## 38.2. Views and the Rule System

Views in Postgres Pro are implemented using the rule system. In fact, there is essentially no difference between:

    CREATE VIEW myview AS SELECT * FROM mytab;

compared against the two commands:

    CREATE TABLE myview (same column list as mytab);
    CREATE RULE "_RETURN" AS ON SELECT TO myview DO INSTEAD
        SELECT * FROM mytab;

because this is exactly what the `CREATE VIEW` command does internally. This has some side effects. One of them is that the information about a view in the Postgres Pro system catalogs is exactly the same as it is for a table. So for the parser, there is absolutely no difference between a table and a view. They are the same thing: relations.

### 38.2.1. How `SELECT` Rules Work

Rules `ON SELECT` are applied to all queries as the last step, even if the command given is an `INSERT`, `UPDATE` or `DELETE`. And they have different semantics from rules on the other command types in that they modify the query tree in place instead of creating a new one. So `SELECT` rules are described first.

Currently, there can be only one action in an `ON SELECT` rule, and it must be an unconditional `SELECT` action that is `INSTEAD`. This restriction was required to make rules safe enough to open them for ordinary users, and it restricts `ON SELECT` rules to act like views.

The examples for this chapter are two join views that do some calculations and some more views using them in turn. One of the two first views is customized later by adding rules for `INSERT`, `UPDATE`, and `DELETE` operations so that the final result will be a view that behaves like a real table with some magic functionality. This is not such a simple example to start from and this makes things harder to get into. But it's better to have one example that covers all the points discussed step by step rather than having many different ones that might mix up in mind.

For the example, we need a little `min` function that returns the lower of 2 integer values. We create that as:

    CREATE FUNCTION min(integer, integer) RETURNS integer AS $$
        SELECT CASE WHEN $1 < $2 THEN $1 ELSE $2 END
    $$ LANGUAGE SQL STRICT;

The real tables we need in the first two rule system descriptions are these:

    CREATE TABLE shoe_data (
        shoename   text,          -- primary key
        sh_avail   integer,       -- available number of pairs
        slcolor    text,          -- preferred shoelace color
        slminlen   real,          -- minimum shoelace length
        slmaxlen   real,          -- maximum shoelace length
        slunit     text           -- length unit
    );

    CREATE TABLE shoelace_data (
        sl_name    text,          -- primary key
        sl_avail   integer,       -- available number of pairs
        sl_color   text,          -- shoelace color
        sl_len     real,          -- shoelace length
        sl_unit    text           -- length unit
    );

    CREATE TABLE unit (
        un_name    text,          -- primary key
        un_fact    real           -- factor to transform to cm
    );

As you can see, they represent shoe-store data.

The views are created as:

    CREATE VIEW shoe AS
        SELECT sh.shoename,
               sh.sh_avail,
               sh.slcolor,
               sh.slminlen,
               sh.slminlen * un.un_fact AS slminlen_cm,
               sh.slmaxlen,
               sh.slmaxlen * un.un_fact AS slmaxlen_cm,
               sh.slunit
          FROM shoe_data sh, unit un
         WHERE sh.slunit = un.un_name;

    CREATE VIEW shoelace AS
        SELECT s.sl_name,
               s.sl_avail,
               s.sl_color,
               s.sl_len,
               s.sl_unit,
               s.sl_len * u.un_fact AS sl_len_cm
          FROM shoelace_data s, unit u
         WHERE s.sl_unit = u.un_name;

    CREATE VIEW shoe_ready AS
        SELECT rsh.shoename,
               rsh.sh_avail,
               rsl.sl_name,
               rsl.sl_avail,
               min(rsh.sh_avail, rsl.sl_avail) AS total_avail
          FROM shoe rsh, shoelace rsl
         WHERE rsl.sl_color = rsh.slcolor
           AND rsl.sl_len_cm >= rsh.slminlen_cm
           AND rsl.sl_len_cm <= rsh.slmaxlen_cm;

The `CREATE VIEW` command for the `shoelace` view (which is the simplest one we have) will create a relation `shoelace` and an entry in `pg_rewrite` that tells that there is a rewrite rule that must be applied whenever the relation `shoelace` is referenced in a query's range table. The rule has no rule qualification (discussed later, with the non-`SELECT` rules, since `SELECT` rules currently cannot have them) and it is `INSTEAD`. Note that rule qualifications are not the same as query qualifications. The action of our rule has a query qualification. The action of the rule is one query tree that is a copy of the `SELECT` statement in the view creation command.

### Note

The two extra range table entries for `NEW` and `OLD` that you can see in the `pg_rewrite` entry aren't of interest for `SELECT` rules.

