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Check Time Left

This snippet helps make a routing decision whether to add a new contact to the queue or take another action (like terminating the call) based on the queue length and the business hours for that day.

The solution includes 2 AWS Lambda functions, and an Amazon DynamoDB table:

  • UpdateQueueHours Lambda function:

    • Triggered by AWS EventBridge scheduled rule
    • UpdateRate parameter defines Hours of Operation refresh rate, between DynamoDB and Amazon Connect Instance
    • Retrieves all Queues from an Amazon Connect instance
    • For each queue, retrieves the queue Hours of Operation
    • Stores Queue and Hours of Operation configuration in a DynamoDB table

  • GetTimeLeft Lambda function:

    • Triggered by Amazon Connect Contact Flow
    • Receives contact's Queue ARN and Oldest contact in queue
    • Retrieves Queue and Hours of Operation configuration from the DynamoDB table
    • Checks the Hours of Operation and determines HoursOfOperationStatus as:
      • QUEUE_NOT_FOUND - in case the Queue was not found in the DynamoDB table
      • OUT_OF_HOURS - in case:
        • current day was not found in the Hours of Operation, or
        • current time was outside of Start and End time window for the current day
      • OPEN_ALL_HOURS - in case Start and End time were equal for the current day
      • IN_HOURS - in case current time was between Start and End time window for the current day
    • Only if HoursOfOperationStatus was IN_HOURS, the function adds the following to the response:
      • TimeLeftMinutes = time difference between current time and End time
      • TimeDeltaMinutes = time difference between TimeLeftMinutes and Oldest contact in queue

Based on GetTimeLeft function response, you can design your Amazon Connect Contact Flow to either add the contact to the queue, if there was enough time left before the queue closes, or take another action.

To invoke GetTimeLeft function from your Amazon Connect Contact Flow:

  • Place Set working queue block and set your Queue (i.e. BasicQueue)
  • Place Get queue metrics block
  • Place Invoke AWS Lambda function and select GetTimeLeft Lambda function
  • In Lambda function input parameters add:
    • Select Use attribute
    • Destination key: OldestContactTimeSeconds
    • Type: Queue metrics
    • Attribute: Oldest contact in queue
  • Place Check contact attributes and select Type: External, Attribute: HoursOfOperationStatus, then add conditions: QUEUE_NOT_FOUND, OUT_OF_HOURS, OPEN_ALL_HOURS, IN_HOURS, and complete the contact treatment based on the result.
  • For IN_HOURS result, you can add another Check contact attributes block to check External->TimeLeftMinutes and External->TimeDeltaMinutes response parameters, then complete the contact treatment based on the result.

An AWS Serverless Application Model (SAM) defines the application's AWS resources in template.yaml file.

The application includes AWS resources such as AWS Lambda functions, Amazon DynamoDB and Amazon CloudWatchEvents. You can update the template to add additional AWS resources, through the same deployment process that updates your application code.

If you prefer to use an integrated development environment (IDE) to build and test your application, you can use the AWS Toolkit.
The AWS Toolkit is an open source plug-in for popular IDEs that uses the SAM CLI to build and deploy serverless applications on AWS. The AWS Toolkit also adds a simplified step-through debugging experience for Lambda function code. See the following links to get started.

Deploy the sample application

The Serverless Application Model Command Line Interface (SAM CLI) is an extension of the AWS CLI that adds functionality for building and testing serverless applications. It uses Docker to run your functions in an Amazon Linux environment that matches Lambda. It can also emulate your application's build environment and API.

To use the SAM CLI, you need the following tools.

To build and deploy your application for the first time, run the following in your shell:

sam build
sam deploy --guided

If you no longer need the AWS resources that you created by running this script, you can remove them by deleting the AWS CloudFormation stack that you deployed.

aws cloudformation delete-stack --stack-name <stack_name> --region <region>