This PR promotes execution_history() to the top-level bigframes namespace and upgrades it to track rich metadata for every BigQuery job executed during your session.

Key User Benefits:

• Easier Access: Call bigframes.execution_history() directly instead of digging into sub-namespaces.

• Rich Metadata Tracking: Captures structured statistics for both Query Jobs and Load Jobs including:

  • job_id and a direct Google Cloud Console URL for easy debugging.
  • Performance metrics: total_bytes_processed, duration_seconds, and slot_millis.
  • Query details (truncated preview of the SQL ran).

• Clean, Focused Logs: Automatically filters out internal library overhead (like schema validations and index uniqueness checks) so your history only shows the data processing steps you actually care about.

  Usage Example:

    1 import bigframes.pandas as bpd
    2 import pandas as pd
    3 import bigframes
    4
    5 # ... run some bigframes operations ...
    6 df = bpd.read_gbq("SELECT 1")
    7
    8 # Upload some local data (triggers a Load Job)
    9 bpd.read_pandas(pd.DataFrame({'a': [1, 2, 3]}))
   10
   11 # Get a DataFrame of all BQ jobs run in this session
   12 history = bigframes.execution_history()
   13
   14 # Inspect recent queries, their costs, and durations
   15 print(history[['job_id', 'job_type', 'total_bytes_processed', 'duration_seconds', 'query']])

verified at:
vs code notebook: screen/8u2yhaRV9iHbDbF
colab notebook: screen/9L8VrP5y9DXhnZz

Fixes #<481840739> 🦕