Java Streams

You are a Java developer specializing in functional programming patterns. The user wants to refactor imperative loops into declarative Java Streams and apply functional composition techniques.

What to check first

• Verify Java version is 8 or higher: java -version (Streams API requires Java 8+)
• Check existing loop structures in your codebase for candidates like for, while, enhanced for, and nested iterations that filter, map, or collect data

Steps

1. Identify imperative loops that iterate over collections and perform filtering, transformation, or aggregation operations
2. Replace for loops with stream() to create a stream from the source collection
3. Chain filter(Predicate<T>) to replace conditional blocks that skip elements
4. Chain map(Function<T,R>) to replace operations that transform each element
5. Use flatMap(Function<T,Stream<R>>) instead of nested loops that flatten results
6. Replace collection building logic (like new ArrayList<>() with .add() in loops) with collect(Collectors.toList())
7. For terminal operations, use forEach(Consumer<T>) for side effects, reduce() for aggregation, or findFirst()/findAny() for searching
8. Chain multiple operations without creating intermediate collections—streams are lazy and evaluate only when a terminal operation is called

Code

import java.util.*;
import java.util.stream.Collectors;

public class StreamRefactoring {

    // BEFORE: Imperative loop
    public static List<String> filterAndTransformOld(List<String> names) {
        List<String> result = new ArrayList<>();
        for (String name : names) {
            if (name.length() > 3) {
                result.add(name.toUpperCase());
            }
        }
        return result;
    }

    // AFTER: Stream with filter and map
    public static List<String> filterAndTransformNew(List<String> names) {
        return names.stream()
                .filter(name -> name.length() > 3)
                .map(String::toUpperCase)
                .collect(Collectors.toList());
    }

    // BEFORE: Nested loops flattening
    public static List<Integer> flattenAndFilterOld(List<List<Integer>> matrix) {
        List<Integer> result = new ArrayList<>();
        for (List<Integer> row : matrix) {
            for (Integer value : row) {
                if (value > 10) {
                    result.add(value);
                }
            }
        }
        return result;
    }

    // AFTER: flatMap and filter
    public static List<Integer> flattenAndFilterNew(List<List<Integer>> matrix) {
        return matrix.stream()
                .flatMap(Collection::stream)
                .filter(value -> value > 10)
                .collect(Collectors.toList());

Note: this example was truncated in the source. See the GitHub repo for the latest full version.

Common Pitfalls

• Treating this skill as a one-shot solution — most workflows need iteration and verification
• Skipping the verification steps — you don't know it worked until you measure
• Applying this skill without understanding the underlying problem — read the related docs first

When NOT to Use This Skill

• When a simpler manual approach would take less than 10 minutes
• On critical production systems without testing in staging first
• When you don't have permission or authorization to make these changes

How to Verify It Worked

• Run the verification steps documented above
• Compare the output against your expected baseline
• Check logs for any warnings or errors — silent failures are the worst kind

Production Considerations

• Test in staging before deploying to production
• Have a rollback plan — every change should be reversible
• Monitor the affected systems for at least 24 hours after the change