We're considering using UUID values as primary keys for our MySQL database. The data being inserted is generated from dozens, hundreds, or even thousands of remote computers and being inserted at a rate of 100-40,000 inserts per second, and we'll never do any updates.

The database itself will typically get to around 50M records before we start to cull data, so not a massive database, but not tiny either. We're also planing to run on InnoDB, though we are open to changing that if there is a better engine for what we're doing.

We were ready to go with Java's Type 4 UUID, but in testing have been seeing some strange behavior. For one, we're storing as varchar(36) and I now realize we'd be better off using binary(16) - though how much better off I'm not sure.

The bigger question is: how badly does this random data screw up the index when we have 50M records? Would we be better off if we used, for example, a type-1 UUID where the leftmost bits were timestamped? Or maybe we should ditch UUIDs entirely and consider auto_increment primary keys?

I'm looking for general thoughts/tips on the performance of different types of UUIDs when they are stored as an index/primary key in MySQL. Thanks!


Solution 1:

At my job, we use UUID as PKs. What I can tell you from experience is DO NOT USE THEM as PKs (SQL Server by the way).

It's one of those things that when you have less than 1000 records it;s ok, but when you have millions, it's the worst thing you can do. Why? Because UUID are not sequential, so everytime a new record is inserted MSSQL needs to go look at the correct page to insert the record in, and then insert the record. The really ugly consequence with this is that the pages end up all in different sizes and they end up fragmented, so now we have to do de-fragmentation periodic.

When you use an autoincrement, MSSQL will always go to the last page, and you end up with equally sized pages (in theory) so the performance to select those records is much better (also because the INSERTs will not block the table/page for so long).

However, the big advantage of using UUID as PKs is that if we have clusters of DBs, there will not be conflicts when merging.

I would recommend the following model: 1. PK INT Identity 2. Additional column automatically generated as UUID.

This way, the merge process is possible (UUID would be your REAL key, while the PK would just be something temporary that gives you good performance).

NOTE: That the best solution is to use NEWSEQUENTIALID (like I was saying in the comments), but for legacy app with not much time to refactor (and even worse, not controlling all inserts), it is not possible to do. But indeed as of 2017, I'd say the best solution here is NEWSEQUENTIALID or doing Guid.Comb with NHibernate.

Hope this helps

Solution 2:

A UUID is a Universally Unique ID. It's the universally part that you should be considering here.

Do you really need the IDs to be universally unique? If so, then UUIDs may be your only choice.

I would strongly suggest that if you do use UUIDs, you store them as a number and not as a string. If you have 50M+ records, then the saving in storage space will improve your performance (although I couldn't say by how much).

If your IDs do not need to be universally unique, then I don't think that you can do much better then just using auto_increment, which guarantees that IDs will be unique within a table (since the value will increment each time)

Solution 3:

Something to take into consideration is that Autoincrements are generated one at a time and cannot be solved using a parallel solution. The fight for using UUIDs eventually comes down to what you want to achieve versus what you potentially sacrifice.

On performance, briefly:

A UUID like the one above is 36 characters long, including dashes. If you store this VARCHAR(36), you're going to decrease compare performance dramatically. This is your primary key, you don't want it to be slow.

At its bit level, a UUID is 128 bits, which means it will fit into 16 bytes, note this is not very human readable, but it will keep storage low, and is only 4 times larger than a 32-bit int, or 2 times larger than a 64-bit int. I will use a VARBINARY(16) Theoretically, this can work without a lot of overhead.

I recommend reading the following two posts:

  • Brian "Krow" Aker's Idle Thoughts - Myths, GUID vs Autoincrement
  • To UUID or not to UUID ?

I reckon between the two, they answer your question.

Solution 4:

I tend to avoid UUID simply because it is a pain to store and a pain to use as a primary key but there are advantages. The main one is they are UNIQUE.

I usually solve the problem and avoid UUID by using dual key fields.

COLLECTOR = UNIQUE ASSIGNED TO A MACHINE

ID = RECORD COLLECTED BY THE COLLECTOR (auto_inc field)

This offers me two things. Speed of auto-inc fields and uniqueness of data being stored in a central location after it is collected and grouped together. I also know while browsing the data where it was collected which is often quite important for my needs.

I have seen many cases while dealing with other data sets for clients where they have decided to use UUID but then still have a field for where the data was collected which really is a waste of effort. Simply using two (or more if needed) fields as your key really helps.

I have just seen too many performance hits using UUID. They feel like a cheat...

Solution 5:

Instead of centrally generating unique keys for each insertion, how about allocating blocks of keys to individual servers? When they run out of keys, they can request a new block. Then you solve the problem of overhead by connecting for each insert.

Keyserver maintains next available id

  • Server 1 requests id block.
  • Keyserver returns (1,1000)
    Server 1 can insert a 1000 records until it needs to request a new block
  • Server 2 requests index block.
  • Keyserver returns (1001,2000)
  • etc...

You could come up with a more sophisticated version where a server could request the number of needed keys, or return unused blocks to the keyserver, which would then of course need to maintain a map of used/unused blocks.