AWS Toolkit for JetBrains
User Guide

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Key Tasks for the AWS Toolkit for JetBrains

Use the following brief instructions to complete key tasks with the AWS Toolkit for JetBrains.

Install the AWS Toolkit for JetBrains

  1. Create an AWS account, if you don't have an account already.

  2. Create an administrator user and group in AWS Identity and Access Management (IAM) in the account, if you haven't done that already.

    Note

    We recommend that you create or use a special type of user and group in the account for the AWS Toolkit for JetBrains to use, which we call an administrator IAM user and group. Although you can create a regular IAM user and group in the account for the toolkit to use, this approach might not allow the toolkit to have full access to all of the AWS resources and AWS serverless applications in that account. We support, but strongly discourage, using an AWS account root user with the AWS Toolkit for JetBrains.

  3. Create an access key for the user, if you don't have an access key for that user already.

    Note

    An access key contains both an access key ID value and a secret access key value. The AWS Toolkit for JetBrains needs to use both of these values later. Be sure to store them in a secure location. If you lose them, they're gone forever and can't be retrieved. However, you can always delete a lost access key, and then create a replacement access key. If you ever do this, you also need to change your toolkit connection settings. We support, but strongly discourage, creating an access key for an AWS account root user for the AWS Toolkit for JetBrains to use.

  4. Ensure that IntelliJ IDEA or PyCharm 2018.3 or later is installed and running.

  5. For macOS, on the main menu, choose IntelliJ IDEA, Preferences or PyCharm, Preferences.

    For Windows and other operating systems, on the main menu, choose File, Settings.

  6. Choose Plugins.

  7. On the Marketplace tab, in Search plugins in marketplace, begin entering AWS Toolkit. When AWS Toolkit by Amazon Web Servicesis displayed, choose it.

    
        Finding the AWS Toolkit

    Note: AWS Toolkit is also available online at plugins.jetbrains.com

  8. Choose Install.

    
        Installing the AWS Toolkit for JetBrains
  9. When the Third-party Plugins Privacy Note is displayed, choose Accept.

  10. Choose Restart IDE.

  11. When prompted, choose Restart.

  12. Before you can use the AWS Toolkit for JetBrains to develop, test, analyze, and deploy AWS serverless applications or AWS Lambda functions, you must first also install the following tools, if you haven't done so already. These tools must be installed in the following order:

    1. AWS Command Line Interface (AWS CLI)

    2. Docker (Docker must always be running whenever you develop, test, analyze, or deploy serverless applications or functions)

    3. AWS Serverless Application Model Command Line Interface (AWS SAM CLI)

  13. After you install the AWS Toolkit for JetBrains (and, if you're working with AWS serverless applications or Lambda functions, you've installed the preceding additional required tools, in order), connect to an AWS account for the first time.

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Update the AWS Toolkit for JetBrains

After you install the AWS Toolkit for JetBrains, you can check for updates to the toolkit at any time and install them. To do this, with IntelliJ IDEA or PyCharm already running, do the following.

  1. For macOS, on the main menu, choose IntelliJ IDEA, Preferences or PyCharm, Preferences. For other operating systems, on the main menu, choose File, Settings.

  2. Choose Updates. (If no updates are displayed, you might need to choose Check new updates.)

    
        Checking for updates to the AWS Toolkit for JetBrains
  3. Follow any on-screen instructions to finish updating the AWS Toolkit for JetBrains.

  4. Restart IntelliJ IDEA or PyCharm.

Configure the AWS Toolkit for JetBrains to Use an HTTP Proxy

After you install the AWS Toolkit for JetBrains, you can configure it to use an HTTP proxy. With IntelliJ IDEA or PyCharm already running, do one of the following:

  • For IntelliJ IDEA, see HTTP Proxy on the IntelliJ IDEA Help website.

  • For PyCharm, see HTTP Proxy on the PyCharm Help website.

After you complete the preceding instructions, the toolkit begins using those HTTP proxy settings.

