Unleash the Speed Demon Within: Top 20 SQL Query Optimization Techniques
Tags: sql, performance, backend, discuss
Published: 2024-02-29 13:18:54

In the domain of data management, query performance reigns supreme. Sluggish SQL queries can impede information retrieval from your database, leading to frustration and inefficiencies. However, through the strategic application of optimization techniques, you can unlock the inherent potential for swift query execution, empowering you to harness the true power of your data.

Here are the top 20 techniques, presented in a structured order for easier learning, to transform your queries from sluggish snails to supersonic jets:

**1. Embrace the Power of Indexes:**
 Think of indexes as roadmaps for your data. For large tables, creating indexes helps your database quickly locate the information you need.

> **How It Works:** Indexes function similarly to an index in the back of a book. The database builds an ordered list of values from specific columns, along with pointers directly to the rows containing those values. When you query the indexed column, the database uses the index to find the matching rows instead of scanning through the entire table.
> **Performance Boost:** Reduces the number of disk accesses required to find the specific data. Think of having to flip through every page of a book versus using the index to jump straight to a topic!


**2. Use EXISTS() Wisely:**
Ditch the `COUNT()` function when simply checking for an element's existence. `EXISTS()` is your new best friend for efficient element hunting.

> **How It Works:** `EXISTS()` checks for the presence of a row within a subquery and returns either TRUE or FALSE. As soon as it finds the first matching row, it stops scanning. `COUNT(*)`, on the other hand, has to scan through all the potential matching rows and count them.
> **Performance Boost:** Improves efficiency by short-circuiting the search as soon as a match is found, especially in situations where you only need to know if a row exists or not.


**3. Be Selective, Not Greedy:**
 Instead of grabbing everything with `SELECT *`, specify the exact fields you need. This streamlines the process and saves precious time.

> **How It Works:** By only requesting specific columns with `SELECT`, the database engine has to retrieve and transfer less data from disk to memory and then to your application.
> **Performance Boost:** Reduces network traffic and the memory footprint required to hold and process the results.


**4. Subqueries? Not Today:**
 Subqueries within the `WHERE` clause can be cumbersome. Break them down into separate queries or use derived tables for a smoother ride.

> **How It Works:** A subquery within the `WHERE` clause can be forced to run for every single row that your main query is evaluating. Rewriting these as separate queries or derived tables lets the database engine optimize their execution independently, sometimes leading to massive performance gains.
> **Performance Boost:** Improves the database engine's ability to optimize how the query runs and avoids repetitive computations across rows.


**5. Avoid the DISTINCT Detour:**
If possible, steer clear of `SELECT DISTINCT`. Filtering within the `WHERE` clause can often achieve the same result more efficiently.

> **How It Works:** The `DISTINCT` keyword forces the database to compare every result row to all the others to ensure it's unique before it's returned. Intelligent filtering in the `WHERE` clause often lets you achieve the same result without this sorting and comparison process.
> **Performance Boost:** Avoids the overhead of checking for duplicate rows after the main data set has been retrieved.


**6. Filter First, Aggregate Later:** 
Utilize the `WHERE` clause for initial filtering before applying aggregation functions with `HAVING`. This targeted approach optimizes performance.

> **How It Works:** The `WHERE` clause filters rows before aggregation functions (`SUM`, `MIN`, `MAX`, etc.) are applied. `HAVING` is used to filter the results after the aggregations have been calculated.
> **Performance Boost:** Shrinks the dataset that aggregations are performed on, leading to fewer calculations.


**7. JOIN the Right Way:**
For precise data merging, use `INNER JOIN` instead of relying solely on the `WHERE` clause. This ensures cleaner results.

> **How It Works:** An `INNER JOIN` matches rows between tables based on a defined condition. Filtering in the `WHERE` clause, while possible, creates a less organized approach to performing those matches.
> **Performance Boost:** The database engine can optimize how it performs lookups across the tables when the join condition is directly expressed as a join.


**8. Peek, Don't Pour:** 
When testing or debugging, use `LIMIT` to retrieve a limited sample of data instead of the entire dataset. This saves time and resources.

> **How It Works:** `LIMIT` restricts the number of returned rows. During development and troubleshooting, you often want a sample to validate your query rather than the complete result set.
> **Performance Boost:** Significantly speeds up queries, especially when your table is large, by reducing the amount of data retrieved and processed.


**9. Embrace UNION ALL's Simplicity:** 
When combining result sets without duplicates, opt for the speedier `UNION ALL` over `UNION`.

> **How It Works:** `UNION ALL` combines result sets and includes all rows, including duplicates. `UNION` removes duplicates, requiring an extra sorting and comparison step.
> **Performance Boost:** If you don't need duplicate removal, `UNION ALL` skips the unnecessary processing, improving the query's speed.


**10. Leverage UNION WHERE for Efficiency:** 
Need to remove duplicates while combining results? Use `UNION WHERE` for a more efficient approach than `WHERE ... OR ....`

> **How It Works:** This is an advanced optimization technique that allows you to perform `UNION`-like operations and apply filtering simultaneously. This is more efficient when you potentially need the same filter (`WHERE` condition) multiple times on different parts of a `UNION`.
> **Performance Boost:** Improves performance by avoiding the redundancy of specifying the same filter within each part of a complex UNION.


**11. Off-Peak Hours: Your Performance Ally:** 
Schedule resource-intensive queries for off-peak hours to minimize impact on regular operations.

