datasetq is a high-performance data processing tool that extends jq-like syntax to work with structured data formats including Parquet, Avro, CSV, JSON Lines, Arrow, and more. Built on Polars, dsq provides fast data manipulation across multiple file formats with familiar filter syntax.

Key Features

• Format Flexibility - Process Parquet, Avro, CSV, TSV, JSON Lines, Arrow, and more with automatic format detection
• Performance - Built on Polars DataFrames with lazy evaluation, columnar operations, and efficient memory usage
• Familiar Syntax - jq-inspired filter syntax extended to tabular data operations
• Correctness - Proper type handling and clear error messages

Installation

Binaries

Download binaries for Linux, Mac, and Windows from the releases page.

On Linux:

curl -fsSL https://github.com/datasetq/datasetq/releases/latest/download/dsq-$(uname -m)-unknown-linux-musl -o dsq && chmod +x dsq

From Source

Install with Rust toolchain (see https://rustup.rs/):

cargo install --locked dsq-cli
cargo install --locked --git https://github.com/datasetq/datasetq  # development version

Or build from the repository:

cargo build --release  # creates target/release/dsq
cargo install --locked --path dsq-cli  # installs binary

Quick Start

Process CSV data:

dsq 'map(select(.age > 30))' people.csv

Convert between formats:

dsq '.' data.csv --output data.parquet

Aggregate data:

dsq 'group_by(.department) | map({dept: .[0].department, count: length})' employees.parquet

Filter and transform:

dsq 'map(select(.status == "active") | {name, email})' users.json

Process multiple files:

dsq 'flatten | group_by(.category)' sales_*.csv

Use lazy evaluation for large datasets:

dsq --lazy 'filter(.amount > 1000)' transactions.parquet

Interactive Mode

Start an interactive REPL to experiment with filters:

dsq --interactive

Available REPL commands:

• load <file> - Load data from a file
• show - Display current data
• explain <filter> - Explain what a filter does
• history - Show command history
• help - Show help message
• quit - Exit

Common Operations

Format Conversion

dsq convert input.csv output.parquet

Data Inspection

dsq inspect data.parquet --schema --sample 10 --stats

File Merging

dsq merge data1.csv data2.csv --output combined.csv

Shell Completions

dsq completions bash >> ~/.bashrc

Supported Formats

Input/Output:

• CSV/TSV - Delimited text with customizable options
• Parquet - Columnar storage with compression
• JSON/JSON Lines - Standard and newline-delimited JSON
• Arrow - Columnar in-memory format
• Avro - Row-based serialization
• ADT - ASCII delimited text (control characters)

Output Only:

• Excel (.xlsx)
• ORC - Optimized row columnar

Format detection is automatic based on file extensions. Override with --input-format and --output-format.

Documentation

• Architecture - Core library structure and modules
• Functions - Built-in function reference
• Formats - Format support and options
• API - Library usage examples
• Configuration - Configuration file reference
• Language - Filter language syntax

Command-Line Options

Input/Output

• -i, --input-format <FORMAT> - Specify input format
• -o, --output <FILE> - Output file (stdout by default)
• --output-format <FORMAT> - Specify output format
• -f, --filter-file <FILE> - Read filter from file

Processing

• --lazy - Enable lazy evaluation
• --dataframe-optimizations - Enable DataFrame optimizations
• --threads <N> - Number of threads
• --memory-limit <LIMIT> - Memory limit (e.g., 1GB)

Output Formatting

• -c, --compact-output - Compact output
• -r, --raw-output - Raw strings without quotes
• -S, --sort-keys - Sort object keys

Debugging

• -v, --verbose - Increase verbosity
• --explain - Show execution plan
• --stats - Show execution statistics
• -I, --interactive - Start REPL mode

Configuration

Configuration files are searched in:

1. Current directory (.dsq.toml, dsq.yaml)
2. Home directory (~/.config/dsq/)
3. System directory (/etc/dsq/)

Manage configuration:

dsq config show                  # Show current configuration
dsq config set filter.lazy_evaluation true
dsq config init                  # Create default config

See Configuration for details.

Contributing

Contributions are welcome! Please ensure:

1. Compatibility with jq syntax where possible
2. Tests pass with cargo test
3. Documentation updated for new features
4. Performance implications considered

See CONTRIBUTING.md for details.

Acknowledgements

dsq builds on excellent foundations from:

• jq - The original and inimitable jq
• jaq - jq clone inspiring our syntax compatibility
• Polars - High-performance DataFrame library
• Arrow - Columnar memory format

Special thanks to Ronald Duncan for defining the ASCII Delimited Text (ADT) format.

Our GitHub Actions disk space cleanup script was inspired by the Apache Flink project.

License

See LICENSE file for details.