GUID/UUID Performance Breakthrough
Brought to you by Rick James
GUIDs/UUIDs are very random.
Therefore, INSERTing into an index means jumping around a lot.
Once the index is too big to be cached, most INSERTs involve a disk hit.
Even on a beefy system, this limits you to a few hundred INSERTs per second.
MySQL's UUID function
This blog is mostly eliminated in MySQL 8.0 with the advent of the following functions:
Why it is a Problem
A 'standard' GUID/UUID is composed of the time, machine identification
and some other stuff. The combination should be unique, even
without coordination between different computers
that could be generating UUIDs simultaneously.
The top part of the GUID/UUID is the bottom part of the current time.
The top part is the primary part of what would be used for
placing the value in an ordered list (INDEX).
This cycles in about 7.16 minutes.
If the index is small enough to be cached in RAM, each insert into the index
is CPU only, with the writes being delayed and batched.
If the index is 20 times as big as can be cached, then 19 out of 20
inserts will be a cache miss. (This math applies to any "random" index. MD5 and SHA1 are other randomly distributed functions.)
36 characters is bulky. If you are using that as a PRIMARY KEY in InnoDB
and you have secondary keys, remember that each secondary key has an implicit
copy of the PK, thereby making it bulky.
It is tempting to declare the UUID VARCHAR(36).
And, since you probably are thinking globally, so you have CHARACTER SET utf8 (or utf8mb4).
⚈ 2 - Overhead for VAR
⚈ 36 - chars
⚈ 3 (or 4) bytes per character for utf8 (or utf8mb4)
So, max length = 2+3*36 = 110 (or 146) bytes.
For temp tables 108 (or 144) is actually used if a MEMORY table is used.
⚈ utf8 is unnecessary (ascii would do); but this is obviated by the next two steps
⚈ Toss dashes
Now it will fit in 16 bytes: BINARY(16)
Combining the Problems and Crafting a Solution
But first, a caveat. This solution only works for
"Time based" / "Version 1" UUIDs
They are recognizable by the "1" at the beginning of the third clump.
The manual's sample: 6ccd780c-baba-1026-9564-0040f4311e29 .
A more current value (after a few years): 49ea2de3-17a2-11e2-8346-001eecac3efa .
Notice how the 3rd part has slowly changed over time?
Let's data is rearranged, thus:
11e2-17ac-106762a5-8346-001eecac3efa -- after a few more minutes
Now we have a number that increases nicely over time.
Multiple sources won't be quite in time order, but they will be close.
The "hot" spot for inserting into an INDEX(uuid) will be rather narrow,
thereby making it quite cacheable and efficient.
If your SELECTs tend to be for "recent" uuids, then they, too, will be
If, on the other hand, your SELECTs often reach for old uuids, they will
be random and not well cached. Still, improving the INSERTs will help
the system overall.
Code to do it
Again, this code is obviated in MySQL 8.0 by
Let's make Stored Functions to do the messy work of the two actions:
⚈ Rearrange fields
⚈ Convert to/from BINARY(16)
CREATE FUNCTION UuidToBin(_uuid BINARY(36))
LANGUAGE SQL DETERMINISTIC CONTAINS SQL SQL SECURITY INVOKER
SUBSTR(_uuid, 15, 4),
SUBSTR(_uuid, 10, 4),
SUBSTR(_uuid, 1, 8),
SUBSTR(_uuid, 20, 4),
SUBSTR(_uuid, 25) ));
CREATE FUNCTION UuidFromBin(_bin BINARY(16))
LANGUAGE SQL DETERMINISTIC CONTAINS SQL SQL SECURITY INVOKER
HEX(SUBSTR(_bin, 5, 4)),
HEX(SUBSTR(_bin, 3, 2)),
HEX(SUBSTR(_bin, 1, 2)),
HEX(SUBSTR(_bin, 9, 2)),
Then you would do things like the following. (Applies to 8.0, but with function names changed to UUID_TO_BIN() and BIN_TO_UUID().)
