Cogs and Levers A blog full of technical stuff

Getting Started with WebAssembly

29 Jan 2025

Introduction

Previously, we’ve explored WASM in rust as well as some more advanced concepts with Pixel Buffer Rendering again from Rust. In today’s article, we’ll go through WebAssembly from a more fundamental perspective.

WebAssembly (Wasm) is a powerful technology that enables high-performance execution in web browsers and beyond. If you’re just getting started, this guide will walk you through writing a simple WebAssembly program from scratch, running it in a browser using JavaScript.

What is WebAssembly?

WebAssembly is a low-level binary instruction format that runs at near-native speed. It provides a sandboxed execution environment, making it secure and highly portable. While it was initially designed for the web, Wasm is now expanding into cloud computing, serverless, and embedded systems.

Unlike JavaScript, Wasm allows near-native performance, making it ideal for gaming, video processing, and even AI in the browser.

First program

Before we start, we need to make sure all of the tools are available on your system. Make sure you have wabt installed on your system:

sudo pacman -S wabt

WAT

We’ll start by writing a WebAssembly module using the WebAssembly Text Format (WAT). Create a file called add.wat with the following code:

(module
  (func $add (param $a i32) (param $b i32) (result i32)
    local.get $a
    local.get $b
    i32.add
  )
  (export "add" (func $add))
)

This module defines a function $add that takes two 32-bit integers (i32) as parameters and returns their sum.

  • local.get retrieves the parameters.
  • i32.add performs the addition.
  • The function is exported as "add", making it accessible from JavaScript

wat2wasm

To convert our add.wat file into a .wasm binary, we’ll use a tool called wat2wasm from the WebAssembly Binary Toolkit (wabt) that we installed earlier:

wat2wasm add.wat -o add.wasm

This produces a binary add.wasm file, ready for execution.

Running WebAssembly from Javascript

Now, let’s create a JavaScript file (index.js) to load and execute our Wasm module:

async function runWasm() {
    // Fetch and compile the Wasm module
    const response = await fetch("add.wasm");
    const buffer = await response.arrayBuffer();
    const wasmModule = await WebAssembly.instantiate(buffer);

    // Get the exported add function
    const add = wasmModule.instance.exports.add;

    // Call the function
    console.log("5 + 7 = ", add(5, 7));
}

runWasm();

We can execute this javascript by referencing it from a html file, and running this in a browser.

Create index.html with the following:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>WebAssembly Example</title>
</head>
<body>
    <h1>WebAssembly Add Example</h1>
    <script src="index.js"></script>
</body>
</html>

Testing

Now we can serve our mini-website via python’s http server.

python3 -m http.server 8080

When you hit http://localhost:8080 in your browser, pop open your javascript console and you should see the following text:

5 + 7 = 12

Next

Ok, it’s not the most exciting but it is the start of what you can achieve with these technologies.

Delaunay Triangulation

27 Jan 2025

Introduction

In the world of computational geometry, Delaunay triangulation stands out as one of the most versatile and powerful algorithms. Its ability to transform a scattered set of points into a structured mesh of triangles has applications ranging from terrain modeling to wireless network optimization.

This blog explores the concept of Delaunay triangulation, the algorithms that implement it, and its real-world applications.

What is Delaunay Triangulation?

Delaunay triangulation is a method for connecting a set of points in a plane (or higher dimensions) to form a network of triangles. The primary property of this triangulation is that no point lies inside the circumcircle of any triangle. This ensures the triangulation is “optimal” in the sense that it avoids skinny triangles and maximizes the smallest angles in the mesh.

A key relationship is its duality with the Voronoi diagram: Delaunay triangulation and Voronoi diagrams together provide complementary ways to describe the spatial relationships between points.

Why is Delaunay Triangulation Important?

The importance of Delaunay triangulation stems from its geometric and computational properties:

  1. Optimal Mesh Quality: By avoiding narrow angles, it produces meshes suitable for simulations, interpolation, and rendering.
  2. Simplicity and Efficiency: It reduces computational overhead by connecting points with minimal redundant edges.
  3. Wide Applicability: From geographic information systems (GIS) to computer graphics and engineering, Delaunay triangulation plays a foundational role.

Real-World Applications

  1. Geographic Information Systems (GIS)
    • Terrain Modeling: Delaunay triangulation is used to create Triangulated Irregular Networks (TINs), which model landscapes by connecting elevation points into a mesh.
    • Watershed Analysis: Helps analyze water flow and drainage patterns.
  2. Computer Graphics
    • Mesh Generation: Triangles are the fundamental building blocks for 3D modeling and rendering.
    • Collision Detection: Used in simulations to detect interactions between objects.
  3. Telecommunications
    • Wireless Network Optimization: Helps optimize the placement of cell towers and the connections between them.
    • Voronoi-based Coverage Analysis: Delaunay edges represent backhaul connections between towers.
  4. Robotics and Pathfinding
    • Motion Planning: Robots use triangulated graphs to navigate efficiently while avoiding obstacles.
    • Terrain Navigation: Triangulation simplifies understanding of the environment for autonomous vehicles.
  5. Engineering and Simulation
    • Finite Element Analysis (FEA): Generates triangular meshes for simulating physical systems, such as stress distribution in materials.
    • Fluid Dynamics: Simulates the flow of fluids over surfaces.
  6. Environmental Science
    • Flood Modeling: Simulates how water flows across landscapes.
    • Resource Management: Models the distribution of natural resources like water or minerals.

A Practical Example

To illustrate the concept, let’s consider a set of points representing small towns scattered across a region. Using Delaunay triangulation:

  1. The towns (points) are connected with lines (edges) to form a network.
  2. These edges represent potential road connections, ensuring the shortest and most efficient routes between towns.
  3. By avoiding sharp angles, this network is both practical and cost-effective for infrastructure planning.

Here’s a Python script that demonstrates this idea:

import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay

# Generate random points in 2D space
np.random.seed(42)  # For reproducibility
points = np.random.rand(20, 2)  # 20 points in 2D

# Perform Delaunay triangulation
tri = Delaunay(points)

# Plot the points and the triangulation
plt.figure(figsize=(8, 6))
plt.triplot(points[:, 0], points[:, 1], tri.simplices, color='blue', linewidth=0.8)
plt.scatter(points[:, 0], points[:, 1], color='red', s=50, label='Points')
plt.title("Delaunay Triangulation Example")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()
plt.grid(True)
plt.show()

The following is the output from this program.

Example

Note how the paths between the points are the most optimal for connecting the towns efficiently. The triangulation avoids unnecessary overlaps or excessively sharp angles, ensuring practicality and simplicity in the network design.

Limitations and Challenges

While Delaunay triangulation is powerful, it has its challenges:

  1. Degenerate Cases: Points that are collinear or on the same circle can cause issues.
  2. Scalability: Large datasets may require optimized algorithms to compute triangulations efficiently.
  3. Extensions to Higher Dimensions: In 3D or higher, the algorithm becomes more complex and computationally expensive.

Conclusion

Delaunay triangulation is a cornerstone of computational geometry, offering an elegant way to structure and connect scattered points. Its versatility makes it applicable across diverse domains, from GIS to robotics and environmental science. Whether you’re modeling terrains, optimizing networks, or simulating physical systems, Delaunay triangulation is an indispensable tool for solving real-world problems.

Writing Safe Abstractions for Unsafe Rust Code

26 Jan 2025

Introduction

Rust is celebrated for its emphasis on safety and performance, largely thanks to its robust compile-time checks. However, there are situations where you need to bypass these checks to perform low-level operations—this is where Rust’s unsafe keyword comes in. While unsafe opens the door to powerful features, it also comes with significant risks.

The solution?

Encapsulating unsafe code in safe abstractions.

This post explores what that means, why it’s important, and how to do it effectively.

