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Table of Contents

Objective

You have a working Azure Java Function and the source code is managed in a Git repository. The goal of the tutorial is to automate the deployment of one or more Java Functions to an Azure Function App.  This automation will include:

  • cloning the Java application from a Git repository

  • compiling the Java Function

  • package packaging the Java executable and Application Setting/Connection String properties file

  • update relevant Application Setting and Connection String properties in Azure Function App

  • deploying the Java Function(s) to Azure Function App

  • retrieve the deployment log (optional) against the deployment executed

We will walk through each of the FlexDeploy features that will be created/configured to accomplish this goal and have the Java Function deployed in a very short amount of time.

Checklist

Checklist

Description

Azure Tenant Id

Azure Tenant Id for the company

Azure Subscription Id

Azure Subscription where the Function App exists

Azure Client ID

Client ID to connect to desired Azure Subscription

Azure Client Key

Password for the Client ID

Azure Client ID permission

The Client ID must have relevant access in target Function App to deploy code

Azure Function App

The Function App with relevant runtime should already be created

Azure Resource Group

The Resource Group containing the Function App

Azure CLI installation

Azure CLI needs to be installed where the plugin operation

shall

will run (generally on FlexDeploy server)

Azure CLI in class path

Azure CLI should be added to

class

the path on the

FlexDeploy Server. Else the

server where the plugin operation will run. Otherwise, the path can also be set

under

using FlexDeploy

environment

target level property

Configure

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Cloud Account

First, an Azureaccount needs to be configured under Topology. FlexDeploy will connect to the right Azure Subscription with provided details and do to run the deployment.

  • Navigate to the Integrationssection through the menu

  • Access Open the Cloud tab under Integraions

  • Create a new Cloud account of the by pressing the Create button. The provider type Azure” with the “Create” button.should be Azure

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It should have a Client ID, Client Key, Tenant Id, and Subscription Id configured in it. The Client ID must have relevant access in the target Function App to deploy code and make other associated changes. (Application Setting update, etc.).

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Git

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Repository Structure

The Git repository should contain the function codebase. Alongside it can also contain any Application Setting and Connection String application setting and connection string related attributes in respective files (any naming convention allowed). These files are not mandatory, however, in case someone has any Application Setting and Connection String property to update this is recommended. Please review the Application Setting and Connection String property updateProperty Update section for a more detailed explanation.

The Sample A sample Git repository structure is given below.:

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Build and Deploy Workflows

Build Workflow

Navigate to the Workflows tab and create a workflow using the +”(Click to create new Workflow) (blue star) button as highlighted below.

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Next, create one Build and Deploy workflow as shown below. The workflow Type field defines the type of workflow.

Build Workflow

  1. Navigate to the Workflows

  2. Select the “+” button from the left-hand pane to create a new workflow

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Deploy Workflow

  1. navigate to the Workflows

  2. Select the “+” button from the left-hand pane to create a new workflow

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The Workflow Group and Subgroup define the folder hierarchy. Once both workflows are created it should look like the below. No constraint on workflow or folder naming convention.

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The steps of the workflow execution can be configured through the Workflow Definition section.

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Below given is a sample build workflow to build a Java executable(jar file) and create an artifact from it.

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Step-i: Clone Git Repository

This step will clone the Git repository codebase into the project execution working directory. The Git URL will be retrieved from Source Control configured under Project Configuration.

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Below given is the Git code structure and corresponding exported codebase during execution as a result of the above step.

Git Codebase

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Exported Codebase

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Step-ii: Execute Maven Build

The below step will execute the maven build with the project pom.xml and will create a Java executable(.jar) file inside the target directory.

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note: Each application will have separate directory structure hence the all paths need to be modified accordingly.

note: Its essential to make sure Consumes Artifacts and Produces Artifacts checkbox is marked only when required. Please follow the snapshots for all execution steps to avoid any discrepancy.

If the pom.xml is properly configured the java executable will be generated along other essential files as shown below under target/azure-functions folder.

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Also for each Function sperate folder will be generated with corresponding function.json file in it. These will be automatically generated.

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All of above files need to be copied to Azure Function App to execute properly.

Step-iii: Copy the properties file to the intermediate dir

Application Setting and Connection String json files(if applicable/present) will be moved to the intermediate directory. An intermediate directory can have any name, in this case, we are using the artifactsObjects folder inside the temp directory. File names are mentioned under File Filter with ‘##’ separated(as advised in the description). Application Setting and Connection String values can also be passed as input to deploy workflow. Application Setting and Connection String files are optional, please review the Application Setting and Connection String property update for a detailed explanation.

