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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.

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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The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

  • Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
  • Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
  • Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

  • Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
  • Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
  • Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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