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<!-- doc/src/sgml/rules.sgml -->
<chapter id="rules">
<title>The Rule System</title>
<indexterm zone="rules">
<primary>rule</primary>
</indexterm>
<para>
This chapter discusses the rule system in
<productname>PostgreSQL</productname>. Production rule systems
are conceptually simple, but there are many subtle points
involved in actually using them.
</para>
<para>
Some other database systems define active database rules, which
are usually stored procedures and triggers. In
<productname>PostgreSQL</productname>, these can be implemented
using functions and triggers as well.
</para>
<para>
The rule system (more precisely speaking, the query rewrite rule
system) is totally different from stored procedures and triggers.
It modifies queries to take rules into consideration, and then
passes the modified query to the query planner for planning and
execution. It is very powerful, and can be used for many things
such as query language procedures, views, and versions. The
theoretical foundations and the power of this rule system are
also discussed in <xref linkend="ston90b"/> and <xref
linkend="ong90"/>.
</para>
<sect1 id="querytree">
<title>The Query Tree</title>
<indexterm zone="querytree">
<primary>query tree</primary>
</indexterm>
<para>
To understand how the rule system works it is necessary to know
when it is invoked and what its input and results are.
</para>
<para>
The rule system is located between the parser and the planner.
It takes the output of the parser, one query tree, and the user-defined
rewrite rules, which are also
query trees with some extra information, and creates zero or more
query trees as result. So its input and output are always things
the parser itself could have produced and thus, anything it sees
is basically representable as an <acronym>SQL</acronym> statement.
</para>
<para>
Now what is a query tree? It is an internal representation of an
<acronym>SQL</acronym> statement where the single parts that it is
built from are stored separately. These query trees can be shown
in the server log if you set the configuration parameters
<varname>debug_print_parse</varname>,
<varname>debug_print_rewritten</varname>, or
<varname>debug_print_plan</varname>. The rule actions are also
stored as query trees, in the system catalog
<structname>pg_rewrite</structname>. They are not formatted like
the log output, but they contain exactly the same information.
</para>
<para>
Reading a raw query tree requires some experience. But since
<acronym>SQL</acronym> representations of query trees are
sufficient to understand the rule system, this chapter will not
teach how to read them.
</para>
<para>
When reading the <acronym>SQL</acronym> representations of the
query trees in this chapter it is necessary to be able to identify
the parts the statement is broken into when it is in the query tree
structure. The parts of a query tree are
<variablelist>
<varlistentry>
<term>
the command type
</term>
<listitem>
<para>
This is a simple value telling which command
(<command>SELECT</command>, <command>INSERT</command>,
<command>UPDATE</command>, <command>DELETE</command>) produced
the query tree.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
the range table
<indexterm><primary>range table</primary></indexterm>
</term>
<listitem>
<para>
The range table is a list of relations that are used in the query.
In a <command>SELECT</command> statement these are the relations given after
the <literal>FROM</literal> key word.
</para>
<para>
Every range table entry identifies a table or view and tells
by which name it is called in the other parts of the query.
In the query tree, the range table entries are referenced by
number rather than by name, so here it doesn't matter if there
are duplicate names as it would in an <acronym>SQL</acronym>
statement. This can happen after the range tables of rules
have been merged in. The examples in this chapter will not have
this situation.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
the result relation
</term>
<listitem>
<para>
This is an index into the range table that identifies the
relation where the results of the query go.
</para>
<para>
<command>SELECT</command> queries don't have a result
relation. (The special case of <command>SELECT INTO</command> is
mostly identical to <command>CREATE TABLE</command> followed by
<literal>INSERT ... SELECT</literal>, and is not discussed
separately here.)
</para>
<para>
For <command>INSERT</command>, <command>UPDATE</command>, and
<command>DELETE</command> commands, the result relation is the table
(or view!) where the changes are to take effect.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
the target list
<indexterm><primary>target list</primary></indexterm>
</term>
<listitem>
<para>
The target list is a list of expressions that define the
result of the query. In the case of a
<command>SELECT</command>, these expressions are the ones that
build the final output of the query. They correspond to the
expressions between the key words <command>SELECT</command>
and <command>FROM</command>. (<literal>*</literal> is just an
abbreviation for all the column names of a relation. It is
expanded by the parser into the individual columns, so the
rule system never sees it.)
</para>
<para>
<command>DELETE</command> commands don't need a normal target list
because they don't produce any result. Instead, the planner
adds a special <acronym>CTID</acronym> entry to the empty target list,
to allow the executor to find the row to be deleted.
(<acronym>CTID</acronym> is added when the result relation is an ordinary
table. If it is a view, a whole-row variable is added instead, by
the rule system, as described in <xref linkend="rules-views-update"/>.)
</para>
<para>
For <command>INSERT</command> commands, the target list describes
the new rows that should go into the result relation. It consists of the
expressions in the <literal>VALUES</literal> clause or the ones from the
<command>SELECT</command> clause in <literal>INSERT
... SELECT</literal>. The first step of the rewrite process adds
target list entries for any columns that were not assigned to by
the original command but have defaults. Any remaining columns (with
neither a given value nor a default) will be filled in by the
planner with a constant null expression.
</para>
<para>
For <command>UPDATE</command> commands, the target list
describes the new rows that should replace the old ones. In the
rule system, it contains just the expressions from the <literal>SET
column = expression</literal> part of the command. The planner will
handle missing columns by inserting expressions that copy the values
from the old row into the new one. Just as for <command>DELETE</command>,
a <acronym>CTID</acronym> or whole-row variable is added so that
the executor can identify the old row to be updated.
</para>
<para>
Every entry in the target list contains an expression that can
be a constant value, a variable pointing to a column of one
of the relations in the range table, a parameter, or an expression
tree made of function calls, constants, variables, operators, etc.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
the qualification
</term>
<listitem>
<para>
The query's qualification is an expression much like one of
those contained in the target list entries. The result value of
this expression is a Boolean that tells whether the operation
(<command>INSERT</command>, <command>UPDATE</command>,
<command>DELETE</command>, or <command>SELECT</command>) for the
final result row should be executed or not. It corresponds to the <literal>WHERE</literal> clause
of an <acronym>SQL</acronym> statement.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
the join tree
</term>
<listitem>
<para>
The query's join tree shows the structure of the <literal>FROM</literal> clause.
For a simple query like <literal>SELECT ... FROM a, b, c</literal>, the join tree is just
a list of the <literal>FROM</literal> items, because we are allowed to join them in
any order. But when <literal>JOIN</literal> expressions, particularly outer joins,
are used, we have to join in the order shown by the joins.
In that case, the join tree shows the structure of the <literal>JOIN</literal> expressions. The
restrictions associated with particular <literal>JOIN</literal> clauses (from <literal>ON</literal> or
<literal>USING</literal> expressions) are stored as qualification expressions attached
to those join-tree nodes. It turns out to be convenient to store
the top-level <literal>WHERE</literal> expression as a qualification attached to the
top-level join-tree item, too. So really the join tree represents
both the <literal>FROM</literal> and <literal>WHERE</literal> clauses of a <command>SELECT</command>.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
the others
</term>
<listitem>
<para>
The other parts of the query tree like the <literal>ORDER BY</literal>
clause aren't of interest here. The rule system
substitutes some entries there while applying rules, but that
doesn't have much to do with the fundamentals of the rule
system.
</para>
</listitem>
</varlistentry>
</variablelist>
</para>
</sect1>
<sect1 id="rules-views">
<title>Views and the Rule System</title>
<indexterm zone="rules-views">
<primary>rule</primary>
<secondary>and views</secondary>
</indexterm>
<indexterm zone="rules-views">
<primary>view</primary>
<secondary>implementation through rules</secondary>
</indexterm>
<para>
Views in <productname>PostgreSQL</productname> are implemented
using the rule system. In fact, there is essentially no difference
between:
<programlisting>
CREATE VIEW myview AS SELECT * FROM mytab;
</programlisting>
compared against the two commands:
<programlisting>
CREATE TABLE myview (<replaceable>same column list as mytab</replaceable>);
CREATE RULE "_RETURN" AS ON SELECT TO myview DO INSTEAD
SELECT * FROM mytab;
</programlisting>
because this is exactly what the <command>CREATE VIEW</command>
command does internally. This has some side effects. One of them
is that the information about a view in the
<productname>PostgreSQL</productname> 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.
</para>
<sect2 id="rules-select">
<title>How <command>SELECT</command> Rules Work</title>
<indexterm zone="rules-select">
<primary>rule</primary>
<secondary sortas="SELECT">for SELECT</secondary>
</indexterm>
<para>
Rules <literal>ON SELECT</literal> are applied to all queries as the last step, even
if the command given is an <command>INSERT</command>,
<command>UPDATE</command> or <command>DELETE</command>. 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
<command>SELECT</command> rules are described first.
