CS121Lec05.pdf

SQL QUERIES
CS121: Relational Databases
Fall 2018 – Lecture 5

SQL Queries
¨ SQL queries use the SELECT statement
¨ General form is:
SELECT A1, A2, ...
FROM r1, r2, ...
WHERE P;
¤ ri are the relations (tables)
¤ Ai are attributes (columns)
¤ P is the selection predicate
¨ Equivalent to: P (sP(r1 ´ r2 ´ …))
A1, A2, …
2

Ordered Results
¨ SQL query results can be ordered by particular
attributes
¨ Two main categories of query results:
¤ “Not ordered by anything”
n Tuples can appear in any order
¤ “Ordered by attributes A1, A2, …”
n Tuples are sorted by specified attributes
n Results are sorted by A1 first
n Within each value of A1, results are sorted by A2
n etc.
¨ Specify an ORDER BY clause at end of SELECT
statement
3

Ordered Results (2)
¨ Find bank accounts with a balance under $700:
SELECT account_number, balance
FROM account
WHERE balance < 700;
¨ Order results in increasing
order of bank balance:
SELECT account_number, balance
FROM account
WHERE balance < 700
ORDER BY balance;
¤ Default order is ascending order
+----------------+---------+
| account_number | balance |
+----------------+---------+
| A-102 | 400.00 |
| A-101 | 500.00 |
| A-444 | 625.00 |
| A-305 | 350.00 |
+----------------+---------+
+----------------+---------+
| account_number | balance |
+----------------+---------+
| A-305 | 350.00 |
| A-102 | 400.00 |
| A-101 | 500.00 |
| A-444 | 625.00 |
+----------------+---------+
4

Aggregate Functions in SQL
¨ SQL provides grouping and aggregate operations,
just like relational algebra
¨ Aggregate functions:
SUM sums the values in the collection
AVG computes average of values in the collection
COUNT counts number of elements in the collection
MIN returns minimum value in the collection
MAX returns maximum value in the collection
¨ SUM and AVG require numeric inputs (obvious)
6

Aggregate Examples
¨ Find average balance of accounts at Perryridge
branch
SELECT AVG(balance) FROM account
WHERE branch_name = 'Perryridge';
¨ Find maximum amount of any loan in the bank
SELECT MAX(amount) AS max_amt FROM loan;
¤ Can name computed values, like usual
+--------------+
| AVG(balance) |
+--------------+
| 650.000000 |
+--------------+
+---------+
| max_amt |
+---------+
| 7500.00 |
+---------+
7

Aggregate Examples (2)
¨ This query produces an error:
SELECT branch_name,
MAX(amount) AS max_amt
FROM loan;
¨ Aggregate functions compute a single value from a
multiset of inputs
¤ Doesn’t make sense to combine individual attributes and
aggregate functions like this
¨ This does work:
SELECT MIN(amount) AS min_amt,
MAX(amount) AS max_amt
FROM loan; +---------+---------+
| min_amt | max_amt |
+---------+---------+
| 500.00 | 7500.00 |
+---------+---------+
8

Eliminating Duplicates
¨ Sometimes need to eliminate duplicates in SQL
queries
¤ Can use DISTINCT keyword to eliminate duplicates
¨ Example:
“Find the number of branches that currently have loans.”
SELECT COUNT(branch_name) FROM loan;
¤ Doesn’t work, because branches may have multiple loans
¤ Instead, do this:
SELECT COUNT(DISTINCT branch_name) FROM loan;
¤ Duplicates are eliminated from input multiset before
aggregate function is applied
9

Computing Counts
¨ Can count individual attribute values
COUNT(branch_name)
COUNT(DISTINCT branch_name)
¨ Can also count the total number of tuples
COUNT(*)
¤ If used with grouping, counts total number of tuples in each group
¤ If used without grouping, counts total number of tuples
¨ Counting a specific attribute is useful when:
¤ Need to count (possibly distinct) values of a particular attribute
¤ Cases where some values in input multiset may be NULL
n As before, COUNT ignores NULL values (more on this next week)
10

