# Advanced Features

## Introduction

In the previous chapter we have covered the basics of using SQL to store and access your data in PostgreSQL. We will now discuss some more advanced features of SQL that simplify management and prevent loss or corruption of your data. Finally, we will look at some PostgreSQL extensions.

This chapter will on occasion refer to examples found in tutorial-sql to change or improve them, so it will be useful to have read that chapter. Some examples from this chapter can also be found in `advanced.sql` in the tutorial directory. This file also contains some sample data to load, which is not repeated here. (Refer to tutorial-sql-intro for how to use the file.)

## Views

Refer back to the queries in tutorial-join. Suppose the combined listing of weather records and city location is of particular interest to your application, but you do not want to type the query each time you need it. You can create a view over the query, which gives a name to the query that you can refer to like an ordinary table:

    CREATE VIEW myview AS
        SELECT name, temp_lo, temp_hi, prcp, date, location
            FROM weather, cities
            WHERE city = name;

    SELECT * FROM myview;

Making liberal use of views is a key aspect of good SQL database design. Views allow you to encapsulate the details of the structure of your tables, which might change as your application evolves, behind consistent interfaces.

Views can be used in almost any place a real table can be used. Building views upon other views is not uncommon.

## Foreign Keys

Recall the `weather` and `cities` tables from tutorial-sql. Consider the following problem: You want to make sure that no one can insert rows in the `weather` table that do not have a matching entry in the `cities` table. This is called maintaining the referential integrity of your data. In simplistic database systems this would be implemented (if at all) by first looking at the `cities` table to check if a matching record exists, and then inserting or rejecting the new `weather` records. This approach has a number of problems and is very inconvenient, so PostgreSQL can do this for you.

The new declaration of the tables would look like this:

    CREATE TABLE cities (
            name     varchar(80) primary key,
            location point
    );

    CREATE TABLE weather (
            city      varchar(80) references cities(name),
            temp_lo   int,
            temp_hi   int,
            prcp      real,
            date      date
    );

Now try inserting an invalid record:

    INSERT INTO weather VALUES ('Berkeley', 45, 53, 0.0, '1994-11-28');

    ERROR:  insert or update on table "weather" violates foreign key constraint "weather_city_fkey"
    DETAIL:  Key (city)=(Berkeley) is not present in table "cities".

The behavior of foreign keys can be finely tuned to your application. We will not go beyond this simple example in this tutorial, but just refer you to ddl for more information. Making correct use of foreign keys will definitely improve the quality of your database applications, so you are strongly encouraged to learn about them.

## Transactions

Transactions are a fundamental concept of all database systems. The essential point of a transaction is that it bundles multiple steps into a single, all-or-nothing operation. The intermediate states between the steps are not visible to other concurrent transactions, and if some failure occurs that prevents the transaction from completing, then none of the steps affect the database at all.

For example, consider a bank database that contains balances for various customer accounts, as well as total deposit balances for branches. Suppose that we want to record a payment of $100.00 from Alice's account to Bob's account. Simplifying outrageously, the SQL commands for this might look like:

    UPDATE accounts SET balance = balance - 100.00
        WHERE name = 'Alice';
    UPDATE branches SET balance = balance - 100.00
        WHERE name = (SELECT branch_name FROM accounts WHERE name = 'Alice');
    UPDATE accounts SET balance = balance + 100.00
        WHERE name = 'Bob';
    UPDATE branches SET balance = balance + 100.00
        WHERE name = (SELECT branch_name FROM accounts WHERE name = 'Bob');

The details of these commands are not important here; the important point is that there are several separate updates involved to accomplish this rather simple operation. Our bank's officers will want to be assured that either all these updates happen, or none of them happen. It would certainly not do for a system failure to result in Bob receiving $100.00 that was not debited from Alice. Nor would Alice long remain a happy customer if she was debited without Bob being credited. We need a guarantee that if something goes wrong partway through the operation, none of the steps executed so far will take effect. Grouping the updates into a transaction gives us this guarantee. A transaction is said to be atomic: from the point of view of other transactions, it either happens completely or not at all.

