/hyperexecute-playwright-python-sample

Demonstration of Playwright Python testing on HyperExecute Grid

Primary LanguagePython

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HyperExecute is a smart test orchestration platform to run end-to-end Playwright tests at the fastest speed possible. HyperExecute lets you achieve an accelerated time to market by providing a test infrastructure that offers optimal speed, test orchestration, and detailed execution logs.

The overall experience helps teams test code and fix issues at a much faster pace. HyperExecute is configured using a YAML file. Instead of moving the Hub close to you, HyperExecute brings the test scripts close to the Hub!

To know more about how HyperExecute does intelligent Test Orchestration, do check out HyperExecute Getting Started Guide

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Gitpod

Follow the below steps to run Gitpod button:

  1. Click 'Open in Gitpod' button (You will be redirected to Login/Signup page).
  2. Login with Lambdatest credentials and it will be redirected to Gitpod editor in new tab and current tab will show hyperexecute dashboard.

Run in Gitpod

How to run Playwright automation tests on HyperExecute (using Python framework)

Pre-requisites

Before using HyperExecute, you have to download HyperExecute CLI corresponding to the host OS. Along with it, you also need to export the environment variables LT_USERNAME and LT_ACCESS_KEY that are available in the LambdaTest Profile page.

Download HyperExecute CLI

HyperExecute CLI is the CLI for interacting and running the tests on the HyperExecute Grid. The CLI provides a host of other useful features that accelerate test execution. In order to trigger tests using the CLI, you need to download the HyperExecute CLI binary corresponding to the platform (or OS) from where the tests are triggered:

Also, it is recommended to download the binary in the project's parent directory. Shown below is the location from where you can download the HyperExecute CLI binary:

Configure Environment Variables

Before the tests are run, please set the environment variables LT_USERNAME & LT_ACCESS_KEY from the terminal. The account details are available on your LambdaTest Profile page.

For macOS:

export LT_USERNAME=LT_USERNAME
export LT_ACCESS_KEY=LT_ACCESS_KEY

For Linux:

export LT_USERNAME=LT_USERNAME
export LT_ACCESS_KEY=LT_ACCESS_KEY

For Windows:

set LT_USERNAME=LT_USERNAME
set LT_ACCESS_KEY=LT_ACCESS_KEY

Auto-Split Execution with Python

Auto-split execution mechanism lets you run tests at predefined concurrency and distribute the tests over the available infrastructure. Concurrency can be achieved at different levels - file, module, test suite, test, scenario, etc.

For more information about auto-split execution, check out the Auto-Split Getting Started Guide

Core

Auto-split YAML file (yaml/win/.hyperexecute_autosplits.yaml) in the repo contains the following configuration:

globalTimeout: 90
testSuiteTimeout: 90
testSuiteStep: 90

Global timeout, testSuite timeout, and testSuite timeout are set to 90 minutes.   The runson key determines the platform (or operating system) on which the tests are executed. Here we have set the target OS as Windows.

runson: win

Auto-split is set to true in the YAML file.

 autosplit: true

retryOnFailure is set to true, instructing HyperExecute to retry failed command(s). The retry operation is carried out till the number of retries mentioned in maxRetries are exhausted or the command execution results in a Pass. In addition, the concurrency (i.e. number of parallel sessions) is set to 2.

retryOnFailure: true
maxRetries: 5
concurrency: 2

Pre Steps and Dependency Caching

To leverage the advantage offered by Dependency Caching in HyperExecute, the integrity of requirements.txt is checked using the checksum functionality.

cacheKey: '{{ checksum "requirement.txt" }}'

By default, pip in Python saves the downloaded packages in the cache so that next time, the package download request can be serviced from the cache (rather than re-downloading it again).

The caching advantage offered by pip can be leveraged in HyperExecute, whereby the downloaded packages can be stored (or cached) in a secure server for future executions. The packages available in the cache will only be used if the checksum stage results in a Pass.

The cacheDirectories directive is used for specifying the directory where the packages have to be cached. The mentioned directory will override the default directory where Python packages are usually cached; further information about caching in pip is available here. The packages downloaded using pip will be cached in the directory (or location) mentioned under the cacheDirectories directive.

In our case, the downloaded packages are cached in the CacheDir folder in the project's root directory. The folder is automatically created when the packages mentioned in requirements.txt are downloaded.  

cacheDirectories:
  - CacheDir

Content under the pre directive is the precondition that will run before the tests are executed on the HyperExecute grid. The --cache-dir option in pip3 is used for specifying the cache directory. It is important to note that downloaded cached packages are securely uploaded to a secure cloud before the execution environment is auto-purged after build completion. Please modify requirements.txt as per the project requirements.

pip3 install -r requirements.txt  --cache-dir CacheDir

Post Steps

The post directive contains a list of commands that run as a part of post-test execution. Here, the contents of yaml/win/.hyperexecute_autosplits.yaml are read using the cat command as a part of the post step.

post:
  - cat yaml/win/*.*hyperexecute_autosplits.yaml

The testDiscovery directive contains the command that gives details of the mode of execution, along with detailing the command that is used for test execution. Here, we are fetching the list of Python files that would be further executed using the value passed in the testRunnerCommand

testDiscovery:
  type: raw
  mode: dynamic
  command: grep -lr 'def' *.py

testRunnerCommand: python $test

Running the above command on the terminal will give a list of Python files that are located in the Project folder:

  • test_app.py

The testRunnerCommand contains the command that is used for triggering the test. The output fetched from the testDiscoverer command acts as an input to the testRunner command.

testRunnerCommand: python $test

Test Execution

The CLI option --config is used for providing the custom HyperExecute YAML file (i.e. yaml/win/.hyperexecute_autosplits.yaml for Windows, yaml/linux/.hyperexecute_autosplits.yaml for Linux and yaml/linux/.hyperexecute_autosplits.yaml for Max).

