Ceph Dashboard Developer Documentation

Table of Contents

Feature Design

To promote collaboration on new Ceph Dashboard features, the first step is the definition of a design document. These documents then form the basis of implementation scope and permit wider participation in the evolution of the Ceph Dashboard UI.

Design Documents:

Preliminary Steps

The following documentation chapters expect a running Ceph cluster and at least a running dashboard manager module (with few exceptions). This chapter gives an introduction on how to set up such a system for development, without the need to set up a full-blown production environment. All options introduced in this chapter are based on a so called vstart environment.

Note

Every vstart environment needs Ceph to be compiled from its Github repository, though Docker environments simplify that step by providing a shell script that contains those instructions.

One exception to this rule are the build-free capabilities of ceph-dev. See below for more information.

vstart

“vstart” is actually a shell script in the src/ directory of the Ceph repository (src/vstart.sh). It is used to start a single node Ceph cluster on the machine where it is executed. Several required and some optional Ceph internal services are started automatically when it is used to start a Ceph cluster. vstart is the basis for the three most commonly used development environments in Ceph Dashboard.

You can read more about vstart in Deploying a development cluster. Additional information for developers can also be found in the Developer Guide.

Host-based vs Docker-based Development Environments

This document introduces you to three different development environments, all based on vstart. Those are:

  • vstart running on your host system

  • vstart running in a Docker environment

    Besides their independent development branches and sometimes slightly different approaches, they also differ with respect to their underlying operating systems.

    Release

    ceph-dev-docker

    ceph-dev

    Mimic

    openSUSE Leap 15

    CentOS 7

    Nautilus

    openSUSE Leap 15

    CentOS 7

    Octopus

    openSUSE Leap 15.2

    CentOS 8

    Master

    openSUSE Tumbleweed

    CentOS 8

Note

Independently of which of these environments you will choose, you need to compile Ceph in that environment. If you compiled Ceph on your host system, you would have to recompile it on Docker to be able to switch to a Docker based solution. The same is true vice versa. If you previously used a Docker development environment and compiled Ceph there and you now want to switch to your host system, you will also need to recompile Ceph (or compile Ceph using another separate repository).

ceph-dev is an exception to this rule as one of the options it provides is build-free. This is accomplished through a Ceph installation using RPM system packages. You will still be able to work with a local Github repository like you are used to.

Development environment on your host system

  • No need to learn or have experience with Docker, jump in right away.

  • Limited amount of scripts to support automation (like Ceph compilation).

  • No pre-configured easy-to-start services (Prometheus, Grafana, etc).

  • Limited amount of host operating systems supported, depending on which Ceph version is supposed to be used.

  • Dependencies need to be installed on your host.

  • You might find yourself in the situation where you need to upgrade your host operating system (for instance due to a change of the GCC version used to compile Ceph).

Development environments based on Docker

  • Some overhead in learning Docker if you are not used to it yet.

  • Both Docker projects provide you with scripts that help you getting started and automate recurring tasks.

  • Both Docker environments come with partly pre-configured external services which can be used to attach to or complement Ceph Dashboard features, like

    • Prometheus

    • Grafana

    • Node-Exporter

    • Shibboleth

    • HAProxy

  • Works independently of the operating system you use on your host.

vstart on your host system

The vstart script is usually called from your build/ directory like so:

../src/vstart.sh -n -d

In this case -n ensures that a new vstart cluster is created and that a possibly previously created cluster isn’t re-used. -d enables debug messages in log files. There are several more options to chose from. You can get a list using the --help argument.

At the end of the output of vstart, there should be information about the dashboard and its URLs:

vstart cluster complete. Use stop.sh to stop. See out/* (e.g. 'tail -f out/????') for debug output.

dashboard urls: https://192.168.178.84:41259, https://192.168.178.84:43259, https://192.168.178.84:45259
  w/ user/pass: admin / admin
restful urls: https://192.168.178.84:42259, https://192.168.178.84:44259, https://192.168.178.84:46259
  w/ user/pass: admin / 598da51f-8cd1-4161-a970-b2944d5ad200

During development (especially in backend development), you also want to check on occasions if the dashboard manager module is still running. To do so you can call ./bin/ceph mgr services manually. It will list all the URLs of successfully enabled services. Only URLs of services which are available over HTTP(S) will be listed there. Ceph Dashboard is one of these services. It should look similar to the following output:

$ ./bin/ceph mgr services
{
    "dashboard": "https://home:41931/",
    "restful": "https://home:42931/"
}

By default, this environment uses a randomly chosen port for Ceph Dashboard and you need to use this command to find out which one it has become.

Docker

Docker development environments usually ship with a lot of useful scripts. ceph-dev-docker for instance contains a file called start-ceph.sh, which cleans up log files, always starts a Rados Gateway service, sets some Ceph Dashboard configuration options and automatically runs a frontend proxy, all before or after starting up your vstart cluster.

Instructions on how to use those environments are contained in their respective repository README files.

Frontend Development

Before you can start the dashboard from within a development environment, you will need to generate the frontend code and either use a compiled and running Ceph cluster (e.g. started by vstart.sh) or the standalone development web server.

The build process is based on Node.js and requires the Node Package Manager npm to be installed.

Prerequisites

  • Node 12.18.2 or higher

  • NPM 6.13.4 or higher

nodeenv:

During Ceph’s build we create a virtualenv with node and npm installed, which can be used as an alternative to installing node/npm in your system.

If you want to use the node installed in the virtualenv you just need to activate the virtualenv before you run any npm commands. To activate it run . build/src/pybind/mgr/dashboard/node-env/bin/activate.

Once you finish, you can simply run deactivate and exit the virtualenv.

Angular CLI:

If you do not have the Angular CLI installed globally, then you need to execute ng commands with an additional npm run before it.

Package installation

Run npm ci in directory src/pybind/mgr/dashboard/frontend to install the required packages locally.

Adding or updating packages

Run the following commands to add/update a package:

npm install <PACKAGE_NAME>
npm ci

Setting up a Development Server

Create the proxy.conf.json file based on proxy.conf.json.sample.

Run npm start for a dev server. Navigate to http://localhost:4200/. The app will automatically reload if you change any of the source files.

Code Scaffolding

Run ng generate component component-name to generate a new component. You can also use ng generate directive|pipe|service|class|guard|interface|enum|module.

Build the Project

Run npm run build to build the project. The build artifacts will be stored in the dist/ directory. Use the --prod flag for a production build (npm run build -- --prod). Navigate to https://localhost:8443.

Build the Code Documentation

Run npm run doc-build to generate code docs in the documentation/ directory. To make them accessible locally for a web browser, run npm run doc-serve and they will become available at http://localhost:8444. With npm run compodoc -- <opts> you may fully configure it.

Code linting and formatting

We use the following tools to lint and format the code in all our TS, SCSS and HTML files:

We added 2 npm scripts to help run these tools:

  • npm run lint, will check frontend files against all linters

  • npm run fix, will try to fix all the detected linting errors

Ceph Dashboard and Bootstrap

Currently we are using Bootstrap on the Ceph Dashboard as a CSS framework. This means that most of our SCSS and HTML code can make use of all the utilities and other advantages Bootstrap is offering. In the past we often have used our own custom styles and this lead to more and more variables with a single use and double defined variables which sometimes are forgotten to be removed or it led to styling be inconsistent because people forgot to change a color or to adjust a custom SCSS class.

To get the current version of Bootstrap used inside Ceph please refer to the package.json and search for:

  • bootstrap: For the Bootstrap version used.

  • @ng-bootstrap: For the version of the Angular bindings which we are using.

So for the future please do the following when visiting a component:

  • Does this HTML/SCSS code use custom code? - If yes: Is it needed? –> Clean it up before changing the things you want to fix or change.

  • If you are creating a new component: Please make use of Bootstrap as much as reasonably possible! Don’t try to reinvent the wheel.

  • If possible please look up if Bootstrap has guidelines on how to extend it properly to do achieve what you want to achieve.

The more bootstrap alike our code is the easier it is to theme, to maintain and the less bugs we will have. Also since Bootstrap is a framework which tries to have usability and user experience in mind we increase both points exponentially. The biggest benefit of all is that there is less code for us to maintain which makes it easier to read for beginners and even more easy for people how are already familiar with the code.

Writing Unit Tests

To write unit tests most efficient we have a small collection of tools, we use within test suites.

Those tools can be found under src/pybind/mgr/dashboard/frontend/src/testing/, especially take a look at unit-test-helper.ts.

There you will be able to find:

configureTestBed that replaces the initial TestBed methods. It takes the same arguments as TestBed.configureTestingModule. Using it will run your tests a lot faster in development, as it doesn’t recreate everything from scratch on every test. To use the default behaviour pass true as the second argument.

PermissionHelper to help determine if the correct actions are shown based on the current permissions and selection in a list.

FormHelper which makes testing a form a lot easier with a few simple methods. It allows you to set a control or multiple controls, expect if a control is valid or has an error or just do both with one method. Additional you can expect a template element or multiple elements to be visible in the rendered template.

