BigQuery's BI Engine enhances query performance by intelligently caching data from your designated BigQuery tables in its in-memory layer. When you query data from a table accelerated by BI Engine, it typically serves the results from this cached copy, providing faster response times.
How BI Engine Handles Updates (Automatic):
BI Engine automatically synchronizes its cache with the base BigQuery table to reflect changes such as updates, inserts, or deletions. This synchronization process is designed to be near real-time, ensuring low latency in most cases. However, there might be a brief lag between the updates in BigQuery and their reflection in the BI Engine's cache, depending on system load and update frequency.
Key Points to Remember:
- Data Consistency: BI Engine prioritizes data consistency, striving to provide query results equivalent to direct queries on the base BigQuery table. However, users should be aware of the potential brief lag in data synchronization.
- No Manual Intervention: The cache update process in BI Engine is automatic, requiring no manual triggers or management.
- Complex Queries: For complex queries involving operations like JOINs or aggregations across multiple tables, BI Engine may partially use cached data along with real-time computation. The effectiveness of caching in these scenarios can vary.
- Cost and Reservation: Managing BI Engine involves creating a memory reservation. It's important to optimize cost and manage reservation size, considering your query patterns and data usage.
Official Documentation and Resources:
Additional Considerations:
- Cache Refresh Mechanism: Understand the nuances of the cache refresh mechanism, especially for datasets that are highly dynamic.
- Performance Optimization: Optimize table design and query structure for better performance and efficient use of BI Engine.
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