Now we populate `unit`, `shoe_data` and `shoelace_data` and run a simple query on a view:

    INSERT INTO unit VALUES ('cm', 1.0);
    INSERT INTO unit VALUES ('m', 100.0);
    INSERT INTO unit VALUES ('inch', 2.54);

    INSERT INTO shoe_data VALUES ('sh1', 2, 'black', 70.0, 90.0, 'cm');
    INSERT INTO shoe_data VALUES ('sh2', 0, 'black', 30.0, 40.0, 'inch');
    INSERT INTO shoe_data VALUES ('sh3', 4, 'brown', 50.0, 65.0, 'cm');
    INSERT INTO shoe_data VALUES ('sh4', 3, 'brown', 40.0, 50.0, 'inch');

    INSERT INTO shoelace_data VALUES ('sl1', 5, 'black', 80.0, 'cm');
    INSERT INTO shoelace_data VALUES ('sl2', 6, 'black', 100.0, 'cm');
    INSERT INTO shoelace_data VALUES ('sl3', 0, 'black', 35.0 , 'inch');
    INSERT INTO shoelace_data VALUES ('sl4', 8, 'black', 40.0 , 'inch');
    INSERT INTO shoelace_data VALUES ('sl5', 4, 'brown', 1.0 , 'm');
    INSERT INTO shoelace_data VALUES ('sl6', 0, 'brown', 0.9 , 'm');
    INSERT INTO shoelace_data VALUES ('sl7', 7, 'brown', 60 , 'cm');
    INSERT INTO shoelace_data VALUES ('sl8', 1, 'brown', 40 , 'inch');

    SELECT * FROM shoelace;

     sl_name   | sl_avail | sl_color | sl_len | sl_unit | sl_len_cm
    -----------+----------+----------+--------+---------+-----------
     sl1       |        5 | black    |     80 | cm      |        80
     sl2       |        6 | black    |    100 | cm      |       100
     sl7       |        7 | brown    |     60 | cm      |        60
     sl3       |        0 | black    |     35 | inch    |      88.9
     sl4       |        8 | black    |     40 | inch    |     101.6
     sl8       |        1 | brown    |     40 | inch    |     101.6
     sl5       |        4 | brown    |      1 | m       |       100
     sl6       |        0 | brown    |    0.9 | m       |        90
    (8 rows)

This is the simplest `SELECT` you can do on our views, so we take this opportunity to explain the basics of view rules. The `SELECT * FROM shoelace` was interpreted by the parser and produced the query tree:

    SELECT shoelace.sl_name, shoelace.sl_avail,
           shoelace.sl_color, shoelace.sl_len,
           shoelace.sl_unit, shoelace.sl_len_cm
      FROM shoelace shoelace;

and this is given to the rule system. The rule system walks through the range table and checks if there are rules for any relation. When processing the range table entry for `shoelace` (the only one up to now) it finds the `_RETURN` rule with the query tree:

    SELECT s.sl_name, s.sl_avail,
           s.sl_color, s.sl_len, s.sl_unit,
           s.sl_len * u.un_fact AS sl_len_cm
      FROM shoelace old, shoelace new,
           shoelace_data s, unit u
     WHERE s.sl_unit = u.un_name;

To expand the view, the rewriter simply creates a subquery range-table entry containing the rule's action query tree, and substitutes this range table entry for the original one that referenced the view. The resulting rewritten query tree is almost the same as if you had typed:

    SELECT shoelace.sl_name, shoelace.sl_avail,
           shoelace.sl_color, shoelace.sl_len,
           shoelace.sl_unit, shoelace.sl_len_cm
      FROM (SELECT s.sl_name,
                   s.sl_avail,
                   s.sl_color,
                   s.sl_len,
                   s.sl_unit,
                   s.sl_len * u.un_fact AS sl_len_cm
              FROM shoelace_data s, unit u
             WHERE s.sl_unit = u.un_name) shoelace;

There is one difference however: the subquery's range table has two extra entries `shoelace old` and `shoelace new`. These entries don't participate directly in the query, since they aren't referenced by the subquery's join tree or target list. The rewriter uses them to store the access privilege check information that was originally present in the range-table entry that referenced the view. In this way, the executor will still check that the user has proper privileges to access the view, even though there's no direct use of the view in the rewritten query.

That was the first rule applied. The rule system will continue checking the remaining range-table entries in the top query (in this example there are no more), and it will recursively check the range-table entries in the added subquery to see if any of them reference views. (But it won't expand `old` or `new` — otherwise we'd have infinite recursion!) In this example, there are no rewrite rules for `shoelace_data` or `unit`, so rewriting is complete and the above is the final result given to the planner.

Now we want to write a query that finds out for which shoes currently in the store we have the matching shoelaces (color and length) and where the total number of exactly matching pairs is greater or equal to two.

