NQL SOUNDEX Function

Overview of Soundex

The SOUNDEX function is a phonetic algorithm that converts a word (typically a name) into a code based on how it sounds when pronounced in English. In other words, Soundex helps normalize words by pronunciation – words that sound alike will share the same Soundex code even if their spellings differ. This algorithm was originally developed in the early 1900s to encode homophones (e.g. treating “Smith” and “Smyth” as equivalents) so that they can be matched despite minor spelling differences. The output of the Soundex function is a four-character code (one letter followed by three numbers) that represents the phonetic signature of the input string. For example, SOUNDEX("Smith") returns a code of “S530” and SOUNDEX("Smyth") also returns “S530”, indicating they are pronounced similarly. By using this standardized code, Soundex allows you to group together variations of a word based on pronunciation, which is extremely helpful in data normalization and matching tasks.

Why Use Soundex?

SOUNDEX is useful when you need to match text strings that are phonetically similar, rather than exactly identical. Here are some common use cases where Soundex can help:

• Name Searching and Matching: Searching for names in a database can be tricky due to spelling variations or typos. Soundex enables you to find names that “sound like” the search term. For instance, a query for “Catherine” could also match “Katherine” or “Kathryn” if you use Soundex-based matching. This is because all these variations would share the same phonetic code, allowing the database to identify them as potential matches. In practice, this means users can get relevant results without needing an exact spelling match.
• Data Deduplication and Record Linkage: In data integration or cleaning projects, you might encounter duplicate records where the same entity (person, place, etc.) is spelled differently in different sources. Soundex is a classic fuzzy matching technique for identifying such duplicates. For example, two customer records – one with last name “McDonnell” and another with “MacDonell” – would produce the same Soundex code. This alerts you that they could be the same person, even though the strings aren’t exact. By grouping on Soundex codes, you can catch inconsistencies and consolidate records that refer to the same entity.
• Ensuring Consistent Matching Across Variations: Soundex can be used to normalize data for joining tables or comparing lists. If one dataset uses American spelling (“color”) and another uses British spelling (“colour”), a direct join would fail, but a phonetic comparison might still succeed for certain types of differences. In general, converting strings to their Soundex codes before comparison ensures that pronunciation is the basis of the match. This can be especially useful when integrating data from multiple sources with inconsistent spelling conventions or data-entry errors, providing a common ground for comparison.

Database-Specific Gotchas

While Soundex is based on a standard algorithm, different SQL databases have slight variations in their Soundex implementations that are important to be aware of. These quirks can affect how the function behaves and the results it returns. For example:

• Snowflake: Snowflake’s implementation of SOUNDEX preserves the case of the first letter in the returned code. This means the Soundex code’s first character will have the same case (upper or lower) as the input string’s first character. For instance, SOUNDEX('tom'); in Snowflake might return “t500” (lowercase t), whereas SOUNDEX('Tom') would return T500. The phonetic encoding is the same, but the letter case differs. This is a bit unusual, because most Soundex implementations traditionally output the first letter in uppercase. Snowflake essentially leaves the input’s case intact in the Soundex result.
• Spark: Apache Spark’s SOUNDEX function does not preserve the input case – it always returns an uppercase code. In Spark (and many other SQL engines), soundex('tom') and soundex('TOM') will both yield “T500”, with a capital T. Spark treats the function as case-insensitive, which is in line with the Soundex algorithm’s typical usage (the first letter of the code is normally uppercase by definition). In other words, Spark’s version ensures a consistent format (all caps for the letter) regardless of how the input is cased.

These differences mean that the same Soundex call can behave differently across systems. A direct comparison of Soundex codes between Snowflake and Spark could fail purely due to letter case mismatches – for example, Snowflake’s "t500" vs Spark’s "T500" would not be equal, even though they represent the same sound. Additionally, some databases may have variations in the Soundex algorithm itself. (Snowflake, for instance, offers a special function SOUNDEX_P123 to handle a slight variant of the algorithm used by certain systems like Teradata) It’s important to be mindful of these nuances when working in a multi-database environment.

How Narrative Handles These Issues

One of the advantages of using Narrative’s data collaboration platform is that it abstracts away these database-specific quirks. Narrative’s engine uses a combination of Rosetta Stone mappings and the Narrative Query Language (NQL) to ensure that your queries behave consistently across different underlying databases. In practice, this means when you use the Soundex function in an NQL query, you don’t have to worry about whether the query will run on Snowflake, Spark, or another data plane engine – Narrative handles the differences automatically.

Cross-Compilation with NQL: NQL is an SQL-like language that Narrative compiles into the proper SQL dialect for the target execution engine. When you write an NQL query using SOUNDEX(...), the platform’s compiler will translate it into the correct function call and syntax for whichever database is being used (e.g., Snowflake SQL or Spark SQL). As part of this translation, Narrative applies Rosetta Stone mappings – these are mappings that align functions and data types across systems. For example, if Snowflake’s Soundex needs an extra tweak (like wrapping the result with an UPPER() function to normalize case), Narrative’s execution engine can inject that, so the behavior matches Spark’s output. Conversely, if a function isn’t natively available in one engine, the platform knows how to substitute or simulate it.

Consistency Across Databases: Thanks to this cross-compilation layer, the platform ensures that a phonetic match logic you write is consistent no matter where it runs. Narrative essentially standardizes the behavior of Soundex for you. So if you perform a join on Soundex codes or filter using Soundex in NQL, you’ll get uniform results whether your query executes in Snowflake or Spark. The case difference issue described above is handled behind the scenes – you won’t need to manually uppercase or lowercase anything, because Narrative will do it in the generated SQL if necessary. This preserves data integrity and consistency across disparate sources and query engines.

In summary, Narrative’s platform takes care of the heavy lifting when it comes to database-specific Soundex differences. You can confidently use the Soundex function for phonetic matching and data normalization in your queries, and trust that Narrative will automatically reconcile any quirks between Snowflake, Spark, or other supported databases. This allows you to focus on insights and analysis, rather than worrying about the underlying SQL dialect nuances.