Here are examples of converting a Unix timestamp to a date/time value in some of the major RDBMSs.
The Unix timestamp (also known as Unix Epoch time, Unix time, or POSIX time) is the number of seconds that have elapsed since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC).
Some RDBMSs provide an ISNULL() function that can be used when dealing with potentially null values.
MySQL, MariaDB, and Oracle Database each have an ISNULL() function that returns 1 if its argument is null, and 0 if it’s not.
SQL Server also has an ISNULL() function, but it works differently. It works more like how the IFNULL() function works in some other RDBMSs.
Other RDBMSs, such as PostgreSQL and SQLite don’t include an ISNULL() function, but they do support the IS NULL predicate (as do the other RDBMSs).
In PostgreSQL, the COALESCE() operator returns the first non-null value from its list of arguments. If all arguments are null, it returns null.
Below are two options for removing duplicate rows from a table in PostgreSQL when those rows have a primary key or unique identifier column. The primary key is used in the query, but it’s ignored when comparing duplicates (due to the fact that primary keys prevent duplicate rows by definition).
The following examples delete duplicate rows but keep one. So in the case of say, three identical rows, it deletes two and keeps one.
In PostgreSQL, we can use the - operator to subtract one or more hours from a time value.
By “time” value, this could be an actual time value, a timestamp, or an interval. We can also subtract hours from a date value or a date and time combination.
Here are four options for returning rows that contain lowercase characters in PostgreSQL.
Some RDBMSs provide an IFNULL() function that can be used when dealing with potentially null values. In particular, MySQL, MariaDB, and SQLite each have an IFNULL() function that replaces any null values with another value.
Other RDBMSs, such as SQL Server, Oracle, and PostgreSQL provide similar functionality via functions of a different name.
If you ever encounter a character column that should be numeric, there’s always a possibility that it contains non-numeric data that you don’t know about.
For example, someone might have set up a Price column as a varchar column that should have been a numeric column, and now you need to clean up after them. You might start by identifying all non-numeric data so that you can work out what to do with it before converting the column to a numeric type.
In SQL, you can run a query to return non-numeric data from the column. The query you use will largely depend on your DBMS.