Have you ever wondered why your database sorts strings differently than expected, or why a seemingly simple query delivers "quirky" results? If you’ve worked with databases like MySQL, PostgreSQL, or SQL Server, you’ve likely stumbled across the mysterious world of collation and the subtle but critical differences between VARBINARY and VARCHAR data types.
What Is Collation and Why Does It Matter?
Collation refers to the set of rules a database uses to compare and sort character strings. It governs how strings are ordered (e.g., is “Apple” less than “apple”?), how accents are treated (e.g., does “é” equal “e”?), and even how special characters are handled. Collation is critical because it directly impacts query results, sorting, and indexing, especially in multilingual applications or systems handling diverse datasets.
Different databases handle collation in different ways:
- MySQL: Offers a wide range of collations (e.g., utf8mb4_unicode_ci for case-insensitive Unicode or utf8mb4_bin for binary comparisons). You can set collation at the server, database, table, or column level, providing fine-grained control.
- PostgreSQL: Uses the operating system’s locale settings for collation (e.g., en_US.UTF-8) but allows custom collation definitions for specific columns or queries. It’s flexible but can be sensitive to system-level settings.
- SQL Server: Relies on collations like SQL_Latin1_General_CP1_CI_AS (case-insensitive, accent-sensitive). It ties collation to code pages, which can affect character encoding and sorting behavior.
Why is collation a “nice thing” to have? It ensures consistent string handling across diverse datasets, supports internationalization (e.g., proper sorting for non-English languages), and prevents unexpected query behavior. Without collation, your database might treat “Café” and “Cafe” as entirely different strings or worse, sort them inconsistently.
VARBINARY vs VARCHAR: The Core Differences
To understand how collation interacts with data types, let’s compare VARBINARY and VARCHAR:
VARCHAR: Stores character data with a specific character set (e.g., UTF-8) and collation. It interprets data as text, applying collation rules during comparisons and sorting. For example, in MySQL’s utf8mb4_general_ci collation, “Apple” and “apple” are treated as equal in a case-insensitive comparison. It can handle the difference even in those situation but you would need to specify that need.
VARBINARY: Stores raw binary data as a sequence of bytes, ignoring character sets and collation. Comparisons are byte-by-byte, making VARBINARY case-sensitive and accent-sensitive by default. For instance, “Apple” and “apple” are distinct because their ASCII byte values differ.
Diferencies on DBMSs
VARBINARY may have different names across database systems, such as BINARY, BYTEA, or even BIN. These types are used to store binary data (i.e., raw bytes), but they are not necessarily bit-specific fields, they store byte sequences, not individual bits.
| Term | Database System | Description |
|------------|---------------------|-----------------------------------------------------|
| `VARBINARY`| SQL Server, MySQL | Variable-length binary data |
| `BINARY` | SQL Server, MySQL | Fixed-length binary data |
| `BYTEA` | PostgreSQL | Binary string type for storing raw bytes |
| `BIN` | Not a standard type | Sometimes used informally or in proprietary systems |
Storage and Performance
VARCHAR: Stores text in the specified character set, which may use varying byte lengths per character depending on the encoding. For example, latin1 (used in MySQL) uses 1 byte per character, supporting Western European languages with simpler storage needs but limited character range compared to UTF-8 (1–4 bytes for broader Unicode support). Collation-aware comparisons, such as "case-insensitive" or "accent-sensitive" sorting, may incur additional processing overhead, particularly for complex Unicode rules or collations like latin1_swedish_ci that prioritize specific linguistic conventions.
VARBINARY: Stores data as a sequence of raw bytes, with each character occupying exactly the number of bytes required by its binary representation (e.g., a string like “Café” stored as VARBINARY uses the exact byte sequence of its encoding, such as 5 bytes in ASCII or UTF-8). Binary comparisons are faster because they bypass collation rules, directly comparing byte values without linguistic interpretation, but this sacrifices text-specific features like "case-insensitive" searches or "accent normalization", making it unsuitable for text data requiring human-readable sorting or matching.
Database-Specific Behaviors
MySQL: VARBINARY is unaffected by collation, treating data as raw bytes. VARCHAR respects the column’s collation, which can lead to slower queries if complex rules (e.g., Unicode sorting) are involved.
PostgreSQL: Similar to MySQL, BYTEA (the equivalent of VARBINARY) stores raw bytes, while TEXT or VARCHAR uses collation. PostgreSQL’s locale-based collation can cause platform-specific sorting differences.
SQL Server: VARBINARY stores binary data, while NVARCHAR or VARCHAR applies collation rules. SQL Server’s collation choices can impact performance, especially for large datasets with accent-sensitive comparisons.
Experimental Case Study: VARBINARY vs VARCHAR in MySQL 8
Let’s dive into a hands-on experiment to see how VARBINARY and VARCHAR behave differently under collation in MySQL 8. We’ll create a sample dataset, run queries, and observe the results.
Setup
Create a table with both VARCHAR and VARBINARY columns containing identical data:
CREATE TABLE test_collation (
id INT AUTO_INCREMENT PRIMARY KEY,
text_varchar VARCHAR(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci,
text_varbinary VARBINARY(50)
);
INSERT INTO test_collation (text_varchar, text_varbinary) VALUES
('Apple', 'Apple'),
('apple', 'apple'),
('Café', 'Café'),
('Cafe', 'Cafe');
Query 1: Sorting Behavior
SELECT text_varchar FROM test_collation ORDER BY text_varchar;
SELECT text_varbinary FROM test_collation ORDER BY text_varbinary;
Apple
apple
Café
Cafe
Apple
Cafe
Café
apple
Query 2: Comparison Behavior
SELECT text_varchar FROM test_collation WHERE text_varchar = 'apple';
SELECT text_varbinary FROM test_collation WHERE text_varbinary = 'apple';
Apple
apple
apple
An Unusual Twist: Collation in Mixed Queries
CREATE TABLE test_collation2 (
id INT AUTO_INCREMENT PRIMARY KEY,
text_varbinary VARBINARY(50)
);
INSERT INTO test_collation2 (text_varbinary) VALUES ('apple');
SELECT t1.text_varchar, t2.text_varbinary
FROM test_collation t1
JOIN test_collation2 t2 ON t1.text_varchar = t2.text_varbinary;
text_varchar | text_varbinary
-------------|---------------
Apple | apple
apple | apple
Collation Pitfalls to Avoid
- Join Issues: As shown in the experiment, joining VARCHAR and VARBINARY can lead to implicit conversions, skewing results.
- Performance Traps: Case-insensitive collations (e.g., utf8mb4_unicode_ci) are slower than binary comparisons, especially for large datasets.
- Cross-Database Portability: Collation behavior varies across databases, so a MySQL query may behave differently in PostgreSQL or SQL Server.
Best Practices for Choosing Between VARBINARY and VARCHAR
- Use VARCHAR for Text Data: If you need collation-aware sorting or searching (e.g., for user-facing data), choose VARCHAR with an appropriate collation.
- Use VARBINARY for Binary Data: Store raw bytes (e.g., hashes, encrypted data) in VARBINARY to avoid collation overhead.
- Test Collation Impact: Always test queries with your chosen collation, especially in multilingual applications.
- Explicit Casting in Joins: When mixing types, use explicit casts (e.g., CAST(text_varbinary AS CHAR)) to avoid surprises.
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