Build a serverless analytics application with Amazon Redshift and Amazon API Gateway

Serverless applications are a modernized way to perform analytics among business departments and engineering teams. Business teams can gain meaningful insights by simplifying their reporting through web applications and distributing it to a broader audience.

Use cases can include the following:

Dashboarding – A webpage consisting of tables and charts where each component can offer insights to a specific business department.
Reporting and analysis – An application where you can trigger large analytical queries with dynamic inputs and then view or download the results.
Management systems – An application that provides a holistic view of the internal company resources and systems.
ETL workflows – A webpage where internal company individuals can trigger specific extract, transform, and load (ETL) workloads in a user-friendly environment with dynamic inputs.
Data abstraction – Decouple and refactor underlying data structure and infrastructure.
Ease of use – An application where you want to give a large set of user-controlled access to analytics without having to onboard each user to a technical platform. Query updates can be completed in an organized manner and maintenance has minimal overhead.

In this post, you will learn how to build a serverless analytics application using Amazon Redshift Data API and Amazon API Gateway WebSocket and REST APIs.

Amazon Redshift is fully managed by AWS, so you no longer need to worry about data warehouse management tasks such as hardware provisioning, software patching, setup, configuration, monitoring nodes and drives to recover from failures, or backups. The Data API simplifies access to Amazon Redshift because you don’t need to configure drivers and manage database connections. Instead, you can run SQL commands to an Amazon Redshift cluster by simply calling a secured API endpoint provided by the Data API. The Data API takes care of managing database connections and buffering data. The Data API is asynchronous, so you can retrieve your results later.

API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. With API Gateway, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication applications. API Gateway supports containerized and serverless workloads, as well as web applications. API Gateway acts as a reverse proxy to many of the compute resources that AWS offers.

Event-driven model

Event-driven applications are increasingly popular among customers. Analytical reporting web applications can be implemented through an event-driven model. The applications run in response to events such as user actions and unpredictable query events. Decoupling the producer and consumer processes allows greater flexibility in application design and building decoupled processes. This design can be achieved with the Data API and API Gateway WebSocket and REST APIs.

Both REST API calls and WebSocket establish communication between the client and the backend. Due to the popularity of REST, you may wonder why WebSockets are present and how they contribute to an event-driven design.

What are WebSockets and why do we need them?

Unidirectional communication is customary when building analytical web solutions. In traditional environments, the client initiates a REST API call to run a query on the backend and either synchronously or asynchronously waits for the query to complete. The “wait” aspect is engineered to apply the concept of polling. Polling in this context is when the client doesn’t know when a backend process will complete. Therefore, the client will consistently make a request to the backend and check.

What is the problem with polling? Main challenges include the following:

Increased traffic in your network bandwidth – A large number of users performing empty checks will impact your backend resources and doesn’t scale well.
Cost usage – Empty requests don’t deliver any value to the business. You pay for the unnecessary cost of resources.
Delayed response – Polling is scheduled in time intervals. If the query is complete in-between these intervals, the user can only see the results after the next check. This delay impacts the user experience and, in some cases, may result in UI deadlocks.

For more information on polling, check out From Poll to Push: Transform APIs using Amazon API Gateway REST APIs and WebSockets.

WebSockets is another approach compared to REST when establishing communication between the front end and backend. WebSockets enable you to create a full duplex communication channel between the client and the server. In this bidirectional scenario, the client can make a request to the server and is notified when the process is complete. The connection remains open, with minimal network overhead, until the response is received.

You may wonder why REST is present, since you can transfer response data with WebSockets. A WebSocket is a light weight protocol designed for real-time messaging between systems. The protocol is not designed for handling large analytical query data and in API Gateway, each frame’s payload can only hold up to 32 KB. Therefore, the REST API performs large data retrieval.

By using the Data API and API Gateway, you can build decoupled event-driven web applications for your data analytical needs. You can create WebSocket APIs with API Gateway and establish a connection between the client and your backend services. You can then initiate requests to perform analytical queries with the Data API. Due to the Data API’s asynchronous nature, the query completion generates an event to notify the client through the WebSocket channel. The client can decide to either retrieve the query results through a REST API call or perform other follow-up actions. The event-driven architecture enables bidirectional interoperable messages and data while keeping your system components agnostic.

