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CREATE RULE

CREATE RULE

CREATE RULE — define a new rewrite rule

Synopsis

CREATE [ OR REPLACE ] RULE name AS ON event
    TO table_name [ WHERE condition ]
    DO [ ALSO | INSTEAD ] { NOTHING | command | ( command ; command ... ) }

where event can be one of:

    SELECT | INSERT | UPDATE | DELETE

Description

CREATE RULE defines a new rule applying to a specified table or view. CREATE OR REPLACE RULE will either create a new rule, or replace an existing rule of the same name for the same table.

The PostgreSQL rule system allows one to define an alternative action to be performed on insertions, updates, or deletions in database tables. Roughly speaking, a rule causes additional commands to be executed when a given command on a given table is executed. Alternatively, an INSTEAD rule can replace a given command by another, or cause a command not to be executed at all. Rules are used to implement SQL views as well. It is important to realize that a rule is really a command transformation mechanism, or command macro. The transformation happens before the execution of the command starts. If you actually want an operation that fires independently for each physical row, you probably want to use a trigger, not a rule. More information about the rules system is in Chapter 41.

Presently, ON SELECT rules must be unconditional INSTEAD rules and must have actions that consist of a single SELECT command. Thus, an ON SELECT rule effectively turns the table into a view, whose visible contents are the rows returned by the rule's SELECT command rather than whatever had been stored in the table (if anything). It is considered better style to write a CREATE VIEW command than to create a real table and define an ON SELECT rule for it.

You can create the illusion of an updatable view by defining ON INSERT , ON UPDATE , and ON DELETE rules (or any subset of those that's sufficient for your purposes) to replace update actions on the view with appropriate updates on other tables. If you want to support INSERT RETURNING and so on, then be sure to put a suitable RETURNING clause into each of these rules.

There is a catch if you try to use conditional rules for complex view updates: there must be an unconditional INSTEAD rule for each action you wish to allow on the view. If the rule is conditional, or is not INSTEAD , then the system will still reject attempts to perform the update action, because it thinks it might end up trying to perform the action on the dummy table of the view in some cases. If you want to handle all the useful cases in conditional rules, add an unconditional DO INSTEAD NOTHING rule to ensure that the system understands it will never be called on to update the dummy table. Then make the conditional rules non- INSTEAD ; in the cases where they are applied, they add to the default INSTEAD NOTHING action. (This method does not currently work to support RETURNING queries, however.)

Note

A view that is simple enough to be automatically updatable (see CREATE VIEW ) does not require a user-created rule in order to be updatable. While you can create an explicit rule anyway, the automatic update transformation will generally outperform an explicit rule.

Another alternative worth considering is to use INSTEAD OF triggers (see CREATE TRIGGER ) in place of rules.

Parameters

name

The name of a rule to create. This must be distinct from the name of any other rule for the same table. Multiple rules on the same table and same event type are applied in alphabetical name order.

event

The event is one of SELECT , INSERT , UPDATE , or DELETE . Note that an INSERT containing an ON CONFLICT clause cannot be used on tables that have either INSERT or UPDATE rules. Consider using an updatable view instead.

table_name

The name (optionally schema-qualified) of the table or view the rule applies to.

condition

Any SQL conditional expression (returning boolean ). The condition expression cannot refer to any tables except NEW and OLD , and cannot contain aggregate functions.

INSTEAD

INSTEAD indicates that the commands should be executed instead of the original command.

ALSO

ALSO indicates that the commands should be executed in addition to the original command.

If neither ALSO nor INSTEAD is specified, ALSO is the default.

command

The command or commands that make up the rule action. Valid commands are SELECT , INSERT , UPDATE , DELETE , or NOTIFY .

Within condition and command , the special table names NEW and OLD can be used to refer to values in the referenced table. NEW is valid in ON INSERT and ON UPDATE rules to refer to the new row being inserted or updated. OLD is valid in ON UPDATE and ON DELETE rules to refer to the existing row being updated or deleted.

Notes

You must be the owner of a table to create or change rules for it.

In a rule for INSERT , UPDATE , or DELETE on a view, you can add a RETURNING clause that emits the view's columns. This clause will be used to compute the outputs if the rule is triggered by an INSERT RETURNING , UPDATE RETURNING , or DELETE RETURNING command respectively. When the rule is triggered by a command without RETURNING , the rule's RETURNING clause will be ignored. The current implementation allows only unconditional INSTEAD rules to contain RETURNING ; furthermore there can be at most one RETURNING clause among all the rules for the same event. (This ensures that there is only one candidate RETURNING clause to be used to compute the results.) RETURNING queries on the view will be rejected if there is no RETURNING clause in any available rule.

It is very important to take care to avoid circular rules. For example, though each of the following two rule definitions are accepted by PostgreSQL , the SELECT command would cause PostgreSQL to report an error because of recursive expansion of a rule:

CREATE RULE "_RETURN" AS
    ON SELECT TO t1
    DO INSTEAD
        SELECT * FROM t2;

CREATE RULE "_RETURN" AS
    ON SELECT TO t2
    DO INSTEAD
        SELECT * FROM t1;

SELECT * FROM t1;

Presently, if a rule action contains a NOTIFY command, the NOTIFY command will be executed unconditionally, that is, the NOTIFY will be issued even if there are not any rows that the rule should apply to. For example, in:

CREATE RULE notify_me AS ON UPDATE TO mytable DO ALSO NOTIFY mytable;

UPDATE mytable SET name = 'foo' WHERE id = 42;

one NOTIFY event will be sent during the UPDATE , whether or not there are any rows that match the condition id = 42 . This is an implementation restriction that might be fixed in future releases.

