SQL (Structured Query Language) is a homogeneous programming language which is widely used for organizing relational databases, and for accomplishment of several operations on the data stored in them. To trace the beginning, it was first created and used in the 1970s, SQL is frequently used by mainly the database managers, as well as by developers who are working in the profile of writing data integration scripts. Data analysts who are looking to set up and run analytical queries also use SQL.
SQL statements can be easily used to retrieve relevant data from any database that exists. To get similar outcomes you are required to write dissimilar SQL queries. For optimum performance you need to use the queries that are not only best and faster but also efficient. Therefore, you need a SQL query tuning, which is based on the business in addition to the user requirements.
By choosing only the columns that you require, you are dropping the size of the result table substantially thereby decreasing the network traffic and increasing the general performance of your query.
In the below screenshot, you can see how ‘International Journal of Scientific & Technology Research’ has very well explained the concept-
The HAVING clause is generally used like a filter and that makes is quite impractical in a SELECT statement. It goes through the final result table of the query analyzing the rows that don’t even meet the HAVING condition. See the concept explained in the below screenshot
This problem is very common, because using UPDATE has a natural flow that seems rational but it is better to use CASE. An UPDATE statement has to write twofold for every single write to the table and thus slowing your results.
Many developers tend to copy someone else’s as it pulls the data that they need. The issue is that it pulls much more data than you actually need. Trim the code to your exact needs.
The IN-list predicate can be used substantially for indexed recovery and also, the optimizer can sort the IN-list to match the sort sequence of the index, leading to more efficient retrieval.
See the concept explained in the below screenshot by ‘International Journal of Scientific & Technology Research’
It is quite easy to code all your queries with SELECT* command instead of listing the columns separately every time.
If you already have a report or a procedure that can do analogous joins to large tables, it can be an advantage for you to pre-stage the data by linking the tables ahead of time and persisting them into a table.
It helps you increase concurrency because your database can go online quickly.
Cursor operations suffer from speed and block problem. To avoid that try to run them against a temp table.
This is because you will end up in a loop where you have views that call views that call views that call views. You will have more data retrieved than is needed.
Whenever you put a scalar function in the SELECT list of the query, the function is called for each single row in the result set.
The DISTINCT keyword effectively works by picking all the columns in the table then analyzesany duplicates.In its place, if you use sub query with the EXISTS keyword, you can circumvent having to return an entire table as a result.
Stored procedures have quite a lot of benefits. The best ones are that you’re pushing much less data across the network; the procedure call is shorter etc.
Every time when you place an ‘OR’ in the join condition, the query will slow down by at least a factor of two.
Functions or methods are utilized very frequently with their SQL queries. Rewriting the query by eliminating collective functions will escalate the performance extremely.
There will be instances where you will be performing mathematical queries within an SQL statement. They can really drag on the performance if written wrongly. So removing any preventable math in the statement will make it perform more rapidly.
The UNION ALL statement is more rapid than UNION, as a UNION ALL statement never considers copies, and UNION statement looks for duplicates in a table irrespective of their existence.
We hope that these tips will help you write better queries. For more insight, read our other blogs on SQL.
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