are situations where a communications model using asynchronous messaging can make your life easier than using REST APIs.

Infrastructure complexity

Start with looking at the infrastructure complexity for the backends of your services. Depending on your implementation paradigm, you have to include different components in your infrastructure that you don’t have to deal with when using messaging.

Imagine your services each expose a REST API. Typically, this means you add a load balancer in front of your compute layer, and your backend implementation includes an HTTP server. It is usually a good idea to decouple your services APIs from their concrete implementations, so you could also consider adding Amazon API Gateway in front of your load balancer.

For a serverless approach, you don’t need to worry about load balancing and scaling out infrastructure. Amazon API Gateway with AWS Lambda integration provides a fully managed solution for removing complexities around infrastructure management.

Using Amazon SQS as a cloud-native messaging service for queues, you don’t employ any of the above mentioned components. As described in a prior post, an SQS queue can act as a load balancer in itself. The consumers, or target services, don’t need an HTTP server, but simply ask a queue for available messages. If you use AWS Lambda for your consumers, this process is even simpler, as the Lambda functions are automatically invoked when messages appear in an SQS queue. See Using AWS Lambda with Amazon SQS to learn more.

The same applies to Serverless architectures implementing a publish/subscribe pattern. Lambda function executions can be directly triggered by SNS messages. Without AWS Lambda, you need load balancers and web servers in your backend implementations to receive SNS notifications, as those are injected via web hooks into your services. SNS also provides the fan-out functionality that you would otherwise have to build using an intermediary component to implement a recipient list of subscribers.

Reliability, resilience

For synchronous systems, if a service crashes while it processes the payload of an API request, the information is lost. A good way to prevent this on a microservice is to explicitly persist an incoming request immediately after receiving it. Then process and reprocess, until the request is finally marked as resolved.

This approach requires additional work, and it requires the microservice to not crash while persisting an incoming API request. The microservice sending a request must also resend if the target service doesn’t acknowledge receipt. For example, it doesn’t respond with a successful HTTP status code, or the connection drops.

When sending messages to a queue, this additional work is addressed by the messaging infrastructure. A message will remain in a queue unless a consumer explicitly states that processing is finished by acknowledging the message reception. As long as message reception is not acknowledged by a consumer, it will stay in the queue. Messages can be retained in an SQS queue for a maximum of 14 days.

Scale out latency

Under increased load, your services must scale out to process the requests. You must then consider scale-out latency, which may be managed for you with serverless implementations. It takes a few moments from when an Auto Scaling group triggers the launch of additional instances until these are ready to operate. Also launching new container tasks takes time. When your scaling threshold is not optimal and the scaling event occurs late, your available resources may be unable to serve all incoming requests. These requests may be lost or answered with HTTP status code 5xx.

Using message queues that buffer messages during a scaling event help prevent this. Even in use cases where the EUC is waiting for an immediate response, this is the more reliable architecture. If your infrastructure needs time to scale out and you are not able to process all requests in time, the requests are persisted.

When messaging is your only choice

What happens when your services must respond to peak loads at scale?

For many applications, the scale-out latency, including load balancer pre-warming, will eventually become too large to handle steeply ascending loads fast enough. With a serverless architecture, exposing your Lambda functions with API Gateway can handle steeply ascending loads. But you must still consider downstream systems, which may be easily overwhelmed.

In these scenarios, where rapid scaling without overwhelming downstream systems is important, messaging may be your best choice. Message queues help protect your downstream services by buffering incoming payloads for consumption at the pace of the consuming service. This helps not only for the communications between microservices, but also when peak loads flood your client-facing API. Often, the most important goal is to accept an incoming request, while the actual processing of that request can happen later. You decouple these steps from each other by using queues.

Serverless messaging systems like Amazon SQS and Amazon SNS can respond quickly to support high scale. These are often the best solution when scale is unpredictable.  While the instance-based messaging system, Amazon MQ, provides compatibility with open standards, it requires manual scaling for large workloads, unlike serverless messaging services.

Conclusion

We hope you got some inspiration to also employ asynchronous messaging for your microservices communications architecture. In blog XYZ we provide concrete examples of these patterns. For more information, feel free to consume the following resources:

  1. AWS whitepaper: Implementing Microservices on AWS
  2. AWS blog: Implementing enterprise integration patterns with AWS messaging services: point-to-point channels
  3. AWS blog: Implementing enterprise integration patterns with AWS messaging services: publish-subscribe channels
  4. AWS blog: Building Scalable Applications and Microservices: Adding Messaging to Your Toolbox

Read the next blog in the series,  Application Integration Patterns for Microservices: Fan-out Strategies.

Designing durable serverless apps with DLQs for Amazon SNS, Amazon SQS, AWS Lambda

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/designing-durable-serverless-apps-with-dlqs-for-amazon-sns-amazon-sqs-aws-lambda/

This post is courtesy of Otavio Ferreira, Sr Manager, SNS.

In a postal system, a dead-letter office is a facility for processing undeliverable mail. In pub/sub messaging, a dead-letter queue (DLQ) is a queue to which messages published to a topic can be sent, in case those messages cannot be delivered to a subscribed endpoint.

Amazon SNS supports DLQs, making your applications more resilient and durable upon delivery failure modes.

Understanding message delivery failures and retries

The delivery of a message fails when it’s not possible for Amazon SNS to access the subscribed endpoint. There are two reasons why this might happen:

  • Client errors, where the client is SNS (the message sender).
  • Server errors, where the server is the system that hosts the subscription endpoint (the message receiver), such as Amazon SQS or AWS Lambda.

Client errors

Client errors happen when SNS has stale subscription metadata. One common cause of client errors is when you (the endpoint owner) delete the endpoint. For example, you might delete the SQS queue that is subscribed to your SNS topic, without also deleting the SNS subscription corresponding to the queue. Another common cause is when you change the resource policy attached to your endpoint in a way that prevents SNS from delivering messages to that endpoint.

These errors are considered client errors because the client has attempted the delivery of a message to a destination that, from the client’s perspective, is no longer accessible. SNS does not retry the delivery of messages that failed as the result of client errors.

Server errors

Server errors happen when the system that powers the subscribed endpoint is unavailable, or when it returns an exception response indicating that it failed to process a valid request from SNS.

When server errors occur, SNS retries the failed deliveries according to a backoff function, which can be either linear or exponential. When a server error occurs for an AWS managed endpoint, backed by either SQS or Lambda, then SNS retries the delivery for up to 100,015 times, over 23 days.

Server errors can also happen with customer managed endpoints, namely HTTP, SMS, email, and mobile push endpoints. SNS also retries the delivery for these types of endpoints. HTTP endpoints support customer-defined retry policies, while SNS sets an internal delivery retry policy for SMS, email, and mobile push endpoints to 50 times, over 6 hours.

Delivery retries

SNS may receive a client error, or continue to receive a server error for a message beyond the number of retries defined by the corresponding retry policy. In that event, SNS discards the message. Setting a DLQ to your SNS subscription enables you to keep this message, regardless of the type of error, either client or server. DLQs give you more control over messages that cannot be delivered.

For more information on the delivery retry policy for each delivery protocol supported by SNS, see Amazon SNS Message Delivery Retry.

Using DLQs for AWS services

SNS, SQS, and Lambda support DLQs, addressing different failure modes. All DLQs are regular queues powered by SQS.

In SNS, DLQs store the messages that failed to be delivered to subscribed endpoints. For more information, see Amazon SNS Dead-Letter Queues.

In SQS, DLQs store the messages that failed to be processed by your consumer application. This failure mode can happen when producers and consumers fail to interpret aspects of the protocol that they use to communicate. In that case, the consumer receives the message from the queue, but fails to process it, as the message doesn’t have the structure or content that the consumer expects. The consumer can’t delete the message from the queue either. After exhausting the receive count in the redrive policy, SQS can sideline the message to the DLQ. For more information, see Amazon SQS Dead-Letter Queues.

In Lambda, DLQs store the messages that resulted in failed asynchronous executions of your Lambda function. An execution can result in an error for several reasons. Your code might raise an exception, time out, or run out of memory. The runtime executing your code might encounter an error and stop. Your function might hit its concurrency limit and be throttled. Regardless of the error type, when the error occurs, your code might have run completely, partially, or not at all. By default, Lambda retries an asynchronous execution twice. After exhausting the retries, Lambda can sideline the message to the DQL. For more information, see AWS Lambda Dead-Letter Queues.

When you have a fan-out architecture, with SQS queues and Lambda functions subscribed to an SNS topic, we recommend that you set DLQs to your SNS subscriptions, and to your destination queues and functions as well. This approach gives your application resilience against message delivery failures, message processing failures, and function execution failures too.

Applying DLQs in a use case

Here’s how everything comes together. The following diagram shows a serverless backend architecture that supports a car rental application. This is a durable serverless architecture based on DLQs for SNS, SQS, and Lambda.

Dead Letter Queue - DLQ SNS use case with architecture diagram

When a customer places an order to rent a car, the application sends that request to an API, which is powered by Amazon API Gateway. The REST API is backed by an SNS topic named Rental-Orders, and deployed onto an Amazon VPC subnet. The topic then fans out that order to the following two subscribed endpoints, for parallel processing:

  • An SQS queue, named Rental-Fulfilment, which feeds the integration with an internal fulfilment system hosted on Amazon EC2.
  • A Lambda function, named Rental-Billing, which processes and loads the customer order into a third-party billing system, also hosted on Amazon EC2.

To increase the durability of this serverless backend API, the following DLQs have been set up:

  • Two SNS DLQs, namely Rental-Fulfilment-Fanout-DLQ and Rental-Billing-Fanout-DLQ, which store the order in case either the subscribed SQS queue or Lambda function ever becomes unreachable.
  • An SQS DLQ, named Rental-Fulfilment-DLQ, which stores the order when the fulfilment system fails to process the order.
  • A Lambda DLQ, named Rental-Billing-DLQ, which stores the order when the function fails to process and load the order into the billing system.

When the DLQ captures the message, you can inspect the message for troubleshooting purposes. After you address the error at hand, you can poll the DLQ to retry the processing of the message.

Setting up DLQs for subscriptions, queues, and functions can be done using the AWS Management Console, SDK, CLI, API, or AWS CloudFormation. You can use the SDK, CLI, and API for polling the DLQs as well.

Configuring DLQs for subscriptions

You can attach a DLQ to an SNS subscription by setting the subscription’s RedrivePolicy parameter. The policy is a JSON object that refers to the DLQ ARN. The ARN must point to an SQS queue in the same AWS account as that of the SNS subscription. Also, both the DLQ and the subscription must be in the same AWS Region.

Here’s how you can configure one of the SNS DLQs applied in the car rental application example, presented earlier.

The following JSON object is a CloudFormation template that subscribes the SQS queue Rental-Fulfilment to the SNS topic Rental-Orders. The template also sets a RedrivePolicy that targets Rental-Fulfilment-Fanout-DLQ as a DLQ.

Lastly, the template sets a FilterPolicy value. It makes SNS deliver a message to the subscribed queue only if the published message carries an attribute named order-status with value set to either confirmed or canceled. As Amazon SNS Message Filtering happens before message delivery, messages that are filtered out aren’t sent to that subscription’s DLQ.

Internally, the CloudFormation template uses the SNS Subscribe API action for deploying the subscription and setting both policies, all part of the same API request.

{  
   "Resources": {
      "mySubscription": {
         "Type" : "AWS::SNS::Subscription",
         "Properties" : {
            "Protocol": "sqs",
            "Endpoint": "arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment",
            "TopicArn": "arn:aws:sns:us-east-1:123456789012:Rental-Orders",
            "RedrivePolicy": {
               "deadLetterTargetArn": 
                  "arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment-Fanout-DLQ"
            },
            "FilterPolicy": { 
               "order-status": [ "confirmed", "canceled" ]
            }
         }
      }
   }
}

Maybe the SNS topic and subscription are already deployed. In that case, you can use the SNS SetSubscriptionAttributes API action to set the RedrivePolicy, as shown by the following code examples, based on the AWS CLI and the AWS SDK for Java.

$ aws sns set-subscription-attributes 
   --region us-east-1
   --subscription-arn arn:aws:sns:us-east-1:123456789012:Rental-Orders:44019880-ffa0-4067-9cb4-b974443bcck2
   --attribute-name RedrivePolicy 
   --attribute-value '{"deadLetterTargetArn":"arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment-Fanout-DLQ"}'
AmazonSNS sns = AmazonSNSClientBuilder.defaultClient();

String subscriptionArn = "arn:aws:sns:us-east-1:123456789012:Rental-Orders:44019880-ffa0-4067-9cb4-b974443bcck2";

String redrivePolicy = "{\"deadLetterTargetArn\":\"arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment-Fanout-DLQ\"}";

SetSubscriptionAttributesRequest request = new SetSubscriptionAttributesRequest(
  subscriptionArn, 
  "RedrivePolicy", 
  redrivePolicy
);

sns.setSubscriptionAttributes(request);

Monitoring DLQs

You can use Amazon CloudWatch metrics and alarms to monitor the DLQs associated with your SNS subscriptions. In the car rental example, you can monitor the DLQs to be notified when the API failed to distribute any car rental order to the fulfillment or billing systems.

As regular SQS queues, the DLQs in SNS emit a number of metrics to CloudWatch, in 5-minute data points, such as NumberOfMessagesSent, NumberOfMessagesReceived and NumberOfMessagesDeleted. You can use these SQS metrics to be notified upon activity in your DLQs in SNS, so you may trigger a message recovery protocol.

You might have a case where you expect the DLQ to be always empty. In that case, create an CloudWatch alarm on NumberOfMessagesSent, set the alarm threshold to zero, and provide a separate SNS topic to be notified when the alarm goes off. The SNS topic, in its turn, can delivery your alarm notification to any endpoint type that you choose, such as email address, phone number, or mobile pager app.

Additionally, SNS itself provides its own set of metrics that are relevant to DLQs. Specifically, SNS metrics include the following:

  • NumberOfNotificationsRedrivenToDlq – Used when sending the message to the DLQ succeeds.
  • NumberOfNotificationsFailedToRedriveToDlq – Used when sending the message to the DLQ fails. This can happen because the DLQ either doesn’t exist anymore or doesn’t have the required access permissions to allow SNS to send messages to it. For more information about setting up the required access policy, see Giving Permissions for Amazon SNS to Send Messages to Amazon SQS.

Debugging with DLQs

Use CloudWatch Logs to see the exceptions that caused your SNS deliveries to fail and your messages to be sidelined to DLQs. In the car rental example, you can inspect the rental orders in the DLQs, as well as the logs associated with these queues. Then you can understand why those orders failed to be fanned out to the fulfilment or billing systems.

SNS can log both successful and failed deliveries in CloudWatch. You can enable Amazon SNS Delivery Status Logging by setting three SNS topic attributes, which are delivery protocol-specific. As an example, for SNS deliveries to SQS queues, you must set the following topic attributes: SQSSuccessFeedbackRoleArn,  SQSFailureFeedbackRoleArn, and SQSSuccessFeedbackSampleRate.

The following JSON object represents a successful SNS delivery in an CloudWatch Logs entry. The status code logged is 200 (SUCCESS). The attribute RedrivePolicy shows that the SNS subscription in question had its DLQ set.

