Getting Started

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Welcome to Spring Batch RS! This guide will walk you through creating your first batch processing application in Rust.

Prerequisites

Before you begin, ensure you have:

Rust 1.70+ installed (Install Rust)
Basic familiarity with Rust programming
A text editor or IDE (VS Code with rust-analyzer recommended)

Quick Start

Step 1: Create a New Project

cargo new my-batch-app
cd my-batch-app

Step 2: Add Dependencies

Add Spring Batch RS to your Cargo.toml:

[dependencies]
spring-batch-rs = { version = "0.3", features = ["csv", "json"] }
serde = { version = "1.0", features = ["derive"] }

Step 3: Write Your First Batch Job

Create a simple CSV to JSON converter:

use spring_batch_rs::{
    core::{job::JobBuilder, step::StepBuilder, item::PassThroughProcessor},
    item::{csv::CsvItemReaderBuilder, json::JsonItemWriterBuilder},
    BatchError,
};
use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Deserialize, Serialize)]
struct Product {
    id: u32,
    name: String,
    price: f64,
    category: String,
}

fn main() -> Result<(), BatchError> {
    // Sample CSV data
    let csv_data = r#"id,name,price,category
1,Laptop,999.99,Electronics
2,Coffee Mug,12.99,Kitchen
3,Notebook,5.99,Office
4,Wireless Mouse,29.99,Electronics"#;

    // Create CSV reader
    let reader = CsvItemReaderBuilder::<Product>::new()
        .has_headers(true)
        .from_reader(csv_data.as_bytes());

    // Create JSON writer
    let writer = JsonItemWriterBuilder::<Product>::new()
        .pretty_formatter(true)
        .from_path("products.json");

    // Create processor (pass-through in this case)
    let processor = PassThroughProcessor::<Product>::new();

    // Build the step
    let step = StepBuilder::new("csv-to-json-step")
        .chunk(10)
        .reader(&reader)
        .processor(&processor)
        .writer(&writer)
        .build();

    // Build and run the job
    let job = JobBuilder::new()
        .start(&step)
        .build();

    // Execute the job
    let result = job.run()?;

    println!("✅ Job completed successfully!");
    println!("📊 Processed {} steps", result.get_step_executions().len());

    Ok(())
}

Step 4: Run Your Job

cargo run

You should see:

✅ Job completed successfully!
📊 Processed 1 steps

And a products.json file with your converted data!

Understanding the Core Concepts

The Job

A Job is the top-level container for your entire batch process. It’s composed of one or more Steps that execute sequentially.

let job = JobBuilder::new()
    .start(&step1)      // First step
    .next(&step2)       // Second step (optional)
    .next(&step3)       // Third step (optional)
    .build();

The Step

A Step represents an independent phase of processing. There are two types:

Chunk-Oriented
Tasklet

Process large datasets in configurable chunks using the read-process-write pattern:

let step = StepBuilder::new("process-data")
    .chunk(100)              // Process 100 items at a time
    .reader(&reader)         // Read data source
    .processor(&processor)   // Transform items
    .writer(&writer)         // Write results
    .build();

Execute a single task or operation:

let step = StepBuilder::new("cleanup")
    .tasklet(&cleanup_tasklet)
    .build();

ItemReader

An ItemReader retrieves input data one item at a time from various sources.

use spring_batch_rs::item::csv::CsvItemReaderBuilder;

let reader = CsvItemReaderBuilder::<Product>::new()
    .has_headers(true)
    .delimiter(b',')
    .from_path("products.csv")?;

use spring_batch_rs::item::json::JsonItemReaderBuilder;

let reader = JsonItemReaderBuilder::<Product>::new()
    .from_path("products.json")?;

use spring_batch_rs::item::orm::OrmItemReaderBuilder;
use sea_orm::{Database, EntityTrait};

let db = Database::connect("sqlite::memory:").await?;
let query = ProductEntity::find();

let reader = OrmItemReaderBuilder::new()
    .connection(&db)
    .query(query)
    .page_size(100)
    .build();

ItemProcessor

An ItemProcessor applies business logic to transform or filter items.

use spring_batch_rs::core::item::ItemProcessor;

struct PriceDiscountProcessor {
    discount_rate: f64,
}

impl ItemProcessor<Product, Product> for PriceDiscountProcessor {
    fn process(&self, item: Product) -> Result<Option<Product>, BatchError> {
        let mut product = item;

        // Apply discount
        product.price *= (1.0 - self.discount_rate);

        // Filter out items below minimum price
        if product.price < 5.0 {
            return Ok(None);  // Skip this item
        }

        Ok(Some(product))
    }
}

// Usage
let processor = PriceDiscountProcessor { discount_rate: 0.15 };

ItemWriter

An ItemWriter outputs processed items to various destinations.