-- Letting MySQL create the UUID:
INSERT INTO t (uuid, ...) VALUES (UuidToBin(UUID()), ...);
-- Creating the UUID elsewhere:
INSERT INTO t (uuid, ...) VALUES (UuidToBin(?), ...);
-- Retrieving (point query using uuid):
SELECT ... FROM t WHERE uuid = UuidToBin(?);
-- Retrieving (other):
SELECT UuidFromBin(uuid), ... FROM t ...;
Do not flip the WHERE; this will be inefficent because it won't use INDEX(uuid):
WHERE UuidFromBin(uuid) = '1026-baba-6ccd780c-9564-0040f4311e29' -- NO
InnoDB -- how/when indexes are updated
When adding a record to an InnoDB table, here are (roughly) the steps performed to write the data (and PK) and secondary indexes to disk. (I leave out logging, provision for rollback, etc.) First the PRIMARY KEY and data:
⚈ Check for UNIQUEness constraints
⚈ Fetch the BTree block (normally 16KB) that should contain the row (based on the PRIMARY KEY). Remember that the PK is unique and the data is stored in the BTree in PK order.
⚈ Insert the row, splitting the block if necessary. (Typically a block split occurs 1% of the time.)
⚈ Leave the page “dirty” in the buffer_pool, hoping that more rows are added before it is bumped out of cache (buffer_pool).. Note that for AUTO_INCREMENT and TIMESTAMP-based PKs, the “last” block in the data will be updated repeatedly before splitting; this delayed write adds greatly to the efficiency. OTOH, a UUID will be very random; when the table is big enough, the block will almost always be flushed before a second insert occurs in that block. <– This is the inefficiency in UUIDs.
UNIQUE secondary keys (in InnoDB) work essentially like the PK: fetch the block (possibly cached); check uniqueness (give error if dup); mark block dirty; eventually write to disk.
Non-unique secondary keys use the "Change Buffer":
⚈ Updates to non-unique indexes are stored in the Change Buffer (which is a part of the buffer_pool, defaults to 25%).
⚈ These are kept sorted, but the write to disk is delayed.
⚈ Eventually changes are batched together and poured into the actual index blocks.
⚈ Therefore, both reads and writes are delayed and somewhat cached.
⚈ Meanwhile, index lookups look both in the Change buffer and the actually index BTree; so, the CB is totally transparent.
⚈ If a crash or rollback occurs, the stuff delayed in the CB is magically reconstructed; so the indexes do not get corrupted.
But... If the index is big and random, the read/write caching may be minimal.
That is, the Change Buffer helps some, but only very little for 'huge' indexes or tables.
Changes to indexes for Update and Delete also go through the Change Buffer.
TokuDB is a viable engine if you must have UUIDs (even non-type-1) in a huge table.
TokuDB is available in MariaDB as a 'standard' engine, making the barrier to entry very low.
There are a small number of differences between InnoDB and TokuDB; I will not go into them here.
Tokudb, with its “fractal” indexing strategy builds the indexes in stages.
In contrast, InnoDB inserts index entries “immediately” — actually that indexing is buffered by most of the size of the buffer_pool. To elaborate…
Tokudb, work a bit like the Change Buffer but on disk; it does something like
⚈ Write data/index partially sorted records to disk before finding out exactly where it belongs.
⚈ In the background, combine these partially digested blocks. Repeat as needed.
⚈ Eventually move the info into the real table/indexes.
If you are familiar with how sort-merge works, consider the parallels to Tokudb. Each "sort" does some work of ordering things; each "merge" is quite efficient.
⚈ In the extreme (data/index much larger than buffer_pool), InnoDB must read-modify-write one 16KB disk block for each UUID entry.
⚈ Tokudb makes each I/O "count" by merging several UUIDs for each disk block. (Yeah, Toku rereads blocks, but it comes out ahead in the long run.)
⚈ Tokudb excels when the table is really big, which implies high ingestion rate.
This shows three thing for speeding up usage of GUIDs/UUIDs:
⚈ Shrink footprint (Smaller → more cacheable → less I/o → faster).
⚈ Rearrange uuid to make a "hot spot" to improve cachability.
⚈ Use TokuDB
Note that the benefit of the "hot spot" is only partial:
⚈ Chronologically ordered (or approximately ordered) INSERTs benefit; random ones don't.
⚈ SELECTs/UPDATEs by "recent" uuids benefit; old ones don't benefit.
With these new features in MySQL 8.0.0 (Sep. 2016; GA will be later), this blog is rendered mostly useless.