We also want a guarantee that once a transaction is completed and acknowledged by the database system, it has indeed been permanently recorded and won't be lost even if a crash ensues shortly thereafter. For example, if we are recording a cash withdrawal by Bob, we do not want any chance that the debit to his account will disappear in a crash just after he walks out the bank door. A transactional database guarantees that all the updates made by a transaction are logged in permanent storage (i.e., on disk) before the transaction is reported complete.

Another important property of transactional databases is closely related to the notion of atomic updates: when multiple transactions are running concurrently, each one should not be able to see the incomplete changes made by others. For example, if one transaction is busy totalling all the branch balances, it would not do for it to include the debit from Alice's branch but not the credit to Bob's branch, nor vice versa. So transactions must be all-or-nothing not only in terms of their permanent effect on the database, but also in terms of their visibility as they happen. The updates made so far by an open transaction are invisible to other transactions until the transaction completes, whereupon all the updates become visible simultaneously.

In PostgreSQL, a transaction is set up by surrounding the SQL commands of the transaction with `BEGIN` and `COMMIT` commands. So our banking transaction would actually look like:

    BEGIN;
    UPDATE accounts SET balance = balance - 100.00
        WHERE name = 'Alice';
    -- etc etc
    COMMIT;

If, partway through the transaction, we decide we do not want to commit (perhaps we just noticed that Alice's balance went negative), we can issue the command `ROLLBACK` instead of `COMMIT`, and all our updates so far will be canceled.

PostgreSQL actually treats every SQL statement as being executed within a transaction. If you do not issue a `BEGIN` command, then each individual statement has an implicit `BEGIN` and (if successful) `COMMIT` wrapped around it. A group of statements surrounded by `BEGIN` and `COMMIT` is sometimes called a transaction block.

Some client libraries issue `BEGIN` and `COMMIT` commands automatically, so that you might get the effect of transaction blocks without asking. Check the documentation for the interface you are using.

It's possible to control the statements in a transaction in a more granular fashion through the use of savepoints. Savepoints allow you to selectively discard parts of the transaction, while committing the rest. After defining a savepoint with `SAVEPOINT`, you can if needed roll back to the savepoint with `ROLLBACK TO`. All the transaction's database changes between defining the savepoint and rolling back to it are discarded, but changes earlier than the savepoint are kept.

After rolling back to a savepoint, it continues to be defined, so you can roll back to it several times. Conversely, if you are sure you won't need to roll back to a particular savepoint again, it can be released, so the system can free some resources. Keep in mind that either releasing or rolling back to a savepoint will automatically release all savepoints that were defined after it.

All this is happening within the transaction block, so none of it is visible to other database sessions. When and if you commit the transaction block, the committed actions become visible as a unit to other sessions, while the rolled-back actions never become visible at all.

Remembering the bank database, suppose we debit $100.00 from Alice's account, and credit Bob's account, only to find later that we should have credited Wally's account. We could do it using savepoints like this:

    BEGIN;
    UPDATE accounts SET balance = balance - 100.00
        WHERE name = 'Alice';
    SAVEPOINT my_savepoint;
    UPDATE accounts SET balance = balance + 100.00
        WHERE name = 'Bob';
    -- oops ... forget that and use Wally's account
    ROLLBACK TO my_savepoint;
    UPDATE accounts SET balance = balance + 100.00
        WHERE name = 'Wally';
    COMMIT;

This example is, of course, oversimplified, but there's a lot of control possible in a transaction block through the use of savepoints. Moreover, `ROLLBACK TO` is the only way to regain control of a transaction block that was put in aborted state by the system due to an error, short of rolling it back completely and starting again.

## Window Functions

A window function performs a calculation across a set of table rows that are somehow related to the current row. This is comparable to the type of calculation that can be done with an aggregate function. However, window functions do not cause rows to become grouped into a single output row like non-window aggregate calls would. Instead, the rows retain their separate identities. Behind the scenes, the window function is able to access more than just the current row of the query result.