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Connect the AWS Toolkit for JetBrains to AWS Accounts

After you install the AWS Toolkit for JetBrains, use the toolkit to do the following with AWS accounts:

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Connect to an AWS Account for the First Time

We assume that you already installed the AWS Toolkit for JetBrains. To complete this procedure, you need an access key (which contains both an access key ID value and a secret access key value) for a user in IAM (which we recommend), or for an AWS account root user (which we strongly discourage). (If you don't have an access key for a user in IAM already, create one.)

  1. With your access key ID value and secret access key value ready, do one of the following:

    • On the status bar, choose AWS: No credentials selected, and then choose Edit AWS Credential file(s).

      
            AWS no credentials selected on the status bar
      
            Edit AWS credentials from the status bar
    • Open AWS Explorer, if it is not already open. Choose Configure AWS Connection, and then choose Edit AWS Credential file(s).

      
            Configure AWS connection from AWS Explorer
      
            Edit AWS credentials from AWS Explorer
  2. In the file, under [default], for aws_access_key_id, replace [accessKey1] with your access key ID value (for example, AKIAIOSFODNN7EXAMPLE).

    If prompted, select I want to edit this file anyway, and then choose OK.

  3. For aws_secret_access_key, replace [secretKey1] with your secret access key value (for example, wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY).

    The final results should look as shown here, following the named profile format.

    ... Other file contents omitted for brevity ... [default] # ... Some comments ... aws_access_key_id = AKIAIOSFODNN7EXAMPLE # ... Some more comments ... # ... Some more comments ... # ... Some more comments ... # ... Some more comments ... aws_secret_access_key = wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY ... Other file contents omitted for brevity ...

    Note

    The AWS Toolkit for JetBrains currently supports the following configuration variables:

    • aws_access_key_id

    • aws_secret_access_key

    • aws_session_token

    • credential_process

    • mfa_serial

    • role_arn

    • source_profile

    For more information, see AWS CLI Configuration Variables in the AWS CLI Command Reference.

  4. Save and then close the file. The AWS Toolkit for JetBrains tries to connect to the account by using the preceding access key.

    After connecting, you can use the AWS Toolkit for JetBrains to work with AWS resources in that account, such as AWS serverless applications, AWS Lambda functions, and AWS CloudFormation stacks.

You can also have more than one connection available, so that you can switch between them.

After you connect, the AWS Toolkit for JetBrains selects the default AWS Region automatically. You might need to switch connections to work with different AWS resources.

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Add Multiple Connections

To complete this procedure, you must first have the additional access key (which contains both an access key ID value and a secret access key value) for a user in IAM (which we recommend) or AWS account root user (which we strongly discourage). If you don't have an access key for a user IAM already, create one.

  1. Connect for the first time, if you have not done so already.

  2. With the additional access key ID value and secret access key value ready, do one of the following:

    • On the status bar, choose AWS Connection Settings, and then choose All Credentials, Edit AWS Credential file(s).

      
            Choosing to edit AWS credentials from the status bar
    • Open AWS Explorer, if it isn't already open, and then choose Show Options Menu (the gear icon). Choose AWS Connection Settings, All Credentials, Edit AWS Credential file(s).

      
            Choosing to edit AWS credentials from AWS Explorer
  3. In the file, add a named profile for each additional connection. Profile names can contain only the uppercase letters A through Z, the lowercase letters a through z, the numbers 0 through 9, the hyphen character (-), and the underscore character (_). Profile names must be less than 64 characters in length.

    For example, for a named profile named myuser, use the following format.

    [profile myuser] aws_access_key_id = AKIAIOSFODNN7EXAMPLE aws_secret_access_key = wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

    Note

    The AWS Toolkit for JetBrains currently supports named profiles with only the following characters: A-Z, a-z, 0-9, underscore (_), and hyphen (-).

    The toolkit also currently supports only the following configuration variables:

    • aws_access_key_id

    • aws_secret_access_key

    • aws_session_token

    • credential_process

    • mfa_serial

    • role_arn

    • source_profile

    For more information, see AWS CLI Configuration Variables in the AWS CLI Command Reference.

