<!-- $Header: /cvsroot/pgsql/doc/src/sgml/rules.sgml,v 1.14 2001/07/09 23:50:32 tgl Exp $ -->

<Chapter Id="rules">
<Title>The <ProductName>Postgres</ProductName> Rule System</Title>

 <indexterm zone="rules">
  <primary>rules</primary>
 </indexterm>

   <note>
    <title>Author</title>
    <para>
     Written by Jan Wieck.  Updates for 7.1 by Tom Lane.
    </para>
   </note>

<Para>
     Production rule systems are conceptually simple, but
     there are many subtle points involved in actually using
     them. Some of these points and
     the theoretical foundations of the <ProductName>Postgres</ProductName>
     rule system can be found in
[<XRef LinkEnd="STON90b" EndTerm="STON90b">].
</Para>

<Para>
     Some other database systems define active database rules. These
     are usually stored procedures and triggers and are implemented
     in <ProductName>Postgres</ProductName> as functions and triggers.
</Para>

<Para>
     The query rewrite rule system (the "rule system" from now on) 
     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
     power  of  this  rule system is discussed in 
[<XRef LinkEnd="ONG90" EndTerm="ONG90">]
 as well as
[<XRef LinkEnd="STON90b" EndTerm="STON90b">].
</para>
<Sect1 id="querytree">
<Title>What is a Querytree?</Title>

<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 query parser and the planner.
    It takes the output of the parser, one querytree, and the rewrite
    rules from the <FileName>pg_rewrite</FileName> catalog, which are
    querytrees too with some extra information, and creates zero or many
    querytrees 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 querytree? It is an internal representation of an
    <Acronym>SQL</Acronym> statement where the single parts that built
    it are stored separately. These querytrees are visible when starting
    the <ProductName>Postgres</ProductName> backend with debuglevel 4
    and typing queries into the interactive backend interface. The rule
    actions in the <FileName>pg_rewrite</FileName> system catalog are
    also stored as querytrees. They are not formatted like the debug
    output, but they contain exactly the same information.
</Para>

<Para>
    Reading a querytree requires some experience and it was a hard
    time when I started to work on the rule system. I can remember
    that I was standing at the coffee machine and I saw the cup
    in a targetlist, water and coffee powder in a rangetable and all
    the buttons in a qualification expression. Since 
    <Acronym>SQL</Acronym> representations of querytrees are
    sufficient to understand the rule system, this document will
    not teach how to read them. It might help to learn
    it and the naming conventions are required in the later following
    descriptions.
</Para>

<Sect2>
<Title>The Parts of a Querytree</Title>

<Para>
    When reading the <Acronym>SQL</Acronym> representations of the 
    querytrees in this document it is necessary to be able to identify
    the parts the statement is broken into when it is in the querytree
    structure. The parts of a querytree are
</Para>

<Para>
<VariableList>
    <VarListEntry>
    <Term>
        the commandtype
    </Term>
    <ListItem>
    <Para>
        This is a simple value telling which command
	(SELECT, INSERT, UPDATE, DELETE) produced the parsetree.
    </Para>
    </ListItem>
    </VarListEntry>

    <VarListEntry>
    <Term>
        the rangetable
    </Term>
    <ListItem>
    <Para>
        The rangetable is a list of relations that are used in the query.
	In a SELECT statement these are the relations given after
	the FROM keyword.
    </Para>

    <Para>
        Every rangetable entry identifies a table or view and tells
	by which name it is called in the other parts of the query.
	In the querytree the rangetable entries are referenced by
	index 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 rangetables of rules
	have been merged in. The examples in this document will not have
	this situation.
    </Para>
    </ListItem>
    </VarListEntry>

    <VarListEntry>
    <Term>
        the resultrelation
    </Term>
    <ListItem>
    <Para>
        This is an index into the rangetable that identifies the
	relation where the results of the query go.
    </Para>

    <Para>
	SELECT queries
	normally don't have a result relation. The special case
	of a SELECT INTO is mostly identical to a CREATE TABLE,
	INSERT ... SELECT sequence and is not discussed separately
	here.
    </Para>

    <Para>
        On INSERT, UPDATE and DELETE queries the resultrelation
	is the table (or view!) where the changes take effect.
    </Para>
    </ListItem>
    </VarListEntry>

    <VarListEntry>
    <Term>
        the targetlist
    </Term>
    <ListItem>
    <Para>
        The targetlist is a list of expressions that define the result
	of the query. In the case of a SELECT, the expressions are what
	builds the final output of the query. They are the expressions
	between the SELECT and the FROM keywords.  (* is just an
	abbreviation for all the attribute names of a relation.  It is
	expanded by the parser into the individual attributes, so the
	rule system never sees it.)
    </Para>

    <Para>
        DELETE queries don't need a targetlist because they don't
	produce any result. In fact the planner will add a special CTID
	entry to the empty targetlist. But this is after the rule
	system and will be discussed later. For the rule system the
	targetlist is empty.
    </Para>

    <Para>
        In INSERT queries the targetlist describes the new rows that
	should go into the resultrelation. It is the expressions in the VALUES
	clause or the ones from the SELECT clause in INSERT ... SELECT.
	Missing columns of the resultrelation will be filled in by the
	planner with a constant NULL expression.
    </Para>

    <Para>
        In UPDATE queries, the targetlist describes the new rows that should
	replace the old ones. In the rule system, it contains just the
	expressions from the SET attribute = expression part of the query.
	The planner will add missing columns by inserting expressions that
	copy the values from the old row into the new one. And it will add
	the special CTID entry just as for DELETE too.
    </Para>

    <Para>
        Every entry in the targetlist contains an expression that can
	be a constant value, a variable pointing to an attribute of one
	of the relations in the rangetable, 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 targetlist entries. The result value of this
	expression is a boolean that tells if the operation
	(INSERT, UPDATE, DELETE or SELECT) for the final result row should be
	executed or not. It is the WHERE 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 FROM clause.
	For a simple query like SELECT FROM a, b, c the join tree is just
	a list of the FROM items, because we are allowed to join them in
	any order.  But when JOIN expressions --- particularly outer joins
	--- are used, we have to join in the order shown by the JOINs.
	The join tree shows the structure of the JOIN expressions.  The
	restrictions associated with particular JOIN clauses (from ON or
	USING expressions) are stored as qualification expressions attached
	to those join tree nodes.  It turns out to be convenient to store
	the top-level WHERE expression as a qualification attached to the
	top-level join tree item, too.  So really the join tree represents
	both the FROM and WHERE clauses of a SELECT.
    </Para>
    </ListItem>
    </VarListEntry>

