Optimizing BigQuery Query Performance

Optimizing BigQuery Query Performance

• Query Plan Inspection: Review the query plan in the Google Cloud console for insights into execution phases and steps. Use INFORMATION_SCHEMA.JOBS* views or the jobs.get REST API method for execution details. Identify bottlenecks, like excessively large result sets from certain steps, to target areas for improvement.

Reducing Data Processed

• Column Projection: Avoid reading unnecessary columns (SELECT *). Use SELECT * EXCEPT or query a smaller data subset to minimize the data volume scanned and materialized.

• Generic Table Prefixes: For generic tables with wildcard characters, use the most specific prefix possible. More specific prefixes, such as FROM bigquery-public-data.noaa_gsod.gsod194* are superior to broader prefixes like FROM bigquery-public-data.noaa_gsod.* This significantly reduces the number of tables scanned.

• Partitioning vs. Segmentation: Partition data by time rather than segmenting tables. Partitioned tables offer superior performance, as BigQuery handles schema, metadata, and authorizations more efficiently than tables segmented by prefix/suffix (named by date). Segmentation is not recommended due to increased query processing overhead.

• Filtering Partitions: When querying partitioned tables, employ partitioning columns for filtering. When time-partitioned, specify dates or date ranges using the _PARTITIONTIME pseudo-column to optimize performance and reduce costs. Example: WHERE _PARTITIONTIME BETWEEN '2016-01-01' AND '2016-01-31'.

Data Aggregation and Filtering

• Aggregation Before JOIN: Aggregate data using GROUP BY and aggregate functions before joining large tables. This dramatically reduces the data volume before joining, improving performance. Avoid WITH statements for performance, unless absolutely required, because they might materialize intermediate temporary tables and not be optimized effectively.

• WHERE Clause Optimizations: Leverage BOOL, INT64, FLOAT64, and DATE columns in the WHERE clause to speed up filtering. Operations on these columns are generally faster than those on STRING or BYTE columns.

Key Considerations

• Data Integrity: Use primary and foreign key constraints in your table schemas to significantly enhance query optimization. BigQuery doesn't automatically enforce data integrity, so verify that data meets schema constraints.

• Materialized Views: Utilize materialized views for pre-calculated results to speed up queries, caching frequently accessed query outputs. BigQuery exploits pre-calculated data, requiring only the change sets from the base tables to generate the updated results.

• BI Engine: Use BigQuery BI Engine for faster SELECT-based queries if required by frequently accessed query volume and data size.

• Search Indexes: Consider search indexes to speed up row lookups using the SEARCH function, as well as other operators.

• ETL Considerations: Avoid redundant transformations, specifically for ETL processes. Store transformed data in separate tables to avoid unnecessary repeated processing in subsequent steps.

Procedural, Temporary Tables, and Variables

• CTE (Common Table Expressions): Employ CTEs for query readability, but optimize by reworking into simple, reusable components in your main query – or materializing them to temporary tables via variables or named temporary tables. Avoid redundant CTE references that generate repetitive evaluations.

• Repeated Joins and Subqueries: Avoid redundant joins and subqueries involving the same datasets. If reused, materialize subquery or CTE results in temporary tables to avoid repetitive processing and further improve performance and reduce overall data volume.

Join Optimization Strategies

• Larger Table First: When joining data from multiple tables, start with the largest table to improve join performance. Order tables by size, from largest to smallest in your join queries. Avoid unnecessary cross-joins if possible (where every row from one table joins with every row from another table).

Complex Operations and Ordering

• ORDER BY and Handling Large Sorts: Place ORDER BY clauses at the end of the query or inside window functions to minimize upfront sorting. Utilize LIMIT clauses where possible to reduce the data being sorted.

• Window Functions: Inside window functions, limit the data set prior to the window function calculation. To significantly improve window function performance, pre-filter the dataset.

Complex Queries and Smaller Queries

• Decompose Complex Queries: Break down multi-step complex queries involving regular expressions, subqueries, or layered joins into smaller, more manageable queries and store in temporary tables. This is crucial, because excessively complex queries may exceed BigQuery internal plan complexity limits. Store results from intermediate steps to optimize access in downstream steps.

Data Types and Results

• INT64 and String Types in JOINs: Prioritize INT64 data types in joins for better comparison performance and cost reduction.

• Result Set Size: When dealing with large result sets, consider caching or materializing data for better performance. Consider if results need immediate access or can be consumed later, reducing the storage/caching overhead and performance pressure.

• Error Handling: Avoid errors generated by BigQuery from exceeding limits on processed output size and/or caching limits (Response too large). Use pagination with APIs to fetch and store larger datasets in parts to minimize this problem.