  4. Save and then close the file. The AWS Toolkit for JetBrains displays the new connection in the AWS Connection Settings menu in both the status bar and in AWS Explorer.

Now that you have multiple connections, you can switch between them.

After you connect, you might need to switch connections to work with.

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Switch between Connections

  1. Add multiple connections, if you haven't done so already.

  2. Do one of the following:

    • On the status bar, choose AWS Connection Settings.

    • Open AWS Explorer, if it isn't already open, and then choose AWS Connection Settings.

  3. Choose the name of the named profile to use for the new connection. If it isn't listed, first choose All Credentials, and then choose the name of the named profile to use.

    Switching the current connection

    The AWS Toolkit for JetBrains switches to using the new connection. This connection is now selected in the AWS Connection Settings menu in both the status bar and AWS Explorer.

After you connect, you might need to switch to working with AWS resources in that account that are in a different AWS Region.

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Change Connection Settings

  1. Do one of the following:

    • On the status bar, choose AWS Connection Settings, All Credentials, Edit AWS Credential file(s).

      
            Choosing the Edit AWS Credential files command
    • Open AWS Explorer, if it isn't already open, and then choose Show Options Menu (the gear icon). Then choose AWS Connection Settings, All Credentials, Edit AWS Credential file(s).

      
            Choosing the Edit AWS Credential files command
  2. Make your changes to the file, and then save and close the file.

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Delete a Connection

  1. Do one of the following:

    • On the status bar, choose AWS Connection Settings, All Credentials, Edit AWS Credential file(s).

      
            Choosing the Edit AWS Credential files command
    • Open AWS Explorer, if it isn't already open, and then choose Show Options Menu (the gear icon). Then choose AWS Connection Settings, All Credentials, Edit AWS Credential file(s).

      
            Choosing the Edit AWS Credential files command
  2. In the file, completely delete the named profile (including the named profile's name, access key ID, and secret access key) for the connection that you want to delete.

  3. Save and then close the file. The AWS Toolkit for JetBrains removes the deleted connection from the AWS Connection Settings menu in both the status bar and in AWS Explorer.

After you delete a connection, you might need to switch to a different connection or connect for the first time again.

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Get the Current Connection

To check which connection the AWS Toolkit for JetBrains is currently using, do one of the following:

  • On the status bar, see the current connection displayed in the AWS Connection Settings area.

    
        The current connection in the status bar
  • Open AWS Explorer, if it's not already open, and then choose Show Options Menu (the gear icon). Choose AWS Connection Settings. The current connection is selected.

    
        The current connection in AWS Explorer

You can also have more than one connection available, so that you can switch between them.

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Get the Current AWS Region That Is In Use

To check which AWS Region the AWS Toolkit for JetBrains is currently using, do one of the following:

  • On the status bar, see the current Region displayed in the AWS Connection Settings area.

    
        The current AWS Region in the status bar
  • Open AWS Explorer, if it isn't already open, and then choose Show Options Menu (the gear icon). Choose AWS Connection Settings. The current Region is selected.

    
        The current AWS Region in AWS Explorer

You can also switch to a different AWS Region.

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Switch between AWS Regions

To switch AWS Regions, do one of the following:

  • On the status bar, choose AWS Connection Settings, and then choose the AWS Region that you want to switch to.

    Choosing a different AWS Region in the status bar
  • Open AWS Explorer, if it isn't already open. Choose Show Options Menu (the gear icon), and then choose AWS Connection Settings. If the AWS Region that you want to switch to is listed, choose it. Otherwise, choose All Regions, and then choose the Region to switch to.

    
        Choosing a different AWS Region in AWS Explorer

The AWS Toolkit for JetBrains switches to using the new Region. The Region is now selected in the AWS Connection Settings menu in both the status bar and AWS Explorer.

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Open AWS Explorer within the AWS Toolkit for JetBrains

To complete this procedure, you must first install the AWS Toolkit. Then, with IntelliJ IDEA or PyCharm already running, do one of the following:

  • On the tool window bar, choose AWS Explorer.

