A spec for unique IDs in distributed systems based on the Snowflake design, i.e. a coordination-based ID variant. It aims to be friendly to both machines and humans, compact, versatile and fast.
This repository contains a Go package for generating such IDs.
go get -u github.com/muyo/sno- Compact - 10 bytes in its binary representation, canonically encoded as 16 characters.
URL-safe and non-ambiguous encoding which also happens to be at the binary length of UUIDs - snos can be stored as UUIDs in your database of choice. - K-sortable in either representation.
- Embedded timestamp with a 4msec resolution, bounded within the years 2010 - 2079.
Handles clock drifts gracefully, without waiting. - Embedded byte for arbitrary data.
- Simple data layout - straightforward to inspect or encode/decode.
- Optional and flexible configuration and coordination.
- Fast, wait-free, safe for concurrent use.
Clocks in at about 500 LoC, has no external dependencies and minimal dependencies on std. - A pool of ≥ 16,384,000 IDs per second.
65,536 guaranteed unique IDs per 4msec per partition (65,536 combinations) per metabyte (256 combinations) per tick-tock (1 bit adjustment for clock drifts). 549,755,813,888,000 is the global pool per second when all components are taken into account.
- True randomness. snos embed a counter and have no entropy. They are not suitable in a context where unpredictability of IDs is a must. They still, however, meet the common requirement of keeping internal counts (e.g. total number of entitites) unguessable and appear obfuscated;
- Time precision. While good enough for many use cases, not quite there for others. The ➜ Metabyte can be used to get around this limitation, however.
- It's 10 bytes, not 8. This is suboptimal as far as memory alignment is considered (platform dependent).
Usage (➜ API)
sno comes with a package-level generator on top of letting you configure your own generators.
Generating a new ID using the defaults takes no more than importing the package and:
id := sno.New(0)Where 0 is the ➜ Metabyte.
The global generator is immutable and private. It's therefore also not possible to restore it using a Snapshot. Its Partition is based on time and changes across restarts.
Partitions (➜ doc)
As soon as you run more than 1 generator, you should start coordinating the creation of Generators to actually guarantee a collision-free ride. This applies to all specs of the Snowflake variant.
Partitions are one of several friends you have to get you those guarantees. A Partition is 2 bytes. What they mean and how you define them is up to you.
generator, err := sno.NewGenerator(&sno.GeneratorSnapshot{
Partition: sno.Partition{'A', 10}
}, nil)Multiple generators can share a partition by dividing the sequence pool between them (➜ Sequence sharding).
Snapshots (➜ doc)
Snapshots happen to serve both as configuration and a means of saving and restoring generator data. They are
optional - simply pass nil to NewGenerator(), to get a Generator with sane defaults and a unique (in-process)
Partition.
Snapshots can be taken at runtime:
s := generator.Snapshot()This exposes most of a Generator's internal bookkeeping data. In an ideal world where programmers are not lazy until their system runs into an edge case - you'd persist that snapshot across restarts and restore generators instead of just creating them from scratch each time. This will keep you safe both if a large clock drift happens during the restart -- or before, and you just happen to come back online again "in the past", relative to IDs that had already been generated.
A snapshot is a sample in time - it will very quickly get stale. Only take snapshots meant for restoring them later when generators are already offline - or for metrics purposes when online.
A sno is simply 80-bits comprised of two 40-bit blocks: the timestamp and the payload. The bytes are
stored in big-endian order in all representations to retain their sortable property.
Both blocks can be inspected and mutated independently in either representation. Bits of the components in the binary
representation don't spill over into other bytes which means no additional bit twiddling voodoo is necessary* to extract
them.
*The tick-tock bit in the timestamp is the only exception (➜ Time and sequence).
snos embed a timestamp comprised of 39 bits with the epoch milliseconds at a 4msec resolution (floored, unsigned) and one bit, the LSB of the entire block - for the tick-tock toggle.
The epoch is custom and constant. It is bounded within 2010-01-01 00:00:00 UTC and
2079-09-07 15:47:35.548 UTC. The lower bound is 1262304000 seconds relative to Unix.
If you really have to break out of the epoch - or want to store higher precision - the metabyte is your friend.
Higher precision is not necessarily a good thing. Think in dataset and sorting terms, or in sampling rates. You
want to grab all requests with an error code of 403 in a given second, where the code may be encoded in the metabyte.