Execute Python tests using Autosplit mechanism on Windows platform

Run the following command on the terminal to trigger the tests in Python files with HyperExecute platform set to Windows. The --download-artifacts option is used to inform HyperExecute to download the artifacts for the job.

./hyperexecute --download-artifacts --config --verbose yaml/win/.hyperexecute_autosplits.yaml

Execute Python tests using Autosplit mechanism on Linux platform

Run the following command on the terminal to trigger the tests in Python files with HyperExecute platform set to Linux. The -ifacts option is used to inform HyperExecute to download the artifacts for the job.

./hyperexecute --download-artifacts --config --verbose yaml/linux/.hyperexecute_autosplits.yaml

Execute Python tests using Autosplit mechanism on Mac platform

Run the following command on the terminal to trigger the tests in Python files with HyperExecute platform set to MAc. The -ifacts option is used to inform HyperExecute to download the artifacts for the job.

./hyperexecute --download-artifacts --config --verbose yaml/mac/.hyperexecute_autosplits.yaml

Matrix Execution with Python

Matrix-based test execution is used for running the same tests across different test (or input) combinations. The Matrix directive in HyperExecute YAML file is a key:value pair where value is an array of strings.

Also, the key:value pairs are opaque strings for HyperExecute. For more information about matrix multiplexing, check out the Matrix Getting Started Guide

Core

In the current example, matrix YAML file (yaml/win/.hyperexecute_matrix.yaml) in the repo contains the following configuration:

globalTimeout: 90
testSuiteTimeout: 90
testSuiteStep: 90

Global timeout, testSuite timeout, and testSuite timeout are set to 90 minutes.   The target platform is set to Windows. Please set the [runson] key to [mac] if the tests have to be executed on the macOS platform.

runson: win

Python files in the 'tests' folder contain the test suites run on the HyperExecute grid. In the example, the tests in the files test_app.py run in parallel using the specified input combinations.

files: ["test_app.py"]

The testSuites object contains a list of commands (that can be presented in an array). In the current YAML file, commands for executing the tests are put in an array (with a '-' preceding each item). The Python command is used to run tests in .py files. The files are mentioned as an array to the files key that is a part of the matrix.

testSuites:
  - python $files

Pre Steps and Dependency Caching

Dependency caching is enabled in the YAML file to ensure that the package dependencies are not downloaded in subsequent runs. The first step is to set the Key used to cache directories.

cacheKey: '{{ checksum "requirement.txt" }}'

Set the array of files & directories to be cached. In the example, all the packages will be cached in the CacheDir directory.

cacheDirectories:
  - CacheDir

Steps (or commands) that must run before the test execution are listed in the pre run step. In the example, the packages listed in requirements.txt are installed using the pip3 command.

The --cache-dir option is used for specifying the location of the directory used for caching the packages (i.e. CacheDir). It is important to note that downloaded cached packages are securely uploaded to a secure cloud before the execution environment is auto-purged after build completion. Please modify requirements.txt as per the project requirements.

pre:
  - pip3 install -r requirements.txt --cache-dir CacheDir

Post Steps

Steps (or commands) that need to run after the test execution are listed in the post step. In the example, we cat the contents of yaml/win/.hyperexecute_matrix.yaml

post:
  - cat yaml/win/.hyperexecute_matrix.yaml

Test Execution

The CLI option --config is used for providing the custom HyperExecute YAML file (i.e. yaml/win/.hyperexecute_matrix.yaml or yaml/linux/.hyperexecute_matrix.yaml or yaml/mac/.hyperexecute_matrix.yaml).

Execute Python tests using Matrix mechanism on Windows platform

Run the following command on the terminal to trigger the tests in Python files with HyperExecute platform set to Windows. The --download-artifacts option is used to inform HyperExecute to download the artifacts for the job.

./hyperexecute --download-artifacts --config --verbose yaml/win/.hyperexecute_matrix.yaml

Execute Python tests using Matrix mechanism on Linux platform

Run the following command on the terminal to trigger the tests in Python files with HyperExecute platform set to Linux. The --download-artifacts option is used to inform HyperExecute to download the artifacts for the job.

./hyperexecute --download-artifacts --config --verbose yaml/linux/.hyperexecute_matrix.yaml

Execute Python tests using Matrix mechanism on Mac platform

Run the following command on the terminal to trigger the tests in Python files with HyperExecute platform set to MAC. The --download-artifacts option is used to inform HyperExecute to download the artifacts for the job.

./hyperexecute --download-artifacts --config --verbose yaml/mac/.hyperexecute_matrix.yaml

Secrets Management

In case you want to use any secret keys in the YAML file, the same can be set by clicking on the Secrets button the dashboard.

pytest_secrets_key_1

Now create a secret key that you can use in the HyperExecute YAML file.

secrets_management_1

All you need to do is create an environment variable that uses the secret key:

env:
  PAT: ${{ .secrets.testKey }}

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License

Licensed under the MIT license.