    SELECT * FROM shoe_ready WHERE total_avail >= 2;

     shoename | sh_avail | sl_name | sl_avail | total_avail
    ----------+----------+---------+----------+-------------
     sh1      |        2 | sl1     |        5 |           2
     sh3      |        4 | sl7     |        7 |           4
    (2 rows)

The output of the parser this time is the query tree:

    SELECT shoe_ready.shoename, shoe_ready.sh_avail,
           shoe_ready.sl_name, shoe_ready.sl_avail,
           shoe_ready.total_avail
      FROM shoe_ready shoe_ready
     WHERE shoe_ready.total_avail >= 2;

The first rule applied will be the one for the `shoe_ready` view and it results in the query tree:

    SELECT shoe_ready.shoename, shoe_ready.sh_avail,
           shoe_ready.sl_name, shoe_ready.sl_avail,
           shoe_ready.total_avail
      FROM (SELECT rsh.shoename,
                   rsh.sh_avail,
                   rsl.sl_name,
                   rsl.sl_avail,
                   min(rsh.sh_avail, rsl.sl_avail) AS total_avail
              FROM shoe rsh, shoelace rsl
             WHERE rsl.sl_color = rsh.slcolor
               AND rsl.sl_len_cm >= rsh.slminlen_cm
               AND rsl.sl_len_cm <= rsh.slmaxlen_cm) shoe_ready
     WHERE shoe_ready.total_avail >= 2;

Similarly, the rules for `shoe` and `shoelace` are substituted into the range table of the subquery, leading to a three-level final query tree:

    SELECT shoe_ready.shoename, shoe_ready.sh_avail,
           shoe_ready.sl_name, shoe_ready.sl_avail,
           shoe_ready.total_avail
      FROM (SELECT rsh.shoename,
                   rsh.sh_avail,
                   rsl.sl_name,
                   rsl.sl_avail,
                   min(rsh.sh_avail, rsl.sl_avail) AS total_avail
              FROM (SELECT sh.shoename,
                           sh.sh_avail,
                           sh.slcolor,
                           sh.slminlen,
                           sh.slminlen * un.un_fact AS slminlen_cm,
                           sh.slmaxlen,
                           sh.slmaxlen * un.un_fact AS slmaxlen_cm,
                           sh.slunit
                      FROM shoe_data sh, unit un
                     WHERE sh.slunit = un.un_name) rsh,
                   (SELECT s.sl_name,
                           s.sl_avail,
                           s.sl_color,
                           s.sl_len,
                           s.sl_unit,
                           s.sl_len * u.un_fact AS sl_len_cm
                      FROM shoelace_data s, unit u
                     WHERE s.sl_unit = u.un_name) rsl
             WHERE rsl.sl_color = rsh.slcolor
               AND rsl.sl_len_cm >= rsh.slminlen_cm
               AND rsl.sl_len_cm <= rsh.slmaxlen_cm) shoe_ready
     WHERE shoe_ready.total_avail > 2;

It turns out that the planner will collapse this tree into a two-level query tree: the bottommost `SELECT` commands will be “pulled up” into the middle `SELECT` since there's no need to process them separately. But the middle `SELECT` will remain separate from the top, because it contains aggregate functions. If we pulled those up it would change the behavior of the topmost `SELECT`, which we don't want. However, collapsing the query tree is an optimization that the rewrite system doesn't have to concern itself with.

### 38.2.2. View Rules in Non-`SELECT` Statements

Two details of the query tree aren't touched in the description of view rules above. These are the command type and the result relation. In fact, the command type is not needed by view rules, but the result relation may affect the way in which the query rewriter works, because special care needs to be taken if the result relation is a view.

There are only a few differences between a query tree for a `SELECT` and one for any other command. Obviously, they have a different command type and for a command other than a `SELECT`, the result relation points to the range-table entry where the result should go. Everything else is absolutely the same. So having two tables `t1` and `t2` with columns `a` and `b`, the query trees for the two statements:

    SELECT t2.b FROM t1, t2 WHERE t1.a = t2.a;

    UPDATE t1 SET b = t2.b FROM t2 WHERE t1.a = t2.a;

are nearly identical. In particular:

- The range tables contain entries for the tables `t1` and `t2`.

- The target lists contain one variable that points to column `b` of the range table entry for table `t2`.

- The qualification expressions compare the columns `a` of both range-table entries for equality.

- The join trees show a simple join between `t1` and `t2`.

