How bad was my Google interview...
How Bad Was My Google Interview…?
Introduction
Job interviews at tech giants like Google can often be a rollercoaster of emotions, especially during coding rounds. I want to share my experience from one of the final coding rounds for an L3/Early Career position in the US. The challenge involved processing a log file of conversations and returning the most talkative users. While I felt I was on the right path, there were some critical aspects that I could have improved on, particularly regarding time complexity.
The Problem Statement
I was provided with a log file containing conversation snippets formatted as follows:
“maria: hi, john: hello hi bye bye, maria: pls dont go”
The goal was to create a helper function, parse_log, that would process this log and yield a list of tuples representing each user’s total word count:
[(‘maria’, 1), (‘john’, 4), (‘maria’, 3)]
From this list, I needed to return the top N most talkative users.
My Approach
I decided to use a dictionary to tally the word counts for each user. After that, I utilized a min-heap to manage the top N users. To simulate a max-heap (since Python only provides a min-heap), I stored negative word counts. However, I made a critical mistake: I created a heap of size P (the number of people), instead of capping it at N (the number of top users I needed).
Time Complexity Analysis
I concluded that the time complexity of my solution was O(M log U), where:
- M = number of messages
- U = number of unique users
While I felt confident explaining this, I realized afterward that my heap implementation didn’t offer any advantages over a simple sorting approach due to its size being P.
If I had capped the heap at size N, the time complexity would have instead been O(M log N). This distinction is significant in cases where N « U, as it would lead to more efficient processing.
Debugging and Feedback
After coding, I had around 20-25 minutes left for debugging. The interviewer pointed out a bug in my code, which I quickly identified. However, she pointed to another section that contained a syntactical bug, and while I was able to fix it, it took longer than expected.
I provided various test cases, including those for ties and cases where parse_log returned an empty list. The interviewer then asked about the time complexity, and I explained my reasoning. Unfortunately, we ran out of time before I could explore further optimizations or alternative algorithms, like Quickselect, which could solve the problem in linear time.
Reflection
Looking back, I realize that I could have significantly improved my solution. The choice of a priority queue is critical here, and using a heap of size N would have been much more efficient. Moreover, employing the Quickselect algorithm could have allowed me to achieve an even better time complexity of O(M + U log K), where K is the number of top users needed.
This experience taught me the importance of understanding not just the solution but also the intricacies of time complexity and algorithmic efficiency.
Conclusion
While I felt I performed reasonably well, the few missteps in my implementation and optimization choices made me question how I fared in the interview. However, I was reassured by the interviewer’s feedback that I was on the right track.
Interviews are learning experiences, and this round was no exception. If you’re preparing for similar challenges, remember to focus on both the correctness of your solution and the efficiency of your algorithms.
Top Comments Discussion
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Dynamic Logs: A clarifying question arose about whether the log file was dynamic. If logs are continuously added or if older logs are discarded, this would significantly change the approach needed for the problem.
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Heap Size: A comment pointed out that using a heap of size P defeats the purpose of using a priority queue, as it becomes akin to sorting the entire array.
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Quickselect: It was mentioned that if I had implemented Quickselect, it could have easily elevated my solution to a higher level of competency.
In conclusion, every coding interview is a learning opportunity. Whether you succeed or face challenges, there is always room for growth and understanding. Good luck to everyone preparing for their next tech interview!