{
  "notification": {
    "messageMD5Sum": "7bb3327ac55e49485bad42e159ca4d4b",
    "messageId": "e8c2bb09-235c-5f5d-b583-efd8df0f7d74",
    "topicArn": "arn:aws:sns:us-east-1:123456789012:Rental-Orders",
    "timestamp": "2019-10-04 05:13:55.876"
  },
  "delivery": {
    "deliveryId": "6adf232e-fb12-5062-a564-27ff3741051f",
    "redrivePolicy": "{\"deadLetterTargetArn\": \"arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment-Fanout-DLQ\"}",
    "destination": "arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment",
    "providerResponse": "{\"sqsRequestId\":\"b2608a46-ccc4-51cc-003d-de972097debc\",\"sqsMessageId\":\"05fecd22-60a1-4d7d-bb79-026d49700b5a\"}",
    "dwellTimeMs": 58,
    "attempts": 1,
    "statusCode": 200
  },
  "status": "SUCCESS"
}

The following JSON object represents a failed SNS delivery in CloudWatch Logs. In the following code example, the subscribed queue doesn’t exist. As a client error, the status code logged is 400 (FAILURE). Again, the RedrivePolicy attribute refers to a DLQ.

{
  "notification": {
    "messageMD5Sum": "81c395cbd350da6bedfe3b24db9517b0",
    "messageId": "9959db9d-25c8-57a6-9439-8e5be8f71a1f",
    "topicArn": "arn:aws:sns:us-east-1:123456789012:Rental-Orders",
    "timestamp": "2019-10-04 05:16:51.116"
  },
  "delivery": {
    "deliveryId": "be743821-4c2c-5acc-a586-6cf0807f6fb1",
    "redrivePolicy": "{\"deadLetterTargetArn\": \"arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment-Fanout-DLQ\"}",
    "destination": "arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment",
    "providerResponse": "{\"ErrorCode\":\"AWS.SimpleQueueService.NonExistentQueue\", \"ErrorMessage\":\"The specified queue does not exist or you do not have access to it.\",\"sqsRequestId\":\"Unrecoverable\"}",
    "dwellTimeMs": 53,
    "attempts": 1,
    "statusCode": 400
  },
  "status": "FAILURE"
}

When the message delivery fails and there is a DLQ attached to the subscription, the message is sent to the DLQ and an additional entry is logged in CloudWatch. This new entry is specific to the delivery to the DLQ and refers to the DLQ ARN as the destination, as shown in the following JSON object.

{
  "notification": {
    "messageMD5Sum": "81c395cbd350da6bedfe3b24db9517b0",
    "messageId": "8959db9d-25c8-57a6-9439-8e5be8f71a1f",
    "topicArn": "arn:aws:sns:us-east-1:123456789012:Rental-Orders",
    "timestamp": "2019-10-04 05:16:52.876"
  },
  "delivery": {
    "deliveryId": "a877c79f-a3ee-5105-9bbd-92596eae0232",
    "destination":"arn:aws:sqs:us-east-1:123456789012:Rental-Fulfilment-Fanout-DLQ",
    "providerResponse": "{\"sqsRequestId\":\"8cef1af5-e86a-519e-ad36-4f33252aa5ec\",\"sqsMessageId\":\"2b742c5c-0750-4ec5-a717-b95897adda8e\"}",
    "dwellTimeMs": 51,
    "attempts": 1,
    "statusCode": 200
  },
  "status": "SUCCESS"
}

By analyzing Amazon CloudWatch Logs entries, you can understand why an SNS message was moved to a DLQ, and then take the required set of steps to recover the message. When you enable delivery status logging in SNS, you can configure the sample rate in which deliveries are logged, from 0% to 100%.

Encrypting DLQs

When your SNS subscription targets an SQS encrypted queue, then you probably want your DLQ to be an SQS encrypted queue as well. This configuration provides consistency in the form that your messages are encrypted at rest.

To follow this security recommendation, give the CMK you used to encrypt your DLQ a key policy that grants the SNS service principal access to AWS KMS API actions. For example, see the following sample key policy:

{
    "Sid": "GrantSnsAccessToKms",
    "Effect": "Allow",
    "Principal": { "Service": "sns.amazonaws.com" },
    "Action": [ "kms:Decrypt", "kms:GenerateDataKey*" ],
    "Resource": "*"
}

If you have an SNS encrypted topic, but a subscription in this topic points to a DLQ that isn’t an SQS encrypted queue, then messages sidelined to the DLQ aren’t encrypted at rest.

For more information, see Enabling Server-Side Encryption (SSE) for an Amazon SNS Topic with an Amazon SQS Encrypted Queue Subscribed.

Summary

DLQs for SNS, SQS, and Lambda increase the resiliency and durability of your applications. These DLQs address different failure modes, and can be used together.

  • SNS DLQs store messages that failed to be delivered to subscribed endpoints.
  • SQS DLQs store messages that the consumer system failed to process.
  • Lambda DLQs store the messages that resulted in failed asynchronous executions of your functions.

Setting up DLQs for subscriptions, queues, and functions can be done using the AWS Management Console, SDK, CLI, API, or CloudFormation. DLQs are available in all AWS Regions. Start today by running the tutorials:

Visit the AWS Digital User Engagement team at AWS re:Invent 2019

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/visit-the-aws-digital-user-engagement-team-at-aws-reinvent-2019/

AWS re:Invent 2019 is less than 50 days away, and that means it’s time to start planning your agenda. The Digital User Engagement team is hosting several builders sessions, chalk talks, and workshops this year. Come join us and learn more about using Amazon Pinpoint and Amazon SES to engage with and delight your customers.

Visit our booth

You’ll find our booth in the Expo Hall in the Venetian. Stop by to meet the team, see a demo, and pick up some swag!

Leadership session

EUC206: How AWS is defining the future of engagement and messaging

  • What: Simon Poile, the General Manager of the AWS Digital User Engagement team, talks about how AWS is building on Amazon’s customer-centric culture of innovation to help you better engage your customers. You’ll also hear from AWS customer Coinbase, which uses Amazon Pinpoint to delight its customers while growing its business.
  • When: Wednesday, Dec 4, 10:45 AM – 11:45 AM
  • Where: MGM, Level 3, Chairman’s Ballroom 368

Sessions

EUC207: Build high-volume email applications with Amazon SES

  • What: Companies in many industries use AWS to send millions of emails every day, including Amazon.com. In this session, learn how to build applications using the highly scalable, highly reliable, and multi-tenant-capable email infrastructure of Amazon Simple Email Service (Amazon SES). You also learn how to monitor delivery rates and other important metrics, and how to use this data to improve deliverability. Members of the Amazon.com team discuss the architecture of their multi-tenant email-sending platform, the historical challenges they faced, and the ways Amazon Pinpoint and Amazon SES helped them meet their goals around Prime Day, Cyber Monday, and other retail events.
  • When: Monday, Dec 2, 1:00 PM – 2:00 PM
  • Where: MGM, Level 1, Grand Ballroom 119

Chalk talks

EUC328: Engage with your customers using SMS text messages

  • What: Text messages form a vital part of customer-engagement strategy for organizations around the world. In this workshop, learn how to use Amazon Pinpoint to send promotional, transactional, and two-way SMS messages. You also see demonstrations of how other AWS customers use SMS messaging to engage with their customers.
  • When: Wednesday, Dec 4, 2:30 PM – 3:30 PM
  • Where: Bellagio, Bellagio Ballroom 5

EUC336: Surprise and delight customers with location-based notifications

  • What: In this chalk talk, learn how to use AWS Amplify, AWS AppSync, and Amazon Pinpoint to geo-target customers. We teach you how to build and configure geofences to trigger location-based mobile-app notifications. We also walk you through the published solution and provide dedicated time for Q&A with an AWS solutions architect.
  • When: Thursday, Dec 5, 2:30 PM – 3:30 PM
  • Where: Aria, Plaza Level East, Orovada 3

AIM346-R and AIM346-R1: Personalized user engagement with machine learning

  • What: In this chalk talk, we discuss how to use Amazon Personalize and Amazon Pinpoint to provide a personalized, omni-channel experience starting in your mobile application. We discuss best practices for real-time updates, personalized notifications (push), and messaging (email and text) that drives user engagement and product discovery. We also demonstrate how other mobile services can be used to facilitate rapid prototyping.
Session #WhenWhere
AIM346-RMonday, Dec 2, 11:30 AM – 12:30 PMBellagio, Bellagio Ballroom 7
AIM346-R1Tuesday, Dec 3, 4:00 PM – 5:00 PMAria, Plaza Level East, Orovada 3

ENT315: Improve message deliverability to ensure customer reach

  • What: Do you have outbound and inbound email requirements? Is email a critical workload for your enterprise? Several factors determine whether your email messages reach your recipients. In this chalk talk, learn how to safely migrate your outbound and inbound email volumes over to AWS and Amazon Simple Email Service (Amazon SES). Learn how to onboard, safely ramp up, and ensure that business continues without disruption. Also learn best practices for delivering email messages into your customers’ inboxes rather than their spam folders, and receive guidance on scaling and improving the deliverability of your email campaigns.
  • When: Thursday, Dec 5, 11:30 AM – 12:30 PM
  • Where: Aria, Plaza Level East, Orovada 3

Builder’s sessions

EUC308-R and EUC308-R1: Build and deploy your own two-way text chatbot

  • What: In this builders session, you build an AI-powered chatbot that your customers can engage with by sending SMS messages. Your chatbot can help customers quickly ask questions, get answers, book appointments, check order status, and much more.
Session #WhenWhere
EUC308-RTuesday, Dec 3, 1:00 PM – 2:00 PMMirage, Grand Ballroom B – Table 9
EUC308-R1Wednesday, Dec 4, 4:00 PM – 5:00 PMAria, Level 1 West, Bristlecone 2 – Table 2

EUC309-R and EUC309-R1: Build your own omnichannel e-commerce experience

  • What: In this hands-on session, you learn how to integrate AWS Amplify and Amazon Pinpoint to create a retail website. You use the event data that’s generated by customers’ activities on your site to send custom-tailored emails and push notifications, creating a curated, omnichannel experience. This session is intended for builders who want to expand the user-engagement capabilities of their sites and apps.
Session #WhenWhere
EUC309-RMonday, Dec 2, 12:15 PM – 1:15 PMAria, Level 1 West, Bristlecone 4 – Table 8
EUC309-R1Tuesday, Dec 3, 11:30 AM – 12:30 PMAria, Level 1 West, Bristlecone 2 – Table 1

EUC322: Improve customer engagement by predicting user behavior

  • What: In this hands-on session, you learn how to use Amazon SageMaker and Amazon Pinpoint to create customer engagement scenarios powered by machine learning. You use cross-channel customer-activity and demographic data to train your own behavioral models. After you use your model to categorize your customers, you use Amazon Pinpoint to send engagement campaigns that are optimized to reengage users. This session is intended for builders, marketers, or data scientists who want to improve user engagement using machine learning.
  • When: Monday, Dec 2, 10:45 AM – 11:45 AM
  • Where: Aria, Level 1 West, Bristlecone 4 – Table 2

Sending Push Notifications to iOS 13 Devices with Amazon SNS

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/sending-push-notifications-to-ios-13-devices-with-amazon-sns/

Note: This post was written by Alan Chuang, a Senior Product Manager on the AWS Messaging team.


On September 19, 2019, Apple released iOS 13. This update introduced changes to the Apple Push Notification service (APNs) that can impact your existing workloads. Amazon SNS has made some changes that ensure uninterrupted delivery of push notifications to iOS 13 devices.

iOS 13 introduced a new and required header called apns-push-type. The value of this header informs APNs of the contents of your notification’s payload so that APNs can respond to the message appropriately. The possible values for this header are:

  • alert
  • background
  • voip
  • complication
  • fileprovider
  • mdm

Apple’s documentation indicates that the value of this header “must accurately reflect the contents of your notification’s payload. If there is a mismatch, or if the header is missing on required systems, APNs may return an error, delay the delivery of the notification, or drop it altogether.”

We’ve made some changes to the Amazon SNS API that make it easier for developers to handle this breaking change. When you send push notifications of the alert or background type, Amazon SNS automatically sets the apns-push-type header to the appropriate value for your message. For more information about creating an alert type and a background type notification, see Generating a Remote Notification and Pushing Background Updates to Your App on the Apple Developer website.

Because you might not have enough time to react to this breaking change, Amazon SNS provides two options:

  • If you want to set the apns-push-type header value yourself, or the contents of your notification’s payload require the header value to be set to voip, complication, fileprovider, or mdm, Amazon SNS lets you set the header value as a message attribute using the Amazon SNS Publish API action. For more information, see Specifying Custom APNs Header Values and Reserved Message Attributes for Mobile Push Notifications in the Amazon SNS Developer Guide.
  • If you send push notifications as alert or background type, and if the contents of your notification’s payload follow the format described in the Apple Developer documentation, then Amazon SNS automatically sets the correct header value. To send a background notification, the format requires the aps dictionary to have only the content-available field set to 1. For more information about creating an alert type and a background type notification, see Generating a Remote Notification and Pushing Background Updates to Your App on the Apple Developer website.

We hope these changes make it easier for you to send messages to your customers who use iOS 13. If you have questions about these changes, please open a ticket with our Customer Support team through the AWS Management Console.

Predictive User Engagement using Amazon Pinpoint and Amazon Personalize

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/predictive-user-engagement-using-amazon-pinpoint-and-amazon-personalize/

Note: This post was written by John Burry, a Solution Architect on the AWS Customer Engagement team.


Predictive User Engagement (PUE) refers to the integration of machine learning (ML) and customer engagement services. By implementing a PUE solution, you can combine ML-based predictions and recommendations with real-time notifications and analytics, all based on your customers’ behaviors.

This blog post shows you how to set up a PUE solution by using Amazon Pinpoint and Amazon Personalize. Best of all, you can implement this solution even if you don’t have any prior machine learning experience. By completing the steps in this post, you’ll be able to build your own model in Personalize, integrate it with Pinpoint, and start sending personalized campaigns.

Prerequisites

Before you complete the steps in this post, you need to set up the following:

  • Create an admin user in Amazon Identity and Account Management (IAM). For more information, see Creating Your First IAM Admin User and Group in the IAM User Guide. You need to specify the credentials of this user when you set up the AWS Command Line Interface.
  • Install Python 3 and the pip package manager. Python 3 is installed by default on recent versions of Linux and macOS. If it isn’t already installed on your computer, you can download an installer from the Python website.
  • Use pip to install the following modules:
    • awscli
    • boto3
    • jupyter
    • matplotlib
    • sklearn
    • sagemaker

    For more information about installing modules, see Installing Python Modules in the Python 3.X Documentation.

  • Configure the AWS Command Line Interface (AWS CLI). During the configuration process, you have to specify a default AWS Region. This solution uses Amazon Sagemaker to build a model, so the Region that you specify has to be one that supports Amazon Sagemaker. For a complete list of Regions where Sagemaker is supported, see AWS Service Endpoints in the AWS General Reference. For more information about setting up the AWS CLI, see Configuring the AWS CLI in the AWS Command Line Interface User Guide.
  • Install Git. Git is installed by default on most versions of Linux and macOS. If Git isn’t already installed on your computer, you can download an installer from the Git website.

