When BQ returns empty results (e.g., data not yet refreshed), the
scheduler was marking sync as complete for the day. This meant the
next 15-min tick would skip it ("none are due") and data would stay
stale until the next day's scheduled run.
Now: if partitioned sync processes partitions but gets 0 new rows,
last_sync is NOT updated. The scheduler will retry on the next tick
(15 min later) when data may be available.
Replace to_arrow() (loads entire result into RAM) with
to_arrow_iterable() (streams RecordBatches). Each batch is written
directly to disk via ParquetWriter - constant memory regardless
of table size. Prevents OOM on 8GB server for multi-million row tables.
Propagate column selection and row filtering from data_description.md
through the BigQuery adapter to the BQ client. This enables exporting
only needed columns and applying date range filters at the SQL level,
critical for large DataView tables (e.g., 412-col unit_economics).
BigQuery connector that syncs BQ tables to local Parquet files via PyArrow
(no CSV intermediate step). Supports full refresh, timestamp-based
incremental (via incremental_column), and partition-based sync strategies.
- connectors/bigquery/client.py: BQ API wrapper with ADC auth, parameterized
queries, metadata cache, cross-project support (job project != data project)
- connectors/bigquery/adapter.py: DataSource implementation with merge/dedup
- src/config.py: Add incremental_column field to TableConfig
- 72 unit tests (mocked, no GCP SDK required)