Grouping and Aggregates
¨ Can also perform grouping on a relation before
computing aggregates
¤ Specify a GROUP BY A1,A2,... clause at end of query
¨ Example:
“Find the average loan amount for each branch.”
SELECT branch_name, AVG(amount) AS avg_amt
FROM loan GROUP BY branch_name;
¤ First, tuples in loan are
grouped by branch_name
¤ Then, aggregate functions
are applied to each group
+-------------+-------------+
| branch_name | avg_amt |
+-------------+-------------+
| Central | 570.000000 |
| Downtown | 1250.000000 |
| Mianus | 500.000000 |
| North Town | 7500.000000 |
| Perryridge | 1400.000000 |
| Redwood | 2000.000000 |
| Round Hill | 900.000000 |
+-------------+-------------+
11

Grouping and Aggregates (2)
¨ Can group on multiple attributes
¤ Each group has unique values for the entire set of grouping
attributes
¨ Example:
“How many accounts does each customer have at each branch?”
¤ Group by both customer name and branch name
¤ Compute count of tuples in each group
¤ Can write the SQL statement yourself, and try it out
12

¨ Note the difference between relational algebra
notation and SQL syntax
¨ Relational algebra syntax:
G (E)
¤ Grouping attributes only appear on left of G
¨ SQL syntax:
SELECT G1,G2,..., F1(A1),F2(A2),...
FROM r1,r2,... WHERE P
GROUP BY G1,G2,...
¤ Frequently, grouping attributes are specified in both the
SELECT clause and GROUP BY clause
G1, G2, …, Gn F1(A1), F2(A2), …, Fm(Am)
13

¨ SQL doesn’t require that you specify the grouping
attributes in the SELECT clause
¤ Only requirement is that the grouping attributes are
specified in the GROUP BY clause
¤ e.g. if you only want the aggregated results, could do this:
SELECT F1(A1),F2(A2),...
GROUP BY G1,G2,...
¨ Also, can use expressions for grouping and aggregates
¤ Example (very uncommon, but also valid):
SELECT MIN(a + b) – MAX(c)
FROM t GROUP BY d * e;
14

Filtering Tuples
¨ The WHERE clause is applied before any grouping
occurs
SELECT G1,G2,..., F1(A1),F2(A2),...
GROUP BY G1,G2,...
¤ Translates into relational algebra expression:
P…( G (sP(r1 ´ r2 ´ …)))
¤ A WHERE clause constrains the set of tuples that
grouping and aggregation are applied to
G1, G2, … F1(A1), F2(A2), …
15

Filtering Results
¨ To apply filtering to the results of grouping and
aggregation, use a HAVING clause
¤ Exactly like WHERE clause, except applied after
grouping and aggregation
SELECT G1,G2,..., F1(A1),F2(A2),...
FROM r1,r2,... WHERE PW
GROUP BY G1,G2,...
HAVING PH
¤ Translates into:
P…(s ( G (s (r1 ´ r2 ´ …))))
G1, G2, … F1(A1), F2(A2), … PW
PH
16

The HAVING Clause
¨ The HAVING clause can use aggregate functions in its
predicate
¤ It’s applied after grouping/aggregation, so those values
are available
¤ The WHERE clause cannot do this, of course
¨ Example:
“Find all customers with more than one loan.”
SELECT customer_name, COUNT(*) AS num_loans
FROM borrower GROUP BY customer_name
HAVING COUNT(*) > 1;
+---------------+-----------+
| customer_name | num_loans |
+---------------+-----------+
| Smith | 3 |
+---------------+-----------+
17

Nested Subqueries
¨ SQL provides broad support for nested subqueries
¤ A SQL query is a “select-from-where” expression
¤ Nested subqueries are “select-from-where” expressions
embedded within another query
¨ Can embed queries in WHERE clauses
¤ Sophisticated selection tests
¨ Can embed queries in FROM clauses
¤ Issuing a query against a derived relation
¨ Can even embed queries in SELECT clauses!
¤ Appeared in SQL:2003 standard; many DBs support this
¤ Makes many queries easier to write, but can be slow too
18

Kinds of Subqueries
¨ Some subqueries produce only a single result
SELECT MAX(assets) FROM branch;
¤ Called a scalar subquery
¤ Still a relation, just with one attribute and one tuple
¨ Most subqueries produce a relation containing
multiple tuples
¤ Nested queries often produce relation with single attribute
n Very common for subqueries in WHERE clause
¤ Nested queries can also produce multiple-attribute relation
n Very common for subqueries in FROM clause
n Can also be used in the WHERE clause in some cases
19