Running Unit Tests

Run npm run test to execute the unit tests via Jest.

If you get errors on all tests, it could be because Jest or something else was updated. There are a few ways how you can try to resolve this:

  • Remove all modules with rm -rf dist node_modules and run npm install again in order to reinstall them

  • Clear the cache of jest by running npx jest --clearCache

Running End-to-End (E2E) Tests

We use Cypress to run our frontend E2E tests.

E2E Prerequisites

You need to previously build the frontend.

In some environments, depending on your user permissions and the CYPRESS_CACHE_FOLDER, you might need to run npm ci with the --unsafe-perm flag.

You might need to install additional packages to be able to run Cypress. Please run npx cypress verify to verify it.

run-frontend-e2e-tests.sh

Our run-frontend-e2e-tests.sh script is the go to solution when you wish to do a full scale e2e run. It will verify if everything needed is installed, start a new vstart cluster and run the full test suite.

Start all frontend E2E tests by running:

$ ./run-frontend-e2e-tests.sh
Report:

You can follow the e2e report on the terminal and you can find the screenshots of failed test cases by opening the following directory:

src/pybind/mgr/dashboard/frontend/cypress/screenshots/
Device:

You can force the script to use a specific device with the -d flag:

$ ./run-frontend-e2e-tests.sh -d <chrome|chromium|electron|docker>
Remote:

By default this script will stop and start a new vstart cluster. If you want to run the tests outside the ceph environment, you will need to manually define the dashboard url using -r and, optionally, credentials (-u, -p):

$ ./run-frontend-e2e-tests.sh -r <DASHBOARD_URL> -u <E2E_LOGIN_USER> -p <E2E_LOGIN_PWD>
Note:

When using docker, as your device, you might need to run the script with sudo permissions.

run-cephadm-e2e-tests.sh

run-cephadm-e2e-tests.sh runs a subset of E2E tests to verify that the Dashboard and cephadm as Orchestrator backend behave correctly.

Prerequisites: you need to install KCLI and Node.js in your local machine.

Configure KCLI plan requirements:

$ sudo chown -R $(id -un) /var/lib/libvirt/images
$ mkdir -p /var/lib/libvirt/images/ceph-dashboard dashboard
$ kcli create pool -p /var/lib/libvirt/images/ceph-dashboard dashboard
$ kcli create network -c 192.168.100.0/24 dashboard
Note:

This script is aimed to be run as jenkins job so the cleanup is triggered only in a jenkins environment. In local, the user will shutdown the cluster when desired (i.e. after debugging).

Start E2E tests by running:

$ cd <your/ceph/repo/dir>
$ sudo chown -R $(id -un) src/pybind/mgr/dashboard/frontend/{dist,node_modules,src/environments}
$ ./src/pybind/mgr/dashboard/ci/cephadm/run-cephadm-e2e-tests.sh
Note:

In fedora 35, there can occur a permission error when trying to mount the shared_folders. This can be fixed by running:

$ sudo setfacl -R -m u:qemu:rwx <abs-path-to-your-user-home>

or also by setting the appropriate permission to your $HOME directory

You can also start a cluster in development mode (so the frontend build starts in watch mode and you only have to reload the page for the changes to be reflected) by running:

$ ./src/pybind/mgr/dashboard/ci/cephadm/start-cluster.sh --dev-mode
Note:

Add --expanded if you need a cluster ready to deploy services (one with enough monitor daemons spread across different hosts and enough OSDs).

Test your changes by running:

$ ./src/pybind/mgr/dashboard/ci/cephadm/run-cephadm-e2e-tests.sh

Shutdown the cluster by running:

$ kcli delete plan -y ceph $ # In development mode, also kill the npm build watch process (e.g., pkill -f “ng build”)

Other running options

During active development, it is not recommended to run the previous script, as it is not prepared for constant file changes. Instead you should use one of the following commands:

  • npm run e2e - This will run ng serve and open the Cypress Test Runner.

  • npm run e2e:ci - This will run ng serve and run the Cypress Test Runner once.

  • npx cypress run - This calls cypress directly and will run the Cypress Test Runner. You need to have a running frontend server.

  • npx cypress open - This calls cypress directly and will open the Cypress Test Runner. You need to have a running frontend server.

Calling Cypress directly has the advantage that you can use any of the available flags to customize your test run and you don’t need to start a frontend server each time.

Using one of the open commands, will open a cypress application where you can see all the test files you have and run each individually. This is going to be run in watch mode, so if you make any changes to test files, it will retrigger the test run. This cannot be used inside docker, as it requires X11 environment to be able to open.

By default Cypress will look for the web page at https://localhost:4200/. If you are serving it in a different URL you will need to configure it by exporting the environment variable CYPRESS_BASE_URL with the new value. E.g.: CYPRESS_BASE_URL=https://localhost:41076/ npx cypress open

CYPRESS_CACHE_FOLDER

When installing cypress via npm, a binary of the cypress app will also be downloaded and stored in a cache folder. This removes the need to download it every time you run npm ci or even when using cypress in a separate project.

By default Cypress uses ~/.cache to store the binary. To prevent changes to the user home directory, we have changed this folder to /ceph/build/src/pybind/mgr/dashboard/cypress, so when you build ceph or run run-frontend-e2e-tests.sh this is the directory Cypress will use.

When using any other command to install or run cypress, it will go back to the default directory. It is recommended that you export the CYPRESS_CACHE_FOLDER environment variable with a fixed directory, so you always use the same directory no matter which command you use.

Writing End-to-End Tests

The PagerHelper class

The PageHelper class is supposed to be used for general purpose code that can be used on various pages or suites.

Examples are

  • navigateTo() - Navigates to a specific page and waits for it to load

  • getFirstTableCell() - returns the first table cell. You can also pass a string with the desired content and it will return the first cell that contains it.

  • getTabsCount() - returns the amount of tabs

Every method that could be useful on several pages belongs there. Also, methods which enhance the derived classes of the PageHelper belong there. A good example for such a case is the restrictTo() decorator. It ensures that a method implemented in a subclass of PageHelper is called on the correct page. It will also show a developer-friendly warning if this is not the case.

Subclasses of PageHelper

Helper Methods

In order to make code reusable which is specific for a particular suite, make sure to put it in a derived class of the PageHelper. For instance, when talking about the pool suite, such methods would be create(), exist() and delete(). These methods are specific to a pool but are useful for other suites.

Methods that return HTML elements which can only be found on a specific page, should be either implemented in the helper methods of the subclass of PageHelper or as own methods of the subclass of PageHelper.

Using PageHelpers

In any suite, an instance of the specific Helper class should be instantiated and called directly.

const pools = new PoolPageHelper();

it('should create a pool', () => {
  pools.exist(poolName, false);
  pools.navigateTo('create');
  pools.create(poolName, 8);
  pools.exist(poolName, true);
});

Code Style

Please refer to the official Cypress Core Concepts for a better insight on how to write and structure tests.

describe() vs it()

Both describe() and it() are function blocks, meaning that any executable code necessary for the test can be contained in either block. However, Typescript scoping rules still apply, therefore any variables declared in a describe are available to the it() blocks inside of it.

describe() typically are containers for tests, allowing you to break tests into multiple parts. Likewise, any setup that must be made before your tests are run can be initialized within the describe() block. Here is an example:

describe('create, edit & delete image test', () => {
  const poolName = 'e2e_images_pool';

  before(() => {
    cy.login();
    pools.navigateTo('create');
    pools.create(poolName, 8, 'rbd');
    pools.exist(poolName, true);
  });

  beforeEach(() => {
    cy.login();
    images.navigateTo();
  });

  //...

});

As shown, we can initiate the variable poolName as well as run commands before our test suite begins (creating a pool). describe() block messages should include what the test suite is.

it() blocks typically are parts of an overarching test. They contain the functionality of the test suite, each performing individual roles. Here is an example:

describe('create, edit & delete image test', () => {
  //...

  it('should create image', () => {
    images.createImage(imageName, poolName, '1');
    images.getFirstTableCell(imageName).should('exist');
  });

  it('should edit image', () => {
    images.editImage(imageName, poolName, newImageName, '2');
    images.getFirstTableCell(newImageName).should('exist');
  });

  //...
});

As shown from the previous example, our describe() test suite is to create, edit and delete an image. Therefore, each it() completes one of these steps, one for creating, one for editing, and so on. Likewise, every it() blocks message should be in lowercase and written so long as “it” can be the prefix of the message. For example, it('edits the test image' () => ...) vs. it('image edit test' () => ...). As shown, the first example makes grammatical sense with it() as the prefix whereas the second message does not. it() should describe what the individual test is doing and what it expects to happen.

Differences between Frontend Unit Tests and End-to-End (E2E) Tests / FAQ

General introduction about testing and E2E/unit tests

What are E2E/unit tests designed for?