At a resolution of 1 second, you binary search for just one index and then proceed straight up linearly.
That's simple enough.
At a resolution of 1msec however, you now need to find the corresponding 1000 potential starting offsets because
your 403 requests are interleaved with the 200 requests (potentially). At 4msec, this is 250 steps.
Everything has tradeoffs. This was a compromise between precision, size, simple data layout -- and factors like that above.
snos embed a sequence (2 bytes) that is relative to time. It does not overflow and resets on each new time unit (4msec). A higher sequence within a given timeframe does not necessarily indicate order of creation. It is not advertised as monotonic because its monotonicity is dependent on usage. A single generator writing to a single partition, ceteris paribus, will result in monotonic increments and will represent order of creation.
With multiple writers in the same partition, increment order is undefined. If the generator moves back in time, the order will still be monotonic but sorted either 2msec after or before IDs previously already written at that time (see tick-tock).
The sequence pool has a range of [0..65535] (inclusive). sno supports partition sharing out of the box
by further sharding the sequence - that is multiple writers (generators) in the same partition.
This is done by dividing the pool between all writers, via user-specified bounds.
A generator will reset to its lower bound on each new time unit - and will never overflow its upper bound. Collisions are therefore guaranteed impossible unless misconfigured and they overlap with another currently online generator.
Star Trek: Voyager mode, How to shard sequences
This can be useful when multiple containers on one physical machine are to write as a cluster to a partition defined by the machine's ID (or simpler - multiple processes on one host). Or if multiple remote services across the globe were to do that.
var PeoplePartition = sno.Partition{'P', 0}
// In process/container/remote host #1
generator1, err := sno.NewGenerator(&sno.GeneratorSnapshot{
Partition: PeoplePartition,
SequenceMin: 0,
SequenceMax: 32767 // 32768 - 1
}, nil)
// In process/container/remote host #2
generator2, err := sno.NewGenerator(&sno.GeneratorSnapshot{
Partition: PeoplePartition,
SequenceMin: 32768,
SequenceMax: 65535 // 65536 - 1
}, nil)You will notice that we have simply divided our total pool of 65,536 into 2 even and non-overlapping
sectors. In the first snapshot SequenceMin could be omitted - and SequenceMax in the second, as those are the
defaults used when they are not defined. You will get an error when trying to set limits above the capacity of
generators, but since the library is oblivious to your setup - it cannot warn you about overlaps and cannot
resize on its own either.
The pools can be defined arbitrarily - as long as you make sure they don't overlap across currently online generators.
It is safe for a range previously used by another generator to be assigned to a different generator under the following conditions:
- it happens in a different timeframe in the future, i.e. no sooner than after 4msec have passed (no orchestrator is fast enough to get a new container online to replace a dead one for this to be a worry);
- if you can guarantee the new Generator won't regress into a time the previous Generator was running in.
If you create the new Generator using a Snapshot of the former as it went offline, you do not need to worry about those conditions and can resume writing to the same range immediately - the obvious tradeoff being the need to coordinate the exchange of Snapshots.
If your clusters are always fixed size - reserving ranges is straightforward. With dynamic sizes, a potential simple scheme is to reserve the lower byte of the partition for scaling. Divide your sequence pool by, say, 8, keep assigning higher ranges until you hit your divider. When you do, increment partition by 1, start assigning ranges from scratch. This gave us 2048 identifiable origins by using just one byte of the partition.
That said, the partition pool available is large enough that the likelihood you'll ever need this is slim to none. Suffice to know you can if you want to.
Besides for guaranteeing a collision-free ride, this approach can also be used to attach more semantic meaning to partitions themselves, them being placed higher in the sort order. In other words - with it, the origin of an ID can be determined by inspecting the sequence alone, which frees up the partition for another meaning.
How about...
var requestIDGenerator, _ = sno.NewGenerator(&GeneratorSnapshot{
SequenceMax: 32767,
}, nil)
type Service byte
type Call byte
const (
UsersSvc Service = 1
UserList Call = 1
UserCreate Call = 2
UserDelete Call = 3
)
func genRequestID(svc Service, methodID Call) sno.ID {
id := requestIDGenerator.New(byte(svc))
// Overwrites the upper byte of the fixed partition.
