## Chapter 39. The Rule System

This chapter discusses the rule system in Postgres Pro. Production rule systems are conceptually simple, but there are many subtle points involved in actually using them.

Some other database systems define active database rules, which are usually stored procedures and triggers. In Postgres Pro, these can be implemented using functions and triggers as well.

The rule system (more precisely speaking, the query rewrite rule system) is totally different from stored procedures and triggers. It modifies queries to take rules into consideration, and then passes the modified query to the query planner for planning and execution. It is very powerful, and can be used for many things such as query language procedures, views, and versions. The theoretical foundations and the power of this rule system are also discussed in [ston90b] and [ong90].

## 39.1. The Query Tree

To understand how the rule system works it is necessary to know when it is invoked and what its input and results are.

The rule system is located between the parser and the planner. It takes the output of the parser, one query tree, and the user-defined rewrite rules, which are also query trees with some extra information, and creates zero or more query trees as result. So its input and output are always things the parser itself could have produced and thus, anything it sees is basically representable as an SQL statement.

Now what is a query tree? It is an internal representation of an SQL statement where the single parts that it is built from are stored separately. These query trees can be shown in the server log if you set the configuration parameters `debug_print_parse`, `debug_print_rewritten`, or `debug_print_plan`. The rule actions are also stored as query trees, in the system catalog `pg_rewrite`. They are not formatted like the log output, but they contain exactly the same information.

Reading a raw query tree requires some experience. But since SQL representations of query trees are sufficient to understand the rule system, this chapter will not teach how to read them.

When reading the SQL representations of the query trees in this chapter it is necessary to be able to identify the parts the statement is broken into when it is in the query tree structure. The parts of a query tree are

the command type

  This is a simple value telling which command (`SELECT`, `INSERT`, `UPDATE`, `DELETE`) produced the query tree.

the range table

  The range table is a list of relations that are used in the query. In a `SELECT` statement these are the relations given after the `FROM` key word.

  Every range table entry identifies a table or view and tells by which name it is called in the other parts of the query. In the query tree, the range table entries are referenced by number rather than by name, so here it doesn't matter if there are duplicate names as it would in an SQL statement. This can happen after the range tables of rules have been merged in. The examples in this chapter will not have this situation.

the result relation

  This is an index into the range table that identifies the relation where the results of the query go.

  `SELECT` queries don't have a result relation. (The special case of `SELECT INTO` is mostly identical to `CREATE TABLE` followed by `INSERT ... SELECT`, and is not discussed separately here.)

  For `INSERT`, `UPDATE`, and `DELETE` commands, the result relation is the table (or view!) where the changes are to take effect.

the target list

  The target list is a list of expressions that define the result of the query. In the case of a `SELECT`, these expressions are the ones that build the final output of the query. They correspond to the expressions between the key words `SELECT` and `FROM`. (`*` is just an abbreviation for all the column names of a relation. It is expanded by the parser into the individual columns, so the rule system never sees it.)

  `DELETE` commands don't need a normal target list because they don't produce any result. Instead, the rule system adds a special CTID entry to the empty target list, to allow the executor to find the row to be deleted. (CTID is added when the result relation is an ordinary table. If it is a view, a whole-row variable is added instead, as described in Section 39.2.4, “Updating a View”.)

  For `INSERT` commands, the target list describes the new rows that should go into the result relation. It consists of the expressions in the `VALUES` clause or the ones from the `SELECT` clause in `INSERT ... SELECT`. The first step of the rewrite process adds target list entries for any columns that were not assigned to by the original command but have defaults. Any remaining columns (with neither a given value nor a default) will be filled in by the planner with a constant null expression.

  For `UPDATE` commands, the target list describes the new rows that should replace the old ones. In the rule system, it contains just the expressions from the `SET column = expression` part of the command. The planner will handle missing columns by inserting expressions that copy the values from the old row into the new one. Just as for `DELETE`, the rule system adds a CTID or whole-row variable so that the executor can identify the old row to be updated.

  Every entry in the target list contains an expression that can be a constant value, a variable pointing to a column of one of the relations in the range table, a parameter, or an expression tree made of function calls, constants, variables, operators, etc.

the qualification

  The query's qualification is an expression much like one of those contained in the target list entries. The result value of this expression is a Boolean that tells whether the operation (`INSERT`, `UPDATE`, `DELETE`, or `SELECT`) for the final result row should be executed or not. It corresponds to the `WHERE` clause of an SQL statement.

the join tree

  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. In that case, 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`.

the others

  The other parts of the query tree like the `ORDER BY` clause aren't of interest here. The rule system substitutes some entries there while applying rules, but that doesn't have much to do with the fundamentals of the rule system.