</para>
<para>
Currently, there can be only one action in an <literal>ON SELECT</literal> rule, and it must
be an unconditional <command>SELECT</command> action that is <literal>INSTEAD</literal>. This restriction was
required to make rules safe enough to open them for ordinary users, and
it restricts <literal>ON SELECT</literal> rules to act like views.
</para>
<para>
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
<command>INSERT</command>, <command>UPDATE</command>, and
<command>DELETE</command> 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.
</para>
<para>
The real tables we need in the first two rule system descriptions
are these:
<programlisting>
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
);
</programlisting>
As you can see, they represent shoe-store data.
</para>
<para>
The views are created as:
<programlisting>
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 &gt;= rsh.slminlen_cm
AND rsl.sl_len_cm &lt;= rsh.slmaxlen_cm;
</programlisting>
The <command>CREATE VIEW</command> command for the
<literal>shoelace</literal> view (which is the simplest one we
have) will create a relation <literal>shoelace</literal> and an entry in
<structname>pg_rewrite</structname> that tells that there is a
rewrite rule that must be applied whenever the relation <literal>shoelace</literal>
is referenced in a query's range table. The rule has no rule
qualification (discussed later, with the non-<command>SELECT</command> rules, since
<command>SELECT</command> rules currently cannot have them) and it is <literal>INSTEAD</literal>. 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
<command>SELECT</command> statement in the view creation command.
</para>
<note>
<para>
The two extra range
table entries for <literal>NEW</literal> and <literal>OLD</literal> that you can see in
the <structname>pg_rewrite</structname> entry aren't of interest
for <command>SELECT</command> rules.
</para>
</note>
<para>
Now we populate <literal>unit</literal>, <literal>shoe_data</literal>
and <literal>shoelace_data</literal> and run a simple query on a view:
<programlisting>
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)
</programlisting>
</para>
<para>
This is the simplest <command>SELECT</command> you can do on our
views, so we take this opportunity to explain the basics of view
rules. The <literal>SELECT * FROM shoelace</literal> was
interpreted by the parser and produced the query tree:
<programlisting>
SELECT shoelace.sl_name, shoelace.sl_avail,
shoelace.sl_color, shoelace.sl_len,
shoelace.sl_unit, shoelace.sl_len_cm
FROM shoelace shoelace;
</programlisting>
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
<literal>shoelace</literal> (the only one up to now) it finds the
<literal>_RETURN</literal> rule with the query tree:
<programlisting>
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;
</programlisting>
</para>
<para>
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:
<programlisting>
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;
</programlisting>
There is one difference however: the subquery's range table has two
extra entries <literal>shoelace old</literal> and <literal>shoelace new</literal>. 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.
</para>
<para>
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 <literal>old</literal> or <literal>new</literal> &mdash; otherwise we'd have infinite recursion!)
In this example, there are no rewrite rules for <literal>shoelace_data</literal> or <literal>unit</literal>,
so rewriting is complete and the above is the final result given to
the planner.
</para>
<para>
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.
<programlisting>
SELECT * FROM shoe_ready WHERE total_avail &gt;= 2;
shoename | sh_avail | sl_name | sl_avail | total_avail
----------+----------+---------+----------+-------------
sh1 | 2 | sl1 | 5 | 2
sh3 | 4 | sl7 | 7 | 4
(2 rows)
</programlisting>
</para>
<para>
The output of the parser this time is the query tree:
<programlisting>
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 &gt;= 2;
</programlisting>
The first rule applied will be the one for the
<literal>shoe_ready</literal> view and it results in the
query tree:
<programlisting>
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,
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 &gt;= rsh.slminlen_cm
AND rsl.sl_len_cm &lt;= rsh.slmaxlen_cm) shoe_ready
WHERE shoe_ready.total_avail &gt;= 2;
</programlisting>
Similarly, the rules for <literal>shoe</literal> and
<literal>shoelace</literal> are substituted into the range table of
the subquery, leading to a three-level final query tree:
<programlisting>
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,
least(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 &gt;= rsh.slminlen_cm
AND rsl.sl_len_cm &lt;= rsh.slmaxlen_cm) shoe_ready
WHERE shoe_ready.total_avail &gt; 2;
</programlisting>
</para>
<para>
This might look inefficient, but the planner will collapse this into a
single-level query tree by <quote>pulling up</quote> the subqueries,
and then it will plan the joins just as if we'd written them out
manually. So collapsing the query tree is an optimization that the
rewrite system doesn't have to concern itself with.
</para>
</sect2>
<sect2>
<title>View Rules in Non-<command>SELECT</command> Statements</title>
<para>
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.
</para>
<para>
There are only a few differences between a query tree for a
<command>SELECT</command> and one for any other
command. Obviously, they have a different command type and for a
command other than a <command>SELECT</command>, the result
relation points to the range-table entry where the result should
go. Everything else is absolutely the same. So having two tables
<literal>t1</literal> and <literal>t2</literal> with columns <literal>a</literal> and
<literal>b</literal>, the query trees for the two statements:
<programlisting>
SELECT t2.b FROM t1, t2 WHERE t1.a = t2.a;
UPDATE t1 SET b = t2.b FROM t2 WHERE t1.a = t2.a;
</programlisting>
are nearly identical. In particular:
<itemizedlist>
<listitem>
<para>
The range tables contain entries for the tables <literal>t1</literal> and <literal>t2</literal>.
</para>
</listitem>
<listitem>
<para>
The target lists contain one variable that points to column
<literal>b</literal> of the range table entry for table <literal>t2</literal>.
</para>
</listitem>
<listitem>
<para>
The qualification expressions compare the columns <literal>a</literal> of both
range-table entries for equality.
</para>
</listitem>
<listitem>
<para>
The join trees show a simple join between <literal>t1</literal> and <literal>t2</literal>.
</para>
</listitem>
</itemizedlist>
</para>
<para>
The consequence is, that both query trees result in similar
execution plans: They are both joins over the two tables. For the
<command>UPDATE</command> the missing columns from <literal>t1</literal> are added to
the target list by the planner and the final query tree will read
as:
<programlisting>
UPDATE t1 SET a = t1.a, b = t2.b FROM t2 WHERE t1.a = t2.a;
</programlisting>
and thus the executor run over the join will produce exactly the
same result set as:
<programlisting>
SELECT t1.a, t2.b FROM t1, t2 WHERE t1.a = t2.a;
</programlisting>
But there is a little problem in
<command>UPDATE</command>: 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 <command>SELECT</command> command and the other is an
<command>UPDATE</command> is handled higher up in the executor, where
it knows that this is an <command>UPDATE</command>, and it knows that
this result should go into table <literal>t1</literal>. But which of the rows
that are there has to be replaced by the new row?
</para>
<para>
To resolve this problem, another entry is added to the target list
in <command>UPDATE</command> (and also in
<command>DELETE</command>) statements: the current tuple ID
(<acronym>CTID</acronym>).<indexterm><primary>CTID</primary></indexterm>
This is a system column containing the
file block number and position in the block for the row. Knowing
the table, the <acronym>CTID</acronym> can be used to retrieve the
original row of <literal>t1</literal> to be updated. After adding the
<acronym>CTID</acronym> to the target list, the query actually looks like:
<programlisting>
SELECT t1.a, t2.b, t1.ctid FROM t1, t2 WHERE t1.a = t2.a;
</programlisting>
Now another detail of <productname>PostgreSQL</productname> enters
the stage. Old table rows aren't overwritten, and this
is why <command>ROLLBACK</command> is fast. In an <command>UPDATE</command>,
the new result row is inserted into the table (after stripping the
<acronym>CTID</acronym>) and in the row header of the old row, which the
<acronym>CTID</acronym> pointed to, the <literal>cmax</literal> and
<literal>xmax</literal> 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.
</para>
<para>
Knowing all that, we can simply apply view rules in absolutely
the same way to any command. There is no difference.
</para>
</sect2>
<sect2>
<title>The Power of Views in <productname>PostgreSQL</productname></title>
<para>
The above demonstrates how the rule system incorporates view
definitions into the original query tree. In the second example, a
simple <command>SELECT</command> from one view created a final
query tree that is a join of 4 tables (<literal>unit</literal> was used twice with
different names).
</para>
<para>
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 <productname>PostgreSQL</productname>
ensures, that this is all information available about the query
up to that point.
