Why High-Frequency Trading Firms Care About Microseconds | Sajima Solutions

In high-frequency trading, a microsecond delay can cost millions. In competitive gaming, 10ms of latency is the difference between winning and losing. In network infrastructure, processing millions of packets per second is table stakes.

These industries share a common requirement: maximum performance with zero compromise. The code must be as fast as hand-optimized assembly, yet maintainable enough for teams to work on. Here’s how they achieve both—and how you can apply these patterns to your systems.

What Are Zero-Cost Abstractions?

The principle is simple: you don’t pay for what you don’t use, and what you do use, you couldn’t hand-code any better. Rust’s iterators, Option types, and generics all compile to code that’s as fast as manual loops and null checks.

Iterators vs Manual Loops

Let’s compare iterators to manual indexing:

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// Iterator approach
fn sum_squares_iter(numbers: &[i32]) -> i32 {
    numbers.iter()
        .map(|x| x * x)
        .sum()
}

// Manual loop approach
fn sum_squares_manual(numbers: &[i32]) -> i32 {
    let mut sum = 0;
    for i in 0..numbers.len() {
        sum += numbers[i] * numbers[i];
    }
    sum
}

Both compile to essentially the same assembly. But the iterator version is:

Easier to read
Less prone to off-by-one errors
Often faster due to SIMD optimizations

Monomorphization: Generics Without Runtime Cost

When you write generic code in Rust, the compiler generates specialized versions for each concrete type at compile time:

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fn process<T: std::fmt::Display>(item: T) {
    println!("{}", item);
}

fn main() {
    process(42);        // Generates process_i32
    process("hello");   // Generates process_&str
    process(3.14);      // Generates process_f64
}

Each call goes directly to the specialized function—no virtual dispatch, no runtime type checks. Compare this to dynamic dispatch in other languages where every call goes through a vtable.

Option and Result: No Null Pointer Overhead

Rust’s Option<T> seems like it would add overhead compared to nullable pointers. But the compiler optimizes it away:

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fn find_user(id: u64) -> Option<User> {
    // Returns Some(user) or None
}

// The compiler uses "niche optimization"
// Option<&T> is the same size as *T
// None is represented as null pointer internally

For types that have “niches” (invalid bit patterns), Option adds zero overhead:

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use std::mem::size_of;

assert_eq!(size_of::<&i32>(), size_of::<Option<&i32>>());
assert_eq!(size_of::<Box<i32>>(), size_of::<Option<Box<i32>>>());

Closures: Inlined for Free

Closures in Rust are just structs that implement the Fn traits. When passed to generic functions, they’re monomorphized and often inlined:

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fn apply_twice<F>(f: F, x: i32) -> i32 
where
    F: Fn(i32) -> i32 
{
    f(f(x))
}

fn main() {
    let double = |x| x * 2;
    let result = apply_twice(double, 5);
    // Compiles to: result = ((5 * 2) * 2) = 20
    // The closure is completely inlined
}

Benchmarking: Proving Zero-Cost

Let’s write a benchmark to verify:

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use criterion::{black_box, criterion_group, criterion_main, Criterion};

fn iterator_sum(data: &[i64]) -> i64 {
    data.iter().sum()
}

fn manual_sum(data: &[i64]) -> i64 {
    let mut sum = 0i64;
    for i in 0..data.len() {
        sum += data[i];
    }
    sum
}

fn benchmark_sums(c: &mut Criterion) {
    let data: Vec<i64> = (0..10000).collect();
    
    c.bench_function("iterator_sum", |b| {
        b.iter(|| iterator_sum(black_box(&data)))
    });
    
    c.bench_function("manual_sum", |b| {
        b.iter(|| manual_sum(black_box(&data)))
    });
}

criterion_group!(benches, benchmark_sums);
criterion_main!(benches);

Run with:

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cargo bench

You’ll find both implementations perform identically—often the iterator is faster due to better SIMD vectorization.

When Abstractions Have Cost

Not all abstractions are free. Dynamic dispatch (dyn Trait) has runtime overhead:

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// Zero-cost: static dispatch
fn process_static<T: Processor>(p: T) {
    p.process();
}

// Has cost: dynamic dispatch
fn process_dynamic(p: &dyn Processor) {
    p.process(); // vtable lookup
}

Use dyn Trait when you need heterogeneous collections or want to reduce binary size. Use generics when performance is critical.

String Formatting Cost

String operations often have hidden costs:

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// Allocates a new String
let greeting = format!("Hello, {}!", name);

// Zero allocation for simple concatenation
let mut greeting = String::with_capacity(7 + name.len() + 1);
greeting.push_str("Hello, ");
greeting.push_str(name);
greeting.push('!');

For hot paths, prefer write! to a pre-allocated buffer:

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use std::fmt::Write;

let mut buffer = String::with_capacity(256);
write!(&mut buffer, "Count: {}", count).unwrap();

Practical Example: High-Performance Parser

Here’s a JSON-like parser using zero-cost abstractions:

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#[derive(Debug, Clone)]
pub enum Value {
    Null,
    Bool(bool),
    Number(f64),
    String(String),
    Array(Vec<Value>),
}

pub struct Parser<'a> {
    input: &'a [u8],
    pos: usize,
}

impl<'a> Parser<'a> {
    pub fn new(input: &'a str) -> Self {
        Self { input: input.as_bytes(), pos: 0 }
    }
    
    #[inline]
    fn peek(&self) -> Option<u8> {
        self.input.get(self.pos).copied()
    }
    
    #[inline]
    fn advance(&mut self) {
        self.pos += 1;
    }
    
    #[inline]
    fn skip_whitespace(&mut self) {
        while self.peek().map_or(false, |c| c.is_ascii_whitespace()) {
            self.advance();
        }
    }
    
    pub fn parse_value(&mut self) -> Option<Value> {
        self.skip_whitespace();
        
        match self.peek()? {
            b'n' => self.parse_null(),
            b't' | b'f' => self.parse_bool(),
            b'"' => self.parse_string(),
            b'[' => self.parse_array(),
            c if c.is_ascii_digit() || c == b'-' => self.parse_number(),
            _ => None,
        }
    }
    
    // ... implementation details
}

The #[inline] hints help the compiler inline small functions, and the byte-slice operations compile to direct memory access.

Conclusion

This is why trading firms, game studios, and network equipment manufacturers choose these approaches:

Expressive code that’s still fast — Your team can maintain it without performance penalties
Compile-time optimization — The heavy lifting happens before deployment
Predictable performance — No garbage collection pauses, no runtime surprises
Memory safety — Critical for financial systems and any production workload

Whether you’re building a trading engine, a game server, a network protocol, or any system where latency matters, these patterns give you the performance of C with code your team can actually work with.

At Sajima Solutions, we build high-performance systems for finance, gaming, and infrastructure. Contact us when microseconds matter.