Let's build a Full-Text Search engine

We are going to search a part of the abstract of English Wikipedia. The latest dump is available at dumps.wikimedia.org . As of today, the file size after decompression is 913 MB. The XML file contains over 600K documents.

Document example:

<title>Wikipedia: Kit-Cat Klock</title>
<url>https://en.wikipedia.org/wiki/Kit-Cat_Klock</url>
<abstract>The Kit-Cat Klock is an art deco novelty wall clock shaped like a grinning cat with cartoon eyes that swivel in time with its pendulum tail.</abstract>

Loading documents

First, we need to load all the documents from the dump. The built-in encoding/xml package comes very handy:

import (
    "encoding/xml"
    "os"
)

type document struct {
    Title string `xml:"title"`
    URL   string `xml:"url"`
    Text  string `xml:"abstract"`
    ID    int
}

func loadDocuments(path string) ([]document, error) {
    f, err := os.Open(path)
    if err != nil {
        return nil, err
    }
    defer f.Close()

    dec := xml.NewDecoder(f)
    dump := struct {
        Documents []document `xml:"doc"`
    }{}
    if err := dec.Decode(&dump); err != nil {
        return nil, err
    }

    docs := dump.Documents
    for i := range docs {
        docs[i].ID = i
    }
    return docs, nil
}

Every loaded document gets assigned a unique identifier. To keep things simple, the first loaded document gets assigned ID=0, the second ID=1 and so on.

To make search queries faster, we'll preprocess the text and build an index in advance.

The core of FTS is a data structure called Inverted Index . The Inverted Index associates every word in documents with documents that contain the word.

Example:

documents = {
    1: "a donut on a glass plate",
    2: "only the donut",
    3: "listen to the drum machine",
}

index = {
    "a": [1],
    "donut": [1, 2],
    "on": [1],
    "glass": [1],
    "plate": [1],
    "only": [2],
    "the": [2, 3],
    "listen": [3],
    "to": [3],
    "drum": [3],
    "machine": [3],
}

Below is a real-world example of the Inverted Index. An index in a book where a term references a page number:

Before we start building the index, we need to break the raw text down into a list of words (tokens) suitable for indexing and searching.

The text analyzer consists of a tokenizer and multiple filters.

The tokenizer is the first step of text analysis. Its job is to convert text into a list of tokens. Our implementation splits the text on a word boundary and removes punctuation marks:

func tokenize(text string) []string {
    return strings.FieldsFunc(text, func(r rune) bool {
        // Split on any character that is not a letter or a number.
        return !unicode.IsLetter(r) && !unicode.IsNumber(r)
    })
}

> tokenize("A donut on a glass plate. Only the donuts.")

["A", "donut", "on", "a", "glass", "plate", "Only", "the", "donuts"]

In most cases, just converting text into a list of tokens is not enough. To make the text easier to index and search, we'll need to do additional normalization.

func lowercaseFilter(tokens []string) []string {
    r := make([]string, len(tokens))
    for i, token := range tokens {
        r[i] = strings.ToLower(token)
    }
    return r
}

> lowercaseFilter([]string{"A", "donut", "on", "a", "glass", "plate", "Only", "the", "donuts"})

["a", "donut", "on", "a", "glass", "plate", "only", "the", "donuts"]

var stopwords = map[string]struct{}{ // I wish Go had built-in sets.
    "a": {}, "and": {}, "be": {}, "have": {}, "i": {},
    "in": {}, "of": {}, "that": {}, "the": {}, "to": {},
}

func stopwordFilter(tokens []string) []string {
    r := make([]string, 0, len(tokens))
    for _, token := range tokens {
        if _, ok := stopwords[token]; !ok {
            r = append(r, token)
        }
    }
    return r
}

> stopwordFilter([]string{"a", "donut", "on", "a", "glass", "plate", "only", "the", "donuts"})

["donut", "on", "glass", "plate", "only", "donuts"]

import snowballeng "github.com/kljensen/snowball/english"

func stemmerFilter(tokens []string) []string {
    r := make([]string, len(tokens))
    for i, token := range tokens {
        r[i] = snowballeng.Stem(token, false)
    }
    return r
}

> stemmerFilter([]string{"donut", "on", "glass", "plate", "only", "donuts"})

["donut", "on", "glass", "plate", "only", "donut"]

Putting the analyzer together

func analyze(text string) []string {
    tokens := tokenize(text)
    tokens = lowercaseFilter(tokens)
    tokens = stopwordFilter(tokens)
    tokens = stemmerFilter(tokens)
    return tokens
}

The tokenizer and filters convert sentences into a list of tokens:

> analyze("A donut on a glass plate. Only the donuts.")

