Java vs. Rust: 6 Key Lessons for Backend Developers Making the Switch

Introduction: Why Try Rust After 8 Years of Java

After 8 years as a backend engineer at Google working primarily with Java, I finally decided to see what all the Rust hype was about. I've built and maintained large-scale Java services, becoming well-acquainted with Google's internal build systems and custom frameworks. But despite Java's maturity, I've still experienced the pain of NullPointerExceptions, JVM overhead, and the verbosity that comes with large-scale Java development.

TL;DR for this section: Despite working with Java for years at Google, I wanted to evaluate Rust's safety guarantees and performance benefits firsthand, especially after hearing colleagues both praise it and complain about the borrow checker.

Did you know? Rust has been voted the "most loved programming language" in the Stack Overflow Developer Survey for seven consecutive years (2016-2023).

The Project: A CLI Log Analyzer

For my first Rust project, I built a command-line tool called LogRusticate that parses and analyzes log files. The tool can:

• Count log entries by log level (ERROR, WARN, INFO, DEBUG)

• Filter logs by level

• Search for specific terms

• Show a summary of logs with time range and entry count

How you can use this in your next project: Log analysis tools are universally useful for debugging and monitoring. Building one yourself is an excellent way to learn a new language while creating something practical.

Lesson 1: The Ecosystem Is Surprisingly Mature (But Different)

Coming from Google's vast internal Java ecosystem with decades of maturity, I was skeptical about Rust's crate ecosystem but was pleasantly surprised.

// Rust dependencies
use chrono::{DateTime, NaiveDateTime, Utc};
use clap::{Parser, Subcommand};
use lazy_static::lazy_static;
use regex::Regex;

// Java equivalent
import java.time.ZonedDateTime;
import org.apache.commons.cli.CommandLine;
import java.util.regex.Pattern;

The clap crate for CLI argument parsing is honestly better than the internal flag parsing libraries I'd use in Java at Google. The derive macros make it incredibly clean:

// Rust with clap
#[derive(Parser, Debug)]
#[command(version, about, long_about = None)]
struct Cli {
    /// File from where to read the logs.
    logs_filename: String,

    #[arg(short, long, value_enum)]
    output_format: OutputFormat,

    #[command(subcommand)]
    command: CliCommand,
}

Compared to the Java approach:

// Java with Apache Commons CLI
Options options = new Options();
options.addOption(Option.builder("f")
    .longOpt("file")
    .hasArg()
    .required()
    .desc("File from where to read the logs")
    .build());
options.addOption(Option.builder("o")
    .longOpt("output-format")
    .hasArg()
    .desc("Output format (tiny or json)")
    .build());
// And more boilerplate...

In other words: Rust's ecosystem might be younger than Java's, but it's remarkably mature and often more elegantly designed. The declarative approach in Rust eliminates significant boilerplate compared to imperative Java libraries.

Technical Definition: A crate in Rust is a compilation unit, which can be compiled into a binary or a library. Crates can be published to crates.io, Rust's package registry, making them available for other developers to use.

Lesson 2: Enums Are POWERFUL (Not Just Constants Like in Java)

Java vs. Rust Enums: A Comparison

Feature	Java Enums	Rust Enums
Basic Type	Glorified constants	Algebraic data types
Can Contain Data	Yes (but it's a pain to set up)	Yes (different types per variant)
Pattern Matching	No (switch statements)	Yes (exhaustive)
Type Safety	Limited	Comprehensive

In Java, I've used enums extensively, but they're essentially glorified constants with some methods:

// Java enum
public enum LogLevel {
    ERROR, WARN, INFO, DEBUG, UNKNOWN;
    
    // Maybe some utility methods
    public boolean isErrorOrWarn() {
        return this == ERROR || this == WARN;
    }
}

But in Rust? They're full-blown algebraic data types with pattern matching that completely changes how you structure your code:

// Rust enum with data
#[derive(Subcommand, Debug)]
enum CliCommand {
    /// Count the number of processed logs.
    Count {},

    /// Filter logs by level and return the resulting logs.
    Filter {
        /// The level to filter the logs by.
        level: logs::LogLevel,
    },

    /// Search the logs for a specific term.
    Search {
        /// The term to search the logs for.
        term: String,
    },

    /// Summarize the logs.
    Summary {},
}

Pattern matching in Rust eliminates entire categories of bugs that plague Java code:

// Rust pattern matching
let output = match &cli.command {
    CliCommand::Count {} => {
        // Handle count command
    },
    CliCommand::Filter { level } => {
        // Handle filter command with bound variable
    },
    // ... other commands
};

The Java equivalent would be much more verbose:

// Java equivalent (pre-Java 17 pattern matching)
if (command instanceof CountCommand) {
    // Handle count command
} else if (command instanceof FilterCommand) {
    LogLevel level = ((FilterCommand) command).getLevel();
    // Handle filter command
} else if ...

