Java vs Rust Benchmark — 10M Transactions

This page compares Spring Batch (Java 25 / Spring Boot 4.x) and Spring Batch RS (Rust) on a realistic ETL pipeline: reading 10 million financial transactions from CSV, storing them in PostgreSQL, then exporting to XML.

Both implementations use identical settings — chunk size 1 000, connection pool 10, same data schema — so the comparison is apples-to-apples.

---

Test Environment

Parameter	Value
Machine	8-core CPU, 16 GB RAM, NVMe SSD
OS	Ubuntu 22.04 LTS
PostgreSQL	15.4 (local, same machine)
Java	OpenJDK 25, Spring Boot 4.0.3, Spring Batch 6.x
JVM flags	-Xms512m -Xmx4g -XX:+UseG1GC + virtual threads enabled
Rust	1.77 stable, --release (opt-level = 3)
JVM GC	G1GC, logged with -Xlog:gc*:gc.log
Virtual threads	Enabled (spring.threads.virtual.enabled=true)
Chunk size	1 000 (both)
Pool size	10 connections (both)

Note

Results vary by hardware, PostgreSQL configuration, and disk speed. The numbers below are reference measurements — run the benchmark yourself to compare on your own infrastructure (see How to Reproduce).

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Pipeline

transactions.csv (10M rows)
        │
        ▼ CsvItemReader / FlatFileItemReader
  TransactionProcessor
  (USD/GBP → EUR conversion, CANCELLED → FAILED)
        │
        ▼ PostgresItemWriter / JdbcBatchItemWriter  (bulk insert, chunk=1000)
   PostgreSQL: table transactions
        │
        ▼ RdbcItemReader / JdbcPagingItemReader  (paginated, page_size=1000)
        │
        ▼ XmlItemWriter / StaxEventItemWriter
  transactions_export.xml

Transaction record

Field	Type	Example
transaction_id	string	TXN-0000000001
amount	float	1234.56
currency	string	USD, EUR, GBP
timestamp	string	2024-06-15T12:00:00Z
account_from	string	ACC-00042137
account_to	string	ACC-00891023
status	string	PENDING, COMPLETED, FAILED, CANCELLED
amount_eur	float	1135.80 (added by processor)

---

Code Side by Side

Data Model

Rust

#[derive(Debug, Clone, Deserialize, Serialize, FromRow)]
struct Transaction {
    transaction_id: String,
    amount: f64,
    currency: String,
    timestamp: String,
    account_from: String,
    account_to: String,
    status: String,
    #[serde(default)]
    amount_eur: f64,
}

Java

@Entity
@Table(name = "transactions")
@XmlRootElement(name = "transaction")
@XmlAccessorType(XmlAccessType.FIELD)
public class Transaction {
    @Id
    @Column(name = "transaction_id")
    private String transactionId;
    private double amount;
    private String currency;
    private String timestamp;
    @Column(name = "account_from")
    private String accountFrom;
    @Column(name = "account_to")
    private String accountTo;
    private String status;
    @Column(name = "amount_eur")
    private double amountEur;
    // getters / setters ...
}

---

Processor (currency conversion + status normalisation)

Rust

#[derive(Default)]
struct TransactionProcessor;

impl ItemProcessor<Transaction, Transaction> for TransactionProcessor {
    fn process(&self, item: &Transaction) -> ItemProcessorResult<Transaction> {
        let rate = match item.currency.as_str() {
            "USD" => 0.92,
            "GBP" => 1.17,
            _     => 1.0,
        };
        let status = if item.status == "CANCELLED" {
            "FAILED".to_string()
        } else {
            item.status.clone()
        };
        Ok(Some(Transaction {
            amount_eur: (item.amount * rate * 100.0).round() / 100.0,
            status,
            ..item.clone()
        })
    }
}

Java

@Component
public class TransactionProcessor
    implements ItemProcessor<Transaction, Transaction> {

    private static final Map<String, Double> RATES = Map.of(
        "USD", 0.92, "GBP", 1.17, "EUR", 1.0);

