How to load data from K6 Cloud to ElasticSearch

Learn how to use Airbyte to synchronize your K6 Cloud data into ElasticSearch within minutes.

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Set up a K6 Cloud connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up ElasticSearch for your extracted K6 Cloud data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the K6 Cloud to ElasticSearch in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Set Up K6 Cloud Output Configuration

First, configure K6 to output its test results in a JSON format. You can do this by creating a local script that includes specifying the output in K6's configuration. For example, use the command: `k6 run --out json=output.json your_test_script.js`. This will generate a JSON file (`output.json`) containing the test results.

Execute your K6 test script locally with the configured output settings. Ensure that the test results are written to the specified JSON file. This step will effectively capture the data from K6 Cloud into a format that can be processed further.

Once the test is complete, parse the JSON data to extract the necessary information that needs to be transferred to Elasticsearch. You can use a programming language like Python or JavaScript to read the JSON file and transform the data as needed. Focus on extracting relevant fields such as timestamps, metrics, and labels.

Before sending data to Elasticsearch, create an appropriate index that will receive the K6 data. Use the Elasticsearch API or Kibana to define the index mapping that corresponds to the structure of your parsed K6 data. Make sure that the index can handle the data types you intend to store, such as numbers, strings, or dates.

Transform the parsed data into a format suitable for Elasticsearch's Bulk API. This typically involves creating a newline-delimited JSON formatted file where each line contains an action-and-meta-data line followed by the source line. For example, the first line might be `{"index": {"_index": "k6_results"}}`, followed by the actual data line.

Use a tool like `curl` or a programming language with HTTP client capabilities to send the formatted data to Elasticsearch. The Bulk API endpoint is typically `http://localhost:9200/_bulk` if running Elasticsearch locally. Ensure that you handle any errors or responses from Elasticsearch to confirm that the data has been successfully ingested.

After the data is sent, verify that it has been correctly indexed in Elasticsearch. Use Kibana or an Elasticsearch query to check the index and review the data. Verify the accuracy and completeness of the data, ensuring it matches what was outputted by the K6 test. If any discrepancies are found, revisit previous steps to troubleshoot and resolve them.