157 docs tagged with "release"

In Release, the phases in a template or release represent blocks of work that happen in succession.

You can now add Stage Owners to Deliveries.

This topic provides information about tasks in Release, which are the core components of activities within a template or release, logically grouped into phases.

Learn how to manage application images in air-gapped environments. Following are the five different options you can use to ensure your application images are available in air-gapped environments.

Application Onboarding is a modernized control panel for Developers to enable efficient application onboarding in a hybrid or multi-cloud environments. With Digital.ai Release 23.1, we are bringing in new self-service workflows so that Developers can onboard their applications easier with minimum knowledge of tools. Newly introduced workflows guide users through step-by-step execution of tasks, reducing complexity in setting up environments. Application Onboarding leverages cloud benefits such as scalability, availability, and optimizing applications for the cloud for improved performance and reliability. It also helps ensure adherence to cloud security and compliance best practices.

This topic provides information about how to automatically handle failures in Release tasks.

Here are some examples how to backup the content of the Release installation on K8s cluster.

This topic provides an overview of the calendar features in Digital.ai Release, including different views (day, week, month, year) and how to filter and export the calendar.

This topic explains how to change the type of a task in Release, including the necessary permissions for modifying tasks in templates, planned releases, and active releases.

This topic explains how Digital.ai Release can define a release from a Groovy-based DSL script, that describes all the phases, tasks, and task-groups in the release. This enables you to store release definitions as code in version control and gives you programmatic control over a release when creating it.

This topic illustrates how to configure the settings required to install Release with HTTP2 backend enabled on a Kubernetes cluster.

This topic illustrates how to configure SSL/TLS with Digital.ai Release. A self-signed certificate is used for illustrative purposes in this procedure. However, you may want to replace it with your own trusted certificate for production environments, which you can do by creating a new Secret object in Kubernetes that contains your certificate and then configuring the ingress controller to use it.

The DORA dashboard is pre-configured with certain default configurations, such as threshold values, bucket ranges, labels, and so on. These values can be modified to suit customers' requirements.

This topic provides an overview of the core concepts and processes in Digital.ai Release, including releases, phases, tasks, release flow, release owners, templates, and teams. It explains how Digital.ai Release helps plan, track, and execute releases, serving as a central source of truth for all involved stakeholders.

Using a Git trigger you can start a release that will execute an External Script task that contains a DSL. The External Script task should point to a file from a Git repository. You must provide HTTP/HTTPS access to the file with a direct link. HTTP authentication provides security.

Release triggers are an automated way to create and run a release. When you create a trigger for a release template and enable it, Release will execute the script associated with the trigger at a specified interval. When the trigger starts, it will create and start a new release from the template. A good example of a trigger is one that polls a source code management system like GitHub for a change and starts a release as a result.

A Create Delivery task is an automatic task that creates a new delivery from a delivery pattern.

You can use variables to manage information that you don't know in advance or that may change. Unlike global variables, release variables can only be used in the template or release in which they are created. You can create a release variable using the release flow editor or the Variables screen.

From 23.3 version, there are changes how is working answer to the question:

Prerequisites

This topic explains how to use dashboard templates in Digital.ai Release to quickly build a dashboard that provides information on your deployments and releases.

The out-of-the-box datasets are created using data that is available in Digital.ai Release and Digital.ai Deploy. You can use these ready-to-use datasets to create customized dashboards based on your requirements.

Delivery Patterns in Release allow you to create pipelines where each team can work at its own timelines and tools, and converge at synchronized points.

DevOps Research and Assessment as a dashboard helps you analyze details to streamline or improve your DevOps process using key DORA metrics such as Deployment frequency, Lead time for change, Mean time to restore, and Change failure rate. DORA metrics have now become the standard for gauging the efficacy of software development teams and can provide crucial insights into areas for growth.

The Change Request Analysis dataset helps you analyze the change requests within a project or organization. This dataset facilitates the Change Executives or Change leaders to monitor, evaluate, and track change requests throughout their cycle. Using this dataset helps you gain insights into business scenarios such as:

The DORA common filter details dataset allows you to filter a range of data based on the criteria you define. For example, the Parent folder attribute as a filter allows you to select any level from the parent attribute and displays a flattened view of all underlying child release folders that are associated either directly or indirectly with the parent folder. And individuals who work together collaboratively to achieve a task represented as teams.