Solution overview

In this post, we show how to create a serverless event-driven web application by querying with the Data API in the backend, establishing a bidirectional communication channel between the user and the backend with the WebSocket feature in API Gateway, and retrieving the results using its REST API feature. Instead of designing an application with long-running API calls, you can use the Data API. The Data API allows you to run SQL queries asynchronously, removing the need to hold long, persistent database connections.

The web application is protected using Amazon Cognito, which is used to authenticate the users before they can utilize the web app and also authorize the REST API calls when made from the application.

Other relevant AWS services in this solution include AWS Lambda and Amazon EventBridge. Lambda is a serverless, event-driven compute resource that enables you to run code without provisioning or managing servers. EventBridge is a serverless event bus allowing you to build event-driven applications.

The solution creates a lightweight WebSocket connection between the browser and the backend. When a user submits a request using WebSockets to the backend, a query is submitted to the Data API. When the query is complete, the Data API sends an event notification to EventBridge. EventBridge signals the system that the data is available and notifies the client. Afterwards, a REST API call is performed to retrieve the query results for the client to view.

We have published this solution on the AWS Samples GitHub repository and will be referencing it during the rest of this post.

The following architecture diagram highlights the end-to-end solution, which you can provision automatically with AWS CloudFormation templates run as part of the shell script with some parameter variables.

The application performs the following steps (note the corresponding numbered steps in the process flow):

A web application is provisioned on AWS Amplify; the user needs to sign up first by providing their email and a password to access the site.
The user verifies their credentials using a pin sent to their email. This step is mandatory for the user to then log in to the application and continue access to the other features of the application.
After the user is signed up and verified, they can sign in to the application and requests data through their web or mobile clients with input parameters. This initiates a WebSocket connection in API Gateway. (Flow 1, 2)
The connection request is handled by a Lambda function, OnConnect, which initiates an asynchronous database query in Amazon Redshift using the Data API. The SQL query is taken from a SQL script in Amazon Simple Storage Service (Amazon S3) with dynamic input from the client. (Flow 3, 4, 6, 7)
In addition, the OnConnect Lambda function stores the connection, statement identifier, and topic name in an Amazon DynamoDB database. The topic name is an extra parameter that can be used if users want to implement multiple reports on the same webpage. This allows the front end to map responses to the correct report. (Flow 3, 4, 5)
The Data API runs the query, mentioned in step 2. When the operation is complete, an event notification is sent to EventBridge. (Flow 8)
EventBridge activates an event rule to redirect that event to another Lambda function, SendMessage. (Flow 9)
The SendMessage function notifies the client that the SQL query is complete via API Gateway. (Flow 10, 11, 12)
After the notification is received, the client performs a REST API call (GET) to fetch the results. (Flow 13, 14, 15, 16)
The GetResult function is triggered, which retrieves the SQL query result and returns it to the client.
The user is now able to view the results on the webpage.
When clients disconnect from their browser, API Gateway automatically deletes the connection information from the DynamoDB table using the onDisconnect function. (Flow 17, 18,19)

Prerequisites

Prior to deploying your event-driven web application, ensure you have the following:

An Amazon Redshift cluster in your AWS environment – This is your backend data warehousing solution to run your analytical queries. For instructions to create your Amazon Redshift cluster, refer to Getting started with Amazon Redshift.
- After you create your Amazon Redshift cluster, add the AmazonS3ReadOnlyAccess permission to the associated cluster AWS Identity and Access Management (IAM) role.
An S3 bucket that you have access to – The S3 bucket will be your object storage solution where you can store your SQL scripts. To create your S3 bucket, refer to Create your first S3 bucket.

Deploy CloudFormation templates

The code associated to the design is available in the following GitHub repository. You can clone the repository inside an AWS Cloud9 environment in our AWS account. The AWS Cloud9 environment comes with AWS Command Line Interface (AWS CLI) installed, which is used to run the CloudFormation templates to set up the AWS infrastructure. Make sure that the jQuery library is installed; we use it to parse the JSON output during the run of the script.

