Primary keys are the backbone of a relational database. Everything leans on on these keys. So when developers pick a string as the primary key (often the string representation of a GUID or something more “readable”), they are adding friction where things should be fast.
On paper, a string key might seem harmless. But underneath, you are dealing with bloated indexes, slower inserts, sluggish joins, and unnecessary storage overhead.
Here is what is really going on.
1. Indexes Get Sluggish
Databases are tuned to handle sequential, numeric keys efficiently. They are fast to compare, quick to sort, and easy to compress.
Strings slow everything down:
- Comparisons use more CPU
- Indexes grow larger and eat more memory
- Sorting takes longer
- Fragmentation becomes more likely
If you use a random string like a GUID as your clustered key, new inserts will scatter across your table. This triggers frequent page splits and degrades performance across the entire index.
2. Insert Performance Gets Worse
Sequential integers keep clustered indexes clean. New rows go to the end. Pages stay compact. Inserts are fast.
Random strings break that rhythm:
- Inserts land in unpredictable places
- Page splits become frequent
- Cache usage becomes inefficient
- Insert performance drops
If your system writes data often, this adds up quickly. What looks like a harmless design choice will quietly sabotage your system’s performance.
3. The Hidden Cost of Strings
Let’s compare some common key types:
| Type | Storage Size | Max Value |
|---|---|---|
INT | 4 bytes | 2,147,483,647 |
BIGINT | 8 bytes | 9,223,372,036,854,775,807 |
CHAR(36) | 36 bytes | Fixed size for GUID-style strings |
VARCHAR(36) | 38 to 40 bytes | Varies by encoding and internal format |
Strings can use five to ten times more space than integers. Every time your primary key is referenced as a foreign key, that extra space gets duplicated.
Ten million rows with a string primary key can burn hundreds of megabytes just to store identifiers. And that storage cost compounds across your schema.
4. Foreign Keys Spread the Pain
Your primary key gets copied into every related table as a foreign key. That means the performance and storage problems follow it.
Every join runs slower. Every related index gets larger. Every lookup gets just a bit more expensive.
The design choice you made in one table now affects every table it touches.
5. What You Should Use Instead
Here is the better approach:
- Use
INTorBIGINTas your primary key and wrap them in strongly-typed IDs for compile time safety. - Make it a surrogate key if needed
- If you need GUIDs, store them in a separate column
- Use
BINARY(16)for GUIDs to save space if necessary
This gives you fast, compact indexes and keeps your schema easy to scale.
Wrap-Up
String primary keys may feel convenient now. But they quietly erode performance with every insert. The longer your app runs, the more it hurts.
Stick with numeric keys:
- Faster inserts
- Smaller, faster indexes
- Leaner foreign key references
- Less wasted space
Your database will run faster. Your queries will stay sharp. And your system will scale with a lot less pain.
Whether you’re building a new application or optimizing an existing one, implementing proper database design patterns is crucial for long-term success. If you need help architecting scalable database solutions, our custom software development team has extensive experience with high-performance database design and optimization.