gharc 0.1.4

A stream-processing tool for GitHub Archive data filtering.

gharc: GitHub Archive Stream-Processor

Mine the GitHub Archive on a standard laptop.

gharc is a command-line tool and Python library that filters the GitHub Archive dataset on consumer hardware. Each hourly archive is streamed through memory, filtered against your criteria, and written out as Parquet or JSONL. Peak local storage stays bounded by the downloads in flight at once, one temporary file per worker (each hourly archive is roughly 60 to 150 MB in 2024), so disk use scales with --workers rather than with how long a window you process.

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Why gharc?

The full GitHub Archive spans every public event since 2011: tens of terabytes compressed, and several petabytes uncompressed. Traditional analysis requires either massive local storage or a cloud-warehouse account (BigQuery, Snowflake).

gharc solves this by implementing a Stream-and-Filter architecture:

1. Streaming: Downloads each hourly archive (~60 to 150 MB compressed in 2024) to a temporary file.
2. Filtering: Extracts only events matching your criteria (e.g., specific repos or event types).
3. Writing: Streams matching events into a single Parquet or JSONL file via pyarrow.ParquetWriter for true append.
4. Cleanup: Deletes the temporary download immediately after, so disk usage never accumulates.

Ideal for:

• Academic research on Open Source Software (OSS).
• Large scale data mining on consumer hardware.
• Creating custom datasets for specific organizations or ecosystems.

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Key Features

• Bounded Storage: Processes terabytes of source data while keeping only the in-flight downloads on disk, one temporary file per worker (about 250 MB at the default 4 workers, about 85 MB with a single worker). For selective filters the working memory stays near 100 MB; a very wide or empty filter buffers more of each hour and uses more.
• Resumable Downloads: Recovers from network interruptions (common on residential connections) using HTTP Range requests.
• Parallel processing: Hours in the range are downloaded and filtered across a thread pool.
• Filtering before parsing: A byte-level token check rejects irrelevant lines before any JSON parsing, so most events are skipped without paying the parser cost.
• Optional orjson: Uses orjson for JSON parsing when it is installed, which is faster than the standard library parser.
• Parquet output: Writes columnar data ready for Pandas, Spark, or Polars, typically several times smaller than the equivalent JSONL.

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Performance

Measured on a Windows 11 laptop (12 logical cores, 15 GB RAM) over a typical residential connection. Reproducible scripts in benchmarks/.

A six-hour window of GHArchive (2024-01-01 00:00 to 06:00 UTC), filtered to apache/spark:

Workers	Wall-clock	Hours/sec	Spark events	Peak RSS
1	76.0 s	0.079	14	94.2 MB
4	58.1 s	0.103	14	106.7 MB

Both runs recovered the same events, so concurrency does not affect output. Peak RSS stays below 110 MB. The bottleneck on residential links is HTTPS download throughput rather than CPU; additional workers help up to a point and then saturate the connection.

Across the same six-hour window gharc streams about 416 MB of compressed source from GHArchive (six hourly files of roughly 60 to 85 MB each) but never retains it. The full source is still transferred, so this is not a bandwidth saving; what stays bounded is local disk. Peak disk is held to the temporary files in flight, one per worker: about 85 MB with a single worker and about 250 MB at the default four workers. The filtered Parquet output for apache/spark over that window is 53 KB, and local disk does not grow with the length of the window processed.

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Installation

Prerequisites

• Python 3.10 or higher
• pip

Install from PyPI

pip install gharc

Install from Source

git clone https://github.com/aravpanwar/gharc.git
cd gharc
python -m venv venv
# macOS / Linux:
source venv/bin/activate
# Windows PowerShell:
#   .\venv\Scripts\Activate.ps1
pip install -e .

Optional Performance Boost

For maximum speed, install with the fast extra. gharc detects and uses orjson automatically when available.

pip install "gharc[fast]"

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Usage

Basic Command

Download all activity for a specific repository over a one-day window. Note that --end is exclusive, so this covers all 24 hours of 2024-01-01.

gharc download \
    --start 2024-01-01 \
    --end 2024-01-02 \
    --repos "apache/spark" \
    --output spark_data.parquet

For multi-hour or multi-day runs, prefer --output run.jsonl so the run can resume from where it left off if it crashes; convert to Parquet at the end with gharc convert run.jsonl run.parquet. See Resumable runs below for details.