The Deployment Analysis dataset provides an overview of the deployment process for software updates of an organization. This dataset helps you gain valuable insights into the deployment process, enabling them to make informed decisions, improve efficiency, and ensure the reliability of software. Using this dataset, you can answer business questions related to deployments such as:

The Incident Analysis dataset helps you analyze the incidents that occurred within a system or organization. This dataset provides valuable insights into incident management, helping you track, analyze, and improve their response to incidents, ultimately providing user satisfaction. Using this dataset helps you gain insights into business scenarios such as:

The DORA Industry Standard Grid dataset is used to view the industry standard thresholds set by the DORA team for comparison and performance enhancements.

The Lead Time Analysis dataset provides insights into the time it takes for a process to be completed, from the initiation of work to its final delivery. This dataset helps you understand the development process better, optimize workflows, and deliver software more efficiently while meeting customer expectations.

The DORA release last refresh datetime dataset is used to view the time stamp of when the data load occurred and presented in a machine-readable format yyyy-MM-dd HHss.SSS.

Add the following entries to the xl-release.conf file to enable caching in Release.

This topic explains how to enable file logging in the Kubernetes cluster for Release application.

A Find Delivery task is an automatic task that searches for an existing delivery by tracked item or by name.

A Find Or Create Delivery task is an automatic task that searches for an existing delivery or creates a new one from a delivery pattern.

This page defines the terms you will encounter in Release.

To use the Release REST API, you need to know the unique identifiers for templates, releases, phases and tasks. This topic explains where you can find them.

This topic explains how to use templates to model the ideal process of a release flow.

You can install Release in an air-gapped environment disconnected from the public internet. This topic provides information about installing and upgrading Release in such environments using a Minikube cluster and a custom image registry. For other Kubernetes platforms, steps are similar to the ones listed here for Minikube as long as the custom image registry contains all the required images.

Note: Here is a basic setup for the AWS EKS cluster, use it as a guideline to create K8s cluster to have minimal K8s environment for Digital.ai Deploy or Release installation.

Note: Here is a basic setup for the Azure K8S cluster, use it as a guideline to create K8s cluster to have minimal K8s environment for Digital.ai Deploy or Release installation.

Note: Here is a basic setup for the GKE cluster, use it as a guideline to create K8s cluster to have minimal K8s environment for Digital.ai Deploy or Release installation.

Note: Here is a basic setup for the AWS Openshift cluster,

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Important: Use these instructions to install Digital.ai Deploy or Release on a minikube multi-node cluster for testing or illustration purposes. Do not use these instructions to set up a production environment.

* Here's a list of questions that you would have to answer to install Digital.ai Release Runner using the xl kube install command.

* Here's a list of questions that you would have to answer to install Digital.ai Release or Runner using the xl kube install command.

Learn how to install and use this product on your Red Hat platform.

The Delivery Patterns feature in Release allows you to use deliveries and tracked items to synchronize multiple releases together. This allows you to design delivery patterns that conform to the release standards being used by your organization, such as SAFe.

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Here's what it takes to manage Digital.ai Release plugins, if Release is not working on a Release cluster that was created using the Operator-based installer:

This tutorial is intended to help you get started with DevOps as Code in Release. It describes how to generate a DevOps as Code YAML file from an existing Release template and manage it in source control.

Folders provide an intuitive way to organize your templates, releases, configurations and dashboards by project, by team, or by any other model that fits your organization. With folders, you can easily apply security settings to a large number of templates and releases by setting role-based access control at any level of your folder hierarchy.

This topic describes how to create, run, and manage workflows.

This topic describes how to create and manage delivery patterns. A delivery pattern allows you to design a flow of stages that the tracked items in a delivery must go through. You can reuse these patterns in multiple deliveries.

PostgreSQL cannot be automatically upgraded during an operator-to-operator or Helm-to-operator upgrade due to incompatible data formats between different PostgreSQL server versions. In such cases, a manual upgrade is necessary. The process involves first backing up the PostgreSQL data, then upgrading to the new PostgreSQL version, and finally restoring the data in the upgraded version.

RabbitMQ during operator2operator upgrade or helm2operator upgrade to version 23.3 of operator will not upgrade automatically to the latest RabbitMQ server version.

A Mark Tracked Items task is an automatic task that completes or skips one or more tracked items on a running delivery.