// In our case - we didn't define it but gave a non-nil snapshot, so it is {0, 0}.
id[6] = byte(methodID)
return id
}Remember that limiting the sequence pool also limits max throughput of each generator. For an explanation on what happens when you're running at or over capacity, see the details below or take a look at ➜ Benchmarks which explains the numbers involved.
Star Trek: Voyager mode, Behaviour on sequence overflow
The sequence never overflows and the generator is designed with a single-return New() method that does not return
errors nor invalid IDs. Realistically the default generator will never overflow simply because you won't saturate
the capacity.
But since you can set bounds yourself, the capacity could shrink to 4 per 4msec (smallest allowed).
Now that's more likely. So when you start overflowing, the generator will stall and pray for a
reduction in throughput sometime in the near future.
From sno's persective requesting more IDs than it can safely give you immediately is not an error - but it may require correcting on your end. And you should know about that. Therefore, if you want to know when it happens - simply give sno a channel along with its configuration snapshot.
When a thread requests an ID and gets stalled, once per time unit, you will get a SequenceOverflowNotification
on that channel.
type SequenceOverflowNotification struct {
Now time.Time // Time of tick.
Count uint32 // Number of currently overflowing generation calls.
Ticks uint32 // For how many ticks in total we've already been dealing with the *current* overflow.
}Keep track of the counter. If it keeps increasing, you're no longer bursting - you're simply over capacity
and eventually need to slow down or you'll eventually starve your system. The Ticks count lets you estimate
how long the generator has already been overflowing without keeping track of time yourself. A tick is roughly 1ms.
The order of generation when stalling occurs is undefined. It is not a FIFO queue, it's a race. Previously stalled
goroutines get woken up alongside inflight goroutines which have not yet been stalled, where the order of the former is
handled by the runtime. A livelock is therefore possible if demand doesn't decrease. This behaviour may change and
inflight goroutines may get thrown onto the stalling wait list if one is up and running, but this requires careful
inspection. And since this is considered an unrealistic scenario which can be avoided with simple configuration,
it's not a priority.
Just like all other specs that rely on clock times to resolve ambiguity, snos are prone to clock drifts. But unlike all those others specs, sno adjusts itself to the new time - instead of waiting (blocking), it tick-tocks.
The tl;dr applying to any system, really: ensure your deployments use properly synchronized system clocks (via NTP) to mitigate the size of drifts. Ideally, use a NTP server pool that applies a gradual smear for leap seconds. Despite the original Snowflake spec suggesting otherwise, using NTP in slew mode (to avoid regressions entirely) is not always a good idea.
Also remember that containers tend to get paused meaning their clocks are paused with them.
As far as sno, collisions and performance are concerned, in typical scenarios you can enjoy a wait-free ride
without requiring slew mode nor having to worry about even large drifts.
Star Trek: Voyager mode, How tick-tocking works
sno attempts to eliminate the issue entirely - both despite and because of its small pool of bits to work with.
The approach it takes is simple - each generator keeps track of the highest wall clock time it got from the OS*, each time it generates a new timestamp. If we get a time that is lower than the one we recorded, i.e. the clock drifted backwards and we'd risk generating colliding IDs, we toggle a bit - stored from here on out in each sno generated until the next regression. Rinse, repeat - tick, tock.
(*IDs created with a user-defined time are exempt from this mechanism as their time is arbitrary. The means to bring your own time are provided to make porting old IDs simpler and is assumed to be done before an ID scheme goes online)
In practice this means that we switch back and forth between two alternating timelines. Remember how the pool we've got is 16,384,000 IDs per second? When we tick or tock, we simply jump between two pools with the same capacity.
Why not simply use that bit to store a higher resolution time fraction? True, we'd get twice the pool which seemingly boils down to the same - except it doesn't. That is due to how the sequence increases. Even if you had a throughput of merely 1 ID per hour, while the chance would be astronomically low - if the clock drifted back that whole hour, you could get a collision. The higher your throughput, the bigger the probability. ID's of the Snowflake variant, sno being one of them, are about guarantees - not probabilities. So this is a sno-go.
(I will show myself out...)
The simplistic approach of tick-tocking entirely eliminates that collision chance - but with a rigorous assumption: regressions happen at most once into a specific period, i.e. from the highest recorded time into the past and never back into that particular timeframe (let alone even further into the past).
