A need for the unique identifier: an IoT case study Let’s build an IoT application with weather sensors deployed around the globe. The sensors will collect data and the data will be stored along with the IDs of the sensors. We’ll run multiple database instances, and the sensors will write to the geographically closest database. All databases will regularly exchange data, so eventually, all the databases will have data from all the sensors. We need each sensor to have a globally unique ID. How can we achieve it? We could, for instance, run a service assigning sensor IDs as a part of the sensor installation procedure. It would mean additional architectural complexity, but it's doable. Sensor IDs are immutable, so each sensor needs to talk to the ID service only once - right after the installation. That’s not too bad. What if we need to store a unique ID for each data reading? Hitting the centralized ID service whenever we need to store data is not an option. That would stress the ID service too much and when the ID service is unavailable no sensor would be able to write any data. What are the possible solutions? In the simplest case, each sensor could talk to the remote ID service and reserve a block of IDs. It could then assign locally without further coordination. When it exhausts the block, it asks the ID service for a new one. This strategy would reduce the load on the ID service, and sensors could function even when the ID service is temporarily unavailable. We could also generate local reading IDs and prefix them with our unique immutable sensor ID. We could also be smart and use fancy ID algorithms like FlakeIDs. The strategies mentioned aim to minimize the need for coordination while still making sure that the IDs are unique globally. The goal is to generate unique IDs without any coordination at all. This is what we call coordination-free unique IDs. UUID enters the scene Flip a coin 128 times and write down 1 for each head and 0 for each tail. This gives you a sequence of 128 1s and 0s, or 128 bits of randomness. That’s a space large enough that the probability of generating the same sequence twice is so extremely low that you can rule it out for practical purposes. How is that related to UUIDs? If you have ever seen a UUID then you know they look similar to this: . This format is just a textual representation of 128 bits. 420cd09a-4d56-4749-acc2-40b2e8aa8c42 How does it work? The UUID string has 36 characters in total. If we remove the 4 dashes, which are there just to make it a bit more human-readable, we are left with 32 hexadecimal digits: 0-F. Each digit represents 4 bits and 32 * 4 bits = 128 bits. So UUIDs are 128-bit values. We often represent them as strings, but that's just a convenience. UUID has been explicitly designed to be unique and generated without coordination. When you have a good random generator then 128 random bits are enough to practically guarantee uniqueness. At the same time, 128 bits are not too much, so UUIDs do not occupy too much space when stored. UUID versions There are multiple versions of UUIDs. Versions 1 - 5 are defined in and they are the most widely used. Versions 6 - 8 are currently in , and they might be approved in the future. Let's take a brief look at the different versions. RFC 4122 draft status Version 1 Version 1 is generated by using a MAC address and time as inputs. The MAC address is used to ensure uniqueness across multiple machines. The time is used to ensure uniqueness across multiple processes on the same machine. The usage of the MAC means that generated UUIDs can be tracked to a specific machine. This can be useful occasionally, but it might not be desirable in other cases, as a MAC address can be considered private information. Interestingly enough, the time portion is not based on the usual Unix epoch, but it uses a count of 100-nanosecond intervals since 00:000:00.00 on the 15th of October 1582. What is special about October 1582? It's the . See version 7 for a UUID with a standard Unix epoch. Gregorian calendar reform Version 2 Version 2 is similar to version 1, but adds a local domain ID to the UUID. It's not widely used. Versions 3 and 5 These versions use a hash function to generate the UUID. The hash function is seeded with a namespace UUID and a name. The namespace UUID is used to ensure uniqueness across multiple namespaces. The name is used to ensure uniqueness within a namespace. Version 3 uses MD5 as a hash function, while version 5 uses SHA-1. SHA-1 generates 160 bits so the digest is truncated to 128 bits. Version 4 Version 4 UUID is probably the most popular one. It relies solely on a random generator to generate UUIDs, it's similar to the coin flip example above. This means that the is critical. quality of the random generator Version 6 Version 6 is similar to Version 1, but it has a different ordering of bytes. It encodes the time from the most significant bits to the least