Both V6 and V7 include timestamps just like V1 and V2, but with an option of a nanosecond precision instead of 100ms precision. To combat this locality issue, there are new UUID draft that include version V6, V7, and V8. In other words, new items are far away from each other. It keeps jumping between nodes which means more pages (memory) and cpu cycles needed to find the nest item. ![]() As you can see, going from the first to the second UUID ( 4ab20c83 to 62135145) is relatively close, but going to the third one which is 6e2b46f6 is relatively further away. The items above only use the first eight characters because only the first few characters are important to demonstrate locality. When we index using b-tree, it will look like thisįigure 4: An Example of UUID V4 b-tree index For example, take a look at the following sequence eight UUID V4 where the top comes first and the bottom is the last item in a table. If a random UUID like V4 is used, a sequence of them will be all over the place, making lookup longer. ![]() Here we can see that the integers in the leaf nodes are close to each other. Serial integer has no issue with indexes because the way they are indexed in a database are next to each other.įigure 3: Where the numbers are stored in a tree The reason against using UUID instead of serial ID is that is bad for indexex. To visualise this, the chance of getting two identical UUID is less than a combination of getting hit by an asteroid (1 in 1,600,000 ), and hit by a tsunami (1 in 5000 years ) (1 -12 A file containing this many UUIDs, at 16 bytes per UUID, would be about 45 exabytes.” “This number is equivalent to generating 1 billion UUIDs per second for about 85 years. We know V4 has 122 bits left for random part, thus: To find out, we use the Birthday Paradox formula. Generally, collisions have a low chance of happening. This is a significant issue when used as a primary key which requires uniqueness. CollisionĬollision happens when generated results have an identical value. V3 and V5 are namespace-based with different hash, MD5 and SHA1 respectively. More details on what k-sortable is in the later section. These include timestamps of 100 nanosecond precision in the leading portion of UUID, so they are k-sortable. So, there’s no collision of primary keys between the shards. V1 and V2 include hardware MAC for easier scaling between nodes in a sharded database. That leaves 122 bits for other random bits for a total of 2 122 It has 6 bits predetermined that contains a version (V4) and a variant. There are many versions ranging from V1 to V5. They look something like this c50ab83b-b632-419d-91b9-626895e705ecįigure 1: An example of a UUID Version of UUIDs It has an 8-4-4-4-12 format length separated by dashes where each section corresponds to a specific way of encoding. They are 32 hexadecimal characters consisting of 16 octets, thus 16*8 = 128 bits. Leak fewer information on HTTP response or URL compared to integer primary key.We generally do not have to worry about ID clashing between databases held in different servers. So multiple machines can generate them independently which means potentially faster write. Can create records concurrently because it does not have to wait for the next ID like in sequential ID. ![]() Why, and when do you use a universally unique identifier (UUID) as a primary key in a database?
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |