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Top 10 SQL Query Optimization Tips to Improve Database Performance

5 minutes, 18 seconds read

SQL Query optimization is a process of writing thoughtful SQL queries to improve database performance. During development, the amount of data accessed and tested is less. Hence, developers get a quick response to the queries they write. But the problem starts when the project goes live and enormous data starts flooding the database. Such instances slow down SQL queries response drastically and create performance issues.

When working with large-scale data, even the most minor change can have a dramatic impact on performance.

SQL performance tuning can be an incredibly difficult task. Even a minor change can have a dramatic impact on performance. Here are the 10 most effective ways to optimize your SQL queries. 

  1. Indexing: Ensure proper indexing for quick access to the database.
  2. Select query: Specify the columns in SELECT query instead of SELECT* to avoid extra fetching load on the database.
  3. Running queries: Loops in query structure slows the sequence. Thus, avoid them.
  4. Matching records: Use EXITS() for matching if the record exists.
  5. Subqueries: Avoid correlated sub queries as it searches row by row, impacting the speed of SQL query processing.
  6. Wildcards: Use wildcards (e.g. %xx%) wisely as they search the entire database for matching results.
  7. Operators: Avoid using function at RHS of the operator.
  8. Fetching data: Always fetch limited data.
  9. Loading: Use a temporary table to handle bulk data.
  10. Selecting Rows: Use the clause WHERE instead of HAVING for primary filters.

SQL Query Optimization Tips with Examples

Tip 1: Proper Indexing

An index is a data structure that improves the speed of data retrieval operations on a database table. A unique index creates separate data columns without overlapping each other. Proper indexing ensures quicker access to the database, i.e. you’ll be able to select or sort rows faster. The following diagram explains the basics of indexing while structuring tables.

SQL query optimization - basics of structuring tables

TIP 2: Use SELECT <columns> instead of SELECT *

Specify the columns in the SELECT clause instead of using SELECT *. The unnecessary columns place extra load on the database, which slows down not just the single SQL, but the whole system.

Inefficient

SELECT * FROM employees

This query fetches all the data stored in the “employees” table such as phone number, activity dates, notes from sales, etc. which might not be required for a particular scenario.

Efficient

SELECT first_name, last_name, mobile, city, state FROM employees

This query will fetch only selected columns.

Tip 3: Avoid running queries in a loop

Coding SQL queries in loops slows down the entire sequence. Instead of writing a query that runs in a loop, you can use bulk insert and update depending on the situation. Suppose there are 1000 records. Here, the query will execute 1000 times.

Inefficient

for ($i = 0; $i < 10; $i++) {  
  $query = “INSERT INTO TBL (A,B,C) VALUES . . . .”;  
  $mysqli->query($query);  
  printf (“New Record has id %d.\ “, $mysqli->insert_id);
}

Efficient

INSERT INTO TBL (A,B,C) VALUES (1,2,3), (4,5,6). . . .

Tip 4: Does My record exists?

Normally, developers use EXITS() or COUNT() queries for matching a record entry. However, EXIT() is more efficient as it will exit as soon as finding a matching record; whereas, COUNT() will scan the entire table even if the record is found in the first row.

Inefficient

IF (SELECT COUNT(1) FROM EMPLOYEES WHERE FIRSTNAME LIKE ‘%JOHN%’) > 0 PRINT ‘YES’

Efficient

IF EXISTS(SELECT FIRSTNAME FROM EMPLOYEES WHERE FIRSTNAME LIKE ‘%JOHN%’)
PRINT ‘YES’

Tip 5: A big NO for correlated subqueries

A correlated subquery depends on the parent or outer query. Since it executes row by row, it decreases the overall speed of the process.

Inefficient

SELECT c.Name, c.City,(SELECT CompanyName FROM Company WHERE ID = c.CompanyID) AS CompanyName FROM Customer c

Here, the problem is — the inner query is run for each row returned by the outer query. Going over the “company” table again and again for every row processed by the outer query creates process overhead. Instead, for SQL query optimization, use JOIN to solve such problems.

Efficient

SELECT c.Name, c.City, co.CompanyName FROM Customer c LEFT JOIN Company co   ON c.CompanyID = co.CompanyID

Tip 6: Use wildcard characters wisely

Wildcard characters can be either used as a prefix or a suffix. Using leading wildcard (%) in combination with an ending wildcard will search all records for a match anywhere within the selected field.

Inefficient

Select name from employees where name like ‘%avi%’

This query will pull the expected results of Avishek, Avinash, Avik and so on . However, it will also pull unexpected results, such as David, Xavier, Davin.    

Efficient

Select name from employees where name like ‘avi%’.

This query will pull only the expected results of Avishek, Avinash, Avik and so on. 

Tip 7: Avoid using SQL function on the RHS of the operator

Often developers use functions or methods with their SQL queries. 

Inefficient

Select * from Customer where YEAR(AccountCreatedOn) == 2005 and  MONTH(AccountCreatedOn) = 6

Note that even though AccountCreatedOn has an index, the above query changes the WHERE clause in such a way that this index cannot be used anymore.

Efficient

Select * From Customer Where AccountCreatedOn between ‘6/1/2005’ and ‘6/30/2005’

Tip 8: Always fetch limited data and target accurate results

Lesser the data retrieved, the faster the query will run. Rather than applying too many filters on the client-side, filter the data as much as possible at the server. This limits the data being sent on the wire and you’ll be able to see the results much faster.

Tip 9: Drop index before loading bulk data

If you want to insert thousands of rows in an online system, use a temporary table to load data. Ensure that this temporary table does not have any index. Since moving data from one table to another is much faster than loading them from an external source; you can now drop indexes on your primary table, move data from temporary to the final table, and finally recreate the indexes.

Tip 10: Use WHERE instead of HAVING

HAVING clause filters the rows after all the rows are selected. It is just like a filter. Do not use the HAVING clause for any other purposes. 

In the SQL Order of Operations, HAVING statements are calculated after WHERE statements. Therefore, executing the WHERE query is faster.

Hope you enjoyed reading these tips for SQL query optimization. If you have any questions, feel free to drop a comment or write to us at hello@mantralabsglobal.com.

You can learn more about SQL queries and syntax at W3Schools tutorial.

About Author: Avishek Kumar Singh is a Senior Tech Lead at Mantra Labs —  a leading application development service provider in insurtech and e-commerce domains. He has years of experience in developing robust web and mobile applications for enterprises.

Suggest reading – LAMP/MEAN Stack: Business and Developer Perspective

Common FAQs

What is SQL optimization?

SQL optimization is a process of using SQL queries in the best possible way to get accurate and fast database results. The most common database queries are INSERT, SELECT, UPDATE, DELETE, and CALL. These are coupled with subqueries to filter the results. This is where people need to think of optimization to get accurate results with fewer resources and improve database performance.

What is SQL query tuning?

SQL optimization is also known as SQL query tuning. Basically, it is a process of smartly using SQL queries to increase the speed of fetching data and improve overall database performance.

What are the different query optimization techniques?

There are two most common query optimization techniques – cost-based optimization and rule (logic) based optimization. For large databases, a cost-based query optimization technique is useful as it table join methods to deliver the required output. Rule-based optimization combines two or more queries based on relational expressions. The following example illustrates rule-based query optimization.
rule-based query optimization

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Open Finance: Reality or Hype?

3 minutes read

Open Banking has reshaped the fintech industry. Customers want a seamless experience with more convenient and flexible access to services. Technological innovation and digital transformation have led to the emergence of neobanks which offer a banking experience similar to delivery apps. Now the customers can avail of services like opening an account in minutes. In the last few years, another new concept- Open Finance has joined the queue. What exactly is open finance? Is it just hype or reality? And how open finance might improve customer experience (CX). These are some of the questions that we’re going to talk about in this blog. 

Open Banking

In open banking, banks and other financial institutions allow third-party financial service providers to access the bank’s customers’ data via APIs (application programming interfaces). This helps banks to create more personalized offerings and meet the changing needs of their customers.

What is Open Finance?

Open Banking and Open Finance are similar. However, Open Finance is slightly more advanced in the process. Simply put, it is the next step in open banking. 

Open Finance is a more customer-centric approach. It gives users a safe and dependable way to share their data with the financial tools and apps they prefer to use.

How is Open Finance different from Open Banking?

How is Open Finance different from Open Banking?

Source: Accenture

Open Banking has certain limitations when it comes to sharing of financial data. Here, only that data can be shared which is related to financial operations made within the bank’s app or in a branch office. Open finance goes beyond this limitation.

In Open Finance, non-banking financial data including mortgages, savings, pensions, insurance, and consumer credit – basically your entire financial footprint – could be opened up to trusted third-party APIs if you agree.

Open finance will help open new gateways for financial institutions to improve CX. Let’s dig deeper to understand how this concept will change CX in the Fintech world for the next-Gen customers. 

  1. 360-degree Customer Insights: Data acts as a tool to study deeply about your customers. Organizations can analyze the customer data and extract some valuable insights to design the complete customer journey. Open Finance opens a more secure pathway for financial institutions and gives a more complete picture of their customer’s finances. 
  2. Partnerships & Collaborations: With open finance, comes an opportunity for the financial institutions to network and collaborate with various providers. This means they could deliver a wider variety of services based on consumer data, uncovering new business models and innovations.
  3. Transparency for the Lenders: Lenders can evaluate and measure the creditworthiness of potential borrowers, audit documents, and offer customized solutions by securely collecting customer data. Machine learning algorithms may help to extract valuable insights from raw data.

Open Finance offers freedom and flexibility to consumers giving more options and control over the data they share and how they engage with their finances. With just 8 seconds of attention span, the new age consumers want better experiences to get hooked to one brand. Open finance creates unparalleled access to a broader range of products and services. With data sharing, banking organizations can keep track on the changing customer expectations who want frictionless interactions and hyper-personalized experiences across all touchpoints of the customer journey.

The Road Ahead

Statista predicts that there will be 63.8 million open banking users globally by 2024, increasing at an average annual rate of about 50% between 2020 and 2024. This means there will be more demand for innovative products and services in the industry. Banking organizations would need to analyze the rising customer expectations more closely than ever. And for this, data would act as a key to designing the experience of tomorrow. 

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