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Answer: Collect and maintain table statistics (e.g., via ANALYZE TABLE COMPUTE STATISTICS) for all tables involved in the query to provide the CBO with necessary information.
To fully leverage Spark's Cost-Based Optimizer (CBO) for complex queries, it's essential to collect and maintain table statistics for all involved tables. This practice provides the CBO with crucial data about distribution, cardinality, and skewness, enabling it to make informed decisions for generating an optimal execution plan. Accurate statistics allow the CBO to estimate the cost of various execution plans accurately, leading to improved query performance by avoiding suboptimal plans. Regularly updating these statistics ensures the CBO works with the most current data, optimizing join order, selectivity estimation, and partitioning strategies for complex queries.
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How can you maximize the efficiency of Spark's Cost-Based Optimizer (CBO) for complex SQL queries involving multiple data sources and transformations?
A
Increase the value of spark.sql.cbo.enabled to a higher level than the default to enhance the optimizer's capabilities.
B
Manually define the execution plan for complex queries, bypassing the CBO, to ensure optimal performance.
C
Annotate queries with explicit optimizer hints to guide the CBO in choosing the most efficient execution plan.
D
Collect and maintain table statistics (e.g., via ANALYZE TABLE COMPUTE STATISTICS) for all tables involved in the query to provide the CBO with necessary information.