BigQuery now offers column-granularity indexing, currently in public preview, to improve query performance and cost efficiency
BigQuery stores data in a columnar format, with each column having its own file block, enabling efficient data organization
BigQuery stores data in a columnar format, with each column having its own file block, enabling efficient data organization
The default search index maps data tokens to files, narrowing the search space by scanning only relevant files
File-level indexing struggles when search tokens are widespread across columns but selective within specific columns, leading to unnecessary file scans
Column-granularity indexing adds column-specific information to indexes, allowing BigQuery to pinpoint relevant data within specific columns
Column-granularity indexing adds column-specific information to indexes, allowing BigQuery to pinpoint relevant data within specific columns
By identifying files with relevant tokens in specific columns, column-granularity indexing reduces the number of files scanned
Searching for "Google Cloud Logging" in the Title column scans only files containing all tokens in the Title column, improving efficiency
Column-granularity indexing significantly speeds up query execution times by accurately locating data within columns
Column-granularity indexing significantly speeds up query execution times by accurately locating data within columns
Cost Savings: Reduced processed bytes and slot time lower query costs, making data analysis more cost-effective
Particularly beneficial for queries filtering or aggregating data based on specific columns or when tokens are common across files but selective within columns
Particularly beneficial for queries filtering or aggregating data based on specific columns or when tokens are common across files but selective within columns