</para>
</sect2>
<sect2 id="rules-views-update">
<title>Updating a View</title>
<para>
What happens if a view is named as the target relation for an
<command>INSERT</command>, <command>UPDATE</command>, or
<command>DELETE</command>? 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 <productname>PostgreSQL</productname>
can support the appearance of updating a view, however.
</para>
<para>
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 <command>INSERT</command>,
<command>UPDATE</command>, or <command>DELETE</command> is applied to
the base relation in the appropriate way. Views that are
<quote>simple enough</quote> for this are called <firstterm>automatically
updatable</firstterm>. For detailed information on the kinds of view that can
be automatically updated, see <xref linkend="sql-createview"/>.
</para>
<para>
Alternatively, the operation may be handled by a user-provided
<literal>INSTEAD OF</literal> trigger on the view.
Rewriting works slightly differently
in this case. For <command>INSERT</command>, the rewriter does
nothing at all with the view, leaving it as the result relation
for the query. For <command>UPDATE</command> and
<command>DELETE</command>, it's still necessary to expand the
view query to produce the <quote>old</quote> 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.
</para>
<para>
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 <acronym>CTID</acronym> 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 <acronym>CTID</acronym>, since its rows do not have
actual physical locations. Instead, for an <command>UPDATE</command>
or <command>DELETE</command> operation, a special <literal>wholerow</literal>
entry is added to the target list, which expands to include all
columns from the view. The executor uses this value to supply the
<quote>old</quote> row to the <literal>INSTEAD OF</literal> trigger. It is
up to the trigger to work out what to update based on the old and
new row values.
</para>
<para>
Another possibility is for the user to define <literal>INSTEAD</literal>
rules that specify substitute actions for <command>INSERT</command>,
<command>UPDATE</command>, and <command>DELETE</command> 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 <xref linkend="rules-update"/>.
</para>
<para>
Note that rules are evaluated first, rewriting the original query
before it is planned and executed. Therefore, if a view has
<literal>INSTEAD OF</literal> triggers as well as rules on <command>INSERT</command>,
<command>UPDATE</command>, or <command>DELETE</command>, then the rules will be
evaluated first, and depending on the result, the triggers may not be
used at all.
</para>
<para>
Automatic rewriting of an <command>INSERT</command>,
<command>UPDATE</command>, or <command>DELETE</command> 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.
</para>
<para>
If there are no <literal>INSTEAD</literal> rules or <literal>INSTEAD OF</literal>
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.
</para>
</sect2>
</sect1>
<sect1 id="rules-materializedviews">
<title>Materialized Views</title>
<indexterm zone="rules-materializedviews">
<primary>rule</primary>
<secondary>and materialized views</secondary>
</indexterm>
<indexterm zone="rules-materializedviews">
<primary>materialized view</primary>
<secondary>implementation through rules</secondary>
</indexterm>
<indexterm zone="rules-materializedviews">
<primary>view</primary>
<secondary>materialized</secondary>
</indexterm>
<para>
Materialized views in <productname>PostgreSQL</productname> use the
rule system like views do, but persist the results in a table-like form.
The main differences between:
<programlisting>
CREATE MATERIALIZED VIEW mymatview AS SELECT * FROM mytab;
</programlisting>
and:
<programlisting>
CREATE TABLE mymatview AS SELECT * FROM mytab;
</programlisting>
are that the materialized view cannot subsequently be directly updated
and that the query used to create the materialized view is stored in
exactly the same way that a view's query is stored, so that fresh data
can be generated for the materialized view with:
<programlisting>
REFRESH MATERIALIZED VIEW mymatview;
</programlisting>
The information about a materialized view in the
<productname>PostgreSQL</productname> system catalogs is exactly
the same as it is for a table or view. So for the parser, a
materialized view is a relation, just like a table or a view. When
a materialized view is referenced in a query, the data is returned
directly from the materialized view, like from a table; the rule is
only used for populating the materialized view.
</para>
<para>
While access to the data stored in a materialized view is often much
faster than accessing the underlying tables directly or through a view,
the data is not always current; yet sometimes current data is not needed.
Consider a table which records sales:
<programlisting>
CREATE TABLE invoice (
invoice_no integer PRIMARY KEY,
seller_no integer, -- ID of salesperson
invoice_date date, -- date of sale
invoice_amt numeric(13,2) -- amount of sale
);
</programlisting>
If people want to be able to quickly graph historical sales data, they
might want to summarize, and they may not care about the incomplete data
for the current date:
<programlisting>
CREATE MATERIALIZED VIEW sales_summary AS
SELECT
seller_no,
invoice_date,
sum(invoice_amt)::numeric(13,2) as sales_amt
FROM invoice
WHERE invoice_date &lt; CURRENT_DATE
GROUP BY
seller_no,
invoice_date
ORDER BY
seller_no,
invoice_date;
CREATE UNIQUE INDEX sales_summary_seller
ON sales_summary (seller_no, invoice_date);
</programlisting>
This materialized view might be useful for displaying a graph in the
dashboard created for salespeople. A job could be scheduled to update
the statistics each night using this SQL statement:
<programlisting>
REFRESH MATERIALIZED VIEW sales_summary;
</programlisting>
</para>
<para>
Another use for a materialized view is to allow faster access to data
brought across from a remote system through a foreign data wrapper.
A simple example using <literal>file_fdw</literal> is below, with timings,
but since this is using cache on the local system the performance
difference compared to access to a remote system would usually be greater
than shown here. Notice we are also exploiting the ability to put an
index on the materialized view, whereas <literal>file_fdw</literal> does
not support indexes; this advantage might not apply for other sorts of
foreign data access.
</para>
<para>
Setup:
<programlisting>
CREATE EXTENSION file_fdw;
CREATE SERVER local_file FOREIGN DATA WRAPPER file_fdw;
CREATE FOREIGN TABLE words (word text NOT NULL)
SERVER local_file
OPTIONS (filename '/usr/share/dict/words');
CREATE MATERIALIZED VIEW wrd AS SELECT * FROM words;
CREATE UNIQUE INDEX wrd_word ON wrd (word);
CREATE EXTENSION pg_trgm;
CREATE INDEX wrd_trgm ON wrd USING gist (word gist_trgm_ops);
VACUUM ANALYZE wrd;
</programlisting>
Now let's spell-check a word. Using <literal>file_fdw</literal> directly:
<programlisting>
SELECT count(*) FROM words WHERE word = 'caterpiler';
count
-------
0
(1 row)
</programlisting>
With <command>EXPLAIN ANALYZE</command>, we see:
<programlisting>
Aggregate (cost=21763.99..21764.00 rows=1 width=0) (actual time=188.180..188.181 rows=1 loops=1)
-&gt; Foreign Scan on words (cost=0.00..21761.41 rows=1032 width=0) (actual time=188.177..188.177 rows=0 loops=1)
Filter: (word = 'caterpiler'::text)
Rows Removed by Filter: 479829
Foreign File: /usr/share/dict/words
Foreign File Size: 4953699
Planning time: 0.118 ms
Execution time: 188.273 ms
</programlisting>
If the materialized view is used instead, the query is much faster:
<programlisting>
Aggregate (cost=4.44..4.45 rows=1 width=0) (actual time=0.042..0.042 rows=1 loops=1)
-&gt; Index Only Scan using wrd_word on wrd (cost=0.42..4.44 rows=1 width=0) (actual time=0.039..0.039 rows=0 loops=1)
Index Cond: (word = 'caterpiler'::text)
Heap Fetches: 0
Planning time: 0.164 ms
Execution time: 0.117 ms
</programlisting>
Either way, the word is spelled wrong, so let's look for what we might
have wanted. Again using <literal>file_fdw</literal>:
<programlisting>
SELECT word FROM words ORDER BY word &lt;-&gt; 'caterpiler' LIMIT 10;
word
---------------
cater
caterpillar
Caterpillar
caterpillars
caterpillar's
Caterpillar's
caterer
caterer's
caters
catered
(10 rows)
</programlisting>
<programlisting>
Limit (cost=11583.61..11583.64 rows=10 width=32) (actual time=1431.591..1431.594 rows=10 loops=1)
-&gt; Sort (cost=11583.61..11804.76 rows=88459 width=32) (actual time=1431.589..1431.591 rows=10 loops=1)
Sort Key: ((word &lt;-&gt; 'caterpiler'::text))
Sort Method: top-N heapsort Memory: 25kB
-&gt; Foreign Scan on words (cost=0.00..9672.05 rows=88459 width=32) (actual time=0.057..1286.455 rows=479829 loops=1)
Foreign File: /usr/share/dict/words
Foreign File Size: 4953699
Planning time: 0.128 ms
Execution time: 1431.679 ms
</programlisting>
Using the materialized view:
<programlisting>
Limit (cost=0.29..1.06 rows=10 width=10) (actual time=187.222..188.257 rows=10 loops=1)
-&gt; Index Scan using wrd_trgm on wrd (cost=0.29..37020.87 rows=479829 width=10) (actual time=187.219..188.252 rows=10 loops=1)
Order By: (word &lt;-&gt; 'caterpiler'::text)
Planning time: 0.196 ms
Execution time: 198.640 ms
</programlisting>
If you can tolerate periodic update of the remote data to the local
database, the performance benefit can be substantial.
