Databases and SQL

Filtering

Overview

Teaching: 10 min
Exercises: 10 min
Questions
  • How can I select subsets of data?

Objectives
  • Write queries that select records that satisfy user-specified conditions.

  • Explain the order in which the clauses in a query are executed.

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:

SELECT * FROM Visited WHERE site='DR-1';
id site dated
619 DR-1 1927-02-08
622 DR-1 1927-02-10
844 DR-1 1932-03-22

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 which columns to display.

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

SELECT id FROM Visited WHERE site='DR-1';
id
619
622
844

SQL Filtering in Action

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 before 1930:

SELECT * FROM Visited WHERE site='DR-1' AND dated<'1930-01-01';
id site dated
619 DR-1 1927-02-08
622 DR-1 1927-02-10

Date Types

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 (Julian days, the number of days since November 24, 4714 BCE), or integers (Unix time, 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:

SELECT * FROM Survey WHERE person='lake' OR person='roe';
taken person quant reading
734 lake sal 0.05
751 lake sal 0.1
752 lake rad 2.19
752 lake sal 0.09
752 lake temp -16.0
752 roe sal 41.6
837 lake rad 1.46
837 lake sal 0.21
837 roe sal 22.5
844 roe rad 11.25

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

SELECT * FROM Survey WHERE person IN ('lake', 'roe');
taken person quant reading
734 lake sal 0.05
751 lake sal 0.1
752 lake rad 2.19
752 lake sal 0.09
752 lake temp -16.0
752 roe sal 41.6
837 lake rad 1.46
837 lake sal 0.21
837 roe sal 22.5
844 roe rad 11.25

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:

SELECT * FROM Survey WHERE quant='sal' AND person='lake' OR person='roe';
taken person quant reading
734 lake sal 0.05
751 lake sal 0.1
752 lake sal 0.09
752 roe sal 41.6
837 lake sal 0.21
837 roe sal 22.5
844 roe rad 11.25

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

SELECT * FROM Survey WHERE quant='sal' AND (person='lake' OR person='roe');
taken person quant reading
734 lake sal 0.05
751 lake sal 0.1
752 lake sal 0.09
752 roe sal 41.6
837 lake sal 0.21
837 roe sal 22.5

We can also filter by partial matches. For example, if we want to know something just about the site names beginning with “DR” we can use the LIKE keyword. The percent symbol acts as a wildcard, matching any characters in that place. It can be used at the beginning, middle, or end of the string:

SELECT * FROM Visited WHERE site LIKE 'DR%';
id site dated
619 DR-1 1927-02-08
622 DR-1 1927-02-10
734 DR-3 1930-01-07
735 DR-3 1930-01-12
751 DR-3 1930-02-26
752 DR-3  
844 DR-1 1932-03-22

Finally, we can use DISTINCT with WHERE to give a second level of filtering:

SELECT DISTINCT person, quant FROM Survey WHERE person='lake' OR person='roe';
person quant
lake sal
lake rad
lake temp
roe sal
roe rad

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

Growing Queries

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.

Fix This Query

Suppose we want to select all sites that lie more than 42 degrees from the poles. Our first query is:

SELECT * FROM Site WHERE (lat > -48) OR (lat < 48);

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

Solution

Because we used OR, a site on the South Pole for example will still meet the second criteria and thus be included. Instead, we want to restrict this to sites that meet both criteria:

SELECT * FROM Site WHERE (lat > -48) AND (lat < 48);

Finding Outliers

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.

Solution

SELECT * FROM Survey WHERE quant='sal' AND ((reading > 1.0) OR (reading < 0.0));
taken person quant reading
752 roe sal 41.6
837 roe sal 22.5

Matching Patterns

Which of these expressions are true?

  1. 'a' LIKE 'a'
  2. 'a' LIKE '%a'
  3. 'beta' LIKE '%a'
  4. 'alpha' LIKE 'a%%'
  5. 'alpha' LIKE 'a%p%'

Solution

  1. True because these are the same character.
  2. True because the wildcard can match zero or more characters.
  3. True because the % matches bet and the a matches the a.
  4. True because the first wildcard matches lpha and the second wildcard matches zero characters (or vice versa).
  5. True because the first wildcard matches l and the second wildcard matches ha.

Key Points