wiki:DatabaseUuidEfficiency

Efficiency, UUIDs and SQLite

A UUID can be represented in at least three ways: as a 32 or 36 character string using printable ASCII, a 16 byte string (with each byte in the range 0x00 - 0xff, making the string non-printable) or a 16 byte integer.

We store our UUIDs in the most readable format which is also the fattest -- as a 36 character string like e8d1860e-0a09-41b89357-c3024e8394b2. (It's the same UUID with or without the four dashes which is why they can be represented as 32 or 36 character strings.)

This document examines the pros and cons of storing UUIDs this way and examines alternatives.

If you like, you can jump straight to the conclusion.

Advantages and Disadvantages of the Current Scheme

The advantages of this scheme are simple. The UUID strings are recognizable to some as UUIDs (to some) and easy to read for everyone. They also match the strings we write to XML files.

The disadvantage is mainly that of space. It's my understanding that SQLite is generally very good at not wasting space. Nevertheless, a 36 character string is going to take at least 36 bytes to store.

However, for almost all of the tables in the current database, the difference hardly matters. Most tables that contain UUIDs will contain at most maybe 2000 rows (and often more like 200). 2000 * 36 = 72000, and 72000/1024 = 70 kilobytes of data. In other words, peanuts.

However, the simulations and experiment_dims tables are likely to be large (tens of thousands of rows or more) and they contain one UUID each. That means there's 36 UUID bytes per row. Assuming one million rows, that's 34 megabytes of UUIDs.

In terms of available disk space on the average computer, that's still peanuts. But in terms of the amount of data that SQLite needs to read from disk during a query, it's bad. The operating system reads from the disk in blocks (4k is a common size) and the more rows that can fit in a block, the more likely it is that a row will be cached in memory when SQLite requests it from disk. Bigger rows mean fewer rows per block and fewer rows cached in memory.

It would be nice to shrink our UUID storage. Is that possible?

Shrinking UUIDs - INTEGERs and REALs

As mentioned above, a 36 character UUID can also be represented as a 128 bit (16 byte) integer. Unfortunately, SQLite's INT type is 64 bit (8 bytes) and it offers no facility for storing larger ints.

SQLite has a REAL type which is a 64 bit/8 byte IEEE float. It's possible to convert a 16 byte integer into an 8 byte float, but the number doesn't survive the round trip due to floating point noise. Here's an example from a Python interpreter session:

>>> import uuid
>>> my_uuid = uuid.uuid4()
>>> my_uuid
UUID('379cdd33-f231-4122-b78d-7481557f13c1')
>>> my_uuid.int
73922024606215418702568719629565236161L
>>> # It's possible to recreate my_uuid from this int value
... 
>>> my_uuid == uuid.UUID(int=73922024606215418702568719629565236161L)
True
>>> # But I can't do so if the value passes through float() on the way
... 
>>> my_uuid == uuid.UUID(int=long(float(my_uuid.int)))
False
>>> # ...and we can see why...
... 
>>> uuid.UUID(int=long(float(my_uuid.int)))
UUID('379cdd33-f231-4200-0000-000000000000')

Shrinking UUIDs - Double INTEGERs

A 128 bit integer can be represented as two 64 bit integers. This would require two columns to represent each UUID, however. That could possibly be made transparent on some level with clever use of VIEWs and possibly custom functions, but this is too complicated for us.

Shrinking UUIDs - BLOBs

A SQLite BLOB can store arbitrary data and can be indexed. By use of the (undocumented!) sqlite.Binary() call, one can convert a UUID into a BLOB. This works, but see the "Real World Testing" section below.

Shrinking UUIDs - Shadow ids

Remember that the problem isn't so much the UUIDs on the unique objects themselves, it's the table(s) that point to these objects. Instead of trying to shrink UUIDs, an alternative is to give each object a second, more compact unique identifier. A traditional AUTOINCREMENT INTEGER is an obvious choice. This id would be randomly assigned by the database and would have nothing to do with the UUID other than occupying the same row. In other words, it would shadow the UUID, following it around but always subordinate to it.

Integers are very compact in SQLite. They use 1 to 9 bytes; as little space as necessary is allocated to represent the number. Most of the ids we would use would fit into 2-4 bytes which is a lot smaller than 36.

Another way to implement shadow ids is to create a table that maps UUIDs to integer ids and vice versa. The UUID would still be the "public" id of the object, but it wouldn't appear in any tables except the mapper table. Internally, the tables would use the integer ids. This is a bit nicer than embedding the shadow ids directly in the tables alongside the UUID since this solution doesn't violate normalization.

It would be possible to implement shadow ids for some tables and not others.

In any case, using shadow ids would require rewriting Vespa' SQL for all of the affected tables. The rewrite wouldn't be extensive, but it would touch most queries.

Real World Testing

I compared our current UUID representation (36 byte strings) to a 16 byte BLOB representation.

I generated one million (1m) UUIDs and wrote them to a text file. Next, I created two SQLite databases, each with just one table with one primary key column called id. The only difference is that in one database, id was CHAR and in the other it was BLOB. I then inserted the 1m UUIDs into each database.

The text database was 94M while the BLOB database was 51M. This averages out to about 98 bytes/row for the text representation versus 53 bytes/row for the BLOB representation. It's a significant difference.

I tested to see how this would affect select times. I read the 1m UUIDs from the text file, converted them to UUID objects, and then used Python's random.choice() to select a UUID and select it from the database. I repeated that loop 100,000 times.

Results were inconclusive. According to Python's profiler (cProfile), selecting from the BLOB database was about 8% faster. However, that's if we only measure the execute() method.

The BLOB method was slower if we consider the time it takes to convert the BLOB into the UUID string that we actually need. How much slower depends on how the code is structured. When using text UUIDs, we never need to convert them to or from strings. When using BLOBs, given a variable uuid that contains a string, we have to call sqlite.Binary(uuid_module.UUID(uuid).bytes) to convert a UUID for use in a query and str(uuid_module.UUID(bytes=result)) to convert the BLOB result returned by the database to a UUID string. Those calls chew up a significant amount of time.

I don't know how this would play out in the real world. Since the select improvement when using BLOBs is < 10% and the increase is cancelled out by time spent in the uuid module, I don't think the real world change would be more then +/- 5%.

The sparsely-commented code that I used to run these tests is attached as test.py.

Conclusion

Storing a more compact UUID representation in SQLite via Python might be possible, but it isn't easy. More to the point, it's not worth the trouble for us.

If the size of UUIDs becomes too painful at some point, the best solution is probably using some variant of the shadow ids described above.

Last modified 8 years ago Last modified on Jun 16, 2011, 9:45:09 AM

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