Step 1: Create an Amazon Pinpoint Project

In this section, you create and configure a project in Amazon Pinpoint. This project contains all of the customers that we will target, as well as the recommendation data that’s associated with each one. Later, we’ll use this data to create segments and campaigns.

To set up the Amazon Pinpoint project

  1. Sign in to the Amazon Pinpoint console at http://console.aws.amazon.com/pinpoint/.
  2. On the All projects page, choose Create a project. Enter a name for the project, and then choose Create.
  3. On the Configure features page, under SMS and voice, choose Configure.
  4. Under General settings, select Enable the SMS channel for this project, and then choose Save changes.
  5. In the navigation pane, under Settings, choose General settings. In the Project details section, copy the value under Project ID. You’ll need this value later.

Step 2: Create an Endpoint

In Amazon Pinpoint, an endpoint represents a specific method of contacting a customer, such as their email address (for email messages) or their phone number (for SMS messages). Endpoints can also contain custom attributes, and you can associate multiple endpoints with a single user. In this example, we use these attributes to store the recommendation data that we receive from Amazon Personalize.

In this section, we create a new endpoint and user by using the AWS CLI. We’ll use this endpoint to test the SMS channel, and to test the recommendations that we receive from Personalize.

To create an endpoint by using the AWS CLI

  1. At the command line, enter the following command:
    aws pinpoint update-endpoint --application-id <project-id> \
    --endpoint-id 12456 --endpoint-request "Address='<mobile-number>', \
    ChannelType='SMS',User={UserAttributes={recommended_items=['none']},UserId='12456'}"

    In the preceding example, replace <project-id> with the Amazon Pinpoint project ID that you copied in Step 1. Replace <mobile-number> with your phone number, formatted in E.164 format (for example, +12065550142).

Note that this endpoint contains hard-coded UserId and EndpointId values of 12456. These IDs match an ID that we’ll create later when we generate the Personalize data set.

Step 3: Create a Segment and Campaign in Amazon Pinpoint

Now that we have an endpoint, we need to add it to a segment so that we can use it within a campaign. By sending a campaign, we can verify that our Pinpoint project is configured correctly, and that we created the endpoint correctly.

To create the segment and campaign

  1. Open the Pinpoint console at http://console.aws.amazon.com/pinpoint, and then choose the project that you created in Step 1.
  2. In the navigation pane, choose Segments, and then choose Create a segment.
  3. Name the segment “No recommendations”. Under Segment group 1, on the Add a filter menu, choose Filter by user.
  4. On the Choose a user attribute menu, choose recommended-items. Set the value of the filter to “none”.
  5. Confirm that the Segment estimate section shows that there is one eligible endpoint, and then choose Create segment.
  6. In the navigation pane, choose Campaigns, and then choose Create a campaign.
  7. Name the campaign “SMS to users with no recommendations”. Under Choose a channel for this campaign, choose SMS, and then choose Next.
  8. On the Choose a segment page, choose the “No recommendations” segment that you just created, and then choose Next.
  9. In the message editor, type a test message, and then choose Next.
  10. On the Choose when to send the campaign page, keep all of the default values, and then choose Next.
  11. On the Review and launch page, choose Launch campaign. Within a few seconds, you receive a text message at the phone number that you specified when you created the endpoint.

Step 4: Load sample data into Amazon Personalize

At this point, we’ve finished setting up Amazon Pinpoint. Now we can start loading data into Amazon Personalize.

To load the data into Amazon Personalize

  1. At the command line, enter the following command to clone the sample data and Jupyter Notebooks to your computer:
    git clone https://github.com/markproy/personalize-car-search.git

  2. At the command line, change into the directory that contains the data that you just cloned. Enter the following command:
    jupyter notebook

    A new window opens in your web browser.

  3. In your web browser, open the first notebook (01_generate_data.ipynb). On the Cell menu, choose Run all. Wait for the commands to finish running.
  4. Open the second notebook (02_make_dataset_group.ipynb). In the first step, replace the value of the account_id variable with the ID of your AWS account. Then, on the Cell menu, choose Run all. This step takes several minutes to complete. Make sure that all of the commands have run successfully before you proceed to the next step.
  5. Open the third notebook (03_make_campaigns.ipynb). In the first step, replace the value of the account_id variable with the ID of your AWS account. Then, on the Cell menu, choose Run all. This step takes several minutes to complete. Make sure that all of the commands have run successfully before you proceed to the next step.
  6. Open the fourth notebook (04_use_the_campaign.ipynb). In the first step, replace the value of the account_id variable with the ID of your AWS account. Then, on the Cell menu, choose Run all. This step takes several minutes to complete.
  7. After the fourth notebook is finished running, choose Quit to terminate the Jupyter Notebook. You don’t need to run the fifth notebook for this example.
  8. Open the Amazon Personalize console at http://console.aws.amazon.com/personalize. Verify that Amazon Personalize contains one dataset group named car-dg.
  9. In the navigation pane, choose Campaigns. Verify that it contains all of the following campaigns, and that the status for each campaign is Active:
    • car-popularity-count
    • car-personalized-ranking
    • car-hrnn-metadata
    • car-sims
    • car-hrnn

Step 5: Create the Lambda function

We’ve loaded the data into Amazon Personalize, and now we need to create a Lambda function to update the endpoint attributes in Pinpoint with the recommendations provided by Personalize.

The version of the AWS SDK for Python that’s included with Lambda doesn’t include the libraries for Amazon Personalize. For this reason, you need to download these libraries to your computer, put them in a .zip file, and upload the entire package to Lambda.

To create the Lambda function

  1. In a text editor, create a new file. Paste the following code.
    # Copyright 2010-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
    #
    # This file is licensed under the Apache License, Version 2.0 (the "License").
    # You may not use this file except in compliance with the License. A copy of the
    # License is located at
    #
    # http://aws.amazon.com/apache2.0/
    #
    # This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS
    # OF ANY KIND, either express or implied. See the License for the specific
    # language governing permissions and limitations under the License.
    
    AWS_REGION = "<region>"
    PROJECT_ID = "<project-id>"
    CAMPAIGN_ARN = "<car-hrnn-campaign-arn>"
    USER_ID = "12456"
    endpoint_id = USER_ID
    
    from datetime import datetime
    import json
    import boto3
    import logging
    from botocore.exceptions import ClientError
    
    DATE = datetime.now()
    
    personalize           = boto3.client('personalize')
    personalize_runtime   = boto3.client('personalize-runtime')
    personalize_events    = boto3.client('personalize-events')
    pinpoint              = boto3.client('pinpoint')
    
    def lambda_handler(event, context):
        itemList = get_recommended_items(USER_ID,CAMPAIGN_ARN)
        response = update_pinpoint_endpoint(PROJECT_ID,endpoint_id,itemList)
    
        return {
            'statusCode': 200,
            'body': json.dumps('Lambda execution completed.')
        }
    
    def get_recommended_items(user_id, campaign_arn):
        response = personalize_runtime.get_recommendations(campaignArn=campaign_arn, 
                                                           userId=str(user_id), 
                                                           numResults=10)
        itemList = response['itemList']
        return itemList
    
    def update_pinpoint_endpoint(project_id,endpoint_id,itemList):
        itemlistStr = []
        
        for item in itemList:
            itemlistStr.append(item['itemId'])
    
        pinpoint.update_endpoint(
        ApplicationId=project_id,
        EndpointId=endpoint_id,
        EndpointRequest={
                            'User': {
                                'UserAttributes': {
                                    'recommended_items': 
                                        itemlistStr
                                }
                            }
                        }
        )    
    
        return
    

    In the preceding code, make the following changes:

    • Replace <region> with the name of the AWS Region that you want to use, such as us-east-1.
    • Replace <project-id> with the ID of the Amazon Pinpoint project that you created earlier.
    • Replace <car-hrnn-campaign-arn> with the Amazon Resource Name (ARN) of the car-hrnn campaign in Amazon Personalize. You can find this value in the Amazon Personalize console.
  2. Save the file as pue-get-recs.py.
  3. Create and activate a virtual environment. In the virtual environment, use pip to download the latest versions of the boto3 and botocore libraries. For complete procedures, see Updating a Function with Additional Dependencies With a Virtual Environment in the AWS Lambda Developer Guide. Also, add the pue-get-recs.py file to the .zip file that contains the libraries.
  4. Open the IAM console at http://console.aws.amazon.com/iam. Create a new role. Attach the following policy to the role:
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "logs:CreateLogStream",
                    "logs:DescribeLogGroups",
                    "logs:CreateLogGroup",
                    "logs:PutLogEvents",
                    "personalize:GetRecommendations",
                    "mobiletargeting:GetUserEndpoints",
                    "mobiletargeting:GetApp",
                    "mobiletargeting:UpdateEndpointsBatch",
                    "mobiletargeting:GetApps",
                    "mobiletargeting:GetEndpoint",
                    "mobiletargeting:GetApplicationSettings",
                    "mobiletargeting:UpdateEndpoint"
                ],
                "Resource": "*"
            }
        ]
    }
    
  5. Open the Lambda console at http://console.aws.amazon.com/lambda, and then choose Create function.
  6. Create a new Lambda function from scratch. Choose the Python 3.7 runtime. Under Permissions, choose Use an existing role, and then choose the IAM role that you just created. When you finish, choose Create function.
  7. Upload the .zip file that contains the Lambda function and the boto3 and botocore libraries.
  8. Under Function code, change the Handler value to pue-get-recs.lambda_handler. Save your changes.

When you finish creating the function, you can test it to make sure it was set up correctly.

To test the Lambda function

  1. On the Select a test event menu, choose Configure test events. On the Configure test events window, specify an Event name, and then choose Create.
  2. Choose the Test button to execute the function.
  3. If the function executes successfully, open the Amazon Pinpoint console at http://console.aws.amazon.com/pinpoint.
  4. In the navigation pane, choose Segments, and then choose the “No recommendations” segment that you created earlier. Verify that the number under total endpoints is 0. This is the expected value; the segment is filtered to only include endpoints with no recommendation attributes, but when you ran the Lambda function, it added recommendations to the test endpoint.

Step 7: Create segments and campaigns based on recommended items

In this section, we’ll create a targeted segment based on the recommendation data provided by our Personalize dataset. We’ll then use that segment to create a campaign.

To create a segment and campaign based on personalized recommendations

  1. Open the Amazon Pinpoint console at http://console.aws.amazon.com/pinpoint. On the All projects page, choose the project that you created earlier.
  2. In the navigation pane, choose Segments, and then choose Create a segment. Name the new segment “Recommendations for product 26304”.
  3. Under Segment group 1, on the Add a filter menu, choose Filter by user. On the Choose a user attribute menu, choose recommended-items. Set the value of the filter to “26304”. Confirm that the Segment estimate section shows that there is one eligible endpoint, and then choose Create segment.
  4. In the navigation pane, choose Campaigns, and then choose Create a campaign.
  5. Name the campaign “SMS to users with recommendations for product 26304”. Under Choose a channel for this campaign, choose SMS, and then choose Next.
  6. On the Choose a segment page, choose the “Recommendations for product 26304” segment that you just created, and then choose Next.
  7. In the message editor, type a test message, and then choose Next.
  8. On the Choose when to send the campaign page, keep all of the default values, and then choose Next.
  9. On the Review and launch page, choose Launch campaign. Within a few seconds, you receive a text message at the phone number that you specified when you created the endpoint.

Next steps

Your PUE solution is now ready to use. From here, there are several ways that you can make the solution your own:

  • Expand your usage: If you plan to continue sending SMS messages, you should request a spending limit increase.
  • Extend to additional channels: This post showed the process of setting up an SMS campaign. You can add more endpoints—for the email or push notification channels, for example—and associate them with your users. You can then create new segments and new campaigns in those channels.
  • Build your own model: This post used a sample data set, but Amazon Personalize makes it easy to provide your own data. To start building a model with Personalize, you have to provide a data set that contains information about your users, items, and interactions. To learn more, see Getting Started in the Amazon Personalize Developer Guide.
  • Optimize your model: You can enrich your model by sending your mobile, web, and campaign engagement data to Amazon Personalize. In Pinpoint, you can use event streaming to move data directly to S3, and then use that data to retrain your Personalize model. To learn more about streaming events, see Streaming App and Campaign Events in the Amazon Pinpoint User Guide.
  • Update your recommendations on a regular basis: Use the create-campaign API to create a new recurring campaign. Rather than sending messages, include the hook property with a reference to the ARN of the pue-get-recs function. By completing this step, you can configure Pinpoint to retrieve the most up-to-date recommendation data each time the campaign recurs. For more information about using Lambda to modify segments, see Customizing Segments with AWS Lambda in the Amazon Pinpoint Developer Guide.

Forward Incoming Email to an External Destination

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/forward-incoming-email-to-an-external-destination/

Note: This post was written by Vesselin Tzvetkov, an AWS Senior Security Architect, and by Rostislav Markov, an AWS Senior Engagement Manager.


Amazon SES has included support for incoming email for several years now. However, some customers have told us that they need a solution for forwarding inbound emails to domains that aren’t managed by Amazon SES. In this post, you’ll learn how to use Amazon SES, Amazon S3, and AWS Lambda to create a solution that forwards incoming email to an email address that’s managed outside of Amazon SES.

Architecture

This solution uses several AWS services to forward incoming emails to a single, external email address. The following diagram shows the flow of information in this solution.

The following actions occur in this solution:

  1. A new email is sent from an external sender to your domain. Amazon SES handles the incoming email for your domain.
  2. An Amazon SES receipt rule saves the incoming message in an S3 bucket.
  3. An Amazon SES receipt rule triggers the execution of a Lambda function.
  4. The Lambda function retrieves the message content from S3, and then creates a new message and sends it to Amazon SES.
  5. Amazon SES sends the message to the destination server.

Limitations

This solution works in all AWS Regions where Amazon SES is currently available. For a complete list of supported Regions, see AWS Service Endpoints in the AWS General Reference.

Prerequisites

In order to complete this procedure, you need to have a domain that receives incoming email. If you don’t already have a domain, you can purchase one through Amazon Route 53. For more information, see Registering a New Domain in the Amazon Route 53 Developer Guide. You can also purchase a domain through one of several third-party vendors.

Procedures

Step 1: Set up Your Domain

  1. In Amazon SES, verify the domain that you want to use to receive incoming email. For more information, see Verifying Domains in the Amazon SES Developer Guide.
  2. Add the following MX record to the DNS configuration for your domain:
    10 inbound-smtp.<regionInboundUrl>.amazonaws.com

    Replace <regionInboundUrl> with the URL of the email receiving endpoint for the AWS Region that you use Amazon SES in. For a complete list of URLs, see AWS Service Endpoints – Amazon SES in the AWS General Reference.

  3. If your account is still in the Amazon SES sandbox, submit a request to have it removed. For more information, see Moving Out of the Sandbox in the Amazon SES Developer Guide.