E2E test:

It requires a fully functional system and tests the interaction of all components of the application (Ceph, back-end, front-end). E2E tests are designed to mimic the behavior of the user when interacting with the application - for example when it comes to workflows like creating/editing/deleting an item. Also the tests should verify that certain items are displayed as a user would see them when clicking through the UI (for example a menu entry or a pool that has been created during a test and the pool and its properties should be displayed in the table).

Angular Unit Tests:

Unit tests, as the name suggests, are tests for smaller units of the code. Those tests are designed for testing all kinds of Angular components (e.g. services, pipes etc.). They do not require a connection to the backend, hence those tests are independent of it. The expected data of the backend is mocked in the frontend and by using this data the functionality of the frontend can be tested without having to have real data from the backend. As previously mentioned, data is either mocked or, in a simple case, contains a static input, a function call and an expected static output. More complex examples include the state of a component (attributes of the component class), that define how the output changes according to the given input.

Which E2E/unit tests are considered to be valid?

This is not easy to answer, but new tests that are written in the same way as already existing dashboard tests should generally be considered valid. Unit tests should focus on the component to be tested. This is either an Angular component, directive, service, pipe, etc.

E2E tests should focus on testing the functionality of the whole application. Approximately a third of the overall E2E tests should verify the correctness of user visible elements.

How should an E2E/unit test look like?

Unit tests should focus on the described purpose and shouldn’t try to test other things in the same it block.

E2E tests should contain a description that either verifies the correctness of a user visible element or a complete process like for example the creation/validation/deletion of a pool.

What should an E2E/unit test cover?

E2E tests should mostly, but not exclusively, cover interaction with the backend. This way the interaction with the backend is utilized to write integration tests.

A unit test should mostly cover critical or complex functionality of a component (Angular Components, Services, Pipes, Directives, etc).

What should an E2E/unit test NOT cover?

Avoid duplicate testing: do not write E2E tests for what’s already been covered as frontend-unit tests and vice versa. It may not be possible to completely avoid an overlap.

Unit tests should not be used to extensively click through components and E2E tests shouldn’t be used to extensively test a single component of Angular.

Best practices/guideline

As a general guideline we try to follow the 70/20/10 approach - 70% unit tests, 20% integration tests and 10% end-to-end tests. For further information please refer to this document and the included “Testing Pyramid”.

Further Help

To get more help on the Angular CLI use ng help or go check out the Angular CLI README.

Example of a Generator

# Create module 'Core'
src/app> ng generate module core -m=app --routing

# Create module 'Auth' under module 'Core'
src/app/core> ng generate module auth -m=core --routing
or, alternatively:
src/app> ng generate module core/auth -m=core --routing

# Create component 'Login' under module 'Auth'
src/app/core/auth> ng generate component login -m=core/auth
or, alternatively:
src/app> ng generate component core/auth/login -m=core/auth

Frontend Typescript Code Style Guide Recommendations

Group the imports based on its source and separate them with a blank line.

The source groups can be either from Angular, external or internal.

Example:

import { Component } from '@angular/core';
import { Router } from '@angular/router';

import { ToastrManager } from 'ngx-toastr';

import { Credentials } from '../../../shared/models/credentials.model';
import { HostService } from './services/host.service';

Frontend components

There are several components that can be reused on different pages. This components are declared on the components module: src/pybind/mgr/dashboard/frontend/src/app/shared/components.

Helper

This component should be used to provide additional information to the user.

Example:

<cd-helper>
  Some <strong>helper</strong> html text
</cd-helper>

Terminology and wording

Instead of using the Ceph component names, the approach suggested is to use the logical/generic names (Block over RBD, Filesystem over CephFS, Object over RGW). Nevertheless, as Ceph-Dashboard cannot completely hide the Ceph internals, some Ceph-specific names might remain visible.

Regarding the wording for action labels and other textual elements (form titles, buttons, etc.), the chosen approach is to follow these guidelines. As a rule of thumb, ‘Create’ and ‘Delete’ are the proper wording for most forms, instead of ‘Add’ and ‘Remove’, unless some already created item is either added or removed to/from a set of items (e.g.: ‘Add permission’ to a user vs. ‘Create (new) permission’).

In order to enforce the use of this wording, a service ActionLabelsI18n has been created, which provides translated labels for use in UI elements.

Frontend branding

Every vendor can customize the ‘Ceph dashboard’ to his needs. No matter if logo, HTML-Template or TypeScript, every file inside the frontend folder can be replaced.

To replace files, open ./frontend/angular.json and scroll to the section fileReplacements inside the production configuration. Here you can add the files you wish to brand. We recommend to place the branded version of a file in the same directory as the original one and to add a .brand to the file name, right in front of the file extension. A fileReplacement could for example look like this:

{
  "replace": "src/app/core/auth/login/login.component.html",
  "with": "src/app/core/auth/login/login.component.brand.html"
}

To serve or build the branded user interface run:

$ npm run start – –prod

or

$ npm run build – –prod

Unfortunately it’s currently not possible to use multiple configurations when serving or building the UI at the same time. That means a configuration just for the branding fileReplacements is not an option, because you want to use the production configuration anyway (https://github.com/angular/angular-cli/issues/10612). Furthermore it’s also not possible to use glob expressions for fileReplacements. As long as the feature hasn’t been implemented, you have to add the file replacements manually to the angular.json file (https://github.com/angular/angular-cli/issues/12354).

Nevertheless you should stick to the suggested naming scheme because it makes it easier for you to use glob expressions once it’s supported in the future.

To change the variable defaults or add your own ones you can overwrite them in ./frontend/src/styles/vendor/_variables.scss. Just reassign the variable you want to change, for example $color-primary: teal; To overwrite or extend the default CSS, you can add your own styles in ./frontend/src/styles/vendor/_style-overrides.scss.

UI Style Guide

The style guide is created to document Ceph Dashboard standards and maintain consistency across the project. Its an effort to make it easier for contributors to process designing and deciding mockups and designs for Dashboard.

The development environment for Ceph Dashboard has live reloading enabled so any changes made in UI are reflected in open browser windows. Ceph Dashboard uses Bootstrap as the main third-party CSS library.

Avoid duplication of code. Be consistent with the existing UI by reusing existing SCSS declarations as much as possible.

Always check for existing code similar to what you want to write. You should always try to keep the same look-and-feel as the existing code.

Colors

All the colors used in Ceph Dashboard UI are listed in frontend/src/styles/defaults/_bootstrap-defaults.scss. If using new color always define color variables in the _bootstrap-defaults.scss and use the variable instead of hard coded color values so that changes to the color are reflected in similar UI elements.

The main color for the Ceph Dashboard is $primary. The primary color is used in navigation components and as the $border-color for input components of form.

The secondary color is $secondary and is the background color for Ceph Dashboard.

Buttons

Buttons are used for performing actions such as: “Submit”, “Edit, “Create” and “Update”.

Forms: When using to submit forms anywhere in the Dashboard, the main action button should use the cd-submit-button component and the secondary button should use cd-back-button component. The text on the action button should be same as the form title and follow a title case. The text on the secondary button should be Cancel. Perform action button should always be on right while Cancel button should always be on left.

Modals: The main action button should use the cd-submit-button component and the secondary button should use cd-back-button component. The text on the action button should follow a title case and correspond to the action to be performed. The text on the secondary button should be Close.

Disclosure Button: Disclosure buttons should be used to allow users to display and hide additional content in the interface.

Action Button: Use the action button to perform actions such as edit or update a component. All action button should have an icon corresponding to the actions they perform and button text should follow title case. The button color should be the same as the form’s main button color.

Drop Down Buttons: Use dropdown buttons to display predefined lists of actions. All drop down buttons have icons corresponding to the action they perform.

Forms

Mark invalid form fields with red outline and show a meaningful error message. Use red as font color for message and be as specific as possible. This field is required. should be the exact error message for required fields. Mark valid forms with a green outline and a green tick at the end of the form. Sections should not have a bigger header than the parent.

Modals

Blur any interface elements in the background to bring the modal content into focus. The heading of the modal should reflect the action it can perform and should be clearly mentioned at the top of the modal. Use cd-back-button component in the footer for closing the modal.

Icons

We use Fork Awesome classes for icons. We have a list of used icons in src/app/shared/enum/icons.enum.ts, these should be referenced in the HTML, so its easier to change them later. When icons are next to text, they should be center-aligned horizontally. If icons are stacked, they should also be center-aligned vertically. Use small icons with buttons. For notifications use large icons.

Alerts and notifications

Default notification should have text-info color. Success notification should have text-success color. Failure notification should have text-danger color.

Error Handling

For handling front-end errors, there is a generic Error Component which can be found in ./src/pybind/mgr/dashboard/frontend/src/app/core/error. For reporting a new error, you can simply extend the DashboardError class in error.ts file and add specific header and message for the new error. Some generic error classes are already in place such as DashboardNotFoundError and DashboardForbiddenError which can be called and reused in different scenarios.

For example - throw new DashboardNotFoundError().

I18N

How to extract messages from source code?

