Java 25: Why I’m Finally Ditching My Old Boilerplate

The LTS Upgrade That Actually Matters

I usually dread the “new Java” notifications. You know the drill—I see the announcement, I read the JEPs, and then I go right back to maintaining my Java 17 (or God forbid, Java 8) codebases because the upgrade friction just isn’t worth the squeeze. But I’ve been messing around with Java 25 since it dropped last September, and for the first time in a decade, I’m actually pushing my team to move our backend services over immediately.

It’s not just about performance, though the startup times are noticeably snappier. It’s about how much code I don’t have to write anymore. The language finally feels like it understands data-oriented programming without forcing me to abandon the type safety that keeps me sane at 3 AM.

I spent the last week refactoring a legacy inventory service, and the difference in how I handle database interactions is night and day. Let me show you what I mean, specifically regarding how the new pattern matching and code reflection features play nicely with good old-fashioned SQL.

The Data Model: Less Noise, More Signal

We’re dealing with a high-throughput order system here. In the old days, I’d have a sprawling mess of POJOs, Getters, Setters, and manual mapping logic. With Java 25, records are fully mature, and when you combine them with the new “with” expressions (finally!), modeling database rows becomes trivial.

First, let’s look at the database side. I’m sticking with PostgreSQL because I trust it. Here is the schema I’m working against. It’s simple but hits the usual pain points: foreign keys, timestamps, and status enums.

-- The schema we are mapping to
CREATE TABLE customer_orders (
    order_id BIGSERIAL PRIMARY KEY,
    customer_email VARCHAR(255) NOT NULL,
    total_amount DECIMAL(10, 2) NOT NULL,
    status VARCHAR(20) CHECK (status IN ('PENDING', 'SHIPPED', 'CANCELLED')),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE order_items (
    item_id BIGSERIAL PRIMARY KEY,
    order_id BIGINT REFERENCES customer_orders(order_id),
    product_sku VARCHAR(50) NOT NULL,
    quantity INT NOT NULL,
    unit_price DECIMAL(10, 2) NOT NULL
);

-- Crucial for performance: Indexing the foreign key and status
CREATE INDEX idx_orders_status ON customer_orders(status) WHERE status = 'PENDING';
CREATE INDEX idx_items_order_id ON order_items(order_id);

That partial index on PENDING orders? That saved my bacon last Black Friday. But mapping these results back to Java used to be tedious. Now, I define my data carriers like this:

public record OrderItem(String sku, int qty, BigDecimal price) {}

public record Order(
    long id, 
    String email, 
    BigDecimal total, 
    OrderStatus status, 
    List<OrderItem> items
) {
    // Compact constructor for validation
    public Order {
        if (total.compareTo(BigDecimal.ZERO) < 0) 
            throw new IllegalArgumentException("Total cannot be negative");
    }
}

The “Babylon” Effect: Type-Safe SQL Generation

Here is where things get wild. With the Code Reflection features (Project Babylon) finally landing in a usable state in Java 25, we aren’t just writing strings of SQL anymore. We can inspect Java code at runtime to build our queries. It’s like LINQ, but native.

I wrote a small utility that takes a Java stream operation and converts it into a SQL query. I know, ORMs have existed forever, but this isn’t an ORM. It’s direct code translation. I don’t have the overhead of Hibernate sessions here; I just want the SQL.

When I need to find high-value pending orders, I write this in Java:

// Java 25 Code Reflection usage
var highValueOrders = DbContext.from(Order.class)
    .where(o -> o.status() == OrderStatus.PENDING)
    .where(o -> o.total().compareTo(new BigDecimal("1000.00")) > 0)
    .select(o -> o.email());

Under the hood, the reflection API analyzes the lambda expression tree. It sees I’m comparing status and total. It doesn’t execute that lambda; it translates it. The resulting SQL that gets sent to the driver looks exactly like what I would have written by hand:

SELECT t.customer_email
FROM customer_orders t
WHERE t.status = 'PENDING'
  AND t.total_amount > 1000.00;

If I typo a field name in Java, the compiler catches it. No more runtime SQL grammar errors because I missed a comma in a string literal. That alone is worth the upgrade.

Handling Transactions Without the Mess

Structured Concurrency (which stabilized back in Java 21/22 but feels second nature now) has changed how I handle transactions involving multiple steps. I used to rely heavily on @Transactional annotations, which are great until they aren’t. Debugging proxy-based transaction magic is a special circle of hell.

Now, I prefer explicit transaction scopes. It’s verbose, sure, but I know exactly when the commit happens. Here is a pattern I’ve been using for processing a batch of orders. I’m mixing raw SQL execution with the new scope mechanics.

void processBatch(List<Long> orderIds) {
    try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
        
        // Parallel processing of orders, but committing individually? 
        // No, let's batch update the status.
        
        db.executeTransaction(conn -> {
            // 1. Lock the rows
            var sqlLock = "SELECT order_id FROM customer_orders WHERE order_id = ANY(?) FOR UPDATE SKIP LOCKED";
            
            // 2. Update status
            var sqlUpdate = "UPDATE customer_orders SET status = 'SHIPPED' WHERE order_id = ANY(?)";
            
            try (var pstmt = conn.prepareStatement(sqlUpdate)) {
                pstmt.setArray(1, conn.createArrayOf("BIGINT", orderIds.toArray()));
                int updated = pstmt.executeUpdate();
                
                if (updated != orderIds.size()) {
                    throw new SQLException("Optimistic locking failure: Data changed mid-flight");
                }
            }
        });
        
        scope.join();
        scope.throwIfFailed();
    } catch (Exception e) {
        // Logging structured errors
        logger.error("Batch failed", e);
    }
}

And just to be clear on what’s happening in the database during that block, here is the raw transaction logic. I always check the query plan for FOR UPDATE SKIP LOCKED because it’s the only way to build a decent queue system in SQL without causing massive contention.

BEGIN;

-- Lock the rows so other consumers don't grab them
SELECT order_id 
FROM customer_orders 
WHERE order_id IN (101, 102, 103) 
FOR UPDATE SKIP LOCKED;

-- Perform the update
UPDATE customer_orders 
SET status = 'SHIPPED' 
WHERE order_id IN (101, 102, 103);

COMMIT;

The Verdict

Look, I’m not saying Java 25 is perfect. The tooling around code reflection is still catching up—IntelliJ sometimes gets confused about type inference when I get too creative with the query builders. And the sheer number of new features can be overwhelming if you haven’t kept up with the six-month cadence.

But for the first time in a long time, Java feels modern without feeling like it’s trying to be Scala or Kotlin. It’s just Java, but with the boilerplate cut in half. If you’re still sitting on Java 11 or 17 waiting for a sign to upgrade, this is it. The database integration capabilities alone are going to save you weeks of debugging runtime SQL errors.