</para>
</sect1>
<sect1 id="rules-update">
<title>Rules on <command>INSERT</command>, <command>UPDATE</command>, and <command>DELETE</command></title>
<indexterm zone="rules-update">
<primary>rule</primary>
<secondary sortas="INSERT">for INSERT</secondary>
</indexterm>
<indexterm zone="rules-update">
<primary>rule</primary>
<secondary sortas="UPDATE">for UPDATE</secondary>
</indexterm>
<indexterm zone="rules-update">
<primary>rule</primary>
<secondary sortas="DELETE">for DELETE</secondary>
</indexterm>
<para>
Rules that are defined on <command>INSERT</command>, <command>UPDATE</command>,
and <command>DELETE</command> are significantly different from the view rules
described in the previous section. First, their <command>CREATE
RULE</command> command allows more:
<itemizedlist>
<listitem>
<para>
They are allowed to have no action.
</para>
</listitem>
<listitem>
<para>
They can have multiple actions.
</para>
</listitem>
<listitem>
<para>
They can be <literal>INSTEAD</literal> or <literal>ALSO</literal> (the default).
</para>
</listitem>
<listitem>
<para>
The pseudorelations <literal>NEW</literal> and <literal>OLD</literal> become useful.
</para>
</listitem>
<listitem>
<para>
They can have rule qualifications.
</para>
</listitem>
</itemizedlist>
Second, they don't modify the query tree in place. Instead they
create zero or more new query trees and can throw away the
original one.
</para>
<caution>
<para>
In many cases, tasks that could be performed by rules
on <command>INSERT</command>/<command>UPDATE</command>/<command>DELETE</command> are better done
with triggers. Triggers are notationally a bit more complicated, but their
semantics are much simpler to understand. Rules tend to have surprising
results when the original query contains volatile functions: volatile
functions may get executed more times than expected in the process of
carrying out the rules.
</para>
<para>
Also, there are some cases that are not supported by these types of rules at
all, notably including <literal>WITH</literal> clauses in the original query and
multiple-assignment sub-<literal>SELECT</literal>s in the <literal>SET</literal> list
of <command>UPDATE</command> queries. This is because copying these constructs
into a rule query would result in multiple evaluations of the sub-query,
contrary to the express intent of the query's author.
</para>
</caution>
<sect2>
<title>How Update Rules Work</title>
<para>
Keep the syntax:
<programlisting>
CREATE [ OR REPLACE ] RULE <replaceable class="parameter">name</replaceable> AS ON <replaceable class="parameter">event</replaceable>
TO <replaceable class="parameter">table</replaceable> [ WHERE <replaceable class="parameter">condition</replaceable> ]
DO [ ALSO | INSTEAD ] { NOTHING | <replaceable class="parameter">command</replaceable> | ( <replaceable class="parameter">command</replaceable> ; <replaceable class="parameter">command</replaceable> ... ) }
</programlisting>
in mind.
In the following, <firstterm>update rules</firstterm> means rules that are defined
on <command>INSERT</command>, <command>UPDATE</command>, or <command>DELETE</command>.
</para>
<para>
Update rules get applied by the rule system when the result
relation and the command type of a query tree are equal to the
object and event given in the <command>CREATE RULE</command> command.
For update rules, the rule system creates a list of query trees.
Initially the query-tree list is empty.
There can be zero (<literal>NOTHING</literal> key word), one, or multiple actions.
To simplify, we will look at a rule with one action. This rule
can have a qualification or not and it can be <literal>INSTEAD</literal> or
<literal>ALSO</literal> (the default).
</para>
<para>
What is a rule qualification? It is a restriction that tells
when the actions of the rule should be done and when not. This
qualification can only reference the pseudorelations <literal>NEW</literal> and/or <literal>OLD</literal>,
which basically represent the relation that was given as object (but with a
special meaning).
</para>
<para>
So we have three cases that produce the following query trees for
a one-action rule.
<variablelist>
<varlistentry>
<term>No qualification, with either <literal>ALSO</literal> or
<literal>INSTEAD</literal></term>
<listitem>
<para>
the query tree from the rule action with the original query
tree's qualification added
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Qualification given and <literal>ALSO</literal></term>
<listitem>
<para>
the query tree from the rule action with the rule
qualification and the original query tree's qualification
added
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Qualification given and <literal>INSTEAD</literal></term>
<listitem>
<para>
the query tree from the rule action with the rule
qualification and the original query tree's qualification; and
the original query tree with the negated rule qualification
added
</para>
</listitem>
</varlistentry>
</variablelist>
Finally, if the rule is <literal>ALSO</literal>, the unchanged original query tree is
added to the list. Since only qualified <literal>INSTEAD</literal> rules already add the
original query tree, we end up with either one or two output query trees
for a rule with one action.
</para>
<para>
For <literal>ON INSERT</literal> rules, the original query (if not suppressed by <literal>INSTEAD</literal>)
is done before any actions added by rules. This allows the actions to
see the inserted row(s). But for <literal>ON UPDATE</literal> and <literal>ON
DELETE</literal> rules, the original query is done after the actions added by rules.
This ensures that the actions can see the to-be-updated or to-be-deleted
rows; otherwise, the actions might do nothing because they find no rows
matching their qualifications.
</para>
<para>
The query trees generated from rule actions are thrown into the
rewrite system again, and maybe more rules get applied resulting
in more or less query trees.
So a rule's actions must have either a different
command type or a different result relation than the rule itself is
on, otherwise this recursive process will end up in an infinite loop.
(Recursive expansion of a rule will be detected and reported as an
error.)
</para>
<para>
The query trees found in the actions of the
<structname>pg_rewrite</structname> system catalog are only
templates. Since they can reference the range-table entries for
<literal>NEW</literal> and <literal>OLD</literal>, some substitutions have to be made before they can be
used. For any reference to <literal>NEW</literal>, the target list of the original
query is searched for a corresponding entry. If found, that
entry's expression replaces the reference. Otherwise, <literal>NEW</literal> means the
same as <literal>OLD</literal> (for an <command>UPDATE</command>) or is replaced by
a null value (for an <command>INSERT</command>). Any reference to <literal>OLD</literal> is
replaced by a reference to the range-table entry that is the
result relation.
</para>
<para>
After the system is done applying update rules, it applies view rules to the
produced query tree(s). Views cannot insert new update actions so
there is no need to apply update rules to the output of view rewriting.
</para>
<sect3>
<title>A First Rule Step by Step</title>
<para>
Say we want to trace changes to the <literal>sl_avail</literal> column in the
<literal>shoelace_data</literal> relation. So we set up a log table
and a rule that conditionally writes a log entry when an
<command>UPDATE</command> is performed on
<literal>shoelace_data</literal>.
<programlisting>
CREATE TABLE shoelace_log (
sl_name text, -- shoelace changed
sl_avail integer, -- new available value
log_who text, -- who did it
log_when timestamp -- when
);
CREATE RULE log_shoelace AS ON UPDATE TO shoelace_data
WHERE NEW.sl_avail &lt;&gt; OLD.sl_avail
DO INSERT INTO shoelace_log VALUES (
NEW.sl_name,
NEW.sl_avail,
current_user,
current_timestamp
);
</programlisting>
</para>
<para>
Now someone does:
<programlisting>
UPDATE shoelace_data SET sl_avail = 6 WHERE sl_name = 'sl7';
</programlisting>
and we look at the log table:
<programlisting>
SELECT * FROM shoelace_log;
sl_name | sl_avail | log_who | log_when
---------+----------+---------+----------------------------------
sl7 | 6 | Al | Tue Oct 20 16:14:45 1998 MET DST
(1 row)
</programlisting>
</para>
<para>
That's what we expected. What happened in the background is the following.