Understanding unsafe in Rust

Rust enforces strict memory safety guarantees by default. However, some operations are inherently unsafe and require explicit acknowledgment from the programmer. These include:

  • Raw pointer manipulation: Directly accessing memory without bounds or validity checks.
  • Foreign Function Interface (FFI): Interacting with non-Rust code (e.g., calling C functions).
  • Manual memory management: Allocating and freeing memory without Rust’s usual safeguards.
  • Concurrency primitives: Implementing data structures that require custom synchronization logic.

When you write unsafe code, you’re essentially telling the compiler, “I know what I’m doing; trust me.”

While this is sometimes necessary, it’s critical to minimize the potential for misuse by others.

Why Wrap Unsafe Code in Safe Abstractions?

Using unsafe is a trade-off. It gives you access to low-level features and optimizations but requires you to manually uphold the invariants that Rust would otherwise enforce. Safe abstractions address this challenge by:

  • Avoiding Undefined Behavior: Preventing common pitfalls like null pointer dereferences, data races, or buffer overflows.
  • Improving Maintainability: Reducing the scattering of unsafe blocks across the codebase makes it easier to audit and debug.
  • Providing Ease of Use: Enabling most developers to rely on Rust’s safety guarantees without needing to understand the intricacies of the underlying unsafe implementation.

What is a Safe Abstraction?

A safe abstraction is an API or module where the internal implementation may use unsafe code, but the external interface ensures that incorrect usage is either impossible or extremely difficult.

Let’s look at how to create one.

Example: Safe Wrapping of Unsafe Memory Allocation

Here’s a simplified example of wrapping unsafe memory management into a safe abstraction:

pub struct SafeAllocator {
    // Internal raw pointer or other unsafe constructs
    ptr: *mut u8,
    size: usize,
}

impl SafeAllocator {
    pub fn new(size: usize) -> Self {
        let ptr = unsafe { libc::malloc(size) as *mut u8 };
        if ptr.is_null() {
            panic!("Failed to allocate memory");
        }
        Self { ptr, size }
    }

    pub fn allocate(&self, offset: usize, len: usize) -> &[u8] {
        if offset + len > self.size {
            panic!("Out of bounds access");
        }
        unsafe {
            std::slice::from_raw_parts(self.ptr.add(offset), len)
        }
    }

    pub fn deallocate(self) {
        unsafe {
            libc::free(self.ptr as *mut libc::c_void);
        }
    }
}

impl Drop for SafeAllocator {
    fn drop(&mut self) {
        unsafe {
            libc::free(self.ptr as *mut libc::c_void);
        }
    }
}

In this example:

  • unsafe is confined to specific, well-defined sections of the code.
  • The API ensures that users cannot misuse the allocator (e.g., by accessing out-of-bounds memory).
  • Drop ensures memory is automatically freed when the allocator goes out of scope.

Example Usage of SafeAllocator

Here’s how you might use the SafeAllocator in practice:

fn main() {
    // Create a new SafeAllocator with 1024 bytes of memory
    let allocator = SafeAllocator::new(1024);

    // Allocate a slice of 128 bytes starting from offset 0
    let slice = allocator.allocate(0, 128);
    println!("Allocated slice of length: {}", slice.len());

    // The allocator will automatically deallocate memory when it goes out of scope
}

This usage demonstrates:

  • How to create and interact with the SafeAllocator API.
  • That memory is automatically managed via Rust’s Drop trait, preventing leaks.

Leveraging Rust’s Type System

Rust’s type system is another powerful tool for enforcing invariants. For example, you can use:

  • Lifetimes: To ensure references don’t outlive the data they point to.
  • PhantomData: To associate types or lifetimes with otherwise untyped data.
  • Ownership and Borrowing Rules: To enforce safe access patterns at compile time.

Documentation of Safety Contracts

Any unsafe code should include clear documentation of the invariants it relies on. For example:

// Safety:
// - `ptr` must be non-null and point to a valid memory region.
// - `len` must not exceed the bounds of the allocated memory.
unsafe {
    std::slice::from_raw_parts(ptr, len)
}

This makes it easier for future maintainers to understand and verify the correctness of the code.