Here is an example that shows how to compare each employee's salary with the average salary in his or her department:

    SELECT depname, empno, salary, avg(salary) OVER (PARTITION BY depname) FROM empsalary;

      depname  | empno | salary |          avg
    -----------+-------+--------+-----------------------
     develop   |    11 |   5200 | 5020.0000000000000000
     develop   |     7 |   4200 | 5020.0000000000000000
     develop   |     9 |   4500 | 5020.0000000000000000
     develop   |     8 |   6000 | 5020.0000000000000000
     develop   |    10 |   5200 | 5020.0000000000000000
     personnel |     5 |   3500 | 3700.0000000000000000
     personnel |     2 |   3900 | 3700.0000000000000000
     sales     |     3 |   4800 | 4866.6666666666666667
     sales     |     1 |   5000 | 4866.6666666666666667
     sales     |     4 |   4800 | 4866.6666666666666667
    (10 rows)

The first three output columns come directly from the table empsalary, and there is one output row for each row in the table. The fourth column represents an average taken across all the table rows that have the same depname value as the current row. (This actually is the same function as the non-window `avg` aggregate, but the `OVER` clause causes it to be treated as a window function and computed across the window frame.)

A window function call always contains an `OVER` clause directly following the window function's name and argument(s). This is what syntactically distinguishes it from a normal function or non-window aggregate. The `OVER` clause determines exactly how the rows of the query are split up for processing by the window function. The `PARTITION BY` clause within `OVER` divides the rows into groups, or partitions, that share the same values of the `PARTITION BY` expression(s). For each row, the window function is computed across the rows that fall into the same partition as the current row.

You can also control the order in which rows are processed by window functions using `ORDER BY` within `OVER`. (The window `ORDER BY` does not even have to match the order in which the rows are output.) Here is an example:

    SELECT depname, empno, salary,
           rank() OVER (PARTITION BY depname ORDER BY salary DESC)
    FROM empsalary;

      depname  | empno | salary | rank
    -----------+-------+--------+------
     develop   |     8 |   6000 |    1
     develop   |    10 |   5200 |    2
     develop   |    11 |   5200 |    2
     develop   |     9 |   4500 |    4
     develop   |     7 |   4200 |    5
     personnel |     2 |   3900 |    1
     personnel |     5 |   3500 |    2
     sales     |     1 |   5000 |    1
     sales     |     4 |   4800 |    2
     sales     |     3 |   4800 |    2
    (10 rows)

As shown here, the `rank` function produces a numerical rank for each distinct `ORDER BY` value in the current row's partition, using the order defined by the `ORDER BY` clause. `rank` needs no explicit parameter, because its behavior is entirely determined by the `OVER` clause.

The rows considered by a window function are those of the virtual table produced by the query's `FROM` clause as filtered by its `WHERE`, `GROUP BY`, and `HAVING` clauses if any. For example, a row removed because it does not meet the `WHERE` condition is not seen by any window function. A query can contain multiple window functions that slice up the data in different ways using different `OVER` clauses, but they all act on the same collection of rows defined by this virtual table.

We already saw that `ORDER BY` can be omitted if the ordering of rows is not important. It is also possible to omit `PARTITION BY`, in which case there is a single partition containing all rows.

There is another important concept associated with window functions: for each row, there is a set of rows within its partition called its window frame. Some window functions act only on the rows of the window frame, rather than of the whole partition. By default, if `ORDER BY` is supplied then the frame consists of all rows from the start of the partition up through the current row, plus any following rows that are equal to the current row according to the `ORDER BY` clause. When `ORDER BY` is omitted the default frame consists of all rows in the partition.

There are options to define the window frame in other ways, but this tutorial does not cover them. See syntax-window-functions for details.

Here is an example using `sum`:

    SELECT salary, sum(salary) OVER () FROM empsalary;

     salary |  sum
    --------+-------
       5200 | 47100
       5000 | 47100
       3500 | 47100
       4800 | 47100
       3900 | 47100
       4200 | 47100
       4500 | 47100
       4800 | 47100
       6000 | 47100
       5200 | 47100
    (10 rows)

Above, since there is no `ORDER BY` in the `OVER` clause, the window frame is the same as the partition, which for lack of `PARTITION BY` is the whole table; in other words each sum is taken over the whole table and so we get the same result for each output row. But if we add an `ORDER BY` clause, we get very different results:

    SELECT salary, sum(salary) OVER (ORDER BY salary) FROM empsalary;

     salary |  sum
    --------+-------
       3500 |  3500
       3900 |  7400
       4200 | 11600
       4500 | 16100
       4800 | 25700
       4800 | 25700
       5000 | 30700
       5200 | 41100
       5200 | 41100
       6000 | 47100
    (10 rows)

Here the sum is taken from the first (lowest) salary up through the current one, including any duplicates of the current one (notice the results for the duplicated salaries).