The consequence is, that both query trees result in similar execution plans: They are both joins over the two tables. For the `UPDATE` the missing columns from `t1` are added to the target list by the planner and the final query tree will read as:

    UPDATE t1 SET a = t1.a, b = t2.b FROM t2 WHERE t1.a = t2.a;

and thus the executor run over the join will produce exactly the same result set as:

    SELECT t1.a, t2.b FROM t1, t2 WHERE t1.a = t2.a;

But there is a little problem in `UPDATE`: the part of the executor plan that does the join does not care what the results from the join are meant for. It just produces a result set of rows. The fact that one is a `SELECT` command and the other is an `UPDATE` is handled higher up in the executor, where it knows that this is an `UPDATE`, and it knows that this result should go into table `t1`. But which of the rows that are there has to be replaced by the new row?

To resolve this problem, another entry is added to the target list in `UPDATE` (and also in `DELETE`) statements: the current tuple ID (CTID). This is a system column containing the file block number and position in the block for the row. Knowing the table, the CTID can be used to retrieve the original row of `t1` to be updated. After adding the CTID to the target list, the query actually looks like:

    SELECT t1.a, t2.b, t1.ctid FROM t1, t2 WHERE t1.a = t2.a;

Now another detail of Postgres Pro enters the stage. Old table rows aren't overwritten, and this is why `ROLLBACK` is fast. In an `UPDATE`, the new result row is inserted into the table (after stripping the CTID) and in the row header of the old row, which the CTID pointed to, the `cmax` and `xmax` entries are set to the current command counter and current transaction ID. Thus the old row is hidden, and after the transaction commits the vacuum cleaner can eventually remove the dead row.

Knowing all that, we can simply apply view rules in absolutely the same way to any command. There is no difference.

### 38.2.3. The Power of Views in Postgres Pro

The above demonstrates how the rule system incorporates view definitions into the original query tree. In the second example, a simple `SELECT` from one view created a final query tree that is a join of 4 tables (`unit` was used twice with different names).

The benefit of implementing views with the rule system is, that the planner has all the information about which tables have to be scanned plus the relationships between these tables plus the restrictive qualifications from the views plus the qualifications from the original query in one single query tree. And this is still the situation when the original query is already a join over views. The planner has to decide which is the best path to execute the query, and the more information the planner has, the better this decision can be. And the rule system as implemented in Postgres Pro ensures, that this is all information available about the query up to that point.

### 38.2.4. Updating a View

What happens if a view is named as the target relation for an `INSERT`, `UPDATE`, or `DELETE`? Doing the substitutions described above would give a query tree in which the result relation points at a subquery range-table entry, which will not work. There are several ways in which Postgres Pro can support the appearance of updating a view, however.

If the subquery selects from a single base relation and is simple enough, the rewriter can automatically replace the subquery with the underlying base relation so that the `INSERT`, `UPDATE`, or `DELETE` is applied to the base relation in the appropriate way. Views that are “simple enough” for this are called automatically updatable. For detailed information on the kinds of view that can be automatically updated, see CREATE VIEW.

Alternatively, the operation may be handled by a user-provided `INSTEAD OF` trigger on the view. Rewriting works slightly differently in this case. For `INSERT`, the rewriter does nothing at all with the view, leaving it as the result relation for the query. For `UPDATE` and `DELETE`, it's still necessary to expand the view query to produce the “old” rows that the command will attempt to update or delete. So the view is expanded as normal, but another unexpanded range-table entry is added to the query to represent the view in its capacity as the result relation.

The problem that now arises is how to identify the rows to be updated in the view. Recall that when the result relation is a table, a special CTID entry is added to the target list to identify the physical locations of the rows to be updated. This does not work if the result relation is a view, because a view does not have any CTID, since its rows do not have actual physical locations. Instead, for an `UPDATE` or `DELETE` operation, a special `wholerow` entry is added to the target list, which expands to include all columns from the view. The executor uses this value to supply the “old” row to the `INSTEAD OF` trigger. It is up to the trigger to work out what to update based on the old and new row values.

Another possibility is for the user to define `INSTEAD` rules that specify substitute actions for `INSERT`, `UPDATE`, and `DELETE` commands on a view. These rules will rewrite the command, typically into a command that updates one or more tables, rather than views. That is the topic of Section 38.4.

Note that rules are evaluated first, rewriting the original query before it is planned and executed. Therefore, if a view has `INSTEAD OF` triggers as well as rules on `INSERT`, `UPDATE`, or `DELETE`, then the rules will be evaluated first, and depending on the result, the triggers may not be used at all.

Automatic rewriting of an `INSERT`, `UPDATE`, or `DELETE` query on a simple view is always tried last. Therefore, if a view has rules or triggers, they will override the default behavior of automatically updatable views.

If there are no `INSTEAD` rules or `INSTEAD OF` triggers for the view, and the rewriter cannot automatically rewrite the query as an update on the underlying base relation, an error will be thrown because the executor cannot update a view as such.