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Step-iv: Create an archive of the executable in the intermediate dir

Per the Azure Function App deployment requirement, we have created an archive with the java executable and other associated files(generated through maven) in it.

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note: Each application will have separate directory structure hence the all paths need to be modified accordingly.

If we look inside the deploy.zip, it contains the jar file along with other associated files. Basically, it contains all relevant files that need to be copied to the Azure Function App backend. Azure Function App treats the jar file as an executable package and runs it.

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note: the jar file(or any executable files) inside the .zip file shouldn’t be inside any subfolder. Else once the zip file is deployed to Azure Function App may not work.

Step-v: Save all as Artifact from the intermediate dir

Post execution of the above steps the intermediate directory(artifactObjects) should contain files as shown below.

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These objects are required as part of the deployment flow. Hence we create an archive(artifacts.zip) with them inside the Artifacts directory. We also enable Produces Artifacts checkbox to save the zip file as an artifact.

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As part of the above step, all files inside the artifactObjects folder are archived to create an artifacts.zip which will be saved as an artifact for future reference from the Deployment workflow.

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note: The directory name/artifact name can be anything. The deployment workflow expects only one .zip file inside the artifact containing the executable(.jar file in this case). It will search for a .zip extension and treat it as a package archive for deployment.

note: the deployFunctions operation(Azure plugin) will search for the Application Setting and Connection String file name provided as part of Project properties. Irrespective of folder structure it will find the file. In case more than one file exists with the same name, flow shall fail.

Below given is a sample deployment workflow to deploy the archive to Azure Function App.

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This step first updates the Application Setting/Connection String values from the files present in the artifact(optional). Instead of using Application Setting and Connection String files, one can directly provide the values as part of plugin input.

In such case, we can create input variables on deployment workflow like given below and pass them as deployFunctions operation input argument. This is optional and any other way to pass the arguments(e.g. hardcoded value) is also acceptable.

First, navigate to the Workflow Definition tab and click on the Create/View Inputs button as shown below.

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Create two input variables, one each for Application Setting and Connection String as shown below.

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The input arguments are then mapped to the input properties of the deployFunctions operation as shown below. The plugin will take the input values and process them accordingly.

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The deployFunctions operation returns four output variables. To capture them and use them for further processing/evaluation, one can create four variables and map them against the output parameters of deployFunctions operation. Return As Output drop down option should be set as Yes.

  1. First, navigate to the Workflow Definition tab and click on the Variables button

  2. Next use Create option to add four output variables.

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Map the variables against the output of the deployFunctions operation to capture the responses.

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Project configuration

Navigate to the Project tab and create a Project with a logical name(JavaAzureFunction in this case)

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Configure the Build and Deploy workflow that has been created in previous steps as shown below.

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Configure the Source SCM repository under Source Control as shown below.

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  1. To configure Project specific Source Control one first need to navigate to the Project Configuration tab.

  2. Next, expand the SOURCE CONTROL option from the left-hand pane.

  3. Select SOURCES for configuring the Source Repository

  4. Select the appropriate Source Control Type

  5. Configure Source Repository.

For detailed steps of Source Control configuration please refer to Configure Source Control in FlexDeploy

Project Properties

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  1. To configure Project specific settings one can navigate to the Project Configuration tab as shown above.

  2. Next, select the PROPERTIES option from the left-hand pane.

  3. To deploy a Function to Azure Function App, the first two mandatory properties are the Function App Name and Resource Group Name. These need to be configured under Project properties.

  4. Remote Build: This flag is applicable in case we want to upload the code to the Azure Function App backend and build it there itself (e.g.: .NET/Node.js). We are creating the Java executable(jar) file explicitly as part of the build workflow, hence this checkbox can be left unchecked for this flow.

  5. Under Project properties, one also needs to provide the Application Setting/Connection String file names(if applicable).

  6. Show deployment log: Once deployment is completed, in case someone wants to verify the execution steps, they need to check the below checkbox under Project properties.

    In this case, the plugin will internally retrieve the deployment Id and will fetch the Deployment log(if available), and return the same as an Output variable.

    This is applicable only for Code archive deployment(not for docker image deployment.)