    <VarListEntry>
    <Term>
        the others
    </Term>
    <ListItem>
    <Para>
        The other parts of the querytree like the ORDER BY 
	clause aren't of interest here. The rule system
	substitutes 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>
</Sect2>
</Sect1>

<Sect1 id="rules-views">
<Title>Views and the Rule System</Title>

<Sect2>
<Title>Implementation of Views in <ProductName>Postgres</ProductName></Title>

<Para>
    Views in <ProductName>Postgres</ProductName> are implemented
    using the rule system. In fact there is absolutely no difference
    between a

<ProgramListing>
    CREATE VIEW myview AS SELECT * FROM mytab;
</ProgramListing>
    
    compared against the two commands

<ProgramListing>
    CREATE TABLE myview (<Replaceable>same attribute list as for mytab</Replaceable>);
    CREATE RULE "_RETmyview" AS ON SELECT TO myview DO INSTEAD
        SELECT * FROM mytab;
</ProgramListing>
    
    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 <ProductName>Postgres</ProductName>
    system catalogs is exactly the same as it is for a table. So for the
    query parser, there is absolutely no difference between
    a table and a view. They are the same thing - relations. That is the
    important one for now.
</Para>
</Sect2>

<Sect2>
<Title>How SELECT Rules Work</Title>

<Para>
    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 the others in that they modify the parsetree in
    place instead of creating a new one.
    So SELECT rules are described first.
</Para>

<Para>
    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 rules ON SELECT to real view rules.
</Para>

<Para>
    The examples for this document 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.
    It 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 database needed to play with the examples is named al_bundy.
    You'll see soon why this is the database name. And it needs the
    procedural language PL/pgSQL installed, because
    we need a little min() function returning the lower of 2
    integer values. We create that as

<ProgramListing>
    CREATE FUNCTION min(integer, integer) RETURNS integer AS
        'BEGIN
            IF $1 < $2 THEN
                RETURN $1;
            END IF;
            RETURN $2;
        END;'
    LANGUAGE 'plpgsql';
</ProgramListing>
</Para>

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

<ProgramListing>
    CREATE TABLE shoe_data (
        shoename   char(10),      -- primary key
        sh_avail   integer,       -- available # of pairs
        slcolor    char(10),      -- preferred shoelace color
        slminlen   float,         -- miminum shoelace length
        slmaxlen   float,         -- maximum shoelace length
        slunit     char(8)        -- length unit
    );

    CREATE TABLE shoelace_data (
        sl_name    char(10),      -- primary key
        sl_avail   integer,       -- available # of pairs
        sl_color   char(10),      -- shoelace color
        sl_len     float,         -- shoelace length
        sl_unit    char(8)        -- length unit
    );

    CREATE TABLE unit (
        un_name    char(8),       -- the primary key
        un_fact    float          -- factor to transform to cm
    );
</ProgramListing>

    I think most of us wear shoes and can realize that this is
    really useful data. Well there are shoes out in the world
    that don't require shoelaces, but this doesn't make Al's
    life easier and so we ignore it.
</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,
               min(rsh.sh_avail, rsl.sl_avail) AS total_avail
          FROM shoe rsh, shoelace rsl
         WHERE rsl.sl_color = rsh.slcolor
           AND rsl.sl_len_cm >= rsh.slminlen_cm
           AND rsl.sl_len_cm <= rsh.slmaxlen_cm;
</ProgramListing>

    The CREATE VIEW command for the <Filename>shoelace</Filename> 
    view (which is the simplest one we have)
    will create a relation shoelace and an entry 
    in <FileName>pg_rewrite</FileName>
    that tells that there is a rewrite rule that must be applied
    whenever the relation shoelace is referenced in a query's rangetable.
    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 rule's action has a query qualification.
</Para>

<Para>
    The rule's action is one querytree that is a copy of the
    SELECT statement in the view creation command.
    
    <Note>
    <Title>Note</Title>
    <Para>
    The two extra range
    table entries for NEW and OLD (named *NEW* and *CURRENT* for
    historical reasons in the printed querytree) you can see in
    the <Filename>pg_rewrite</Filename> entry aren't of interest
    for SELECT rules.
    </Para>
    </Note>

    Now we populate <Filename>unit</Filename>, <Filename>shoe_data</Filename>
    and <Filename>shoelace_data</Filename> and Al types the first 
    SELECT in his life:

<ProgramListing>
    al_bundy=> INSERT INTO unit VALUES ('cm', 1.0);
    al_bundy=> INSERT INTO unit VALUES ('m', 100.0);
    al_bundy=> INSERT INTO unit VALUES ('inch', 2.54);
    al_bundy=> 
    al_bundy=> INSERT INTO shoe_data VALUES 
    al_bundy->     ('sh1', 2, 'black', 70.0, 90.0, 'cm');
    al_bundy=> INSERT INTO shoe_data VALUES 
    al_bundy->     ('sh2', 0, 'black', 30.0, 40.0, 'inch');
    al_bundy=> INSERT INTO shoe_data VALUES 
    al_bundy->     ('sh3', 4, 'brown', 50.0, 65.0, 'cm');
    al_bundy=> INSERT INTO shoe_data VALUES 
    al_bundy->     ('sh4', 3, 'brown', 40.0, 50.0, 'inch');
    al_bundy=> 
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl1', 5, 'black', 80.0, 'cm');
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl2', 6, 'black', 100.0, 'cm');
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl3', 0, 'black', 35.0 , 'inch');
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl4', 8, 'black', 40.0 , 'inch');
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl5', 4, 'brown', 1.0 , 'm');
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl6', 0, 'brown', 0.9 , 'm');
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl7', 7, 'brown', 60 , 'cm');
    al_bundy=> INSERT INTO shoelace_data VALUES 
    al_bundy->     ('sl8', 1, 'brown', 40 , 'inch');
    al_bundy=> 
    al_bundy=> 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>

