Google has announced a significant update to BigQuery DataFrames, introducing a new 'partial ordering mode' feature. This enhancement, currently in Preview, aims to generate more efficient queries and potentially reduce costs for users working with large datasets.
Key Features of Partial Ordering Mode:
Efficiency Boost: Generates faster and more resource-efficient queries, especially for large clustered or partitioned tables.
Cost Reduction: Can lower costs by reducing the number of bytes processed when using row filters on cluster and partition columns.
Contrast to Strict Mode: Differs from the default 'strict' mode, which creates a total ordering over all rows.
Null Index: Uses a null index instead of a sequential index over the ordering.
Important Considerations:
- Feature Limitations: Turns off features requiring total row ordering, such as the
DataFrame.ilocproperty. - Pandas Compatibility: While still pandas-like, it may differ from common pandas behavior in some aspects.
- No Implicit Joins: Does not perform implicit joins by index.
How to Use:
Users can activate this mode by setting the ordering_mode property to partial in their BigQuery DataFrame operations.
Impact on Query Processing:
- Eliminates the need to compute missing rows in the sequential index during filtering operations.
- Avoids full data scans that ignore row and column filters, which can occur in strict mode.
This update represents Google's ongoing efforts to enhance BigQuery's performance and cost-effectiveness. While it may require some adjustments in workflow for users accustomed to pandas-like behavior, the potential for improved efficiency and reduced costs makes it a valuable option for those working with large-scale data in BigQuery.
Users are encouraged to explore this new feature, particularly when dealing with substantial clustered or partitioned tables where query efficiency is crucial.
