Using Between Clause To Compare in Oracle

There are a handful of different ways you can use the BETWEEN clause in Oracle SQL queries, but most often you will use the "greater than equal" and "less than equal" conditions to get to your desired data.

As an example, if you want to compare the sales numbers between all salespeople at your organization, you may pull a SQL table that includes employee names and their daily sales numbers. You may then want to go a step further by filtering the data to find sales numbers between two figures to identify which salespeople are at the bottom and the top of the query and who is most representative of the average.

In this instance, when writing your code, you could write out: -> WHERE daily_sales BETWEEN 50 and 100, or you could use the >= and <= conditions as an alternate. For example, with these conditions, your SQL query may look like this: -> WHERE daily_sales >= 50 AND -> daily_sales <= 100.

Both of these lines of SQL query will generate sales numbers that are between 50 and 100 for a given group (that you identify in a second line of query). However, a general rule of thumb is to write as little code as possible in your queries, as fewer lines of code tend to be better and more concise.

The BETWEEN clause can be applied to dates, numbers, or any text, but it's most common with numerical queries. You can write SQL queries that contain BETWEEN to display data that fall between two values or to display data that does not fall between a range of values. For example, if your data supports it, you can run a SQL query for a list of employees whose salary is not between $30,000 and $50,000 per year. This line of query may look like this: -> WHERE salary NOT BETWEEN 30000 AND 50000.

Ultimately, if you are looking for a range of data that falls between two value points, you can use the BETWEEN clause in your SQL query as a way to extract that important data.

SQL Query Best Practices

There are certain best practices when writing SQL queries. Here is what to keep in mind when generating the code for your data extraction:

• Write out the full syntax for tables. Instead of using the SELECT * operator, write out the full names of only the columns you need. This should improve the SQL server performance and make for less disk input/output error. Plus, you will only receive data you need.
• Do not use column numbers. This applies to SQL queries that include the ORDER BY clause. If you list column names in the query rather than numbers, you have more of a chance that the data results you get are correct. Column names are very clear directions to the server, whereas column numbers are less readable.
• Do not use the "sp_" prefix. If you use this prefix, your SQL server will likely return a procedure from the master database rather than the data you're looking for.
• Use a standard SQL format. It's important to maintain the same rules for your SQL queries so anyone who comes after you can both understand your previous queries and produce some of their own following the same format. This uniformity in SQL query writing will provide a better process that's both efficient and effective in extracting the correct data.
• Minimize lines of code. Because the server will be reading your query, it's best to not muddle your code with unnecessary lines. Clearer, more concise code will perform better and faster.
• Update your SQL statements slowly. If you are updating or deleting lines of SQL syntax from your transaction log, do so a couple of lines at a time. This will prevent the server from becoming overloaded and help minimize errors in the rewrite.
• Use column names that are easy to read. In an effort to maintain the efficiency of the SQL servers and keep it easy on all coders to both write and read columns, keep column names simple. Consider column names that do not have special characters or capitalization, and make sure that all column names follow the same format.

SQL servers take some time to learn, which is why we offer SQL server database support to our customers. Contact us today to learn more about how we can support your business and the important data you have.