refactor(api): Optimize data structures to avoid unnecessary allocations and enable parallel searches

[PyDataBlog]

Description

A complete (and breaking) refactor of API and internal data structures which allows for native parallelism.

Fixes #8

Type of change
Switching to string interning and the adoption of atomic referencing allows enables true parallel searches. Initial benchmarks suggest a significant improvement (2-6x faster) in search speeds as well as database indexing compared to old API and other implementations.

Please choose the type of change that best describes your PR.

• feat!(api): Switch to string interning and atomic reference to avoid unnecessary allocations.
• perf(api): native parallel searches

How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration.

• test_measures
• test_database
• test_features
• test_search

Checklist:

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• I have added an entry to the CHANGELOG.md file
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• Any dependent changes have been merged and published in downstream modules