Rust Backend Development

Overview

Learn Rust programming for building high-performance, memory-safe backend services in the Sravz platform. This section covers NSQ messaging, Polars DataFrame processing, and real-time data pipelines for financial applications.

What You’ll Learn

• Rust Fundamentals: Ownership, borrowing, and lifetimes
• NSQ Integration: Message queue for distributed systems
• Polars DataFrames: Fast data processing library (pandas alternative)
• Async Programming: Tokio runtime for concurrent I/O
• Python Interop: PyO3 for calling Rust from Python
• Historical Ticker Plant: Process and store historical market data

Key Technologies

• Rust 1.70+: Modern systems programming language
• NSQ: Distributed real-time messaging platform
• Polars: Lightning-fast DataFrame library
• Tokio: Async runtime for Rust
• MongoDB: Rust driver for document storage
• PyO3: Python bindings for Rust

Documentation Index

1. Rust NSQ Reader/Writer - Build historical ticker plant using NSQ for message queuing
2. Polars DataFrame Service - High-performance data processing with Polars
3. Real-time Data Processing - Real-time market data processing patterns

Use Cases

Message Processing

• NSQ Consumer: Read messages from NSQ topics
• NSQ Producer: Publish processed data to NSQ
• Historical Data: Process and store historical quotes
• Data Transformation: Clean and normalize market data

Data Analysis

• Polars DataFrames: Fast columnar data processing
• Aggregations: Calculate statistics across tickers
• Time-series Operations: Resample and downsample data
• Joins & Merges: Combine multiple data sources

Backend Services

• High Throughput: Process thousands of messages/second
• Low Latency: Sub-millisecond processing times
• Memory Safety: Zero-cost abstractions without crashes
• Concurrent Processing: Tokio async for parallel work

Architecture

NSQ Message Pipeline

Producer → NSQ Topic → Rust Consumer → Process → MongoDB/S3
                           ↓
                    Polars DataFrame
                           ↓
                    Analytics & Storage

Python Integration

Python → PyO3 → Rust (Polars) → Fast Processing → Return Results

Why Rust?

Performance

• Zero-cost Abstractions: High-level code, low-level performance
• No Garbage Collection: Predictable latency
• SIMD Optimizations: Polars leverages vectorization
• Compiled Binary: Native machine code execution

Safety

• Memory Safety: No null pointers, no dangling references
• Thread Safety: Ownership prevents data races
• Type Safety: Strong static typing catches bugs at compile time
• Error Handling: Result type forces explicit error handling

Concurrency

• Fearless Concurrency: Compiler prevents data races
• Async/Await: Tokio for async I/O
• Rayon: Data parallelism for CPU-bound work
• Channels: Message passing between threads

Polars vs Pandas

*Pandas with Dask can be parallel

NSQ Benefits

Distributed

• Topology-free: No brokers, no SPOFs
• Auto-Discovery: nsqlookupd for service discovery
• Horizontal Scaling: Add more nsqd instances

Reliable

• Guaranteed Delivery: Messages persisted to disk
• Requeuing: Automatic retry on failure
• Backpressure: Flow control to prevent overload

Simple

• HTTP/TCP: Easy to produce and consume
• No Dependencies: Self-contained binaries
• Monitoring: Built-in stats and admin UI

Getting Started

1. Learn Basics: Understand Rust ownership and borrowing
2. NSQ Setup: Install and run NSQ locally
3. Follow Tutorial: Build Historical Ticker Plant
4. Explore Polars: Learn DataFrame operations in Polars Service
5. Real-time Processing: Implement Real-time Patterns

Code Examples

NSQ Consumer

use nsq::Consumer;

let mut consumer = Consumer::new("ticker_quotes", "channel");
consumer.on_message(|msg| {
    // Process message
    process_quote(msg.body());
    msg.finish();
});

Polars DataFrame

use polars::prelude::*;

let df = df! {
    "ticker" => ["AAPL", "GOOGL", "MSFT"],
    "price" => [150.0, 2800.0, 300.0],
}?;

let filtered = df.filter(&df["price"].gt(200.0)?)?;

Best Practices

Error Handling

• Use Result<T, E> for recoverable errors
• Use panic! only for unrecoverable bugs
• Implement custom error types with thiserror
• Propagate errors with ? operator

Performance

• Use cargo build --release for production
• Profile with cargo flamegraph
• Benchmark with criterion crate
• Optimize hot paths with #[inline]

Async Code

• Use Tokio for I/O-bound work
• Use Rayon for CPU-bound work
• Avoid blocking in async context
• Use tokio::spawn for parallel tasks

Source Code: backend-rust | Related: C++ | Golang | Python