ferroid

ferroid is a Rust crate for generating and parsing Snowflake and ULID identifiers.

Features

• 📌 Bit-level compatibility with major Snowflake and ULID formats
• 🧩 Pluggable clocks and RNGs via TimeSource and RandSource
• 🧵 Lock-free, lock-based, and single-threaded generators
• 📐 Custom layouts via define_snowflake_id! and define_ulid! macros
• 🔢 Crockford base32 support with base32 feature flag

📦 Supported Layouts

Snowflake

Ulid

🔧 Generator Comparison

🚀 Usage

Generate an ID

Synchronous

Calling next_id() may yield Pending if the current sequence is exhausted. In that case, you can spin, yield, or sleep depending on your environment:

#[cfg(feature = "snowflake")]
{
    use ferroid::{MonotonicClock, TWITTER_EPOCH, BasicSnowflakeGenerator, SnowflakeTwitterId, IdGenStatus};

    let clock = MonotonicClock::with_epoch(TWITTER_EPOCH);
    let generator = BasicSnowflakeGenerator::new(0, clock);

    let id: SnowflakeTwitterId = loop {
        match generator.next_id() {
            IdGenStatus::Ready { id } => break id,
            IdGenStatus::Pending { yield_for } => {
                println!("Exhausted; wait for: {}ms", yield_for);
                core::hint::spin_loop(); // Blocking spin: burns CPU, but yields the lowest latency.
                // std::thread::yield_now(); // Optional: yields to OS, still busy-waits.
                // std::thread::sleep(Duration::from_millis(yield_for.to_u64().unwrap())); // Lowest CPU use, but imprecise and may oversleep.
                //
                // For non-blocking ID generation, use the async API (see below).
            }
        }
    };
}

#[cfg(feature = "ulid")]
{
    use ferroid::{MonotonicClock, IdGenStatus, TWITTER_EPOCH, ThreadRandom, BasicUlidGenerator, ULID};

    let clock = MonotonicClock::with_epoch(TWITTER_EPOCH);
    let rand = ThreadRandom::default();
    let generator = BasicUlidGenerator::new(clock, rand);

    let id: ULID = loop {
        match generator.next_id() {
            IdGenStatus::Ready { id } => break id,
            IdGenStatus::Pending { yield_for } => {
                println!("Exhausted; wait for: {}ms", yield_for);
                core::hint::spin_loop(); // Blocking spin: burns CPU, but yields the lowest latency.
                // std::thread::yield_now(); // Optional: yields to OS, still busy-waits.
                // std::thread::sleep(Duration::from_millis(yield_for.to_u64().unwrap())); // Lowest CPU use, but imprecise and may oversleep.
                //
                // For non-blocking ID generation, use the async API (see below).
            }
        }
    };

    println!("Generated ID: {}", id);
}

Asynchronous

If you're in an async context (e.g., using Tokio or Smol), you can enable one of the following features:

• async-tokio
• async-smol

#[cfg(feature = "async-tokio")]
{
    use ferroid::{Result, MonotonicClock, MASTODON_EPOCH};

    #[tokio::main]
    async fn main() -> Result<()> {
        #[cfg(feature = "snowflake")]
        {
            use ferroid::{
                AtomicSnowflakeGenerator, SnowflakeMastodonId,
                SnowflakeGeneratorAsyncTokioExt
            };

            let clock = MonotonicClock::with_epoch(MASTODON_EPOCH);
            let generator = AtomicSnowflakeGenerator::new(0, clock);

            let id: SnowflakeMastodonId = generator.try_next_id_async().await?;
            println!("Generated ID: {}", id);
        }

        #[cfg(feature = "ulid")]
        {
            use ferroid::{ThreadRandom, UlidGeneratorAsyncTokioExt, BasicUlidGenerator, ULID};

            let clock = MonotonicClock::with_epoch(MASTODON_EPOCH);
            let rand = ThreadRandom::default();
            let generator = BasicUlidGenerator::new(clock, rand);

            let id: ULID = generator.try_next_id_async().await?;
            println!("Generated ID: {}", id);
        }
        Ok(())
    }
    main().expect("failed to run")
}

