Boosting Your Database Performance: A Comprehensive Guide to Query Re-Writing for Query Optimization
Published: 2023-04-21 20:26:37

---
title: Boosting Your Database Performance: A Comprehensive Guide to Query Re-Writing for Query Optimization
published: true
date: 2023-04-16 08:07:44 UTC
tags: 
canonical_url: https://harshmange.hashnode.dev/boosting-your-database-performance-a-comprehensive-guide-to-query-re-writing-for-query-optimization
---

Query rewriting is a technique used to optimize database query performance by transforming a complex or inefficient query into an equivalent, more efficient form. This technique involves using rules or algorithms to modify the query structure or execution plan, which can help to reduce the total execution time of a query.

Here are some examples of query rewriting techniques that can be used to optimize database queries:

1. 
## Subquery optimization

This technique involves transforming a subquery (a query embedded within another query) into an equivalent join operation. For example, consider the following query:

```
SELECT *
FROM orders
WHERE order_id IN (SELECT order_id FROM order_items WHERE product_id = '123');

```

This query can be rewritten as a join operation, as follows:

```
SELECT *
FROM orders
JOIN order_items ON orders.order_id = order_items.order_id
WHERE order_items.product_id = '123';

```

1. 
## View materialization

This technique involves precomputing the result of a view (a virtual table created by a query) and storing it as a physical table. For example, consider the following view:

```
CREATE VIEW order_totals AS
SELECT order_id, SUM(total_price) AS order_total
FROM order_items
GROUP BY order_id;

```

This view can be rewritten as a materialized view, which precomputes the result and stores it as a physical table:

```
CREATE MATERIALIZED VIEW order_totals_mv AS
SELECT order_id, SUM(total_price) AS order_total
FROM order_items
GROUP BY order_id;

```

1. 
## Predicate pushdown

This technique involves moving a filter condition (a predicate) from a later stage of the query execution plan to an earlier stage, where it can be used to reduce the amount of data that needs to be processed. For example, consider the following query:

```
SELECT *
FROM orders
JOIN customers ON orders.customer_id = customers.customer_id
WHERE customers.country = 'USA' AND order_date >= '2022-01-01';

```

This query can be rewritten using predicate pushdown, which moves the filter condition from the join operation to the table scan:

```
SELECT *
FROM (SELECT * FROM customers WHERE country = 'USA') AS c
JOIN (SELECT * FROM orders WHERE order_date >= '2022-01-01') AS o
ON c.customer_id = o.customer_id;

```

1. 
## Index optimization

Queries can be rewritten to take advantage of database indexes, which can improve query performance by reducing the number of table scans required. For example, the following query:

```
SELECT * FROM orders WHERE order_date BETWEEN '01-JAN-2022' AND '31-DEC-2022';

```

Can be rewritten as:

```
SELECT * FROM orders WHERE order_date >= '01-JAN-2022' AND order_date < '01-JAN-2023';

```

This rewritten query takes advantage of an index on the order\_date column, which can improve query performance by avoiding a full table scan.

1. 
## Query simplification

Queries can be simplified by removing unnecessary expressions or conditions, which can improve query performance by reducing the amount of work required by the database engine. For example, the following query:

```
SELECT * FROM orders WHERE order_date >= '01-JAN-2022' AND order_date < '01-JAN-2023' AND (customer_id = 100 OR customer_id = 200);

```

Can be simplified as:

```
SELECT * FROM orders WHERE order_date >= '01-JAN-2022' AND order_date < '01-JAN-2023' AND customer_id IN (100, 200);

```

This simplified query produces the same result but is more efficient because it reduces the number of OR conditions that need to be evaluated.