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A logistics company uses a BigQuery data warehouse for analyzing shipment data. They notice that queries filtering on `delivery_date` and `warehouse_id` columns are consistently slow and expensive, even though these columns are frequently used together in WHERE clauses. The table contains billions of rows and is partitioned by `shipment_date`. Which BigQuery feature should they implement to improve query performance and reduce costs for these specific queries?
- AExport the filtered data to Cloud Storage and query from there.
- BImplement clustering on `delivery_date` and `warehouse_id`.
- CCreate a materialized view on the `delivery_date` and `warehouse_id` columns.
- DChange the table partitioning to `delivery_date`.
Show answer & explanationAnswer & explanation
Correct answer: B. Implement clustering on `delivery_date` and `warehouse_id`.
Clustering in BigQuery organizes data within each partition based on the specified columns, significantly improving performance and reducing costs for queries that filter or aggregate on those clustered columns.
Why the other options are wrong
- A. Exporting data to Cloud Storage would not improve BigQuery query performance; it's an extra step and adds complexity.
- C. Materialized views pre-compute results, but clustering directly optimizes the underlying table for filtering on specific columns.
- D. Changing partitioning would help queries on `delivery_date` but not `warehouse_id` in combination, and would require re-ingestion.
BigQuery Clustering
A BigQuery feature that organizes data within each partition based on the values of one or more specified columns, improving query performance and reducing cost for filtering and aggregation.
- Data is physically co-located within partitions.
- Optimizes queries with `WHERE` clauses and aggregations on clustered columns.
- Can be combined with partitioning for multi-level optimization.
Memory trick: Cluster your data for faster access.