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These Application Setting/Connection String properties are non-mandatory and can be directly provided as plugin Input as well. Or even may not be passed at all. However this is the recommended way and the files should exist in working directory(TEMP) during execution(folder hierarchy is not required, the plugin will search the file based on the file name).

Although only one Application Setting and one Connection String file are allowed per project.

Application Setting and Connection String are two separate entities so whichever files exist, the same will be considered during deployment.

Let’s review the sample Application Setting file first(same structure as the local.settings.json file). In our scenario, there are two dynamic values that need to be propagated to Function App Application Setting.

One of them DYNAMIC_TESTKEY1: is project specific and env agnostic

DYNAMIC_ENV_TESTKEY2: another one however changes env-wise

Both are already added to the AppSettings.json file present in Git Repository.

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Both properties need to be configured in workflow Properties with relevant Property Scope.

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  1. To pass the value for DYNAMIC_TESTKEY1(Project specific) one first needs to navigate to the Configuration tab

  2. Next, select the PROPERTIES option from the left-hand pane and provide the proper value against the property.

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For Environment property, the same needs to be added against Environment-Target Group properties.

  1. First, navigate to the Topology.

  2. Next, navigate to the Environment-Target Group properties section for the intended Target.

  3. Next, select the PROPERTIES tab and provide the value for DYNAMIC_ENV_TESTKEY2(environment specific) as shown below.

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Application Setting/Connection String can also be passed as an input argument to the deployFunction operation. The sample is shown as given below.

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Please refer to this link for the input structure of Application Setting and Connection String both when passed as a file or as input parameter - Application Setting/Connection String file and input content acceptable structure

In case both the Application Setting file and Application Setting input argument are passed, a consolidated list of properties will be considered while updating Function App. In case the same attribute is used both in the file and input, the value provided as part of the input will take precedence. The same applies to Connection String as well.

Also, it’s not mandatory to have dynamic values. One can always pass static hard-coded value if needed (although not recommended).

Application Setting and Connection String values will be updated against the Function Slot Name configured at the environment level. Refer to the Deploy to Slot section for further details.

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The steps of the workflow execution can be configured from the Definition tab. Below is a sample build workflow.

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Step 1: Clone Git Repository

This step will clone the Git repository codebase into the Project’s execution working directory. The Git URL will be retrieved from Source Control configured under Project configuration.

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Below given is the Git code structure and corresponding exported codebase during execution as a result of the above step.

Git Codebase

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Exported Codebase

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Step 2: Execute Maven Build

The below step will execute the Maven build with the project’s pom.xml and create a Java executable (.jar) file inside the target directory.

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If the pom.xml is properly configured, the java executable will be generated along other essential files as shown below under target/azure-functions folder.

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Also, for each Function, a separate folder will be automatically generated with a corresponding function.json file in it.

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All of above files need to be copied to Azure Function App to execute properly.

Step 3: Copy Properties File to Intermediate Directory

Application Setting and Connection String json files (if applicable/present) will be moved to the intermediate directory. An intermediate directory can have any name. In this case, we are using the artifactsObjects folder inside the temp directory. File names are mentioned under File Filter separated by ‘##’ (as advised in the description). Application Setting and Connection String values can also be passed as inputs to the deploy workflow. Application Setting and Connection String files are optional. Please review the Application Setting and Connection String property update section for a detailed explanation.

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Step 4: Create an Archive of the Executable in Intermediate Directory

Per the Azure Function App deployment requirement, we have created an archive with the java executable and other associated files (generated through Maven) in it.

Source Path: FD_TEMP_DIR + "/JavaFunction/target/azure-functions/azure-functions-sample"

Target Path: FD_TEMP_DIR + '/artifactObjects/deploy.zip'

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If we look inside the deploy.zip, it contains the jar file along with other associated files. Basically, it contains all relevant files that need to be copied to the Azure Function App backend. Azure Function App treats the jar file as an executable package and runs it.

...

Note

The jar file (or any executable files) inside the .zip file shouldn’t be inside any subfolder. If they are, the zip file deployment to Azure may not work.

Step 5: Save All as Artifact from Intermediate Directory

After executing the above steps, the intermediate directory (artifactObjects) should contain files as shown below.

...

These objects are required as part of the deployment flow. Hence we create an archive (artifacts.zip) with them inside the Artifacts directory. We also check the Produces Artifacts checkbox to save the zip file as an artifact.