Step 2: Configure Your S3 Bucket

  1. In Amazon S3, create a new bucket. For more information, see Create a Bucket in the Amazon S3 Getting Started Guide.
  2. Apply the following policy to the bucket:
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Sid": "AllowSESPuts",
                "Effect": "Allow",
                "Principal": {
                    "Service": "ses.amazonaws.com"
                },
                "Action": "s3:PutObject",
                "Resource": "arn:aws:s3:::<bucketName>/*",
                "Condition": {
                    "StringEquals": {
                        "aws:Referer": "<awsAccountId>"
                    }
                }
            }
        ]
    }

  3. In the policy, make the following changes:
    • Replace <bucketName> with the name of your S3 bucket.
    • Replace <awsAccountId> with your AWS account ID.

    For more information, see Using Bucket Policies and User Policies in the Amazon S3 Developer Guide.

Step 3: Create an IAM Policy and Role

  1. Create a new IAM Policy with the following permissions:
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Sid": "VisualEditor0",
                "Effect": "Allow",
                "Action": [
                    "logs:CreateLogStream",
                    "logs:CreateLogGroup",
                    "logs:PutLogEvents"
                ],
                "Resource": "*"
            },
            {
                "Sid": "VisualEditor1",
                "Effect": "Allow",
                "Action": [
                    "s3:GetObject",
                    "ses:SendRawEmail"
                ],
                "Resource": [
                    "arn:aws:s3:::<bucketName>/*",
                    "arn:aws:ses:<region>:<awsAccountId>:identity/*"
                ]
            }
        ]
    }

    In the preceding policy, make the following changes:

    • Replace <bucketName> with the name of the S3 bucket that you created earlier.
    • Replace <region> with the name of the AWS Region that you created the bucket in.
    • Replace <awsAccountId> with your AWS account ID. For more information, see Create a Customer Managed Policy in the IAM User Guide.
  2. Create a new IAM role. Attach the policy that you just created to the new role. For more information, see Creating Roles in the IAM User Guide.

Step 4: Create the Lambda Function

  1. In the Lambda console, create a new Python 3.7 function from scratch. For the execution role, choose the IAM role that you created earlier.
  2. In the code editor, paste the following code.
    # Copyright 2010-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
    #
    # This file is licensed under the Apache License, Version 2.0 (the "License").
    # You may not use this file except in compliance with the License. A copy of the
    # License is located at
    #
    # http://aws.amazon.com/apache2.0/
    #
    # This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS
    # OF ANY KIND, either express or implied. See the License for the specific
    # language governing permissions and limitations under the License.
    
    import os
    import boto3
    import email
    import re
    from botocore.exceptions import ClientError
    from email.mime.multipart import MIMEMultipart
    from email.mime.text import MIMEText
    from email.mime.application import MIMEApplication
    
    region = os.environ['Region']
    
    def get_message_from_s3(message_id):
    
        incoming_email_bucket = os.environ['MailS3Bucket']
        incoming_email_prefix = os.environ['MailS3Prefix']
    
        if incoming_email_prefix:
            object_path = (incoming_email_prefix + "/" + message_id)
        else:
            object_path = message_id
    
        object_http_path = (f"http://s3.console.aws.amazon.com/s3/object/{incoming_email_bucket}/{object_path}?region={region}")
    
        # Create a new S3 client.
        client_s3 = boto3.client("s3")
    
        # Get the email object from the S3 bucket.
        object_s3 = client_s3.get_object(Bucket=incoming_email_bucket,
            Key=object_path)
        # Read the content of the message.
        file = object_s3['Body'].read()
    
        file_dict = {
            "file": file,
            "path": object_http_path
        }
    
        return file_dict
    
    def create_message(file_dict):
    
        sender = os.environ['MailSender']
        recipient = os.environ['MailRecipient']
    
        separator = ";"
    
        # Parse the email body.
        mailobject = email.message_from_string(file_dict['file'].decode('utf-8'))
    
        # Create a new subject line.
        subject_original = mailobject['Subject']
        subject = "FW: " + subject_original
    
        # The body text of the email.
        body_text = ("The attached message was received from "
                  + separator.join(mailobject.get_all('From'))
                  + ". This message is archived at " + file_dict['path'])
    
        # The file name to use for the attached message. Uses regex to remove all
        # non-alphanumeric characters, and appends a file extension.
        filename = re.sub('[^0-9a-zA-Z]+', '_', subject_original) + ".eml"
    
        # Create a MIME container.
        msg = MIMEMultipart()
        # Create a MIME text part.
        text_part = MIMEText(body_text, _subtype="html")
        # Attach the text part to the MIME message.
        msg.attach(text_part)
    
        # Add subject, from and to lines.
        msg['Subject'] = subject
        msg['From'] = sender
        msg['To'] = recipient
    
        # Create a new MIME object.
        att = MIMEApplication(file_dict["file"], filename)
        att.add_header("Content-Disposition", 'attachment', filename=filename)
    
        # Attach the file object to the message.
        msg.attach(att)
    
        message = {
            "Source": sender,
            "Destinations": recipient,
            "Data": msg.as_string()
        }
    
        return message
    
    def send_email(message):
        aws_region = os.environ['Region']
    
    # Create a new SES client.
        client_ses = boto3.client('ses', region)
    
        # Send the email.
        try:
            #Provide the contents of the email.
            response = client_ses.send_raw_email(
                Source=message['Source'],
                Destinations=[
                    message['Destinations']
                ],
                RawMessage={
                    'Data':message['Data']
                }
            )
    
        # Display an error if something goes wrong.
        except ClientError as e:
            output = e.response['Error']['Message']
        else:
            output = "Email sent! Message ID: " + response['MessageId']
    
        return output
    
    def lambda_handler(event, context):
        # Get the unique ID of the message. This corresponds to the name of the file
        # in S3.
        message_id = event['Records'][0]['ses']['mail']['messageId']
        print(f"Received message ID {message_id}")
    
        # Retrieve the file from the S3 bucket.
        file_dict = get_message_from_s3(message_id)
    
        # Create the message.
        message = create_message(file_dict)
    
        # Send the email and print the result.
        result = send_email(message)
        print(result)
  3. Create the following environment variables for the Lambda function:

    KeyValue
    MailS3BucketThe name of the S3 bucket that you created earlier.
    MailS3PrefixThe path of the folder in the S3 bucket where you will store incoming email.
    MailSenderThe email address that the forwarded message will be sent from. This address has to be verified.
    MailRecipientThe address that you want to forward the message to.
    RegionThe name of the AWS Region that you want to use to send the email.
  4. Under Basic settings, set the Timeout value to 30 seconds.

(Optional) Step 5: Create an Amazon SNS Topic

You can optionally create an Amazon SNS topic. This step is helpful for troubleshooting purposes, or if you just want to receive additional notifications when you receive a message.

  1. Create a new Amazon SNS topic. For more information, see Creating a Topic in the Amazon SNS Developer Guide.
  2. Subscribe an endpoint, such as an email address, to the topic. For more information, see Subscribing an Endpoint to a Topic in the Amazon SNS Developer Guide.

Step 6: Create a Receipt Rule Set

  1. In the Amazon SES console, create a new Receipt Rule Set. For more information, see Creating a Receipt Rule Set in the Amazon SES Developer Guide.
  2. In the Receipt Rule Set that you just created, add a Receipt Rule. In the Receipt Rule, add an S3 Action. Set up the S3 Action to send your email to the S3 bucket that you created earlier.
  3. Add a Lambda action to the Receipt Rule. Configure the Receipt Rule to invoke the Lambda function that you created earlier.

For more information, see Setting Up a Receipt Rule in the Amazon SES Developer Guide.

Step 7: Test the Function

  • Send an email to an address that corresponds with an address in the Receipt Rule you created earlier. Make sure that the email arrives in the correct S3 bucket. In a minute or two, the email arrives in the inbox that you specified in the MailRecipient variable of the Lambda function.

Troubleshooting

If you send a test message, but it is never forwarded to your destination email address, do the following:

  • Make sure that the Amazon SES Receipt Rule is active.
  • Make sure that the email address that you specified in the MailRecipient variable of the Lambda function is correct.
  • Subscribe an email address or phone number to the SNS topic. Send another test email to your domain. Make sure that SNS sends a Received notification to your subscribed email address or phone number.
  • Check the CloudWatch Log for your Lambda function to see if any errors occurred.

If you send a test email to your receiving domain, but you receive a bounce notification, do the following:

  • Make sure that the verification process for your domain completed successfully.
  • Make sure that the MX record for your domain specifies the correct Amazon SES receiving endpoint.
  • Make sure that you’re sending to an address that is handled by the receipt rule.

Costs of using this solution

The cost of implementing this solution is minimal. If you receive 10,000 emails per month, and each email is 2KB in size, you pay $1.00 for your use of Amazon SES. For more information, see Amazon SES Pricing.

You also pay a small charge to store incoming emails in Amazon S3. The charge for storing 1,000 emails that are each 2KB in size is less than one cent. Your use of Amazon S3 might qualify for the AWS Free Usage Tier. For more information, see Amazon S3 Pricing.

Finally, you pay for your use of AWS Lambda. With Lambda, you pay for the number of requests you make, for the amount of compute time that you use, and for the amount of memory that you use. If you use Lambda to forward 1,000 emails that are each 2KB in size, you pay no more than a few cents. Your use of AWS Lambda might qualify for the AWS Free Usage Tier. For more information, see AWS Lambda Pricing.

Note: These cost estimates don’t include the costs associated with purchasing a domain, since many users already have their own domains. The cost of obtaining a domain is the most expensive part of implementing this solution.

Conclusion

This solution makes it possible to forward incoming email from one of your Amazon SES verified domains to an email address that isn’t necessarily verified. It’s also useful if you have multiple AWS accounts, and you want incoming messages to be sent from each of those accounts to a single destination. We hope you’ve found this tutorial to be helpful!

Sending Push Notifications to iOS 13 and watchOS 6 Devices

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/sending-push-notifications-to-ios-13-and-watchos-6-devices/

Last week, we made some changes to the way that Amazon Pinpoint sends Apple Push Notification service (APNs) push notifications.

In June, Apple announced that push notifications sent to iOS 13 and watchOS devices would require the new apns-push-type header. APNs uses this header to determine whether a notification should be shown on the display of the recipient’s device, or if it should be sent to the background.

When you use Amazon Pinpoint to send an APNs message, you can choose whether you want to send the message as a standard message, or as a silent notification. Amazon Pinpoint uses your selection to determine which value to apply to the apns-push-type header: if you send the message as a standard message, Amazon Pinpoint automatically sets the value of the apns-push-type header to alert; if you send the message as a silent notification, it sets the apns-push-type header to silent. Amazon Pinpoint applies these settings automatically—you don’t have to do any additional work in order to send messages to recipients with iOS 13 and watchOS 6 devices.

One last thing to keep in mind: if you specify the raw content of an APNs push notification, the message payload has to include the content-available key. The value of the content-available key has to be an integer, and can only be 0 or 1. If you’re sending an alert, set the value of content-available to 0. If you’re sending a background notification, set the value of content-available to 1. Additionally, background notification payloads can’t include the alert, badge, or sound keys.

To learn more about sending APNs notifications, see Generating a Remote Notification and Pushing Background Updates to Your App on the Apple Developer website.

Creating static custom domain endpoints with Amazon MQ to simplify broker modification and scaling

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/creating-static-custom-domain-endpoints-with-amazon-mq/

This post is courtesy of Wallace Printz, Senior Solutions Architect, AWS, and Christian Mueller, Senior Solutions Architect, AWS.

Many cloud-native application architectures take advantage of the point-to-point and publish-subscribe (“pub-sub”) model of message-based communication between application components. This architecture is generally more resilient to failure because of the loose coupling and because message processing failures can be retried. It’s also more efficient because individual application components can independently scale up or down to maintain message-processing SLAs, compared to monolithic application architectures. Synchronous (REST-based) systems are tightly coupled. A problem in a synchronous downstream dependency has an immediate impact on the upstream callers.

Retries from upstream callers can all too easily fan out and amplify problems. Amazon SQS and Amazon SNS are fully managed message queuing services, but are not necessarily the right tool for the job in some cases. For applications requiring messaging protocols including JMS, NMS, AMQP, STOMP, MQTT, and WebSocket, Amazon provides Amazon MQ. Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud.

Amazon MQ provides two managed broker deployment connection options: public brokers and private brokers. Public brokers receive internet-accessible IP addresses, while private brokers receive only private IP addresses from the corresponding CIDR range in their VPC subnet.

In some cases, for security purposes, you may prefer to place brokers in a private subnet. You can also allow access to the brokers through a persistent public endpoint, such as a subdomain of their corporate domain like mq.example.com.

In this post, we explain how to provision private Amazon MQ brokers behind a secure public load balancer endpoint using an example subdomain.

Architecture overview

There are several reasons one might want to deploy this architecture beyond the security aspects.

First, human-readable URLs are easier for people to parse when reviewing operations and troubleshooting, such as deploying updates to mq-dev.example.com before mq-prod.example.com.

Second, maintaining static URLs for your brokers helps reduce the necessity of modifying client code when performing maintenance on the brokers.

Third, this pattern allows you to vertically scale your brokers without changing the client code or even notifying the clients that changes have been made.

Finally, the same architecture described here works for a network of brokers configuration as well, whereby you could horizontally scale your brokers without impacting the client code.

Prerequisites

This blog post assumes some familiarity with AWS networking fundamentals, such as VPCs, subnets, load balancers, and Amazon Route 53.

When you are finished, the architecture should be set up as shown in the following diagram. For ease of visualization, we demonstrate with a pair of brokers using the active-standby option.

Solution Overview

Amazon MQ solution overview

The client to broker traffic flow is as follows.

  • First, the client service tries to connect with a failover URL to the domain endpoint setup in Route 53. If a client loses the connection, using the failover URL allows the client to automatically try to reconnect to the broker.
  • The client looks up the domain name from Route 53, and Route 53 returns the IP address of the Network Load Balancer.
  • The client creates a secure socket layer (SSL) connection to the Network Load Balancer with an SSL certificate provided from AWS Certificate Manager (ACM). The Network Load Balancer selects from the healthy brokers in its target group and creates a separate SSL connection between the Network Load Balancer and the broker. This provides secure, end-to-end SSL encrypted messaging between client and brokers.

In this diagram, the healthy broker connection is shown in the solid line. The standby broker, which does not reply to connection requests and is therefore marked as unhealthy in the target group, is shown in the dashed line.

Solution walkthrough

To build this architecture, build the network segmentation first, then the Amazon MQ brokers, and finally the network routing.

Setup

First, you need the following resources:

  • A VPC
  • One private subnet per Availability Zone
  • One public subnet for your bastion host (if desired)

This demonstration VPC uses the 20.0.0.0/16 CIDR range.

Additionally, you must create a custom security group for your brokers. Set up this security group to allow traffic from your Network Load Balancer and, if using a network of brokers, among the brokers as well.

This VPC is not being used for any other workloads. This demonstration allows all incoming traffic originating within the VPC, including the Network Load Balancer, through to the brokers on the following ports:

  • OpenWire communication port of 61617
  • Apache ActiveMQ console port of 8162

If you are using a different protocol, adjust the port numbers accordingly.

Create an amazon mq security group

Building the Amazon MQ brokers

Now that you have the network segmentation set up, build the Amazon MQ brokers. As mentioned previously, this demonstration uses the active-standby pair of private brokers option.

Configure the broker settings by selecting a broker name, instance type, ActiveMQ console user, and password first.