This topic illustrates how to set up the persistent volumes on the Release containers.

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The Live Deployments Discovery wizard allows you to create connections to ArgoCD or your server's Deploy applications. You can use this wizard to discover and onboard applications with ease.

Out-of-the-box datasets are pre-built and ready-to-use datasets that you can use to create visually appealing reports. These datasets can be used to create customized dashboards based on your requirements. You initiate analysis, development, or testing without the need to collect, clean, or prepare data from scratch. These datasets serve as a convenient resource for users to promptly commence data-related tasks, learn new tools, and develop solutions with minimal setup effort.

Out-of-the-box datasets are pre-built and ready-to-use datasets that you can use to create visually appealing reports. These datasets can be used to create customized dashboards based on your requirements. You initiate analysis, development, or testing without the need to collect, clean, or prepare data from scratch. These datasets serve as a convenient resource for users to promptly commence data-related tasks, learn new tools, and develop solutions with minimal setup effort.

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The release dashboard tiles topic provides you with an overview of your planning information with the help of graphical representations. The release dashboard can be customized as per your requirement by adding, configuring, moving, and removing tiles to show the planning information and details about the release status.

This topic provides an overview of Digital.ai Release reports, including types of dashboards, report permissions, and report caching. Digital.ai Release reports display graphs and statistics based on historical release data stored in the archive database, and are available to users with the appropriate permissions.

This topic describes how release reports display graphs and statistics based on historical release data.

Here is the list of the main parameters for the Digital.ai Release Custom Resource (CR). The following table lists the parameters available in the Digital.ai Release's dairelease_cr.yaml file and their default values.

You use the XL CLI's xl kube command to install or upgrade Digital.ai Deploy or Release, or Release Runner. For more information, see XL Kube Command Reference.

From 23.3 plugin management is possible with XL CLI utility. For more information, see Plugin Manager CLI.

Follow these instructions if you have chosen Keycloak for OIDC authentication.

This topic describes how to add one or more tracked items to a delivery. If you want to track a new feature as part of your end to end delivery of a release, you can add the feature as a tracked item in the delivery.

A Register Tracked Items task is an automatic task that registers one or more tracked items to a running delivery.

This topic covers the audit report in Digital.ai Release, which provides full traceability and auditability for auditors. You can generate an audit report for releases that are in progress, completed, or archived.

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The Flow Editor shows the phases and tasks in a release or a template. You can use this view to add, move, edit, and delete phases and tasks.

This topic describes Release History, which records all the events occurring in a release, providing a detailed audit trail of user actions with corresponding dates and times.

This topic explains the different stages and states a release goes through during its lifecycle in Digital.ai Release.

The release overview shows the list of releases that you have permission to view and that are planned, active (including releases that are in progress, paused, failing, or failed), or completed (including aborted).

The Release planner view provides an interactive Gantt chart to view and edit the duration of the phases and tasks in a release or template. The Gantt chart is a combined timeline of the template or release, its phases, and the tasks within.

This topic covers Release properties, which are configurable attributes that influence the behavior and execution of a release.

The Release table view provides an alternative view of a template or release that is optimized for working with tasks.

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This topic explains the structure of users, roles, and permissions in Release, focusing on how they ensure secure and efficient access control.

In Release, templates are like blueprints for releases. You can use templates to model the ideal process of a release flow. A template can describe a procedure that is used to deliver different applications, or it can describe a procedure that is used to release a particular application and that will be reused for different versions of the application.

This topic describes the Release value stream report, which focuses on the quality of completed and aborted releases. The data for this report is sourced from the archive database, meaning releases that are completed or aborted but not yet archived will not be included in the report. For more information, see completed and aborted and

This topic provides information on the Release value stream report, which evaluates the quality of completed and aborted releases. The report draws data from the Release archive database, so it does not include releases that are completed or aborted but not yet archived. For more information, see completed and aborted and archive database.

The Release user interface includes multiple release planning and management views to accommodate all of the types of users that participate in the release process:

This topic provides key information on flagged releases and highlights those with the highest level of automation.

In an active release, you can abort the current phase and restart the execution from any past phase. This can be required if some parts of the release procedure must be repeated. For example, QA rejects a version of the application for release and the test phase must be repeated with an updated version.