The complete architecture is set up using three CloudFormation templates:

cognito-setup.yaml – Creates the Amazon Cognito user pool to web app client, which is used for authentication and protecting the REST API
backend-setup.yaml – Creates all the required Lambda functions and the WebSocket and Rest APIs, and configures them on API Gateway
webapp-setup.yaml – Creates the web application hosting using Amplify to connect and communicate with the WebSocket and Rest APIs.

These CloudFormation templates are run using the script.sh shell script, which takes care of all the dependencies as required.

A generic template is provided for you to customize your own DDL SQL scripts as well as your own query SQL scripts. We have created sample scripts for you to follow along.

Download the sample DDL script and upload it to an existing S3 bucket.
Change the IAM role value to your Amazon Redshift cluster’s IAM role with permissions to AmazonS3ReadOnlyAccess.

For this post, we copy the New York Taxi Data 2015 dataset from a public S3 bucket.

Download the sample query script and upload it to an existing S3 bucket.
Upload the modified sample DDL script and the sample query script into a preexisting S3 bucket that you own, and note down the S3 URI path.

If you want to run your own customized version, modify the DDL and query script to fit your scenario.

Edit the script.sh file before you run it and set the values for the following parameters:

- RedshiftClusterEndpoint (aws_redshift_cluster_ep) – Your Amazon Redshift cluster endpoint available on the AWS Management Console
- DBUsername (aws_dbuser_name) – Your Amazon Redshift database user name
- DDBTableName (aws_ddbtable_name) – The name of your DynamoDB table name that will be created
- WebsocketEndpointSSMParameterName (aws_wsep_param_name) – The parameter name that stores the WebSocket endpoint in AWS Systems Manager Parameter Store.
- RestApiEndpointSSMParameterName (aws_rapiep_param_name) – The parameter name that stores the REST API endpoint in Parameter Store.
- DDLScriptS3Path (aws_ddl_script_path) – The S3 URI to the DDL script that you uploaded.
- QueryScriptS3Path (aws_query_script_path) – The S3 URI to the query script that you uploaded.
- AWSRegion (aws_region) – The Region where the AWS infrastructure is being set up.
- CognitoPoolName (aws_user_pool_name) – The name you want to give to your Amazon Cognito user pool
- ClientAppName (aws_client_app_name) – The name of the client app to be configured for the web app to handle the user authentication for the users

The default acceptable values are already provided as part of the downloaded code.

Run the script using the following command:

./script.sh

During deployment, AWS CloudFormation creates and triggers the Lambda function SetupRedshiftLambdaFunction, which sets up an Amazon Redshift database table and populates data into the table. The following diagram illustrates this process.

Use the demo app

When the shell script is complete, you can start interacting with the demo web app:

On the Amplify console, under All apps in the navigation pane, choose DemoApp.
Choose Run build.

The DemoApp web application goes through a phase of Provision, Build, Deploy.

When it’s complete, use the URL provided to access the web application.

The following screenshot shows the web application page. It has minimal functionality: you can sign in, sign up, or verify a user.

Choose Sign Up.

For Email ID, enter an email.
For Password, enter a password that is at least eight characters long, has at least one uppercase and lowercase letter, at least one number, and at least one special character.
Choose Let’s Enroll.

The Verify your Login to Demo App page opens.

Enter your email and the verification code sent to the email you specified.
Choose Verify.

You’re redirected to a login page.

You’re redirected to the demoPage.html website.

Choose Open Connection.

You now have an active WebSocket connection between your browser and your backend AWS environment.

For Trip Month, specify a month (for this example, December) and choose Submit.

You have now defined the month and year you want to query your data upon. After a few seconds, you can to see the output delivered from the WebSocket.

You may continue using the active WebSocket connection for additional queries—just choose a different month and choose Submit again.

When you’re done, choose Close Connection to close the WebSocket connection.

For exploratory purposes, while your WebSocket connection is active, you can navigate to your DynamoDB table on the DynamoDB console to view the items that are currently stored. After the WebSocket connection is closed, the items stored in DynamoDB are deleted.

