Spruce ·

Spruce is the React UI for MongoDB's continuous integration software.

Table of Contents

• Getting Started
  • Running Locally
  • Environment Variables
• Deployment
  • Requirements
  • How to Deploy

Getting Started

Running Locally

1. Clone the Spruce Github repository
2. Ensure you have Node.js 16+ installed
3. Ask a colleague for their .cmdrc.json file and follow the instructions here
4. Run yarn
5. Start a local evergreen server by doing the following:

• Clone the evergreen repo
• Run make local-evergreen

6. Run yarn run dev. This will launch the app and point it at the local evergreen server you just ran.

Storybook

Run yarn run storybook to launch storybook and view our shared components.

Code Formatting

Install the Prettier code formatting plugin in your code editor if you don't have it already. The plugin will use the .prettierrc settings file found at the root of Spruce to format your code.

GQL Query Linting

Follow these directions to enable query linting during local development so your Evergreen GraphQL schema changes are reflected in your Spruce query linting results.

1. Symlink the standard definition language GraphQL schema used in your backend to a file named sdlschema in the root of the Spruce directory to enable query linting with ESlint like so ln -s /path/to/evergreen/schema sdlschema
2. Run yarn run eslint to see the results of query linting in your terminal or install a plugin to integrate ESlint into your editor. If you are using VSCode, we recommend ESLint by Dirk Baeumer.

Environment Variables

env-cmd is used to configure build environments for production, staging and development. This file is git ignored because it contains API keys that we do not want to publish. It should be named .cmdrc.json and placed in the env/ folder at the root of the project. This file is required to deploy Spruce to production and to staging. Ask a team member to send you their copy of the file, which should look like the following:

{
  "devLocal": {
    "REACT_APP_SIGNAL_PROCESSING_URL": "https://performance-monitoring-and-analysis.server-tig.staging.corp.mongodb.com",
    "REACT_APP_GQL_URL": "http://localhost:9090/graphql/query",
    "REACT_APP_API_URL": "http://localhost:3000/api",
    "REACT_APP_UI_URL": "http://localhost:9090",
    "REACT_APP_SPRUCE_URL": "http://localhost:3000"
  },
  "staging": {
    "REACT_APP_API_URL": "https://evergreen-staging.corp.mongodb.com/api",
    "REACT_APP_UI_URL": "https://evergreen-staging.corp.mongodb.com",
    "REACT_APP_SPRUCE_URL": "https://evergreen-staging.spruce.s3-website-us-east-1.amazonaws.com"
  },
  "prod": {
    "REACT_APP_SIGNAL_PROCESSING_URL": "https://performance-monitoring-and-analysis.server-tig.prod.corp.mongodb.com",
    "REACT_APP_DEPLOYS_EMAIL": "something@something.com", 
    "REACT_APP_SPRUCE_URL": "https://spruce.mongodb.com",
    "REACT_APP_BUGSNAG_API_KEY": "this-is-the-api-key",
    "REACT_APP_API_URL": "https://evergreen.mongodb.com/api",
    "REACT_APP_UI_URL": "https://evergreen.mongodb.com",
    "REACT_APP_LOBSTER_URL": "https://evergreen.mongodb.com",
    "REACT_APP_NEW_RELIC_ACCOUNT_ID": "dummy-new-relic-account-id",
    "REACT_APP_NEW_RELIC_AGENT_ID": "dummy-new-relic-agent-id",
    "REACT_APP_NEW_RELIC_APPLICATION_ID": "dummy-new-relic-application-id",
    "REACT_APP_NEW_RELIC_LICENSE_KEY": "dummy-new-relic-license-key",
    "REACT_APP_NEW_RELIC_TRUST_KEY": "dummy-new-relic-trust-key"
  }
}

GraphQL Type Generation

We use Code generation to generate our types for our GraphQL queries and mutations. When you create a query or mutation you can run the code generation script with the steps below. The types for your query/mutation response and variables will be generated and saved to gql/generated/types.ts. Much of the underlying types for subfields in your queries will likely be generated there as well and you can refer to those before creating your own.

Setting up code generation

• create a symlink from the schema folder from evergreen with the spruce folder using ln -s path-to-evergreen-schema sdlschema

Using code generation

• From within the spruce folder run yarn run codegen
• As long as your queries are declared correctly the types should generate

Code generation troubleshooting and tips

• Queries should be declared with a query name so the code generation knows what to name the corresponding type.
• Each query and mutation should have a unique name.
• Since query analysis for type generation occurs statically we cant place dynamic variables with in query strings we instead have to hard code the variable in the query or pass it in as query variable.

Testing

Spruce has a combination of unit tests using Jest, and integration tests using Cypress.

Unit tests

Unit Tests are used to test individual features in isolation. We utilize the Jest Test Runner to execute our Unit Tests and generate reports.

There are 3 types of unit tests you may encounter in this codebase.