    
            AWS Explorer tool window button
  • On the View menu, choose Tool Windows, AWS Explorer.

    
            Opening AWS Explorer from the main menu

After you open AWS Explorer for the first time, use it to connect to an AWS account for the first time. After you connect for the first time, you can use AWS Explorer to work with AWS Lambda functions and AWS CloudFormation stacks in the account.

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Work with AWS Serverless Applications

After you install the AWS Toolkit for JetBrains and then used the toolkit to connect to an AWS account for the first time, you can use the toolkit to work with AWS serverless applications in an account, as follows:

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Create a Serverless Application

To complete this procedure, you must first install the AWS Toolkit for JetBrains, and if you haven't yet, connect to an AWS account for the first time. With IntelliJ IDEA or PyCharm already running, do the following:

  1. Choose File, New Project.

  2. For IntelliJ IDEA, choose AWS, AWS Serverless Application, and then choose Next.

    Choosing to create an AWS serverless application in IntelliJ IDEA

    For PyCharm, choose AWS Serverless Application.

    Choosing to create an AWS serverless application in PyCharm
  3. Complete the New Project dialog box, and then choose Finish (for IntelliJ IDEA) or Create (for PyCharm). The AWS Toolkit for JetBrains creates the project and adds the serverless application's code files to the new project.

  4. If you're using IntelliJ IDEA, with the Project tool window already open and displaying the project that contains the serverless application's files, do one of the following:

    • For Maven-based projects, right-click the project's pom.xml file, and then choose Add as Maven Project.

      Choosing to add the POM file as a Maven project
    • For Gradle-based projects, right-click the project's build.gradle file, and then choose Import Gradle project.

      Choosing to import the Gradle project

      Complete the Import Module from Gradle dialog box, and then choose OK.

After you create the serverless application, you can run (invoke) or debug the local version of an AWS Lambda function that is contained in that application.

You can also deploy the serverless application. After you deploy it, you can run (invoke) the remote version of a Lambda function that is part of that deployed application.

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Deploy a Serverless Application

Before you can use this procedure to deploy an AWS serverless application, you must first create the AWS serverless application. Then follow these steps.

Note

To deploy a serverless application that contains an AWS Lambda function, and deploy that function with any nondefault or optional properties, you must first set those properties in the function's corresponding AWS Serverless Application Model (AWS SAM) template file (for example, in a file named template.yaml within the project). For a list of available properties, see AWS::Serverless::Function in the awslabs/serverless-application-model repository on GitHub.

  1. If you need to switch to a different AWS Region to deploy the serverless application, do that now.

  2. With the Project tool window already open and displaying the project that contains the serverless application's files, right-click the project's template.yaml file. Then choose Deploy Serverless Application.

    Choosing the Deploy Serverless Application command
  3. Complete the Deploy Serverless Application dialog box, and then choose Deploy.

    The AWS Toolkit for JetBrains creates a corresponding AWS CloudFormation stack for the deployment. It also adds the name of the stack to the CloudFormation list in AWS Explorer. If the deployment fails, you can try to figure out why by viewing event logs for the stack.

After you deploy it, you can run (invoke) the remote version of an AWS Lambda function that is part of that deployed application.

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Change (Update) the Settings for a Serverless Application

Before you can use this procedure to change settings for a serverless application, you must first deploy the AWS serverless application that you want to change. Then follow these steps.

Note

To deploy a serverless application that contains an AWS Lambda function, and deploy that function with any nondefault or optional properties, you must first set those properties in the function's corresponding AWS SAM template file (for example, in a file named template.yaml within the project). For a list of available properties, see AWS::Serverless::Function in the awslabs/serverless-application-model repository on GitHub.

  1. With the Project tool window already open and displaying the project that contains the serverless application's files, open the project's template.yaml file. Change the file's contents to reflect the new settings, and then save and close the file.

  2. If you need to switch to a different AWS Region to deploy the serverless application to, do that now.

  3. Right-click the project's template.yaml file, and then choose Deploy Serverless Application.

    Choosing the Deploy Serverless Application command
  4. Complete the Deploy Serverless Application dialog box, and then choose Deploy. The AWS Toolkit for JetBrains updates the corresponding AWS CloudFormation stack for the deployment.