The parser created the query tree:
<programlisting>
UPDATE shoelace_data SET sl_avail = 6
FROM shoelace_data shoelace_data
WHERE shoelace_data.sl_name = 'sl7';
</programlisting>
There is a rule <literal>log_shoelace</literal> that is <literal>ON UPDATE</literal> with the rule
qualification expression:
<programlisting>
NEW.sl_avail &lt;&gt; OLD.sl_avail
</programlisting>
and the action:
<programlisting>
INSERT INTO shoelace_log VALUES (
new.sl_name, new.sl_avail,
current_user, current_timestamp )
FROM shoelace_data new, shoelace_data old;
</programlisting>
(This looks a little strange since you cannot normally write
<literal>INSERT ... VALUES ... FROM</literal>. The <literal>FROM</literal>
clause here is just to indicate that there are range-table entries
in the query tree for <literal>new</literal> and <literal>old</literal>.
These are needed so that they can be referenced by variables in
the <command>INSERT</command> command's query tree.)
</para>
<para>
The rule is a qualified <literal>ALSO</literal> rule, so the rule system
has to return two query trees: the modified rule action and the original
query tree. In step 1, the range table of the original query is
incorporated into the rule's action query tree. This results in:
<programlisting>
INSERT INTO shoelace_log VALUES (
new.sl_name, new.sl_avail,
current_user, current_timestamp )
FROM shoelace_data new, shoelace_data old,
<emphasis>shoelace_data shoelace_data</emphasis>;
</programlisting>
In step 2, the rule qualification is added to it, so the result set
is restricted to rows where <literal>sl_avail</literal> changes:
<programlisting>
INSERT INTO shoelace_log VALUES (
new.sl_name, new.sl_avail,
current_user, current_timestamp )
FROM shoelace_data new, shoelace_data old,
shoelace_data shoelace_data
<emphasis>WHERE new.sl_avail &lt;&gt; old.sl_avail</emphasis>;
</programlisting>
(This looks even stranger, since <literal>INSERT ... VALUES</literal> doesn't have
a <literal>WHERE</literal> clause either, but the planner and executor will have no
difficulty with it. They need to support this same functionality
anyway for <literal>INSERT ... SELECT</literal>.)
</para>
<para>
In step 3, the original query tree's qualification is added,
restricting the result set further to only the rows that would have been touched
by the original query:
<programlisting>
INSERT INTO shoelace_log VALUES (
new.sl_name, new.sl_avail,
current_user, current_timestamp )
FROM shoelace_data new, shoelace_data old,
shoelace_data shoelace_data
WHERE new.sl_avail &lt;&gt; old.sl_avail
<emphasis>AND shoelace_data.sl_name = 'sl7'</emphasis>;
</programlisting>
</para>
<para>
Step 4 replaces references to <literal>NEW</literal> by the target list entries from the
original query tree or by the matching variable references
from the result relation:
<programlisting>
INSERT INTO shoelace_log VALUES (
<emphasis>shoelace_data.sl_name</emphasis>, <emphasis>6</emphasis>,
current_user, current_timestamp )
FROM shoelace_data new, shoelace_data old,
shoelace_data shoelace_data
WHERE <emphasis>6</emphasis> &lt;&gt; old.sl_avail
AND shoelace_data.sl_name = 'sl7';
</programlisting>
</para>
<para>
Step 5 changes <literal>OLD</literal> references into result relation references:
<programlisting>
INSERT INTO shoelace_log VALUES (
shoelace_data.sl_name, 6,
current_user, current_timestamp )
FROM shoelace_data new, shoelace_data old,
shoelace_data shoelace_data
WHERE 6 &lt;&gt; <emphasis>shoelace_data.sl_avail</emphasis>
AND shoelace_data.sl_name = 'sl7';
</programlisting>
</para>
<para>
That's it. Since the rule is <literal>ALSO</literal>, we also output the
original query tree. In short, the output from the rule system
is a list of two query trees that correspond to these statements:
<programlisting>
INSERT INTO shoelace_log VALUES (
shoelace_data.sl_name, 6,
current_user, current_timestamp )
FROM shoelace_data
WHERE 6 &lt;&gt; shoelace_data.sl_avail
AND shoelace_data.sl_name = 'sl7';
UPDATE shoelace_data SET sl_avail = 6
WHERE sl_name = 'sl7';
</programlisting>
These are executed in this order, and that is exactly what
the rule was meant to do.
</para>
<para>
The substitutions and the added qualifications
ensure that, if the original query would be, say:
<programlisting>
UPDATE shoelace_data SET sl_color = 'green'
WHERE sl_name = 'sl7';
</programlisting>
no log entry would get written. In that case, the original query
tree does not contain a target list entry for
<literal>sl_avail</literal>, so <literal>NEW.sl_avail</literal> will get
replaced by <literal>shoelace_data.sl_avail</literal>. Thus, the extra
command generated by the rule is:
<programlisting>
INSERT INTO shoelace_log VALUES (
shoelace_data.sl_name, <emphasis>shoelace_data.sl_avail</emphasis>,
current_user, current_timestamp )
FROM shoelace_data
WHERE <emphasis>shoelace_data.sl_avail</emphasis> &lt;&gt; shoelace_data.sl_avail
AND shoelace_data.sl_name = 'sl7';
</programlisting>
and that qualification will never be true.
</para>
<para>
It will also work if the original query modifies multiple rows. So
if someone issued the command:
<programlisting>
UPDATE shoelace_data SET sl_avail = 0
WHERE sl_color = 'black';
</programlisting>
four rows in fact get updated (<literal>sl1</literal>, <literal>sl2</literal>, <literal>sl3</literal>, and <literal>sl4</literal>).
But <literal>sl3</literal> already has <literal>sl_avail = 0</literal>. In this case, the original
query trees qualification is different and that results
in the extra query tree:
<programlisting>
INSERT INTO shoelace_log
SELECT shoelace_data.sl_name, 0,
current_user, current_timestamp
FROM shoelace_data
WHERE 0 &lt;&gt; shoelace_data.sl_avail
AND <emphasis>shoelace_data.sl_color = 'black'</emphasis>;
</programlisting>
being generated by the rule. This query tree will surely insert
three new log entries. And that's absolutely correct.
</para>
<para>
Here we can see why it is important that the original query tree
is executed last. If the <command>UPDATE</command> had been
executed first, all the rows would have already been set to zero, so the
logging <command>INSERT</command> would not find any row where
<literal>0 &lt;&gt; shoelace_data.sl_avail</literal>.
</para>
</sect3>
</sect2>
<sect2 id="rules-update-views">
<title>Cooperation with Views</title>
<indexterm zone="rules-update-views"><primary>view</primary><secondary>updating</secondary></indexterm>
<para>
A simple way to protect view relations from the mentioned
possibility that someone can try to run <command>INSERT</command>,
<command>UPDATE</command>, or <command>DELETE</command> on them is
to let those query trees get thrown away. So we could create the rules:
<programlisting>
CREATE RULE shoe_ins_protect AS ON INSERT TO shoe
DO INSTEAD NOTHING;
CREATE RULE shoe_upd_protect AS ON UPDATE TO shoe
DO INSTEAD NOTHING;
CREATE RULE shoe_del_protect AS ON DELETE TO shoe
DO INSTEAD NOTHING;
</programlisting>
If someone now tries to do any of these operations on the view
relation <literal>shoe</literal>, the rule system will
apply these rules. Since the rules have
no actions and are <literal>INSTEAD</literal>, the resulting list of
query trees will be empty and the whole query will become
nothing because there is nothing left to be optimized or
executed after the rule system is done with it.
</para>
<para>
A more sophisticated way to use the rule system is to
create rules that rewrite the query tree into one that
does the right operation on the real tables. To do that
on the <literal>shoelace</literal> view, we create
the following rules:
<programlisting>
CREATE RULE shoelace_ins AS ON INSERT TO shoelace
DO INSTEAD
INSERT INTO shoelace_data VALUES (
NEW.sl_name,
NEW.sl_avail,
NEW.sl_color,
NEW.sl_len,
NEW.sl_unit
);
CREATE RULE shoelace_upd AS ON UPDATE TO shoelace
DO INSTEAD
UPDATE shoelace_data
SET sl_name = NEW.sl_name,
sl_avail = NEW.sl_avail,
sl_color = NEW.sl_color,
sl_len = NEW.sl_len,
sl_unit = NEW.sl_unit
WHERE sl_name = OLD.sl_name;
CREATE RULE shoelace_del AS ON DELETE TO shoelace
DO INSTEAD
DELETE FROM shoelace_data
WHERE sl_name = OLD.sl_name;
</programlisting>
</para>
<para>
If you want to support <literal>RETURNING</literal> queries on the view,
you need to make the rules include <literal>RETURNING</literal> clauses that
compute the view rows. This is usually pretty trivial for views on a
single table, but it's a bit tedious for join views such as
<literal>shoelace</literal>. An example for the insert case is:
<programlisting>
CREATE RULE shoelace_ins AS ON INSERT TO shoelace
DO INSTEAD
INSERT INTO shoelace_data VALUES (
NEW.sl_name,
NEW.sl_avail,
NEW.sl_color,
NEW.sl_len,
NEW.sl_unit
)
RETURNING
shoelace_data.*,
(SELECT shoelace_data.sl_len * u.un_fact
FROM unit u WHERE shoelace_data.sl_unit = u.un_name);
</programlisting>
Note that this one rule supports both <command>INSERT</command> and
<command>INSERT RETURNING</command> queries on the view &mdash; the
<literal>RETURNING</literal> clause is simply ignored for <command>INSERT</command>.
