Java Streams Side Effects (Interview Questions)

Sharing 4 Interesting Java Interview Questions

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1. ParallelStream returns missing/duplicated elements

• Why does the code below produce missing or duplicated integers?

• How would you fix it without removing parallelism?

public static void main(String[] args) {
    List<Integer> out = new ArrayList<>();
    IntStream.rangeClosed(1, 100)
            .parallel().
            forEach(out::add);
}

Explanation:

• ArrayList is not thread-safe.

• Parallel threads race on:

  • internal resize,

  • index increments,

  • writes to the same backing array.

• This causes:

  • lost writes,

  • duplicated writes,

  • ArrayIndexOutOfBoundsException under heavy load.

Fix:

Option 1:

This option is preferred due to:

• No shared mutable state,

• No locks,

• No contention

• Used exactly as parallel streams are designed

// use parallel stream with collector to_list()
var out1 = IntStream.rangeClosed(1, 100)
        .parallel()
        .boxed()
        .toList();
System.out.println(out1.size()==100); //true

Option 2:

• Shared mutable state across all threads

• Every add() requires acquiring a monitor lock

• Causes severe contention

• Scaling collapses as thread count increases

// use syncronizedList
List<Integer> out2 = Collections.synchronizedList(new ArrayList<>());
IntStream.rangeClosed(1, 100)
        .parallel().
        forEach(out2::add);
System.out.println(out2.size()==100);//true

2. Counter modified inside forEach gives incorrect totals

Below works in sequential streams, but becomes subtly dangerous if someone changes an earlier part of the pipeline to parallel stream , why?

AtomicLong total = new AtomicLong();
invoices.stream().forEach(i -> total.addAndGet(i.amount()));

invoices.parallelStream()...

Explanation:

Streams guarantee correct behavior only if the mapping/reduction steps are side-effect-free.

We have:

• Shared mutable state (AtomicLong)

• Parallel execution

Even with AtomicLong, this can produce incorrect totals in production billing systems.

When we mutate the shared state inside forEach, the stream framework:

• Cannot optimize

• Cannot reorder safely

• Cannot parallelize correctly

• May drop operations when cancellation happens (e.g., findFirst, limit)

So we get missed increments or double increments in rare edge cases.

If parallel is desired, we can do the following:

long total = invoices.parallelStream()
        .mapToLong(Invoice::amount)
        .sum();

3. Updating the database inside the map() causes inconsistent writes

Question:

• What subtle bug can occur here related to lazy evaluation or short-circuiting

• Where should DB calls appear in a stream pipeline?

orders.stream()
      .map(o -> { repo.update(o.id); return o; })
      .map(this::process)
      .forEach(repo::save);

Explanation:

map() is a lazy intermediate operation. It runs only when downstream operations demand its value.
This means:

• If someone adds .limit(10), then only the first 10 updates run.

• If someone later uses .findFirst() or .anyMatch()Updates for the rest of the elements never execute.

• If exceptions occur later in the pipeline, the update already happened → partial writes.

• If the stream ever becomes parallel, map() may be:

  • executed out of order

  • executed more than once (task retries)

  • skipped due to cancellation

All of these lead to an incomplete or inconsistent database state. In short, A database write is hidden inside map() does not have the execution guarantees you expect.

Where should DB calls appear in a stream pipeline?

In the terminal operation.Not inside map(), filter(), flatMap(), etc.

orders.stream()
      .map(this::process)
      .toList()                  // materialize results
      .forEach(o -> repo.update(o.id));

4. Accidental memory leak from capturing a large object in a lambda

Inside the lambda d -> log.debug(...):

• The lambda captures the outer variable report because it references report.size().

• This captured reference becomes part of a synthetic class instance created by the JVM to represent the lambda.

• That lambda instance gets stored inside:

  • the Stream pipeline,

  • the forEach consumer,

  • and most importantly the logging framework’s async buffer.

Why report stays in memory too long

If your logger (Log4j2, Logback, SLF4J) uses:

• asynchronous appenders

• ring buffers

• message formatting queues

• deferred string evaluation

Then the lambda or the formatted message object is queued and held long after the stream finishes.

Because the lambda contains a reference to report, the entire list—sometimes hundreds of thousands of objects—cannot be garbage collected.

So a simple debug log line ends up keeping:

Lambda -> reference to report -> entire List<ReportData> -> thousands of ReportData rows

This results in:

• high memory spikes

• long GC pauses

• out-of-memory on larger batch sizes

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Happy Coding 🚀
Suraj