Como você pode otimizar consultas SQL para valores NULL?

Here are some strategies to optimize SQL queries involving NULL values: 1. Proper Indexing: Indexes might not be used efficiently for columns with many NULL values. 2. Use IS NULL and IS NOT NULL operators to filter NULL values efficiently. 3.Use COALESCE() or NVL to handle NULL values and substitute them with default values. 4.Utilize CASE statements to handle NULL values and perform conditional operations. 5.Ensure that join conditions handle NULLs appropriately to avoid unexpected results. Consider using LEFT JOIN or RIGHT JOIN instead of INNER JOIN when dealing with NULLs in join conditions. 6.Design the database schema and data model to minimize the presence of NULLs where possible by using default values.

Consider using constraints like NOT NULL or DEFAULT when appropriate for your data model to minimize the occurrence of NULL values. Use the COALESCE function in SELECT statements to provide a default value when dealing with NULLs.

Empty Box Rule: Treat NULL like an empty treasure box. Only ask questions or invite friends if their boxes have treasures (information). No Invisible Friends: When joining tables, only bring friends with treasures. Leave out the ones with invisible boxes (NULL) to keep things simple. So, remember the "Empty Box Rule" and don't invite invisible friends to the data party!

It depends on the database but in most of the scenarios I'd prefer to use ISNULL or DEFAULT to optimize. The choices may be considerably different for other dbs like Oracle.

in some database engines, we can use IFNULL functions. but I think it is not supported in all database engines. anyway handling null values in queries is an essential aspect of database development, and the approach depends on the specific use case, the database system, and the nature of the data

The decision should be between (NOT) EXISTS and a JOIN/SEMI JOIN approach. The performance and quirks of NOT IN make it a losing proposition in my experience. Be careful and test when deciding between EXISTS and a JOIN, as the nature of the two sets (one large, the other a smaller subset versus two large unrelated sets, etc.), constraints (NOT NULL, FK, etc.), and indexes can drastically change the plan (and thus IO and CPU) used in different situations. Please forget about IN/NOT IN.

Is not a best practice have nulls in key fields like cliente_id. We must restrict not null use in key fields when table data set is defined. In a long term its avoids have to joins table set with nulls in a key fields. This practice avoids have result sets with empty fields

Articulate caution! Avoiding NOT IN with NULL-containing subqueries is crucial for accuracy. Opting for NOT EXISTS with a correlated subquery offers reliability, sidestepping pitfalls tied to NOT IN and ensuring results align with expectations. This approach is especially crucial when finding clients without orders, ensuring precision and maintaining expectations in scenarios involving NULL values.

When you use not in or not like function with column which has null values , you will miss a null values as you can’t compare null value with these function , so I suggest to use if null function while using not in or not like function.

If you're using Oracle, you can always use functions such as NVL and NVL2 to optimize your query's execution plan and enhance its readability. More often than not, Oracle engine won't be taking indexes into consideration if you write your queries with (X = 'Y' OR X is NULL), but it definitely will take them into account if you use NVL function.

Although their performance is similar, ISNULL is a T-SQL extension and thus less portable than COALESCE. Plus, down the road, if you need to compare yet another expression, COALESCE can take more than the limited two arguments allowed in ISNULL. Minimal code change is required! And don't use a hammer to drive a screw! Several other options may be more appropriate to your situation. Learn the different functions that may better serve your purpose, like MIN/MAX, FIRST_VALUE/LAST_VALUE, and GREATEST/LEAST, leveraging the OVER clause if appropriate.

There are many ways to handle null values. 1- if we don't want null values we can filter those values. 2- Use a random function to avoid null values. 3- Use the COALESCE function to replace Null with some values. 4- First we can process null values and in second with not null and do it union all.

We also could define field "default value" for numeric fields in "0". This could improve efficient in calculated fields or fields that provides calcs with funtions like AVG

using function like nvl,coalesce in condition or query will provide improvement in performance of the query and help in handling of the output of query if needed in report

If your table has many NULL values, you can also test the efficacy of creating a filtered index by adding WHERE NullableColumn IS NOT NULL. That will keep the index smaller. But you'll have to test whether the index is actually used in the wild. SQL Server can be finicky about using/ignoring filtered indexes.

Is a best practice have table indexes with key fields that provides uniqueness and add value. For example Client_id plus department_Id plus invoice_id, this will returns a clean field to the query. But create a table index by for example fields like Date of Birth, Nickname, Address, Second Name can't guarantee result sets affordable and providers of quality. Have to consider data quality to the data set

NVL and NVL2 are great way to avoid null values in query outputs. while NVL can only take two arguments ; NVL(commission, 0), NVL2 can take multiple arguments ; NVL2(commission, 0, not_available). From the example below we should be able go understand more: SELECT last_name, salary, commission_pct, NVL2(commission_pct, ’SAL+COMM’, ’SAL’) income FROM employees; From the above example, Instead of getting null as result for employees that are not earning commission, NVL2 will return their salary.

I experienced this when I was working on a postgres query. As said using nulls first or nulls last allows you to order the result set returned from the query.

If it exists in the database engine you're using, embrace the IS (NOT) DISTINCT FROM function for comparisons. If speed is important to you (and it should be) and you wish to treat NULL as a 0 (frequently a mistake, BTW), consider using ISNULL (or NVL in some engines) rather than COALESCE. For "DateEnd" columns, consider the use of a surrogate future value such a 99990101 for the date to avoid having to use non-SARGable or OR criteria, both of which have major impacts on performance. Performance is second only to accuracy and, IMHO, is tied for importance in the world of data... especially but not limited to supposed "big data". It can be incredibly important on "small data" that is "hit" upon thousands or even "just" a few times/minute

You have to think about the Null values in your dataset. check if you have any and think what to do about it like replacing these values or get rid of them before building your query. Because using ISNULL in the select-clause has neglible influence on the performance while using it In the where-clause can have a huge impact on performance, it can induce a bad execution plan since it prevents the optimizer from using the index on that column. So yes, optimization wise avoid using the ISNULL in the where clause.

Do the due diligence on defining the column nullabilty and default value specification at column level. When you want to select or exclude rows from the result set with NULL values, use of IS NULL or IS NOT NULL is the way to go for any relational db. If the table is large creating a filter index (SQL server) will save on storage and hence less IOs and hence faster performance. Avoid using any function on the column in the WHERE clause to replace and compare null values. for example WHERE ISNULL(col1,0)=0. Using row compression consolidate the NULLs, less IOs, hence less buffer pool memory required. Results in overall better performance with less memory. Slightly more CPU usage. hence you should test thoroughly to make it worth.

When joining tables, be mindful of NULL values. Use LEFT JOIN or RIGHT JOIN as needed to include or exclude rows with NULL values based on your requirements.

Too much mention of nulls in queries could be a database normalisation smell. I guess it's important when adding nullable fields to at least consider creating a child table for that element. Yes, it means more joins, but that need not matter if you can use Views or stored procedures to abstract these away. Personally, so long as it does not bring your database to its knees, it's nice to keep your SQL/S-procs clean and dry. Still, normalisation, refactoring and clean coding, SOLID and DRY got many a coder sacked, so if your null handling sucks, you are probably on the right track. Even Kent Beck agrees. Just don't let Uncle Bob know you are doing it. LOL.