padelnomics/extract/padelnomics_extract

Padelnomics Extraction

Fetches raw data from external sources to the local landing zone. The pipeline then reads from the landing zone — extraction and transformation are fully decoupled.

Running

# One-shot (most recent data only)
LANDING_DIR=data/landing uv run extract

# First-time full backfill (add your own backfill entry point)
LANDING_DIR=data/landing uv run python -m padelnomics_extract.execute

Design: filesystem as state

The landing zone is an append-only store of raw files. Each file is named by its content fingerprint (etag or SHA256 hash), so:

• Idempotency: running twice writes nothing if the source hasn't changed
• Debugging: every historical raw file is preserved — reprocess any window by re-running transforms
• Safety: extraction never mutates existing files, only appends new ones

Etag-based dedup (preferred)

When the source provides an ETag header, use it as the filename:

data/landing/padelnomics/{year}/{month:02d}/{etag}.csv.gz

The file existing on disk means the content matches the server's current version. No content download needed.

Hash-based dedup (fallback)

When the source has no etag (static files that update in-place), download the content and use its SHA256 prefix as the filename:

data/landing/padelnomics/{year}/{date}_{sha256[:8]}.csv.gz

Two runs that produce identical content produce the same hash → same filename → skip.

State tracking

Every run writes one row to data/landing/.state.sqlite. Query it to answer operational questions:

# When did extraction last succeed?
sqlite3 data/landing/.state.sqlite \
  "SELECT extractor, started_at, status, files_written, files_skipped, cursor_value
   FROM extraction_runs ORDER BY run_id DESC LIMIT 10"

# Did anything fail in the last 7 days?
sqlite3 data/landing/.state.sqlite \
  "SELECT * FROM extraction_runs WHERE status = 'failed'
   AND started_at > datetime('now', '-7 days')"

State table schema:

Column	Type	Description
run_id	INTEGER	Auto-increment primary key
extractor	TEXT	Extractor name (e.g. padelnomics)
started_at	TEXT	ISO 8601 UTC timestamp
finished_at	TEXT	ISO 8601 UTC timestamp, NULL if still running
status	TEXT	running → success or failed
files_written	INTEGER	New files written this run
files_skipped	INTEGER	Files already present (content unchanged)
bytes_written	INTEGER	Compressed bytes written
cursor_value	TEXT	Last successful cursor (date, etag, page, etc.)
error_message	TEXT	Exception message if status = failed

Adding a new extractor

1. Add a function in execute.py following the same pattern as extract_file_by_etag() or extract_file_by_hash()
2. Call it from extract_dataset() with its own extractor name in start_run()
3. Store files under a new subdirectory: landing_path(LANDING_DIR, "my_new_source", year)
4. Add a new SQLMesh raw/ model that reads from the new subdirectory glob

Landing zone structure

data/landing/
├── .state.sqlite              # extraction run history
└── padelnomics/        # one subdirectory per source
    └── {year}/
        └── {month:02d}/
            └── {etag}.csv.gz  # immutable, content-addressed files