Subqueries in WHERE Clause
¨ Widely used:
¤ Direct comparison with scalar-subquery results
¤ Set-membership tests: IN, NOT IN
¤ Empty-set tests: EXISTS, NOT EXISTS
¨ Less frequently used:
¤ Set-comparison tests: ANY, SOME, ALL
¤ Uniqueness tests: UNIQUE, NOT UNIQUE
¨ (Can also use these in the HAVING clause)
20

Comparison with Subquery Result
¨ Can use scalar subqueries in WHERE clause
comparisons
¨ Example:
¤ Want to find the name of the branch with the smallest
number of assets.
¤ Can easily find the smallest number of assets:
SELECT MIN(assets) FROM branch;
¤ This is a scalar subquery; can use it in WHERE clause:
SELECT branch_name FROM branch
WHERE assets = (SELECT MIN(assets) FROM branch);
+-------------+
| branch_name |
+-------------+
| Pownal |
+-------------+
21

Set Membership Tests
¨ Can use IN (...) and NOT IN (...) for set
membership tests
¨ Example:
¤ Find customers with both an account and a loan.
¤ Before, did this with a INTERSECT operation
¤ Can also use a set-membership test:
“Select all customer names from depositor relation, that also
appear somewhere in borrower relation.”
SELECT DISTINCT customer_name FROM depositor
WHERE customer_name IN (
SELECT customer_name FROM borrower)
¤ DISTINCT necessary because a customer might appear
multiple times in depositor
22

Set Membership Tests (2)
¨ IN (...) and NOT IN (...) support subqueries
that return multiple columns (!!!)
¨ Example: “Find the ID of the largest loan at each
branch, including the branch name and the amount of
the loan.”
¤ First, need to find the largest loan at each branch
SELECT branch_name, MAX(amount)
FROM loan GROUP BY branch_name
¤ Use this result to identify the rest of the loan details
SELECT * FROM loan
WHERE (branch_name, amount) IN (
SELECT branch_name, MAX(amount)
FROM loan GROUP BY branch_name);
23

Empty-Set Tests
¨ Can test whether or not a subquery generates any
results at all
n EXISTS (...)
n NOT EXISTS (...)
¨ Example:
“Find customers with an account but not a loan.”
SELECT DISTINCT customer_name FROM depositor d
WHERE NOT EXISTS (
SELECT * FROM borrower b
WHERE b.customer_name = d.customer_name);
¤ Result includes every customer that appears in depositor
table, that doesn’t also appear in the borrower table.
24

Empty-Set Tests (2)
WHERE NOT EXISTS (
¤ Inner query refers to an attribute in outer query’s relation
¨ In general, nested subqueries can refer to enclosing
queries’ relations.
¨ However, enclosing queries cannot refer to the nested
queries’ relations.
25

Correlated Subqueries
WHERE NOT EXISTS (
¨ When a nested query refers to an enclosing query’s
attributes, it is a correlated subquery
¤ The inner query must be evaluated once for each tuple
considered by the enclosing query
¤ Generally to be avoided! Very slow.
26

Correlated Subqueries (2)
¨ Many correlated subqueries can be restated using a join
or a Cartesian product
¤ Often the join operation will be much faster
¤ More advanced DBMSes will automatically decorrelate such
queries, but some can’t…
¨ Certain conditions, e.g. EXISTS/NOT EXISTS,
usually indicate presence of a correlated subquery
¨ If it’s easy to decorrelate the subquery, do that! J
¨ If not, test the query for its performance.
¤ If the database can decorrelate it, you’re done!
¤ If the database can’t decorrelate it, may need to come up
with an alternate formulation.
27

Set Comparison Tests
¨ Can compare a value to a set of values
¤ Is a value larger/smaller/etc. than some value in the
set?
¨ Example:
“Find all branches with assets greater than at least one
branch in Brooklyn.”
WHERE assets > SOME (
SELECT assets FROM branch
WHERE branch_name='Brooklyn');
28

Set Comparison Tests (2)
¨ General form of test:
attr compare_op SOME ( subquery )
¤ Can use any comparison operation
= SOME is same as IN
¤ ANY is a synonym for SOME
¨ Can also compare a value with all values in a set
¤ Use ALL instead of SOME
<> ALL is same as NOT IN
29

Set Comparison Tests (3)
¨ Example:
“Find branches with assets greater than all branches in
Brooklyn.”
WHERE assets > ALL (
SELECT assets FROM branch
¤ Could also write this with a scalar subquery
WHERE assets >
(SELECT MAX(assets) FROM branch
30

