Jain Global·Software Engineer·Technical Phone Screen·Senior

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Apr 2026

Summary

Quant Developer interview at Jain Global with a meaty systems question about processing a huge flat file as fast as possible on one machine. Pretty technical, felt more like a design session than a coding screen.

Questions Asked (1)

You have a very large text file where each line is a city name and a temperature reading. How would you compute the min, max, and average temperature per city as fast as possible on a single machine in Python?

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This one went longer than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the file size and memory constraints, then propose a streaming, single-pass solution that aggregates per-city statistics using a dictionary. Emphasize constant memory per city and O(n) time complexity, and mention optimizations like chunked reading and avoiding unnecessary parsing overhead.

Pro tip: Mention that you would first check if the file fits in memory; if not, use a streaming approach with a dictionary of running sums and counts, and consider using `mmap` or `pandas` with `chunksize` for speed. Also, discuss trade-offs between pure Python and using optimized libraries like NumPy or C extensions.

1. Clarify requirements and constraints

Ask about file size, memory limits, and whether the file is static or streaming. Confirm that the goal is to compute min, max, and average per city in a single pass.

2. Choose a streaming aggregation strategy

Propose reading the file line by line (or in chunks) and maintaining a dictionary mapping city to [min, max, sum, count]. Update these values for each line without storing all data.

3. Optimize parsing and I/O

Suggest efficient parsing (e.g., `line.rsplit(' ', 1)` or `split(',')` depending on format) and using `with open(...)` for buffered I/O. Consider `mmap` or `pandas.read_csv` with `chunksize` for faster reading.

4. Handle edge cases and data quality

Mention handling malformed lines, missing values, and cities with a single reading. Ensure the average is computed as sum/count at the end.

5. Discuss performance trade-offs and alternatives

Compare pure Python vs. using libraries like NumPy/pandas, and mention parallelization (e.g., multiprocessing) if the file is huge, but note that single-machine single-pass is often sufficient.

Key Points to Mention

Single-pass streaming algorithm with O(n) time and O(k) memory where k is number of unique cities
Using a dictionary to store running min, max, sum, and count per city
Efficient file reading: buffered I/O, chunking, or memory-mapping
Avoiding storing all lines in memory; processing line by line
Trade-offs between pure Python and optimized libraries (pandas, NumPy)
Handling data quality issues like malformed lines or missing temperatures

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.