Release calculates a risk level for each release based on different factors such as flags, failed or failing states, or due dates. You can see the releases with a high risk level and take the appropriate actions.

This topic illustrates how to use the diagnostic mode in Release.

In Release, you can schedule your releases by setting start dates and times, end dates and times, and durations on templates, releases, phases, and tasks. When you set dates and durations on phases and tasks, Release automatically adjusts other phases and calculates the release duration and end date.

Configuring OIDC is one of the steps in installing or upgrading Digital.ai Deploy or Release using the Operator-based installer.

Configuring OIDC is one of the steps in installing or upgrading Digital.ai Release using the Operator-based installer.

This topic illustrates on how to set up the custom context root on the Release.

This topic illustrates on how to set up JVM arguments for Relase containers.

This topic illustrates on how to set up a truststore to store trusted certificates that are used to verify the identities of parties in a secure communication.

Workflows are a combination of tasks that can be executed in an interactive session, with pre-built best practices. Once you have this setup in place, all you would be doing is to select the workflows from the self-service catalog and execute them in step-by-step view to interactively setup applications in Argo CD and Deploy.

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Starting with Digital.ai Deploy version 24.3.0, there are two types of images available for Kubernetes-based setups with an external database or message queue: slim and non-slim.

In this article, you will learn how to set up a custom image registry to install or upgrade Release. A custom image registry can either be public (not password protected) or private (password protected).

This topic describes how to create a release from a template using various methods and the steps required before starting the release.

This topic explains how to start a new release from an archived release by using the original template and variable values

This topic describes how to configure Digital.ai Release on Kubernetes to store reports and task logs on Amazon S3. This setup eliminates the need for persistent volumes, making storage management easier for Kubernetes-based release instances.

Triggers allow you to set the conditions for an event to create an action, such as a release task.

If you need to do some customization in the CR file or in the operator deployment, check the following sections.

You must use the custom resource definition file (CR file) in case you want to change the Digital.ai Release's license on sites installed using the Operator-based installer.

* Here's a list of questions that you would have to answer to upgrade Release or Runner using the xl kube upgrade command.

* Here's a list of questions that you would have to answer to upgrade Digital.ai Release Runner using the xl kube upgrade command.

Patch upgrades from 23.3

If you plan to use an existing database—one that is not created by default by the Operator-based installer—you must configure the relevant database parameters in the dairelease_cr.yaml file.

If you plan to use an existing database—one that is not created by default by the Operator-based installer—you must configure the relevant database parameters in the dairelease_cr.yaml file.

If you plan to use an existing message queue — one that is not created by default by the Operator-based installer — you must configure the relevant MQ parameters in the dairelease_cr.yaml file.

This topic illustrates how to use an external database instead of the one that is provided with the operator itself.

This topic illustrates how to use an external message queue, instead of the RabbitMQ that is provided with the operator itself.

This topic describes how to create and manage deliveries.

Dependent properties within the XML type-definitions enables dynamic user inputs that adapt based on user selections. This functionality is essential for tasks requiring context-specific input, ensuring that only relevant fields and options are presented, thereby enhancing the user experience.

You can use DevOps as Code to add and generate Software Delivery items, including delivery patterns and all delivery tasks.

The release dashboard is a customizable view where you can add, configure, move, and remove tiles that show planning information and details about release status.

This topic illustrates how to replace the CR values that are in clear-text format in the CR with the secret references.

The Release support accelerator gathers data that helps the Digital.ai Support Team to troubleshoot issues.

This topic shows an example of a template that deploys an application to a test environment and assigns testing to QA. When testing succeeds, Release sends an email notification. If the testing fails, it tries again with the next version of the application.

When creating release templates, you will create tasks that contain information that varies based on the release. For example, you can have one generic release template that is used for the release process of several applications. Different releases based on this template will require different application names.

Note: Live Status of Deployed Applications is a feature in Tech Preview—released in Digital.ai Release 22.3—enabled by default. You must disable this feature (using the feature flag) if you do not want to use it.

A Wait For Stage task is an automatic task that makes the running release wait for a stage to be completed on a running delivery before proceeding.

A Wait For Tracked Items task is an automatic task that waits for items to exist or be completed in a stage on a running delivery.

In Release, phases in a template or release represent blocks of activities that occur in succession. Activities in a template or release are modeled as tasks, which are logically grouped in phases. You can use the release flow editor to manage phases and tasks.

