This chapter covers how one creates the database structures that will hold one's data. In a relational database, the raw data is stored in tables, so the majority of this chapter is devoted to explaining how tables are created and modified and what features are available to control what data is stored in the tables. Subsequently, we discuss how tables can be organized into schemas, and how privileges can be assigned to tables. Finally, we will briefly look at other features that affect the data storage, such as inheritance, views, functions, and triggers. table row column A table in a relational database is much like a table on paper: It consists of rows and columns. The number and order of the columns is fixed, and each column has a name. The number of rows is variable — it reflects how much data is stored at a given moment. SQL does not make any guarantees about the order of the rows in a table. When a table is read, the rows will appear in an unspecified order, unless sorting is explicitly requested. This is covered in . Furthermore, SQL does not assign unique identifiers to rows, so it is possible to have several completely identical rows in a table. This is a consequence of the mathematical model that underlies SQL but is usually not desirable. Later in this chapter we will see how to deal with this issue. Each column has a data type. The data type constrains the set of possible values that can be assigned to a column and assigns semantics to the data stored in the column so that it can be used for computations. For instance, a column declared to be of a numerical type will not accept arbitrary text strings, and the data stored in such a column can be used for mathematical computations. By contrast, a column declared to be of a character string type will accept almost any kind of data but it does not lend itself to mathematical calculations, although other operations such as string concatenation are available. PostgreSQL includes a sizable set of built-in data types that fit many applications. Users can also define their own data types. Most built-in data types have obvious names and semantics, so we defer a detailed explanation to . Some of the frequently used data types are integer for whole numbers, numeric for possibly fractional numbers, text for character strings, date for dates, time for time-of-day values, and timestamp for values containing both date and time. table creating To create a table, you use the aptly named command. In this command you specify at least a name for the new table, the names of the columns and the data type of each column. For example: CREATE TABLE my_first_table ( first_column text, second_column integer ); This creates a table named my_first_table with two columns. The first column is named first_column and has a data type of text; the second column has the name second_column and the type integer. The table and column names follow the identifier syntax explained in . The type names are usually also identifiers, but there are some exceptions. Note that the column list is comma-separated and surrounded by parentheses. Of course, the previous example was heavily contrived. Normally, you would give names to your tables and columns that convey what kind of data they store. So let's look at a more realistic example: CREATE TABLE products ( product_no integer, name text, price numeric ); (The numeric type can store fractional components, as would be typical of monetary amounts.) When you create many interrelated tables it is wise to choose a consistent naming pattern for the tables and columns. For instance, there is a choice of using singular or plural nouns for table names, both of which are favored by some theorist or other. There is a limit on how many columns a table can contain. Depending on the column types, it is between 250 and 1600. However, defining a table with anywhere near this many columns is highly unusual and often a questionable design. table removing If you no longer need a table, you can remove it using the command. For example: DROP TABLE my_first_table; DROP TABLE products; Attempting to drop a table that does not exist is an error. Nevertheless, it is common in SQL script files to unconditionally try to drop each table before creating it, ignoring any error messages, so that the script works whether or not the table exists. (If you like, you can use the DROP TABLE IF EXISTS variant to avoid the error messages, but this is not standard SQL.) If you need to modify a table that already exists, see later in this chapter. With the tools discussed so far you can create fully functional tables. The remainder of this chapter is concerned with adding features to the table definition to ensure data integrity, security, or convenience. If you are eager to fill your tables with data now you can skip ahead to and read the rest of this chapter later. default value A column can be assigned a default value. When a new row is created and no values are specified for some of the columns, those columns will be filled with their respective default values. A data manipulation command can also request explicitly that a column be set to its default value, without having to know what that value is. (Details about data manipulation commands are in .) null valuedefault value If no default value is declared explicitly, the default value is the null value. This usually makes sense because a null value can be considered to represent unknown data. In a table definition, default values are listed after the column data type. For example: CREATE TABLE products ( product_no integer, name text, price numeric DEFAULT 9.99 ); The default value can be an expression, which will be evaluated whenever the default value is inserted (not when the table is created). A common example is for a timestamp column to have a default of CURRENT_TIMESTAMP, so that it gets set to the time of row insertion. Another common example is generating a serial number for each row. In PostgreSQL this is typically done by something like: CREATE TABLE products ( product_no integer DEFAULT nextval('products_product_no_seq'), ... ); where the nextval() function supplies successive values from a sequence object (see ). This arrangement is sufficiently common that there's a special shorthand for it: CREATE TABLE products ( product_no SERIAL, ... ); The SERIAL shorthand is discussed further in . constraint Data types are a way to limit the kind of data that can be stored in a table. For many applications, however, the constraint they provide is too coarse. For example, a column containing a product price should probably only accept positive values. But there is no standard data type that accepts only positive numbers. Another issue is that you might want to constrain column data with respect to other columns or rows. For example, in a table containing product information, there should be only one row for each product number. To that end, SQL allows you to define constraints on columns and tables. Constraints give you as much control over the data in your tables as you wish. If a user attempts to store data in a column that would violate a constraint, an error is raised. This applies even if the value came from the default value definition. check constraint constraint check A check constraint is the most generic constraint type. It allows you to specify that the value in a certain column must satisfy a Boolean (truth-value) expression. For instance, to require positive product prices, you could use: CREATE TABLE products ( product_no integer, name text, price numeric CHECK (price > 0) ); As you see, the constraint definition comes after the data type, just like default value definitions. Default values and constraints can be listed in any order. A check constraint consists of the key word CHECK followed by an expression in parentheses. The check constraint expression should involve the column thus constrained, otherwise the constraint would not make too much sense. constraint name You can also give the constraint a separate name. This clarifies error messages and allows you to refer to the constraint when you need to change it. The syntax is: CREATE TABLE products ( product_no integer, name text, price numeric CONSTRAINT positive_price CHECK (price > 0) ); So, to specify a named constraint, use the key word CONSTRAINT followed by an identifier followed by the constraint definition. (If you don't specify a constraint name in this way, the system chooses a name for you.) A check constraint can also refer to several columns. Say you store a regular price and a discounted price, and you want to ensure that the discounted price is lower than the regular price: CREATE TABLE products ( product_no integer, name text, price numeric CHECK (price > 0), discounted_price numeric CHECK (discounted_price > 0), CHECK (price > discounted_price) ); The first two constraints should look familiar. The third one uses a new syntax. It is not attached to a particular column, instead it appears as a separate item in the comma-separated column list. Column definitions and these constraint definitions can be listed in mixed order. We say that the first two constraints are column constraints, whereas the third one is a table constraint because it is written separately from any one column definition. Column constraints can also be written as table constraints, while the reverse is not necessarily possible, since a column constraint is supposed to refer to only the column it is attached to. (PostgreSQL doesn't enforce that rule, but you should follow it if you want your table definitions to work with other database systems.) The above example could also be written as: CREATE TABLE products ( product_no integer, name text, price numeric, CHECK (price > 0), discounted_price numeric, CHECK (discounted_price > 0), CHECK (price > discounted_price) ); or even: CREATE TABLE products ( product_no integer, name text, price numeric CHECK (price > 0), discounted_price numeric, CHECK (discounted_price > 0 AND price > discounted_price) ); It's a matter of taste. Names can be assigned to table constraints in the same way as column constraints: CREATE TABLE products ( product_no integer, name text, price numeric, CHECK (price > 0), discounted_price numeric, CHECK (discounted_price > 0), CONSTRAINT valid_discount CHECK (price > discounted_price) ); null value with check constraints It should be noted that a check constraint is satisfied if the check expression evaluates to true or the null value. Since most expressions will evaluate to the null value if any operand is null, they will not prevent null values in the constrained columns. To ensure that a column does not contain null values, the not-null constraint described in the next section can be used. not-null constraint constraint NOT NULL A not-null constraint simply specifies that a column must not assume the null value. A syntax example: CREATE TABLE products ( product_no integer NOT NULL, name text NOT NULL, price numeric ); A not-null constraint is always written as a column constraint. A not-null constraint is functionally equivalent to creating a check constraint