Generate random data with integers. The approach is as follows:
- There is one collection for each different cardinality. All collections contain the same fields.
- Each field contains the data generated from a certain data distribution. The data could be anything - same type, mixed types, same mathematical distribution (e.g. normal), or a mixed distribution.
- The committed configuration file, and the corresponding data file are reduced to only two small collections. For actual experiments one needs to add more data sizes, and re-generate the data locally. This is done so that Evergreen tests can run fast, and to reduce the size of the git repository.
- All data is saved in a single JavaScript file: jstests/query_golden/libs/data/ce_accuracy_test.data, with a corresponding schema file jstests/query_golden/libs/data/ce_accuracy_test.schema.
- The data file is a JavaScript file that can be loaded directly inside a JS test. When loading this file, it creates a global variable dataSet. The reason is that this is the only way to load an external JSON file that doesn't need to install external tools in Evergreen.
* Extend the data generation Python framework for cost calibration to support data generation for CE testing as follows:
- the entry point is ce_generate_data.py,
- the configuration of the generated data is in ce_generate_data_settings.py,
- all collection data is exported into a single JSON file stored in 'jstests/query_golden/libs/data', and a schema file stored in the same directory
* Implement a JS data loader function that also creates all indexes specified in the schema file.
* Add a small JS test that shows how to load the generated JSON files into collections.
