Data Indexing for Enhanced Search Performance

[emperorjm]

Background:

Data indexing has been at the core of many search optimizations and performance improvements in the world of databases. While key-value stores offer simplicity and speed, they often miss out on some advanced feature like rich querying capabilities which is inherent to relational databases for example. These features become paramount when you need complex data retrieval and therefore, developers often find themselves creating additional logic or systems to compensate for these shortcomings.

Example:

Consider this scenario:

You want to design a system where you store articles in the following structure:

pub struct Article {
    pub id: u64,
    pub owner: Addr,
    pub title: Option<String>,
    pub content: Option<String>,
}

Given that any Address can create multiple articles, the challenge is finding the most efficient way to store these articles while ensuring easy retrieval. Specifically, the requirements are:

1. View a list of articles linked to a specific Address.
2. Retrieve an article using its unique ID.
3. Search for articles using title phrases or keywords.

Questions:

When working on application-level data structures, the design often determines how quickly data can be accessed, especially when there's a need to avoid scanning the entire dataset. This raises some questions:

• Should you be utilizing basic Maps?
• Have you considered using Maps combined with Mapped Indexes?
• What about Maps paired with tuples that inherently encapsulate all major indexes or do you resort to the use of an IndexMap?
• Which indexing mechanism provides the most balanced approach between speed, memory usage, and ease of implementation?
• How does the choice of data structure affect search performance when the volume of data grows?
• Given the variable nature of search queries, is it more beneficial to build indexes on frequently accessed fields?
• How do we ensure that the indexing mechanism remains scalable as the number of articles and users grows?