Real-World Examples of Safe Abstractions

Many Rust libraries provide excellent examples of safe abstractions over unsafe code:

  • std::sync::Mutex: Internally uses unsafe for thread synchronization but exposes a safe API for locking and unlocking.
  • Vec: The Rust standard library’s Vec type uses unsafe for raw memory allocation and resizing but ensures bounds checks and proper memory management externally.
  • crossbeam: Provides safe concurrency primitives built on low-level atomic operations.

Costs and Benefits

While writing safe abstractions requires extra effort and careful thought, the benefits outweigh the costs:

Benefits:

  • Reduced Risk of Bugs: Encapsulating unsafe code minimizes the chance of introducing undefined behavior.
  • Improved Developer Experience: Safe APIs make it easier for others to use your code without worrying about low-level details.
  • Easier Auditing: With unsafe code isolated, it’s easier to review and verify its correctness.

Costs:

  • Initial Effort: Designing a robust safe abstraction takes time and expertise.
  • Performance Overhead: In rare cases, adding safety layers may incur slight overhead (though usually negligible in well-designed abstractions).

Conclusion

Writing safe abstractions for unsafe Rust code is both an art and a science. It involves understanding the invariants of your unsafe code, leveraging Rust’s type system to enforce safety, and documenting your assumptions clearly. By doing so, you can harness the power of unsafe while maintaining Rust’s guarantees of memory safety and concurrency correctness—the best of both worlds.

Writing a Key Value Server in Rust

26 Jan 2025

Introduction

In today’s post, we’ll build a simple key value server; but we’ll do it in an iterative way. We’ll build it up simple and then add safety, concurrency, and networking as we go.

Implementation

Now we’ll get started with our iterations. The finished code will be available at the end of this post.

Baseline

All of our implementations will deal with a KeyValueStore struct. This struct will hold all of the variables that we want to keep track of in our server.

use std::collections::HashMap;

struct KeyValueStore {
    data: HashMap<String, String>,
}

We define data as the in-memory representation of our database. We use String keys and store String values.

Our implementation is very basic. All we’re really doing is shadowing the functionality that HashMap provides.

impl KeyValueStore {
    fn new() -> Self {
        Self {
            data: HashMap::new(),
        }
    }

    fn insert(&mut self, key: String, value: String) {
        self.data.insert(key, value);
    }

    fn get(&self, key: &str) -> Option<&String> {
        self.data.get(key)
    }

    fn delete(&mut self, key: &str) {
        self.data.remove(key);
    }
}

This is a pretty decent starting point. We can use our KeyValueStore in some basic tests:

fn main() {
    let mut store = KeyValueStore::new();
    store.insert("key1".to_string(), "value1".to_string());
    println!("{:?}", store.get("key1"));
    store.delete("key1");
    println!("{:?}", store.get("key1"));
}

Variants

String is pretty limiting to store as far as the value side is concerned. We can upgrade this to specifically use data types that we will find useful via an enum:

#[derive(Debug, Clone)]
enum Value {
    String(String),
    Integer(i64),
    Float(f64),
    Boolean(bool),
    Binary(Vec<u8>),
    // Add more variants as needed
}

We can swap out the value side of our data member now, too.

struct KeyValueStore {
    data: HashMap<String, Value>,
}

The implementation simply swaps the String for Value:

impl KeyValueStore {
    fn new() -> Self {
        Self {
            data: HashMap::new(),
        }
    }

    fn insert(&mut self, key: String, value: Value) {
        self.data.insert(key, value);
    }

    fn get(&self, key: &str) -> Option<&Value> {
        self.data.get(key)
    }

    fn delete(&mut self, key: &str) {
        self.data.remove(key);
    }
}

We’re now able to not only store strings. We can store integers, floats, binary, and booleans. This makes our key value store a lot more versatile.

