SQL queries are essential for retrieving and manipulating data from databases, but they can also be slow and inefficient if not written well. In this article, you will learn some tips and tricks to make your SQL queries run faster and optimize your database performance.
Indexes are data structures that help the database find and access data faster. They are like the table of contents of a book, where you can quickly locate a specific page or chapter. By creating indexes on the columns that you frequently use in your queries, you can reduce the amount of scanning and sorting that the database has to do. However, indexes also have some drawbacks, such as taking up extra space and slowing down insertions and updates. Therefore, you should only use indexes when necessary and avoid over-indexing.
Using Indexes can also result in performance degradation, specifically when someone is applying too many indexes. I suggest applying clustered indexes for the columns primarily used to filter the table records, for secondary columns might have non-clustered indexes. Always check the execution plan to see how your actions impact the performance.
1. Based on the data access pattern, a single table can have multiple un-clustered indices and these indices can be built for one or more (composite) columns/attributes. 2. A single relation can have at most one clustered index since the leaf nodes of a clustered index are the actual disk pages. 3. Unclustered indices replicate the data on their leaf nodes and can support index only queries if the query involves only the columns which are part of the un-clustered index. 4. There is a tradeoff between memory and speed with Index only queries, as they are faster because of no disk page access, but the data replication on indices takes up additional space.
Choose the clustered index wisely. Sometimes the primary key may not be the best candidate for applying the clustered index on. Make sure the column has some "business value". Identity columns should never be chosen as the clustered index. The non-clustered indexes should be chosen even more wisely. Only those columns should be chosen for the non-clustered index which are used in search criteria most frequently. The inclusion of columns in the non-clustered index should also be done carefully. The frequently selected columns must be included. Unnecessary columns should not be included as it can hamper the performance.
Primary keys are indexed by default. Therefore using primary key columns in your queries would be more efficient. Using functions on indexed columns would nullify the index. So creating functional index is necessary to make use of functions in the queries.
- Use Indexes - Optimize Queries - Limit the Result Set - Use Joins Carefully - Avoid Using Subqueries - Update Statistics - Partitioning - Use Stored Procedures - Normalize Data - Database Maintenance - Hardware Upgrades - Caching - Use Connection Pooling - Optimize Disk I/O - Profiling and Monitoring - Consider Denormalization - Review Execution Plans - Batch Processing - Upgrade Database Software
Joins are operations that combine data from two or more tables based on a common condition. They are useful for creating complex queries, but they can also be costly and time-consuming. To make your queries run faster, you should avoid unnecessary joins that do not add any value to your results. For example, if you only need data from one table, do not join it with another table that has no relation to it. Also, try to use inner joins instead of outer joins, as they are more efficient and return less rows.
One way to reduce actions performed is to start with the smallest table and join larger tables to it. Moreover, apply filters to tables before performing joins to reduce the dataset size.
Stored procedures are precompiled SQL statements that are stored in the database. They can be called from an application or directly from a SQL query. Using stored procedures can improve query performance by reducing the amount of data that is sent between the database and the application, and by reducing the amount of time required to compile and execute the SQL statements. Another way is by optimizing the database design. This includes ensuring that tables are properly normalized and that indexes are used effectively. In addition, it is important to ensure that the database is properly tuned for the expected workload and that it is configured for the appropriate level of concurrency.
Please don't bring cross-checking between two tables to the application. Let SQL JOINs handle those. Design tables so related tables can have a corresponding key to JOIN together.
Joins combine data from multiple tables based on common conditions, but they can be costly and time-consuming. To optimize joins for faster query execution, avoid unnecessary joins that do not add value to your results. I prefer inner joins over outer joins, as they are more efficient and return fewer rows. Ensure that relevant columns used in joins are indexed, which can significantly speed up the query. Apply filters before joining to reduce the dataset size and simplify joins by breaking down complex joins into simpler, more manageable parts when possible. These steps can help improve the performance of your database queries.
One of the simplest ways to make your queries run faster is to limit the amount of data that you retrieve and process. You can do this by using the LIMIT clause to specify the number of rows that you want to return, or the WHERE clause to filter out the rows that do not match your criteria. By limiting the data, you can reduce the network traffic and the memory usage of your queries. However, you should also be careful not to limit the data too much, as you might miss some important information or introduce errors.
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Here are my 6 tips to skyrocket your query performance. Reduce Columns: Ask: Are all columns necessary in the query? Action: SELECT Col1, Col2, to reduce unwanted data instead of SELECT * Reduce Rows: Ask: Can data be limited? Action: Use LIMIT clause to restrict data. Ask: Can data be filtered? Action: Apply WHERE clause for data filtering. Ask: Can data be aggregated? Action: Utilize GROUP BY for data aggregation. Ask: Can aggregated data be filtered? Action: Combine GROUP BY with HAVING to filter aggregated data. Ask: Can historical data be excluded? Action: Use WHERE clause and DATE to exclude historical data. That's a wrap! Feel free to connect with me if you find these tips valuable.