This generally is exactly the case but oddities as far as time synchronization, bad clocks and NTP client behaviour goes do happen. And in distributed systems, every edge case that can happen - will happen. What do?
- Sonyflake goes to sleep until back at the wall clock time it was already at previously. All goroutines attempting to generate are blocked.
- snowflake hammers the OS with syscalls to get the current time until back at the time it was already at previously. All goroutines attempting to generate are blocked.
- xid goes ¯\(ツ)/¯ and does not tackle drifts at all.
- Entropy-based specs (like UUID or KSUID) don't really need to care as they are generally not prone, even to extreme drifts - you run with a risk all the time.
The approach one library took was to keep generating, but timestamp all IDs with the highest time recorded instead. This worked, because it had a large entropy pool to work with, for one (so a potential large spike in IDs generated in the same timeframe wasn't much of a consideration). sno has none. But more importantly - it disagrees on the reasoning about time and clocks. If we moved backwards, it means that an adjustment happened and we are now closer to the correct time from the perspective of a wider system.
sno therefore keeps generating without waiting, using the time as reported by the system - in the "past" so to speak, but with the tick-tock bit toggled.
If another regression happens, into that timeframe or even further back, only then do we tell all contenders to wait. We get a wait-free fast path most of the time - and safety if things go southways.
Even though the toggle is not part of the milliseconds, you can think of it as if it were. Toggling is then like moving two milliseconds back and forth, but since our milliseconds are floored to increments of 4msec, we never hit the range of a previous timeframe. Alternating timelines are as such sorted as if they were 2msec apart from each other, but as far as the actual stored time is considered - they are timestamped at exactly the same millisecond.
They won't sort in an interleaved fashion, but will be right next to the other timeline. Technically they were created at a different time, so being able to make that distinction is considered a plus by the author.
The metabyte is unique to sno across the specs the author researched, but the concept of embedding metadata in IDs is an ancient one. It's effectively just a byte-of-whatever-you-want-it-to-be - but perhaps 8-bits-of-whatever-you-want-them-to-be does a better job of explaining its versatility.
sno is agnostic as to what that byte represents and it is optional. None of the properties of snos
get violated if you simply pass a 0.
However, if you can't find use for it, then you may be better served using a different ID spec/library altogether (➜ Alternatives). You'd be wasting a byte that could give you benefits elsewhere.
Many databases, especially embedded ones, are extremely efficient when all you need is the keys - not all the data all those keys represent. None of the Snowflake-like specs would provide a means to do that without excessive overrides (or too small a pool to work with), essentially a different format altogether, and so - sno.
And simple constants tend to do the trick.
Untyped integers can pass as uint8 (i.e. byte) in Go, so the following would work and keep things tidy:
const (
PersonType = iota
OtherType
)
type Person struct {
ID sno.ID
Name string
}
person := Person{
ID: sno.New(PersonType),
Name: "A Special Snöflinga",
}Information that describes something has the nice property of also helping to identify something across a sea of possibilities. It's a natural fit.
Do everyone a favor, though, and don't embed confidential information. It will stop being confidential and
become public knowledge the moment you do that. Let's stick to nice property, avoiding PEBKAC.
The metabyte follows the timestamp. This clusters IDs by the timestamp and then by the metabyte (for example - the type of the entity), before the fixed partition.
If you were to use machine-ID based partitions across a cluster generating, say, Person entities, where Person
corresponds to a metabyte of 1 - this has the neat property of grouping all People generated across the entirety
of your system in the given timeframe in a sortable manner. In database terms, you could think of the metabyte as
identifying a table that is sharded across many partitions - or as part of a compound key. But that's just one of
many ways it can be utilized.
Placement at the beginning of the second block allows the metabyte to potentially both extend the timestamp block or provide additional semantics to the payload block. Even if you always leave it empty, sort order nor sort/insert performance won't be hampered.
A single byte is plenty.
Here's a few ideas for things you did not know you wanted, yet.
-
IDs for requests in a HTTP context: 1 byte is enough to contain one of all possible standard HTTP status codes. Et voila, you now got all requests that resulted in an error nicely sorted and clustered.