To extract the I18N messages from the templates and the TypeScript files just run the following command in src/pybind/mgr/dashboard/frontend:

$ npm run i18n:extract

This will extract all marked messages from the HTML templates first and then add all marked strings from the TypeScript files to the translation template. Since the extraction from TypeScript files is still not supported by Angular itself, we are using the ngx-translator extractor to parse the TypeScript files.

When the command ran successfully, it should have created or updated the file src/locale/messages.xlf.

The file isn’t tracked by git, you can just use it to start with the translation offline or add/update the resource files on transifex.

Supported languages

All our supported languages should be registered in both exports in supported-languages.enum.ts and have a corresponding test in language-selector.component.spec.ts.

The SupportedLanguages enum will provide the list for the default language selection.

Translating process

To facilitate the translation process of the dashboard we are using a web tool called transifex.

If you wish to help translating to any language just go to our transifex project page, join the project and you can start translating immediately.

All translations will then be reviewed and later pushed upstream.

Updating translated messages

Any time there are new messages translated and reviewed in a specific language we should update the translation file upstream.

To do that, check the settings in the i18n config file src/pybind/mgr/dashboard/frontend/i18n.config.json:: and make sure that the organization is ceph, the project is ceph-dashboard and the resource is the one you want to pull from and push to e.g. Master:master. To find a list of available resources visit https://www.transifex.com/ceph/ceph-dashboard/content/.

After you checked the config go to the directory src/pybind/mgr/dashboard/frontend and run:

$ npm run i18n

This command will extract all marked messages from the HTML templates and TypeScript files. Once the source file has been created it will push it to transifex and pull the latest translations. It will also fill all the untranslated strings with the source string. The tool will ask you for an api token, unless you added it by running:

$ npm run i18n:token

To create a transifex api token visit https://www.transifex.com/user/settings/api/.

After the command ran successfully, build the UI and check if everything is working as expected. You also might want to run the frontend tests.

Suggestions

Strings need to start and end in the same line as the element:

<!-- avoid -->
<span i18n>
  Foo
</span>

<!-- recommended -->
<span i18n>Foo</span>


<!-- avoid -->
<span i18n>
  Foo bar baz.
  Foo bar baz.
</span>

<!-- recommended -->
<span i18n>Foo bar baz.
  Foo bar baz.</span>

Isolated interpolations should not be translated:

<!-- avoid -->
<span i18n>{{ foo }}</span>

<!-- recommended -->
<span>{{ foo }}</span>

Interpolations used in a sentence should be kept in the translation:

<!-- recommended -->
<span i18n>There are {{ x }} OSDs.</span>

Remove elements that are outside the context of the translation:

<!-- avoid -->
<label i18n>
  Profile
  <span class="required"></span>
</label>

<!-- recommended -->
<label>
  <ng-container i18n>Profile<ng-container>
  <span class="required"></span>
</label>

Keep elements that affect the sentence:

<!-- recommended -->
<span i18n>Profile <b>foo</b> will be removed.</span>

Backend Development

The Python backend code of this module requires a number of Python modules to be installed. They are listed in file requirements.txt. Using pip you may install all required dependencies by issuing pip install -r requirements.txt in directory src/pybind/mgr/dashboard.

If you’re using the ceph-dev-docker development environment, simply run ./install_deps.sh from the toplevel directory to install them.

Unit Testing

In dashboard we have two different kinds of backend tests:

  1. Unit tests based on tox

  2. API tests based on Teuthology.

Unit tests based on tox

We included a tox configuration file that will run the unit tests under Python 3, as well as linting tools to guarantee the uniformity of code.

You need to install tox and coverage before running it. To install the packages in your system, either install it via your operating system’s package management tools, e.g. by running dnf install python-tox python-coverage on Fedora Linux.

Alternatively, you can use Python’s native package installation method:

$ pip install tox
$ pip install coverage

To run the tests, run src/script/run_tox.sh in the dashboard directory (where tox.ini is located):

## Run Python 3 tests+lint commands:
$ ../../../script/run_tox.sh --tox-env py3,lint,check

## Run Python 3 arbitrary command (e.g. 1 single test):
$ ../../../script/run_tox.sh --tox-env py3 "" tests/test_rgw_client.py::RgwClientTest::test_ssl_verify

You can also run tox instead of run_tox.sh:

## Run Python 3 tests command:
$ tox -e py3

## Run Python 3 arbitrary command (e.g. 1 single test):
$ tox -e py3 tests/test_rgw_client.py::RgwClientTest::test_ssl_verify

Python files can be automatically fixed and formatted according to PEP8 standards by using run_tox.sh --tox-env fix or tox -e fix.

We also collect coverage information from the backend code when you run tests. You can check the coverage information provided by the tox output, or by running the following command after tox has finished successfully:

$ coverage html

This command will create a directory htmlcov with an HTML representation of the code coverage of the backend.

API tests based on Teuthology

How to run existing API tests:

To run the API tests against a real Ceph cluster, we leverage the Teuthology framework. This has the advantage of catching bugs originated from changes in the internal Ceph code.

Our run-backend-api-tests.sh script will start a vstart Ceph cluster before running the Teuthology tests, and then it stops the cluster after the tests are run. Of course this implies that you have built/compiled Ceph previously.

Start all dashboard tests by running:

$ ./run-backend-api-tests.sh

Or, start one or multiple specific tests by specifying the test name:

$ ./run-backend-api-tests.sh tasks.mgr.dashboard.test_pool.PoolTest

Or, source the script and run the tests manually:

$ source run-backend-api-tests.sh
$ run_teuthology_tests [tests]...
$ cleanup_teuthology
How to write your own tests:

There are two possible ways to write your own API tests:

The first is by extending one of the existing test classes in the qa/tasks/mgr/dashboard directory.

The second way is by adding your own API test module if you’re creating a new controller for example. To do so you’ll just need to add the file containing your new test class to the qa/tasks/mgr/dashboard directory and implement all your tests here.

Note

Don’t forget to add the path of the newly created module to modules section in qa/suites/rados/mgr/tasks/dashboard.yaml.

Short example: Let’s assume you created a new controller called my_new_controller.py and the related test module test_my_new_controller.py. You’ll need to add tasks.mgr.dashboard.test_my_new_controller to the modules section in the dashboard.yaml file.

Also, if you’re removing test modules please keep in mind to remove the related section. Otherwise the Teuthology test run will fail.

Please run your API tests on your dev environment (as explained above) before submitting a pull request. Also make sure that a full QA run in Teuthology/sepia lab (based on your changes) has completed successfully before it gets merged. You don’t need to schedule the QA run yourself, just add the ‘needs-qa’ label to your pull request as soon as you think it’s ready for merging (e.g. make check was successful, the pull request is approved and all comments have been addressed). One of the developers who has access to Teuthology/the sepia lab will take care of it and report the result back to you.

How to add a new controller?

A controller is a Python class that extends from the BaseController class and is decorated with either the @Controller, @ApiController or @UiApiController decorators. The Python class must be stored inside a Python file located under the controllers directory. The Dashboard module will automatically load your new controller upon start.

@ApiController and @UiApiController are both specializations of the @Controller decorator.

The @ApiController should be used for controllers that provide an API-like REST interface and the @UiApiController should be used for endpoints consumed by the UI but that are not part of the ‘public’ API. For any other kinds of controllers the @Controller decorator should be used.

A controller has a URL prefix path associated that is specified in the controller decorator, and all endpoints exposed by the controller will share the same URL prefix path.

A controller’s endpoint is exposed by implementing a method on the controller class decorated with the @Endpoint decorator.

For example create a file ping.py under controllers directory with the following code:

from ..tools import Controller, ApiController, UiApiController, BaseController, Endpoint

@Controller('/ping')
class Ping(BaseController):
  @Endpoint()
  def hello(self):
    return {'msg': "Hello"}

@ApiController('/ping')
class ApiPing(BaseController):
  @Endpoint()
  def hello(self):
    return {'msg': "Hello"}

@UiApiController('/ping')
class UiApiPing(BaseController):
  @Endpoint()
  def hello(self):
    return {'msg': "Hello"}

The hello endpoint of the Ping controller can be reached by the following URL: https://mgr_hostname:8443/ping/hello using HTTP GET requests. As you can see the controller URL path /ping is concatenated to the method name hello to generate the endpoint’s URL.

In the case of the ApiPing controller, the hello endpoint can be reached by the following URL: https://mgr_hostname:8443/api/ping/hello using a HTTP GET request. The API controller URL path /ping is prefixed by the /api path and then concatenated to the method name hello to generate the endpoint’s URL. Internally, the @ApiController is actually calling the @Controller decorator by passing an additional decorator parameter called base_url:

@ApiController('/ping') <=> @Controller('/ping', base_url="/api")

UiApiPing works in a similar way than the ApiPing, but the URL will be prefixed by /ui-api: https://mgr_hostname:8443/ui-api/ping/hello. UiApiPing is also a @Controller extension:

@UiApiController('/ping') <=> @Controller('/ping', base_url="/ui-api")

The @Endpoint decorator also supports many parameters to customize the endpoint:

  • method="GET": the HTTP method allowed to access this endpoint.

  • path="/<method_name>": the URL path of the endpoint, excluding the controller URL path prefix.

  • path_params=[]: list of method parameter names that correspond to URL path parameters. Can only be used when method in ['POST', 'PUT'].

  • query_params=[]: list of method parameter names that correspond to URL query parameters.

  • json_response=True: indicates if the endpoint response should be serialized in JSON format.

  • proxy=False: indicates if the endpoint should be used as a proxy.

An endpoint method may have parameters declared. Depending on the HTTP method defined for the endpoint the method parameters might be considered either path parameters, query parameters, or body parameters.

For GET and DELETE methods, the method’s non-optional parameters are considered path parameters by default. Optional parameters are considered query parameters. By specifying the query_parameters in the endpoint decorator it is possible to make a non-optional parameter to be a query parameter.

For POST and PUT methods, all method parameters are considered body parameters by default. To override this default, one can use the path_params and query_params to specify which method parameters are path and query parameters respectively. Body parameters are decoded from the request body, either from a form format, or from a dictionary in JSON format.

Let’s use an example to better understand the possible ways to customize an endpoint:

from ..tools import Controller, BaseController, Endpoint

@Controller('/ping')
class Ping(BaseController):

  # URL: /ping/{key}?opt1=...&opt2=...
  @Endpoint(path="/", query_params=['opt1'])
  def index(self, key, opt1, opt2=None):
    """..."""

  # URL: /ping/{key}?opt1=...&opt2=...
  @Endpoint(query_params=['opt1'])
  def __call__(self, key, opt1, opt2=None):
    """..."""

  # URL: /ping/post/{key1}/{key2}
  @Endpoint('POST', path_params=['key1', 'key2'])
  def post(self, key1, key2, data1, data2=None):
    """..."""

In the above example we see how the path option can be used to override the generated endpoint URL in order to not use the method’s name in the URL. In the index method we set the path to "/" to generate an endpoint that is accessible by the root URL of the controller.

An alternative approach to generate an endpoint that is accessible through just the controller’s path URL is by using the __call__ method, as we show in the above example.

From the third method you can see that the path parameters are collected from the URL by parsing the list of values separated by slashes / that come after the URL path /ping for index method case, and /ping/post for the post method case.

Defining path parameters in endpoints’s URLs using python methods’s parameters is very easy but it is still a bit strict with respect to the position of these parameters in the URL structure. Sometimes we may want to explicitly define a URL scheme that contains path parameters mixed with static parts of the URL. Our controller infrastructure also supports the declaration of URL paths with explicit path parameters at both the controller level and method level.

Consider the following example:

from ..tools import Controller, BaseController, Endpoint

@Controller('/ping/{node}/stats')
class Ping(BaseController):

  # URL: /ping/{node}/stats/{date}/latency?unit=...
  @Endpoint(path="/{date}/latency")
  def latency(self, node, date, unit="ms"):
    """ ..."""

In this example we explicitly declare a path parameter {node} in the controller URL path, and a path parameter {date} in the latency method. The endpoint for the latency method is then accessible through the URL: https://mgr_hostname:8443/ping/{node}/stats/{date}/latency .

For a full set of examples on how to use the @Endpoint decorator please check the unit test file: tests/test_controllers.py. There you will find many examples of how to customize endpoint methods.

Implementing Proxy Controller

Sometimes you might need to relay some requests from the Dashboard frontend directly to an external service. For that purpose we provide a decorator called @Proxy. (As a concrete example, check the controllers/rgw.py file where we implemented an RGW Admin Ops proxy.)

The @Proxy decorator is a wrapper of the @Endpoint decorator that already customizes the endpoint for working as a proxy. A proxy endpoint works by capturing the URL path that follows the controller URL prefix path, and does not do any decoding of the request body.

Example:

from ..tools import Controller, BaseController, Proxy

@Controller('/foo/proxy')
class FooServiceProxy(BaseController):

  @Proxy()
  def proxy(self, path, **params):
    """
    if requested URL is "/foo/proxy/access/service?opt=1"
    then path is "access/service" and params is {'opt': '1'}
    """

How does the RESTController work?

We also provide a simple mechanism to create REST based controllers using the RESTController class. Any class which inherits from RESTController will, by default, return JSON.

The RESTController is basically an additional abstraction layer which eases and unifies the work with collections. A collection is just an array of objects with a specific type. RESTController enables some default mappings of request types and given parameters to specific method names. This may sound complicated at first, but it’s fairly easy. Lets have look at the following example:

import cherrypy
from ..tools import ApiController, RESTController

@ApiController('ping')
class Ping(RESTController):
  def list(self):
    return {"msg": "Hello"}

  def get(self, id):
    return self.objects[id]

In this case, the list method is automatically used for all requests to api/ping where no additional argument is given and where the request type is GET. If the request is given an additional argument, the ID in our case, it won’t map to list anymore but to get and return the element with the given ID (assuming that self.objects has been filled before). The same applies to other request types:

Request type

Arguments

Method

Status Code

GET

No

list

200

PUT

No

bulk_set

200

POST

No

create

201

DELETE

No

bulk_delete

204

GET

Yes

get

200

PUT

Yes

set

200

DELETE

Yes

delete

204

To use a custom endpoint for the above listed methods, you can use @RESTController.MethodMap

import cherrypy
from ..tools import ApiController, RESTController

  @RESTController.MethodMap(version='0.1')
  def create(self):
    return {"msg": "Hello"}

This decorator supports three parameters to customize the endpoint:

  • resource": resource id.

  • status=200: set the HTTP status response code

  • version: version

How to use a custom API endpoint in a RESTController?

If you don’t have any access restriction you can use @Endpoint. If you have set a permission scope to restrict access to your endpoints, @Endpoint will fail, as it doesn’t know which permission property should be used. To use a custom endpoint inside a restricted RESTController use @RESTController.Collection instead. You can also choose @RESTController.Resource if you have set a RESOURCE_ID in your RESTController class.

import cherrypy
from ..tools import ApiController, RESTController

@ApiController('ping', Scope.Ping)
class Ping(RESTController):
  RESOURCE_ID = 'ping'

  @RESTController.Resource('GET')
  def some_get_endpoint(self):
    return {"msg": "Hello"}

  @RESTController.Collection('POST')
  def some_post_endpoint(self, **data):
    return {"msg": data}

Both decorators also support five parameters to customize the endpoint:

  • method="GET": the HTTP method allowed to access this endpoint.

  • path="/<method_name>": the URL path of the endpoint, excluding the controller URL path prefix.

  • status=200: set the HTTP status response code

  • query_params=[]: list of method parameter names that correspond to URL query parameters.

  • version: version

How to restrict access to a controller?

All controllers require authentication by default. If you require that the controller can be accessed without authentication, then you can add the parameter secure=False to the controller decorator.

Example:

import cherrypy
from . import ApiController, RESTController


@ApiController('ping', secure=False)
class Ping(RESTController):
  def list(self):
    return {"msg": "Hello"}

How to create a dedicated UI endpoint which uses the ‘public’ API?

Sometimes we want to combine multiple calls into one single call to save bandwidth or for other performance reasons. In order to achieve that, we first have to create an @UiApiController which is used for endpoints consumed by the UI but that are not part of the ‘public’ API. Let the ui class inherit from the REST controller class. Now you can use all methods from the api controller.

Example:

import cherrypy
from . import UiApiController, ApiController, RESTController


@ApiController('ping', secure=False)  # /api/ping
class Ping(RESTController):
  def list(self):
    return self._list()

  def _list(self):  # To not get in conflict with the JSON wrapper
    return [1,2,3]


@UiApiController('ping', secure=False)  # /ui-api/ping
class PingUi(Ping):
  def list(self):
    return self._list() + [4, 5, 6]

How to access the manager module instance from a controller?

We provide the manager module instance as a global variable that can be imported in any module.

Example:

import logging
import cherrypy
from .. import mgr
from ..tools import ApiController, RESTController

logger = logging.getLogger(__name__)

@ApiController('servers')
class Servers(RESTController):
  def list(self):
    logger.debug('Listing available servers')
    return {'servers': mgr.list_servers()}

How to write a unit test for a controller?

We provide a test helper class called ControllerTestCase to easily create unit tests for your controller.

If we want to write a unit test for the above Ping controller, create a test_ping.py file under the tests directory with the following code:

from .helper import ControllerTestCase
from .controllers.ping import Ping


class PingTest(ControllerTestCase):
    @classmethod
    def setup_test(cls):
        cp_config = {'tools.authenticate.on': True}
        cls.setup_controllers([Ping], cp_config=cp_config)

    def test_ping(self):
        self._get("/api/ping")
        self.assertStatus(200)
        self.assertJsonBody({'msg': 'Hello'})

The ControllerTestCase class starts by initializing a CherryPy webserver. Then it will call the setup_test() class method where we can explicitly load the controllers that we want to test. In the above example we are only loading the Ping controller. We can also provide cp_config in order to update the controller’s cherrypy config (e.g. enable authentication as shown in the example).