Component Tests

These test React Componenents. We utilize React Testing Library to help us write our component tests. React Testing Library provides several utilities that are useful for making assertions on React Componenents. When writing component tests you should import test_utils instead of React Testing Library, test_utils is a wrapper around React Testing Library which provides a series of helpful utilities for common testing scenarios such as queryByDataCy which is a helper for selecting data-cy attributes or renderWithRouterMatch which is helpful for testing components that rely on React Router.

Hook Tests

Often times you may find yourself writing custom react hooks. The best way to test these is using React Hooks Testing Library. React Hooks Testing Library allows you to test your custom Hooks in isolation without needing to wrap them in a Component. It provides several methods that make it easy to assert and test different behaviors in your hooks. Such as waitForNextUpdate which will wait for your hook to rerender before allowing a test to proceed.

Standard utility tests

These are the most basic of tests. They do not require any special libraries to run and often just test standard javascript functions.

• You can run all Unit Tests using yarn test
• You can run a specific Unit Test using yarn test -t <test_name>
• You can run jest in watch mode using yarn test:watch This will open an interactive CLI you can use to automatically run tests as you update them.

E2E tests

At a high level, we use Cypress to start a virtual browser that is running Spruce. Cypress then is able to run our test specs, which tell it to interact with the browser in certain ways and makes assertions about what happens in the UI. Note that you must be running the Evergreen server on localhost:9090 for the front-end to work.

In order to run the Cypress tests, do the following, assuming you have this repo checked out and all the dependencies installed by yarn:

1. Start the evergreen back-end with the sample local test data. You can do this by typing make local-evergreen in your evergreen folder.
2. Start the Spruce dev server by typing yarn dev in this repo.
3. Run Cypress by typing one of the following:
   • yarn cy:open - opens the Cypress app in interactive mode. You can select tests to run from here in the Cypress browser.
   • yarn cy:run - runs all the Cypress tests at the command-line and reports the results
   • yarn cy:test cypress/integration/hosts/hosts-filtering.ts - runs tests in a specific file at the command-line. Replace the final argument with the relative path to your test file

Snapshot Tests

Snapshot tests are automatically generated when we create storybook stories. These Tests create a snapshot of the UI and compare them to previous snapshots which are stored as files along side your storybook stories in a __snapshots__ directory. They try to catch unexpected UI regressions. Read more about them Here.

How to get data for your feature

If you need more data to be able to test out your feature locally the easiest way to do it is to populate the local db using real data from the staging or production environments.

1. You should identify if the data you need is located in the staging or prod db and ssh into them (You should be connected to the office network or vpn before proceeding). The urls for these db servers can be located in the fabfile.py located in the evergreen directory or here.

2. You should ensure you are connected to a secondary node before proceeding.

3. Run mongo to open the the mongo shell.

4. Identify the query you need to fetch the data you are looking for.

   mci:SECONDARY> rs.secondaryOk() // Allows read operations on a secondary node
   mci:SECONDARY> use mci // use the correct db
   switched to db mci
   mci:SECONDARY>  db.distro.find({_id: "archlinux-small"}) // the full query
   

5. Exit from the mongo shell and prepare to run mongoexport

   mongoexport --db=mci --collection=distro --out=distro.json --query='{_id: "archlinux-small"}' 
   2020-07-29T17:41:50.266+0000	connected to: localhost
   2020-07-29T17:41:50.269+0000	exported 1 record
   

   After running this command a file will be saved to your home directory with the results of the mongoexport

   Note you may need to provide the full path to mongoexport on the staging db

   /var/lib/mongodb-mms-automation/mongodb-linux-x86_64-4.0.5/bin/mongoexport --db=mci --collection=distro --out=distro.json --query='{_id: "archlinux-small"}' 
   2020-07-29T17:41:50.266+0000	connected to: localhost
   2020-07-29T17:41:50.269+0000	exported 1 record
   

6. Exit the ssh session using exit or Ctrl + D

7. You can now transfer this json file to your local system by running the following command. scp <db you sshed into>:~/distro.json . This will save a file named distro.json to the current directory

8. You should run this file through the scramble-eggs script to sanitize it and remove any sensitive information make scramble file=<path to file>.json from within the evergreen folder

9. Once you have this file you can copy the contents of it to the relevant testdata/local/<collection>.json file with in the evergreen folder

10. You can then delete /bin/.load-local-data within the evergreen folder and run make local-evergreen to repopulate the local database with your new data.

Notes

When creating your queries you should be sure to limit the amount of documents so you don't accidently export an entire collection you can do this by passing a --limit=<number> flag to mongoexport

Deployment

Requirements

You must be on the main Branch if deploying to prod.

A .cmdrc.json file is required to deploy because it sets the environment variables that the application needs in production and staging environments. See Environment Variables section for more info about this file.

How to Deploy:

Run one of the following commands to deploy to the appropriate environment

1. yarn deploy:prod = deploy to https://spruce.mongodb.com
2. yarn deploy:staging = deploy to https://spruce-staging.corp.mongodb.com
3. yarn deploy:beta = deploy to https://spruce-beta.corp.mongodb.com (Beta connects to the production backend)