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コメント(5)

• 正解だと思う選択肢: B

  The best answer is B. Partition table data by create_date, cluster table data by location_id, and device_version.

  Here's a breakdown of why this structure is optimal:

  Partitioning by create_date:

  Aligns with query pattern: Filters for recent data based on create_date, so partitioning by this column allows BigQuery to quickly narrow down the data to scan, reducing query costs and improving performance. Manages data growth: Partitioning effectively segments data by date, making it easier to manage large datasets and optimize storage costs. Clustering by location_id and device_version:

  Enhances filtering: Frequently filtering by location_id and device_version, clustering physically co-locates related data within partitions, further reducing scan time and improving performance.

  👍 2

  e70ea9e2023/12/30
• 正解だと思う選択肢: B

  Answer is B:

  • Partitioning the table by create_date allows us to efficiently query data based on time, which is common in access patterns that prioritize recent data.
  • Clustering the table by location_id and device_version further organizes the data within each partition, making queries filtered by these columns more efficient and cost-effective.
  👍 2

  raaad2024/01/02
• 正解だと思う選択肢: B

  Only correct answer is B, you can only partition by one field, and you can only cluster on partitioned tables

  👍 1

  Smakyel792024/01/07