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New column-granularity indexing in BigQuery offers a leap in query performance
- BigQuery has introduced indexing with column granularity to enhance query performance and reduce costs by indexing column data.
- This new feature allows BigQuery to pinpoint relevant data within columns for faster search queries.
- Tables in BigQuery are stored in physical files with each column having its dedicated file block in a columnar format.
- The default search index in BigQuery operates at the file level, meaning it reduces the search space by scanning relevant files.
- File-level indexing can face challenges when search tokens are selective within specific columns but common across others.
- Column-granularity indexing enables BigQuery to leverage indexes to locate data within columns even when tokens are prevalent across files.
- By adding column information in the indexes, BigQuery can significantly improve query performance by scanning only relevant files.
- Benchmark tests show that column-granularity indexing results in faster query execution and improved cost efficiency.
- Benefits of column-granularity indexing include enhanced query performance and cost efficiency in scenarios with selective search tokens within columns.
- Best practices for utilizing column granularity indexing include identifying high-impact columns, monitoring performance, and considering indexing and storage costs.
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