Updated on 09th Dec, 23 9.3K Views

It is a very crucial task. Because even a tiny change in SQL queries will improve the performance by a big margin. There is no hard and fast rule for the same, we should always follow general guidelines while writing queries used by operators. After that, look at execution plans to figure out which portion of the query requires the most time, and we can rewrite it again in a more efficient way.

Table of Content:

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Overview of SQL Query Optimization

The process of selecting the most effective way to carry out a SQL statement is known as query optimization. Due to SQL’s non-procedural nature, the optimizer is permitted to merge, rearrange, and process data in any sequence.

Based on statistics gathered regarding the accessed data, the database optimizes each SQL statement. The optimizer evaluates various access techniques, such as full table scans or index scans, various join techniques, such as nested loop joins and hash joins, various join orders, and potential transformations to identify the best plan for a SQL query.

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SQL Query Optimization Techniques

Let’s talk about some of the best SQL Query Optimization techniques:

Add missing indexes

Adding missing indexes in the SQL database can improve query performance. Think of indexes as a roadmap for the database engine to quickly locate data. When an index is missing for columns frequently used in queries, the database has to scan the entire table, which eventually slows down performance. Thus, to identify missing indexes, you can analyze query execution plans or use database management systems. Once identified, carefully adding these indexes can boost query speed by allowing the database to swiftly pinpoint the required data, resulting in faster and more efficient SQL operations.                                                                                                                                                      

Check for unused indexes

Checking for unused indexes is an important aspect of SQL query optimization. While indexes help to deliver speed during database operations, having too many or unnecessary indexes consumes storage space, which again slows down data modification operations like inserts, updates, and deletes. Database administrators can utilize built-in tools or scripts to identify these redundant indexes. 

By periodically examining index usage statistics and removing those that aren’t contributing to query performance, you can smooth database operations, leading to faster and more efficient SQL queries.

SELECT
    Index_name,
    user_seeks,
    user_scans,
    user_lookups,
    user-updates
FROM
sys.dm_db_index_usage_stats
WHERE
  user_seeks = 0
  AND user_scans = 0
  AND user_lookups = 0
  AND user_updates > 0;

This query accesses the system view “sys.dm_db_index_usage_stats” to identify indexes that have had no seeks, scans, or lookups, yet have received updates. It provides a basic overview of potentially unused indexes in your database.

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Avoid using multiple OR in the FILTER predicate

Minimizing the use of multiple OR conditions within the FILTER predicate is a significant strategy in optimizing SQL queries. When numerous OR conditions are used, the database engine has to evaluate each condition individually that impacts query performance. Instead, consider using other logical operators like IN, and CASE statements, or restructuring the query to reduce the number of OR conditions. This consolidation helps the query execution process, leading to faster retrieval of data. By optimizing queries to avoid multiple ORs, experts can enhance database performance and overall system efficiency.

Syntax:-

SELECT column1, column2, ….
WHERE condition_column = ‘value’
   AND (another_condition_column = ‘value1’ OR another_condition_column = ‘value2’);

Use wildcards at the end of a phrase only 

When crafting SQL queries, employing wildcard characters, like “%” or “_”, can enhance search capabilities. To optimize query performance, it’s beneficial to use wildcards, particularly “%” at the end of a phrase in search conditions. Placing the wildcard at the start of a phrase can lead to inefficient search operations since the database engine might need to scan more data. By utilizing wildcards at the phrase’s end, you instruct the database to find matches that start with a particular sequence of characters. Therefore, it limits the scope of the search and improves query execution speed. This optimization technique helps the database efficiently retrieve relevant data, resulting in quicker and more accurate search results.

Syntax:-

SELECT column1, column2, …
FROM your_table
WHERE your_column LIKE ‘starting-phrase%’

This query retrieves specific columns from a table based on specified pattern using the ‘LIKE’ operator.

Avoid too many JOINs

Limiting the number of JOIN operations in SQL queries is a crucial strategy for optimizing performance. While JOINs are essential for combining data from multiple tables, excessive use can lead to increased query complexity and reduced execution speed. To make searches faster, check if all connections are needed. Consider using different ways, like subqueries or changing the structure for better efficiency.By reducing unnecessary JOINs, you streamline query execution, minimize resource usage, and enhance the overall efficiency of SQL operations. A balance between necessary JOINs and queries is key to improving database performance.

Avoid using SELECT DISTINCT

Skipping “SELECT DISTINCT” enhances query speed. Though it fetches unique values, it can slow queries as it scans the whole set for duplicates. Instead, consider alternative approaches like using GROUP BY or refining the query conditions to fetch distinct values efficiently. By minimizing the usage of “SELECT DISTINCT”, you reduce the computational load on the database, resulting in faster query execution. Employing this optimization technique ensures smooth query performance while retrieving the necessary unique values without compromising efficiency.