Source Path: FD_TEMP_DIR+"/artifactObjects"

Target Path: FD_ARTIFACTS_DIR+"/artifacts.zip"

...

As part of the above step, all files inside the artifactObjects folder are archived to create an artifacts.zip file which will be saved as an artifact for future reference from the Deployment workflow.

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Tip

The directory name/artifact name can be anything. The deployment workflow expects only one .zip file inside the artifact containing the executable (.jar file in this case). It will search for a .zip extension and treat it as a package archive for deployment.

The deployFunctions operation of the Azure plugin will search for the Application Setting and Connection String file name provided as part of Project properties. Irrespective of folder structure, it will find the file. In case more than one file exists with the given name, the workflow execution will fail.

Deploy Workflow

Navigate to Workflows and create a workflow using the (blue star) button as highlighted below.

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Below is a sample deployment workflow to deploy the archive to Azure Function App.

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Step 1: Deploy Functions

This step first updates the Application Setting/Connection String values from the files present in the artifact (optional). Instead of using Application Setting and Connection String files, you can also directly provide the values as plugin inputs.

In using inputs is preferred, we can create input variables on the deployment workflow like below and use them in the deployFunctions operation.

Open the (blue star) Inputs popup within the deploy workflow and create the two inputs.

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The input arguments can then be used in the deployFunctions operation as shown below. The plugin will take the input values and process them accordingly.

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The deployFunctions operation returns four output variables. To capture them and use them for further processing/evaluation, you can create variables and map them against the output parameters of deployFunctions operation. Open the X2 variables popup and create the four variables, making sure to check the Return As Output option. Map the variables against the outputs of the deployFunctions operation to capture the responses (shown above).

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Project Configuration

Navigate to Projects and create a Project with a logical name (JavaAzureFunction in this case)

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Configure the build and deploy workflows which were created in previous steps.

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Anchor
SourceControl
SourceControl
Source Control

Configure the SCM repository on the Source Control tab.

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  1. Select the Sources section for configuring your source repository

  2. Select the appropriate Source Control Type

  3. Configure source repositorydetails. For more detailed steps of source control configuration, please refer to Configure Source Control in FlexDeploy

Project Properties

To configure Project-specific properties, navigate to Configuration → Properties.

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  1. To deploy a Function to Azure Function App, the first two mandatory properties are the Function App Name and Resource Group Name.

  2. Remote Build: This flag is applicable in case we want to upload the code to the Azure Function App backend and build it there itself (e.g.: .NET/Node.js). We are creating the Java executable (jar) file explicitly as part of the build workflow, hence this checkbox can be left unchecked for this scenario.

  3. We also need to provide the Application Setting/Connection String file names (if applicable).

  4. Show Deployment Log: Once deployment is completed, this can be checked if you’d like to verify the execution steps.

    If checked, the plugin will internally retrieve the deployment dd and will fetch the deployment log (if available) and return it as an output variable.

    This is applicable only for code archive deployments (not for Docker image deployment.)

Anchor
Application_Setting
Application_Setting
Application Setting and Connection String Property Update

These Application Setting/Connection String properties are not mandatory and can also be provided in the workflow as a plugin input, or you may not need it to be passed at all. However, the following is the recommended way, which assumes the files exist in the working execution directory (folder hierarchy is not required, the plugin will search the file based on the file name).

One application setting and one connection string file are allowed per project.

Application setting and connection string are two separate entities, so whichever files exist will be considered during deployment.

Let’s review the sample application setting file first (same structure as the local.settings.json file). In our scenario, there are two dynamic values that need to be propagated to Function App Application Setting.

DYNAMIC_TESTKEY1: project-specific and independent of environment

DYNAMIC_ENV_TESTKEY2: environment-specific

Both are already added to the AppSettings.json file present in our Git Repository.

...

Both properties need to be configured in the Workflow Properties with relevant property scope.

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To provide the value for DYNAMIC_TESTKEY1 (Project-specific), navigate to the Configuration tab of your Project. Select the Properties tab from the left-hand pane and provide the proper value for the property.

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To provide the value for DYNAMIC_ENV_TESTKEY2 (Environment-specific), the same needs to be done within the Target properties. Navigate to Topology and open the intended Target. Provide the value for the property as shown below.

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These Application Setting/Connection String values can also be passed as a workflow input to the deployFunction operation. The sample is shown as given below.