TL;DR for this section: Rust's enums are far more powerful than Java's, allowing you to encapsulate data with variants and use exhaustive pattern matching to ensure all cases are handled.

❓ Have you ever had a bug because you forgot to handle a case in a switch statement? Rust's pattern matching prevents that entirely.

Lesson 3: The Ownership System Is Both Brilliant and Infuriating

This is where the Java-to-Rust transition is most jarring. In Java, we create objects and let the garbage collector worry about cleanup:

// Java - create and forget
List<LogEntry> entries = readLogFile(filename);
List<LogEntry> filtered = entries.stream()
    .filter(log -> log.getLevel() == level)
    .collect(Collectors.toList());

Memory Management Comparison

Aspect	Java	Rust
Memory Management	Garbage Collection	Ownership System
Reference Types	All objects are references	Owned values, references, and mutable references
Cleanup Timing	Unpredictable (GC)	Deterministic (at end of scope)
Memory Safety	Runtime checks	Compile-time checks
Performance Impact	GC pauses	Zero runtime cost

Rust's approach is fundamentally different. Every value has exactly one owner, and when the owner goes out of scope, the value is dropped:

// This works - borrowing with references
Output::Filter(entries.iter().filter(|log| log.level == *level).collect())

// This might not - moves ownership
Output::Filter(entries.filter(|log| log.level == level).collect())

A Real-World Analogy: Think of Java objects like library books that you check out and a librarian (the garbage collector) periodically comes to reclaim books nobody is holding. Rust's ownership system is more like lending a book to someone - you can't read it while they have it, and if they want to pass it to someone else, they must give up their access first.

The first time I got the dreaded "value borrowed here after move" error, I wanted to throw my laptop out the window. But after a few hours of frustration, something clicked. The compiler wasn't being difficult – it was preventing me from introducing the exact kinds of bugs that lead to production incidents in our Java services.

⚠️ Important Warning: Coming from Java, you might be tempted to use clone() liberally to avoid ownership issues. This works but can lead to performance problems. Take the time to understand borrowing instead.

Lesson 4: Functional Programming Feels More Natural Than Java Streams

Java 8 introduced streams, which I use extensively:

// Java streams
Map<LogLevel, Long> levelCounts = entries.stream()
    .collect(Collectors.groupingBy(
        LogEntry::getLevel,
        Collectors.counting()
    ));

But Rust's iterators feel more integrated with the language:

// Rust iterators
let level_frequency_map = entries.into_iter().fold(HashMap::new(), |mut map, entry| {
    *map.entry(entry.level).or_insert(0) += 1;
    map
});

In other words: Java streams feel bolted-on to an object-oriented language, while Rust's iterators feel like a core part of the language design. And unlike Java streams which create many short-lived objects that the GC must clean up, Rust's iterators are zero-cost abstractions that get optimized away at compile time.

Try this yourself: Rewrite a Java stream operation you use frequently in the Rust iterator style. You'll likely find the Rust version more concise and flexible.

Lesson 5: Error Handling Is Explicit (Not Exceptions)

In Java, I throw exceptions and sometimes forget to catch them:

// Java exception handling
try {
    List<LogEntry> entries = readLogFile(filename);
    // Use entries
} catch (IOException e) {
    logger.error("Failed to read log file", e);
    System.exit(1);
}

Rust's approach requires explicit handling:

// My initial approach (not great)
let entries = match read_log_file(cli.logs_filename) {
    Ok(ent) => ent,
    Err(e) => panic!("Error reading log file: {}", e),
};

I fell into the trap of using panic! as a crutch, essentially treating it like throwing runtime exceptions in Java. My code reviewer pointed out that this isn't idiomatic Rust, and I need to properly propagate errors:

// Better Rust error handling
fn main() -> Result<(), LogAnalyzerError> {
    let cli = Cli::parse();
    let entries = read_log_file(&cli.logs_filename)?;
    
	// ...
    