use spring_batch_rs::item::json::JsonItemWriterBuilder;

// Replace MyType with your actual output type
let writer = JsonItemWriterBuilder::<MyType>::new()
    .pretty_formatter(true)
    .from_path("output.json");

use spring_batch_rs::item::csv::CsvItemWriterBuilder;

let writer = CsvItemWriterBuilder::new()
    .has_headers(true)
    .delimiter(b',')
    .from_path("output.csv")?;

use spring_batch_rs::item::orm::OrmItemWriterBuilder;

let writer = OrmItemWriterBuilder::new()
    .connection(&db)
    .build();

Complete Example: Data Processing Pipeline

Let’s build a more sophisticated batch job that:

Reads products from CSV
Applies business rules and transformations
Filters invalid items
Writes to JSON

use spring_batch_rs::{
    core::{job::JobBuilder, step::StepBuilder, item::ItemProcessor},
    item::{csv::CsvItemReaderBuilder, json::JsonItemWriterBuilder},
    BatchError,
};
use serde::{Deserialize, Serialize};

#[derive(Deserialize, Clone)]
struct RawProduct {
    id: u32,
    name: String,
    price: f64,
    category: String,
    stock: i32,
}

#[derive(Serialize)]
struct ProcessedProduct {
    id: u32,
    name: String,
    final_price: f64,
    category: String,
    stock_status: String,
    price_tier: String,
}

struct ProductProcessor;

impl ItemProcessor<RawProduct, ProcessedProduct> for ProductProcessor {
    fn process(&self, item: RawProduct) -> Result<Option<ProcessedProduct>, BatchError> {
        // Validate price
        if item.price <= 0.0 {
            return Ok(None); // Skip invalid items
        }

        // Apply category-specific discount
        let discount = match item.category.as_str() {
            "Electronics" => 0.15,
            "Kitchen" => 0.10,
            "Office" => 0.05,
            _ => 0.0,
        };

        let final_price = item.price * (1.0 - discount);

        // Determine stock status
        let stock_status = if item.stock == 0 {
            "Out of Stock"
        } else if item.stock < 10 {
            "Low Stock"
        } else {
            "In Stock"
        }.to_string();

        // Classify price tier
        let price_tier = if final_price < 20.0 {
            "Budget"
        } else if final_price < 100.0 {
            "Mid-Range"
        } else {
            "Premium"
        }.to_string();

        Ok(Some(ProcessedProduct {
            id: item.id,
            name: item.name,
            final_price,
            category: item.category,
            stock_status,
            price_tier,
        }))
    }
}

fn main() -> Result<(), BatchError> {
    // Create input file with sample data
    let csv_data = r#"id,name,price,category,stock
1,Laptop,999.99,Electronics,25
2,Coffee Mug,12.99,Kitchen,150
3,Notebook,5.99,Office,8
4,Wireless Mouse,29.99,Electronics,0
5,Desk Lamp,45.00,Office,50"#;

    let reader = CsvItemReaderBuilder::<RawProduct>::new()
        .has_headers(true)
        .from_reader(csv_data.as_bytes());

    let writer = JsonItemWriterBuilder::<ProcessedProduct>::new()
        .pretty_formatter(true)
        .from_path("processed_products.json");

    let processor = ProductProcessor;

    let step = StepBuilder::new("process-products")
        .chunk(10)
        .reader(&reader)
        .processor(&processor)
        .writer(&writer)
        .build();

    let job = JobBuilder::new()
        .start(&step)
        .build();

    job.run()?;

    println!("✅ Processing complete! Check processed_products.json");

    Ok(())
}

Error Handling & Fault Tolerance

Spring Batch RS provides robust error handling with configurable skip limits:

let step = StepBuilder::new("fault-tolerant-step")
    .chunk(100)
    .reader(&reader)
    .processor(&processor)
    .writer(&writer)
    .skip_limit(10)  // Allow up to 10 errors before failing
    .build();

Best Practices

Start Small

Begin with small chunk sizes (10-100) and adjust based on your data and memory constraints

Validate Early

Perform validation in your processor to catch errors before writing

Use Skip Limits Wisely

Set appropriate skip limits based on acceptable data quality thresholds

Log Errors

Implement custom error logging to track skipped items for later review

Next Steps

Now that you understand the basics, explore more advanced features:

Processing Models

Learn about chunk-oriented vs tasklet processing patterns

Item Readers & Writers

Explore all available data sources and destinations

View Options →

Tasklets

Use tasklets for file operations, FTP transfers, and custom tasks

Learn About Tasklets →

Examples

Browse comprehensive examples for every feature

See Examples →

Need Help?

📖 API Documentation - Complete API reference
💬 Discord - Chat with the community
🐛 GitHub Issues - Report bugs or request features
💡 Discussions - Ask questions

Happy batch processing! 🚀