Advanced Filtering

Filter for multiple repositories and specific event types (e.g., only Pull Requests and Pushes). This covers all of June 2023 (June 1 inclusive through July 1 exclusive).

gharc download \
    --start 2023-06-01 \
    --end 2023-07-01 \
    --repos "apache/spark, pandas-dev/pandas, pytorch/pytorch" \
    --event-types "PullRequestEvent, PushEvent" \
    --output oss_summer_2023.parquet \
    --workers 4

Arguments

Argument	Description	Example
--start	Start date in UTC, inclusive (YYYY-MM-DD or YYYY-MM-DD-HH)	2024-01-01
--end	End date in UTC, exclusive (YYYY-MM-DD or YYYY-MM-DD-HH)	2024-02-01
--repos	Comma-separated repositories to keep; supports owner/* wildcards	apache/spark,apache/*
--orgs	Comma-separated repository owners to keep	apache,pandas-dev
--actors	Comma-separated actor logins to keep	dongjoon-hyun,cloud-fan
--event-types	Comma-separated list of GHArchive event types	WatchEvent,ForkEvent
--output	Output filename (.parquet or .jsonl)	data.parquet
--workers	Number of parallel download threads (default: 4)	8

Dates are interpreted as UTC, matching GHArchive's hourly file naming.

Repository names are matched exactly and are case-sensitive, so pass the canonical owner/name as it appears on GitHub (for example apache/spark). Use apache/* or --orgs apache to keep every repository under an owner. --repos and --orgs are combined (an event is kept if it matches either), while --event-types and --actors further narrow the result.

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Resumable runs

For long jobs, gharc keeps a small <output>.state.json next to the output file listing which hours it has already processed. If the run crashes, restarting the same command picks up where it left off rather than redoing completed hours. The state file is written atomically (a temporary file renamed into place) so a crash mid-write cannot corrupt it, and it is removed automatically when the run finishes cleanly. Note that gharc relies on the operating system to flush writes to disk rather than forcing an fsync after each hour, so an abrupt power loss (as opposed to a process crash) could in rare cases leave an hour marked done whose data had not yet reached disk.

Resume support requires JSONL output. Parquet writers cannot append to a closed file, so for multi-hour runs use --output run.jsonl and convert to Parquet at the end:

gharc convert run.jsonl run.parquet

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Python API

The CLI is a thin wrapper around gharc.process_range, which you can call directly:

from datetime import datetime
import gharc

gharc.setup_logging()
gharc.process_range(
    start=datetime(2024, 1, 1),
    end=datetime(2024, 1, 2),
    repos=["apache/spark"],
    event_types=None,
    output="spark_one_day.jsonl",
    workers=4,
)

gharc.jsonl_to_parquet("spark_one_day.jsonl", "spark_one_day.parquet")

__all__ in gharc/__init__.py lists the public surface (process_range, jsonl_to_parquet, DataWriter, parse_date, date_range, get_url_for_time, setup_logging, plus the filter helpers).

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Automating Bulk Downloads

For long date ranges, the included examples/orchestrator.py script runs gharc month by month so each year produces one Parquet file per month rather than one giant output:

python examples/orchestrator.py \
    --start 2023-01-01 \
    --end 2024-01-01 \
    --repos "apache/spark,pandas-dev/pandas" \
    --output-dir ./gharc_out \
    --workers 4

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Repository Layout

gharc/
├── src/gharc/        # Library + CLI entry point
├── tests/            # pytest test suite
├── benchmarks/       # Reproducible runs that back the performance claims
├── examples/         # Driver scripts (e.g. month-by-month orchestrator)
├── paper/            # paper.md, paper.bib, figures (the JOSS submission)
└── CITATION.cff      # GitHub-detectable citation metadata

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Contributing

Contributions are welcome. Please read CONTRIBUTING.md for details on the process for submitting pull requests.

Running Tests:

pip install -e ".[test]"
pytest tests/

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Citation

The accompanying paper is at paper/paper.pdf and is rebuilt automatically on every push by the Paper CI workflow.

If you use gharc in your research, please cite it using the metadata in CITATION.cff or as follows:

@software{gharc2026,
  author = {Panwar, Arav},
  title = {gharc: A stream-and-filter tool for the GitHub Archive on consumer hardware},
  year = {2026},
  url = {https://github.com/aravpanwar/gharc}
}

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License

This project is licensed under the MIT License - see the LICENSE file for details.

Created by Arav Panwar aravpanwar.com