Email Advertisement Databases drive the modern web. Every big or dynamic website uses a database in some way, and when combined with Structured Query Language SQLthe possibilities for manipulating data really are endless.
We usually need a particular sub-set of this data and when we pick up only the part we need, this saves a lot of time in query execution and speeds up performance.
For example, below we pick up all the age and gender values for all employees whose age is greater than 25 years. This means that this data would be returned for ages 26 or more. Also note that the number 25 is written without any double or single quotes.
|Popular Tags||Issue a SQL statement such as the following: This indicates log file 11 is currently being applied.|
On pressing execute you can see that 28 rows have been retrieved and on skimming the data, you realize that the filter was successfully applied. If you wanted to include employees with age 25, just add an equals sign in front of the greater than sign to make it a greater than or equal to condition as shown below.
On execution, you get the data for ages 25 or more as seen below. You can keep adding AND conditions and continue adding more filters like this. I now add the gender filter as male.
Notice that the value has been enclosed in single quotes. This is really important. Whenever the value is a string of characters, it should be in single quotes. Always remember that fields, and object names are always in double quotes but data values that are characters will always be in single quotes.
We see no data. So this can either mean that there is no row in this table that matches this condition or that we messed up the filter somehow. I screwed this up to teach you an important lesson — Always check the data before applying filters. Anything inside a quote is case sensitive.
Now we correct the filter and capitalise the M in male. Executing it this time provides the correct results. The below example shows addition of a third filter. Upon execution you get the result as below.
This statement can be a bit misleading and not understood in the way it was meant to. Read it once again.12 Data Guard Scenarios. This chapter describes scenarios you might encounter while administering your Data Guard configuration. Each scenario can be adapted to your specific environment. The BACKUP statement is not allowed in an explicit or implicit transaction.
Under the simple recovery model, read/write files must all be backed up together. This helps make sure that the database can be restored to a consistent point in time.
Instead of individually specifying each read/write file. 1.
Using Standalone Rowsets to Write a File The following is an example of using standalone rowsets along with a file layout to write a file: writes a file using a file layout that contains parent-child records.
(Jesper Krogh) The MySQL Document Store became general available (GA) with MySQL 8. One of the nice features of the MySQL Document Store is the X DevAPI that allows you to query the data from a multitude of programming languages using the same API (but while retaining the conventions of .
SAP HANA SQL SCRIPT LIKE Operator. Now there are situations where we need to return the list of data where a record starts with/ends with/contains a particular character or set of characters.
12 Data Guard Scenarios. This chapter describes scenarios you might encounter while administering your Data Guard configuration. Each scenario can be adapted to your specific environment.