Configure Amazon MQ broker settings

In the Additional Settings area, place the brokers in your previously selected VPC and the associated private subnets.

Configure Amazon MQ additional settings

Finally, select the existing Security Group previously discussed, and make sure that the Public Accessibility option is set to No.

Set Amazon MQ security group settings

That’s it for the brokers. When it is done provisioning, the Amazon MQ dashboard should look like the one shown in the following screenshot. Note the IP addresses of the brokers and the ActiveMQ web console URLs for later.

Amazon MQ dashboard

Configuring a Load Balancer Target Group

The next step in the build process is to configure the load balancer’s target group. This demonstration uses the private IP addresses of the brokers as targets for the Network Load Balancer.

Create and name a target group, select the IP option under Target type, and make sure to select TLS under Protocol and 61617 under Port, as well as the VPC in which your brokers reside. It is important to configure the health check settings so traffic is only routed to active brokers by selecting the TCP protocol and overriding the health check port to 8162, the Apache ActiveMQ console port.

Do not use the OpenWire port as the target group health check port. Because the Network Load Balancer may not be able to recognize the host as healthy on that port, it is better to use the ActiveMQ web console port.

Next, add the brokers’ IP addresses as targets. You can find the broker IP addresses in the Amazon MQ console page after they complete provisioning. Make sure to add both the active and the standby broker to the target group so that when reboots occur, the Network Load Balancer routes traffic to whichever broker is active.

You may be pursuing a more dynamic environment for scaling brokers up and down to handle the demands of a variable message load. In that case, as you scale to add more brokers, make sure that you also add them to the target group.

AWS Lambda would be a great way to programmatically handle adding or removing the broker’s IP addresses to this target group automatically.

Creating a Network Load Balancer

Next, create a Network Load Balancer. This demo uses an internet-facing load balancer with TLS listeners on port 61617, and routes traffic to brokers’ VPC and private subnets.

Configure a network load balancer

Clients must securely connect to the Network Load Balancer, so this demo uses an ACM certificate for the subdomain registered in Route 53, such as mq.example.com. For simplicity, ACM certificate provisioning is not shown. For more information, see Request a Public Certificate.

Make sure that the ACM certificate is provisioned in the same Region as your Network Load Balancer, or the certificate is not displayed in the selection menu.

Next, select the target group that you just created, and select TLS for the connection between the Network Load Balancer and the brokers. Similarly, select the health checks on TCP port 8162.

If all went well, you see the list of brokers’ IP addresses listed as targets. From here, review your settings and confirm you’d like to deploy the Network Load Balancer.

Configuring Route 53

The last step in this build is to configure Route 53 to serve traffic at the subdomain of your choice to your Network Load Balancer.

Go to your Route 53 Hosted Zone, and create a new subdomain record set, such as mq.example.com, that matches the ACM certificate that you previously created. In the Type field, select A – IPv4 address, then select Yes for Alias. This allows you to select the Network Load Balancer as the alias target. Select the Network Load Balancer that you just created from the Alias Target menu and save the record set.

Testing broker connectivity

And that’s it!

There’s an important advantage to this architecture. When you create Amazon MQ active-standby brokers, the Amazon MQ service provides two endpoints. Only one broker host is active at a time, and when configuration changes or other reboot events occur, the standby broker becomes active and the active broker goes to standby. The typical connection string when there is an option to connect to multiple brokers is something similar to the following string

"failover:(ssl://b-ce452fbe-2581-4003-8ce2-4185b1377b43-1.mq.us-west-2.amazonaws.com:61617,ssl://b-ce452fbe-2581-4003-8ce2-4185b1377b43-2.mq.us-west-2.amazonaws.com:61617)"

In this architecture, you use only a single connection URL, but you still want to use the failover protocol to force re-connection if the connection is dropped for any reason.

For ease of use, this solution relies on the Amazon MQ workshop client application code from re:Invent 2018. To test this solution setting the connection URL to the following:

"failover:(ssl://mq.example.com:61617

Run the producer and consumer clients in separate terminal windows.

The messages are sent and received successfully across the internet, while the brokers are hidden behind the Network Load Balancer.

Logging into the broker’s ActiveMQ console

But what if we want to log in to the broker’s ActiveMQ web console?

There are three options. Due to the security group rules, only traffic originating from inside the VPC is allowed to the brokers.

  • Use a VPN connection from the corporate network to the VPC. Most customers likely use this option, but for rapid testing, there is a simple and cost-effective method.
  • Connect to the brokers’ web console through a Route 53 subdomain, which requires creating a separate port 8162 Listener on the existing Network Load Balancer and creating a separate TLS target group on port 8162 for the brokers.
  • Use a bastion host to proxy traffic to the web console.

To use a bastion host, create a small Linux EC2 instance in your public subnet, and make sure that:

  • The EC2 instance has a public IP address.
  • You have access to the SSH key pair.
  • It is placed in a security group that allows SSH port 22 traffic from your location.

For simplicity, this step is not shown, but this demonstration uses a t3.micro Amazon Linux 2 host with all default options as the bastion.

Creating a forwarding tunnel

Next, create a forwarding tunnel through an SSH connection to the bastion host. Below is an example command in the terminal window. This keeps a persistent SSH connection forwarding port 8162 through the bastion host at the public IP address 54.244.188.53.

For example, the command could be:

ssh -D 8162 -C -q -N -I <my-key-pair-name>.pem [email protected]<ec2-ip-address>

You can also configure a browser to tunnel traffic through your proxy.

We have chosen to demonstrate in Firefox. Configure the network settings to use a manual proxy on localhost on the Apache ActiveMQ console port of 8162.  This can be done by opening the Firefox Connection Settings.  In the Configure Proxy Access to the Internet section, select Manual proxy configuration, then set the SOCKS Host to localhost and Port to 8162, leaving other fields empty.

Finally, use the Apache ActiveMQ console URL provided in the Amazon MQ web console details page to connect to the broker through the proxy.

ActiveMQ screenshot

Conclusion

Congratulations! You’ve successfully built a highly available Amazon MQ broker pair in a private subnet. You’ve layered your security defense by putting the brokers behind a highly scalable Network Load Balancer, and you’ve configured routing from a single custom subdomain URL to multiple brokers with health check built in.

To learn more about Amazon MQ and scalable broker communication patterns, we highly recommend the following resources:

Keep on building!

Creating custom Pinpoint dashboards using Amazon QuickSight, part 3

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/creating-custom-pinpoint-dashboards-using-amazon-quicksight-part-3/

Note: This post was written by Manan Nayar and Aprajita Arora, Software Development Engineers on the AWS Digital User Engagement team.


This is the third and final post in our series about creating custom visualizations of your Amazon Pinpoint metrics using Amazon QuickSight.

In our first post, we used the Metrics APIs to retrieve specific Key Performance Indicators (KPIs), and then created visualizations using QuickSight. In the second post, we used the event stream feature in Amazon Pinpoint to enable more in-depth analyses.

The examples in the first two posts used Amazon S3 to store the metrics that we retrieved from Amazon Pinpoint. This post takes a different approach, using Amazon Redshift to store the data. By using Redshift to store this data, you gain the ability to create visualizations on large data sets. This example is useful in situations where you have a large volume of event data, and situations where you need to store your data for long periods of time.

Step 1: Provision the storage

The first step in setting up this solution is to create the destinations where you’ll store the Amazon Pinpoint event data. Since we’ll be storing the data in Amazon Redshift, we need to create a new Redshift cluster. We’ll also create an S3 bucket, which will house the original event data that’s streamed from Amazon Pinpoint.

To create the Redshift cluster and the S3 bucket

  1. Create a new Redshift cluster. To learn more, see the Amazon Redshift Getting Started Guide.
  2. Create a new table in the Redshift cluster that contains the appropriate columns. Use the following query to create the table:
    create table if not exists pinpoint_events_table(
      rowid varchar(255) not null,
      project_key varchar(100) not null,
      event_type varchar(100) not null,
      event_timestamp timestamp not null,
      campaign_id varchar(100),
      campaign_activity_id varchar(100),
      treatment_id varchar(100),
      PRIMARY KEY (rowid)
    );
  3. Create a new Amazon S3 bucket. For complete procedures, see Create a Bucket in the Amazon S3 Getting Started Guide.

Step 2: Set up the event stream

This example uses the event stream feature of Amazon Pinpoint to send event data to S3. Later, we’ll create a Lambda function that sends the event data to your Redshift cluster when new event data is added to the S3 bucket. This method lets us store the original event data in S3, and transfer a subset of that data to Redshift for analysis.

To set up the event stream

  1. Sign in to the Amazon Pinpoint console at http://console.aws.amazon.com/pinpoint. In the list of projects, choose the project that you want to enable event streaming for.
  2. Under Settings, choose Event stream.
  3. Choose Edit, and then configure the event stream to use Amazon Kinesis Data Firehose. If you don’t already have a Kinesis Data Firehose stream, follow the link to create one in the Kinesis console. Configure the stream to send data to an S3 bucket. For more information about creating streams, see Creating an Amazon Kinesis Data Firehose Delivery Stream.
  4. Under IAM role, choose Automatically create a role. Choose Save.

Step 3: Create the Lambda function

In this section, you create a Lambda function that processes the raw event stream data, and then writes it to a table in your Redshift cluster.
To create the Lambda function:

  1. Download the psycopg2 binary from https://github.com/jkehler/awslambda-psycopg2. This Python library lets you interact with PostgreSQL databases, such as Amazon Redshift. It contains certain libraries that aren’t included in Lambda.
    • Note: This Github repository is not an official AWS-managed repository.
  2. Within the awslambda-psycopg2-master folder, you’ll find a folder called psycopg2-37. Rename the folder to psycopg2 (you may need to delete the existing folder with that name), and then compress the entire folder to a .zip file.
  3. Create a new Lambda function from scratch, using the Python 3.7 runtime.
  4. Upload the psycopg2.zip file that you created in step 1 to Lambda.
  5. In Lambda, create a new function called lambda_function.py. Paste the following code into the function:
    import datetime
    import json
    import re
    import uuid
    import os
    import boto3
    import psycopg2
    from psycopg2 import Error
    
    cluster_redshift = "<clustername>"
    dbname_redshift = "<dbname>"
    user_redshift = "<username>"
    password_redshift = "<password>"
    endpoint_redshift = "<endpoint>"
    port_redshift = "5439"
    table_redshift = "pinpoint_events_table"
    
    # Get the file that contains the event data from the appropriate S3 bucket.
    def get_file_from_s3(bucket, key):
        s3 = boto3.client('s3')
        obj = s3.get_object(Bucket=bucket, Key=key)
        text = obj["Body"].read().decode()
    
        return text
    
    # If the object that we retrieve contains newline-delineated JSON, split it into
    # multiple objects.
    def clean_and_split(json_raw):
        json_delimited = re.sub('}\s{','}---X-DELIMITER---{',json_raw)
        json_clean = re.sub('\s+','',json_delimited)
        data = json_clean.split("---X-DELIMITER---")
    
        return data
    
    # Set all of the variables that we'll use to create the new row in Redshift.
    def set_variables(in_json):
    
        for line in in_json:
            content = json.loads(line)
            app_id = content['application']['app_id']
            event_type = content['event_type']
            event_timestamp = datetime.datetime.fromtimestamp(content['event_timestamp'] / 1e3).strftime('%Y-%m-%d %H:%M:%S')
    
            if (content['attributes'].get('campaign_id') is None):
                campaign_id = ""
            else:
                campaign_id = content['attributes']['campaign_id']
    
            if (content['attributes'].get('campaign_activity_id') is None):
                campaign_activity_id = ""
            else:
                campaign_activity_id = content['attributes']['campaign_activity_id']
    
            if (content['attributes'].get('treatment_id') is None):
                treatment_id = ""
            else:
                treatment_id = content['attributes']['treatment_id']
    
            write_to_redshift(app_id, event_type, event_timestamp, campaign_id, campaign_activity_id, treatment_id)
                
    # Write the event stream data to the Redshift table.
    def write_to_redshift(app_id, event_type, event_timestamp, campaign_id, campaign_activity_id, treatment_id):
        row_id = str(uuid.uuid4())
    
        query = ("INSERT INTO " + table_redshift + "(rowid, project_key, event_type, "
                + "event_timestamp, campaign_id, campaign_activity_id, treatment_id) "
                + "VALUES ('" + row_id + "', '"
                + app_id + "', '"
                + event_type + "', '"
                + event_timestamp + "', '"
                + campaign_id + "', '"
                + campaign_activity_id + "', '"
                + treatment_id + "');")
    
        try:
            conn = psycopg2.connect(user = user_redshift,
                                    password = password_redshift,
                                    host = endpoint_redshift,
                                    port = port_redshift,
                                    database = dbname_redshift)
    
            cur = conn.cursor()
            cur.execute(query)
            conn.commit()
            print("Updated table.")
    
        except (Exception, psycopg2.DatabaseError) as error :
            print("Database error: ", error)
        finally:
            if (conn):
                cur.close()
                conn.close()
                print("Connection closed.")
    
    # Handle the event notification that we receive when a new item is sent to the 
    # S3 bucket.
    def lambda_handler(event,context):
        print("Received event: \n" + str(event))
    
        bucket = event['Records'][0]['s3']['bucket']['name']
        key = event['Records'][0]['s3']['object']['key']
        data = get_file_from_s3(bucket, key)
    
        in_json = clean_and_split(data)
    
        set_variables(in_json)

    In the preceding code, make the following changes:

    • Replace <clustername> with the name of the cluster.
    • Replace <dbname> with the name of the database.
    • Replace <username> with the user name that you specified when you created the Redshift cluster.
    • Replace <password> with the password that you specified when you created the Redshift cluster.
    • Replace <endpoint> with the endpoint address of the Redshift cluster.
  6. In IAM, update the execution role that’s associated with the Lambda function to include the GetObject permission for the S3 bucket that contains the event data. For more information, see Editing IAM Policies in the AWS IAM User Guide.

Step 4: Set up notifications on the S3 bucket

Now that we’ve created the Lambda function, we’ll set up a notification on the S3 bucket. In this case, the notification will refer to the Lambda function that we created in the previous section. Every time a new file is added to the bucket, the notification will cause the Lambda function to run.

To create the event notification

  1. In S3, create a new bucket notification. The notification should be triggered when PUT events occur, and should trigger the Lambda function that you created in the previous section. For more information about creating notifications, see Configuring Amazon S3 Event Notifications in the Amazon S3 Developer Guide.
  2. Test the event notification by sending a test campaign. If you send an email campaign, your Redshift database should contain events such as _campaign.send, _email.send, _email.delivered, and others. You can check the contents of the Redshift table by running the following query in the Query Editor in the Redshift console:
    select * from pinpoint_events_table;

Step 5: Add the data set in Amazon QuickSight

If your Lambda function is sending event data to Redshift as expected, you can use your Redshift database to create a new data set in Amazon QuickSight. QuickSight includes an automatic database discovery feature that helps you add your Redshift database as a data set with only a few clicks. For more information, see Creating a Data Set from a Database in the Amazon QuickSight User Guide.

Step 6: Create your visualizations

Now that QuickSight is retrieving information from your Redshift database, you can use that data to create visualizations. To learn more about creating visualizations in QuickSight, see Creating an Analysis in the Amazon QuickSight User Guide.