Compatibility

CREATE RULE is a PostgreSQL language extension, as is the entire query rewrite system.

See Also

ALTER RULE , DROP RULE
70.1. Introduction

70.1. Introduction

GIN stands for Generalized Inverted Index. GIN is designed for handling cases where the items to be indexed are composite values, and the queries to be handled by the index need to search for element values that appear within the composite items. For example, the items could be documents, and the queries could be searches for documents containing specific words.

We use the word item to refer to a composite value that is to be indexed, and the word key to refer to an element value. GIN always stores and searches for keys, not item values per se.

A GIN index stores a set of (key, posting list) pairs, where a posting list is a set of row IDs in which the key occurs. The same row ID can appear in multiple posting lists, since an item can contain more than one key. Each key value is stored only once, so a GIN index is very compact for cases where the same key appears many times.

GIN is generalized in the sense that the GIN access method code does not need to know the specific operations that it accelerates. Instead, it uses custom strategies defined for particular data types. The strategy defines how keys are extracted from indexed items and query conditions, and how to determine whether a row that contains some of the key values in a query actually satisfies the query.

One advantage of GIN is that it allows the development of custom data types with the appropriate access methods, by an expert in the domain of the data type, rather than a database expert. This is much the same advantage as using GiST .

The GIN implementation in PostgreSQL is primarily maintained by Teodor Sigaev and Oleg Bartunov. There is more information about GIN on their website.

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70.1. Introduction

70.1. Introduction

GIN stands for Generalized Inverted Index. GIN is designed for handling cases where the items to be indexed are composite values, and the queries to be handled by the index need to search for element values that appear within the composite items. For example, the items could be documents, and the queries could be searches for documents containing specific words.

We use the word item to refer to a composite value that is to be indexed, and the word key to refer to an element value. GIN always stores and searches for keys, not item values per se.

A GIN index stores a set of (key, posting list) pairs, where a posting list is a set of row IDs in which the key occurs. The same row ID can appear in multiple posting lists, since an item can contain more than one key. Each key value is stored only once, so a GIN index is very compact for cases where the same key appears many times.

GIN is generalized in the sense that the GIN access method code does not need to know the specific operations that it accelerates. Instead, it uses custom strategies defined for particular data types. The strategy defines how keys are extracted from indexed items and query conditions, and how to determine whether a row that contains some of the key values in a query actually satisfies the query.

One advantage of GIN is that it allows the development of custom data types with the appropriate access methods, by an expert in the domain of the data type, rather than a database expert. This is much the same advantage as using GiST .

The GIN implementation in PostgreSQL is primarily maintained by Teodor Sigaev and Oleg Bartunov. There is more information about GIN on their website.

Read article
Chapter 15. Parallel Query

Chapter 15. Parallel Query

Table of Contents

15.1. How Parallel Query Works
15.2. When Can Parallel Query Be Used?
15.3. Parallel Plans
15.3.1. Parallel Scans
15.3.2. Parallel Joins
15.3.3. Parallel Aggregation
15.3.4. Parallel Append
15.3.5. Parallel Plan Tips
15.4. Parallel Safety
15.4.1. Parallel Labeling for Functions and Aggregates

PostgreSQL can devise query plans that can leverage multiple CPUs in order to answer queries faster. This feature is known as parallel query. Many queries cannot benefit from parallel query, either due to limitations of the current implementation or because there is no imaginable query plan that is any faster than the serial query plan. However, for queries that can benefit, the speedup from parallel query is often very significant. Many queries can run more than twice as fast when using parallel query, and some queries can run four times faster or even more. Queries that touch a large amount of data but return only a few rows to the user will typically benefit most. This chapter explains some details of how parallel query works and in which situations it can be used so that users who wish to make use of it can understand what to expect.

Read article
Chapter 15. Parallel Query

Chapter 15. Parallel Query

Table of Contents

15.1. How Parallel Query Works
15.2. When Can Parallel Query Be Used?
15.3. Parallel Plans
15.3.1. Parallel Scans
15.3.2. Parallel Joins
15.3.3. Parallel Aggregation
15.3.4. Parallel Append
15.3.5. Parallel Plan Tips
15.4. Parallel Safety
15.4.1. Parallel Labeling for Functions and Aggregates

PostgreSQL can devise query plans that can leverage multiple CPUs in order to answer queries faster. This feature is known as parallel query. Many queries cannot benefit from parallel query, either due to limitations of the current implementation or because there is no imaginable query plan that is any faster than the serial query plan. However, for queries that can benefit, the speedup from parallel query is often very significant. Many queries can run more than twice as fast when using parallel query, and some queries can run four times faster or even more. Queries that touch a large amount of data but return only a few rows to the user will typically benefit most. This chapter explains some details of how parallel query works and in which situations it can be used so that users who wish to make use of it can understand what to expect.

Read article
53.35. pg_opfamily

53.35. pg_opfamily

The catalog pg_opfamily defines operator families. Each operator family is a collection of operators and associated support routines that implement the semantics specified for a particular index access method. Furthermore, the operators in a family are all compatible , in a way that is specified by the access method. The operator family concept allows cross-data-type operators to be used with indexes and to be reasoned about using knowledge of access method semantics.

Operator families are described at length in Section 38.16.

Table 53.35. pg_opfamily Columns

Column Type

Description

oid oid

Row identifier

opfmethod oid (references pg_am . oid )

Index access method operator family is for

opfname name

Name of this operator family

opfnamespace oid (references pg_namespace . oid )

Namespace of this operator family

opfowner oid (references pg_authid . oid )

Owner of the operator family


The majority of the information defining an operator family is not in its pg_opfamily row, but in the associated rows in pg_amop , pg_amproc , and pg_opclass .

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