    It's the simplest SELECT Al can do on our views, so we take this
    to explain the basics of view rules.
    The 'SELECT * FROM shoelace' was interpreted by the parser and
    produced the parsetree

<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
    rangetable and checks if there are rules in <Filename>pg_rewrite</Filename>
    for any relation. When processing the rangetable entry for
    <Filename>shoelace</Filename> (the only one up to now) it finds the
    rule '_RETshoelace' with the parsetree

<ProgramListing>
    <FirstTerm>SELECT s.sl_name, s.sl_avail,
           s.sl_color, s.sl_len, s.sl_unit,
           float8mul(s.sl_len, u.un_fact) AS sl_len_cm
      FROM shoelace *OLD*, shoelace *NEW*,
           shoelace_data s, unit u
     WHERE bpchareq(s.sl_unit, u.un_name);</FirstTerm>
</ProgramListing>

    Note that the parser changed the calculation and qualification into
    calls to the appropriate functions. But
    in fact this changes nothing.
</Para>

<Para>
    To expand the view, the rewriter simply creates a subselect rangetable
    entry containing the rule's action parsetree, and substitutes this
    rangetable entry for the original one that referenced the view.  The 
    resulting rewritten parsetree is almost the same as if Al 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 sub-query's rangetable has two
     extra entries shoelace *OLD*, shoelace *NEW*.  These entries don't
     participate directly in the query, since they aren't referenced by
     the sub-query's join tree or targetlist.  The rewriter uses them
     to store the access permission check info that was originally present
     in the rangetable entry that referenced the view.  In this way, the
     executor will still check that the user has proper permissions 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 rangetable entries in the top query (in this example there
    are no more), and it will recursively check the rangetable entries in
    the added sub-query to see if any of them reference views.  (But it
    won't expand *OLD* or *NEW* --- otherwise we'd have infinite recursion!)
    In this example, there are no rewrite rules for shoelace_data or unit,
    so rewriting is complete and the above is the final result given to
    the planner.
</Para>

<Para>
    Now we face Al with the problem that the Blues Brothers appear
    in his shop and
    want to buy some new shoes, and as the Blues Brothers are,
    they want to wear the same shoes. And they want to wear them
    immediately, so they need shoelaces too.
</Para>

<Para>
    Al needs to know for which shoes currently in the store
    he has the matching shoelaces (color and size) and where the
    total number of exactly matching pairs is greater or equal to two.
    We teach him what to do and he asks his database:

<ProgramListing>
    al_bundy=> SELECT * FROM shoe_ready WHERE total_avail >= 2;
    shoename  |sh_avail|sl_name   |sl_avail|total_avail
    ----------+--------+----------+--------+-----------
    sh1       |       2|sl1       |       5|          2
    sh3       |       4|sl7       |       7|          4
    (2 rows)
</ProgramListing>

    Al is a shoe guru and so he knows that only shoes of type sh1
    would fit (shoelace sl7 is brown and shoes that need brown shoelaces
    aren't shoes the Blues Brothers would ever wear).
</Para>

<Para>
    The output of the parser this time is the parsetree

<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 int4ge(shoe_ready.total_avail, 2);
</ProgramListing>

    The first rule applied will be the one for the 
    <Filename>shoe_ready</Filename> view and it results in the
    parsetree

<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,
                   min(rsh.sh_avail, rsl.sl_avail) AS total_avail
              FROM shoe rsh, shoelace rsl
             WHERE rsl.sl_color = rsh.slcolor
               AND rsl.sl_len_cm >= rsh.slminlen_cm
               AND rsl.sl_len_cm <= rsh.slmaxlen_cm) shoe_ready
     WHERE int4ge(shoe_ready.total_avail, 2);
</ProgramListing>

    Similarly, the rules for <Filename>shoe</Filename> and
    <Filename>shoelace</Filename> are substituted into the rangetable of
    the sub-query, leading to a three-level final querytree:

<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,
                   min(rsh.sh_avail, rsl.sl_avail) AS total_avail
              FROM (SELECT sh.shoename,
                           sh.sh_avail,
                           sh.slcolor,
                           sh.slminlen,
                           sh.slminlen * un.un_fact AS slminlen_cm,
                           sh.slmaxlen,
                           sh.slmaxlen * un.un_fact AS slmaxlen_cm,
                           sh.slunit
                      FROM shoe_data sh, unit un
                     WHERE sh.slunit = un.un_name) rsh,
                   (SELECT s.sl_name,
                           s.sl_avail,
                           s.sl_color,
                           s.sl_len,
                           s.sl_unit,
                           s.sl_len * u.un_fact AS sl_len_cm
                      FROM shoelace_data s, unit u
                     WHERE s.sl_unit = u.un_name) rsl
             WHERE rsl.sl_color = rsh.slcolor
               AND rsl.sl_len_cm >= rsh.slminlen_cm
               AND rsl.sl_len_cm <= rsh.slmaxlen_cm) shoe_ready
     WHERE int4ge(shoe_ready.total_avail, 2);
</ProgramListing>

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

    <Note>
    <Title>Note</Title>
    <Para>
    There is currently no recursion stopping mechanism for view
    rules in the rule system (only for the other kinds of rules).
    This doesn't hurt much, because the only way to push this
    into an endless loop (blowing up the
    backend until it reaches the memory limit)
    is to create tables and then setup the
    view rules by hand with CREATE RULE in such a way, that
    one selects from the other that selects from the one.
    This could never happen if CREATE VIEW is used because
    for the first CREATE VIEW, the second relation does not exist
    and thus the first view cannot select from the second.
    </Para>
    </Note>
</Para>

</Sect2>

<Sect2>
<Title>View Rules in Non-SELECT Statements</Title>

<Para>
    Two details of the parsetree aren't touched in the description of
    view rules above. These are the commandtype and the resultrelation.
    In fact, view rules don't need this information.
</Para>

<Para>
    There are only a few differences between a parsetree for a SELECT
    and one for any other command. Obviously they have another commandtype
    and this time the resultrelation points to the rangetable entry where
    the result should go. Everything else is absolutely the same. 
    So having two tables t1 and t2 with attributes
    a and b, the parsetrees for the two statements

<ProgramListing>
    SELECT t2.b FROM t1, t2 WHERE t1.a = t2.a;

    UPDATE t1 SET b = t2.b WHERE t1.a = t2.a;
</ProgramListing>

    are nearly identical.