Workflows are step-by-step interactive sessions designed to accelerate developers' familiarity with internal tools and streamline the onboarding of applications and environments. These guided workflows prompt users for inputs and automatically generate necessary artifacts, facilitating release orchestration and end-to-end deployment. Workflows minimize the need for in-depth tool knowledge, allowing developers to focus on their core tasks. They serve as repeatable paths for addressing various engineering needs, ensuring efficiency, consistency, and ease of use throughout the development and deployment lifecycle.

The Workflow Analytics out-of-the-box datasets are created using data that is available in Digital.ai Release. You can use these ready-to-use datasets to create customized dashboards based on your requirements.

The Workflow Category Details dataset provides insights into the templates linked to various workflow categories, aiding efficiency in workflow execution through effective categorization.

Workflows are bundled within the Release application itself, and are categorized into multiple types based on its use-cases. Additionally, you can also create new workflows and assign it to a specific category.

Workflows are step-by-step interactive sessions designed to accelerate developers’ familiarity with internal tools and streamline the onboarding of applications and environments. The Workflow Details dataset provides an overview of the workflow execution and helps you gain valuable insights using which teams can make data-driven decisions to optimize their processes, increase productivity, and reduce errors.

Before You Begin

The Workflow Tag dataset provides insights into the tags associated with various workflows, used for categorization, thereby aiding in searchability and filtering.

The Workflow Teams dataset provides insights into the teams working on various workflows, aiding in team management and workflow assignment tracking.

The objective is to illustrate how to use the AWS Lambda create function using S3 zip file workflow in Digital.ai Release to create an AWS Lambda function using S3 zip file.

The objective is to illustrate how to use the AWS SecretsManager create secret workflow in Digital.ai Release to create secrets in AWS Secrets Manager, which can again be looked up and substituted across Digital.ai Release templates and workflows.

The objective is to illustrate how to use the Azure KeyVault create secret workflow in Digital.ai Release to create secrets in Azure KeyVault, which can again be looked up and substituted across Digital.ai Release templates and workflows.

The objective is to illustrate how to use the ArgoCD delete application workflow in Digital.ai Release to delete an application in Argo CD that runs in a Kubernetes cluster.

The objective is to illustrate how to use the AWS SecretsManager delete secret workflow in Digital.ai Release to delete secrets in AWS Secrets Manager.

The objective is to illustrate how to use the Azure KeyVault delete secret workflow in Digital.ai Release to delete secrets in Azure KeyVault.

Before You Begin

The objective is to illustrate how to use the Helm install and uninstall application workflow in Digital.ai Release to install and uninstall the application using Helm.

Before You Begin

Before You Begin

Before You Begin

The objective is to illustrate how to use the List Azure Container Registry Images workflow in Digital.ai Release to list container images from the Azure container registry.

Before You Begin

This topic illustrates how to use the CheckmarxOne scan Git repository workflow to initiate a CheckmarxOne scan on your git repository, review scan results, and verify compliance directly from within the Digital.ai Release.

The objective is to illustrate how to use the ArgoCD update application workflow in Digital.ai Release to update an application in Argo CD that runs in a Kubernetes cluster.

The objective is to illustrate how to use the AWS SecretsManager update secret workflow in Digital.ai Release to update secrets in AWS Secrets Manager, which can again be looked up and substituted across Digital.ai Release templates and workflows.

The objective is to illustrate how to use the Azure KeyVault update secret workflow in Digital.ai Release to update secrets in Azure KeyVault, which can again be looked up and substituted across Digital.ai Release templates and workflows.

The Workflow Users Details dataset offers insights into user activity and license status, aiding in the management of user access and license allocation.

Note: Workflows are bundled with the Release application by default. However, you must have the relevant plugins installed to view these workflows in the Workflows folder. For example, you must have the Deploy plugin installed in Release to view the workflows in the Workflows > Digital.ai Deploy folder.

With Digital.ai Release 23.3, we are bringing in new self-service workflows so that Developers can onboard their applications easier with minimum knowledge of tools. Newly introduced workflows guide users through step-by-step execution of tasks, reducing complexity in setting up environments. It leverages cloud benefits such as scalability, availability, and optimizing applications for the cloud for improved performance and reliability. It also helps ensure adherence to cloud security and compliance best practices.