indicium

🔎 A simple in-memory search for collections (Vec, HashMap, BTreeMap, etc) and key-value stores. Features autocompletion.

There are many incredible search engines available for Rust. Many seem to require compiling a separate server binary. I wanted something simple and light-weight - an easy-to-use crate that could conveniently search structs and collections within my own binary. So, I made indicium.

While indicium was made with web apps in mind, it is an in-memory search and it does not scale indefinitely or to cloud size (i.e. Facebook or Google size). Even in such an environment, it would still be a convenient way of searching large lists (such as currencies, languages, countries, etc.) It's also great for applications where there is an anticipated scale limit (i.e. searching a list of company assets, list of users in a corporate intranet, etc.)

Indicium easily can handle millions of records without breaking a sweat thanks to Rust's BTreeMap. This crate is primarily limited by available memory. However, depending on the nature your data-set and if there are keywords that are repeated many times, performance may begin to degrade at a point.

What's New?

• Release notes are available on GitHub.

• The full change log is available on GitHub.

• 0.6.1: Removed eddie as the default string similarity crate, for now, due to a potential panic.

• 0.6.0: Fix for contextual fuzzy matching for Live interactive searches. In some cases Live search would return global results without properly observing the maximum_search_results setting. This has been fixed. This will improve performance and user experience.

• 0.6.0: New, optional eddie feature which is turned on by default. When this feature is enabled, this library will utilize Ilia Schelokov's eddie crate for faster UTF-8 string distance and string similarity calculations.

• 0.6.0: New, optional gxhash feature. ahash is still the default hasher. When this feature is enabled, this library will utilize Olivier Giniaux's bleeding edge gxhash crate for faster HashMap and HashSet hashing.

Quick Start Guide

For our Quick Start Guide example, we will be searching inside of the following struct:

struct MyStruct {
    title: String,
    year: u16,
    body: String,
}

1. Implementing Indexable

To begin, we must make our record indexable. We'll do this by implementing the Indexable trait for our struct. The idea is to return a String for every field that we would like to be indexed. Example:

use indicium::simple::Indexable;

impl Indexable for MyStruct {
    fn strings(&self) -> Vec<String> {
        vec![
            self.title.clone(),
            self.year.to_string(),
            self.body.clone(),
        ]
    }
}

Don't forget that you may make numbers, numeric identifiers, enums, and other types in your struct (or other complex types) indexable by converting them to a String and including them in the returned Vec<String>.

2. Indexing a Collection

To index an existing collection, we can iterate over the collection. For each record, we will insert it into the search index. This should look something like these two examples:

Vec

use indicium::simple::SearchIndex;

let my_vec: Vec<MyStruct> = Vec::new();

// In the case of a `Vec` collection, we use the index as our key. A
// `Vec` index is a `usize` type. Therefore we will instantiate
// `SearchIndex` as `SearchIndex<usize>`.

let mut search_index: SearchIndex<usize> = SearchIndex::default();

my_vec
    .iter()
    .enumerate()
    .for_each(|(index, element)|
        search_index.insert(&index, element)
    );

HashMap

use std::collections::HashMap;
use indicium::simple::SearchIndex;

let my_hash_map: HashMap<String, MyStruct> = HashMap::new();

// In the case of a `HashMap` collection, we use the hash map's key as
// the `SearchIndex` key. In our hypothetical example, we will use
// MyStruct's `title` as a the key which is a `String` type. Therefore
// we will instantiate `HashMap<K, V>` as HashMap<String, MyStruct> and
// `SearchIndex<K>` as `SearchIndex<String>`.

let mut search_index: SearchIndex<String> = SearchIndex::default();

my_hash_map
    .iter()
    .for_each(|(key, value)|
        search_index.insert(key, value)
    );

As long as the Indexable trait was implemented for your value type, the above examples will index a previously populated Vec or HashMap. However, the preferred method for large collections is to insert into the SearchIndex as you insert into your collection (Vec, HashMap, etc.)

It's recommended to wrap your target collection (your Vec, HashMap, etc.) and this SearchIndex together in a new struct type. Then, implement the insert, replace, remove, etc. methods for this new struct type that will update both the collection and search index. This will ensure that both your collection and index are always synchronized.

Once the index has been populated, you can use the search and autocomplete methods.

3. Searching

The search method will return keys as the search results. Each resulting key can then be used to retrieve the full record from its collection.

Basic usage:

let mut search_index: SearchIndex<usize> = SearchIndex::default();

search_index.insert(&0, &"Harold Godwinson");
search_index.insert(&1, &"Edgar Ætheling");
search_index.insert(&2, &"William the Conqueror");
search_index.insert(&3, &"William Rufus");
search_index.insert(&4, &"Henry Beauclerc");

let resulting_keys: Vec<&usize> = search_index.search("William");

assert_eq!(resulting_keys, vec![&2, &3]);

// Demonstrating fuzzy matching:

let resulting_keys: Vec<&usize> = search_index.search("Harry");

assert_eq!(resulting_keys, vec![&0]);

Search only supports exact keyword matches. For Live searches, fuzzy matching is only applied to the last keyword. Consider providing the autocomplete feature to your users as an ergonomic alternative to fuzzy matching.

4. Autocompletion

The autocomplete method will provide several autocompletion options for the last keyword in the supplied string.

Basic usage:

let mut search_index: SearchIndex<usize> =
    SearchIndexBuilder::default()
        .autocomplete_type(&AutocompleteType::Global)
        .build();

search_index.insert(&0, &"apple");
search_index.insert(&1, &"ball");
search_index.insert(&3, &"bird");
search_index.insert(&4, &"birthday");
search_index.insert(&5, &"red");

let autocomplete_options: Vec<String> =
    search_index.autocomplete("a very big bi");

assert_eq!(
    autocomplete_options,
    vec!["a very big bird", "a very big birthday"]
);

// Demonstrating fuzzy matching:

let autocomplete_options: Vec<String> =
    search_index.autocomplete("a very big birf");

assert_eq!(
    autocomplete_options,
    vec!["a very big bird", "a very big birthday"]
);