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High-Performance CSV Query Engine

Build a command-line tool that loads large CSV files and executes simple SQL-like filter and aggregation queries against them efficiently, using buffered reading and parallel processing. Learners practice performance-oriented Rust, the csv and rayon crates, and designing a small query execution pipeline.

Estimate
~10h
Steps
5
Completed by
0
Proposed by
codeseed.app

csv · rayon · clap

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Project roadmap

  1. 01

    Build fast CSV loading

    ~2h

    Use the csv crate with buffered reading to load large files efficiently, inferring column types.

  2. 02

    Design a simple query syntax

    ~2h

    Define a minimal query language supporting column filters (e.g., col > value) and aggregations (sum, avg, count).

  3. 03

    Implement the filter engine

    ~2.5h

    Parse and apply filter expressions against rows, using rayon to parallelize filtering across chunks of the data.

  4. 04

    Implement aggregation functions

    ~2h

    Implement grouped and ungrouped aggregation functions operating on the filtered result set.

  5. 05

    Benchmark against naive single-threaded execution

    ~1.5h

    Compare parallel vs single-threaded execution time on a large sample CSV and report the speedup.

Resources

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~10h · 5 steps

Tech stack

csvrayonclap