Command to delete a view in sql

Whether to use subqueries or connections depends on the specific scenario. 1. When it is necessary to filter data in advance, subqueries are more effective, such as finding today's order customers; 2. When merging large-scale data sets, the connection efficiency is higher, such as obtaining customers and their recent orders; 3. When writing highly readable logic, the subqueries structure is clearer, such as finding hot-selling products; 4. When performing updates or deleting operations that depend on related data, subqueries are the preferred solution, such as deleting users that have not been logged in for a long time.

There are three core methods to find the second highest salary: 1. Use LIMIT and OFFSET to skip the maximum salary and get the maximum, which is suitable for small systems; 2. Exclude the maximum value through subqueries and then find MAX, which is highly compatible and suitable for complex queries; 3. Use DENSE_RANK or ROW_NUMBER window function to process parallel rankings, which is highly scalable. In addition, it is necessary to combine IFNULL or COALESCE to deal with the absence of a second-highest salary.

Filtering NULL value records in SQL cannot use =NULL or !=NULL, 1. ISNULL or ISNOTNULL must be used; 2. For example, users who look for email columns NULL should write SELECT*FROMusersWHEREemailISNULL; 3. Multiple fields can simultaneously determine that multiple ISNULL conditions can be combined, such as OR or AND connection; 4. COALESCE can replace NULL values ​​for display or default processing, but are not applicable to filtering. Because NULL represents an unknown value and does not participate in the comparison operation of equal or non-equal, =NULL will not return the result and will not report an error. The WHERE clause only accepts TRUE lines, ignores FALSE and UNK

You can use SQL's CREATETABLE statement and SELECT clause to create a table with the same structure as another table. The specific steps are as follows: 1. Create an empty table using CREATETABLEnew_tableASSELECT*FROMexisting_tableWHERE1=0;. 2. Manually add indexes, foreign keys, triggers, etc. when necessary to ensure that the new table is intact and consistent with the original table structure.

MySQL supports REGEXP and RLIKE; PostgreSQL uses operators such as ~ and ~*; Oracle is implemented through REGEXP_LIKE; SQLServer requires CLR integration or simulation. 2. Regularly used to match mailboxes (such as WHEREemailREGEXP'^[A-Za-z0-9._% -] @[A-Za-z0-9.-] \.[A-Za-z]{2,}$'), extract area codes (such as SUBSTRING(phoneFROM'^(\d{3})')), filter usernames containing numbers (such as REGEXP_LIKE(username,'[0-9]')). 3. Pay attention to performance issues,

Calculate the conditional sum or count in SQL, mainly using CASE expressions or aggregate functions with filtering. 1. Using CASE expressions nested in the aggregate function, you can count the results according to different conditions in a single line of query, such as COUNT(CASEWHENstatus='shipped'THEN1END) and SUM(CASEWHENstatus='shipped'THENamountELSE0END); 2. PostgreSQL supports FILTER syntax to make the code more concise, such as COUNT(*)FILTER(WHEREstatus='shipped'); 3. Multiple conditions can be processed in the same query,

In predictive analysis, SQL can complete data preparation and feature extraction. The key is to clarify the requirements and use SQL functions reasonably. Specific steps include: 1. Data preparation requires extracting historical data from multiple tables and aggregating and cleaning, such as aggregating sales volume by day and associated promotional information; 2. The feature project can use window functions to calculate time intervals or lag features, such as obtaining the user's recent purchase interval through LAG(); 3. Data segmentation is recommended to divide the training set and test set based on time, such as sorting by date with ROW_NUMBER() and marking the collection type proportionally. These methods can efficiently build the data foundation required for predictive models.

TheHAVINGCOUNT(*)clauseinSQLisusedtofiltergroupsofrowsbasedonthenumberofrecordsineachgroupaftergrouping.1.ItworksaftertheGROUPBYclauseandallowsfilteringbasedonaggregatedvalueslikerowcount.2.Forexample,itcanreturnonlycustomerswithmorethan10ordersbycou