Comparison¶

Below is a comparison of jonq with Pandas and DuckDB, highlighting their differences in use cases, setup, and capabilities.

Aspect

jonq

Pandas

DuckDB

Primary Use Case

Fast, lightweight JSON querying from the command line

General-purpose data manipulation and analysis in Python

Analytical SQL queries on large datasets, including JSON

Setup

Minimal: requires only jq and Python

Requires a Python environment with Pandas installed

Requires installing DuckDB

Query Language

SQL-like syntax (e.g., select name, age if age > 30)

Python code (e.g., df[df['age'] > 30])

SQL with JSON functions (e.g., SELECT * FROM read_json(...))

Footprint

Small (~500 KB for jq)

Larger (~20 MB for Pandas)

Larger (~140 MB for DuckDB)

Streaming

Supports streaming for large JSON files (--stream option)

Can handle large files with chunking (e.g., pd.read_json(..., chunksize=...))

Must load data into tables

Memory Usage

Low, due to streaming capabilities

Higher, typically loads data into memory

Optimized for large datasets with columnar storage

Ecosystem

Leverages jq for post-processing

Integrates with Python data science tools (NumPy, Matplotlib)

Can be used standalone or with Python