sno
Compact, sortable and fast unique IDs with embedded metadata
About sno
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<img src="./.github/logo_200x200.png" alt="sno logo" title="sno" align="left" height="200" />
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.
[](https://pkg.go.dev/github.com/muyo/sno?tab=doc)
[](https://github.com/muyo/sno/releases)
[](https://travis-ci.com/muyo/sno)
[](https://codecov.io/gh/muyo/sno)
[](https://goreportcard.com/report/github.com/muyo/sno)
[](https://raw.githubusercontent.com/muyo/sno/master/LICENSE)
```bash
go get -u github.com/muyo/sno
```
### Features
- **Compact** - **10 bytes** in its binary representation, canonically [encoded](#encoding) as **16 characters**.
<br />URL-safe and non-ambiguous encoding which also happens to be at the binary length of UUIDs -
**sno**s can be stored as UUIDs in your database of choice.
- **K-sortable** in either representation.
- **[Embedded timestamp](#time-and-sequence)** with a **4msec resolution**, bounded within the years **2010 - 2079**.
<br />Handles clock drifts gracefully, without waiting.
- **[Embedded byte](#metabyte)** for arbitrary data.
- **[Simple data layout](#layout)** - straightforward to inspect or encode/decode.
- **[Optional and flexible](#usage)** configuration and coordination.
- **[Fast](./benchmark#results)**, wait-free, safe for concurrent use.
<br />Clocks in at about 500 LoC, has no external dependencies and minimal dependencies on std.
- A pool of **≥ 16,384,000** IDs per second.
<br /> 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.
### Non-features / cons
- True randomness. **sno**s 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](#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).
<br />
## Usage (➜ [API](https://pkg.go.dev/github.com/muyo/sno?tab=doc))
**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:
```go
id := sno.New(0)
```
Where `0` is the ➜ [Metabyte](#metabyte).<br />
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](https://pkg.go.dev/github.com/muyo/sno?tab=doc#Partition))
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.
```go
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](#sequence-sharding)).
### Snapshots (➜ [doc](https://pkg.go.dev/github.com/muyo/sno?tab=doc#GeneratorSnapshot))
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:
```go
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.
<br />
## Layout
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](#time-and-sequence)).
<br />
## Time and sequence
### Time
**sno**s 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.
### Epoch
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.
### Precision
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.
### Sequence
**sno**s 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).
#### Sequence sharding
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.
<details>
<summary>Star Trek: Voyager mode, <b>How to shard sequences</b></summary>
<p>
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.
```go
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...
```go
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
}
```
</p>
</details>
#### Sequence overflow
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](#benchmarks)
which explains the numbers involved.
<details>
<summary>Star Trek: Voyager mode, <b>Behaviour on sequence overflow</b></summary>
<p>
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.
```go
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.
</p>
</details>
#### Clock drift and the tick-tock toggle
Just like all other specs that rely on clock times to resolve ambiguity, **sno**s 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](https://developers.google.com/time/smear). Despite the original Snowflake spec
suggesting otherwise, using NTP in slew mode (to avoid regressions entirely)
[is not always a good idea](https://www.redhat.com/en/blog/avoiding-clock-drift-vms).
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.
<details>
<summary>Star Trek: Voyager mode, <b>How tick-tocking works</b></summary>
<p>
**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?
##### How others do it
- [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.
##### Tick-tocking obviously affects the sort order as it changes the timestamp
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.
Frequently Asked Questions
What is sno?
sno is a UUID library for the Go programming language. Compact, sortable and fast unique IDs with embedded metadata
How do I install sno?
Install sno with the Go module system using `go get muyo/sno`. Check the repository for the current installation instructions.
What category does sno belong to?
sno is listed under UUID, specifically UUID.