+ Databases and SQL
+Aggregation
+Learning Objectives
-Learning Objectives
++- +
- Define “aggregation” and give examples of its use.
- Write queries that compute aggregated values.
- Trace the execution of a query that performs aggregation.
- Explain how missing data is handled during aggregation.
We now want to calculate ranges and averages for our data. We know how to select all of the dates from the Visited table:
SELECT dated FROM Visited;SELECT dated FROM Visited;but to combine them, we must use an aggregation function such as min or max. Each of these functions takes a set of records as input, and produces a single record as output:
SELECT min(dated) FROM Visited;SELECT min(dated) FROM Visited;
SELECT max(dated) FROM Visited;
SELECT max(dated) FROM Visited;min and max are just two of the aggregation functions built into SQL. Three others are avg, count, and sum:
SELECT avg(reading) FROM Survey WHERE quant='sal';SELECT avg(reading) FROM Survey WHERE quant='sal';SELECT count(reading) FROM Survey WHERE quant='sal';SELECT count(reading) FROM Survey WHERE quant='sal';SELECT sum(reading) FROM Survey WHERE quant='sal';SELECT sum(reading) FROM Survey WHERE quant='sal';We used count(reading) here, but we could just as easily have counted quant or any other field in the table, or even used count(*), since the function doesn’t care about the values themselves, just how many values there are.
SQL lets us do several aggregations at once. We can, for example, find the range of sensible salinity measurements:
-SELECT min(reading), max(reading) FROM Survey WHERE quant='sal' AND reading<=1.0;SELECT min(reading), max(reading) FROM Survey WHERE quant='sal' AND reading<=1.0;We can also combine aggregated results with raw results, although the output might surprise you:
-SELECT person, count(*) FROM Survey WHERE quant='sal' AND reading<=1.0;SELECT person, count(*) FROM Survey WHERE quant='sal' AND reading<=1.0;Why does Lake’s name appear rather than Roerich’s or Dyer’s? The answer is that when it has to aggregate a field, but isn’t told how to, the database manager chooses an actual value from the input set. It might use the first one processed, the last one, or something else entirely.
Another important fact is that when there are no values to aggregate — for example here where the there are no rows satisfying the WHERE clause — aggregation’s result is “don’t know” rather than zero or some other arbitrary value:
SELECT person, max(reading), sum(reading) FROM Survey WHERE quant='missing';SELECT person, max(reading), sum(reading) FROM Survey WHERE quant='missing';One final important feature of aggregation functions is that they are inconsistent with the rest of SQL in a very useful way. If we add two values, and one of them is null, the result is null. By extension, if we use sum to add all the values in a set, and any of those values are null, the result should also be null. It’s much more useful, though, for aggregation functions to ignore null values and only combine those that are non-null. This behavior lets us write our queries as:
SELECT min(dated) FROM Visited;SELECT min(dated) FROM Visited;instead of always having to filter explicitly:
-SELECT min(dated) FROM Visited WHERE dated IS NOT NULL;SELECT min(dated) FROM Visited WHERE dated IS NOT NULL;Aggregating all records at once doesn’t always make sense. For example, suppose Gina suspects that there is a systematic bias in her data, and that some scientists’ radiation readings are higher than others. We know that this doesn’t work:
-SELECT person, count(reading), round(avg(reading), 2)
-FROM Survey
-WHERE quant='rad';SELECT person, count(reading), round(avg(reading), 2)
+FROM Survey
+WHERE quant='rad';because the database manager selects a single arbitrary scientist’s name rather than aggregating separately for each scientist. Since there are only five scientists, she could write five queries of the form:
-SELECT person, count(reading), round(avg(reading), 2)
-FROM Survey
-WHERE quant='rad'
-AND person='dyer';SELECT person, count(reading), round(avg(reading), 2)
+FROM Survey
+WHERE quant='rad'
+AND person='dyer';but this would be tedious, and if she ever had a data set with fifty or five hundred scientists, the chances of her getting all of those queries right is small.
What we need to do is tell the database manager to aggregate the hours for each scientist separately using a GROUP BY clause:
SELECT person, count(reading), round(avg(reading), 2)
-FROM Survey
-WHERE quant='rad'
-GROUP BY person;SELECT person, count(reading), round(avg(reading), 2)
+FROM Survey
+WHERE quant='rad'
+GROUP BY person;GROUP BY does exactly what its name implies: groups all the records with the same value for the specified field together so that aggregation can process each batch separately. Since all the records in each batch have the same value for person, it no longer matters that the database manager is picking an arbitrary one to display alongside the aggregated reading values.
Just as we can sort by multiple criteria at once, we can also group by multiple criteria. To get the average reading by scientist and quantity measured, for example, we just add another field to the GROUP BY clause:
SELECT person, quant, count(reading), round(avg(reading), 2)
-FROM Survey
-GROUP BY person, quant;SELECT person, quant, count(reading), round(avg(reading), 2)
+FROM Survey
+GROUP BY person, quant;Note that we have added quant to the list of fields displayed, since the results wouldn’t make much sense otherwise.
Let’s go one step further and remove all the entries where we don’t know who took the measurement:
-SELECT person, quant, count(reading), round(avg(reading), 2)
-FROM Survey
-WHERE person IS NOT NULL
-GROUP BY person, quant
-ORDER BY person, quant;SELECT person, quant, count(reading), round(avg(reading), 2)
+FROM Survey
+WHERE person IS NOT NULL
+GROUP BY person, quant
+ORDER BY person, quant;