    If the deployment fails, you can try to figure out why by viewing event logs for the stack.

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Delete a Serverless Application

Before you can use this procedure to delete a serverless application, you must first deploy the AWS serverless application that you want to delete. Then follow these steps.

  1. Open AWS Explorer, if it isn't already open. If you need to switch to a different AWS Region that contains the serverless application, do that now.

  2. Expand CloudFormation.

  3. Right-click the name of the AWS CloudFormation stack that contains the serverless application you want to delete, and then choose Delete CloudFormation Stack.

    
        Choosing to delete the AWS CloudFormation stack for an AWS serverless application starting from AWS Explorer
  4. Enter the stack's name to confirm the deletion, and then choose OK. If the stack deletion succeeds, the AWS Toolkit for JetBrains removes the stack name from the CloudFormation list in AWS Explorer. If the stack deletion fails, you can try to figure out why by viewing event logs for the stack.

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Work with AWS Lambda Functions

After you install the AWS Toolkit for JetBrains and then used the toolkit to connect to an AWS account for the first time, you can use the toolkit to work with Lambda functions in the account, as follows.

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Create a Function

You can use the AWS Toolkit for JetBrains to create a Lambda function that is part of an AWS serverless application, or you can create a Lambda function by itself.

Create a Serverless Application that Contains a Lambda Function

See the instructions earlier in this topic about creating an AWS serverless application.

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Create a Standalone Function

To complete this procedure, you must first install the AWS Toolkit for JetBrains and, if you haven't yet, connect to an AWS account for the first time. Then with IntelliJ IDEA or PyCharm already running, do one of the following:

  • Open AWS Explorer, if it isn't already open. If you need to switch to a different AWS Region to create the function in, do that now. Then right-click Lambda, and choose Create new AWS Lambda.

    
        Creating an AWS Lambda function by starting from AWS Explorer

    Complete the Create Function dialog box, and then choose Create Function. The AWS Toolkit for JetBrains creates a corresponding AWS CloudFormation stack for the deployment, and adds the function name to the Lambda list in AWS Explorer. If the deployment fails, you can try to figure out why by viewing event logs for the stack.

  • Create a code file that implements a function handler for Java or a function handler for Python.

    To enable running (invoking) the remote version of the function, if you need to switch to a different AWS Region to create the function in, do that now. Then in the code file, choose the Lambda icon in the gutter next to the function handler, and then choose Create new AWS Lambda. Complete the Create Function dialog box, and then choose Create Function.

    
        Creating an AWS Lambda function by starting from an existing function handler in a
          code file

    Note

    If the Lambda icon isn't displayed in the gutter next to the function handler, you can try displaying it for the current project by selecting the following box in Preferences: Tools , AWS, Project settings, Show gutter icons for all potential AWS Lambda handlers. Also, if the function handler is already defined in the corresponding AWS SAM template, the Create new AWS Lambda command won't appear.

    After you choose Create Function, the AWS Toolkit for JetBrains creates a corresponding function in the Lambda service for the connected AWS account. If the operation succeeds, after you refresh AWS Explorer, the Lambda list displays the name of the new function.

  • If you already have a project that contains an AWS Lambda function, and if you need to first switch to a different AWS Region to create the function in, do that now. Then in the code file that contains the function handler for Java or the function handler for Python, choose the Lambda icon in the gutter next to the function handler. Choose Create new AWS Lambda, complete the Create Function dialog box, and then choose Create Function.

    
        Creating an AWS Lambda function by starting from an existing function handler in a
          code file

    Note

    If the Lambda icon isn't displayed in the gutter next to the function handler, you can try displaying it for the current project by selecting the following box in Preferences: Tools, AWS, Project settings, Show gutter icons for all potential AWS Lambda handlers. Also, the Create new AWS Lambda command won't display if the function handler is already defined in the corresponding AWS SAM template.