</para>
<para>
Now assume that once in a while, a pack of shoelaces arrives at
the shop and a big parts list along with it. But you don't want
to manually update the <literal>shoelace</literal> view every
time. Instead we set up two little tables: one where you can
insert the items from the part list, and one with a special
trick. The creation commands for these are:
<programlisting>
CREATE TABLE shoelace_arrive (
arr_name text,
arr_quant integer
);
CREATE TABLE shoelace_ok (
ok_name text,
ok_quant integer
);
CREATE RULE shoelace_ok_ins AS ON INSERT TO shoelace_ok
DO INSTEAD
UPDATE shoelace
SET sl_avail = sl_avail + NEW.ok_quant
WHERE sl_name = NEW.ok_name;
</programlisting>
Now you can fill the table <literal>shoelace_arrive</literal> with
the data from the parts list:
<programlisting>
SELECT * FROM shoelace_arrive;
arr_name | arr_quant
----------+-----------
sl3 | 10
sl6 | 20
sl8 | 20
(3 rows)
</programlisting>
Take a quick look at the current data:
<programlisting>
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 | 6 | 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)
</programlisting>
Now move the arrived shoelaces in:
<programlisting>
INSERT INTO shoelace_ok SELECT * FROM shoelace_arrive;
</programlisting>
and check the results:
<programlisting>
SELECT * FROM shoelace ORDER BY sl_name;
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 | 6 | brown | 60 | cm | 60
sl4 | 8 | black | 40 | inch | 101.6
sl3 | 10 | black | 35 | inch | 88.9
sl8 | 21 | brown | 40 | inch | 101.6
sl5 | 4 | brown | 1 | m | 100
sl6 | 20 | brown | 0.9 | m | 90
(8 rows)
SELECT * FROM shoelace_log;
sl_name | sl_avail | log_who| log_when
---------+----------+--------+----------------------------------
sl7 | 6 | Al | Tue Oct 20 19:14:45 1998 MET DST
sl3 | 10 | Al | Tue Oct 20 19:25:16 1998 MET DST
sl6 | 20 | Al | Tue Oct 20 19:25:16 1998 MET DST
sl8 | 21 | Al | Tue Oct 20 19:25:16 1998 MET DST
(4 rows)
</programlisting>
</para>
<para>
It's a long way from the one <literal>INSERT ... SELECT</literal>
to these results. And the description of the query-tree
transformation will be the last in this chapter. First, there is
the parser's output:
<programlisting>
INSERT INTO shoelace_ok
SELECT shoelace_arrive.arr_name, shoelace_arrive.arr_quant
FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok;
</programlisting>
Now the first rule <literal>shoelace_ok_ins</literal> is applied and turns this
into:
<programlisting>
UPDATE shoelace
SET sl_avail = shoelace.sl_avail + shoelace_arrive.arr_quant
FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
shoelace_ok old, shoelace_ok new,
shoelace shoelace
WHERE shoelace.sl_name = shoelace_arrive.arr_name;
</programlisting>
and throws away the original <command>INSERT</command> on
<literal>shoelace_ok</literal>. This rewritten query is passed to
the rule system again, and the second applied rule
<literal>shoelace_upd</literal> produces:
<programlisting>
UPDATE shoelace_data
SET sl_name = shoelace.sl_name,
sl_avail = shoelace.sl_avail + shoelace_arrive.arr_quant,
sl_color = shoelace.sl_color,
sl_len = shoelace.sl_len,
sl_unit = shoelace.sl_unit
FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
shoelace_ok old, shoelace_ok new,
shoelace shoelace, shoelace old,
shoelace new, shoelace_data shoelace_data
WHERE shoelace.sl_name = shoelace_arrive.arr_name
AND shoelace_data.sl_name = shoelace.sl_name;
</programlisting>
Again it's an <literal>INSTEAD</literal> rule and the previous query tree is trashed.
Note that this query still uses the view <literal>shoelace</literal>.
But the rule system isn't finished with this step, so it continues
and applies the <literal>_RETURN</literal> rule on it, and we get:
<programlisting>
UPDATE shoelace_data
SET sl_name = s.sl_name,
sl_avail = s.sl_avail + shoelace_arrive.arr_quant,
sl_color = s.sl_color,
sl_len = s.sl_len,
sl_unit = s.sl_unit
FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
shoelace_ok old, shoelace_ok new,
shoelace shoelace, shoelace old,
shoelace new, shoelace_data shoelace_data,
shoelace old, shoelace new,
shoelace_data s, unit u
WHERE s.sl_name = shoelace_arrive.arr_name
AND shoelace_data.sl_name = s.sl_name;
</programlisting>
Finally, the rule <literal>log_shoelace</literal> gets applied,
producing the extra query tree:
<programlisting>
INSERT INTO shoelace_log
SELECT s.sl_name,
s.sl_avail + shoelace_arrive.arr_quant,
current_user,
current_timestamp
FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok,
shoelace_ok old, shoelace_ok new,
shoelace shoelace, shoelace old,
shoelace new, shoelace_data shoelace_data,
shoelace old, shoelace new,
shoelace_data s, unit u,
shoelace_data old, shoelace_data new
shoelace_log shoelace_log
WHERE s.sl_name = shoelace_arrive.arr_name
AND shoelace_data.sl_name = s.sl_name
AND (s.sl_avail + shoelace_arrive.arr_quant) &lt;&gt; s.sl_avail;
</programlisting>
After that the rule system runs out of rules and returns the
generated query trees.
</para>
<para>
So we end up with two final query trees that are equivalent to the
<acronym>SQL</acronym> statements:
<programlisting>
INSERT INTO shoelace_log
SELECT s.sl_name,
s.sl_avail + shoelace_arrive.arr_quant,
current_user,
current_timestamp
FROM shoelace_arrive shoelace_arrive, shoelace_data shoelace_data,
shoelace_data s
WHERE s.sl_name = shoelace_arrive.arr_name
AND shoelace_data.sl_name = s.sl_name
AND s.sl_avail + shoelace_arrive.arr_quant &lt;&gt; s.sl_avail;
UPDATE shoelace_data
SET sl_avail = shoelace_data.sl_avail + shoelace_arrive.arr_quant
FROM shoelace_arrive shoelace_arrive,
shoelace_data shoelace_data,
shoelace_data s
WHERE s.sl_name = shoelace_arrive.sl_name
AND shoelace_data.sl_name = s.sl_name;
</programlisting>
The result is that data coming from one relation inserted into another,
changed into updates on a third, changed into updating
a fourth plus logging that final update in a fifth
gets reduced into two queries.
</para>
<para>
There is a little detail that's a bit ugly. Looking at the two
queries, it turns out that the <literal>shoelace_data</literal>
relation appears twice in the range table where it could
definitely be reduced to one. The planner does not handle it and
so the execution plan for the rule systems output of the
<command>INSERT</command> will be
<literallayout class="monospaced">
Nested Loop
-&gt; Merge Join
-&gt; Seq Scan
-&gt; Sort
-&gt; Seq Scan on s
-&gt; Seq Scan
-&gt; Sort
-&gt; Seq Scan on shoelace_arrive
-&gt; Seq Scan on shoelace_data
</literallayout>
while omitting the extra range table entry would result in a
<literallayout class="monospaced">
Merge Join
-&gt; Seq Scan
-&gt; Sort
-&gt; Seq Scan on s
-&gt; Seq Scan
-&gt; Sort
-&gt; Seq Scan on shoelace_arrive
</literallayout>
which produces exactly the same entries in the log table. Thus,
the rule system caused one extra scan on the table
<literal>shoelace_data</literal> that is absolutely not
necessary. And the same redundant scan is done once more in the
<command>UPDATE</command>. But it was a really hard job to make
that all possible at all.
</para>
<para>
Now we make a final demonstration of the
<productname>PostgreSQL</productname> rule system and its power.