This brings us to the end of our series. While these posts focused on using Amazon QuickSight to visualize your analytics data, you can also use these same techniques to create visualizations using 3rd party applications. We hope you enjoyed this series, and we can’t wait to see what you build using these examples!

Creating custom Pinpoint dashboards using Amazon QuickSight, part 2

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/creating-custom-pinpoint-dashboards-using-amazon-quicksight-part-2/

Note: This post was written by Manan Nayar and Aprajita Arora, Software Development Engineers on the AWS Digital User Engagement team.


In our previous post, we discussed the process of visualizing specific, pre-aggregated Amazon Pinpoint metrics—such as delivery rate or open rate—using the Amazon Pinpoint Metrics APIs. In that example, we showed how to create a Lambda function that retrieves your metrics, and then make those metrics available for creating visualizations in Amazon QuickSight.

This post discusses shows a different approach to exporting data from Amazon Pinpoint and using it to build visualizations. Rather than retrieve specific metrics, you can use the event stream feature in Amazon Pinpoint to export raw event data. You can use this data in Amazon QuickSight to create in-depth analyses of your data, as opposed to visualizing pre-calculated metrics. As an added benefit, when you use this solution, you don’t have to modify any code, and the underlying data is updated every few minutes.

Step 1: Configure the event stream in Amazon Pinpoint

The Amazon Pinpoint event stream includes information about campaign events (such as campaign.send) and application events (such as session.start). It also includes response information related to all of the emails and SMS messages that you send from your Amazon Pinpoint project, regardless of whether they were sent from campaigns or on a transactional basis. When you enable event streams, Amazon Pinpoint automatically sends this data to your S3 bucket (via Amazon Kinesis Data Firehose) every few minutes.

To set up the event stream

  1. Sign in to the Amazon Pinpoint console at http://console.aws.amazon.com/pinpoint. In the list of projects, choose the project that you want to enable event streaming for.
  2. Under Settings, choose Event stream.
  3. Choose Edit, and then configure the event stream to use Amazon Kinesis Data Firehose. If you don’t already have a Kinesis Data Firehose stream, follow the link to create one in the Kinesis console. Configure the stream to send data to an S3 bucket. For more information about creating streams, see Creating an Amazon Kinesis Data Firehose Delivery Stream.
  4. Under IAM role, choose Automatically create a role. Choose Save.

Step 2: Add a data set in Amazon QuickSight

Now that you’ve started streaming your Amazon Pinpoint data to S3, you can set Amazon QuickSight to look for data in the S3 bucket. You connect QuickSight to sources of data by creating data sets.

To create a data set

    1. In a text editor, create a new file. Paste the following code:
      {
          "fileLocations": [
              {
                  "URIPrefixes": [ 
                      "s3://<bucketName>/"          
                  ]
              }
          ],
          "globalUploadSettings": {
              "format": "JSON"
          }
      }

      In the preceding code, replace <bucketName> with the name of the S3 bucket that you’re using to store the event stream data. Save the file as manifest.json.

    2. Sign in to the QuickSight console at https://quicksight.aws.amazon.com.
    3. Create a new S3 data set. When prompted, choose the manifest file that you created in step 1. For more information about creating S3 data sets, see Creating a Data Set Using Amazon S3 Files in the Amazon QuickSight User Guide.
    4. Create a new analysis. From here, you can start creating visualizations of your data. To learn more, see Creating an Analysis in the Amazon QuickSight User Guide.

Step 3: Set the refresh rate for the data set

You can configure your data sets in Amazon QuickSight to automatically refresh on a certain schedule. In this section, you configure the data set to refresh every day, one minute before midnight.

To set the refresh schedule

  1. Go to the QuickSight start page at https://quicksight.aws.amazon.com/sn/start. Choose Manage data.
  2. Choose the data set that you created in the previous section.
  3. Choose Schedule refresh. Follow the prompts to set up a daily refresh schedule.

Step 4: Create your visualizations

From this point, you can start creating visualizations of your data. To learn more about creating visualizations, see Creating an Analysis in the Amazon QuickSight User Guide.

Creating custom Pinpoint dashboards using Amazon QuickSight, part 1

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/creating-custom-pinpoint-dashboards-using-amazon-quicksight-part-1/

Note: This post was written by Manan Nayar and Aprajita Arora, Software Development Engineers on the AWS Digital User Engagement team.


Amazon Pinpoint helps you create customer-centric engagement experiences across the mobile, web, and other messaging channels. It also provides a variety of Key Performance Indicators (KPIs) that you can use to track the performance of your messaging programs.

You can access these KPIs through the console, or by using the Amazon Pinpoint API. In some cases, you might want to create custom dashboards that aren’t included by default, or even combine these metrics with other data. Over the next few days, we’ll discuss several different methods that you can use to create your own custom dashboards.

In this post, you’ll learn how to use the Amazon Pinpoint API to retrieve metrics, and then display them in visualizations that you create in Amazon QuickSight. This option is ideal for creating custom dashboards that highlight a specific set of metrics, or for embedding these metrics in your existing application or website.

In the next post (which we’ll post on Monday, August 19), you’ll learn how to export raw event data to an S3 bucket, and use that data to create dashboards in by using QuickSight’s Super-fast, Parallel, In-memory Calculation Engine (SPICE). This option enables you to perform in-depth analyses and quickly update visualizations. It’s also cost-effective, because all of the event data is stored in an S3 bucket.

The final post (which we’ll post on Wednesday, August 21) will also discuss the process of creating visualizations from event stream data. However, in this solution, the data will be sent from Amazon Kinesis to a Redshift cluster. This option is ideal if you need to process very large volumes of event data.

Creating a QuickSight dashboard that uses specific metrics

You can use the Amazon Pinpoint API to programmatically access many of the metrics that are shown on the Analytics pages of the Amazon Pinpoint console. You can learn more about using the API to obtain specific KPIs in our recent blog post, Tracking Campaign Performance Using the Metrics APIs.

The following sections show you how to parse and store those results in Amazon S3, and then create custom dashboards by using Amazon Quicksight. The steps below are meant to provide general guidance, rather than specific procedures. If you’ve used other AWS services in the past, most of the concepts here will be familiar. If not, don’t worry—we’ve included links to the documentation to make things easier.

Step 1: Package the Dependencies

Lambda currently uses a version of the AWS SDK that is a few versions behind the current version. However, the ability to retrieve Pinpoint metrics programmatically is a relatively new feature. For this reason, you have to download the latest version of the SDK libraries to your computer, create a .zip archive, and then upload that archive to Lambda.

To package the dependencies

    1. Paste the following code into a text editor:
      from datetime import datetime
      import boto3
      import json
      
      AWS_REGION = "<us-east-1>"
      PROJECT_ID = "<projectId>"
      BUCKET_NAME = "<bucketName>"
      BUCKET_PREFIX = "quicksight-data"
      DATE = datetime.now()
      
      # Get today's push open rate KPI values.
      def get_kpi(kpi_name):
      
          client = boto3.client('pinpoint',region_name=AWS_REGION)
      
          response = client.get_application_date_range_kpi(
              ApplicationId=PROJECT_ID,
              EndTime=DATE.strftime("%Y-%m-%d"),
              KpiName=kpi_name,
              StartTime=DATE.strftime("%Y-%m-%d")
          )
          rows = response['ApplicationDateRangeKpiResponse']['KpiResult']['Rows'][0]['Values']
      
          # Create a JSON object that contains the values we'll use to build QuickSight visualizations.
          data = construct_json_object(rows[0]['Key'], rows[0]['Value'])
      
          # Send the data to the S3 bucket.
          write_results_to_s3(kpi_name, json.dumps(data).encode('UTF-8'))
      
      # Create the JSON object that we'll send to S3.
      def construct_json_object(kpi_name, value):
          data = {
              "applicationId": PROJECT_ID,
              "kpiName": kpi_name,
              "date": str(DATE),
              "value": value
          }
      
          return data
      
      # Send the data to the designated S3 bucket.
      def write_results_to_s3(kpi_name, data):
          # Create a file path with folders for year, month, date, and hour.
          path = (
              BUCKET_PREFIX + "/"
              + DATE.strftime("%Y") + "/"
              + DATE.strftime("%m") + "/"
              + DATE.strftime("%d") + "/"
              + DATE.strftime("%H") + "/"
              + kpi_name
          )
      
          client = boto3.client('s3')
      
          # Send the data to the S3 bucket.
          response = client.put_object(
              Bucket=BUCKET_NAME,
              Key=path,
              Body=bytes(data)
          )
      
      def lambda_handler(event, context):
          get_kpi('email-open-rate')
          get_kpi('successful-delivery-rate')
          get_kpi('unique-deliveries')

      In the preceding code, make the following changes:

      • Replace <us-east-1> with the name of the AWS Region that you use Amazon Pinpoint in.
      • Replace <projectId> with the ID of the Amazon Pinpoint project that the metrics are associated with.
      • Replace <bucketName> with the name of the Amazon S3 bucket that you want to use to store the data. For more information about creating S3 buckets, see Create a Bucket in the Amazon S3 Getting Started Guide.
      • Optionally, modify the lambda_handler function so that it calls the get_kpi function for the specific metrics that you want to retrieve.

      When you finish, save the file as retrieve_pinpoint_kpis.py.

  1. Use pip to download the latest versions of the boto3 and botocore libraries. Add these libraries to a .zip file. Also add retrieve_pinpoint_kpis.py to the .zip file. You can learn more about all of these tasks in Updating a Function with Additional Dependencies With a Virtual Environment in the AWS Lambda Developer Guide.

Step 2: Set up the Lambda function

In this section, you upload the package that you created in the previous section to Lambda.

To set up the Lambda function

  1. In the Lambda console, create a new function from scratch. Choose the Python 3.7 runtime.
  2. Choose a Lambda execution role that contains the following permissions:
    • Allows the action mobiletargeting:GetApplicationDateRangeKpi for the resource arn:aws:mobiletargeting:<awsRegion>:<yourAwsAccountId>:apps/*/kpis/*/*, where <awsRegion> is the Region where you use Amazon Pinpoint, and <yourAwsAccountId> is your AWS account number.
    • Allows the action s3:PutObject for the resource arn:aws:s3:::<my_bucket>/*, where <my_bucket> is the name of the S3 bucket where you want to store the metrics.
  3. Upload the .zip file that you created in the previous section.
  4. Change the Handler value to retrieve_pinpoint_kpis.lambda_handler.
  5. Save your changes.

Step 3: Schedule the execution of the function

At this point, the Lambda function is ready to run. The next step is to set up the trigger that will cause it to run. In this case, since we’re retrieving an entire day’s worth of data, we’ll set up a scheduled trigger that runs every day at 11:59 PM.

To set up the trigger

  1. In the Lambda console, in the Designer section, choose Add trigger.
  2. Create a new CloudWatch Events rule that uses the Schedule expression rule type.
  3. For the schedule expression, enter cron(59 23 ? * * *).

Step 4: Create QuickSight Analyses

Once the data is populated in S3, you can start creating analyses in Amazon QuickSight. The process of creating new analyses involves a couple of tasks: creating a new data set, and creating your visualizations.

To create analyses in QuickSight
1.    In a text editor, create a new file. Paste the following code:

{
    "fileLocations": [
        {
            "URIPrefixes": [ 
                "s3://<bucketName>/quicksight-data/"          
            ]
        }
    ],
    "globalUploadSettings": {
        "format": "JSON"
    }
}

In the preceding code, replace <bucketName> with the name of the S3 bucket that you’re using to store the metrics data. Save the file as manifest.json.
2.    Sign in to the QuickSight console at https://quicksight.aws.amazon.com.
3.    Create a new S3 data set. When prompted, choose the manifest file that you created in step 1. For more information about creating S3 data sets, see Creating a Data Set Using Amazon S3 Files in the Amazon QuickSight User Guide.
4.    Create a new analysis. From here, you can start creating visualizations of your data. To learn more, see Creating an Analysis in the Amazon QuickSight User Guide.

Simple Two-way Messaging using the Amazon SQS Temporary Queue Client

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/simple-two-way-messaging-using-the-amazon-sqs-temporary-queue-client/

This post is contributed by Robin Salkeld, Sr. Software Development Engineer

Amazon SQS is a fully managed message queuing service that makes it easy to decouple and scale microservices, distributed systems, and serverless applications. Asynchronous workflows have always been the primary use case for SQS. Using queues ensures one component can keep running smoothly without losing data when another component is unavailable or slow.

We were surprised, then, to discover that many customers use SQS in synchronous workflows. For example, many applications use queues to communicate between frontends and backends when processing a login request from a user.

Why would anyone use SQS for this? The service stores messages for up to 14 days with high durability, but messages in a synchronous workflow often must be processed within a few minutes, or even seconds. Why not just set up an HTTPS endpoint?

The more we talked to customers, the more we understood. Here’s what we learned:

  • Creating a queue is often easier and faster than creating an HTTPS endpoint and the infrastructure necessary to ensure the endpoint’s scalability.
  • Queues are safe by default because they are locked down to the AWS account that created them. In addition, any DDoS attempt on your service is absorbed by SQS instead of loading down your own servers.
  • There is generally no need to create firewall rules for the communication between microservices if they use queues.
  • Although SQS provides durable storage (which isn’t necessary for short-lived messages), it is still a cost-effective solution for this use case. This is especially true when you consider all the messaging broker maintenance that is done for you.

However, setting up efficient two-way communication through one-way queues requires some non-trivial client-side code. In our previous two-part post series on implementing enterprise integration patterns with AWS messaging services, Point-to-point channels and Publish-subscribe channels, we discussed the Request-Response Messaging Pattern. In this pattern, each requester creates a temporary destination to receive each response message.

The simplest approach is to create a new queue for each response, but this is like building a road just so a single car can drive on it before tearing it down. Technically, this can work (and SQS can create and delete queues quickly), but we can definitely make it faster and cheaper.

To better support short-lived, lightweight messaging destinations, we are pleased to present the Amazon SQS Temporary Queue Client. This client makes it easy to create and delete many temporary messaging destinations without inflating your AWS bill.

Virtual queues

The key concept behind the client is the virtual queue. Virtual queues let you multiplex many low-traffic queues onto a single SQS queue. Creating a virtual queue only instantiates a local buffer to hold messages for consumers as they arrive; there is no API call to SQS and no costs associated with creating a virtual queue.

The Temporary Queue Client includes the AmazonSQSVirtualQueuesClient class for creating and managing virtual queues. This class implements the AmazonSQS interface and adds support for attributes related to virtual queues. You can create a virtual queue using this client by calling the CreateQueue API action and including the HostQueueURL queue attribute. This attribute specifies the existing SQS queue on which to host the virtual queue. The queue URL for a virtual queue is in the form <host queue URL>#<virtual queue name>. For example:

https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue#MyVirtualQueueName

When you call the SendMessage or SendMessageBatch API actions on AmazonSQSVirtualQueuesClient with a virtual queue URL, the client first extracts the virtual queue name. It then attaches this name as an additional message attribute to each message, and sends the messages to the host queue. When you call the ReceiveMessage API action on a virtual queue, the calling thread waits for messages to appear in the in-memory buffer for the virtual queue. Meanwhile, a background thread polls the host queue and dispatches messages to these buffers according to the additional message attribute.