Yes, WHERE clause, LIMIT, HAVING clause, and Subqueries help narrow down the scope of a query. Which in turn makes the response faster.
1- Don't ever use convert or cast in a where condition 2-Creation of Temp Tables is a good practice with big data, as you put the filtered data in a temp table, and you can use it multiple ways within a single query (it acts as a normal table and very light), but don't forget to drop it at the end of the script
Subqueries are queries that are nested inside another query. They can be useful for breaking down complex queries into smaller parts, or for performing calculations and aggregations that are not possible with a single query. However, subqueries can also slow down your queries if they are not used wisely. For example, if you use a subquery in the SELECT clause, it will run for every row of the outer query, which can be very inefficient. To make your queries run faster, you should use subqueries sparingly and only when necessary. You should also try to use joins or temporary tables instead of subqueries when possible.
A programming language that lets you retrieve data from a database. Performance matters when retrieving this data, which is why it's important to learn how to write efficient SQL queries. Below are few rules to make your SQL faster and more efficient. 1. Minimize the use of wildcard characters 2. Increase Query Performance with Indexes 3. Use appropriate data types 4. Avoid subqueries 5. Use LIMIT or TOP to limit the number of rows returned 6. Avoid using SELECT * 7. Use EXISTS instead of IN 8. Use GROUP BY to group data 9. Use stored procedures 10. Optimize the database design 11. Use query optimization tools 12. Monitor query performance 13.Use cursors less 14.Separate large and small transactions 15.Avoid triggers
1) Keep subqueries simple and avoid complex operations. 2) Limit the use of deeply nested queries. 3) Limit the results and also restrict the columns in the select statement. 4) Ensure the columns used in subqueries are indexed. 5) Try to rewrite the subqueries as joins when possible. 6) Use materialized views for complex subqueries. 7) Optimized sub-queries with Explain or similar tools.
The design of your database can have a significant impact on the performance of your queries. A well-designed database follows the principles of normalization, which means that it eliminates data redundancy and ensures data integrity. A normalized database has smaller and simpler tables, which makes it easier and faster to query. However, normalization also has some disadvantages, such as increasing the number of tables and joins, which can affect the readability and maintainability of your queries. Therefore, you should balance the trade-offs between normalization and denormalization, and choose the optimal level of normalization for your database.
1- If you have a big query with many calculations like Salary or Incentive, saving query in a stored procedure is a good solution, and make the query running faster 2- Function table is not working fast at all, avoid it while using big data
Use data and metadata regardless of the specific DBMS. Although you will lose some speed, thanks to the built-in algorithms, you will gain: 1. Universality of the solution 2. Flexibility to future development 3. More difficult to support than someone else who doesn't have your experience and knowledge Don't forget about statistical metadata (and use it when necessary), and try not to summarize your metadata, but to enrich it. (they should still be significantly less than the main data). Thus, you will get two-level requests (metadata-data), which will not only optimize your performance, but also give you the opportunity to react quickly to changes in the business/data model.
The final tip to make your queries run faster is to test and tune them regularly. You can use various tools and methods to measure and analyze the performance of your queries, such as the EXPLAIN command, the query execution plan, or the query profiler. These tools can help you identify the bottlenecks and problems in your queries, such as missing indexes, inefficient joins, or excessive data. You can then apply the tips and tricks that we discussed in this article, or other techniques, to optimize and improve your queries. You should also monitor and update your queries as your data and requirements change, and keep learning new ways to make your queries run faster.
This one is usually a winner. I use it from most interview questions :) Examining the execution plan of your queries can help identity the exact cause of the problem. Only then, you can switch to think about actual solutions.
After running EXPLAIN, if you find that the QUERY PLAN isn't ideal for faster results, it's probably because the statistics about your TABLE are outdated. To fix this, use the ANALYZE TABLE <table> command. Keeping your statistics accurate is essential because it helps the optimizer make better choices for the Query Plan, which ultimately improves how your queries perform.
Know your database engine. Not all engines will execute statements the same. For example in Oracle (and MySQL), DDL will be committed after the statement runs. No rollback. In MS SQL Server, you can wrap DDL in a transaction and it can be rolled back. Mileage in other engines may vary. Make sure you know the rules of the database engine you're running your code on. There are going to be trade-offs between engines. Get to know it.
Use of right syntax is necessary. Eg. In Oracle, an indexed varchar column containing only numbers would work when using clauses like WHERE col = 123. But internally it would run the query as WHERE TO_NUMBER(col) = 123. This would result in poor performance. So use '123' when working with alphanumeric columns.
Problems come from: 1. Insufficient resources (size, speed) 2. Improper use of available resources (granularity of the model, incorrect structures, non-compliance with the specifics of the technology) 3. The simultaneous use of data from several processes. So that : 1. Check the processes that (will) use the data, when and how (they will) use it (including system ones) 2. Check whether the structures in which the data are (will) be - how adequate they are to the way they are used. 3. Check if you have the necessary resources for reliable and sustainable operation of the system in both normal and above-normal situations