| --- |
| license: osl-3.0 |
| --- |
| # Process to Generate DuckDB Dataset |
|
|
| ## 1. Load Repository Metadata |
| - Read `repo_metadata.json` from [GitHub Public Repository Metadata](https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars/data) |
| - 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`. |
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| ## 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: |
| ```sql |
| CREATE INDEX topic_idx ON repo_topics (topics); |
| |
