Amazon/Google Interview: K-way Merge Log Streams

🔥 Amazon SDE Interview Review

With the real-time assistance of oavoservice, this student not only perfectly solved this classic K-way Merge problem but also turned the algorithm question into a system design discussion by exploring "stream processing" and "external sorting" with the interviewer, earning a Strong Hire rating on the spot.

📘 Problem Description (Engineering Context)

Merge K Sorted Log Streams You are monitoring a distributed system with K application servers. Each server produces a log file where log entries are strictly sorted by timestamp (ascending). Your task is to merge these K log files into a single, chronologically sorted master log stream for analysis.

Example:

Server A: [10:00, 10:05, 10:10]
Server B: [10:01, 10:06]
Server C: [10:00, 10:03]

Target Output:

[10:00 (A), 10:00 (C), 10:01 (B), 10:03 (C), 10:05 (A), ...]

1. Opening: Show Professionalism with Clarifying Questions

Many candidates start coding immediately. oavoservice mentors prompted the student: Confirm boundaries first, this is what a Senior does.

Typical Clarifying Questions:

Tie-breaker Rules: If two servers produce logs at the same millisecond, which comes first? (Usually sorted by Server ID).
Data Scale: Does the log fit in memory, or is it massive data requiring Streaming? (Decides between simple array sort() vs Iterator Pattern + Min-Heap).
Empty Files: Are there cases where a server has no logs? Handle empty lists specifically?

2. Approach: From Brute Force to Optimal

Option A: Brute Force

Method: Read all logs into a large array and call sort().
Time Complexity: (O(N \log N)), where (N) is total log entries.
Issue: Wastes the key information that individual server logs are already sorted.

Option B: Min-Heap K-way Merge (Optimal)

Since each file is internally sorted, we only need to compare the current "head" elements of these K files.

Core Data Structure

Maintain a Min-Heap of size K.
Heap Element: (timestamp, server_id, pointer).

Steps

Init: Push the first log of all K streams into the heap.
Loop:
- Pop the earliest log from the heap, output to result.
- Find the corresponding stream via server_id, push the next log from that stream into the heap.
- If a stream is exhausted, do nothing for that source.
Terminate: When heap is empty.

Complexity

Time: (O(N \log K))
Space: (O(K))

3. oavoservice Real-time Assistance Replay

1️⃣ Breaking the Problem (3 mins)

Student Block: Hesitated whether to read all data first.
oavoservice Hint:

"Don't read all! This is a log stream, emphasize the iterator concept. Propose K-way merge + min-heap directly, complexity O(N log K)."

2️⃣ Coding Phase

Student Block: What to store in the heap? How to get the "next" item?
oavoservice Hint:

"Heap stores (time, server_index, pointer). After popping, use server_index to get pointer + 1 from the array. Check bounds."

3️⃣ Deep Follow-up

Interviewer:

"If the logs are huge (TB-level) and cannot fit into memory, what would you do?"

oavoservice Keywords: External Sort, Streaming, IO-bound

We guided the student to answer:

The current solution is inherently Streaming: memory only holds K candidates.
Bottleneck is Disk/Network IO, not memory.
Combine with External Sorting concepts: read in blocks, sequential scan.

Result: The interviewer immediately elevated the topic to System Design, discussing log collection pipelines.

4. Why You Need oavoservice

Many can write K-way Merge, but few can explain it beautifully, write elegant code, and handle variants.

oavoservice helps you:

🎯 Instantly Pinpoint Concepts: Min-heap / Divide & Conquer / Complexity.
🗣 Optimize Wording: Use terms like latency, throughput, memory footprint.
🛡 Cover Edge Cases: Empty streams, timestamp conflicts.
🚀 Control Pace: Manage Clarification -> Coding -> Testing -> Follow-up.

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