HyperExecute is a smart test orchestration platform to run end-to-end Selenium 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!
- HyperExecute HomePage: https://www.lambdatest.com/hyperexecute
- Lambdatest HomePage: https://www.lambdatest.com
- LambdaTest Support: [email protected]
To know more about how HyperExecute does intelligent Test Orchestration, do check out HyperExecute Getting Started Guide
Follow the below steps to run Gitpod button:
- Click 'Open in Gitpod' button (You will be redirected to Login/Signup page).
- Login with Lambdatest credentials and it will be redirected to Gitpod editor in new tab and current tab will show hyperexecute dashboard.
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.
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:
- Mac: https://downloads.lambdatest.com/hyperexecute/darwin/hyperexecute
- Linux: https://downloads.lambdatest.com/hyperexecute/linux/hyperexecute
- Windows: https://downloads.lambdatest.com/hyperexecute/windows/hyperexecute.exe
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_KEYFor Linux:
export LT_USERNAME=LT_USERNAME
export LT_ACCESS_KEY=LT_ACCESS_KEYFor Windows:
set LT_USERNAME=LT_USERNAME
set LT_ACCESS_KEY=LT_ACCESS_KEYAuto-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
Auto-split YAML file (yaml/win/pyunit_hyperexecute_autosplit_sample.yaml) in the repo contains the following configuration:
globalTimeout: 90
testSuiteTimeout: 90
testSuiteStep: 90Global 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: winAuto-split is set to true in the YAML file.
autosplit: trueretryOnFailure 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: 2To leverage the advantage offered by Dependency Caching in HyperExecute, the integrity of requirements.txt is checked using the checksum functionality.
cacheKey: '{{ checksum "requirements.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:
- CacheDirContent 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 CacheDirThe post directive contains a list of commands that run as a part of post-test execution. Here, the contents of yaml/win/pyunit_hyperexecute_autosplit_sample.yaml are read using the cat command as a part of the post step.
post:
- cat yaml/win/pyunit_hyperexecute_autosplit_sample.yamlThe 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 -nri 'hyperexecutePyUnit' tests -ir --include=\*.py | sed 's/:.*//'
testRunnerCommand: python3 -s $testRunning the above command on the terminal will give a list of Python files that are located in the Project folder:
- tests/lt_selenium_playground.py
- tests/lt_sample_todo.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: python3 -s $testThe mergeArtifacts directive (which is by default false) is set to true for merging the artifacts and combing artifacts generated under each task.
The uploadArtefacts directive informs HyperExecute to upload artifacts [files, reports, etc.] generated after task completion. In the example, path consists of a regex for parsing the directory (i.e. example_1 and example_2 that contains the test reports).
mergeArtifacts: true
uploadArtefacts:
- name: TestReport1
path:
- example_1/**
- name: TestReport2
path:
- example_2/**HyperExecute also facilitates the provision to download the artifacts on your local machine. To download the artifacts, click on Artifacts button corresponding to the associated TestID.
Now, you can download the artifacts by clicking on the Download button as shown below:
The CLI option --config is used for providing the custom HyperExecute YAML file (i.e. yaml/win/pyunit_hyperexecute_autosplit_sample.yaml for Windows and yaml/linux/pyunit_hyperexecute_autosplit_sample.yaml for Linux).
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 --verbose --config yaml/win/pyunit_hyperexecute_autosplit_sample.yamlRun 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 --verbose --config yaml/linux/pyunit_hyperexecute_autosplit_sample.yamlVisit HyperExecute Automation Dashboard to check the status of execution
Shown below is the execution screenshot when the YAML file is triggered from the terminal:
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
In the current example, matrix YAML file (yaml/win/pyunit_hyperexecute_matrix_sample.yaml) in the repo contains the following configuration:
globalTimeout: 90
testSuiteTimeout: 90
testSuiteStep: 90Global 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: winPython files in the 'tests' folder contain the test suites run on the HyperExecute grid. In the example, the tests in the files tests/lt_sample_todo.py and tests/lt_selenium_playground.py run in parallel using the specified input combinations.
files: ["tests/lt_sample_todo.py", "tests/lt_selenium_playground.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:
- python3 -s $filesDependency 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 "requirements.txt" }}'Set the array of files & directories to be cached. In the example, all the packages will be cached in the CacheDir directory.
cacheDirectories:
- CacheDirSteps (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 CacheDirSteps (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/pyunit_hyperexecute_matrix_sample.yaml
post:
- cat yaml/win/pyunit_hyperexecute_matrix_sample.yamlThe mergeArtifacts directive (which is by default false) is set to true for merging the artifacts and combing artifacts generated under each task.
The uploadArtefacts directive informs HyperExecute to upload artifacts [files, reports, etc.] generated after task completion. In the example, path consists of a regex for parsing the directory (i.e. example_1 and example_2 that contains the test reports).
mergeArtifacts: true
uploadArtefacts:
- name: TestReport1
path:
- example_1/**
- name: TestReport2
path:
- example_2/**HyperExecute also facilitates the provision to download the artifacts on your local machine. To download the artifacts, click on Artifacts button corresponding to the associated TestID.
Now, you can download the artifacts by clicking on the Download button as shown below:
The CLI option --config is used for providing the custom HyperExecute YAML file (i.e. yaml/win/pyunit_hyperexecute_matrix_sample.yaml for Windows and yaml/win/pyunit_hyperexecute_matrix_sample.yaml for Linux).
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/pyunit_hyperexecute_matrix_sample.yamlRun 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/pyunit_hyperexecute_matrix_sample.yamlVisit HyperExecute Automation Dashboard to check the status of execution:
Shown below is the execution screenshot when the YAML file is triggered from the terminal:
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.
Now create a secret key that you can use in the HyperExecute YAML file.
All you need to do is create an environment variable that uses the secret key:
env:
PAT: ${{ .secrets.testKey }}HyperExecute lets you navigate from/to Test Logs in Automation Dashboard from/to HyperExecute Logs. You also get relevant get relevant Selenium test details like video, network log, commands, Exceptions & more in the Dashboard. Effortlessly navigate from the automation dashboard to HyperExecute logs (and vice-versa) to get more details of the test execution.
Shown below is the HyperExecute Automation dashboard which also lists the tests that were executed as a part of the test suite:
Here is a screenshot that lists the automation test that was executed on the HyperExecute grid:
The LambdaTest Community allows people to interact with tech enthusiasts. Connect, ask questions, and learn from tech-savvy people. Discuss best practises in web development, testing, and DevOps with professionals from across the globe.
If you want to learn more about the LambdaTest's features, setup, and usage, visit the LambdaTest documentation. You can also find in-depth tutorials around test automation, mobile app testing, responsive testing, manual testing on LambdaTest Blog and LambdaTest Learning Hub.
LambdaTest is a leading test execution and orchestration platform that is fast, reliable, scalable, and secure. It allows users to run both manual and automated testing of web and mobile apps across 3000+ different browsers, operating systems, and real device combinations. Using LambdaTest, businesses can ensure quicker developer feedback and hence achieve faster go to market. Over 500 enterprises and 1 Million + users across 130+ countries rely on LambdaTest for their testing needs.
- Got a query? we are available 24x7 to help. Contact Us
- For more info, visit - https://www.lambdatest.com
Licensed under the MIT license.