Thread Safety

We will have multiple threads of execution trying to perform actions on this structure at the same time, so we will add some thread safety to the process now. Wrapping data in Arc will give us a thread safe, reference counting pointer. We’re also going to need to lock this data structure for reading and for writing. We can use RwLock to take care of that for us.

We update our data structure to include these new types:

struct KeyValueStore {
    data: Arc<RwLock<HashMap<String, Value>>>,
}

Now our implementation functions need to change to work with these new structures. We can keep the structure of functions the same though.

impl KeyValueStore {
    fn new() -> Self {
        Self {
            data: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    fn insert(&self, key: String, value: Value) {
        let mut locked = self.data.write().unwrap();
        locked.insert(key, value);
    }

    fn get(&self, key: &str) -> Option<Value> {
        let mut locked = self.data.read().unwrap();
        locked.get(key).cloned()
    }

    fn delete(&self, key: &str) {
        let mut locked = self.data.write().unwrap();
        locked.remove(key);
    }
}

These functions are now safe, which means calling code can be multithreaded and we can guaranteed that our data structure will be treated consistently.

fn main() {
    let store = Arc::new(KeyValueStore::new());

    // Create a vector to hold thread handles
    let mut handles = vec![];

    // Spawn threads to perform inserts
    for i in 0..5 {
        let store = Arc::clone(&store);
        let handle = thread::spawn(move || {
            let key = format!("key{}", i);
            let value = Value::Integer(i * 10);
            store.insert(key.clone(), value);
            println!("Thread {} inserted: {}", i, key);
        });
        handles.push(handle);
    }

    // Spawn threads to read values
    for i in 0..5 {
        let store = Arc::clone(&store);
        let handle = thread::spawn(move || {
            let key = format!("key{}", i);
            if let Some(value) = store.get(&key) {
                println!("Thread {} read: {} -> {:?}", i, key, value);
            } else {
                println!("Thread {} could not find: {}", i, key);
            }
        });
        handles.push(handle);
    }

    // Spawn threads to delete keys
    for i in 0..5 {
        let store = Arc::clone(&store);
        let handle = thread::spawn(move || {
            let key = format!("key{}", i);
            store.delete(&key);
            println!("Thread {} deleted: {}", i, key);
        });
        handles.push(handle);
    }

    // Wait for all threads to complete
    for handle in handles {
        handle.join().unwrap();
    }

    println!("Final state of the store: {:?}", store.data.read().unwrap());
}

Error handling

You can see that we’re using unwrap in the implementation functions, which might be ok for tests or short scripts. If we’re going to expect to run this code in production, we’d be best replacing these with actual error handling counterparts.

In order to do that, we need to define our error domain first. We create an enum called StoreError. As we fill out our implementation, we’ll run into a number of different error cases. We’ll use StoreError to centralise all of these errors so we can express them clearly.

#[derive(Debug)]
enum StoreError {
    LockError(String),
    KeyNotFound(String),
}

impl<T> From<PoisonError<T>> for StoreError {
    fn from(err: PoisonError<T>) -> Self {
        StoreError::LockError(format!("Lock poisoned: {}", err))
    }
}

We’ve implemented PoisonError for our StoreError because the PoisonError type is an error which can be returned whenever a lock is acquired. If something goes wrong and we’ve acquired a lock, it’s a PoisonError that’s used.

Our insert, get, and delete methods now need an upgrade. We’ll be returning Result<T, E> values from our functions now to accomodate potential failures.

fn insert(&self, key: String, value: Value) -> Result<(), StoreError> {
    let mut locked = self.data.write()?;
    locked.insert(key, value);
    Ok(())
}

fn get(&self, key: &str) -> Result<Option<Value>, StoreError> {
    let locked = self.data.read()?;
    Ok(locked.get(key).cloned()) // Clone the value to return an owned copy
}

fn delete(&self, key: &str) -> Result<(), StoreError> {
    let mut locked = self.data.write()?;
    if locked.remove(key).is_none() {
        return Err(StoreError::KeyNotFound(key.to_string()));
    }
    Ok(())
}

We’ve removed the use of unwrap now, swapping out to using the ? operator. This will allow us to actually handle any failure that is bubbled out of calling code.

