How do I serialize fastnum decimals with serde?

Enable the 'serde' feature in your Cargo.toml, then use serde's derive macros. The README notes that serialization and deserialization are supported via this optional feature, with details available in the docs.

fastnum vs rust_decimal: which should I use?

fastnum offers exact precision, IEEE 754 special values, and const evaluation, while rust_decimal has a stable API but limited 28-digit precision. Choose fastnum if you need advanced features and compile-time calculations for financial apps.

Can fastnum handle Bitcoin's 8 decimal places?

Yes, fastnum can support Bitcoin's precision using types like UD128 or UD256, as shown in the precision table where 8 decimal digits are within the fixed limits for financial units.

Is fastnum suitable for WebAssembly projects?

Absolutely, fastnum is fully compatible with WebAssembly and no_std environments, making it ideal for browser-based or embedded applications requiring exact decimal arithmetic.

How to generate random fastnum decimals?

Enable the 'rand' feature in Cargo.toml, then use the rand crate's traits. The README specifies that random number generation is available via this optional feature for testing or simulations.

What are the memory overheads of using fastnum?

Fastnum uses fixed-size types, so memory usage is predictable and stack-allocated, avoiding heap fragmentation. However, larger types like D512 consume more memory, which should be considered for resource-constrained environments.

fastnum — Fixed-Size Decimal Numbers in Rust

What is fastnum?

fastnum is a Rust library that implements fixed-size decimal numbers with exact precision, designed for financial, cryptocurrency, and other applications requiring accurate fixed-point calculations without rounding errors. It provides high-performance arithmetic operations, support for special values like NaN and Infinity, and avoids dynamic allocation by using fixed-size integer storage.

Target Audience

Rust developers working on financial systems, cryptocurrency applications, trading platforms, or any domain requiring exact decimal precision and high-performance numeric computations.

Value Proposition

Developers choose fastnum for its combination of exact precision, high performance (comparable to native types), fixed-size stack allocation, and comprehensive feature set including const evaluation, no_std support, and optional integrations with popular crates like serde and rand.

Fixed-size decimal numbers implemented in pure Rust

Use Cases

Best For

Financial calculations requiring exact decimal precision (e.g., currency conversions)
Cryptocurrency applications handling BTC, ETH, or other tokens with specific decimal places
High-performance trading systems where allocation overhead must be minimized
Embedded or no_std environments needing decimal arithmetic
WebAssembly projects requiring exact-precision numeric computations
Compile-time constant evaluation of decimal numbers

Not Ideal For

Applications requiring runtime-adjustable precision for numbers exceeding fixed integer size limits
Systems deeply integrated with arbitrary-precision decimal libraries like BigDecimal for interoperability
Performance-critical real-time simulations where native floating-point speed outweighs exact precision needs

Pros & Cons

Pros

Exact Decimal Precision

Eliminates rounding errors such as 0.1 + 0.2 = 0.3, which is critical for financial and crypto applications as emphasized in the README's overview.

Stack-Allocated Efficiency

Uses fixed-size integers to avoid dynamic allocation, making types trivially copyable and improving cache-friendliness, demonstrated in allocation benchmarks where it outperforms bigdecimal.

IEEE 754 Special Values

Supports ±0, ±Infinity, and NaN with standard semantics, enabling robust error handling in mathematical operations as listed in the features.

Const Evaluation Capabilities

Most methods are const, allowing compile-time parsing and calculations, enhancing performance and safety for known values, as highlighted in the 'Why fastnum?' section.

Cons

Pre-1.0 API Instability

The library is pre-1.0.0, so breaking changes in the public API are possible, as warned in the documentation, which could disrupt long-term projects.

Fixed Precision Constraint

Precision is arbitrary but fixed at compile-time and cannot be adjusted dynamically, limiting flexibility compared to arbitrary-precision alternatives like BigDecimal.

Performance Overhead

Benchmarks show operations like division are significantly slower than native f64, which might impact latency-sensitive applications such as high-frequency trading.

What is fastnum?

Target Audience

Rust developers working on financial systems, cryptocurrency applications, trading platforms, or any domain requiring exact decimal precision and high-performance numeric computations.

Value Proposition

Use Cases

Best For

Financial calculations requiring exact decimal precision (e.g., currency conversions)
Cryptocurrency applications handling BTC, ETH, or other tokens with specific decimal places
High-performance trading systems where allocation overhead must be minimized
Embedded or no_std environments needing decimal arithmetic
WebAssembly projects requiring exact-precision numeric computations
Compile-time constant evaluation of decimal numbers

Not Ideal For

Applications requiring runtime-adjustable precision for numbers exceeding fixed integer size limits
Systems deeply integrated with arbitrary-precision decimal libraries like BigDecimal for interoperability
Performance-critical real-time simulations where native floating-point speed outweighs exact precision needs

Pros & Cons

Pros

Exact Decimal Precision

Eliminates rounding errors such as 0.1 + 0.2 = 0.3, which is critical for financial and crypto applications as emphasized in the README's overview.

Stack-Allocated Efficiency

Uses fixed-size integers to avoid dynamic allocation, making types trivially copyable and improving cache-friendliness, demonstrated in allocation benchmarks where it outperforms bigdecimal.

IEEE 754 Special Values

Supports ±0, ±Infinity, and NaN with standard semantics, enabling robust error handling in mathematical operations as listed in the features.

Const Evaluation Capabilities

Most methods are const, allowing compile-time parsing and calculations, enhancing performance and safety for known values, as highlighted in the 'Why fastnum?' section.

Cons

Pre-1.0 API Instability

The library is pre-1.0.0, so breaking changes in the public API are possible, as warned in the documentation, which could disrupt long-term projects.

Fixed Precision Constraint

Precision is arbitrary but fixed at compile-time and cannot be adjusted dynamically, limiting flexibility compared to arbitrary-precision alternatives like BigDecimal.

Performance Overhead

Benchmarks show operations like division are significantly slower than native f64, which might impact latency-sensitive applications such as high-frequency trading.

fastnum

What is fastnum?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

fastnum

What is fastnum?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?