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Creating a Local Full Text Search Index

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Creating a Local Full Text Search Index

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One issue I have with UrzaGatherer is related to the search. As you may know, I have a huge list of cards saved locally and my application allows the user to search specific cards using a part of their name. But browsing more than 20 000 cards using a text search can be really expensive mainly on low-end hardware.

Currently the code looks like something like that:

cards = UrzaGatherer.CardsList.createFiltered(queryFunction)

I use the WinJS.Binding.List object to create a filtered view using my search pattern.

The filter function uses a simple indexOf function:

var queryFunction = function (card) {
    if (card.name.indexOf(textSearch) !== -1) {
        return true;

    return false;

But obviously, it can take ages to perform a search using this solution. The complexity is almost O(n * m) which can be simplified to O(n²) (where n is the number of cards and m the average length of cards name).

So the question is: How can I optimize my search?

Building a full text search tree

One solution can be found with a search tree. This kind of structure allows you to perform a search with a complexity of O(n) where n is the average length of a card’s name.

You have to build the tree by feeding it with the strings you want to search. For each string, the tree will generate a branch with all the characters then a branch with n-1 character (starting after the first one) and so on.

For example, if we use the “urza” string the tree will look like that:


The leafs contain the id of the associated card.

If I add a new string like “Pizza” to my previous tree the resulting tree is:


Please note that some leafs can contain many cards (like for “ZA” and “A”)

The related code is pretty simple:

var root;

var processString = function(string, offset, node, object) {
    if (!string || offset == string.length) {

    var currentNode = node;

    for (var index = offset; index < string.length; index++) {
        var c = string[index];

        if (currentNode[c] === undefined) {
            currentNode[c] = {};

        currentNode = currentNode[c];

    if (currentNode.ref == undefined)
        currentNode.ref = [];

    if (currentNode.ref.indexOf(object.id) == -1) {

    processString(string, offset + 1, root, object);

var addString = function (string, object) {
    if (!string)
    if (!root)
        root = { };

    processString(string.toLowerCase(), 0, root, object);

Searching using the full text search tree

The search function is then a simple tree traversal:

var concatArray = function (source, data) {
    for (var index = 0; index < data.length; index++) {
        source[data[index]] = {};

var gatherResults = function (node, results) {
    for (var prop in node) {
        if (prop == "ref")
            concatArray(results, node[prop]);
            gatherResults(node[prop], results);

var searchString = function (string) {
    var currentNode = root;

    for (var index = 0; index < string.length; index++) {
        var c = string[index];

        if (currentNode[c] === undefined)
            return {};

        currentNode = currentNode[c];

    var results = {};
    gatherResults(currentNode, results);

    return results;

When the searched string is completely found, the algorithm gathers all the children leafs to produce the final result.

You can also persist your tree easily with JSON:

var persistIndex = function(filename) {
    var data = JSON.stringify(root);
    return Windows.Storage.ApplicationData.current.localFolder.createFileAsync(filename, 
        .then(function (file) {
        return Windows.Storage.FileIO.writeTextAsync(file, data);

var resetIndex = function() {
    root = undefined;

var loadIndex = function(filename) {
    return Windows.ApplicationModel.Package.current.installedLocation.getFolderAsync("data")
        .then(function(localFolder) {
        return localFolder.getFileAsync(filename).then(function(file) {
            return Windows.Storage.FileIO.readTextAsync(file).then(function(data) {
                if (data) {
                    root = JSON.parse(data);

For instance, the complete index for my 20 000 cards weights 6 Mb.


Blazing fast !! Nothing more Sourire. Using my search tree I can search through the entire collection in less than 300ms where before it can take up to 20s for the same result.

But beware: this optimization is costly in terms of memory consumption (due to the index).

At the end of the day, it is a good tool for you if you want to search your data without using a backend server (or when you are offline).

What’s the best way to boost the efficiency of your product team and ship with confidence? Check out this ebook to learn how Sentry's real-time error monitoring helps developers stay in their workflow to fix bugs before the user even knows there’s a problem.


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