significant bits. This allows sorting UUIDs correctly by time when you sort just bytes representing the UUIDs. Version 7 Version 7 uses a 48-bit timestamp and random data. Unlike versions 1, 2, or 6, it uses a standard Unix epoch in milliseconds. It also uses a random generator instead of a MAC address. Version 8 Version 8 is meant to be used for experimental and private use. Security considerations UUIDs are designed to be unique, but they are not designed to be secret. What's the difference? If you generate a UUID then you can assume it's different from any other UUID generated before or after, but you should not treat them as a password or a secret session identifier. This is what the says about this: RFC 4122 Do not assume that UUIDs are hard to guess; they should not be used as security capabilities (identifiers whose mere possession grants access), for example. A predictable random number source will exacerbate the situation. UUID in QuestDB UUIDs are popular synthetic IDs because they can be generated without any coordination and do not use too much space. QuestDB users often store UUIDs, but until recently, QuestDB did not have first-class support. Most users stored UUIDs in a string column. It makes sense because as we have seen above UUIDs have a canonical textual representation. Storing UUIDs in a string column is possible, but it's inefficient. Let's do some math: We already know each UUID has 128 bits, that's 16 bytes. The canonical textual representation of UUID has 36 characters. QuestDB uses UTF-16 encoding for strings, so each ASCII character uses 2 bytes. There is also a fixed cost of 4 bytes per string stored. So it takes 36 * 2 + 4 = 76 bytes to store a single UUID which contains just 16 bytes of information! It's not just wasting disk space. QuestDB must read these bytes when evaluating a SQL predicate, joining tables or calculating an aggregation. Thus storing UUIDs as strings also makes your queries slower! That's why QuestDB 6.7 implemented UUID as a first-class data type. This allows user applications to declare a column as UUID and then each UUID stored will use only 16 bytes. Thanks to this, SQL queries will be faster. Demo time The demo creates tables that, in total, occupy just under 100 GB of disk space. Make sure you have enough disk space available. You might also need to increase the query timeout via the query.timeout.sec property. See for more details. Alternatively, you can change the Configuration long_sequence() function to create a smaller number of rows. Let’s create a table with a single string column and populate it with 1 billion random UUIDs. The column is defined as the string type so the UUIDs will be stored as strings: CREATE TABLE tab_s (s string);
INSERT INTO tab_s SELECT rnd_uuid4() FROM long_sequence(1000000000); Let’s try to query it: SELECT * FROM tab_s WHERE s = 'ab632aba-be36-43e5-a4a0-4895e9cd3f0d'; It’s taking around 2.2s. It’s not terrible given it’s a full-table scan over one billion strings, but we can do better! How much better? Let’s see. Create a new table with a UUID column: CREATE TABLE tab_u (u uuid); Populate it with UUID values from the first table: INSERT INTO tab_u SELECT * FROM tab_s; The newly created table has the same values as the first table, but the column is defined as UUID instead of a string, so it eliminates the waste we discussed above. Let’s see how the predicate performs now: SELECT * FROM tab_u WHERE u = 'ab632aba-be36-43e5-a4a0-4895e9cd3f0d'; This query takes around 380ms on my test box. That’s almost 6x better than the original 2.2 seconds! Speed is the key to any real-time analysis so this is certainly important. Let’s check disk space. The command shows the space used by each table. First, the table with strings: du $ du -h
79G    ./default
79G    . The table with UUID: $ du -h
15G    ./default
15G    . Declaring the column as UUID saved 64GB of disk space! Using UUID optimizes query performance and is cost-effective. Last but not least, predicates on UUID values will become even faster in future QuestDB versions as we are looking at how to vectorize them by using the SIMD instructions! Conclusion We use UUIDs to generate globally unique IDs without any coordination. They are 128 bits long so they do not use too much space. This makes them a good fit for distributed applications, IoT, cryptocurrencies, or decentralized finance. When your application stores UUIDs, tell your database it’s a UUID, do not store them in a string column. You will save disk space and CPU cycles! Also published here.

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Understanding How UUIDs Save Time and Storage

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Jaromir Hamala

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Understanding How UUIDs Save Time and Storage

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Jaromir Hamala

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