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Why is query with phone = N'1234' slower than phone = '1234'?

sql
performance
best-practices
indexing
Anton ShumikhinbyAnton Shumikhin·Dec 11, 2024
TLDR

In SQL Server, implicit conversion imposes an overhead on phone = N'1234', converting all data to Unicode. If phone is VARCHAR, using the N prefix triggers the conversion process. Therefore, always match your literal to the column type:

VARCHAR (NON-Unicode):

-- Speedy non-Unicode select, faster than your Gran's broadband SELECT * FROM table WHERE phone = '1234';

NVARCHAR (Unicode):

-- Unicode select, with twice the character, just like a good thriller novel SELECT * FROM table WHERE phone = N'1234';

Literal matching alleviates unnecessary conversions and zooms queries.

What lies beneath: Type precedence and collation

SQL Server uses type precedence when comparing values of different data types. It converts the lower precedence type to the higher one. Here, NVARCHAR trumps VARCHAR, leading to an implicit conversion hindering effective usage of indexes.

The term collation refers to the set of rules dictating string comparison in SQL Server. Mixing collation types (SQL collation vs. Windows collation) can affect performance. Although Windows collations are feature-rich and more linguistically accurate than SQL collations, their complex nature can slow down comparison operations.

Datatypes' impact on query speed

When a VARCHAR column matches against N'1234', SQL Server converts the column to NVARCHAR before the comparison, slowing down the operation. Converting the column type to NVARCHAR or casting the literal to VARCHAR ditches the implicit conversion, causing a noticeable performance boost.

VARCHAR vs NVARCHAR: Trade-offs and Optimizer handling

Selecting between VARCHAR and NVARCHAR involves trade-offs. NVARCHAR accommodates more characters but demands more storage space. If you're dealing with multiple languages or prefer Unicode's precision, opt for NVARCHAR. Yet, for mostly English data with storage or performance issues, VARCHAR can streamline your database.

A savvy query optimizer could tackle type precedence issues, manipulating its execution plan to handle implicit conversions and indexing.

The intelligent usage of indexes

Matching your query's data type with your column's enables the database engine to use indexes directly. However, when data types differ, it resorts to a full table scan instead of an index seek. Explicitly CASTing or CONVERTing your query lets the query optimizer leverage indexes better.

-- Index-friendly conversion, like friendly neighbor, always helpful SELECT * FROM table WHERE phone = CAST(N'1234' AS VARCHAR);

Strategies to combat performance dips

These strategies can help combat performance issues:

  1. Synchronize your data types: Literals in your queries should match the datatype of their respective columns.
  2. Explicit casting: Explicitly cast your literals to the appropriate data type when necessary.
  3. Choose your collation wisely: Consider the impact of collation on string comparisons and select accordingly.
  4. Smart Index design: Indexes should cater to your system's most frequent and performance-critical queries.