README.md

metadata

license: osl-3.0

Process to Generate DuckDB Dataset

1. Load Repository Metadata

• Read repo_metadata.json from GitHub Public Repository Metadata
• Normalize JSON into three lists:
  • Repositories → general metadata (stars, forks, license, etc.).
  • Languages → repo-language mappings with size.
  • Topics → repo-topic mappings.
• Convert lists into Pandas DataFrames: df_repos, df_languages, df_topics.

2. Enhance with BigQuery Data

• Create a temporary BigQuery table (repo_list) containing repository names.
• Query GitHub Archive (githubarchive.month.2024*):
  • Collect contributors from PullRequestEvent.
  • Collect stargazers from WatchEvent.
• Merge BigQuery results back into df_repos.

3. Preprocess for DuckDB

• Aggregate topics by repository:
  • Join multiple topics into a single pipe-separated string.
  • Create df_topics_agg.

4. Write to DuckDB

• Connect to DuckDB using SQLAlchemy.
• Write DataFrames into DuckDB:
  • repos → enriched repository metadata.
  • repo_languages → repo-language relations.
  • repo_topics → aggregated topics.
• Add index on repo_topics.topics for faster querying:

  CREATE INDEX topic_idx ON repo_topics (topics);