Window functions are permitted only in the `SELECT` list and the `ORDER BY` clause of the query. They are forbidden elsewhere, such as in `GROUP BY`, `HAVING` and `WHERE` clauses. This is because they logically execute after the processing of those clauses. Also, window functions execute after non-window aggregate functions. This means it is valid to include an aggregate function call in the arguments of a window function, but not vice versa.

If there is a need to filter or group rows after the window calculations are performed, you can use a sub-select. For example:

    SELECT depname, empno, salary, enroll_date
    FROM
      (SELECT depname, empno, salary, enroll_date,
              rank() OVER (PARTITION BY depname ORDER BY salary DESC, empno) AS pos
         FROM empsalary
      ) AS ss
    WHERE pos < 3;

The above query only shows the rows from the inner query having `rank` less than 3.

When a query involves multiple window functions, it is possible to write out each one with a separate `OVER` clause, but this is duplicative and error-prone if the same windowing behavior is wanted for several functions. Instead, each windowing behavior can be named in a `WINDOW` clause and then referenced in `OVER`. For example:

    SELECT sum(salary) OVER w, avg(salary) OVER w
      FROM empsalary
      WINDOW w AS (PARTITION BY depname ORDER BY salary DESC);

More details about window functions can be found in syntax-window-functions, functions-window, queries-window, and the sql-select reference page.

## Inheritance

Inheritance is a concept from object-oriented databases. It opens up interesting new possibilities of database design.

Let's create two tables: A table `cities` and a table `capitals`. Naturally, capitals are also cities, so you want some way to show the capitals implicitly when you list all cities. If you're really clever you might invent some scheme like this:

    CREATE TABLE capitals (
      name       text,
      population real,
      elevation  int,    -- (in ft)
      state      char(2)
    );

    CREATE TABLE non_capitals (
      name       text,
      population real,
      elevation  int     -- (in ft)
    );

    CREATE VIEW cities AS
      SELECT name, population, elevation FROM capitals
        UNION
      SELECT name, population, elevation FROM non_capitals;

This works OK as far as querying goes, but it gets ugly when you need to update several rows, for one thing.

A better solution is this:

    CREATE TABLE cities (
      name       text,
      population real,
      elevation  int     -- (in ft)
    );

    CREATE TABLE capitals (
      state      char(2) UNIQUE NOT NULL
    ) INHERITS (cities);

In this case, a row of `capitals` inherits all columns (name, population, and elevation) from its parent, `cities`. The type of the column name is `text`, a native PostgreSQL type for variable length character strings. The `capitals` table has an additional column, state, which shows its state abbreviation. In PostgreSQL, a table can inherit from zero or more other tables.

For example, the following query finds the names of all cities, including state capitals, that are located at an elevation over 500 feet:

    SELECT name, elevation
      FROM cities
      WHERE elevation > 500;

which returns:

       name    | elevation
    -----------+-----------
     Las Vegas |      2174
     Mariposa  |      1953
     Madison   |       845
    (3 rows)

On the other hand, the following query finds all the cities that are not state capitals and are situated at an elevation over 500 feet:

    SELECT name, elevation
        FROM ONLY cities
        WHERE elevation > 500;

       name    | elevation
    -----------+-----------
     Las Vegas |      2174
     Mariposa  |      1953
    (2 rows)

Here the `ONLY` before `cities` indicates that the query should be run over only the `cities` table, and not tables below `cities` in the inheritance hierarchy. Many of the commands that we have already discussed — `SELECT`, `UPDATE`, and `DELETE` — support this `ONLY` notation.

Although inheritance is frequently useful, it has not been integrated with unique constraints or foreign keys, which limits its usefulness. See ddl-inherit for more detail.

## Conclusion

PostgreSQL has many features not touched upon in this tutorial introduction, which has been oriented toward newer users of SQL. These features are discussed in more detail in the remainder of this book.

If you feel you need more introductory material, please visit the PostgreSQL web site for links to more resources.