["donut", "on", "glass", "plate", "only", "donut"]

The tokens are ready for indexing.

Building the index

Back to the inverted index. It maps every word in documents to document IDs. The built-in map is a good candidate for storing the mapping. The key in the map is a token (string) and the value is a list of document IDs:

type index map[string][]int

Building the index consists of analyzing the documents and adding their IDs to the map:

func (idx index) add(docs []document) {
    for _, doc := range docs {
        for _, token := range analyze(doc.Text) {
            if ids != nil && ids[len(ids)-1] == doc.ID {
                // Don't add same ID twice.
                continue
            }
            idx[token] = append(idx[token], doc.ID)
        }
    }
}

func main() {
    idx := make(index)
    idx.add([]document{{ID: 1, Text: "A donut on a glass plate. Only the donuts."}})
    idx.add([]document{{ID: 2, Text: "donut is a donut"}})
    fmt.Println(idx)
}

It works! Each token in the map refers to IDs of the documents that contain the token:

map[donut:[1 2] glass:[1] is:[2] on:[1] only:[1] plate:[1]]

To query the index, we are going to apply the same tokenizer and filters we used for indexing:

func (idx index) search(text string) [][]int {
    var r [][]int
    for _, token := range analyze(text) {
        if ids, ok := idx[token]; ok {
            r = append(r, ids)
        }
    }
    return r
}

> idx.search("Small wild cat")

[[24, 173, 303, ...], [98, 173, 765, ...], [[24, 51, 173, ...]]

And finally, we can find all documents that mention cats. Searching 600K documents took less than a millisecond (18µs)!

With the inverted index, the time complexity of the search query is linear to the number of search tokens. In the example query above, other than analyzing the input text, search had to perform only three map lookups.

The query from the previous section returned a disjoined list of documents for each token. What we normally expect to find when we type small wild cat in a search box is a list of results that contain small , wild and cat at the same time. The next step is to compute the set intersection between the lists. This way we'll get a list of documents matching all tokens.

Luckily, IDs in our inverted index are inserted in ascending order. Since the IDs are sorted, it's possible to compute the intersection between two lists in linear time. The intersection function iterates two lists simultaneously and collect IDs that exist in both:

func intersection(a []int, b []int) []int {
    maxLen := len(a)
    if len(b) > maxLen {
        maxLen = len(b)
    }
    r := make([]int, 0, maxLen)
    var i, j int
    for i < len(a) && j < len(b) {
        if a[i] < b[j] {
            i++
        } else if a[i] > b[j] {
            j++
        } else {
            r = append(r, a[i])
            i++
            j++
        }
    }
    return r
}

Updated search analyzes the given query text, lookups tokens and computes the set intersection between lists of IDs:

func (idx index) search(text string) []int {
    var r []int
    for _, token := range analyze(text) {
        if ids, ok := idx[token]; ok {
            if r == nil {
                r = ids
            } else {
                r = intersection(r, ids)
            }
        } else {
            // Token doesn't exist.
            return nil
        }
    }
    return r
}

The Wikipedia dump contains only two documents that match small , wild and cat at the same time:

> idx.search("Small wild cat")

130764  The wildcat is a species complex comprising two small wild cat species, the European wildcat (Felis silvestris) and the African wildcat (F. lybica).
131692  Catopuma is a genus containing two Asian small wild cat species, the Asian golden cat (C. temminckii) and the bay cat.

The search is working as expected!

By the way, this is the first time I hear about catopuma , here is one of them:

We just built a Full-Text Search engine. Despite its simplicity, it can be a solid foundation for more advanced projects.

I didn't touch on a lot of things that can significantly improve the performance and make the engine more user friendly. Here are some ideas for further improvements:

• Persist the index to disk. Rebuilding it on every application restart may take a while.

• Extend boolean queries to support OR and NOT .

• Experiment with memory and CPU-efficient data formats for storing sets of document IDs. Take a look at Roaring Bitmaps .

• Support indexing multiple document fields.

• Sort results by relevance.

The full source code is available on GitHub .