One of the most powerful features of a database is the ability to filter data, i.e., to select only those records that match certain criteria. For example, suppose we want to see when a particular site was visited. We can select these records from the Visited table by using a where clause in our query:

The database manager executes this query in two stages. First, it checks at each row in the Visited table to see which ones satisfy the where. It then uses the column names following the select keyword to determine what columns to display.

This processing order means that we can filter records using where based on values in columns that aren't then displayed:

We can use many other Boolean operators to filter our data. For example, we can ask for all information from the DR-1 site collected since 1930:

(The parentheses around the individual tests aren't strictly required, but they help make the query easier to read.)

Most database managers have a special data type for dates. In fact, many have two: one for dates, such as "May 31, 1971", and one for durations, such as "31 days". SQLite doesn't: instead, it stores dates as either text (in the ISO-8601 standard format "YYYY-MM-DD HH:MM:SS.SSSS"), real numbers (the number of days since November 24, 4714 BCE), or integers (the number of seconds since midnight, January 1, 1970). If this sounds complicated, it is, but not nearly as complicated as figuring out historical dates in Sweden.

If we want to find out what measurements were taken by either Lake or Roerich, we can combine the tests on their names using or:

Alternatively, we can use in to see if a value is in a specific set:

We can combine and with or, but we need to be careful about which operator is executed first. If we don't use parentheses, we get this:

which is salinity measurements by Lake, and any measurement by Roerich. We probably want this instead:

Finally, we can use distinct with where to give a second level of filtering:

But remember: distinct is applied to the values displayed in the chosen columns, not to the entire rows as they are being processed.

What we have just done is how most people "grow" their SQL queries. We started with something simple that did part of what we wanted, then added more clauses one by one, testing their effects as we went. This is a good strategy—in fact, for complex queries it's often the only strategy—but it depends on quick turnaround, and on us recognizing the right answer when we get it.

The best way to achieve quick turnaround is often to put a subset of data in a temporary database and run our queries against that, or to fill a small database with synthesized records. For example, instead of trying our queries against an actual database of 20 million Australians, we could run it against a sample of ten thousand, or write a small program to generate ten thousand random (but plausible) records and use that.

Challenges

1. Suppose we want to select all sites that lie more than 30° from the poles. Our first query is:

   select * from Site where (lat > -60) or (lat < 60);

   Explain why this is wrong, and rewrite the query so that it is correct.

2. Normalized salinity readings are supposed to be between 0.0 and 1.0. Write a query that selects all records from Survey with salinity values outside this range.

3. The SQL test *column-name* like *pattern* is true if the value in the named column matches the pattern given; the character '%' can be used any number of times in the pattern to mean "match zero or more characters".

   Expression	Value
   'a' like 'a'	True
   'a' like '%a'	True
   'b' like '%a'	False
   'alpha' like 'a%'	True
   'alpha' like 'a%p%'	True

   The expression *column-name* not like *pattern* inverts the test. Using like, write a query that finds all the records in Visited that aren't from sites labelled 'DR-something'.