    <ItemizedList>
        <ListItem>
	<Para>
	    The rangetables contain entries for the tables t1 and t2.
	</Para>
        </ListItem>

        <ListItem>
	<Para>
	    The targetlists contain one variable that points to attribute
	    b of the rangetable entry for table t2.
	</Para>
        </ListItem>

        <ListItem>
	<Para>
	    The qualification expressions compare the attributes a of both
	    ranges for equality.
	</Para>
        </ListItem>

        <ListItem>
	<Para>
	    The jointrees show a simple join between t1 and t2.
	</Para>
        </ListItem>
    </ItemizedList>

    The consequence is, that both parsetrees 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 targetlist by the planner 
    and the final parsetree will read as

<ProgramListing>
    UPDATE t1 SET a = t1.a, b = t2.b WHERE t1.a = t2.a;
</ProgramListing>

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

<ProgramListing>
    SELECT t1.a, t2.b FROM t1, t2 WHERE t1.a = t2.a;
</ProgramListing>
    
    will do. But there is a little problem in UPDATE. The executor does
    not care what the results from the join it is doing are meant
    for. It just produces a result set of rows. The difference that one
    is a SELECT command and the other is an UPDATE is handled in the
    caller of the executor. The caller still knows (looking at the
    parsetree) that this is an UPDATE, and he 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?
</Para>

<Para>
    To resolve this problem, another entry is added to the targetlist
    in UPDATE (and also in DELETE) statements: the current tuple ID (ctid).
    This is a system attribute 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 t1 row to be updated.
    After adding the ctid to the targetlist, 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>Postgres</ProductName> enters the
    stage. At this moment, table rows aren't overwritten and this is why
    ABORT TRANSACTION is fast. In an UPDATE, the new result row is inserted
    into the table (after stripping ctid) and in the tuple header of the row 
    that 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 commited the vacuum cleaner can really move
    it out.
</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>Postgres</ProductName></Title>

<Para>
    The above demonstrates how the rule system incorporates
    view definitions into the original parsetree. In the second example
    a simple SELECT from one view created a final parsetree that is
    a join of 4 tables (unit is used twice with different names).
</Para>

<Sect3>
<Title>Benefits</Title>

<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 parsetree. And this is still the situation
    when the original query is already a join over views.
    Now the planner has to decide which is
    the best path to execute the query. The more information
    the planner has, the better this decision can be. And
    the rule system as implemented in <ProductName>Postgres</ProductName>
    ensures, that this is all information available about the query
    up to now.
</Para>
</Sect3>
</Sect2>

<Sect2>
<Title>What about updating a view?</Title>

<Para>
    What happens if a view is named as the target relation for an INSERT,
    UPDATE, or DELETE?  After doing the substitutions described above,
    we will have a querytree in which the resultrelation points at a
    subquery rangetable entry.  This will not work, so the rewriter throws
    an error if it sees it has produced such a thing.
</Para>

<Para>
    To change this we can define rules that modify the behaviour
    of non-SELECT queries. This is the topic of the next section.
</Para>
</Sect2>

</Sect1>

<Sect1 id="rules-insert">
<Title>Rules on INSERT, UPDATE and DELETE</Title>

<Sect2>
<Title>Differences from View Rules</Title>

<Para>
    Rules that are defined ON INSERT, UPDATE and DELETE are
    totally different from the view rules described
    in the previous section. First, their CREATE RULE
    command allows more:

    <ItemizedList>
        <ListItem>
	<Para>
	    They can have no action.
	</Para>
	</ListItem>

        <ListItem>
	<Para>
	    They can have multiple actions.
	</Para>
	</ListItem>

        <ListItem>
	<Para>
	    The keyword INSTEAD is optional.
	</Para>
	</ListItem>

        <ListItem>
	<Para>
	    The pseudo relations NEW and OLD become useful.
	</Para>
	</ListItem>

        <ListItem>
	<Para>
	    They can have rule qualifications.
	</Para>
	</ListItem>
    </ItemizedList>

    Second, they don't modify the parsetree in place. Instead they
    create zero or many new parsetrees and can throw away the
    original one.
</Para>
</sect2>
<Sect2>
<Title>How These Rules Work</Title>

<Para>
    Keep the syntax

<ProgramListing>
    CREATE RULE rule_name AS ON event
        TO object [WHERE rule_qualification]
        DO [INSTEAD] [action | (actions) | NOTHING];
</ProgramListing>

    in mind.
    In the following, "update rules" means rules that are defined
    ON INSERT, UPDATE or DELETE.
</Para>

<Para>
    Update rules get applied by the rule system when the result
    relation and the commandtype of a parsetree are equal to the
    object and event given in the CREATE RULE command.
    For update rules, the rule system creates a list of parsetrees.
    Initially the parsetree list is empty.
    There can be zero (NOTHING keyword), one or multiple actions.
    To simplify, we look at a rule with one action. This rule
    can have a qualification or not and it can be INSTEAD or not.
</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 NEW and/or OLD pseudo relations
    which are basically the relation given as object (but with a
    special meaning).
</Para>

<Para>
    So we have four cases that produce the following parsetrees for
    a one-action rule.
</Para>
<Para>
    <ItemizedList>
    <ListItem>
    <Para>
        No qualification and not INSTEAD:
        <ItemizedList>
	    <ListItem>
	    <Para>
	        The parsetree from the rule action where the
		original parsetree's qualification has been added.
	    </Para>
	    </ListItem>
	</ItemizedList>
    </Para>
    </ListItem>

    <ListItem>
    <Para>
        No qualification but INSTEAD:
        <ItemizedList>
	    <ListItem>
	    <Para>
	        The parsetree from the rule action where the
		original parsetree's qualification has been added.
	    </Para>
	    </ListItem>
	</ItemizedList>
    </Para>
    </ListItem>

    <ListItem>
    <Para>
        Qualification given and not INSTEAD:
        <ItemizedList>
	    <ListItem>
	    <Para>
	        The parsetree from the rule action where the rule
		qualification and the original parsetree's 
		qualification have been added.
	    </Para>
	    </ListItem>
	</ItemizedList>
    </Para>
    </ListItem>