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Please refer to this page for the input structure of Application Setting and Connection String when passed as a file or as an input parameter - Application Setting/Connection String File and Input Content Acceptable Structure

In the case that both the Application Setting file and Application Setting input argument are passed, a consolidated list of properties will be considered while updating the Function App. If the same attribute is used both in the file and input, the input value will take precedence. The same applies to Connection String as well.

Also, it’s not mandatory to have dynamic values. You can always pass static hard-coded values if needed (although not recommended).

Application Setting and Connection String values will be updated against the Function Slot Name configured at the environment-level. Refer to the Deploy to Slot section for further details.

There could be scenarios where someone only need to update the Application Setting or Connection String without actually deploying the code. In that case, you can use upsertFunctionApplicationSetting or upsertFunctionConnectionString operations respectively. The workflow execution will be exactly the same, but the code archive creation/Docker image update step will not be required. Build workflow The build workflow’s generated artifact should only container contain AppSetting.json or ConnString.json. These values can also be passed as input as already mentioned in detail in above section.

Target Properties

Select Topology from the menu and then select Topology Overview from the left menu. You will see a table that has Instances as the rows and Environments as the columns with colored circles representing the Environment Instance. Color Navigate to Topology. Choose a Target Group on the left, or create a new one. You will see a list of Environments on the right with colored circles representing each Target

Color coding represents:

  • RED - no required properties are set and/or the Endpoint is not configured

  • YELLOW - some of the required properties are set and/or the Endpoint is not configured

  • GREEN - all required properties are set and the Endpoint is configured

Selecting Clicking the GREEN circle for DEV will display the configurable properties/Endpoint and allows for the configuration of environment row for Dev allows modifying the Endpoint and properties. Below are the environment-specific values which need to be updated. Also, if we are adding any environment-specific properties for Application Setting/Connection String (as mentioned in the previous step), those values should also be updated here.

Properties

Mandatory Field

Description

Function Slot Name

 

Required

Target slot for deployment

Target Slot to

swap

Swap

Optional

In case slot swap is required

Azure Cloud Account

 

Required 

Azure account with relevant details

Absolute

path

Path of Azure CLI

Optional

Not required if Azure CLI already added to workflow

execution m/c class path

GIT Path

Optional

Path to the Git executable. Required only if git is not on class path.

Below given are the environment-specific values which need to be updated.

Also in case, we are adding any environment-specific properties for Application Setting/Connection String(as mentioned in the previous step), those values should also be updated.

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execution m/c class path

GIT Path

Optional

Path to the Git executable. Required only if git is not on class path.

Anchor
Deploytoslot
Deploytoslot
Deploy to Slot

Azure Function App default slot is production. However, one you can configure multiple slots and deploy Functions on those slots inside within your Function App. Function Slot Name is a mandatory field, where the code needs to deploy.

In case someone wants to do a slot swap, they can provide the Target Slot to swap with as shown below.

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The executeFunctionAppSlotSwap operation can be used to perform the slot swap.

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Build Execution

For detailed steps on how to initiate Build a build for an Azure Function, review artifacts, and ultimately deploy to Azure Function App, please refer to Build and deploy execution Deploy Execution through FlexDeploy

Post

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-Deployment Debugging

After deploying your project in FlexDeploy, it can take a little while for the function application to be up and running.

Currently once Once the application is deployed to Azure Function App through the plugin operation, Azure basically uploads the files to the backend. However, if the application is up and running or have failed , can’t be verified immediately. It takes a little while for the service to start and accept requests.

Issues can happen if host.json has some issue. Sample A sample error message is shown below.

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The function archive generated is not in desired format hence can’t be loaded. Sample A sample error message is shown below.

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In such scenario, functions deployed will reflect as none with and Azure Function may be in an unreachable state as shown below.

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 Best The best way to check logs : is using URL https://$func_name.scm.azurewebsites.net/api/logstream or logstream  logstream from console. As stated already in above scenarios, in case of failure we can easily debug the issue from log logs and get the issue fixed.

Congratulations! You have successfully completed the Java Application deployment to the Azure Function App tutorial.

Now that you have configured FlexDeploy for one Azure Function, it is extremely easy to replicate the same for other Azure Functions. Simply use the Copy Project feature and a new project will be created with all of the configuration completed already. You just need to make the necessary SCM and property configuration changes for your target Azure Function.