    After you choose Create Function, the AWS Toolkit for JetBrains creates a corresponding function in the Lambda service for the connected AWS account. If the operation succeeds, after you refresh AWS Explorer, the new function's name appears in the Lambda list.

After you create the function, you can run (invoke) or debug the local version of the function or run (invoke) the remote version.

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Run (Invoke) or Debug the Local Version of a Function

A local version of an AWS Lambda function is a function whose source code already exists on your local development computer.

To complete this procedure, you must first create the AWS Lambda function that you want to run (invoke) or debug, if you haven't created it already.

Note

If you want to run (invoke) or debug the local version of an AWS Lambda function, and run (invoke) or debug that function locally with any nondefault or optional properties, you must first set those properties in the function's corresponding AWS SAM template file (for example, in a file named template.yaml within the project). For a list of available properties, see AWS::Serverless::Function in the awslabs/serverless-application-model repository on GitHub.

  1. Do one of the following:

    • In the code file that contains the function handler for Java or the function handler for Python, choose the Lambda icon in the gutter next to the function handler. Choose Run '[Local]' or Debug '[Local]'.

      
            Running or debugging the local version of a Lambda function by starting from the
              function handler in the code file
    • With the Project tool window already open and displaying the project that contains the function, open the project's template.yaml file. Choose the Run icon in the gutter next to the function's resource definition, and then choose Run '[Local]' or Debug '[Local]'.

      
            Running or debugging the local version of a Lambda function by starting from the
              function definition in the AWS SAM template file
  2. Complete the Edit configuration dialog box if it is displayed, and then choose Run or Debug. (If the Edit configuration dialog box doesn't appear and you want to change the existing configuration, first change its configuration, and then repeat this procedure from the beginning. If the configuration details are missing, expand Templates, AWS Lambda, and then choose Local. Choose OK, and then repeat this procedure from the beginning.) Results are displayed in the Run or Debug tool window.

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Run (Invoke) the Remote Version of a Function

A remote version of an AWS Lambda function is a function whose source code already exists inside of the Lambda service for an AWS account.

To complete this procedure, you must first install the AWS Toolkit for JetBrains and, if you haven't yet, connect to an AWS account for the first time. Then with IntelliJ IDEA or PyCharm already running, do the following.

  1. Open AWS Explorer, if it isn't already open. If you need to switch to a different AWS Region that contains the function, do that now.

  2. Expand Lambda, and confirm that the name of the function is listed. If it is, skip ahead to step 3 in this procedure.

    If the name of the function isn't listed, create the Lambda function that you want to run (invoke), if you have not done so already.

    If you created the function as part of an AWS serverless application, you must also deploy that application.

    If you created the function by creating a code file that implements a function handler for Java or a function handler for Python, then in the code file, choose the Lambda icon next to the function handler. Then choose Create new AWS Lambda. Complete the Create Function dialog box, and then choose Create Function.

  3. With Lambda already expanded in AWS Explorer, right-click the name of the function, and then choose Run '[Remote]'.

    
        Running the remote version of a Lambda function by starting from AWS
          Explorer
  4. Complete the Edit configuration dialog box if it is displayed, and then choose Run. (If the Edit configuration dialog box isn't displayed and you want to change the existing configuration, first change its configuration, and then repeat this procedure starting from step 3. If the configuration details are missing, expand Templates, AWS Lambda. Then choose Remote, choose OK, and then repeat this procedure from the beginning.) Results are displayed in the Run tool window.

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Change (Update) the Configuration for a Function

Do one of the following:

  • With the code file open that contains the function handler for Java or the function handler for Python, on the main menu, choose Run, Edit Configurations. Complete the Run/Debug Configurations dialog box, and then choose OK.

  • Open AWS Explorer, if it isn't already open. If you need to switch to a different AWS Region that contains the function, do that now. Expand Lambda, choose the name of the function to change the configuration for, and then do one of the following:

    • Change settings such as the timeout, memory, environment variables, and execution role – Right-click the name of the function, and then choose Update Function Configuration.