Say you add some shoelaces with extraordinary colors to your
database:
<programlisting>
INSERT INTO shoelace VALUES ('sl9', 0, 'pink', 35.0, 'inch', 0.0);
INSERT INTO shoelace VALUES ('sl10', 1000, 'magenta', 40.0, 'inch', 0.0);
</programlisting>
We would like to make a view to check which
<literal>shoelace</literal> entries do not fit any shoe in color.
The view for this is:
<programlisting>
CREATE VIEW shoelace_mismatch AS
SELECT * FROM shoelace WHERE NOT EXISTS
(SELECT shoename FROM shoe WHERE slcolor = sl_color);
</programlisting>
Its output is:
<programlisting>
SELECT * FROM shoelace_mismatch;
sl_name | sl_avail | sl_color | sl_len | sl_unit | sl_len_cm
---------+----------+----------+--------+---------+-----------
sl9 | 0 | pink | 35 | inch | 88.9
sl10 | 1000 | magenta | 40 | inch | 101.6
</programlisting>
</para>
<para>
Now we want to set it up so that mismatching shoelaces that are
not in stock are deleted from the database.
To make it a little harder for <productname>PostgreSQL</productname>,
we don't delete it directly. Instead we create one more view:
<programlisting>
CREATE VIEW shoelace_can_delete AS
SELECT * FROM shoelace_mismatch WHERE sl_avail = 0;
</programlisting>
and do it this way:
<programlisting>
DELETE FROM shoelace WHERE EXISTS
(SELECT * FROM shoelace_can_delete
WHERE sl_name = shoelace.sl_name);
</programlisting>
<foreignphrase>Voil&agrave;</foreignphrase>:
<programlisting>
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 | 6 | brown | 60 | cm | 60
sl4 | 8 | black | 40 | inch | 101.6
sl3 | 10 | black | 35 | inch | 88.9
sl8 | 21 | brown | 40 | inch | 101.6
sl10 | 1000 | magenta | 40 | inch | 101.6
sl5 | 4 | brown | 1 | m | 100
sl6 | 20 | brown | 0.9 | m | 90
(9 rows)
</programlisting>
</para>
<para>
A <command>DELETE</command> on a view, with a subquery qualification that
in total uses 4 nesting/joined views, where one of them
itself has a subquery qualification containing a view
and where calculated view columns are used,
gets rewritten into
one single query tree that deletes the requested data
from a real table.
</para>
<para>
There are probably only a few situations out in the real world
where such a construct is necessary. But it makes you feel
comfortable that it works.
</para>
</sect2>
</sect1>
<sect1 id="rules-privileges">
<title>Rules and Privileges</title>
<indexterm zone="rules-privileges">
<primary>privilege</primary>
<secondary sortas="Regeln">with rules</secondary>
</indexterm>
<indexterm zone="rules-privileges">
<primary>privilege</primary>
<secondary sortas="Sichten">with views</secondary>
</indexterm>
<para>
Due to rewriting of queries by the <productname>PostgreSQL</productname>
rule system, other tables/views than those used in the original
query get accessed. When update rules are used, this can include write access
to tables.
</para>
<para>
Rewrite rules don't have a separate owner. The owner of
a relation (table or view) is automatically the owner of the
rewrite rules that are defined for it.
The <productname>PostgreSQL</productname> rule system changes the
behavior of the default access control system. Relations that
are used due to rules get checked against the
privileges of the rule owner, not the user invoking the rule.
This means that users only need the required privileges
for the tables/views that are explicitly named in their queries.
</para>
<para>
For example: A user has a list of phone numbers where some of
them are private, the others are of interest for the assistant of the office.
The user can construct the following:
<programlisting>
CREATE TABLE phone_data (person text, phone text, private boolean);
CREATE VIEW phone_number AS
SELECT person, CASE WHEN NOT private THEN phone END AS phone
FROM phone_data;
GRANT SELECT ON phone_number TO assistant;
</programlisting>
Nobody except that user (and the database superusers) can access the
<literal>phone_data</literal> table. But because of the <command>GRANT</command>,
the assistant can run a <command>SELECT</command> on the
<literal>phone_number</literal> view. The rule system will rewrite the
<command>SELECT</command> from <literal>phone_number</literal> into a
<command>SELECT</command> from <literal>phone_data</literal>.
Since the user is the owner of
<literal>phone_number</literal> and therefore the owner of the rule, the
read access to <literal>phone_data</literal> is now checked against the user's
privileges and the query is permitted. The check for accessing
<literal>phone_number</literal> is also performed, but this is done
against the invoking user, so nobody but the user and the
assistant can use it.
</para>
<para>
The privileges are checked rule by rule. So the assistant is for now the
only one who can see the public phone numbers. But the assistant can set up
another view and grant access to that to the public. Then, anyone
can see the <literal>phone_number</literal> data through the assistant's view.
What the assistant cannot do is to create a view that directly
accesses <literal>phone_data</literal>. (Actually the assistant can, but it will not work since
every access will be denied during the permission checks.)
And as soon as the user notices that the assistant opened
their <literal>phone_number</literal> view, the user can revoke the assistant's access. Immediately, any
access to the assistant's view would fail.
</para>
<para>
One might think that this rule-by-rule checking is a security
hole, but in fact it isn't. But if it did not work this way, the assistant
could set up a table with the same columns as <literal>phone_number</literal> and
copy the data to there once per day. Then it's the assistant's own data and
the assistant can grant access to everyone they want. A
<command>GRANT</command> command means, <quote>I trust you</quote>.
If someone you trust does the thing above, it's time to
think it over and then use <command>REVOKE</command>.
</para>
<para>
Note that while views can be used to hide the contents of certain
columns using the technique shown above, they cannot be used to reliably
conceal the data in unseen rows unless the
<literal>security_barrier</literal> flag has been set. For example,
the following view is insecure:
<programlisting>
CREATE VIEW phone_number AS
SELECT person, phone FROM phone_data WHERE phone NOT LIKE '412%';
</programlisting>
This view might seem secure, since the rule system will rewrite any
<command>SELECT</command> from <literal>phone_number</literal> into a
<command>SELECT</command> from <literal>phone_data</literal> and add the
qualification that only entries where <literal>phone</literal> does not begin
with 412 are wanted. But if the user can create their own functions,
it is not difficult to convince the planner to execute the user-defined
function prior to the <function>NOT LIKE</function> expression.
For example:
<programlisting>
CREATE FUNCTION tricky(text, text) RETURNS bool AS $$
BEGIN
RAISE NOTICE '% =&gt; %', $1, $2;
RETURN true;
END
$$ LANGUAGE plpgsql COST 0.0000000000000000000001;
SELECT * FROM phone_number WHERE tricky(person, phone);
</programlisting>
Every person and phone number in the <literal>phone_data</literal> table will be
printed as a <literal>NOTICE</literal>, because the planner will choose to
execute the inexpensive <function>tricky</function> function before the
more expensive <function>NOT LIKE</function>. Even if the user is
prevented from defining new functions, built-in functions can be used in
similar attacks. (For example, most casting functions include their
input values in the error messages they produce.)
</para>
<para>
Similar considerations apply to update rules. In the examples of
the previous section, the owner of the tables in the example
database could grant the privileges <literal>SELECT</literal>,
<literal>INSERT</literal>, <literal>UPDATE</literal>, and <literal>DELETE</literal> on
the <literal>shoelace</literal> view to someone else, but only
<literal>SELECT</literal> on <literal>shoelace_log</literal>. The rule action to
write log entries will still be executed successfully, and that
other user could see the log entries. But they could not create fake
entries, nor could they manipulate or remove existing ones. In this
case, there is no possibility of subverting the rules by convincing
the planner to alter the order of operations, because the only rule
which references <literal>shoelace_log</literal> is an unqualified
<literal>INSERT</literal>. This might not be true in more complex scenarios.
</para>
<para>
When it is necessary for a view to provide row level security, the
<literal>security_barrier</literal> attribute should be applied to
the view. This prevents maliciously-chosen functions and operators from
being passed values from rows until after the view has done its work. For
example, if the view shown above had been created like this, it would
be secure:
<programlisting>
CREATE VIEW phone_number WITH (security_barrier) AS
SELECT person, phone FROM phone_data WHERE phone NOT LIKE '412%';
</programlisting>
Views created with the <literal>security_barrier</literal> may perform
far worse than views created without this option. In general, there is
no way to avoid this: the fastest possible plan must be rejected
if it may compromise security. For this reason, this option is not
enabled by default.
</para>
<para>
The query planner has more flexibility when dealing with functions that
have no side effects. Such functions are referred to as <literal>LEAKPROOF</literal>, and
include many simple, commonly used operators, such as many equality
operators. The query planner can safely allow such functions to be evaluated
at any point in the query execution process, since invoking them on rows
invisible to the user will not leak any information about the unseen rows.
