Second Lowest Number

Introduction

Practice a focused Java Stream API coding question: Second Lowest Number. Solve it first with a stream pipeline, then compare it with the imperative alternative.

Purpose of this lesson

Build interview-ready Stream API muscle memory. Difficulty: Medium - XP: 50 - Estimated time: 10 min - Tags: distinct, sorted, optional.

Understanding the topic

Problem statement: Find the second lowest distinct number in a list.

Sample input: List.of(10, 5, 20, 5, 15)
Expected output: Optional[10]

Approach: Remove duplicates, sort ascending, skip the lowest, then take the next value.

Syntax reference

Stream solution:

Optional<Integer> secondLowest = numbers.stream()
    .distinct()
    .sorted()
    .skip(1)
    .findFirst();

Alternative solution:

Integer first = null, second = null;
for (int n : new HashSet<>(numbers)) {
    if (first == null || n < first) {
        second = first;
        first = n;
    } else if (second == null || n < second) {
        second = n;
    }
}
Optional<Integer> result = Optional.ofNullable(second);

Informative example

Dry run: Distinct values are 10, 5, 20, 15; ascending order is 5, 10, 15, 20; skipping 5 returns 10.

• Follow-up: Can this be done in one pass?
• Follow-up: What should happen when all values are equal?

Complexity analysis

• Time: O(n log n)
• Space: O(n)

Real-world use

Interactive challenge: complete the starter code, then compare with the reference solution.

static Object solve(List<?> input) {
    // TODO: write the Stream pipeline
    return null;
}

Reference solution:

Optional<Integer> secondLowest = numbers.stream()
    .distinct()
    .sorted()
    .skip(1)
    .findFirst();

Hints:
- Start with the operation that changes the number of elements.
- Use a terminal operation that returns exactly the expected output type.
- Write an empty-list test before the happy path.

Best practices

• Name the terminal operation and explain why it ends the pipeline.
• Mention how nulls, empty inputs, duplicates, and ordering affect the answer.
• State when a loop is clearer than a Stream pipeline.

Common mistakes

• Calling get() on an empty Optional instead of handling absence.
• Using sorted() for a problem that only needs one pass.
• Mutating external state inside map, filter, or forEach.

Debugging tips

• Print the intermediate result after each pipeline stage when the output surprises you.
• Temporarily replace method references with lambdas so you can inspect parameter names and values.

Optimization strategies

• Avoid unnecessary distinct(), sorted(), and boxing on hot paths.
• Use primitive streams such as mapToInt when aggregating large numeric collections.

Advanced interview questions

Hands-on exercise

Practice progress metadata: difficulty=Medium, xp=50, estimatedTime=10 min, tags=distinct, sorted, optional.

Summary

You solved Second Lowest Number with Streams, compared it against an imperative alternative, reviewed complexity, and captured interview follow-ups.

Purpose of this lesson

Practice Stream API interview questions with production-style trade-offs, not just syntax recall.

Step-by-step explanation

1. State the input, output, and edge cases before writing the pipeline.
2. Choose the smallest useful Stream operation: filter, map, flatMap, reduce, or collect.
3. Compare the Stream answer with the imperative alternative and explain readability trade-offs.
4. Check performance notes: stateful operations, boxing, sorting, distinct, and parallel overhead.

Interactive workflow diagram

Debugging tips

• Insert a temporary peek() only while debugging, then remove it before committing.
• If the result is missing rows, inspect every predicate in filter() with a small sample input.
• When collectors produce unexpected maps, print the classifier key for each element.
• When NOT to use Streams: complex branching, heavy mutation, tiny hot loops, or code where a clear loop is easier to review.

Optimization strategies

• Why distinct() can be expensive: it keeps a set of seen values and depends on correct equals/hashCode.
• When parallel streams hurt performance: small data, blocking I/O, shared mutable state, or expensive splitting.
• Common mistakes with reduce(): non-neutral identity values and non-associative accumulators break parallel correctness.
• Stream pipeline optimization: filter early, avoid intermediate collections, prefer primitive streams for numeric aggregation.

Enterprise example

Teams at Netflix, Uber and Goldman Sachs apply Second Lowest Number daily — usually wrapped behind Spring Boot services with observability hooks (Micrometer + OpenTelemetry).

Interview questions & answers

Summary

Use this question to practice both coding and explanation: the strongest interview answers include edge cases, trade-offs, and when not to use Streams.