How to update or create new dashboards in grafana?

We are using jsonnet and grafonnet-lib to write code for the grafana dashboards. All the dashboards are written inside grafana_dashboards.jsonnet file in the monitoring/grafana/dashboards/jsonnet directory.

We generate the dashboard json files directly from this jsonnet file by running this command in the grafana/dashboards directory: jsonnet -m . jsonnet/grafana_dashboards.jsonnet. (For the above command to succeed we need jsonnet package installed and grafonnet-lib directory cloned in our machine. Please refer - https://grafana.github.io/grafonnet-lib/getting-started/ in case you have some trouble.)

To update an existing grafana dashboard or to create a new one, we need to update the grafana_dashboards.jsonnet file and generate the new/updated json files using the above mentioned command. For people who are not familiar with grafonnet or jsonnet implementation can follow this doc - https://grafana.github.io/grafonnet-lib/.

Example grafana dashboard in jsonnet format:

To specify the grafana dashboard properties such as title, uid etc we can create a local function -

local dashboardSchema(title, uid, time_from, refresh, schemaVersion, tags,timezone, timepicker)

To add a graph panel we can spcify the graph schema in a local function such as -

local graphPanelSchema(title, nullPointMode, stack, formatY1, formatY2, labelY1, labelY2, min, fill, datasource)

and then use these functions inside the dashboard definition like -

{
    radosgw-sync-overview.json: //json file name to be generated

    dashboardSchema(
      'RGW Sync Overview', 'rgw-sync-overview', 'now-1h', '15s', .., .., ..
    )

    .addPanels([
      graphPanelSchema(
        'Replication (throughput) from Source Zone', 'Bps', null, .., .., ..)
    ])
}

The valid grafonnet-lib attributes can be found here - https://grafana.github.io/grafonnet-lib/api-docs/.

How to listen for manager notifications in a controller?

The manager notifies the modules of several types of cluster events, such as cluster logging event, etc…

Each module has a “global” handler function called notify that the manager calls to notify the module. But this handler function must not block or spend too much time processing the event notification. For this reason we provide a notification queue that controllers can register themselves with to receive cluster notifications.

The example below represents a controller that implements a very simple live log viewer page:

from __future__ import absolute_import

import collections

import cherrypy

from ..tools import ApiController, BaseController, NotificationQueue


@ApiController('livelog')
class LiveLog(BaseController):
    log_buffer = collections.deque(maxlen=1000)

    def __init__(self):
        super(LiveLog, self).__init__()
        NotificationQueue.register(self.log, 'clog')

    def log(self, log_struct):
        self.log_buffer.appendleft(log_struct)

    @cherrypy.expose
    def default(self):
        ret = '<html><meta http-equiv="refresh" content="2" /><body>'
        for l in self.log_buffer:
            ret += "{}<br>".format(l)
        ret += "</body></html>"
        return ret

As you can see above, the NotificationQueue class provides a register method that receives the function as its first argument, and receives the “notification type” as the second argument. You can omit the second argument of the register method, and in that case you are registering to listen all notifications of any type.

Here is an list of notification types (these might change in the future) that can be used:

  • clog: cluster log notifications

  • command: notification when a command issued by MgrModule.send_command completes

  • perf_schema_update: perf counters schema update

  • mon_map: monitor map update

  • fs_map: cephfs map update

  • osd_map: OSD map update

  • service_map: services (RGW, RBD-Mirror, etc.) map update

  • mon_status: monitor status regular update

  • health: health status regular update

  • pg_summary: regular update of PG status information

How to write a unit test when a controller accesses a Ceph module?

Consider the following example that implements a controller that retrieves the list of RBD images of the rbd pool:

import rbd
from .. import mgr
from ..tools import ApiController, RESTController


@ApiController('rbdimages')
class RbdImages(RESTController):
    def __init__(self):
        self.ioctx = mgr.rados.open_ioctx('rbd')
        self.rbd = rbd.RBD()

    def list(self):
        return [{'name': n} for n in self.rbd.list(self.ioctx)]

In the example above, we want to mock the return value of the rbd.list function, so that we can test the JSON response of the controller.

The unit test code will look like the following:

import mock
from .helper import ControllerTestCase


class RbdImagesTest(ControllerTestCase):
    @mock.patch('rbd.RBD.list')
    def test_list(self, rbd_list_mock):
        rbd_list_mock.return_value = ['img1', 'img2']
        self._get('/api/rbdimages')
        self.assertJsonBody([{'name': 'img1'}, {'name': 'img2'}])

How to add a new configuration setting?

If you need to store some configuration setting for a new feature, we already provide an easy mechanism for you to specify/use the new config setting.

For instance, if you want to add a new configuration setting to hold the email address of the dashboard admin, just add a setting name as a class attribute to the Options class in the settings.py file:

# ...
class Options(object):
  # ...

  ADMIN_EMAIL_ADDRESS = ('admin@admin.com', str)

The value of the class attribute is a pair composed by the default value for that setting, and the python type of the value.

By declaring the ADMIN_EMAIL_ADDRESS class attribute, when you restart the dashboard module, you will automatically gain two additional CLI commands to get and set that setting:

$ ceph dashboard get-admin-email-address
$ ceph dashboard set-admin-email-address <value>

To access, or modify the config setting value from your Python code, either inside a controller or anywhere else, you just need to import the Settings class and access it like this:

from settings import Settings

# ...
tmp_var = Settings.ADMIN_EMAIL_ADDRESS

# ....
Settings.ADMIN_EMAIL_ADDRESS = 'myemail@admin.com'

The settings management implementation will make sure that if you change a setting value from the Python code you will see that change when accessing that setting from the CLI and vice-versa.

How to run a controller read-write operation asynchronously?

Some controllers might need to execute operations that alter the state of the Ceph cluster. These operations might take some time to execute and to maintain a good user experience in the Web UI, we need to run those operations asynchronously and return immediately to frontend some information that the operations are running in the background.

To help in the development of the above scenario we added the support for asynchronous tasks. To trigger the execution of an asynchronous task we must use the following class method of the TaskManager class:

from ..tools import TaskManager
# ...
TaskManager.run(name, metadata, func, args, kwargs)

  • name is a string that can be used to group tasks. For instance for RBD image creation tasks we could specify "rbd/create" as the name, or similarly "rbd/remove" for RBD image removal tasks.

  • metadata is a dictionary where we can store key-value pairs that characterize the task. For instance, when creating a task for creating RBD images we can specify the metadata argument as {'pool_name': "rbd", image_name': "test-img"}.

  • func is the python function that implements the operation code, which will be executed asynchronously.

  • args and kwargs are the positional and named arguments that will be passed to func when the task manager starts its execution.

The TaskManager.run method triggers the asynchronous execution of function func and returns a Task object. The Task provides the public method Task.wait(timeout), which can be used to wait for the task to complete up to a timeout defined in seconds and provided as an argument. If no argument is provided the wait method blocks until the task is finished.

The Task.wait is very useful for tasks that usually are fast to execute but that sometimes may take a long time to run. The return value of the Task.wait method is a pair (state, value) where state is a string with following possible values:

  • VALUE_DONE = "done"

  • VALUE_EXECUTING = "executing"

The value will store the result of the execution of function func if state == VALUE_DONE. If state == VALUE_EXECUTING then value == None.

The pair (name, metadata) should unequivocally identify the task being run, which means that if you try to trigger a new task that matches the same (name, metadata) pair of the currently running task, then the new task is not created and you get the task object of the current running task.

For instance, consider the following example:

task1 = TaskManager.run("dummy/task", {'attr': 2}, func)
task2 = TaskManager.run("dummy/task", {'attr': 2}, func)

If the second call to TaskManager.run executes while the first task is still executing then it will return the same task object: assert task1 == task2.

How to get the list of executing and finished asynchronous tasks?

The list of executing and finished tasks is included in the Summary controller, which is already polled every 5 seconds by the dashboard frontend. But we also provide a dedicated controller to get the same list of executing and finished tasks.

The Task controller exposes the /api/task endpoint that returns the list of executing and finished tasks. This endpoint accepts the name parameter that accepts a glob expression as its value. For instance, an HTTP GET request of the URL /api/task?name=rbd/* will return all executing and finished tasks which name starts with rbd/.

To prevent the finished tasks list from growing unbounded, we will always maintain the 10 most recent finished tasks, and the remaining older finished tasks will be removed when reaching a TTL of 1 minute. The TTL is calculated using the timestamp when the task finished its execution. After a minute, when the finished task information is retrieved, either by the summary controller or by the task controller, it is automatically deleted from the list and it will not be included in further task queries.