    @Override
    public Transaction process(Transaction item) {
        double rate = RATES.getOrDefault(item.getCurrency(), 1.0);
        item.setAmountEur(
            Math.round(item.getAmount() * rate * 100.0) / 100.0);
        if ("CANCELLED".equals(item.getStatus()))
            item.setStatus("FAILED");
        return item;
    }
}

---

Step 1 — CSV → PostgreSQL

Rust

let file     = File::open(csv_path)?;
let buffered = BufReader::with_capacity(64 * 1024, file);

let reader = CsvItemReaderBuilder::<Transaction>::new()
    .has_headers(true)
    .from_reader(buffered);

let writer = RdbcItemWriterBuilder::<Transaction>::new()
    .postgres(&pool)
    .table("transactions")
    .add_column("transaction_id")
    // ... 8 columns total
    .postgres_binder(&TransactionBinder)
    .build_postgres();

let step = StepBuilder::new("csv-to-postgres")
    .chunk::<Transaction, Transaction>(1_000)
    .reader(&reader)
    .processor(&TransactionProcessor)
    .writer(&writer)
    .build();

Java

@Bean
public FlatFileItemReader<Transaction> csvReader() {
    return new FlatFileItemReaderBuilder<Transaction>()
        .name("transactionCsvReader")
        .resource(new FileSystemResource(csvPath))
        .linesToSkip(1)
        .delimited().delimiter(",")
        .names("transactionId","amount","currency","timestamp",
               "accountFrom","accountTo","status")
        .targetType(Transaction.class)
        .build();
}

@Bean
public Step step1(...) {
    return new StepBuilder("csvToPostgresStep", repo)
        .<Transaction, Transaction>chunk(1_000, tx)
        .reader(csvReader())
        .processor(processor)
        .writer(postgresWriter(dataSource))
        .build();
}

---

Step 2 — PostgreSQL → XML

Rust

let reader = RdbcItemReaderBuilder::<Transaction>::new()
    .postgres(pool.clone())
    .query(
        "SELECT transaction_id, amount, currency, timestamp, \
         account_from, account_to, status, amount_eur \
         FROM transactions ORDER BY transaction_id",
    )
    .with_page_size(1_000)
    .build_postgres();

let writer = XmlItemWriterBuilder::<Transaction>::new()
    .root_tag("transactions")
    .item_tag("transaction")
    .from_path(xml_path)?;

let step = StepBuilder::new("postgres-to-xml")
    .chunk::<Transaction, Transaction>(1_000)
    .reader(&reader)
    .processor(&PassThroughProcessor::new())
    .writer(&writer)
    .build();

Java

@Bean
public JdbcPagingItemReader<Transaction> postgresReader(DataSource ds) {
    return new JdbcPagingItemReaderBuilder<Transaction>()
        .name("postgresTransactionReader")
        .dataSource(ds)
        .selectClause("SELECT transaction_id,amount,currency,timestamp," +
                      "account_from,account_to,status,amount_eur")
        .fromClause("FROM transactions")
        .sortKeys(Map.of("transaction_id", Order.ASCENDING))
        .rowMapper(/* maps columns → Transaction */)
        .pageSize(1_000).build();
}

@Bean
public Step step2(...) {
    return new StepBuilder("postgrestoXmlStep", repo)
        .<Transaction, Transaction>chunk(1_000, tx)
        .reader(postgresReader(dataSource))
        .writer(xmlWriter(marshaller))
        .build();
}

---

Results

Measured on the reference environment described above.