CHECK (column_name IS NOT NULL), but in PostgreSQL creating an explicit not-null constraint is more efficient. The drawback is that you cannot give explicit names to not-null constraints created this way. Of course, a column can have more than one constraint. Just write the constraints one after another: CREATE TABLE products ( product_no integer NOT NULL, name text NOT NULL, price numeric NOT NULL CHECK (price > 0) ); The order doesn't matter. It does not necessarily determine in which order the constraints are checked. The NOT NULL constraint has an inverse: the NULL constraint. This does not mean that the column must be null, which would surely be useless. Instead, this simply selects the default behavior that the column might be null. The NULL constraint is not present in the SQL standard and should not be used in portable applications. (It was only added to PostgreSQL to be compatible with some other database systems.) Some users, however, like it because it makes it easy to toggle the constraint in a script file. For example, you could start with: CREATE TABLE products ( product_no integer NULL, name text NULL, price numeric NULL ); and then insert the NOT key word where desired. In most database designs the majority of columns should be marked not null. unique constraint constraint unique Unique constraints ensure that the data contained in a column or a group of columns is unique with respect to all the rows in the table. The syntax is: CREATE TABLE products ( product_no integer UNIQUE, name text, price numeric ); when written as a column constraint, and: CREATE TABLE products ( product_no integer, name text, price numeric, UNIQUE (product_no) ); when written as a table constraint. If a unique constraint refers to a group of columns, the columns are listed separated by commas: CREATE TABLE example ( a integer, b integer, c integer, UNIQUE (a, c) ); This specifies that the combination of values in the indicated columns is unique across the whole table, though any one of the columns need not be (and ordinarily isn't) unique. You can assign your own name for a unique constraint, in the usual way: CREATE TABLE products ( product_no integer CONSTRAINT must_be_different UNIQUE, name text, price numeric ); Adding a unique constraint will automatically create a unique btree index on the column or group of columns used in the constraint. null value with unique constraints In general, a unique constraint is violated when there is more than one row in the table where the values of all of the columns included in the constraint are equal. However, two null values are not considered equal in this comparison. That means even in the presence of a unique constraint it is possible to store duplicate rows that contain a null value in at least one of the constrained columns. This behavior conforms to the SQL standard, but we have heard that other SQL databases might not follow this rule. So be careful when developing applications that are intended to be portable. primary key constraint primary key Technically, a primary key constraint is simply a combination of a unique constraint and a not-null constraint. So, the following two table definitions accept the same data: CREATE TABLE products ( product_no integer UNIQUE NOT NULL, name text, price numeric ); CREATE TABLE products ( product_no integer PRIMARY KEY, name text, price numeric ); Primary keys can also constrain more than one column; the syntax is similar to unique constraints: CREATE TABLE example ( a integer, b integer, c integer, PRIMARY KEY (a, c) ); A primary key indicates that a column or group of columns can be used as a unique identifier for rows in the table. (This is a direct consequence of the definition of a primary key. Note that a unique constraint does not, by itself, provide a unique identifier because it does not exclude null values.) This is useful both for documentation purposes and for client applications. For example, a GUI application that allows modifying row values probably needs to know the primary key of a table to be able to identify rows uniquely. Adding a primary key will automatically create a unique btree index on the column or group of columns used in the primary key. A table can have at most one primary key. (There can be any number of unique and not-null constraints, which are functionally the same thing, but only one can be identified as the primary key.) Relational database theory dictates that every table must have a primary key. This rule is not enforced by PostgreSQL, but it is usually best to follow it. foreign key constraint foreign key referential integrity A foreign key constraint specifies that the values in a column (or a group of columns) must match the values appearing in some row of another table. We say this maintains the referential integrity between two related tables. Say you have the product table that we have used several times already: CREATE TABLE products ( product_no integer PRIMARY KEY, name text, price numeric ); Let's also assume you have a table storing orders of those products. We want to ensure that the orders table only contains orders of products that actually exist. So we define a foreign key constraint in the orders table that references the products table: CREATE TABLE orders ( order_id integer PRIMARY KEY, product_no integer REFERENCES products (product_no), quantity integer ); Now it is impossible to create orders with product_no entries that do not appear in the products table. We say that in this situation the orders table is the referencing table and the products table