Each executing task is represented by the following dictionary:

{
  'name': "name",  # str
  'metadata': { },  # dict
  'begin_time': "2018-03-14T15:31:38.423605Z",  # str (ISO 8601 format)
  'progress': 0  # int (percentage)
}

Each finished task is represented by the following dictionary:

{
  'name': "name",  # str
  'metadata': { },  # dict
  'begin_time': "2018-03-14T15:31:38.423605Z",  # str (ISO 8601 format)
  'end_time': "2018-03-14T15:31:39.423605Z",  # str (ISO 8601 format)
  'duration': 0.0,  # float
  'progress': 0  # int (percentage)
  'success': True,  # bool
  'ret_value': None,  # object, populated only if 'success' == True
  'exception': None,  # str, populated only if 'success' == False
}

How to use asynchronous APIs with asynchronous tasks?

The TaskManager.run method as described in a previous section, is well suited for calling blocking functions, as it runs the function inside a newly created thread. But sometimes we want to call some function of an API that is already asynchronous by nature.

For these cases we want to avoid creating a new thread for just running a non-blocking function, and want to leverage the asynchronous nature of the function. The TaskManager.run is already prepared to be used with non-blocking functions by passing an object of the type TaskExecutor as an additional parameter called executor. The full method signature of TaskManager.run:

TaskManager.run(name, metadata, func, args=None, kwargs=None, executor=None)

The TaskExecutor class is responsible for code that executes a given task function, and defines three methods that can be overridden by subclasses:

def init(self, task)
def start(self)
def finish(self, ret_value, exception)

The init method is called before the running the task function, and receives the task object (of class Task).

The start method runs the task function. The default implementation is to run the task function in the current thread context.

The finish method should be called when the task function finishes with either the ret_value populated with the result of the execution, or with an exception object in the case that execution raised an exception.

To leverage the asynchronous nature of a non-blocking function, the developer should implement a custom executor by creating a subclass of the TaskExecutor class, and provide an instance of the custom executor class as the executor parameter of the TaskManager.run.

To better understand the expressive power of executors, we write a full example of use a custom executor to execute the MgrModule.send_command asynchronous function:

import json
from mgr_module import CommandResult
from .. import mgr
from ..tools import ApiController, RESTController, NotificationQueue, \
                    TaskManager, TaskExecutor


class SendCommandExecutor(TaskExecutor):
    def __init__(self):
        super(SendCommandExecutor, self).__init__()
        self.tag = None
        self.result = None

    def init(self, task):
        super(SendCommandExecutor, self).init(task)

        # we need to listen for 'command' events to know when the command
        # finishes
        NotificationQueue.register(self._handler, 'command')

        # store the CommandResult object to retrieve the results
        self.result = self.task.fn_args[0]
        if len(self.task.fn_args) > 4:
            # the user specified a tag for the command, so let's use it
            self.tag = self.task.fn_args[4]
        else:
            # let's generate a unique tag for the command
            self.tag = 'send_command_{}'.format(id(self))
            self.task.fn_args.append(self.tag)

    def _handler(self, data):
        if data == self.tag:
            # the command has finished, notifying the task with the result
            self.finish(self.result.wait(), None)
            # deregister listener to avoid memory leaks
            NotificationQueue.deregister(self._handler, 'command')


@ApiController('test')
class Test(RESTController):

    def _run_task(self, osd_id):
        task = TaskManager.run("test/task", {}, mgr.send_command,
                               [CommandResult(''), 'osd', osd_id,
                                json.dumps({'prefix': 'perf histogram dump'})],
                               executor=SendCommandExecutor())
        return task.wait(1.0)

    def get(self, osd_id):
        status, value = self._run_task(osd_id)
        return {'status': status, 'value': value}

The above SendCommandExecutor executor class can be used for any call to MgrModule.send_command. This means that we should need just one custom executor class implementation for each non-blocking API that we use in our controllers.

The default executor, used when no executor object is passed to TaskManager.run, is the ThreadedExecutor. You can check its implementation in the tools.py file.

How to update the execution progress of an asynchronous task?

The asynchronous tasks infrastructure provides support for updating the execution progress of an executing task. The progress can be updated from within the code the task is executing, which usually is the place where we have the progress information available.

To update the progress from within the task code, the TaskManager class provides a method to retrieve the current task object:

TaskManager.current_task()

The above method is only available when using the default executor ThreadedExecutor for executing the task. The current_task() method returns the current Task object. The Task object provides two public methods to update the execution progress value: the set_progress(percentage), and the inc_progress(delta) methods.

The set_progress method receives as argument an integer value representing the absolute percentage that we want to set to the task.

The inc_progress method receives as argument an integer value representing the delta we want to increment to the current execution progress percentage.

Take the following example of a controller that triggers a new task and updates its progress:

from __future__ import absolute_import
import random
import time
import cherrypy
from ..tools import TaskManager, ApiController, BaseController


@ApiController('dummy_task')
class DummyTask(BaseController):
    def _dummy(self):
        top = random.randrange(100)
        for i in range(top):
            TaskManager.current_task().set_progress(i*100/top)
            # or TaskManager.current_task().inc_progress(100/top)
            time.sleep(1)
        return "finished"

    @cherrypy.expose
    @cherrypy.tools.json_out()
    def default(self):
        task = TaskManager.run("dummy/task", {}, self._dummy)
        return task.wait(5)  # wait for five seconds

How to deal with asynchronous tasks in the front-end?

All executing and most recently finished asynchronous tasks are displayed on “Background-Tasks” and if finished on “Recent-Notifications” in the menu bar. For each task a operation name for three states (running, success and failure), a function that tells who is involved and error descriptions, if any, have to be provided. This can be achieved by appending TaskManagerMessageService.messages. This has to be done to achieve consistency among all tasks and states.

Operation Object

Ensures consistency among all tasks. It consists of three verbs for each different state f.e. {running: 'Creating', failure: 'create', success: 'Created'}.

  1. Put running operations in present participle f.e. 'Updating'.

  2. Failed messages always start with 'Failed to ' and should be continued with the operation in present tense f.e. 'update'.

  3. Put successful operations in past tense f.e. 'Updated'.

Involves Function

Ensures consistency among all messages of a task, it resembles who’s involved by the operation. It’s a function that returns a string which takes the metadata from the task to return f.e. "RBD 'somePool/someImage'".

Both combined create the following messages:

  • Failure => "Failed to create RBD 'somePool/someImage'"

  • Running => "Creating RBD 'somePool/someImage'"

  • Success => "Created RBD 'somePool/someImage'"

For automatic task handling use TaskWrapperService.wrapTaskAroundCall.

If for some reason wrapTaskAroundCall is not working for you, you have to subscribe to your asynchronous task manually through TaskManagerService.subscribe, and provide it with a callback, in case of a success to notify the user. A notification can be triggered with NotificationService.notifyTask. It will use TaskManagerMessageService.messages to display a message based on the state of a task.

Notifications of API errors are handled by ApiInterceptorService.

Usage example:

export class TaskManagerMessageService {
  // ...
  messages = {
    // Messages for task 'rbd/create'
    'rbd/create': new TaskManagerMessage(
      // Message prefixes
      ['create', 'Creating', 'Created'],
      // Message suffix
      (metadata) => `RBD '${metadata.pool_name}/${metadata.image_name}'`,
      (metadata) => ({
        // Error code and description
        '17': `Name is already used by RBD '${metadata.pool_name}/${
               metadata.image_name}'.`
      })
    ),
    // ...
  };
  // ...
}

export class RBDFormComponent {
  // ...
  createAction() {
    const request = this.createRequest();
    // Subscribes to 'call' with submitted 'task' and handles notifications
    return this.taskWrapper.wrapTaskAroundCall({
      task: new FinishedTask('rbd/create', {
        pool_name: request.pool_name,
        image_name: request.name
      }),
      call: this.rbdService.create(request)
    });
  }
  // ...
}

REST API documentation

Ceph-Dashboard provides two types of documentation for the Ceph RESTful API:

  • Static documentation: available at Ceph RESTful API. This comes from a versioned specification located at src/pybind/mgr/dashboard/openapi.yaml.

  • Interactive documentation: available from a running Ceph-Dashboard instance (top-right ? icon > API Docs).

If changes are made to the controllers/ directory, it’s very likely that they will result in changes to the generated OpenAPI specification. For that reason, a checker has been implemented to block unintended changes. This check is automatically triggered by the Pull Request CI (make check) and can be also manually invoked: tox -e openapi-check.

If that checker failed, it means that the current Pull Request is modifying the Ceph API and therefore:

  1. The versioned OpenAPI specification should be updated explicitly: tox -e openapi-fix.

  2. The team @ceph/api will be requested for reviews (this is automated via Github CODEOWNERS), in order to asses the impact of changes.

Additionally, Sphinx documentation can be generated from the OpenAPI specification with tox -e openapi-doc.

The Ceph RESTful OpenAPI specification is dynamically generated from the Controllers in controllers/ directory. However, by default it is not very detailed, so there are two decorators that can and should be used to add more information:

  • @EndpointDoc() for documentation of endpoints. It has four optional arguments (explained below): description, group, parameters and responses.

