PDE Question 60

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A1

  partitioned table is more efficient

• Chose B1

  Sharded tables, like LOGS_yyyymmdd, are useful for managing data, but querying across a long date range with table wildcards can lead to inefficiencies and exceed the 1,000 table limit in BigQuery. Instead of using multiple sharded tables, you should consider converting these into a partitioned table. A partitioned table allows you to store all the log data in a single table, but logically divides the data into partitions (e.g., by date). This way, you can efficiently query data across long date ranges without hitting the 1,000 table limit.

• Chose B1

  gpt: Thank you for your feedback and additional information. You are correct that partitioned tables have a limit of 4,000 partitions, so partitioning tables by date could potentially run into this limit in the future. In this case, option B, converting sharded tables into a single partitioned table, could be a reasonable solution to avoid exceeding the maximum number of tables in BigQuery. As you mentioned, sharded tables require additional metadata and permissions verification, which can impact query performance. Converting sharded tables into a single partitioned table can improve performance and reduce query overhead. Therefore, based on the information provided, option B seems to be the most appropriate solution for avoiding the limit of 1,000 tables in BigQuery and optimizing query performance.