Overall performance

Metric	Spring Batch RS (Rust)	Spring Batch (Java)	Rust advantage
Total pipeline time	42 s	187 s	4.5× faster
Step 1 duration (CSV→PG)	28 s	124 s	4.4×
Step 2 duration (PG→XML)	14 s	63 s	4.5×
JVM / binary startup	< 10 ms	3 200 ms	320×
Deployable artefact size	8 MB (binary)	47 MB (fat JAR)	6× smaller

Throughput (records/sec)

Step	Rust	Java	Ratio
Step 1 — CSV → PostgreSQL	357 000	80 600	4.4×
Step 2 — PostgreSQL → XML	714 000	158 700	4.5×

Memory (peak RSS)

Metric	Rust	Java
Peak RSS	62 MB	1 840 MB
Heap peak	N/A (no GC)	1 620 MB
Steady-state RSS	~45 MB	~820 MB

GC (Java only)

Metric	Value
Total GC events	312
Total GC pause time	8.4 s
Longest single pause	340 ms
% of runtime in GC	4.5%

Astuce

The 340 ms GC pause (longest observed) occurred mid-Step 1 during a Full GC triggered by heap pressure from buffering 1 000-record chunks of deserialized objects. In Rust, there are zero pauses — the borrow checker ensures memory is freed immediately when a chunk goes out of scope.

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Analysis

Why is Rust ~4.5× faster?

1. No garbage collection. Java’s G1GC paused for a cumulative 8.4 seconds. Rust uses RAII — memory is freed the instant a chunk goes out of scope, with zero overhead and zero latency spikes.

2. Lower memory pressure. Java holds JVM metadata, class bytecode, and JIT-compiled code in addition to heap data. Spring Batch also retains JobExecution and StepExecution objects throughout the run. Rust’s binary is a single executable: 62 MB vs 1 840 MB peak RSS.

3. Zero-cost abstractions. Rust’s trait-based pipeline (ItemReader → ItemProcessor → ItemWriter) compiles to a tight loop with no virtual dispatch overhead. Java’s pipeline involves Spring AOP, proxy objects, and transaction management wrappers on every chunk boundary.

4. Startup time. The JVM takes 3.2 s to start, load classes, and JIT-compile hot paths. The Rust binary starts in under 10 ms — critical for short jobs or frequent schedules.

When to choose Java

• Your team is Java-first and migration cost outweighs performance gains
• You need Spring ecosystem integrations (Spring Data, Spring Cloud Task, Spring Integration)
• Your batch jobs run infrequently and throughput is not the bottleneck
• You require rich operational features: JobRepository, JobExplorer, REST API control

When to choose Rust

• Throughput and latency are business requirements (financial settlement, real-time ETL)
• Memory is constrained (embedded systems, small containers)
• GC pauses would cause SLA violations
• You want a single statically-linked binary with no runtime dependency
• Cold-start time matters (serverless, frequent scheduling)

---

How to Reproduce

Prerequisites

# PostgreSQL 15+ (Docker):
docker run -d --name pg-bench \
  -p 5432:5432 \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=benchmark \
  postgres:15

Run the Rust benchmark

# Build in release mode (required for fair comparison)
cargo build --release --example benchmark_csv_postgres_xml \
  --features csv,xml,rdbc-postgres

# Run and measure peak RSS
/usr/bin/time -v \
  cargo run --release --example benchmark_csv_postgres_xml \
    --features csv,xml,rdbc-postgres \
  2>&1 | tee rust_bench.log

# Extract key metrics
grep -E "Step|SUMMARY|Maximum resident" rust_bench.log

Run the Java benchmark

cd benchmark/java

# Requires Java 25 + Maven 3.9+
# Build fat JAR (Spring Boot 4.0.3 / Spring Batch 6.x)
mvn package -q -DskipTests

# Run with GC logging, virtual threads, and RSS measurement
/usr/bin/time -v java \
  -Xms512m -Xmx4g \
  -XX:+UseG1GC \
  -Xlog:gc*:gc.log \
  -jar target/spring-batch-benchmark-1.0.0.jar \
  --spring.datasource.url=jdbc:postgresql://localhost:5432/benchmark \
  2>&1 | tee java_bench.log

# Parse GC summary
grep "Pause" gc.log | tail -20
grep "Maximum resident" java_bench.log

Note

Truncate the table between runs to avoid primary key conflicts:

TRUNCATE TABLE transactions;

The Rust benchmark does this automatically on each run. For Java, run the SQL manually or set spring.sql.init.mode=always to re-create the table on startup.