  • @ControllerDoc() for documentation of controller or group associated with the endpoints. It only takes the two first arguments: description and group.

description: A a string with a short (1-2 sentences) description of the object.

group: By default, an endpoint is grouped together with other endpoints within the same controller class. group is a string that can be used to assign an endpoint or all endpoints in a class to another controller or a conceived group name.

parameters: A dict used to describe path, query or request body parameters. By default, all parameters for an endpoint are listed on the Swagger UI page, including information of whether the parameter is optional/required and default values. However, there will be no description of the parameter and the parameter type will only be displayed in some cases. When adding information, each parameters should be described as in the example below. Note that the parameter type should be expressed as a built-in python type and not as a string. Allowed values are str, int, bool, float.

@EndpointDoc(parameters={'my_string': (str, 'Description of my_string')})
def method(my_string): pass

For body parameters, more complex cases are possible. If the parameter is a dictionary, the type should be replaced with a dict containing its nested parameters. When describing nested parameters, the same format as other parameters is used. However, all nested parameters are set as required by default. If the nested parameter is optional this must be specified as for item2 in the example below. If a nested parameters is set to optional, it is also possible to specify the default value (this will not be provided automatically for nested parameters).

@EndpointDoc(parameters={
  'my_dictionary': ({
    'item1': (str, 'Description of item1'),
    'item2': (str, 'Description of item2', True),  # item2 is optional
    'item3': (str, 'Description of item3', True, 'foo'),  # item3 is optional with 'foo' as default value
}, 'Description of my_dictionary')})
def method(my_dictionary): pass

If the parameter is a list of primitive types, the type should be surrounded with square brackets.

@EndpointDoc(parameters={'my_list': ([int], 'Description of my_list')})
def method(my_list): pass

If the parameter is a list with nested parameters, the nested parameters should be placed in a dictionary and surrounded with square brackets.

@EndpointDoc(parameters={
  'my_list': ([{
    'list_item': (str, 'Description of list_item'),
    'list_item2': (str, 'Description of list_item2')
}], 'Description of my_list')})
def method(my_list): pass

responses: A dict used for describing responses. Rules for describing responses are the same as for request body parameters, with one difference: responses also needs to be assigned to the related response code as in the example below:

@EndpointDoc(responses={
  '400':{'my_response': (str, 'Description of my_response')}})
def method(): pass

Error Handling in Python

Good error handling is a key requirement in creating a good user experience and providing a good API.

Dashboard code should not duplicate C++ code. Thus, if error handling in C++ is sufficient to provide good feedback, a new wrapper to catch these errors is not necessary. On the other hand, input validation is the best place to catch errors and generate the best error messages. If required, generate errors as soon as possible.

The backend provides few standard ways of returning errors.

First, there is a generic Internal Server Error:

Status Code: 500
{
    "version": <cherrypy version, e.g. 13.1.0>,
    "detail": "The server encountered an unexpected condition which prevented it from fulfilling the request.",
}

For errors generated by the backend, we provide a standard error format:

Status Code: 400
{
    "detail": str(e),     # E.g. "[errno -42] <some error message>"
    "component": "rbd",   # this can be null to represent a global error code
    "code": "3",          # Or a error name, e.g. "code": "some_error_key"
}

In case, the API Endpoints uses @ViewCache to temporarily cache results, the error looks like so:

Status Code 400
{
    "detail": str(e),     # E.g. "[errno -42] <some error message>"
    "component": "rbd",   # this can be null to represent a global error code
    "code": "3",          # Or a error name, e.g. "code": "some_error_key"
    'status': 3,          # Indicating the @ViewCache error status
}

In case, the API Endpoints uses a task the error looks like so:

Status Code 400
{
    "detail": str(e),     # E.g. "[errno -42] <some error message>"
    "component": "rbd",   # this can be null to represent a global error code
    "code": "3",          # Or a error name, e.g. "code": "some_error_key"
    "task": {             # Information about the task itself
        "name": "taskname",
        "metadata": {...}
    }
}

Our WebUI should show errors generated by the API to the user. Especially field-related errors in wizards and dialogs or show non-intrusive notifications.

Handling exceptions in Python should be an exception. In general, we should have few exception handlers in our project. Per default, propagate errors to the API, as it will take care of all exceptions anyway. In general, log the exception by adding logger.exception() with a description to the handler.

We need to distinguish between user errors from internal errors and programming errors. Using different exception types will ease the task for the API layer and for the user interface:

Standard Python errors, like SystemError, ValueError or KeyError will end up as internal server errors in the API.

In general, do not return error responses in the REST API. They will be returned by the error handler. Instead, raise the appropriate exception.

Plug-ins

New functionality can be provided by means of a plug-in architecture. Among the benefits this approach brings in, loosely coupled development is one of the most notable. As the Ceph Dashboard grows in feature richness, its code-base becomes more and more complex. The hook-based nature of a plug-in architecture allows to extend functionality in a controlled manner, and isolate the scope of the changes.

Ceph Dashboard relies on Pluggy to provide for plug-ing support. On top of pluggy, an interface-based approach has been implemented, with some safety checks (method override and abstract method checks).

In order to create a new plugin, the following steps are required:

  1. Add a new file under src/pybind/mgr/dashboard/plugins.

  2. Import the PLUGIN_MANAGER instance and the Interfaces.

  3. Create a class extending the desired interfaces. The plug-in library will check if all the methods of the interfaces have been properly overridden.

  4. Register the plugin in the PLUGIN_MANAGER instance.

  5. Import the plug-in from within the Ceph Dashboard module.py (currently no dynamic loading is implemented).

The available Mixins (helpers) are:

  • CanMgr: provides the plug-in with access to the mgr instance under self.mgr.

The available Interfaces are:

  • Initializable: requires overriding init() hook. This method is run at the very beginning of the dashboard module, right after all imports have been performed.

  • Setupable: requires overriding setup() hook. This method is run in the Ceph Dashboard serve() method, right after CherryPy has been configured, but before it is started. It’s a placeholder for the plug-in initialization logic.

  • HasOptions: requires overriding get_options() hook by returning a list of Options(). The options returned here are added to the MODULE_OPTIONS.

  • HasCommands: requires overriding register_commands() hook by defining the commands the plug-in can handle and decorating them with @CLICommand. The commands can be optionally returned, so that they can be invoked externally (which makes unit testing easier).

  • HasControllers: requires overriding get_controllers() hook by defining and returning the controllers as usual.

  • FilterRequest.BeforeHandler: requires overriding filter_request_before_handler() hook. This method receives a cherrypy.request object for processing. A usual implementation of this method will allow some requests to pass or will raise a cherrypy.HTTPError based on the request metadata and other conditions.

New interfaces and hooks should be added as soon as they are required to implement new functionality. The above list only comprises the hooks needed for the existing plugins.

A sample plugin implementation would look like this:

# src/pybind/mgr/dashboard/plugins/mute.py

from . import PLUGIN_MANAGER as PM
from . import interfaces as I

from mgr_module import CLICommand, Option
import cherrypy

@PM.add_plugin
class Mute(I.CanMgr, I.Setupable, I.HasOptions, I.HasCommands,
                     I.FilterRequest.BeforeHandler, I.HasControllers):
  @PM.add_hook
  def get_options(self):
    return [Option('mute', default=False, type='bool')]

  @PM.add_hook
  def setup(self):
    self.mute = self.mgr.get_module_option('mute')

  @PM.add_hook
  def register_commands(self):
    @CLICommand("dashboard mute")
    def _(mgr):
      self.mute = True
      self.mgr.set_module_option('mute', True)
      return 0

  @PM.add_hook
  def filter_request_before_handler(self, request):
    if self.mute:
      raise cherrypy.HTTPError(500, "I'm muted :-x")

  @PM.add_hook
  def get_controllers(self):
    from ..controllers import ApiController, RESTController

    @ApiController('/mute')
    class MuteController(RESTController):
      def get(_):
        return self.mute

    return [MuteController]

Additionally, a helper for creating plugins SimplePlugin is provided. It facilitates the basic tasks (Options, Commands, and common Mixins). The previous plugin could be rewritten like this:

from . import PLUGIN_MANAGER as PM
from . import interfaces as I
from .plugin import SimplePlugin as SP

import cherrypy

@PM.add_plugin
class Mute(SP, I.Setupable, I.FilterRequest.BeforeHandler, I.HasControllers):
  OPTIONS = [
      SP.Option('mute', default=False, type='bool')
  ]

  def shut_up(self):
    self.set_option('mute', True)
    self.mute = True
    return 0

  COMMANDS = [
      SP.Command("dashboard mute", handler=shut_up)
  ]

  @PM.add_hook
  def setup(self):
    self.mute = self.get_option('mute')

  @PM.add_hook
  def filter_request_before_handler(self, request):
    if self.mute:
      raise cherrypy.HTTPError(500, "I'm muted :-x")

  @PM.add_hook
  def get_controllers(self):
    from ..controllers import ApiController, RESTController

    @ApiController('/mute')
    class MuteController(RESTController):
      def get(_):
        return self.mute

    return [MuteController]