MySQL 8.0.0 improves the usability of UUID manipulations (WL#8920) by implementing three new SQL functions:
⚈ UUID_TO_BIN() - convert from UUID formatted text to VARBINARY(16), optionally shuffling the time parts to the top
⚈ BIN_TO_UUID() - convert from VARBINARY(16) to UUID formatted text, optionally unshuffling the time parts
⚈ IS_UUID() - check the validity of an UUID formatted text
The UUID stored as a VARBINARY(16) can be indexed using functional indexes.
To summarize, MySQL does not have special data types for IPv6 addresses or UUIDs but instead encourages the use of VARBINARY(16).
MySQL provides functions to convert from textual IPv6/UUID representations to and from the more compact VARBINARY(16) datatype. MySQL now offers
bit-wise operations on VARBINARY(16) datatype. IPv6/UUID functions combined with bit-wise operations can be used to test, extract, or combine
on parts (sub-structure) of the IPv6/UUID, i.e. defining a function over IPv6/UUID content. This function can be used to define the content
in a Virtual Generated Column which then can be indexed.
Thanks to Oracle for building in functions (in ver 8) similar to what has been described here.
Thanks to Trey for some of the ideas here.
The tips in this document apply to MySQL, MariaDB, and Percona.
Although the 8.0 functions have not yet made it into MariaDB or Percona,
the Stored Functions described here have similar functionality.
Detailed discussion of UUID indexing
Graphical display of the random nature of UUID on PRIMARY KEY
Benchmarks, etc, by Karthik Appigatla
More details on the clock
A StackExchange discussion
NHibernate can generate sequential GUIDs
but it seems to be backwards.
Written: Oct, 2012;
Added TokuDB: Jan, 2015;
MySQL 8.0: Sep, 2016;
Change Buffering: Sep. 2017
Contact me by posting a question at MySQL Forums :: Performance
-- Rick James
MySQL Documents by Rick James
HowTo Techniques for Optimizing Tough Tasks:
Partition Maintenance (DROP+REORG) for time series (includes list of PARTITION uses)
Big DELETEs - how to optimize -- and other chunking advice, plus a use for PARTITIONing
Chunking lengthy DELETE/UPDATE/etc.
Data Warehouse techniques:
High speed ingestion
Entity-Attribute-Value -- a common, poorly performing, design pattern (EAV); plus an alternative
Find the nearest 10 pizza parlors -- efficient searching on Latitude + Longitude (another PARITION use)
Lat/Long representation choices
Pagination, not with OFFSET, LIMIT
Techniques on efficiently finding a random row (On beyond ORDER BY RAND())
GUID/UUID Performance (type 1 only)
IP Range Table Performance -- or other disjoint ranges
Rollup Unique User Counts
Alter of a Huge table -- Mostly obviated by 5.6
Latest 10 news articles -- how to optimize the schema and code for such
Build and execute a "Pivot" SELECT (showing rows as columns)
Find largest row for each group ("groupwise max")
Other Tips, Tuning, Debugging, Optimizations, etc...
Rick's RoTs (Rules of Thumb -- lots of tips)
Memory Allocation (caching, etc)
Character Set and Collation problem solver
Trouble with UTF-8
utf8mb4 Collations on 8.0
Converting from MyISAM to InnoDB -- includes differences between them
Compound INDEXes plus other insights into the mysteries of INDEXing
Cookbook for Creating Indexes
Many-to-many mapping table
MySQL Limits -- built-in hard limits
767-byte INDEX limit
Galera, tips on converting to (Percona XtraDB Cluster, MariaDB 10, or manually installed)
5.7's Query Rewrite -- perhaps 5.7's best perf gain, at least for this forum's users
Request for tuning / slowlog info
Best of MySQL Forum -- index of lots of tips, discussions, etc
Analyze MySQL Performance
Analyze VARIABLEs and GLOBAL STATUS
My slides from conferences
Percona Live 4/2017 - Rick's RoTs (Rules of Thumb) - MySQL/MariaDB
Percona Live 4/2017 - Index Cookbook - MySQL/MariaDB
Percona Live 9/2015 - PARTITIONing - MySQL/MariaDB
(older ones upon request)
Contact me via LinkedIn; be sure to include a brief teaser in the Invite request: