Collation and Sorting Rules

Sorting and collation in internationalized or multilingual systems present unique challenges due to the diverse ways in which languages, scripts, and locales handle text ordering. What may seem straightforward in one language or region can vary significantly in another, necessitating careful consideration and implementation. From accommodating locale-specific sorting rules to handling multilingual datasets and ensuring performance for large-scale applications, developers must navigate these complexities to deliver consistent and user-friendly experiences. This lesson explores common challenges in sorting and collation, offering examples and practical solutions to address them effectively.

Different Sorting Rules for the Same Script in Different Locales

Consider the difference between German and Swedish in the way umlauted characters like ä, ö, and ü are  sorted. In German, they are considered variations of their base letters a, o, and u and are typically sorted as if they were ae, oe, and ue.

• Example words: Anna, Zebra, Ärger, Über
• German sorting: Anna, Ärger, Über, Zebra

In Swedish, umlauted characters like ä and ö are distinct letters and are sorted after z.

• Example words: Anna, Åsa, Äpple, Öst, Zebra
• Swedish sorting: Anna, Zebra, Åsa, Äpple, Öst

Solution

Locale-specific collation rules must be applied using Unicode Collation Algorithm (UCA) or libraries like ICU (International Components for Unicode) to account for these differences.

Sorting Content Across Different Locales or Scripts

Imagine a dataset that includes names in multiple languages and scripts, e.g., 张伟 (Zhang Wei), Алексей (Alexey), Marie, محمد (Mohammad).

Sorting alphabetically without locale-specific rules might mix scripts randomly, causing confusion:

• Incorrect order: Alexey, Marie, 张伟, محمد

Another example is a list of languages displayed in their own endonyms: Deutsch, العربية, Español, Français, 中文. This is a common use case in language- and locale-selection navigation.

Sorting this list alphabetically in a way that makes sense across scripts is challenging:

• Incorrect order: Deutsch, Español, Français, العربية, 中文 (incorrectly sorted by Latin script first, then others).

Solution

Implement locale-aware collation to sort the dataset appropriately within each script and locale. Use the Unicode Common Locale Data Repository (CLDR), which defines sort order across scripts. Consider assigning weights to characters to ensure uniform sorting. For example, zh-Hans (Simplified Chinese) would use Pinyin order, while ar (Arabic) would follow the Arabic alphabet.

In MySQL, the most suitable collation for such purposes is utf8mb4_unicode_ci, which is based on the Unicode Collation Algorithm (UCA). It is designed to handle multilingual data and sort characters from various scripts in a consistent way.

Performance Concerns for Large Datasets

Imagine an e-commerce platform with millions of product names in multiple locales and scripts. Sorting these names by locale-specific rules for each user dynamically is resource-intensive.

• Example: Sorting a dataset with English (en), French (fr), and Japanese (ja) names while respecting each locale's collation.

Sorting rules must consider locale-specific diacritics, casing, and script differences. Dynamic sorting for every user request can slow down performance on large datasets.

Solution

• Precompute and cache sorted subsets of the data for popular locales.
• Use indexed sorting columns in the database, such as MySQL's COLLATE clause (e.g., COLLATE utf8mb4_0900_as_cs).
• Implement a hybrid solution combining database-side sorting and client-side adjustments for edge cases.

Summary of Challenges and Solutions