Using the File System

We need to be able to persist the state of our key value store out to disk for durability. In order to do this, we need to keep track of where we’ll write the file. We add a file_path member to our structure:

struct KeyValueStore {
    data: Arc<RwLock<HashMap<String, Value>>>,
    file_path: Option<String>,
}

impl KeyValueStore {
    fn new(file_path: Option<String>) -> Self {
        Self {
            data: Arc::new(RwLock::new(HashMap::new())),
            file_path,
        }
    }
}

Starting out this implementation simply, we just write a load and save function that we can call at any time. Before we do this we need some extra dependencies added for serialisation:

[dependencies]
serde = { version = "1.0.217", features = ["derive"] }
serde_json = "1.0.137"

This will allow us to reduce our internal state to JSON.

Loading the database off disk

/// Load the state from a file
fn load(&self) -> Result<(), StoreError> {
    if let Some(ref path) = self.file_path {
        match fs::read_to_string(path) {
            Ok(contents) => {
                let deserialized: HashMap<String, Value> = serde_json::from_str(&contents)?;
                let mut locked = self.data.write()?;
                *locked = deserialized; // Replace the current state with the loaded one
                Ok(())
            }
            Err(e) if e.kind() == ErrorKind::NotFound => {
                // File doesn't exist, just return Ok (no data to load)
                Ok(())
            }
            Err(e) => Err(e.into()),
        }
    } else {
        Err(StoreError::IoError("File path not set".to_string()))
    }
}

We need to make sure that a file_path was specified. We read everything off from the file into contents as a big string. Using serde_json::from_str we can turn that contents into the deserialised representation. From there, we simply swap out the underlying content.

We’ve got some new errors to deal with here in IoError.

#[derive(Debug)]
enum StoreError {
    LockError(String),
    KeyNotFound(String),
    IoError(String),
    SerdeError(String),
}

This will be used for our write implementation which looks like this:

/// Save the current state to a file
fn save(&self) -> Result<(), StoreError> {
    if let Some(ref path) = self.file_path {
        let locked = self.data.read()?;
        let serialized = serde_json::to_string(&*locked)?;
        fs::write(path, serialized)?;
        Ok(())
    } else {
        Err(StoreError::IoError("File path not set".to_string()))
    }
}

The magic here really is the serde_json::to_string taking our internal state and writing it as json.

An example of how this looks is like this:

{
    "key2":{"Integer":20},
    "key4":{"Integer":40},
    "key1":{"Integer":10},
    "key3":{"Integer":30},
    "key0":{"Integer":0}
}

Networking

Finally, we’ll add some networking to the solution. A really basic network interface will allow remote clients to perform the get, set, and delete operations for us.

The handle_client function is the heart of the server process, performing the needed processing on incoming requests and routing them to the database instance:

fn handle_client(mut stream: TcpStream, store: Arc<KeyValueStore>) {
    let mut buffer = [0; 512];

    // Read the incoming request
    match stream.read(&mut buffer) {
        Ok(_) => {
            let request = String::from_utf8_lossy(&buffer);
            let mut parts = request.trim().split_whitespace();
            let command = parts.next();

            let response = match command {
                Some("SET") => {
                    let key = parts.next().unwrap_or_default().to_string();
                    let value = parts.next().unwrap_or_default().to_string();
                    store.insert(key, Value::String(value));
                    "OK\n".to_string()
                }
                Some("GET") => {
                    let key = parts.next().unwrap_or_default();
                    if let Ok(Some(value)) = store.get(key) {
                        format!("{:?}\n", value)
                    } else {
                        "Key not found\n".to_string()
                    }
                }
                Some("DEL") => {
                    let key = parts.next().unwrap_or_default();
                    store.delete(key);
                    "OK\n".to_string()
                }
                _ => "Unknown command\n".to_string(),
            };

            // Send the response back to the client
            stream.write_all(response.as_bytes()).unwrap();
        }
        Err(e) => eprintln!("Failed to read from socket: {}", e),
    }
}

Out networking “protocol” looks like this:

-- set the key "key1" to the value "hello"
SET key1 hello

-- get the value of the key "key1"
GET key1

-- remove the value and key "key1"
DEL key1

This is all made possible by the following:

let request = String::from_utf8_lossy(&buffer);
let mut parts = request.trim().split_whitespace();
let command = parts.next();

We read in the request data from the client into request. This gets split up on white spaces into parts with command given the first of these parts. The code is expecting command to be either SET, GET, or DEL that is then handled in the following pattern match.