    <ListItem>
    <Para>
        Qualification given and INSTEAD:
        <ItemizedList>
	    <ListItem>
	    <Para>
	        The parsetree from the rule action where the rule
		qualification and the original parsetree's 
		qualification have been added.
	    </Para>
	    </ListItem>

	    <ListItem>
	    <Para>
	        The original parsetree where the negated rule
		qualification has been added.
	    </Para>
	    </ListItem>
	</ItemizedList>
    </Para>
    </ListItem>
    </ItemizedList>
</para>
<Para>
    Finally, if the rule is not INSTEAD, the unchanged original parsetree is
    added to the list. Since only qualified INSTEAD rules already add the
    original parsetree, we end up with either one or two output parsetrees
    for a rule with one action.
</Para>

<Para>
    For ON INSERT rules, the original query (if not suppressed by INSTEAD)
    is done before any actions added by rules.  This allows the actions to
    see the inserted row(s).  But for ON UPDATE and ON
    DELETE 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 parsetrees generated from rule actions are thrown into the
    rewrite system again and maybe more rules get applied resulting
    in more or less parsetrees.
    So the parsetrees in the rule actions must have either another commandtype
    or another resultrelation. Otherwise this recursive process will end up in a loop.
    There is a compiled in recursion limit of currently 10 iterations.
    If after 10 iterations there are still update rules to apply the
    rule system assumes a loop over multiple rule definitions and reports
    an error.
</Para>

<Para>
    The parsetrees found in the actions of the <Filename>pg_rewrite</Filename>
    system catalog are only templates. Since they can reference the
    rangetable entries for NEW and OLD, some substitutions have to be made
    before they can be used. For any reference to NEW, the targetlist of
    the original query is searched for a corresponding entry. If found,
    that entry's expression replaces the reference. Otherwise
    NEW means the same as OLD (for an UPDATE) or is replaced by NULL
    (for an INSERT). Any reference to OLD is replaced by a
    reference to the rangetable entry which is the resultrelation.
</Para>

<Para>
    After we are done applying update rules, we apply view rules to the
    produced parsetree(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>
    We want to trace changes to the sl_avail column in the
    <Filename>shoelace_data</Filename> relation. So we setup a
    log table and a rule that conditionally writes a log entry when
    an UPDATE is performed on <Filename>shoelace_data</Filename>.

<ProgramListing>
    CREATE TABLE shoelace_log (
        sl_name    char(10),      -- 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 != OLD.sl_avail
        DO INSERT INTO shoelace_log VALUES (
                                        NEW.sl_name,
                                        NEW.sl_avail,
                                        current_user,
                                        current_timestamp
                                    );
</ProgramListing>
</Para>

<Para>
    Now Al does

<ProgramListing>
    al_bundy=> UPDATE shoelace_data SET sl_avail = 6                       
    al_bundy->     WHERE sl_name = 'sl7';
</ProgramListing>

    and we look at the logtable.

<ProgramListing>
    al_bundy=> 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>

    That's what we expected. What happened in the background is the following.
    The parser created the parsetree (this time the parts of the original
    parsetree are highlighted because the base of operations is the
    rule action for update rules).

<ProgramListing>
    <FirstTerm>UPDATE shoelace_data SET sl_avail = 6
      FROM shoelace_data shoelace_data
     WHERE bpchareq(shoelace_data.sl_name, 'sl7');</FirstTerm>
</ProgramListing>

    There is a rule 'log_shoelace' that is ON UPDATE with the rule
    qualification expression

<ProgramListing>
    int4ne(NEW.sl_avail, OLD.sl_avail)
</ProgramListing>

    and one 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 is a little strange-looking since you can't normally write
    INSERT ... VALUES ... FROM.  The FROM clause here is just to indicate
    that there are rangetable entries in the parsetree for *NEW* and *OLD*.
    These are needed so that they can be referenced by variables in the
    INSERT command's querytree.
</Para>

<Para>
    The rule is a qualified non-INSTEAD rule, so the rule system
    has to return two parsetrees: the modified rule action and the original
    parsetree. In the first step the rangetable of the original query is
    incorporated into the rule's action parsetree. 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*,
           <FirstTerm>shoelace_data shoelace_data</FirstTerm>;
</ProgramListing>

    In step 2 the rule qualification is added to it, so the result set
    is restricted to rows where sl_avail 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
     <FirstTerm>WHERE int4ne(*NEW*.sl_avail, *OLD*.sl_avail)</FirstTerm>;
</ProgramListing>

    This is even stranger-looking, since INSERT ... VALUES doesn't have
    a WHERE clause either, but the planner and executor will have no
    difficulty with it.  They need to support this same functionality
    anyway for INSERT ... SELECT.

    In step 3 the original parsetree's qualification is added,
    restricting the resultset further to only the rows touched
    by the original parsetree.

<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 int4ne(*NEW*.sl_avail, *OLD*.sl_avail)
       <FirstTerm>AND bpchareq(shoelace_data.sl_name, 'sl7')</FirstTerm>;
</ProgramListing>

    Step 4 substitutes NEW references by the targetlist entries from the
    original parsetree or with the matching variable references
    from the result relation.

<ProgramListing>
    INSERT INTO shoelace_log VALUES(
           <FirstTerm>shoelace_data.sl_name</FirstTerm>, <FirstTerm>6</FirstTerm>,
           current_user, current_timestamp
      FROM shoelace_data *NEW*, shoelace_data *OLD*,
           shoelace_data shoelace_data
     WHERE int4ne(<FirstTerm>6</FirstTerm>, *OLD*.sl_avail)
       AND bpchareq(shoelace_data.sl_name, 'sl7');
</ProgramListing>

    Step 5 changes OLD references into resultrelation 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 int4ne(6, <FirstTerm>shoelace_data.sl_avail</FirstTerm>)
       AND bpchareq(shoelace_data.sl_name, 'sl7');
</ProgramListing>

    That's it.  Since the rule is not INSTEAD, we also output the
    original parsetree.  In short, the output from the rule system
    is a list of two parsetrees that are the same as the statements:

