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BigQuery time-partitioning: The hidden cost trap
- When faced with slow analytical queries in PostgreSQL, a team migrated analytics workloads to BigQuery using Airbyte for the initial migration.
- Although the migration to BigQuery initially showed impressive query performance improvements, the inability to define clustering or partitioning with Airbyte resulted in unoptimized data and increased costs.
- To address the issue, the team rebuilt the data ingestion pipeline to stream data directly into BigQuery and recreated tables with proper partitioning and clustering.
- Despite following best practices and implementing partitioning and clustering in BigQuery, the author discovered that partitioning didn't reduce the amount of data processed as expected.
- The author experimented with various ways to query time partitioned tables and discovered that certain functions like CAST() led to processing the entire table rather than pruning partitions.
- For TIMESTAMP data type, partition pruning worked effectively for all methods tested, including CAST().
- The author highlighted the importance of continuously monitoring and analyzing costly queries in BigQuery to identify inefficiencies that may not be explicitly warned about in documentation.
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