BigQuery : How do you optimize query performance in BigQuery

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There are several ways to optimize query performance in BigQuery:

  1. Partitioning: Partitioning a table by a column can significantly improve query performance, as BigQuery only needs to scan the partitions that are relevant to the query, rather than the entire table. For example, if you have a table with billions of rows of data and you frequently query data for a specific month, partitioning the table by the date column will improve query performance.
  2. Clustering: Clustering a table by one or more columns organizes the data based on the values in those columns. This can improve query performance by allowing BigQuery to scan less data when processing a query. For example, if you have a table with data on users and their transactions, clustering the table by user_id will improve query performance when querying data on specific users.
  3. Indexing: You can create an index on a column in BigQuery by using the CREATE INDEX statement. This can improve query performance when filtering or sorting on that column.
  4. Materialized Views: Materialized views in BigQuery are pre-aggregated or denormalized tables that can be queried instead of the original table. Querying a materialized view instead of the original table can improve query performance as the data is pre-aggregated or denormalized.
  5. Use the appropriate data types: When creating tables, choose the appropriate data types for each column. For example, instead of using INTEGER use INT64 for large numbers.
  6. Using Subqueries: BigQuery supports subqueries, which allow you to reference a query within another query. This can help improve performance by reducing the number of scans required to process a query.
  7. Use caching: BigQuery allows you to cache query results for a specified period of time. This can improve query performance for frequently run queries by avoiding the need to re-run the query and retrieve the results from the cache instead.
  8. Use the correct JOIN type: BigQuery supports several types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN and FULL OUTER JOIN. Choose the correct JOIN type based on the requirements of your query.

Here is an example of how to use partitioning and clustering to optimize query performance:

# Create a table with data partitioned by date and clustered by user_id
CREATE TABLE freshers_admin_table
    date DATE,
    user_id INT64,
    gpay_transaction_amount FLOAT64
CLUSTER BY user_id

In this example, the table “freshers_admin_table” is partitioned by the “date” column, and clustered by the “user_id” column.

When running a query on this table such as “SELECT SUM(gpay_transaction_amount ) FROM freshers_admin_table WHERE date = ‘2023-07-01′”, BigQuery will only scan the partition for the specified date and the data will be grouped by user_id, which can greatly improve query performance.

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