That means at least 72 bytes per identifier! While this doesn't seem like a big deal, consider that for every 13, identifiers, your database will consume 1MB of storage. If you use a UUID as an identifier for a table, you're going to have to index it. Unfortunately, UUID's don't index well. The problem is their size and randomness.
Indexes are trees that grow and branch as you add more data. Sequential values tend to index well because they don't require large sections of the index to be realigned e. UUID's are designed to be non-sequential and they are very large compared to an integer. This means the more UUIDs you insert, the larger the insertion penalty will be. Notice the nearly consistent insertion time of a long integer vs. More importantly, note that the scale on the left is "insert time in hours".
You will very certainly see better performance on queries using integers as well, though this might not be as pronounced. On the other hand, the index that stores UUID identifiers will grow much larger and at a faster rate than integers. This means a UUID index will become disk bound because it can't fit completely into memory more quickly than an integer index. The point of a UUID is to have a universally unique identifier. A universally unique identifier UUID is, in short, what it says it is: a string of letters and numbers intended to uniquely identify information.
Within the context of identity and access management at Harvard, UUIDs are used as an internal identifier for users and, in the future, other non-person resources. In a Python environment, the UUID library demonstrated above is a standby tool, though many developers have created other options to serve specific use cases.
Unless your application or database calls for a nuanced usage of UUIDs that a popular package cannot service, the best practice is to use a well-maintained and regularly updated library to ensure that the underlying cryptographic technologies creating your identifiers remain state-of-the-art.
UUIDs are appropriate for a wide variety of use cases across different types of systems, since their universal nature means they can be generated anywhere within a network. This eliminates the need to designate this task to a single system node. Here are some common examples of how UUIDs are used in the wild:. Within a web application, UUIDs can be generated within the front end of the app without necessitating a call to a server or database.
Any third-party application system that integrates into a web or mobile application such as marketing, analytics, and advertising tools will likely have a need to generate unique identifiers. For example, an advertising company that wants to identify the impressions, clicks, and other events within a single user session could generate a UUID at the beginning of a session, then associate individual events with this single ID. In distributed database systems, UUIDs can be very useful for splitting up large tables and storing them across multiple servers.
Interested in the topic of user identity and maintaining cross-system data identification? Check out how Customer Data Platforms help engineering teams simplify cross-channel data collection, streamline data transformation and delivery, and address other technical challenges of the modern data ecosystem.
Note: If you ever have a need to determine the likelihood of a UUID collision, or probability theory is just something that brings joy to your heart, you can use the Birthday Problem. Connect with an mParticle expert to discuss how to integrate and orchestrate customer data the right way for your business. Startups can now receive up to one year of complimentary access to mParticle. Data engineering — July 12, What is a UUID?
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