Introduction
Picture this: you start a new project and, out of sheer habit, decide to slap a UUID as the ID on every single table. “What could possibly go wrong?”—after all, UUIDs are universal, globally unique, and basically the coolest thing out there, right? Sound familiar?
In reality, this widespread fascination with UUIDs can drag down performance, bloat your indexes, and give you unnecessary headaches when maintaining your database. Let’s take a closer look at why UUIDs aren’t always the perfect fix and when they actually make sense.
When UUIDs Actually Make Sense
Distributed Systems
A prime case for using UUIDs is when you’ve got dozens (or even hundreds) of independent nodes, each creating records without a shared counter. In that scenario, generating unique IDs locally is a lifesaver—it avoids collisions and doesn’t rely on a central coordinator.
Generating the ID Up Front
Sometimes, you need an object’s identifier before it even hits the database. Think of a mobile app that pre-creates an order and instantly needs an order number. With auto-increment from the DB, you have to insert first, then wait for a response; with a UUID, you’re good to go right away.
Making It Hard to Guess
Another big plus for UUIDs: they’re not easy to brute-force. If you’re building a public API or dealing with magic links (like password reset URLs), being unpredictable is crucial. While there are alternative ways to protect your endpoints (tokens, hashes, etc.), UUIDs are often chosen for convenience.
Hiding the Insertion Order
If you don’t want people to know how many rows are in your database or how fast they’re being added, auto-increment keys might spill the beans. A random-looking UUID is way less obvious and won’t let anyone figure out when or how many records you’ve inserted.
Common Pitfalls and Gotchas With UUID
Index Size and Overhead
The first and most overlooked point: a UUID is 16 bytes, whereas BIGINT is only 8. No biggie for small datasets, but multiply that difference by hundreds of millions of rows, and your indexes could practically double in size. Bigger indexes mean more I/O, which can eat into performance.
Fragmentation and Insertion Order
Next, when you use sequential IDs, new entries typically end up at the “end” of the index—minimal fragmentation. But a UUID v4 is random, scattering new keys all over the place and forcing frequent page splits. This causes index bloat, more overhead, and extra maintenance (like VACUUM in PostgreSQL), leading your database to bulk up and slow down.
Insert Performance
Which ties into our third point: inserting in ascending order is easier, while random inserts require more fuss. The system has to figure out where to put each new record in the index, and if the index is already beefy, that’s even more overhead.
Read Performance (Exact Lookups)
Both BIGINT and UUIDs theoretically have O(log n) search time in a b-tree, but if your UUID-based index is bloated, you’ll be reading more pages off disk. On small-scale projects, that may not matter, but once you hit massive volumes—billions of rows—those extra reads can stack up and cause noticeable slowdowns.
Index Size vs. Cache
When an index swells beyond what fits in shared_buffers or the OS page cache, the database has to do more disk I/O. In a high-load setting, that’s a recipe for bottlenecks and lag.
The “Magical Sharding” Misconception
Some folks assume that random UUIDs magically deliver perfectly distributed sharding. In reality, proper sharding calls for a deliberate approach—hash-based, range-based, consistent hashing, etc. Simply generating random IDs won’t guarantee load balancing for free.
Uniqueness and ID Generation
Sure, a UUID is fantastic if you need to generate globally unique IDs without coordination—like if multiple nodes are all inserting data on their own. But if you’ve got a single service hitting a single database, “global uniqueness” might be overkill, adding more complexity than actual value.
How to Handle It (and Keep Your DB Happy)
If Possible, Go for Auto-Increment (IDENTITY, BIGSERIAL)
In many situations, a straightforward auto-increment setup is a slam dunk: you get faster performance and smaller indexes. Plus, if your users’ data is protected by authentication and a well-thought-out ACL or RBAC model, there’s really no worry—nobody can peek at someone else’s records by guessing IDs.
The Hybrid Approach
Nothing stops you from using sequential IDs internally and then translating them into some kind of randomized string (token, UUID, hash, etc.) when exposing them via an external API. That way, everything is efficient inside your system, and outsiders remain blissfully unaware of your real IDs under the hood.
Don’t Expect Automatic Sharding Magic
If you’re dealing with sharding, you’ll still need a solid plan. UUIDs alone aren’t a silver bullet for distributing data—hash functions, consistent hashing, or ranges have to be properly tuned. “Random = perfect distribution” is wishful thinking, not a real strategy.
Conclusion
Here’s the bottom line: UUIDs are a lifesaver when you’re dealing with a truly distributed system or when you need to know your IDs upfront or make them unguessable. But if you’ve only got one service writing to one database and don’t actually need that global uniqueness, you’ll likely suffer more from oversized indexes and random insertion patterns than you’ll benefit.
Remember, the choice of which type to use should be deliberate. If you need randomness or that global uniqueness, go ahead and use a UUID. Otherwise, stick with classic sequential IDs. Making a mindful decision about your ID strategy keeps your database lean and your development process running smoothly—no extra baggage required.