This mechanism is similar to how the AmazonSQSBufferedAsyncClient prefetches messages, and the benefits are similar. A single call to SQS can provide messages for up to 10 virtual queues, reducing the API calls that you pay for by up to a factor of ten. Deleting a virtual queue simply removes the client-side resources used to implement them, again without making API calls to SQS.

The diagram below illustrates the end-to-end process for sending messages through virtual queues:

Sending messages through virtual queues

Virtual queues are similar to virtual machines. Just as a virtual machine provides the same experience as a physical machine, a virtual queue divides the resources of a single SQS queue into smaller logical queues. This is ideal for temporary queues, since they frequently only receive a handful of messages in their lifetime. Virtual queues are currently implemented entirely within the Temporary Queue Client, but additional support and optimizations might be added to SQS itself in the future.

In most cases, you don’t have to manage virtual queues yourself. The library also includes the AmazonSQSTemporaryQueuesClient class. This class automatically creates virtual queues when the CreateQueue API action is called and creates host queues on demand for all queues with the same queue attributes. To optimize existing application code that creates and deletes queues, you can use this class as a drop-in replacement implementation of the AmazonSQS interface.

The client also includes the AmazonSQSRequester and AmazonSQSResponder interfaces, which enable two-way communication through SQS queues. The following is an example of an RPC implementation for a web application’s login process.

/**
 * This class handles a user's login request on the client side.
 */
public class LoginClient {

    // The SQS queue to send the requests to.
    private final String requestQueueUrl;

    // The AmazonSQSRequester creates a temporary queue for each response.
    private final AmazonSQSRequester sqsRequester = AmazonSQSRequesterClientBuilder.defaultClient();

    private final LoginClient(String requestQueueUrl) {
        this.requestQueueUrl = requestQueueUrl;
    }

    /**
     * Send a login request to the server.
     */
    public String login(String body) throws TimeoutException {
        SendMessageRequest request = new SendMessageRequest()
                .withMessageBody(body)
                .withQueueUrl(requestQueueUrl);

        // This:
        //  - creates a temporary queue
        //  - attaches its URL as an attribute on the message
        //  - sends the message
        //  - receives the response from the temporary queue
        //  - deletes the temporary queue
        //  - returns the response
        //
        // If something goes wrong and the server's response never shows up, this method throws a
        // TimeoutException.
        Message response = sqsRequester.sendMessageAndGetResponse(request, 20, TimeUnit.SECONDS);
        
        return response.getBody();
    }
}

/**
 * This class processes users' login requests on the server side.
 */
public class LoginServer {

    // The SQS queue to poll for login requests.
    // Assume that on construction a thread is created to poll this queue and call
    // handleLoginRequest() below for each message.
    private final String requestQueueUrl;

    // The AmazonSQSResponder sends responses to the correct response destination.
    private final AmazonSQSResponder sqsResponder = AmazonSQSResponderClientBuilder.defaultClient();

    private final AmazonSQS(String requestQueueUrl) {
        this.requestQueueUrl = requestQueueUrl;
    }

    /**
     * Handle a login request sent from the client above.
     */
    public void handleLoginRequest(Message message) {
        // Assume doLogin does the actual work, and returns a serialized result
        String response = doLogin(message.getBody());

        // This extracts the URL of the temporary queue from the message attribute and sends the
        // response to that queue.
        sqsResponder.sendResponseMessage(MessageContent.fromMessage(message), new MessageContent(response));  
    }
}

Cleaning up

As with any other AWS SDK client, your code should call the shutdown() method when the temporary queue client is no longer needed. The AmazonSQSRequester interface also provides a shutdown() method, which shuts down its internal temporary queue client. This ensures that the in-memory resources needed for any virtual queues are reclaimed, and that the host queue that the client automatically created is also deleted automatically.

However, in the world of distributed systems things are a little more complex. Processes can run out of memory and crash, and hosts can reboot suddenly and unexpectedly. There are even cases where you don’t have the opportunity to run custom code on shutdown.

The Temporary Queue Client client addresses this issue as well. For each host queue with recent API calls, the client periodically uses the TagQueue API action to attach a fresh tag value that indicates the queue is still being used. The tagging process serves as a heartbeat to keep the queue alive. According to a configurable time period (by default, 5 minutes), a background thread uses the ListQueues API action to obtain the URLs of all queues with the configured prefix. Then, it deletes each queue that has not been tagged recently. The mechanism is similar to how the Amazon DynamoDB Lock Client expires stale lock leases.

If you use the AmazonSQSTemporaryQueuesClient directly, you can customize how long queues must be idle before they is deleted by configuring the IdleQueueRetentionPeriodSeconds queue attribute. The client supports setting this attribute on both host queues and virtual queues. For virtual queues, setting this attribute ensures that the in-memory resources do not become a memory leak in the presence of application bugs.

Any API call to a queue marks it as non-idle, including ReceiveMessage calls that don’t return any messages. The only reason to increase the retention period attribute is to give the client more time when it can’t send heartbeats—for example, due to garbage collection pauses or networking issues.

But what if you want to use this client in a fleet of a thousand EC2 instances? Won’t every client spend a lot of time checking every queue for idleness? Doesn’t that imply duplicate work that increases as the fleet is scaled up?

We thought of this too. The Temporary Queue Client creates a shared queue for all clients using the same queue prefix, and uses this queue as a work queue for the distributed task. Instead of every client calling the ListQueues API action every five minutes, a new seed message (which triggers the sweeping process) is sent to this queue every five minutes.

When one of the clients receives this message, it calls the ListQueues API action and sends each queue URL in the result as another kind of message to the same shared work queue. The work of actually checking each queue for idleness is distributed roughly evenly to the active clients, ensuring scalability. There is even a mechanism that works around the fact that the ListQueues API action currently only returns no more than 1,000 queue URLs at time.

Summary

We are excited about how the new Amazon SQS Temporary Queue Client makes more messaging patterns easier and cheaper for you. Download the code from GitHub, have a look at Temporary Queues in the Amazon SQS Developer Guide, try out the client, and let us know what you think!

Amazon Managed Streaming for Apache Kafka (MSK) – Now Generally Available

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/amazon-managed-streaming-for-apache-kafka-msk-now-generally-available/

I am always amazed at how our customers are using streaming data. For example, Thomson Reuters, one of the world’s most trusted news organizations for businesses and professionals, built a solution to capture, analyze, and visualize analytics data to help product teams continuously improve the user experience. Supercell, the social game company providing games such as Hay Day, Clash of Clans, and Boom Beach, is delivering in-game data in real-time, handling 45 billion events per day.

Since we launched Amazon Kinesis at re:Invent 2013, we have continually expanded the ways in in which customers work with streaming data on AWS. Some of the available tools are:

  • Kinesis Data Streams, to capture, store, and process data streams with your own applications.
  • Kinesis Data Firehose, to transform and collect data into destinations such as Amazon S3, Amazon Elasticsearch Service, and Amazon Redshift.
  • Kinesis Data Analytics, to continuously analyze data using SQL or Java (via Apache Flink applications), for example to detect anomalies or for time series aggregation.
  • Kinesis Video Streams, to simplify processing of media streams.

At re:Invent 2018, we introduced in open preview Amazon Managed Streaming for Apache Kafka (MSK), a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data.

I am excited to announce that Amazon MSK is generally available today!

How it works

Apache Kafka (Kafka) is an open-source platform that enables customers to capture streaming data like click stream events, transactions, IoT events, application and machine logs, and have applications that perform real-time analytics, run continuous transformations, and distribute this data to data lakes and databases in real time. You can use Kafka as a streaming data store to decouple applications producing streaming data (producers) from those consuming streaming data (consumers).

While Kafka is a popular enterprise data streaming and messaging framework, it can be difficult to setup, scale, and manage in production. Amazon MSK takes care of these managing tasks and makes it easy to set up, configure, and run Kafka, along with Apache ZooKeeper, in an environment following best practices for high availability and security.

Your MSK clusters always run within an Amazon VPC managed by the MSK service. Your MSK resources are made available to your own VPC, subnet, and security group through elastic network interfaces (ENIs) which will appear in your account, as described in the following architectural diagram:

Customers can create a cluster in minutes, use AWS Identity and Access Management (IAM) to control cluster actions, authorize clients using TLS private certificate authorities fully managed by AWS Certificate Manager (ACM), encrypt data in-transit using TLS, and encrypt data at rest using AWS Key Management Service (KMS) encryption keys.

Amazon MSK continuously monitors server health and automatically replaces servers when they fail, automates server patching, and operates highly available ZooKeeper nodes as a part of the service at no additional cost. Key Kafka performance metrics are published in the console and in Amazon CloudWatch. Amazon MSK is fully compatible with Kafka versions 1.1.1 and 2.1.0, so that you can continue to run your applications, use Kafka’s admin tools, and and use Kafka compatible tools and frameworks without having to change your code.

Based on our customer feedback during the open preview, Amazon MSK added may features such as:

  • Encryption in-transit via TLS between clients and brokers, and between brokers
  • Mutual TLS authentication using ACM private certificate authorities
  • Support for Kafka version 2.1.0
  • 99.9% availability SLA
  • HIPAA eligible
  • Cluster-wide storage scale up
  • Integration with AWS CloudTrail for MSK API logging
  • Cluster tagging and tag-based IAM policy application
  • Defining custom, cluster-wide configurations for topics and brokers

AWS CloudFormation support is coming in the next few weeks.

Creating a cluster

Let’s create a cluster using the AWS management console. I give the cluster a name, select the VPC I want to use the cluster from, and the Kafka version.

I then choose the Availability Zones (AZs) and the corresponding subnets to use in the VPC. In the next step, I select how many Kafka brokers to deploy in each AZ. More brokers allow you to scale the throughtput of a cluster by allocating partitions to different brokers.

I can add tags to search and filter my resources, apply IAM policies to the Amazon MSK API, and track my costs. For storage, I leave the default storage volume size per broker.

I select to use encryption within the cluster and to allow both TLS and plaintext traffic between clients and brokers. For data at rest, I use the AWS-managed customer master key (CMK), but you can select a CMK in your account, using KMS, to have further control. You can use private TLS certificates to authenticate the identity of clients that connect to your cluster. This feature is using Private Certificate Authorities (CA) from ACM. For now, I leave this option unchecked.

In the advanced setting, I leave the default values. For example, I could have chosen here a different instance type for my brokers. Some of these settings can be updated using the AWS CLI.

I create the cluster and monitor the status from the cluster summary, including the Amazon Resource Name (ARN) that I can use when interacting via CLI or SDKs.

When the status is active, the client information section provides specific details to connect to the cluster, such as:

  • The bootstrap servers I can use with Kafka tools to connect to the cluster.
  • The Zookeper connect list of hosts and ports.

I can get similar information using the AWS CLI:

  • aws kafka list-clusters to see the ARNs of your clusters in a specific region
  • aws kafka get-bootstrap-brokers --cluster-arn <ClusterArn> to get the Kafka bootstrap servers
  • aws kafka describe-cluster --cluster-arn <ClusterArn> to see more details on the cluster, including the Zookeeper connect string

Quick demo of using Kafka

To start using Kafka, I create two EC2 instances in the same VPC, one will be a producer and one a consumer. To set them up as client machines, I download and extract the Kafka tools from the Apache website or any mirror. Kafka requires Java 8 to run, so I install Amazon Corretto 8.

On the producer instance, in the Kafka directory, I create a topic to send data from the producer to the consumer:

bin/kafka-topics.sh --create --zookeeper <ZookeeperConnectString> \
--replication-factor 3 --partitions 1 --topic MyTopic

Then I start a console-based producer:

bin/kafka-console-producer.sh --broker-list <BootstrapBrokerString> \
--topic MyTopic

On the consumer instance, in the Kafka directory, I start a console-based consumer:

bin/kafka-console-consumer.sh --bootstrap-server <BootstrapBrokerString> \
--topic MyTopic --from-beginning

Here’s a recording of a quick demo where I create the topic and then send messages from a producer (top terminal) to a consumer of that topic (bottom terminal):

Pricing and availability

Pricing is per Kafka broker-hour and per provisioned storage-hour. There is no cost for the Zookeeper nodes used by your clusters. AWS data transfer rates apply for data transfer in and out of MSK. You will not be charged for data transfer within the cluster in a region, including data transfer between brokers and data transfer between brokers and ZooKeeper nodes.

You can migrate your existing Kafka cluster to MSK using tools like MirrorMaker (that comes with open source Kafka) to replicate data from your clusters into a MSK cluster.

Upstream compatibility is a core tenet of Amazon MSK. Our code changes to the Kafka platform are released back to open source.

Amazon MSK is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), EU (Frankfurt), EU (Ireland), EU (Paris), and EU (London).

I look forward to see how are you going to use Amazon MSK to simplify building and migrating streaming applications to the cloud!

New Regions, New Features, and a New Web Site

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/new-regions-new-features-and-a-new-web-site/

It’s a busy time here on the Digital User Engagement Team at AWS!

Last week, we made Amazon Pinpoint available in the Asia Pacific (Mumbai) and Asia Pacific (Sydney) AWS Regions. This is great news for new Pinpoint customers in these areas of the globe who were previously concerned with issues related to latency and data residency. Existing Amazon Pinpoint customers can also use these new Regions to increase availability and create geographical redundancy.

On Tuesday of this week, we also launched two exciting improvements to the Amazon Pinpoint console. The first improvement is a tool that you can use to import customer segments in just a few clicks. Previously, if you wanted to import customer data into Pinpoint, you had to save the data in a CSV or JSON file, upload it to an S3 bucket, create a segment in Pinpoint, and enter the full path to the S3 bucket. Now, you can drag and drop files right into the segment importer. To learn more, see the Pinpoint User Guide.

The other new feature that we released this week is an improved email editor. Our previous email editor only allowed you to include a limited set of HTML tags in your emails. With our new editor, however, you can include any HTML tags that you want. The new editor also includes a helpful side-by-side view that renders your message in real-time, as shown in the following image.

Users who don’t want to work with HTML code can also use the Design view to create and modify emails in an intuitive, WYSIWYG interface. For more information, see the Pinpoint User Guide.

Finally, we launched a new website for Amazon Pinpoint at https://aws.amazon.com/pinpoint. On our new site, you can learn more about the capabilities of Amazon Pinpoint. You’ll find in-depth information about all of the features, channels, and use cases that Amazon Pinpoint supports.

Every day, we’re amazed by the things that our customers do with Amazon Pinpoint. We hope these changes help you do even more incredible things!

Learn About Amazon Pinpoint at Upcoming Events Around the World

Post Syndicated from Hannah Nilsson original https://aws.amazon.com/blogs/messaging-and-targeting/learn-about-amazon-pinpoint-at-upcoming-events/

Connect with the AWS Customer Engagement team at events around the world to learn how our technology can to help you better engage with your customers. Get demos on recent feature releases, discover how you can use Pinpoint for your specific use case, and attend informative sessions to hear how companies around the world are using AWS Customer Engagement solutions to deliver better experiences for their customers. Plus, read below to find out how Amazon Pinpoint and Amazon SES both enable you to create innovative email experiences with the recent AMP Project launch.