> **How It Works:** Scheduling resource-intensive queries for off-peak hours, when the database is less busy, allows the database engine to allocate its resources more efficiently to your query.
> **Performance Boost:** Reduces the impact on concurrent user queries and overall system performance by leveraging less congested periods.

**12. OR in JOINs? Not the Best Option:** 
While tempting, using `OR` within JOINs can lead to performance bottlenecks. Consider alternative approaches.

> **How It Works:** Using `OR` within `JOIN`s can lead to performance issues because the database engine has to evaluate both sides of the OR condition for each row in the tables being joined. This can involve multiple scans and comparisons.
> **Performance Boost:** Consider using alternative approaches, such as multiple `JOIN`s with specific conditions or filtering with the `WHERE` clause, to avoid the overhead of double evaluations.

**13. GROUP BY vs. Window Functions: Choose Wisely:** 
For complex aggregations, window functions can sometimes outperform `GROUP BY`. Evaluate your needs and choose the most efficient option.

> **How It Works:** `GROUP BY` is used to group rows based on specific columns and then perform aggregate functions (like `SUM`, `AVG`) on each group. Window functions operate on individual rows within a result set, allowing for more complex aggregations and calculations.
> **Performance Boost:** In some cases, window functions can outperform `GROUP BY` for complex aggregations because they avoid the overhead of creating intermediate groups and performing aggregations on those groups.


**14. Derived and Temporary Tables:** 
Break down complex queries into smaller, manageable chunks using derived and temporary tables. This enhances clarity and performance.

> **How It Works:** These tables are created on-the-fly within your query using `SELECT` statements. Derived tables are used within the same query, while temporary tables can be used across multiple queries within a session. They help break down complex queries into smaller, more manageable chunks.
> **Performance Boost:** Improves readability and maintainability of complex queries by separating logic into smaller, reusable components. Can also lead to performance gains by allowing the database to optimize each component independently.


**15. Bulk Data Loading? Drop the Index First:** 
For bulk data loads, temporarily dropping indexes can significantly improve speed. Remember to recreate them afterward.

> **How It Works:** When loading large amounts of data into a table, temporarily dropping indexes can significantly improve the speed of the bulk insert operation. Indexes typically enforce data integrity rules and maintain their own ordered structures, which can become a bottleneck during bulk insertion.
> **Performance Boost:** Allows the database to focus on inserting the data quickly without the overhead of maintaining the indexes. Remember to recreate the indexes after the bulk operation to regain the performance benefits they provide for regular queries.


**16. Materialized Views: Pre-calculated Efficiency:** 
If you frequently query the same complex data, consider materialized views. These pre-calculated snapshots can save you time and resources.

> **How It Works:** Materialized views are pre-computed snapshots of your data, based on a predefined query. They are stored separately from the original tables and are automatically refreshed periodically or based on specific triggers.
> **Performance Boost:** Significantly improves query performance for frequently used complex queries by providing readily available, pre-calculated results. This avoids the need to execute the complex query every time, especially if the underlying data changes infrequently.


**17. Avoid the Not-Equal Operator:** 
The `!=` or `<>` operator can be less efficient than its equal counterpart (`=`). If possible, rewrite your queries to use the equal operator instead.

> **How It Works:** The `!=` or `<>` operator can be less efficient than its equal counterpart `=` because the database engine often needs to scan the entire table or index to find rows that don't meet the specified condition.
> **Performance Boost:** When possible, rewrite your queries to use the equal operator `(=)` for comparisons, especially in situations where you anticipate many matching rows. This allows the database to leverage indexes more effectively.


**18. Minimize Subquery Mayhem:** 
The more subqueries you nest, the slower your query becomes. Try to simplify your logic and reduce the number of subqueries whenever possible.

> **How It Works:** Nesting multiple subqueries within a single query can significantly impact performance. Each subquery is essentially treated as a separate query, leading to increased processing overhead for the database engine.
> **Performance Boost:** Simplify your query logic by reducing the number of subqueries. Explore alternative approaches like using joins, derived tables, or breaking down the logic into separate queries.


**19. INNER JOIN vs. LEFT/RIGHT JOIN:** 
When appropriate, use `LEFT JOIN` or `RIGHT JOIN` instead of multiple `INNER JOIN`s to achieve the same result. This can streamline your query.

> **How It Works:** `INNER JOIN` returns only rows where there's a match in both tables based on the specified condition. `LEFT JOIN` and `RIGHT JOIN`, on the other hand, include all rows from the left or right table, respectively, even if there's no match in the other table. Using multiple `INNER JOIN`s to achieve the same result as a single `LEFT JOIN` or `RIGHT JOIN` can be less efficient.
> **Performance Boost:** When appropriate, use `LEFT JOIN` or `RIGHT JOIN` to simplify your query and potentially improve performance compared to using multiple INNER JOINs.


**20. Temporary Sources for Frequent Queries:** 
For frequently retrieved datasets, consider using temporary sources to improve performance and reduce redundant calculations.

Remember, these are just the tip of the iceberg. By understanding your data and applying these techniques, you can transform your SQL queries into powerful tools that unlock the true potential of your information. So, unleash the speed demon within and conquer the realm of data! Comment down other SQL query optimization tips you know.



```
            யாம் பெற்ற இன்பம் பெறுக இவ்வையகம்     
                                                    -திருமந்திரம்
```