#[cfg(feature = "async-smol")]
{
    use ferroid::{Result, MonotonicClock, CUSTOM_EPOCH};

    fn main() -> Result<()> {
        smol::block_on(async {
            #[cfg(feature = "snowflake")]
            {
                use ferroid::{
                    AtomicSnowflakeGenerator, SnowflakeMastodonId,
                    SnowflakeGeneratorAsyncSmolExt
                };

                let clock = MonotonicClock::with_epoch(CUSTOM_EPOCH);
                let generator = AtomicSnowflakeGenerator::new(0, clock);

                let id: SnowflakeMastodonId = generator.try_next_id_async().await?;
                println!("Generated ID: {}", id);
            }

            #[cfg(feature = "ulid")]
            {
                use ferroid::{ThreadRandom, UlidGeneratorAsyncSmolExt, BasicUlidGenerator, ULID};

                let clock = MonotonicClock::with_epoch(CUSTOM_EPOCH);
                let rand = ThreadRandom::default();
                let generator = BasicUlidGenerator::new(clock, rand);

                let id: ULID = generator.try_next_id_async().await?;
                println!("Generated ID: {}", id);
            }

            Ok(())
        })
    }
    main().expect("failed to run")
}

Custom Layouts

To define a custom layouts, use the define_* macros:

#[cfg(feature = "snowflake")]
{
    use ferroid::{define_snowflake_id};

    // Example: a 64-bit Twitter-like ID layout
    //
    //  Bit Index:  63           63 62            22 21             12 11             0
    //              +--------------+----------------+-----------------+---------------+
    //  Field:      | reserved (1) | timestamp (41) | machine ID (10) | sequence (12) |
    //              +--------------+----------------+-----------------+---------------+
    //              |<----------- MSB ---------- 64 bits ----------- LSB ------------>|
    define_snowflake_id!(
        MyCustomId, u64,
        reserved: 1,
        timestamp: 41,
        machine_id: 10,
        sequence: 12
    );


    // Example: a 128-bit extended ID layout
    //
    //  Bit Index:  127           88 87            40 39             20 19             0
    //              +---------------+----------------+-----------------+---------------+
    //  Field:      | reserved (40) | timestamp (48) | machine ID (20) | sequence (20) |
    //              +---------------+----------------+-----------------+---------------+
    //              |<----- HI 64 bits ----->|<-------------- LO 64 bits ------------->|
    //              |<--- MSB ------ LSB --->|<----- MSB ----- 64 bits ----- LSB ----->|
    define_snowflake_id!(
        MyCustomLongId, u128,
        reserved: 40,
        timestamp: 48,
        machine_id: 20,
        sequence: 20
    );
}

#[cfg(feature = "ulid")]
{
    use ferroid::define_ulid;

    // Example: a 128-bit ULID using the Ulid layout
    //
    // - 0 bits reserved
    // - 48 bits timestamp
    // - 80 bits random
    //
    //  Bit Index:  127            80 79           0
    //              +----------------+-------------+
    //  Field:      | timestamp (48) | random (80) |
    //              +----------------+-------------+
    //              |<-- MSB -- 128 bits -- LSB -->|
    define_ulid!(
        MyULID, u128,
        reserved: 0,
        timestamp: 48,
        random: 80
    );
}

⚠️ Note: All four sections (reserved, timestamp, machine_id, and sequence) must be specified in the snowflake macro, even if a section uses 0 bits. reserved bits are always stored as zero and can be used for future expansion. Similarly, the ulid macro requries (reserved, timestamp, and random) fields.

Behavior

Snowflake

• If the clock advances: reset sequence to 0 → IdGenStatus::Ready
• If the clock is unchanged: increment sequence → IdGenStatus::Ready
• If the clock goes backward: return IdGenStatus::Pending
• If the sequence increment overflows: return IdGenStatus::Pending

Ulid

This implementation respects monotonicity within the same millisecond in a single generator by incrementing the random portion of the ID and guarding against overflow.

• If the clock advances: generate new random → IdGenStatus::Ready
• If the clock is unchanged: increment random → IdGenStatus::Ready
• If the clock goes backward: return IdGenStatus::Pending
• If the random increment overflows: return IdGenStatus::Pending

Probability of ID Collisions

When generating time-sortable IDs that use random bits, it's important to estimate the probability of collisions (i.e., two IDs being the same within the same millisecond), given your ID layout and system throughput.