	Ok(())
}

TL;DR for this section: Rust makes error handling explicit in the type system, unlike Java where unchecked exceptions can surprise you at runtime. This makes programs more robust but requires a different mindset.

Lesson 6: Tests Are Built In (Not an Afterthought Like JUnit)

In Java, I use JUnit and Mockito, which require separate dependencies and configuration:

// Java with JUnit
@Test
public void testParseLogEntry() {
    String line = "2025-03-10 08:01:15 INFO User login: username=johndoe";
    LogEntry result = LogEntry.parse(line);
    
    assertEquals(LogLevel.INFO, result.getLevel());
    assertEquals("User login: username=johndoe", result.getMessage());
    // etc...
}

In Rust, testing is built right into the language:

// Rust integrated testing
#[cfg(test)]
mod tests {
    use chrono::NaiveDate;
    use super::*;

    #[test]
    fn success() {
        let line = "2025-03-10 08:01:15 INFO User login: username=johndoe";

        let result = LogEntry::parse(line).unwrap();

        assert_eq!(
            result,
            LogEntry {
                timestamp: NaiveDate::from_ymd_opt(2025, 3, 10)
                    .unwrap()
                    .and_hms_opt(8, 1, 15)
                    .unwrap()
                    .and_utc(),
                level: LogLevel::Info,
                message: "User login: username=johndoe".to_string(),
                original: line.to_string()
            }
        );
    }
}

How you can use this in your next project: Rust's built-in testing encourages a test-as-you-go approach. You can write tests right alongside your code in the same file, making it easier to maintain high test coverage.

What I'd Do Differently Next Time (Coming from Java)

After my code review, I've identified several areas where my Java habits led me astray:

1. Error handling: Stop thinking in exceptions! Create a proper error type and use the ? operator instead of panicking.

2. Ownership optimization: Think about memory ownership up front, which isn't something I ever had to do in Java.

3. Documentation: Add more doc comments with examples, similar to Javadoc but with a different syntax.

4. JSON output: Actually implement it instead of leaving todo!() markers! In Java, I'd just use Jackson, but here I need to learn the serde crate.

   
   

❓ What Java habits do you think would be hardest to break when learning Rust?

The Verdict: A Java Developer's Take on Rust

Is Rust harder to learn than Java? Absolutely. The ownership model requires rewiring how you think about memory, and that's not easy for someone used to garbage collection.

Is it worth it? I'm starting to think so. Even in this small project, I can see how Rust's guarantees eliminate entire categories of bugs I regularly deal with in Java:

• No more null pointer exceptions

• No more concurrent modification exceptions

• No more thread safety issues

• No more GC pauses

• No more wondering if the JIT compiler will optimize your hot path

The compile times in Rust are painful compared to modern incremental Java compilation. But the payoff is runtime performance and predictability.

I'm not ready to rewrite all our Java microservices in Rust yet, but I'm definitely going to keep learning. My next project will be either:

• A database-integrated version of this log analyzer

• A small HTTP service comparable to a simple Spring Boot service, but in Rust

Will I be fighting with the borrow checker? Certainly. But for a Java developer tired of 3am production incidents caused by NullPointerExceptions, that's a fight worth having.

Pull Quote: "I've gone from 'get annoyed every time I have to write Rust' to 'that's a fight worth having' in just one project. Stockholm syndrome? Maybe. Or maybe there's something to this Rust thing after all..."

Further Reading and Resources

• The Rust Programming Language Book - Comprehensive guide to Rust

• Rust By Example - Learn Rust through examples

• Rust for Java Developers - Guide specifically for Java developers

• The Cargo Book - Learn about Rust's package manager

• Rust Design Patterns - Common patterns in Rust programming

  
  

Are you a Java developer who's tried Rust? I'd love to hear your thoughts in the comments!

Interactive Element: Quiz Yourself!

Test your understanding of the Java vs. Rust differences:

1. In Java, you use garbage collection for memory management. What does Rust use instead?

2. What Rust feature is more powerful than Java enums?

3. How does Rust handle errors compared to Java?

4. What's a key advantage of Rust's built-in testing compared to Java's JUnit?

   
   

Check your answers in the comment section below!