<ProgramListing>
    INSERT INTO shoelace_log VALUES(
           shoelace_data.sl_name, 6,
           current_user, current_timestamp
      FROM shoelace_data
     WHERE 6 != 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 defines. The substitutions and the qualifications
    added 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.  This
    time the original parsetree does not contain a targetlist
    entry for sl_avail, so NEW.sl_avail will get replaced by
    shoelace_data.sl_avail resulting in the extra query

<ProgramListing>
    INSERT INTO shoelace_log VALUES(
           shoelace_data.sl_name, <FirstTerm>shoelace_data.sl_avail</FirstTerm>,
           current_user, current_timestamp)
      FROM shoelace_data
     WHERE <FirstTerm>shoelace_data.sl_avail</FirstTerm> != shoelace_data.sl_avail
       AND shoelace_data.sl_name = 'sl7';
</ProgramListing>

    and that qualification will never be true. It will also
    work if the original query modifies multiple rows. So if Al
    would issue the command

<ProgramListing>
    UPDATE shoelace_data SET sl_avail = 0
     WHERE sl_color = 'black';
</ProgramListing>

    four rows in fact get updated (sl1, sl2, sl3 and sl4).
    But sl3 already has sl_avail = 0. This time, the original
    parsetrees qualification is different and that results
    in the extra parsetree

<ProgramListing>
    INSERT INTO shoelace_log SELECT
           shoelace_data.sl_name, 0,
           current_user, current_timestamp
      FROM shoelace_data
     WHERE 0 != shoelace_data.sl_avail
       AND <FirstTerm>shoelace_data.sl_color = 'black'</FirstTerm>;
</ProgramListing>

    This parsetree 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 parsetree is
    executed last.
    If the UPDATE would have been executed first, all the rows
    are already set to zero, so the logging INSERT
    would not find any row where 0 != shoelace_data.sl_avail.
</Para>
</Sect3>

</Sect2>

<Sect2>
<Title>Cooperation with Views</Title>

<Para>
    A simple way to protect view relations from the mentioned
    possibility that someone can try to INSERT, UPDATE and DELETE
    on them is to let those parsetrees get
    thrown away. We 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 Al now tries to do any of these operations on the view
    relation <Filename>shoe</Filename>, the rule system will
    apply the rules. Since the rules have
    no actions and are INSTEAD, the resulting list of
    parsetrees 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.

    <Note>
    <Title>Note</Title>
    <Para>
    This way might irritate frontend applications because
    absolutely nothing happened on the database and thus, the
    backend will not return anything for the query. Not
    even a PGRES_EMPTY_QUERY will be available in libpq.
    In psql, nothing happens. This might change in the future.
    </Para>
    </Note>

</Para>

<Para>
    A more sophisticated way to use the rule system is to
    create rules that rewrite the parsetree into one that
    does the right operation on the real tables. To do that
    on the <Filename>shoelace</Filename> 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>

    Now there is a pack of shoelaces arriving in Al's shop and it has
    a big partlist. Al is not that good in calculating and so
    we don't want him to manually update the shoelace view.
    Instead we setup two little tables, one where he can
    insert the items from the partlist and one with a special
    trick. The create commands for these are:

<ProgramListing>
    CREATE TABLE shoelace_arrive (
        arr_name    char(10),
        arr_quant   integer
    );

    CREATE TABLE shoelace_ok (
        ok_name     char(10),
        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 Al can sit down and do whatever until

<ProgramListing>
    al_bundy=> SELECT * FROM shoelace_arrive;
    arr_name  |arr_quant
    ----------+---------
    sl3       |       10
    sl6       |       20
    sl8       |       20
    (3 rows)
</ProgramListing>

    is exactly what's on the part list. We take a quick look
    at the current data,
    
<ProgramListing>
    al_bundy=> 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
    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>

    move the arrived shoelaces in

<ProgramListing>
    al_bundy=> INSERT INTO shoelace_ok SELECT * FROM shoelace_arrive;
</ProgramListing>

    and check the results

<ProgramListing>
    al_bundy=> 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)

    al_bundy=> 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>

    It's a long way from the one INSERT ... SELECT to these
    results. And its description will be the last in this
    document (but not the last example :-). First there was the parsers 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 'shoelace_ok_ins' is applied and turns it
    into

<ProgramListing>
    UPDATE shoelace SET
           sl_avail = int4pl(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 bpchareq(shoelace.sl_name, showlace_arrive.arr_name);
</ProgramListing>

    and throws away the original INSERT on <Filename>shoelace_ok</Filename>.
    This rewritten query is passed to the rule system again and
    the second applied rule 'shoelace_upd' produced

<ProgramListing>
    UPDATE shoelace_data SET
           sl_name = shoelace.sl_name,
           sl_avail = int4pl(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 showlace_data
     WHERE bpchareq(shoelace.sl_name, showlace_arrive.arr_name)
       AND bpchareq(shoelace_data.sl_name, shoelace.sl_name);
</ProgramListing>

    Again it's an INSTEAD rule and the previous parsetree is trashed.
    Note that this query still uses the view <Filename>shoelace</Filename>.
    But the rule system isn't finished with this loop so it continues
    and applies the rule '_RETshoelace' on it and we get

<ProgramListing>
    UPDATE shoelace_data SET
           sl_name = s.sl_name,
           sl_avail = int4pl(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 showlace_data,
           shoelace *OLD*, shoelace *NEW*,
           shoelace_data s, unit u
     WHERE bpchareq(s.sl_name, showlace_arrive.arr_name)
       AND bpchareq(shoelace_data.sl_name, s.sl_name);
</ProgramListing>

    Again an update rule has been applied and so the wheel
    turns on and we are in rewrite round 3. This time rule
    'log_shoelace' gets applied what produces the extra
    parsetree

<ProgramListing>
    INSERT INTO shoelace_log SELECT
           s.sl_name,
           int4pl(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 showlace_data,
           shoelace *OLD*, shoelace *NEW*,
           shoelace_data s, unit u,
           shoelace_data *OLD*, shoelace_data *NEW*
           shoelace_log shoelace_log
     WHERE bpchareq(s.sl_name,  showlace_arrive.arr_name)
       AND bpchareq(shoelace_data.sl_name, s.sl_name);
       AND int4ne(int4pl(s.sl_avail, shoelace_arrive.arr_quant),
                                                    s.sl_avail);
</ProgramListing>
    
