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Why develop our own Unicode Library? – The Algolia Blog

At one time or another, most developers come across bugs or problems with Unicode (about 3,720,000 results on google for the request unicode bug developer at the time of this writing). Let me tell you about my experience in the last decade and why we have now implemented our own unicode Library to produce exactly the same result across devices/languages.

I first started to use Unicode in 2004 when I was developing a Text Mining software specialized on information extraction. This software was fully implemented in C++ and I used IBM ICU library to be Unicode compliant (all strings were stored in UTF16). I also used some normalization functions of ICU based on decomposition, but I did not notice any major problem at that time. I started to understand the dark side of Unicode later when I used it in other languages like Java, Python, and later in Objective-C. My first surprise was when I understood that a simple isAlpha(unicodechar c) method can return different results!

I started to look in details at the standard and downloaded UnicodeData.txt (the file that contains most of the information about the standard, you can grab the latest version here).

This file contains descriptions of all Unicode characters. Third column represents “General Category” and is documented as:


General Categories

The values in this field are abbreviations for the following. Some of the values are normative, and some are informative. For more information, see the Unicode Standard.

Normative Categories

    • Lu: Letter, Uppercase
    • Ll: Letter, Lowercase
    • Lt: Letter, Titlecase
    • Mn: Mark, Non-Spacing
    • Mc: Mark, Spacing Combining
    • Me: Mark, Enclosing
    • Nd: Number, Decimal Digit
    • Nl: Number, Letter
    • No: Number, Other
    • Zs: Separator, Space
    • Zl: Separator, Line
    • Zp: Separator, Paragraph
    • Cc: Other, Control
    • Cf: Other, Format
    • Cs: Other, Surrogate
    • Co: Other, Private Use
    • Cn: Other, Not Assigned (no characters in the file have this property)

Informative Categories

    • Lm: Letter, Modifier
    • Lo: Letter, Other
    • Pc: Punctuation, Connector
    • Pd: Punctuation, Dash
    • Ps: Punctuation, Open
    • Pe: Punctuation, Close
    • Pi: Punctuation, Initial quote (may behave like Ps or Pe depending on usage)
    • Pf: Punctuation, Final quote (may behave like Ps or Pe depending on usage)
    • Po: Punctuation, Other
    • Sm: Symbol, Math
    • Sc: Symbol, Currency
    • Sk: Symbol, Modifier
    • So: Symbol, Other

As you can see there is quite a lot of categories, some of them are very easy to understand like “Lu” (Letter, uppercase) and “Ll” (Letter, lowercase) but some of them are more complex like “Lo” (Letter, other)  and “No” (Number, other), and this is exactly where the first problem begins.

Let’s take the unicode character U+00BD(½) as an example. It is quite common to describe spare parts and is defined as “No”… except that some unicode libraries consider that this is not a number and return false to isNumber(unicodeChar) method (e.g., Objective-C).

In fact the two most used methods, isAlpha(unicodeChar) and isNumber(unicodeChar), are not directly defined by the Unicode standard and are subject to interpretation.

The consequence is that results are not the same across devices/languages! In our case this is a problem because our compiled index is portable, and we want to have exactly the same results on different devices/languages.

However, this is not the only problem! Unicode normalization is also a tricky topic. The Unicode standard defines a way to decompose characters (Characters decomposition mapping), for example U+00E0(à) which is decomposed as U+0061(a) + U+0300( ̀). But most of the time you do not want a decomposition but a normalization: get the most basic form of a string (lowercase without accents, marks, …). This is key to be able to search and compare words. For example, the normalization of the French word “Hétérogénéité” will be normalized as “heterogeneite”.

To compute this normalized form, most people compute the lowercase form of a word (well defined by the Unicode standard), then compute the decomposed form and finally remove all the diacritics. However, this is not enough. Normalization can not always be reduced to just a matter of removing marks. For example the standard German letter ß is widely used and replaced/understood as “ss” (you can enter ß in your favorite web search engine and you will discover that it also search for “ss“). The problem is that there is no decomposition for “ß” in the Unicode standard because this letter is not a letter with marks.

To solve that problem, we need to look in the Character Fallback Substitution table that is not part of most of Unicode library implementations. This substitution table defines that “ß can be replaced by “ss,”. There are plenty of other examples; For instance, 0153(œ) and 00E6(æ), letters of the French language, can be replaced by “oe” and “ae”.

At the end, this led us to implement our own Unicode library to ensure that our isAlpha(unicodechar) and isNumber(unicodechar) methods have a unique behavior on all devices/languages and to implement a normalize(unicodestring) method that contains character fallback substitution table. By the way our implementation of normalization is far more efficient because we implemented it in one step instead of three (lowercase + decomposition + diacritics removal).

I hope you found this post useful and gained a better understanding of the Unicode standard and the limits of standard Unicode libraries. Feel free to contribute comments or ask for precisions.

About the author
Julien Lemoine

Co-founder & CTO at Algolia


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