same query runs slow and fasts sql server

One horror story is about a major data loss during a system upgrade. The upgrade process had some untested scripts that ended up deleting crucial data tables instead of modifying them. It was a nightmare as there was no proper backup strategy in place. The company had to spend weeks trying to recover what they could from old backups and logs.

A common horror story is performance issues. For example, a query that was supposed to run in seconds took hours. This was due to bad indexing. Indexes were not created properly or were missing for important columns used in the WHERE clause of the query. Another is security breaches. If a SQL Server has weak authentication or improper user permissions, it can be easily hacked. Hackers can then steal sensitive data like customer information or financial records.

Sure. One success story is a large e - commerce company that used Microsoft SQL Server to manage its vast product database. This allowed for quick retrieval of product information, leading to faster page load times for customers and increased sales. Another example is a financial institution that utilized SQL Server for its transaction processing. It ensured secure and accurate handling of financial transactions, meeting strict regulatory requirements.

The transportation industry has its own success stories. A logistics company utilized Microsoft SQL Server to manage its fleet operations. They could track vehicle locations, maintenance schedules, and delivery schedules all in one system. This led to more efficient deliveries and reduced operational costs. In the energy sector, an oil and gas company used SQL Server for data analytics. They could analyze seismic data and production data, which helped them find new oil reserves and optimize production processes.

When you perform fuzzy queries in an SQL statement, you can use a clause that contains multiple keywords to filter. For example, if you want to query all documents that contain "detective","murder", and "novel", you can use the following SQL statement: ``` <<p>><p></p>></p>> ``` In this example,"title" is the column used to query the document,"LIKE" is the fuzzy matching character " %" means matching any character. This clause can contain multiple keywords such as "Detective % Murder % Fictional". This will filter out documents that contain the keywords "Detective","Murder", and "Fictional". If you want to filter multiple keywords in one sentence, you can use a compound clause that contains multiple keywords to filter. For example, if you want to search for all novels that contain the keywords "detective","murder", and "novel", you can use the following SQL statement: ``` <<p>><p></p>></p>> ``` In this example,"novel" can also be filtered with a compound clause such as "novel %". This can filter out novels that contain the keywords "detective","murder", and "novel".

To check the names of students of the same age as Liu Chen, you can use the following SQL statement: ``` Name of the student WHERE age = (SELECT age FROM student WHERE name = 'Liu Chen') ``` This SQL statement used a subquery to find students of the same age as Liu Chen and used a condition statement to filter the results that matched the conditions. In the condition statement, we used the `=>` operator to compare the ages of the two query results and `('Liu Chen')` to specify the name of the student we want to find. The result of the sub-query is stored in the `age` column of the `SELECT` statement to match the result of the query in the condition statement. After executing the above SQL statement, we will get a result set that contains the names of students of the same age as Liu Chen.

I need more context to answer your question. Can you provide more information about the form name, the field content, the student number, the course number, and the score? This way, I can better help you answer your questions.

One SQL horror story could be when a developer accidentally dropped an important table in the production database. They might have mis-typed a command like 'DROP TABLE' instead of something else. This led to a huge loss of data and hours of downtime to try and restore from backups.

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Poorly written SQL queries can also lead to horror stories. For example, queries with incorrect joins can result in wrong data being retrieved or updated. If a developer doesn't fully understand how to use JOINs correctly, it can mess up the whole data integrity.