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:
They can have no action.
They can have multiple actions.
The keyword INSTEAD is optional.
The pseudo relations NEW and OLD become useful.
They can have rule qualifications.
Keep the syntax
CREATE RULE rule_name AS ON event
TO object [WHERE rule_qualification]
DO [INSTEAD] [action | (actions) | NOTHING];
in mind. In the following, "update rules" means rules that
are defined ON INSERT, UPDATE or DELETE.
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.
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).
So we have four cases that produce the following parsetrees for a one-action rule.
No qualification and not INSTEAD:
The parsetree from the rule action where the original parsetree's qualification has been added.
No qualification but INSTEAD:
The parsetree from the rule action where the original parsetree's qualification has been added.
Qualification given and not INSTEAD:
The parsetree from the rule action where the rule qualification and the original parsetree's qualification have been added.
Qualification given and INSTEAD:
The parsetree from the rule action where the rule qualification and the original parsetree's qualification have been added.
The original parsetree where the negated rule qualification has been added.
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.
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 aborts the transaction.
The parsetrees found in the actions of the pg_rewrite 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.
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.
We want to trace changes to the sl_avail column in the shoelace_data relation. So we setup a log table and a rule that conditionally writes a log entry when an UPDATE is performed on shoelace_data.
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
);
Now Al does
al_bundy=> UPDATE shoelace_data SET sl_avail = 6
al_bundy-> WHERE sl_name = 'sl7';
and we look at the logtable.
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)
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).
UPDATE shoelace_data SET sl_avail = 6
FROM shoelace_data shoelace_data
WHERE bpchareq(shoelace_data.sl_name, 'sl7');
There is a rule 'log_shoelace' that is ON UPDATE with the
rule qualification expression
int4ne(NEW.sl_avail, OLD.sl_avail)
and one action
INSERT INTO shoelace_log VALUES(
*NEW*.sl_name, *NEW*.sl_avail,
current_user, current_timestamp
FROM shoelace_data *NEW*, shoelace_data *OLD*;
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.
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
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;
In step 2 the rule qualification is added to it, so the
result set is restricted to rows where sl_avail changes.
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);
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.
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)
AND bpchareq(shoelace_data.sl_name, 'sl7');
Step 4 substitutes NEW references by the targetlist entries
from the original parsetree or with the matching variable
references from the result relation.
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, *OLD*.sl_avail)
AND bpchareq(shoelace_data.sl_name, 'sl7');
Step 5 changes OLD references into resultrelation references.
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, shoelace_data.sl_avail)
AND bpchareq(shoelace_data.sl_name, 'sl7');
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:
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';
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,
UPDATE shoelace_data SET sl_color = 'green'
WHERE sl_name = 'sl7';
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
INSERT INTO shoelace_log VALUES(
shoelace_data.sl_name, shoelace_data.sl_avail,
current_user, current_timestamp)
FROM shoelace_data
WHERE shoelace_data.sl_avail != shoelace_data.sl_avail
AND shoelace_data.sl_name = 'sl7';
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
UPDATE shoelace_data SET sl_avail = 0
WHERE sl_color = 'black';
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
INSERT INTO shoelace_log SELECT
shoelace_data.sl_name, 0,
current_user, current_timestamp
FROM shoelace_data
WHERE 0 != shoelace_data.sl_avail
AND shoelace_data.sl_color = 'black';
This parsetree will surely insert three new log entries. And
that's absolutely correct.
It is important, that the original parsetree is executed last. The Postgres "traffic cop" does a command counter increment between the execution of the two parsetrees so the second one can see changes made by the first. 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.
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
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;
If Al now tries to do any of these operations on the view
relation shoe, 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: 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.
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 shoelace view, we create the following rules:
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;
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:
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;
Now Al can sit down and do whatever until
al_bundy=> SELECT * FROM shoelace_arrive;
arr_name |arr_quant
----------+---------
sl3 | 10
sl6 | 20
sl8 | 20
(3 rows)
is exactly what's on the part list. We take a quick look at
the current data,
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)
move the arrived shoelaces in
al_bundy=> INSERT INTO shoelace_ok SELECT * FROM shoelace_arrive;
and check the results
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)
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
INSERT INTO shoelace_ok SELECT
shoelace_arrive.arr_name, shoelace_arrive.arr_quant
FROM shoelace_arrive shoelace_arrive, shoelace_ok shoelace_ok;
Now the first rule 'shoelace_ok_ins' is applied and turns it
into
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);
and throws away the original INSERT on
shoelace_ok. This rewritten query is passed to the rule
system again and the second applied rule 'shoelace_upd'
produced
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);
Again it's an INSTEAD rule and the previous parsetree is
trashed. Note that this query still uses the view shoelace. But the rule system isn't finished with
this loop so it continues and applies the rule '_RETshoelace' on it
and we get
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);
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
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);
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 SQL statements
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;
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.
There is a little detail that's a bit ugly. Looking at the two queries turns out, that the shoelace_data 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
Nested Loop
-> Merge Join
-> Seq Scan
-> Sort
-> Seq Scan on s
-> Seq Scan
-> Sort
-> Seq Scan on shoelace_arrive
-> Seq Scan on shoelace_data
while omitting the extra rangetable entry would result in a
Merge Join
-> Seq Scan
-> Sort
-> Seq Scan on s
-> Seq Scan
-> Sort
-> Seq Scan on shoelace_arrive
that totally produces the same entries in the log relation.
Thus, the rule system caused one extra scan on the shoelace_data 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.
A final demonstration of the Postgres 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.
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);
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
CREATE VIEW shoelace_obsolete AS
SELECT * FROM shoelace WHERE NOT EXISTS
(SELECT shoename FROM shoe WHERE slcolor = sl_color);
Its output is
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
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
Postgres, we don't delete it directly. Instead we create
one more view
CREATE VIEW shoelace_candelete AS
SELECT * FROM shoelace_obsolete WHERE sl_avail = 0;
and do it this way:
DELETE FROM shoelace WHERE EXISTS
(SELECT * FROM shoelace_candelete
WHERE sl_name = shoelace.sl_name);
Voila:
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)
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.
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.
The truth is: 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.