This function gets mounted onto the server in the main function which now looks like this:

fn main() {
    let store = Arc::new(
        KeyValueStore::new(None)
    );
    let listener = TcpListener::bind("127.0.0.1:7878").unwrap();

    println!("Server running on 127.0.0.1:7878");

    for stream in listener.incoming() {
        match stream {
            Ok(stream) => {
                let store = Arc::clone(&store);
                std::thread::spawn(move || handle_client(stream, store));
            }
            Err(e) => eprintln!("Connection failed: {}", e),
        }
    }
}

We’re starting our server on port 7878 and handling each connection with our handle_client function.

Running this and giving it a test with telnet gives us the following:

➜  telnet 127.0.0.1 7878
Trying 127.0.0.1...
Connected to 127.0.0.1.
Escape character is '^]'.
SET key1 hello
OK
Connection closed by foreign host.

➜  telnet 127.0.0.1 7878
Trying 127.0.0.1...
Connected to 127.0.0.1.
Escape character is '^]'.
GET key1
String("hello")
Connection closed by foreign host.

➜  telnet 127.0.0.1 7878
Trying 127.0.0.1...
Connected to 127.0.0.1.
Escape character is '^]'.
DEL key1
OK
Connection closed by foreign host.

➜  telnet 127.0.0.1 7878
Trying 127.0.0.1...
Connected to 127.0.0.1.
Escape character is '^]'.
GET key1
Key not found
Connection closed by foreign host.

So, it works. It’s crude and needs to be patched to be a little more production ready than this - but this is a start.

Conclusion

In this article, we walked through building a thread-safe, persistent key-value store in Rust. We started with a simple in-memory implementation and iteratively improved it by:

  • Adding support for multiple data types using an enum.
  • Ensuring thread safety with RwLock and Arc.
  • Replacing unwrap with proper error handling.
  • Adding file persistence using JSON serialization and deserialization.
  • Added some basic network access

This provides a solid foundation for a more robust and scalable key-value server. Next steps could include:

  • Implementing advanced features like snapshots or replication.
  • Optimizing for performance with tools like async I/O or a custom storage engine.
  • Partial reads and memory mapping
  • Clustering

The full implementation can be found here.

Implementing an ML Model in Rust

26 Jan 2025

Introduction

Rust, known for its performance, memory safety, and low-level control, is gaining traction in domains traditionally dominated by Python, such as machine learning (ML). While Python is the go-to for prototyping ML models due to its mature ecosystem, Rust shines in scenarios demanding high performance, safety, and seamless system-level integration.

In this post, we’ll explore how to implement logistic regression in Rust and discuss the implications of the model’s output.

Why use Rust?

Before diving into code, it’s worth asking: why choose Rust for ML when Python’s libraries like TensorFlow and PyTorch exist?

Benefits of Rust:

  • Performance: Rust offers near-C speeds, making it ideal for performance-critical tasks.
  • Memory Safety: Its ownership model ensures memory safety, preventing bugs like segmentation faults and data races.
  • Integration: Rust can easily integrate with low-level systems, making it a great choice for embedding ML models into IoT, edge devices, or game engines.
  • Control: Rust provides fine-grained control over execution, allowing developers to optimize their models at a deeper level.

While Rust’s ML ecosystem is still evolving, libraries like ndarray, linfa, and smartcore provide foundational tools for implementing machine learning models.