Uniqueness Tests
¨ Can test whether a nested query generates any
duplicate tuples
¤ UNIQUE (...)
¤ NOT UNIQUE (...)
¨ Not widely implemented
¤ Expensive operation!
¨ Can emulate in a number of ways
¤ GROUP BY ... HAVING COUNT(*) = 1 or
GROUP BY ... HAVING COUNT(*) > 1 is one
approach
31

Subqueries in FROM Clause
¨ Often need to compute a result in multiple steps
¨ Can query against a subquery’s results
¤ Called a derived relation
¨ A trivial example:
¤ A HAVING clause can be implemented as a nested
query in the FROM clause
32

HAVING vs. Nested Query
“Find all cities with more than two customers living in the city.”
SELECT customer_city, COUNT(*) AS num_customers
FROM customer GROUP BY customer_city
HAVING COUNT(*) > 2;
¨ Or, can write:
SELECT customer_city, num_customers
FROM (SELECT customer_city, COUNT(*)
FROM customer GROUP BY customer_city)
AS counts (customer_city, num_customers)
WHERE num_customers > 2;
¤ Grouping and aggregation is computed by inner query
¤ Outer query selects desired results generated by inner query
33

Derived Relation Syntax
¨ Subquery in FROM clause must be given a name
¤ Many DBMSes also require attributes to be named
SELECT customer_city, num_customers
FROM (SELECT customer_city, COUNT(*)
FROM customer GROUP BY customer_city)
AS counts (customer_city, num_customers)
WHERE num_customers > 2;
¤ Nested query is called counts, and specifies two
attributes
¤ Syntax varies from DBMS to DBMS…
n MySQL requires a name for derived relations, but doesn’t allow
attribute names to be specified.
34

Using Derived Relations
¨ More typical is a query against aggregate values
¨ Example:
“Find the largest total account balance of any branch.”
¤ Need to compute total account balance for each branch first.
SELECT branch_name, SUM(balance) AS total_bal
FROM account GROUP BY branch_name;
¤ Then we can easily find the answer:
SELECT MAX(total_bal) AS largest_total
FROM (SELECT branch_name,
SUM(balance) AS total_bal
FROM account GROUP BY branch_name)
AS totals (branch_name, tot_bal);
35

Aggregates of Aggregates
¨ Always take note when computing aggregates of
aggregates!
“Find the largest total account balance of any branch.”
¤ Two nested aggregates: max of sums
¨ A very common mistake:
SELECT branch_name, SUM(balance) AS tot_bal
FROM account GROUP BY branch_name
HAVING tot_bal = MAX(tot_bal)
¤ A SELECT query can only perform one level of
aggregation
¤ Need a second SELECT to find the maximum total
¤ Unfortunately, MySQL accepts this and returns bogus result
36

More Data Manipulation Operations
¨ SQL provides many other options for inserting,
updating, and deleting tuples
¨ All commands support SELECT-style syntax
¨ Can insert individual tuples into a table:
INSERT INTO table VALUES (1, 'foo', 50);
¨ Can also insert the result of a query into a table:
INSERT INTO table SELECT ...;
¤ Only constraint is that generated results must have a
compatible schema
37

Deleting Tuples
¨ SQL DELETE command can use a WHERE clause
DELETE FROM table;
¤ Deletes all rows in the table
DELETE FROM table WHERE ...;
¤ Only deletes rows that satisfy the conditions
¤ The WHERE clause can use anything that SELECT’s
WHERE clause supports
n Nested queries, in particular!
38

Updating Tables
¨ SQL also has an UPDATE command for modifying
existing tuples in a table
¨ General form:
UPDATE table
SET attr1=val1, attr2=val2, ...
WHERE condition;
¤ Must specify the attributes to update
¤ Attributes being modified must appear in table being
updated (obvious)
¤ The WHERE clause is optional! If unspecified, all rows are
updated.
¤ WHERE condition can contain nested queries, etc.
39

Updating Tables (2)
¨ Values in UPDATE can be arithmetic expressions
¤ Can refer to any attribute in table being updated
¨ Example:
¤ Add 2% interest to all bank account balances with a
balance of $500 or less.
UPDATE account
SET balance = balance * 1.02
WHERE balance <= 500;
40

Next Time
¨ NULL values in SQL
¨ Additional SQL join operations
¤ Natural join
¤ Outer joins
¨ SQL views
42

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