New BigQuery History-Based Optimizations Reduce Slot Time and Query Latency

BigQuery history-based optimizations is a new feature currently in preview that aims to improve query performance. It works by analyzing previous executions of similar queries to implement further optimizations. This process helps in reducing slot time consumed and query latency, making it particularly beneficial for scheduled queries or dashboarding queries that are run multiple times.

To activate BigQuery history-based optimizations, you need to enable it by project. The specific steps to do this are not detailed in the text, but it suggests that there is a method to activate it on a per-project basis. Additionally, the documentation for this feature can be found in the comments section of the post.

History-based optimizations in BigQuery utilize information from previous executions of similar queries. This information is used to implement further optimizations on new query executions, which can lead to a reduction in both the slot time required for queries and the query latency. This optimization process is designed to make repeated query executions more efficient over time.

As of the information provided in the text, the pricing for BigQuery history-based optimizations is not yet known. The feature is still in preview, and it is unclear whether it will be free or if there will be a cost associated with it. Users interested in this feature should stay tuned for updates regarding its pricing.

The text expresses curiosity about what BigQuery considers to be 'similar queries' for the purpose of history-based optimizations. It is a point of interest on how BigQuery detects these similar queries and then optimizes them. Unfortunately, the text does not provide a specific answer to this question, but it implies that there is a mechanism within BigQuery that identifies and utilizes the execution patterns of queries it deems similar to enhance performance.