    After that the rule system runs out of rules and returns the 
    generated parsetrees.
    So we end up with two final parsetrees that are equal 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 != 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 turns out, that the <Filename>shoelace_data</Filename>
    relation appears twice in the rangetable 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 INSERT will be

<ProgramListing>
Nested Loop
  ->  Merge Join
        ->  Seq Scan
              ->  Sort
                    ->  Seq Scan on s
        ->  Seq Scan
              ->  Sort
                    ->  Seq Scan on shoelace_arrive
  ->  Seq Scan on shoelace_data
</ProgramListing>

    while omitting the extra rangetable entry would result in a

<ProgramListing>
Merge Join
  ->  Seq Scan
        ->  Sort
              ->  Seq Scan on s
  ->  Seq Scan
        ->  Sort
              ->  Seq Scan on shoelace_arrive
</ProgramListing>

    that totally produces the same entries in the log relation.
    Thus, the rule system caused one extra scan on the
    <Filename>shoelace_data</Filename> relation that is
    absolutely not necessary. And the same obsolete scan
    is done once more in the UPDATE. But it was a really hard
    job to make that all possible at all.
</Para>

<Para>
    A final demonstration of the <ProductName>Postgres</ProductName>
    rule system and its power. There is a cute blonde that
    sells shoelaces. And what Al could never realize, she's not
    only cute, she's smart too - a little too smart. Thus, it
    happens from time to time that Al orders shoelaces that
    are absolutely not sellable. This time he ordered 1000 pairs
    of magenta shoelaces and since another kind is currently not
    available but he committed to buy some, he also prepared
    his database for pink ones.

<ProgramListing>
    al_bundy=> INSERT INTO shoelace VALUES 
    al_bundy->     ('sl9', 0, 'pink', 35.0, 'inch', 0.0);
    al_bundy=> INSERT INTO shoelace VALUES 
    al_bundy->     ('sl10', 1000, 'magenta', 40.0, 'inch', 0.0);
</ProgramListing>

    Since this happens often, we must lookup for shoelace entries,
    that fit for absolutely no shoe sometimes. We could do that in
    a complicated statement every time, or we can setup a view
    for it. The view for this is

<ProgramListing>
    CREATE VIEW shoelace_obsolete AS
        SELECT * FROM shoelace WHERE NOT EXISTS
            (SELECT shoename FROM shoe WHERE slcolor = sl_color);
</ProgramListing>

    Its output is

<ProgramListing>
    al_bundy=> SELECT * FROM shoelace_obsolete;
    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>

    For the 1000 magenta shoelaces we must debt Al before we can
    throw 'em away, but that's another problem. The pink entry we delete.
    To make it a little harder for <ProductName>Postgres</ProductName>,
    we don't delete it directly. Instead we create one more view

<ProgramListing>
    CREATE VIEW shoelace_candelete AS
        SELECT * FROM shoelace_obsolete WHERE sl_avail = 0;
</ProgramListing>

    and do it this way:

<ProgramListing>
    DELETE FROM shoelace WHERE EXISTS
        (SELECT * FROM shoelace_candelete
                 WHERE sl_name = shoelace.sl_name);
</ProgramListing>

    Voila:

<ProgramListing>
    al_bundy=> 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>

    A DELETE on a view, with a subselect qualification that
    in total uses 4 nesting/joined views, where one of them
    itself has a subselect qualification containing a view
    and where calculated view columns are used,
    gets rewritten into 
    one single parsetree that deletes the requested data
    from a real table.
</Para>

<Para>
    I think there are only a few situations out in the real
    world, where such a construct is necessary. But
    it makes me feel comfortable that it works.

    <Note>
    <Title>The truth is</Title>
    <Para>
    Doing this I found one more bug while writing this document.
    But after fixing that I was a little amazed that it works at all.
    </Para>
    </Note>
</Para>
</Sect2>

</Sect1>

<Sect1 id="rules-permissions">
<Title>Rules and Permissions</Title>

<Para>
    Due to rewriting of queries by the <ProductName>Postgres</ProductName>
    rule system, other tables/views than those used in the original
    query get accessed. Using update rules, 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>Postgres</ProductName> rule system changes the
    behaviour of the default access control system. Relations that
    are used due to rules get checked against the
    permissions of the rule owner, not the user invoking the rule.
    This means, that a user does only need the required permissions
    for the tables/views he names in his 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 secretary of the office.
    He can construct the following:

<ProgramListing>
    CREATE TABLE phone_data (person text, phone text, private bool);
    CREATE VIEW phone_number AS
        SELECT person, phone FROM phone_data WHERE NOT private;
    GRANT SELECT ON phone_number TO secretary;
</ProgramListing>
    
    Nobody except him (and the database superusers) can access the
    phone_data table. But due to the GRANT, the secretary can SELECT from the
    phone_number view. The rule system will rewrite
    the SELECT from phone_number into a SELECT from phone_data and add the qualification
    that only entries where private is false are wanted. Since the
    user is the owner of phone_number, the read access to phone_data
    is now checked against his permissions and the query is considered
    granted. The check for accessing phone_number is also performed,
    but this is done against the invoking user, so nobody but the user and the
    secretary can use it.
</Para>

<Para>
    The permissions are checked rule by rule. So the secretary is for now the
    only one who can see the public phone numbers. But the secretary can setup
    another view and grant access to that to public. Then, anyone
    can see the phone_number data through the secretaries view.
    What the secretary cannot do is to create a view that directly
    accesses phone_data (actually he can, but it will not work since
    every access aborts the transaction during the permission checks).
    And as soon as the user will notice, that the secretary opened
    his phone_number view, he can REVOKE his access. Immediately any
    access to the secretaries view will fail.
</Para>

<Para>
    Someone might think that this rule by rule checking is a security
    hole, but in fact it isn't. If this would not work, the secretary
    could setup a table with the same columns as phone_number and
    copy the data to there once per day. Then it's his own data and
    he can grant access to everyone he wants. A GRANT means "I trust you".
    If someone you trust does the thing above, it's time to
    think it over and then REVOKE.
</Para>

<Para>
    This mechanism does also work for update rules. In the examples
    of the previous section, the owner of the tables in Al's database
    could GRANT SELECT, INSERT, UPDATE and DELETE on the shoelace view to al.
    But only SELECT on shoelace_log. The rule action to write log entries
    will still be executed successfully. And Al could see the log entries.
    But he cannot create fake entries, nor could he manipulate or remove
    existing ones.

