How do you make SQL queries run faster?

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.

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

Keeping the size issue in mind, frequently queried column(s) in your WHERE clause can be considered an index. This will speed up the response time.

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.

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

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.

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.

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