Limit yourself to the non-exotic status codes and you can store the HTTP verb along with the status code. In that single byte. Suddenly even the partition (if it's tied to a machine/cluster) gains relevant semantics, as you've gained a timeseries of requests that started fail-cascading in the cluster. Constrain yourself even further to just one bit forOKorERRORand you made room to also store information about the operation that was requested (think resource endpoint). -
How about storing a (immutable) bitmask along with the ID? Save some 7 bytes of bools by doing so and have the flags readily available during an efficient sequential key traversal using your storage engine of choice.
-
Want to version-control a
Message? Limit yourself to at most 256 versions and it becomes trivial. Take the ID of the last version created, increment its metabyte - and that's it. What you now have is effectively a simplistic versioning schema, where the IDs of all possible versions can be inferred without lookups, joins, indices and whatnot. And many databases will just store them close to each other. Locality is a thing.
How? The only part that changed was the metabyte. All other components remained the same, but we ended up with a new ID pointing to the most recent version. Admittedly the timestamp lost its default semantics of moment of creation and instead is moment of creation of first version, but you'd store arevisedAttimestamp anyways, wouldn't you?
And if you really wanted to support more versions - the IDs have certain properties that can be (ab)used for this. Increment this, decrement that... -
Sometimes a single byte is all the data that you actually need to store, along with the time when something happened. Batch processing succeeded?
sno.New(0), done. Failed?sno.New(1), done. You now have a uniquely identifiable event, know when and where it happened, what the outcome was - and you still had 7 spare bits (for higher precision time, maybe?) -
Polymorphism has already been covered. Consider not just data storage, but also things like (un)marshaling polymorphic types efficiently. Take a JSON of
{id: "aaaaaaaa55aaaaaa", foo: "bar", baz: "bar"}. The 8-th and 9-th (0-indexed) characters of the ID contain the encoded bits of the metabyte. Decode that (use one of the utilities provided by the library) and you now know what internal type the data should unmarshal to without first unmarshaling into an intermediary structure (nor rolling out a custom decoder for this type). There are many approaches to tackle this - an ID just happens to lend itself naturally to solve it and is easily portable. -
2 bytes for partitions not enough for your needs? Use a fixed byte as the metabyte -- you have extended the fixed partition to 3 bytes. Wrap a generator with a custom one to apply that metabyte for you each time you use it. The metabyte is, after all, part of the partition. It's just separated out for semantic purposes but its actual semantics are left to you.
The encoding is a custom base32 variant stemming from base32hex. Let's not call it sno32.
A canonically encoded sno is a regexp of [2-9a-x]{16}.
The following alphabet is used:
23456789abcdefghijklmnopqrstuvwx
This is 2 contiguous ASCII ranges: 50..57 (digits) and 97..120 (strictly lowercase letters).
On amd64 encoding/decoding is vectorized and extremely fast.
| Name | Binary (bytes) | Encoded (chars)* | Sortable | Random** | Metadata | nsec/ID |
|---|---|---|---|---|---|---|
| UUID | 16 | 36 |  |
 |
|
≥36.3 |
| KSUID | 20 | 27 | |
|
|
206.0 |
| ULID | 16 | 26 | |
|
|
≥50.3 |
| Sandflake | 16 | 26 | |
 |
|
224.0 |
| cuid | |
25 | |
|
|
342.0 |
| xid | 12 | 20 | |
|
|
19.4 |
| sno | 10 | 16 | |
|
|
8.8 |
| Snowflake | 8 | ≤20 | |
|
|
28.9 |
* Using canonical encoding.
** When used with a proper CSPRNG. The more important aspect is the distinction between entropy-based and
coordination-based IDs. Sandflake and cuid do contain entropy, but not sufficient to rely on entropy
alone to avoid collisions (3 bytes and 4 bytes respectively).
For performance results see ➜ Benchmark. ≥ values given for libraries which provide more
than one variant, whereas the fastest one is listed.
sno is both based on and inspired by xid - more so than by the original Snowflake - but the changes it introduces are unfortunately incompatible with xid's spec.
- Original Snowflake implementation and related post
- Mongo ObjectIds
- Instagram: Sharding & IDs at Instagram
- Flickr: Ticket Servers: Distributed Unique Primary Keys on the Cheap
- Segment: A brief history of the UUID - about KSUID and the shortcomings of UUIDs.
- Farfetch: Unique integer generation in distributed systems - uint32 utilizing Cassandra to coordinate.
Also potentially of interest:
- Lamport timestamps (vector/logical clocks)
- The Bloom Clock by Lum Ramabaja