# 41.2. Views and the Rule System
41.2.2. View Rules in Non-SELECT
Statements
41.2.3. The Power of Views in PostgreSQL
Views in PostgreSQL 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 PostgreSQL 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.
# 41.2.1. 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.
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,
least(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
### 41.2.2. 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 PostgreSQL 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.
### 41.2.3. The Power of Views in PostgreSQL
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 PostgreSQL ensures that this is all information available about the query up to that point.
### 41.2.4. 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 PostgreSQL can support the appearance of updating a view, however. In order of user-experienced complexity those are: automatically substitute in the underlying table for the view, execute a user-defined trigger, or rewrite the query per a user-defined rule. These options are discussed below.
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](sql-createview.html).
Alternatively, the operation may be handled by a user-provided `INSTEAD OF` trigger on the view (see [CREATE TRIGGER](sql-createtrigger.html)). 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 41.4](rules-update.html).
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.