Monotonic IDs with Multiple ULID Generators

If you have $g$ generators (e.g., distributed nodes), and each generator produces $k$ sequential (monotonic) IDs per millisecond by incrementing from a random starting point, the probability that any two generators produce overlapping IDs in the same millisecond is approximately:

$$P_\text{collision} \approx \frac{g(g-1)(2k-1)}{2 \cdot 2^r}$$

Where:

• $g$ = number of generators
• $k$ = number of monotonic IDs per generator per millisecond
• $r$ = number of random bits per ID
• $P_\text{collision}$ = probability of at least one collision

Note: The formula above uses the approximate (birthday bound) model, which assumes that:

• $k \ll 2^{r$ and $g \ll 2}r$
• Each generator's range of $k$ IDs starts at a uniformly random position within the $r$-bit space

Serialize as padded string

Use .to_padded_string() or .encode() for sortable string representations:

#[cfg(feature = "snowflake")]
{
    use ferroid::{Snowflake, SnowflakeTwitterId};

    let id = SnowflakeTwitterId::from(123456, 1, 42);
    assert_eq!(format!("default: {id}"), "default: 517811998762");
    assert_eq!(format!("padded: {}", id.to_padded_string()), "padded: 00000000517811998762");

    #[cfg(feature = "base32")]
    {
        use ferroid::Base32Ext;

        let encoded = id.encode();
        assert_eq!(format!("base32: {encoded}"), "base32: 00000Y4G0082M");

        let decoded = SnowflakeTwitterId::decode(&encoded).expect("decode should succeed");
        assert_eq!(id, decoded);
    }
}

#[cfg(feature = "ulid")]
{
    use ferroid::{Ulid, ULID};

    let id = ULID::from(123456, 42);
    assert_eq!(format!("default: {id}"), "default: 149249145986343659392525664298");
    assert_eq!(format!("padded: {}", id.to_padded_string()), "padded: 000000000149249145986343659392525664298");

    #[cfg(feature = "base32")]
    {
        use ferroid::Base32Ext;

        let encoded = id.encode();
        assert_eq!(format!("base32: {encoded}"), "base32: 000000F2800000000000000058");

        let decoded = ULID::decode(&encoded).expect("decode should succeed");
        assert_eq!(id, decoded);
    }
}

📈 Benchmarks

Snowflake ID generation is theoretically capped by:

max IDs/sec = 2^sequence_bits × 1000ms

For example, Twitter-style IDs (12 sequence bits) allow:

4096 IDs/ms × 1000 ms/sec = ~4M IDs/sec

To benchmark this, we generate IDs in chunks of 4096, which aligns with the sequence limit per millisecond in Snowflake layouts. For ULIDs, we use the same chunk size for consistency, but this number does not represent a hard throughput cap - ULID generation is probabilistic: monotonicity within the same millisecond increments the random bit value. Chunking here primarily serves to keep the benchmark code consistent.

Async benchmarks are tricky because a single generator's performance is affected by task scheduling, which is not predictable and whose scheduler typically has a resolution of 1 millisecond. By the time a task is scheduled to execute (i.e., generate an ID), a millisecond may have already passed, potentially resetting any sequence counter or monotonic increment - thus, never truly testing the hot path. To mitigate this, async tests measure maximum throughput: each task generates a batch of IDs and may await on any of them. This approach offsets idle time on one generator with active work on another, yielding more representative throughput numbers.

Snowflake:

• Sync: Benchmarks the hot path without yielding to the clock.
• Async: Also uses 4096-ID batches, but may yield (sequence exhaustion/CAS failure) or await due to task scheduling, reducing throughput.

ULID:

• Sync & Async: Uses the same 4096-ID batches. Due to random number generation, monotonic increments may overflow randomly, reflecting real-world behavior. In general, it is rare for ULIDs to overflow.

Tests were ran on an M1 Macbook Pro 14", 32GB, 10 cores (8 performance, 2 efficiency).

Synchronous Generators

Async (Tokio Runtime) - Peak throughput

Async (Smol Runtime) - Peak throughput

To run all benchmarks:

cargo criterion --all-features

🧪 Testing

Run all tests with:

cargo test --all-features

📄 License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT License (LICENSE-MIT)

at your option.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.