    <Note>
    <Title>Warning</Title>
    <Para>
    GRANT ALL currently includes RULE permission. This means the granted
    user could drop the rule, do the changes and reinstall it. I think
    this should get changed quickly.
    </Para>
    </Note>
</Para>
</Sect1>

<Sect1 id="rules-triggers">
<Title>Rules versus Triggers</Title>

<Para>
    Many things that can be done using triggers can also be
    implemented using the <ProductName>Postgres</ProductName>
    rule system. What currently cannot be implemented by
    rules are some kinds of constraints. It is possible,
    to place a qualified rule that rewrites a query to NOTHING
    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 for now.
</Para>

<Para>
    On the other hand a trigger that is fired on INSERT 
    on a view can do the same as a rule, put the data somewhere
    else and suppress the insert in the view. But it cannot
    do the same thing on UPDATE or DELETE, because there is
    no real data in the view relation that could be scanned
    and thus the trigger would never get called. Only a rule
    will help.
</Para>

<Para>
    For the things that can be implemented by both,
    it depends on the usage of the database, which is the best.
    A trigger is fired for any row affected once. A rule manipulates
    the parsetree or generates an additional one. So if many
    rows are affected in one statement, a rule issuing one extra
    query would usually do a better job than a trigger that is
    called for any single row and must execute his operations
    this many times.
</Para>

<Para>
    For example: 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 index on hostname is unique.
    The hostname column contains the full qualified domain
    name of the computer. The rule/trigger should constraint
    delete rows from software that reference the deleted host.
    Since the trigger is called for each individual row
    deleted from computer, it can use the statement

<ProgramListing>
    DELETE FROM software WHERE hostname = $1;
</ProgramListing>

    in a prepared and saved plan and pass the hostname 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>

    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 computer is scanned by index (fast) and the
    query issued by the trigger would also be an index scan (fast too).
    The extra query from the rule would be a

<ProgramListing>
    DELETE FROM software WHERE computer.hostname = 'mypc.local.net'
                           AND software.hostname = computer.hostname;
</ProgramListing>

    Since there are appropriate indexes setup, the planner
    will create a plan of

<ProgramListing>
    Nestloop
      ->  Index Scan using comp_hostidx on computer
      ->  Index Scan using soft_hostidx on software
</ProgramListing>

    So there would be not that much difference in speed between
    the trigger and the rule implementation. With the next delete
    we want to get rid of all the 2000 computers where the hostname starts
    with 'old'. There are two possible queries to do that. One is

<ProgramListing>
    DELETE FROM computer WHERE hostname >= 'old'
                           AND hostname <  'ole'
</ProgramListing>

    Where the plan for the rule query will be a 

<ProgramListing>
    Hash Join
      ->  Seq Scan on software
      ->  Hash
	    ->  Index Scan using comp_hostidx on computer
</ProgramListing>

    The other possible query is a

<ProgramListing>
    DELETE FROM computer WHERE hostname ~ '^old';
</ProgramListing>

    with the execution plan

<ProgramListing>
    Nestloop
      ->  Index Scan using comp_hostidx on computer
      ->  Index Scan using soft_hostidx on software
</ProgramListing>

    This shows, that the planner does not realize that the
    qualification for the hostname on computer could also be
    used for an index scan on software when there are
    multiple qualification expressions combined with AND, what
    he does in the regexp version of the query. The trigger will
    get invoked once for any of the 2000 old computers that
    have to be deleted and that will result in one index scan
    over computer and 2000 index scans for the software. The
    rule implementation will do it with two queries over indexes.
    And it depends on the overall size of the software table if
    the rule will still be faster in the seqscan situation. 2000
    query executions over the SPI manager take some time, even
    if all the index blocks to look them up will soon appear in
    the cache.
</Para>

<Para>
    The last query we look at is a

<ProgramListing>
    DELETE FROM computer WHERE manufacurer = 'bim';
</ProgramListing>

    Again this could result in many rows to be deleted from
    computer. So the trigger will again fire many queries into
    the executor. But the rule plan will again be the Nestloop over
    two IndexScan's. Only using another index on computer:

<ProgramListing>
    Nestloop
      ->  Index Scan using comp_manufidx on computer
      ->  Index Scan using soft_hostidx on software
</ProgramListing>

    resulting from the rules query

<ProgramListing>
    DELETE FROM software WHERE computer.manufacurer = 'bim'
                           AND software.hostname = computer.hostname;
</ProgramListing>

    In any of these cases, the extra queries from the rule system will be
    more or less independent from the number of affected rows
    in a query. 
</Para>

<Para>
    Another situation is cases on UPDATE where it depends on the
    change of an attribute if an action should be performed or
    not. In <ProductName>Postgres</ProductName> version 6.4, the
    attribute specification for rule events is disabled (it will have
    its comeback latest in 6.5, maybe earlier 
    - stay tuned). So for now the only way to
    create a rule as in the shoelace_log example is to do it with
    a rule qualification. That results in an extra query that is
    performed always, even if the attribute of interest cannot
    change at all because it does not appear in the targetlist
    of the initial query. When this is enabled again, it will be
    one more advantage of rules over triggers. Optimization of
    a trigger must fail by definition in this case, because the
    fact that its actions will only be done when a specific attribute
    is updated is hidden in its functionality. The definition of
    a trigger only allows to specify it on row level, so whenever a
    row is touched, the trigger must be called to make its
    decision. The rule system will know it by looking up the
    targetlist and will suppress the additional query completely
    if the attribute isn't touched. So the rule, qualified or not,
    will only do its scans if there ever could be something to do.
</Para>

<Para>
    Rules will only be significantly slower than triggers if
    their actions result in large and bad qualified joins, a situation
    where the planner fails. They are a big hammer.
    Using a big hammer without caution can cause big damage. But
    used with the right touch, they can hit any nail on the head.
</Para>
</Sect1>

</Chapter>

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