AWS Customer Engagement in the news: Amazon SES and Amazon Pinpoint support build the future of email with AMP

The AMP Project’s mission is to enable more user-first experiences on the web, including web-based technology like email. On March 26, the AMP Project announced that they are bringing AMP technology to email in order to give users an interactive, real-time experience that also keeps inboxes safe.

Amazon Pinpoint and Amazon SES both provide out-of-the-box support for AMP for email with no additional configuration. This allows you to easily create experiences for your customers such as  submitting RSVPs to events, filling out questionnaires, browsing catalogs, or responding to comments right within the email.

Read the AMP announcement for more information about these new capabilities. To learn how to use the AMP format with Amazon SES, visit the SES Developer Guide. To learn how to use the AMP format with Amazon Pinpoint, read this Amazon Pinpoint API Reference. View these instructions for more information on how to add AMP to an existing email.

Amazon Pinpoint has been busy building. You can now:

  • Learn how to set up an email preference management web page that enables customers to manage their email subscription preferences. Read now.
  • Learn how to set up a web form that collects information from new customers, and then sends them an SMS message to confirm that they want to receive content from you. Read now.
  • Use Amazon Pinpoint in the US West (Oregon), EU (Frankfurt), and EU (Ireland) regions in addition to the US East (Virginia) region. Learn more.
  • Deliver voice messages to your users with Amazon Pinpoint Voice. Learn more.
  • Set up campaigns that auto-send messages to your customers when they take specific actions. Learn more.
  • Detect and understand issues impacting your email deliverability with the Amazon Pinpoint Deliverability Dashboard. Learn more. 

Meet an Amazon Pinpoint expert at these upcoming events. We will teach you how to take advantage of recent updates so that you can create better engagement experiences for your customers. Plus, we can give you an inside look on what’s on our roadmap, and we’ll be giving out custom Pinpoint swag!

AWS Summit, Singapore 

April 10, 2019
Singapore Expo Convention & Exhibition Centre
Amazon Pinpoint will host an informative session about our Customer Engagement solutions at the AWS Singapore Summit. In this session, we will describe how AWS enables companies to better understand and engage their customers with personalized, timely, and relevant communications on multiple channels. You will also learn how Disney Streaming Services is using Amazon Pinpoint to engage their users.
Register for the Summit here.

“Mobile Days” at the AWS San Francisco Loft   

April 24, 2019 
AWS San Francisco Loft
Join us for an engaging day of discussion and education. Amazon Pinpoint experts will host the following sessions:

  • 2:30pm – 3:30pm: How Do You Measure Customer Success? Featuring Amazon Pinpoint. 
  • 3:30pm – 4:30pm: Using ML to Create Enhance Your Marketing. Featuring Amazon Pinpoint and Amazon Personalize. 

Space for this event is limited, please reserve your seat here.

AWS Summit, Sydney

May 1-2, 2019
International Convention Centre (ICC), Darling Harbour, Sydney
Don’t miss the customer engagement session on April 30th. This session, part of Amazon’s Innovation Day event, features a keynote address by Neil Lindsay, Vice President of Global Marketing at Amazon. The session explores how AWS technologies power organizations that deliver customer-centric innovations. Learn about how Australia’s largest brands and digital agencies use AWS technologies to engage customers, build new business models, and transform customer experiences.
Register for the Summit here

AWS Summit, Mumbai

May 15, 2019
Bombay Exhibition Center, Mumbai
The Amazon Pinpoint team will be at the “Ask an Expert” booth. Stop by to meet the team, ask questions, and pick up Amazon Pinpoint swag!
Register for the summit here

Enriching Event-Driven Architectures with AWS Event Fork Pipelines

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/enriching-event-driven-architectures-with-aws-event-fork-pipelines/

This post is courtesy of Otavio Ferreira, Mgr, Amazon SNS, and James Hood, Sr. Software Dev Engineer

Many customers are choosing to build event-driven applications in which subscriber services automatically perform work in response to events triggered by publisher services. This architectural pattern can make services more reusable, interoperable, and scalable.

Customers often fork event processing into pipelines that address common event handling requirements, such as event storage, backup, search, analytics, or replay. To help you build event-driven applications even faster, AWS introduces Event Fork Pipelines, a collection of open-source event handling pipelines that you can subscribe to Amazon SNS topics in your AWS account.

Event Fork Pipelines is a suite of open-source nested applications, based on the AWS Serverless Application Model (AWS SAM). You can deploy it directly from the AWS Serverless Application Repository into your AWS account.

Event Fork Pipelines is built on top of serverless services, including Amazon SNS, Amazon SQS, and AWS Lambda. These services provide serverless building blocks that help you build fully managed, highly available, and scalable event-driven platforms. Lambda enables you to build event-driven microservices as serverless functions. SNS and SQS provide serverless topics and queues for integrating these microservices and other distributed systems in your architecture. These building blocks are at the core of the modern application development best practices.

Surfacing the event fork pattern

At AWS, we’ve worked closely with customers across market segments and geographies on event-driven architectures. For example:

  • Financial platforms that handle events related to bank transactions and stock ticks
  • Retail platforms that trigger checkout and fulfillment events

At scale, event-driven architectures often require a set of supporting services to address common requirements such as system auditability, data discoverability, compliance, business insights, and disaster recovery. Translated to AWS, customers often connect event-driven applications to services such as Amazon S3 for event storage and backup, and to Amazon Elasticsearch Service for event search and analytics. Also, customers often implement an event replay mechanism to recover from failure modes in their applications.

AWS created Event Fork Pipelines to encapsulate these common requirements, reducing the amount of effort required for you to connect your event-driven architectures to these supporting AWS services.

AWS then started sharing this pattern more broadly, so more customers could benefit. At the 2018 AWS re:Invent conference in Las Vegas, Amazon CTO Werner Vogels announced the launch of nested applications in his keynote. Werner shared the Event Fork Pipelines pattern with the audience as an example of common application logic that had been encapsulated as a set of nested applications.

The following reference architecture diagram shows an application supplemented by three nested applications:

Each pipeline is subscribed to the same SNS topic, and can process events in parallel as these events are published to the topic. Each pipeline is independent and can set its own subscription filter policy. That way, it processes only the subset of events that it’s interested in, rather than all events published to the topic.

Amazon SNS Fork pipelines reference architecture

Figure 1 – Reference architecture using Event Fork Pipelines

The three event fork pipelines are placed alongside your regular event processing pipelines, which are potentially already subscribed to your SNS topic. Therefore, you don’t have to change any portion of your current message publisher to take advantage of Event Fork Pipelines in your existing workloads. The following sections describe these pipelines and how to deploy them in your system architecture.

Understanding the catalog of event fork pipelines

In the abstract, Event Fork Pipelines is a serverless design pattern. Concretely, Event Fork Pipelines is also a suite of nested serverless applications, based on AWS SAM. You deploy the nested applications directly from the AWS Serverless Application Repository to your AWS account, to enrich your event-driven platforms. You can deploy them individually in your architecture, as needed.

Here’s more information about each nested application in the Event Fork Pipelines suite.

Event Storage & Backup pipeline

Event Fork Pipeline for Event Storage & Backup

Figure 2 – Event Fork Pipeline for Event Storage & Backup

The preceding diagram shows the Event Storage & Backup pipeline. You can subscribe this pipeline to your SNS topic to automatically back up the events flowing through your system. This pipeline is composed of the following resources:

  • An SQS queue that buffers the events delivered by the SNS topic
  • A Lambda function that automatically polls for these events in the queue and pushes them into an Amazon Kinesis Data Firehose delivery stream
  • An S3 bucket that durably backs up the events loaded by the stream

You can configure this pipeline to fine-tune the behavior of your delivery stream. For example, you can configure your pipeline so that the underlying delivery stream buffers, transforms, and compresses your events before loading them into the bucket. As events are loaded, you can use Amazon Athena to query the bucket using standard SQL queries. Also, you can configure the pipeline to either reuse an existing S3 bucket or create a new one for you.

Event Search & Analytics pipeline

Event Fork Pipeline for Event Search & Analytics

Figure 3 – Event Fork Pipeline for Event Search & Analytics

The preceding diagram shows the Event Search & Analytics pipeline. You can subscribe this pipeline to your SNS topic to index in a search domain the events flowing through your system, and then run analytics on them. This pipeline is composed of the following resources:

  • An SQS queue that buffers the events delivered by the SNS topic
  • A Lambda function that polls events from the queue and pushes them into a Data Firehose delivery stream
  • An Amazon ES domain that indexes the events loaded by the delivery stream
  • An S3 bucket that stores the dead-letter events that couldn’t be indexed in the search domain

You can configure this pipeline to fine-tune your delivery stream in terms of event buffering, transformation and compression. You can also decide whether the pipeline should reuse an existing Amazon ES domain in your AWS account or create a new one for you. As events are indexed in the search domain, you can use Kibana to run analytics on your events and update visual dashboards in real time.

Event Replay pipeline

Event Fork Pipeline for Event Replay

Figure 4 – Event Fork Pipeline for Event Replay

The preceding diagram shows the Event Replay pipeline. You can subscribe this pipeline to your SNS topic to record the events that have been processed by your system for up to 14 days. You can then reprocess them in case your platform is recovering from a failure or a disaster. This pipeline is composed of the following resources:

  • An SQS queue that buffers the events delivered by the SNS topic
  • A Lambda function that polls events from the queue and redrives them into your regular event processing pipeline, which is also subscribed to your topic

By default, the replay function is disabled, which means it isn’t redriving your events. If the events need to be reprocessed, your operators must enable the replay function.

Applying event fork pipelines in a use case

This is how everything comes together. The following scenario describes an event-driven, serverless ecommerce application that uses the Event Fork Pipelines pattern. This example ecommerce application is available in AWS Serverless Application Repository. You can deploy it to your AWS account using the Lambda console, test it, and look at its source code in GitHub.

Example ecommerce application using Event Fork Pipelines

Figure 5 – Example e-commerce application using Event Fork Pipelines

The ecommerce application takes orders from buyers through a RESTful API hosted by Amazon API Gateway and backed by a Lambda function named CheckoutFunction. This function publishes all orders received to an SNS topic named CheckoutEventsTopic, which in turn fans out the orders to four different pipelines. The first pipeline is the regular checkout-processing pipeline designed and implemented by you as the ecommerce application owner. This pipeline has the following resources:

  • An SQS queue named CheckoutQueue that buffers all orders received
  • A Lambda function named CheckoutFunction that polls the queue to process these orders
  • An Amazon DynamoDB table named CheckoutTable that securely saves all orders as they’re placed

The components of the system described thus far handle what you might think of as the core business logic. But in addition, you should address the set of elements necessary for making the system resilient, compliant, and searchable:

  • Backing up all orders securely. Compressed backups must be encrypted at rest, with sensitive payment details removed for security and compliance purposes.
  • Searching and running analytics on orders, if the amount is $100 or more. Analytics are needed for key ecommerce metrics, such as average ticket size, average shipping time, most popular products, and preferred payment options.
  • Replaying recent orders. If the fulfillment process is disrupted at any point, you should be able to replay the most recent orders from up to two weeks. This is a key requirement that guarantees the continuity of the ecommerce business.

Rather than implementing all the event processing logic yourself, you can choose to subscribe Event Fork Pipelines to your existing SNS topic CheckoutEventsTopic. The pipelines are configured as follows:

  • The Event Storage & Backup pipeline is configured to transform data as follows:
    • Remove credit card details
    • Buffer data for 60 seconds
    • Compress data using GZIP
    • Encrypt data using the default customer master key (CMK) for S3

This CMK is managed by AWS and powered by AWS Key Management Service (AWS KMS). For more information, see Choosing Amazon S3 for Your Destination, Data Transformation, and Configuration Settings in the Amazon Kinesis Data Firehose Developer Guide.

  • The Event Search & Analytics pipeline is configured with:
    • An index retry duration of 30 seconds
    • A bucket for storing orders that failed to be indexed in the search domain
    • A filter policy to restrict the set of orders that are indexed

For more information, see Choosing Amazon ES for Your Destination, in the Amazon Kinesis Data Firehose Developer Guide.

  • The Event Replay pipeline is configured with the SQS queue name that is part of the regular checkout processing pipeline. For more information, see Queue Name and URL in the Amazon SQS Developer Guide.

The filter policy, shown in JSON format, is set in the configuration for the Event Search & Analytics pipeline. This filter policy matches only incoming orders in which the total amount is $100 or more. For more information, see Message Filtering in the Amazon SNS Developer Guide.


{

    "amount": [

        { "numeric": [ ">=", 100 ] }

    ]

}

By using the Event Fork Pipelines pattern, you avoid the development overhead associated with coding undifferentiated logic for handling events.

Event Fork Pipelines can be deployed directly from AWS Serverless Application Repository into your AWS account.

Deploying event fork pipelines

Event Fork Pipelines is available as a set of public apps in the AWS Serverless Application Repository (to find the apps, select the ‘Show apps that create custom IAM roles or resource policies’ check box under the search bar). It can be deployed and tested manually via the Lambda console. In a production scenario, we recommend embedding fork pipelines within the AWS SAM template of your overall application. The nested applications feature enables you to do this by adding an AWS::Serverless::Application resource to your AWS SAM template. The resource references the ApplicationId and SemanticVersion values of the application to nest.

For example, you can include the Event Storage & Backup pipeline as a nested application by adding the following YAML snippet to the Resources section of your AWS SAM template:


Backup:

  Type: AWS::Serverless::Application

  Properties:

    Location:

      ApplicationId: arn:aws:serverlessrepo:us-east-1:012345678901:applications/fork-event-storage-backup-pipeline

      SemanticVersion: 1.0.0

    Parameters:

      # SNS topic ARN whose messages should be backed up to the S3 bucket.

      TopicArn: !Ref MySNSTopic

When specifying parameter values, you can use AWS CloudFormation intrinsic functions to reference other resources in your template. In the preceding example, the TopicArn parameter is filled in by referencing an AWS::SNS::Topic called MySNSTopic, defined elsewhere in the AWS SAM template. For more information, see Intrinsic Function Reference in the AWS CloudFormation User Guide.

To copy the YAML required for nesting, in the Lambda console page for an AWS Serverless Application Repository application, choose Copy as SAM Resource.

Authoring new event fork pipelines

We invite you to fork the Event Fork Pipelines repository in GitHub and submit pull requests for contributing with new pipelines. In addition to event storage and backup, event search and analytics, and event replay, what other common event handling requirements have you seen?

We look forward to seeing what you’ll come up with for extending the Event Fork Pipelines suite.

Summary

Event Fork Pipelines is a serverless design pattern and a suite of open-source nested serverless applications, based on AWS SAM. You can deploy it directly from AWS Serverless Application Repository to enrich your event-driven system architecture. Event Fork Pipelines lets you store, back up, replay, search, and run analytics on the events flowing through your system. There’s no need to write code, manually stitch resources together, or set up infrastructure.

You can deploy Event Fork Pipelines in any AWS Region that supports the underlying AWS services used in the pipelines. There are no additional costs associated with Event Fork Pipelines itself, and you pay only for using the AWS resources inside each nested application.

Get started today by deploying the example ecommerce application or searching for Event Fork Pipelines in AWS Serverless Application Repository.