Logistic Regression

Logistic regression is a simple yet powerful algorithm for binary classification. It predicts whether a data point belongs to class 0 or 1 based on a weighted sum of features passed through a sigmoid function.

Below is a Rust implementation of logistic regression using the ndarray crate for numerical operations.

use ndarray::{Array2, Array1};
use ndarray_rand::RandomExt;
use ndarray_rand::rand_distr::Uniform;

fn sigmoid(x: f64) -> f64 {
    1.0 / (1.0 + (-x).exp())
}

fn logistic_regression(X: &Array2<f64>, y: &Array1<f64>, learning_rate: f64, epochs: usize) -> Array1<f64> {
    let (n_samples, n_features) = X.dim();
    let mut weights = Array1::<f64>::random(n_features, Uniform::new(-0.01, 0.01));
    let mut bias = 0.0;

    for _ in 0..epochs {
        let linear_model = X.dot(&weights) + bias;
        let predictions = linear_model.mapv(sigmoid);

        // Compute the error
        let error = &predictions - y;

        // Compute gradients
        let gradient_weights = X.t().dot(&error) / n_samples as f64;
        let gradient_bias = error.sum() / n_samples as f64;

        // Update weights and bias
        weights -= &(learning_rate * gradient_weights);
        bias -= learning_rate * gradient_bias;
    }

    weights
}

fn main() {
    let X = Array2::random((100, 2), Uniform::new(-1.0, 1.0)); // Random features
    let y = Array1::random(100, Uniform::new(0.0, 1.0)).mapv(|v| if v > 0.5 { 1.0 } else { 0.0 }); // Random labels

    let weights = logistic_regression(&X, &y, 0.01, 1000);
    println!("Trained Weights: {:?}", weights);
}

Key Concepts:

  • Sigmoid Function: Converts the linear combination of inputs into a value between 0 and 1.
  • Gradient Descent: Updates weights and bias iteratively to minimize the error between predictions and actual labels.
  • Random Initialization: Weights start with small random values and are fine-tuned during training.

Output

When you run the code, you’ll see output similar to this:

Trained Weights: [0.034283492207871635, 0.3083430316223569], shape=[2], strides=[1], layout=CFcf (0xf), const ndim=1

What Does This Mean?

  1. Weights: Each weight corresponds to a feature in your dataset. For example, with 2 input features, the model learns two weights.
    • A positive weight means the feature increases the likelihood of predicting 1.
    • A negative weight means the feature decreases the likelihood of predicting 1.
  2. Bias (Optional): The bias adjusts the decision boundary and accounts for data not centered at zero. To view the bias, modify the print statement:
println!("Trained Weights: {:?}, Bias: {}", weights, bias);
  1. Predictions: To test the model, use new data points and calculate their predictions:
let new_data = array![0.5, -0.2];
let linear_combination = new_data.dot(&weights) + bias;
let prediction = sigmoid(linear_combination);
println!("Prediction Probability: {}", prediction);

Predictions close to 1 indicate class 1, while predictions close to 0 indicate class 0.

Why Does This Matter?

This simple implementation demonstrates the flexibility and control Rust provides for machine learning tasks. While Python excels in rapid prototyping, Rust’s performance and safety make it ideal for deploying models in production, especially in resource-constrained or latency-critical environments.

When Should You Use Rust for ML?

Rust is a great choice if:

  • Performance is critical: For example, in real-time systems or embedded devices.
  • Memory safety is a priority: Rust eliminates common bugs like memory leaks.
  • Integration with system-level components is needed: Rust can seamlessly work in environments where Python may not be ideal.
  • Custom ML Implementations: You want more control over how the algorithms are built and optimized.

For research or quick prototyping, Python remains the best choice due to its rich ecosystem and community. However, for production-grade systems, Rust’s strengths make it a compelling alternative.

Conclusion

While Rust’s machine learning ecosystem is still maturing, it’s already capable of handling fundamental ML tasks like logistic regression. By combining performance, safety, and control, Rust offers a unique proposition for ML developers building high-performance or production-critical applications.