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Begin by configuring your K6 test script to run on the K6 Cloud. Ensure your test script is ready and includes the relevant metrics you want to export. Sign in to your K6 Cloud account and upload your script to the cloud environment to initiate the test execution.
Run your test on the K6 Cloud by starting the test execution. Monitor the test run through the K6 Cloud interface to ensure that it proceeds without errors. Take note of the test execution ID or result ID, as it will be essential for retrieving test data.
After the test completes, use the K6 Cloud API to access the test results. Authenticate using your K6 Cloud API token. Make a GET request to the appropriate endpoint (`/v1/runs/{run_id}/results`) using the test execution ID obtained earlier. This endpoint provides detailed test results in JSON format.
Once you have the JSON data, download it to your local machine. Use a scripting language such as Python, Node.js, or any preferred language to parse the JSON data. Extract the specific metrics or data points you need to include in your CSV file.
Utilize your script to transform the parsed JSON data into CSV format. Ensure that you define the CSV headers to match your extracted data fields. Loop through the JSON data to write each data point as a row in the CSV format, adhering to the CSV structure.
Create a new CSV file on your local machine. Using your script, write the transformed CSV data into this file. Ensure that your script handles file writing operations properly, including opening the file in write mode and closing it after writing.
Open the resulting CSV file to verify that the data has been exported correctly. Check for any discrepancies or missing fields. Ensure that the CSV file structure aligns with your expectations and that the data accurately reflects the results from the K6 Cloud test execution.
By following these steps, you can effectively move your test data from K6 Cloud to a CSV file without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
k6 Cloud is a commercial SaaS product that we designed to be the perfect companion to k6 OSS. It brings ease of use, team management, and continuous testing capabilities to your performance testing projects. k6 Cloud Docs assist you to learn and use k6 Cloud features and functionality. The k6 Cloud is a fully-managed load testing service that complements k6 to accelerate your performance testing. k6 is an open-source load testing tool and cloud service for developers, DevOps, QA, and SRE teams.
K6 Cloud's API provides access to various types of data related to performance testing and monitoring. The following are the categories of data that can be accessed through the API:
1. Test Results: This category includes data related to the results of performance tests, such as response times, error rates, and throughput.
2. Metrics: This category includes data related to various performance metrics, such as CPU usage, memory usage, and network traffic.
3. User Behavior: This category includes data related to user behavior during performance tests, such as the number of users, their actions, and their locations.
4. Environment: This category includes data related to the environment in which the performance tests are conducted, such as the hardware and software configurations.
5. Alerts: This category includes data related to alerts generated during performance tests, such as threshold breaches and error notifications.
6. Reports: This category includes data related to performance test reports, such as summary reports, detailed reports, and trend analysis reports.
Overall, K6 Cloud's API provides a comprehensive set of data that can be used to analyze and optimize the performance of web applications and services.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
