3 · Reliability and project · 25 MIN

Streams and grouping without mutation

A stream describes a pipeline over data.

filter discards values that do not meet the predicate, mapToInt changes the stream to primitive integers, and sum is a terminal operation. Nothing runs until a terminal operation requests the result. This sample uses a pure pipeline without shared mutable state. Do not assume parallelStream is faster: scheduling, ordering and the cost of each element matter. Streams can be consumed only once; create a fresh stream for a separate result. For large monetary totals use a range-appropriate representation and checked arithmetic.

Prefer collectors or reductions with defined identities.

Read the example

import java.util.*;
public class Main {
  record Expense(String category, int amount) {}
  public static void main(String[] args) {
    var rows = List.of(new Expense("food",5),new Expense("travel",3),new Expense("food",7));
    int total = rows.stream().filter(e -> e.category().equals("food")).mapToInt(Expense::amount).sum();
    System.out.println(total);
    System.out.println(rows.size());
  }
}

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3

Your challenge

Return a Map of category totals using groupingBy and summingInt, then print categories in sorted order.

Solution cost: O(n) traversal; grouping requires O(k) category storage. time · Account for collection storage separately from the returned result. space

Common trap

Mutating a shared accumulator inside a parallel stream introduces races.

import java.util.*;
public class Main {
  record Expense(String category, int amount) {}
  public static void main(String[] args) {
    var rows = List.of(new Expense("food",5),new Expense("travel",3),new Expense("food",7));
    int total = rows.stream().filter(e -> e.category().equals("food")).mapToInt(Expense::amount).sum();
    System.out.println(total);
    System.out.println(rows.size());
  }
}

Further reading: Official documentation