Syntax:- 

SELECT column1, column2, …
FROM your_table
GROUP BY column1, column2, …

This syntax demonstrates an alternative approach to achieve similar results as ‘SELECT DISTINCT’ using the ‘GROUP BY’ clause.

Use SELECT fields instead of SELECT *

When constructing SQL queries, favoring “SELECT fields” over “SELECT *” enhances query performance. While “SELECT *” fetches all columns from a table, “SELECT fields” specifically retrieves only the necessary columns. This optimization technique minimizes unnecessary data retrieval, reduces network traffic, and speeds up query execution. By explicitly listing the required fields, you optimize database operations, improve query readability, and reduce resource consumption. Adopting this practice ensures efficient data retrieval, especially when dealing with large tables or complex queries, resulting in faster and more responsive database operations.

Syntax:-

SELECT column1, column2, …
FROM your_table

This query retrieves specific columns (column1, column2, etc) from a table (your_table) instead of fetching all columns using ‘SELECT *’.

Use TOP to sample query results

Utilizing the “TOP” clause to sample query results is a beneficial strategy in SQL query optimization. When dealing with large databases, the “TOP” clause allows you to retrieve a specific number or percentage of rows, providing a quick preview of the data. This helps in analyzing query performance before running the entire query, especially in scenarios where extensive data processing might be resource-intensive. 

By employing “TOP” to sample query results, you gain insights into the data structure, ensure query accuracy, and improve overall query efficiency. This technique helps in fine-tuning queries, optimizing database performance, and expediting result retrieval.

Syntax:-

SELECT TOP 10 column1, column2, …
FROM your_table

This query retrieves a limited number of rows ( Top 10 ) based on specified columns from a table ( your_table ).

Run the query during off-peak hours 

Running SQL queries during off-peak hours is a strategic approach to optimizing query performance. Off-peak hours are times when the database experiences lower user activity, reducing the strain on system resources. By scheduling resource-intensive or complex queries during these periods, you can avoid contention for resources. This helps in ensuring smoother and faster query execution. Therefore, the practice minimizes the impact on concurrent user operations and maximizes the available system resources for query processing. 

By optimizing query timing, you enhance overall system performance, maintain a consistent user experience, and prevent potential slowdowns during peak usage times.

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Minimize the usage of any query hint 

Reducing the use of query hints is a valuable approach to SQL query optimization. Query hints are instructions provided to the query optimizer, guiding them on how to execute a query. While query hints can influence query performance, they can also limit the optimizer’s flexibility, potentially leading to suboptimal execution plans. Minimizing query hints encourages the optimizer to autonomously determine the more efficient execution plan based on database statistics and configurations. By allowing the optimizer more freedom, you enable it to adapt to changing conditions and select the best execution strategy, leading to improved query performance and overall database efficiency.

Minimize large write operations

Minimizing large write operations is a crucial strategy in optimizing SQL queries. Write operations, such as INSERT, UPDATE, or DELETE, involving a vast amount of data can impact database performance. Breaking down these large write operations into smaller, manageable batches helps prevent system resource overload and reduces contention for database resources. Additionally, consider optimizing indexes and ensuring proper transaction management to mitigate the impact of large write operations. By adopting this approach, you maintain database responsiveness, prevent excessive resource consumption, and enhance overall query execution efficiency.

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Create JOINs with INNER JOIN (not WHERE)

When creating joins in SQL queries, using INNER JOIN instead of WHERE clauses is a beneficial technique for optimizing query performance. INNER JOIN explicitly defines the relationship between tables, providing the optimizer with a clearer introduction on how to merge data. This method helps the database engine generate more efficient execution plans by focusing on retrieving only the relevant data needed to satisfy the join conditions. By using INNER JOIN, you improve query readability, ensure accurate result sets, and enable the query optimizer to better optimize the query execution path. This optimization technique contributes to faster and more efficient SQL queries, enhancing overall database performance.

Syntax:-

SELECT t1.column_name1, t1.column_name2, t2.column_name3
FROM table t1
INNER JOIN table2 t2 ON t1.common_column = t2.common_column;

Replace ‘t1.column_name1’, ‘t2.column_name2’, etc, with the specific columns you want to retrieve. And replace ‘table1’ and ‘table2’ with the actual names of the tables you’re joining.

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Conclusion

We covered many important tips and techniques to improve SQL Query performance. You are suggested to keep them in mind while writing queries as they will improve the performance and provide a great user experience of applications.

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