Performance Predictor | SQL

Code Analysis

The code performs a data transformation task to create a new table called "new_customers_each_month". It calculates the count of distinct new customers for each year and month based on the "order_date" field in the "sales" table. The analysis identif...


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This Query related with thread "Creating a Table to Track New Customers Each Month"

Prompt

CREATE TABLE new_customers_each_month AS
SELECT EXTRACT(YEAR FROM order_date) AS year,
       EXTRACT(MONTH FROM order_date) AS month,
       COUNT(DISTINCT customer_id) AS new_customers
FROM sales
WHERE customer_id NOT IN (
    SELECT DISTINCT customer_id
    FROM sales
    WHERE EXTRACT(YEAR FROM order_date) > (SELECT MIN(EXTRACT(YEAR FROM order_date)) FROM sales)
)
GROUP BY year, month;

Answer

Code Analysis

The code performs a data transformation task to create a new table called "new_customers_each_month". It calculates the count of distinct new customers for each year and month based on the "order_date" field in the "sales" table.

Potential Performance Bottlenecks / Areas of Concern

  1. Nested Subquery: The code uses a nested subquery to filter out customer_ids that have made orders in the previous years. This can be a performance bottleneck, especially if the "sales" table is large and the subquery is executed for each row in the main query.

  2. Redundant Subquery: The code uses two separate subqueries to get the minimum year from the "sales" table. This can be optimized by using a single subquery to retrieve the minimum year once and using it in both places where needed.

  3. COUNT(DISTINCT): The use of the COUNT DISTINCT function can be computationally expensive, particularly if the "sales" table contains a large number of records. This can impact query performance.

Recommendations for Optimization / Alternative Approaches

  1. Replace Nested Subquery: To optimize the nested subquery, you can rewrite the query using a LEFT JOIN to exclude customer_ids that have made orders in previous years. This will improve performance by executing the subquery only once instead of per row.

  2. Optimize Subquery: Instead of using two separate subqueries to retrieve the minimum year, use a single subquery and store the result in a variable. Then, reference the variable in the main query as needed. This will eliminate redundant subquery execution.

  3. Consider Materialized View: If this transformation is performed frequently, you could consider creating a materialized view with the pre-calculated aggregated data. This will improve query performance by eliminating the need to calculate the same aggregation repeatedly.

  4. Avoid COUNT(DISTINCT): If possible, consider alternative approaches to calculating the count of distinct customers. For example, you could use a separate table to keep track of new customers, update it as new orders come in, and retrieve the count from there. Alternatively, you could use window functions or other techniques to calculate the count without resorting to COUNT DISTINCT.

Overall, the recommended optimizations aim to improve query performance by reducing the number of subqueries, eliminating redundant operations, and considering alternative approaches to aggregations.

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