Clean up

To clean up your resources, complete the following steps:

On the Amazon S3 console, navigate to the S3 bucket containing the sample DDL script and query script and delete them from the bucket.
On the Amazon Redshift console, navigate to your Amazon Redshift cluster and delete the data you copied over from the sample DDL script.
1. Run truncate nyc_yellow_taxi;
2. Run drop table nyc_yellow_taxi;
On the AWS CloudFormation console, navigate to the CloudFormation stacks and choose Delete. Delete the stacks in the following order:
1. WebappSetup
2. BackendSetup
3. CognitoSetup

All resources created in this solution will be deleted.

Monitoring

You can monitor your event-driven web application events, user activity, and API usage with Amazon CloudWatch and AWS CloudTrail. Most areas of this solution already have logging enabled. To view your API Gateway logs, you can turn on CloudWatch Logs. Lambda comes with default logging and monitoring and can be accessed with CloudWatch.

Security

You can secure access to the application using Amazon Cognito, which is a developer-centric and cost-effective customer authentication, authorization, and user management solution. It provides both identity store and federation options that can scale easily. Amazon Cognito supports logins with social identity providers and SAML or OIDC-based identity providers, and supports various compliance standards. It operates on open identity standards (OAuth2.0, SAML 2.0, and OpenID Connect). You can also integrate it with API Gateway to authenticate and authorize the REST API calls either using the Amazon Cognito client app or a Lambda function.

Considerations

The nature of this application includes a front-end client initializing SQL queries to Amazon Redshift. An important component to consider are potential malicious activities that the client can perform, such as SQL injections. With the current implementation, that is not possible. In this solution, the SQL queries preexist in your AWS environment and are DQL statements (they don’t alter the data or structure). However, as you develop this application to fit your business, you should evaluate these areas of risk.

AWS offers a variety of security services to help you secure your workloads and applications in the cloud, including AWS Shield, AWS Network Firewall, AWS Web Application Firewall, and more. For more information and a full list, refer to Security, Identity, and Compliance on AWS.

Cost optimization

The AWS services that the CloudFormation templates provision in this solution are all serverless. In terms of cost optimization, you only pay for what you use. This model also allows you to scale without manual intervention. Review the following pages to determine the associated pricing for each service:

Conclusion

In this post, we showed you how to create an event-driven application using the Amazon Redshift Data API and API Gateway WebSocket and REST APIs. The solution helps you build data analytical web applications in an event-driven architecture, decouple your application, optimize long-running database queries processes, and avoid unnecessary polling requests between the client and the backend.

You also used severless technologies, API Gateway, Lambda, DynamoDB, and EventBridge. You didn’t have to manage or provision any servers throughout this process.

This event-driven, serverless architecture offers greater extensibility and simplicity, making it easier to maintain and release new features. Adding new components or third-party products is also simplified.

With the instructions in this post and the generic CloudFormation templates we provided, you can customize your own event-driven application tailored to your business. For feedback or contributions, we welcome you to contact us through the AWS Samples GitHub Repository by creating an issue.

About the Authors

David Zhang is an AWS Data Architect in Global Financial Services. He specializes in designing and implementing serverless analytics infrastructure, data management, ETL, and big data systems. He helps customers modernize their data platforms on AWS. David is also an active speaker and contributor to AWS conferences, technical content, and open-source initiatives. During his free time, he enjoys playing volleyball, tennis, and weightlifting. Feel free to connect with him on LinkedIn.

Manash Deb is a Software Development Manager in the AWS Directory Service team. With over 18 years of software dev experience, his passion is designing and delivering highly scalable, secure, zero-maintenance applications in the AWS identity and data analytics space. He loves mentoring and coaching others and to act as a catalyst and force multiplier, leading highly motivated engineering teams, and building large-scale distributed systems.

Pavan Kumar Vadupu Lakshman Manikya is an AWS Solutions Architect who helps customers design robust, scalable solutions across multiple industries. With a background in enterprise architecture and software development, Pavan has contributed in creating solutions to handle API security, API management, microservices, and geospatial information system use cases for his customers. He is passionate about learning new technologies and solving, automating, and simplifying customer problems using these solutions.