Further, functions which do not take arguments or which are not passed any
arguments from the security barrier view do not have to be marked as
<literal>LEAKPROOF</literal> to be pushed down, as they never receive data
from the view. In contrast, a function that might throw an error depending
on the values received as arguments (such as one that throws an error in the
event of overflow or division by zero) is not leak-proof, and could provide
significant information about the unseen rows if applied before the security
view's row filters.
</para>
<para>
It is important to understand that even a view created with the
<literal>security_barrier</literal> option is intended to be secure only
in the limited sense that the contents of the invisible tuples will not be
passed to possibly-insecure functions. The user may well have other means
of making inferences about the unseen data; for example, they can see the
query plan using <command>EXPLAIN</command>, or measure the run time of
queries against the view. A malicious attacker might be able to infer
something about the amount of unseen data, or even gain some information
about the data distribution or most common values (since these things may
affect the run time of the plan; or even, since they are also reflected in
the optimizer statistics, the choice of plan). If these types of "covert
channel" attacks are of concern, it is probably unwise to grant any access
to the data at all.
</para>
</sect1>
<sect1 id="rules-status">
<title>Rules and Command Status</title>
<para>
The <productname>PostgreSQL</productname> server returns a command
status string, such as <literal>INSERT 149592 1</literal>, for each
command it receives. This is simple enough when there are no rules
involved, but what happens when the query is rewritten by rules?
</para>
<para>
Rules affect the command status as follows:
<itemizedlist>
<listitem>
<para>
If there is no unconditional <literal>INSTEAD</literal> rule for the query, then
the originally given query will be executed, and its command
status will be returned as usual. (But note that if there were
any conditional <literal>INSTEAD</literal> rules, the negation of their qualifications
will have been added to the original query. This might reduce the
number of rows it processes, and if so the reported status will
be affected.)
</para>
</listitem>
<listitem>
<para>
If there is any unconditional <literal>INSTEAD</literal> rule for the query, then
the original query will not be executed at all. In this case,
the server will return the command status for the last query
that was inserted by an <literal>INSTEAD</literal> rule (conditional or
unconditional) and is of the same command type
(<command>INSERT</command>, <command>UPDATE</command>, or
<command>DELETE</command>) as the original query. If no query
meeting those requirements is added by any rule, then the
returned command status shows the original query type and
zeroes for the row-count and OID fields.
</para>
</listitem>
</itemizedlist>
</para>
<para>
The programmer can ensure that any desired <literal>INSTEAD</literal> rule is the one
that sets the command status in the second case, by giving it the
alphabetically last rule name among the active rules, so that it
gets applied last.
</para>
</sect1>
<sect1 id="rules-triggers">
<title>Rules Versus Triggers</title>
<indexterm zone="rules-triggers">
<primary>rule</primary>
<secondary sortas="Trigger">compared with triggers</secondary>
</indexterm>
<indexterm zone="rules-triggers">
<primary>trigger</primary>
<secondary sortas="Regeln">compared with rules</secondary>
</indexterm>
<para>
Many things that can be done using triggers can also be
implemented using the <productname>PostgreSQL</productname>
rule system. One of the things that cannot be implemented by
rules are some kinds of constraints, especially foreign keys. It is possible
to place a qualified rule that rewrites a command to <literal>NOTHING</literal>
if the value of a column does not appear in another table.
But then the data is silently thrown away and that's
not a good idea. If checks for valid values are required,
and in the case of an invalid value an error message should
be generated, it must be done by a trigger.
</para>
<para>
In this chapter, we focused on using rules to update views. All of
the update rule examples in this chapter can also be implemented
using <literal>INSTEAD OF</literal> triggers on the views. Writing such
triggers is often easier than writing rules, particularly if complex
logic is required to perform the update.
</para>
<para>
For the things that can be implemented by both, which is best
depends on the usage of the database.
A trigger is fired once for each affected row. A rule modifies
the query or generates an additional query. So if many
rows are affected in one statement, a rule issuing one extra
command is likely to be faster than a trigger that is
called for every single row and must re-determine what to do
many times. However, the trigger approach is conceptually far
simpler than the rule approach, and is easier for novices to get right.
</para>
<para>
Here we show an example of how the choice of rules versus triggers
plays out in one situation. There are two tables:
<programlisting>
CREATE TABLE computer (
hostname text, -- indexed
manufacturer text -- indexed
);
CREATE TABLE software (
software text, -- indexed
hostname text -- indexed
);
</programlisting>
Both tables have many thousands of rows and the indexes on
<structfield>hostname</structfield> are unique. The rule or trigger should
implement a constraint that deletes rows from <literal>software</literal>
that reference a deleted computer. The trigger would use this command:
<programlisting>
DELETE FROM software WHERE hostname = $1;
</programlisting>
Since the trigger is called for each individual row deleted from
<literal>computer</literal>, it can prepare and save the plan for this
command and pass the <structfield>hostname</structfield> value in the
parameter. The rule would be written as:
<programlisting>
CREATE RULE computer_del AS ON DELETE TO computer
DO DELETE FROM software WHERE hostname = OLD.hostname;
</programlisting>
</para>
<para>
Now we look at different types of deletes. In the case of a:
<programlisting>
DELETE FROM computer WHERE hostname = 'mypc.local.net';
</programlisting>
the table <literal>computer</literal> is scanned by index (fast), and the
command issued by the trigger would also use an index scan (also fast).
The extra command from the rule would be:
<programlisting>
DELETE FROM software WHERE computer.hostname = 'mypc.local.net'
AND software.hostname = computer.hostname;
</programlisting>
Since there are appropriate indexes set up, the planner
will create a plan of
<literallayout class="monospaced">
Nestloop
-&gt; Index Scan using comp_hostidx on computer
-&gt; Index Scan using soft_hostidx on software
</literallayout>
So there would be not that much difference in speed between
the trigger and the rule implementation.
</para>
<para>
With the next delete we want to get rid of all the 2000 computers
where the <structfield>hostname</structfield> starts with
<literal>old</literal>. There are two possible commands to do that. One
is:
<programlisting>
DELETE FROM computer WHERE hostname &gt;= 'old'
AND hostname &lt; 'ole'
</programlisting>
The command added by the rule will be:
<programlisting>
DELETE FROM software WHERE computer.hostname &gt;= 'old' AND computer.hostname &lt; 'ole'
AND software.hostname = computer.hostname;
</programlisting>
with the plan
<literallayout class="monospaced">
Hash Join
-&gt; Seq Scan on software
-&gt; Hash
-&gt; Index Scan using comp_hostidx on computer
</literallayout>
The other possible command is:
<programlisting>
DELETE FROM computer WHERE hostname ~ '^old';
</programlisting>
which results in the following executing plan for the command
added by the rule:
<literallayout class="monospaced">
Nestloop
-&gt; Index Scan using comp_hostidx on computer
-&gt; Index Scan using soft_hostidx on software
</literallayout>
This shows, that the planner does not realize that the
qualification for <structfield>hostname</structfield> in
<literal>computer</literal> could also be used for an index scan on
<literal>software</literal> when there are multiple qualification
expressions combined with <literal>AND</literal>, which is what it does
in the regular-expression version of the command. The trigger will
get invoked once for each of the 2000 old computers that have to be
deleted, and that will result in one index scan over
<literal>computer</literal> and 2000 index scans over
<literal>software</literal>. The rule implementation will do it with two
commands that use indexes. And it depends on the overall size of
the table <literal>software</literal> whether the rule will still be faster in the
sequential scan situation. 2000 command executions from the trigger over the SPI
manager take some time, even if all the index blocks will soon be in the cache.
</para>
<para>
The last command we look at is:
<programlisting>
DELETE FROM computer WHERE manufacturer = 'bim';
</programlisting>
Again this could result in many rows to be deleted from
<literal>computer</literal>. So the trigger will again run many commands
through the executor. The command generated by the rule will be:
<programlisting>
DELETE FROM software WHERE computer.manufacturer = 'bim'
AND software.hostname = computer.hostname;
</programlisting>
The plan for that command will again be the nested loop over two
index scans, only using a different index on <literal>computer</literal>:
<programlisting>
Nestloop
-&gt; Index Scan using comp_manufidx on computer
-&gt; Index Scan using soft_hostidx on software
</programlisting>
In any of these cases, the extra commands from the rule system
will be more or less independent from the number of affected rows
in a command.
</para>
<para>
The summary is, rules will only be significantly slower than
triggers if their actions result in large and badly qualified
joins, a situation where the planner fails.
</para>
</sect1>
</chapter>
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