      Choosing the Update Function Configuration command

      Complete the Update Configuration dialog box, and then choose Update.

    • Change settings such as the input payload – On the main menu, choose Run, Edit Configurations. Complete the Run/Debug Configurations dialog box, and then choose OK.

      Choosing the Edit Configurations command

      If the configuration details are missing, first expand Templates, AWS Lambda, and then choose Local (for the local version of the function) or Remote (for the remote version of that same function). Choose OK, and then repeat this procedure from the beginning.)

    • Change settings such as the function handler name or Amazon Simple Storage Service (Amazon S3) source bucket – Right-click the function name, and then choose Update Function Code.

      Choosing the Update Function Code command

      Complete the Update Code dialog box, and then choose Update.

    • Change other available property settings that are not listed in the preceding bullets – Change those settings in the function's corresponding AWS SAM template file (for example, in a file named template.yaml within the project).

      For a list of available property settings, see AWS::Serverless::Function in the awslabs/serverless-application-model repository on GitHub.

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Delete a Function

You can use the AWS Toolkit for JetBrains to delete an AWS Lambda function that is part of an AWS serverless application. Or you can delete a standalone Lambda function.

Delete a Serverless Application that Contains a Function

See the instructions on deleting a serverless application, earlier in this topic.

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Delete a Standalone Function

  1. Open AWS Explorer, if it isn't already open. If you need to switch to a different AWS Region that contains the function, do that now.

  2. Expand Lambda.

  3. Right-click the name of the function to delete, and then choose Delete Function.

    Choosing the Delete Function command
  4. Enter the function's name to confirm the deletion, and then choose OK. If the function deletion succeeds, the AWS Toolkit for JetBrains removes the function name from the Lambda list.

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Working with AWS CloudFormation Stacks

After you install the AWS Toolkit for JetBrains and then use the toolkit to connect to an AWS account for the first time, you can use the toolkit to work with AWS CloudFormation stacks in the account, as follows.

Currently you cannot use the AWS Toolkit for JetBrains to directly create stacks or to change stack settings. However, you can do these tasks indirectly as part of working with AWS serverless applications and AWS Lambda functions, as follows.

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Create a Stack

Currently, you can't use the AWS Toolkit for JetBrains to create an AWS CloudFormation stack directly. However, whenever you use the AWS Toolkit for JetBrains to deploy an AWS serverless application or to create and then deploy an AWS Lambda function, the toolkit deploys these by first creating a corresponding stack in AWS CloudFormation, and then using that stack for the deployment.

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Change Stack Settings

Currently, you can't use the AWS Toolkit for JetBrains to change the settings for an AWS CloudFormation stack directly. However, you can change (update) the settings for an AWS serverless application that belongs to a stack, or change (update) the configuration for an AWS Lambda function that belongs to a stack. Then you deploy that serverless application again or deploy that function (as part of the lifecycle of running (invoking) the remote version of that function) again.

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View Event Logs for a Stack

  1. Open AWS Explorer, if it isn't already open. If the stack is in an AWS Region that's different from the current one, switch to a different AWS Region that contains it.

  2. Expand CloudFormation.

  3. To view event logs for the stack, right-click the stack's name. The AWS Toolkit for JetBrains displays the event logs in the CloudFormation tool window.

    To hide or show the CloudFormation tool window, on the main menu, choose View, Tool Windows, CloudFormation.

    
        Choosing to view event logs for an AWS CloudFormation stack starting from AWS
          Explorer

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Deleting a Stack

  1. Open AWS Explorer, if it isn't already open. If you need to switch to a different AWS Region that contains the stack, do that now.

  2. Expand CloudFormation.

  3. Right-click the name of the stack to delete, and then choose Delete CloudFormation Stack.

    
        Choosing to delete a AWS CloudFormation stack starting from AWS Explorer
  4. Enter the stack's name to confirm it's deleted, and then choose OK. If the stack deletion succeeds, the AWS Toolkit for JetBrains removes the stack name from the CloudFormation list in AWS Explorer. If the stack deletion fails, you can troubleshoot by viewing the event logs for the stack.

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