is the referenced table. Similarly, there are referencing and referenced columns. You can also shorten the above command to: CREATE TABLE orders ( order_id integer PRIMARY KEY, product_no integer REFERENCES products, quantity integer ); because in absence of a column list the primary key of the referenced table is used as the referenced column(s). A foreign key can also constrain and reference a group of columns. As usual, it then needs to be written in table constraint form. Here is a contrived syntax example: CREATE TABLE t1 ( a integer PRIMARY KEY, b integer, c integer, FOREIGN KEY (b, c) REFERENCES other_table (c1, c2) ); Of course, the number and type of the constrained columns need to match the number and type of the referenced columns. You can assign your own name for a foreign key constraint, in the usual way. A table can contain more than one foreign key constraint. This is used to implement many-to-many relationships between tables. Say you have tables about products and orders, but now you want to allow one order to contain possibly many products (which the structure above did not allow). You could use this table structure: CREATE TABLE products ( product_no integer PRIMARY KEY, name text, price numeric ); CREATE TABLE orders ( order_id integer PRIMARY KEY, shipping_address text, ... ); CREATE TABLE order_items ( product_no integer REFERENCES products, order_id integer REFERENCES orders, quantity integer, PRIMARY KEY (product_no, order_id) ); Notice that the primary key overlaps with the foreign keys in the last table. CASCADE foreign key action RESTRICT foreign key action We know that the foreign keys disallow creation of orders that do not relate to any products. But what if a product is removed after an order is created that references it? SQL allows you to handle that as well. Intuitively, we have a few options: Disallow deleting a referenced product Delete the orders as well Something else? To illustrate this, let's implement the following policy on the many-to-many relationship example above: when someone wants to remove a product that is still referenced by an order (via order_items), we disallow it. If someone removes an order, the order items are removed as well: CREATE TABLE products ( product_no integer PRIMARY KEY, name text, price numeric ); CREATE TABLE orders ( order_id integer PRIMARY KEY, shipping_address text, ... ); CREATE TABLE order_items ( product_no integer REFERENCES products ON DELETE RESTRICT, order_id integer REFERENCES orders ON DELETE CASCADE, quantity integer, PRIMARY KEY (product_no, order_id) ); Restricting and cascading deletes are the two most common options. RESTRICT prevents deletion of a referenced row. NO ACTION means that if any referencing rows still exist when the constraint is checked, an error is raised; this is the default behavior if you do not specify anything. (The essential difference between these two choices is that NO ACTION allows the check to be deferred until later in the transaction, whereas RESTRICT does not.) CASCADE specifies that when a referenced row is deleted, row(s) referencing it should be automatically deleted as well. There are two other options: SET NULL and SET DEFAULT. These cause the referencing columns to be set to nulls or default values, respectively, when the referenced row is deleted. Note that these do not excuse you from observing any constraints. For example, if an action specifies SET DEFAULT but the default value would not satisfy the foreign key, the operation will fail. Analogous to ON DELETE there is also ON UPDATE which is invoked when a referenced column is changed (updated). The possible actions are the same. Since a DELETE of a row from the referenced table or an UPDATE of a referenced column will require a scan of the referencing table for rows matching the old value, it is often a good idea to index the referencing columns. Because this is not always needed, and there are many choices available on how to index, declaration of a foreign key constraint does not automatically create an index on the referencing columns. More information about updating and deleting data is in . Finally, we should mention that a foreign key must reference columns that either are a primary key or form a unique constraint. If the foreign key references a unique constraint, there are some additional possibilities regarding how null values are matched. These are explained in the reference documentation for . exclusion constraint constraint exclusion Exclusion constraints ensure that if any two rows are compared on the specified columns or expressions using the specified operators, at least one of these operator comparisons will return false or null. The syntax is: CREATE TABLE circles ( c circle, EXCLUDE USING gist (c WITH &&) ); See also CREATE TABLE ... CONSTRAINT ... EXCLUDE for details. Adding an exclusion constraint will automatically create an index of the type specified in the constraint declaration. Every table has several system columns that are implicitly defined by the system. Therefore, these names cannot be used as names of user-defined columns. (Note that these restrictions are separate from whether the name is a key word or not; quoting a name will not allow you to escape these restrictions.) You do not really need to be concerned about these columns; just know they exist. column system column oid OID column The object identifier (object ID) of a row. This column is only present if the table was created using WITH OIDS, or if the configuration variable was set at the time. This column is of type oid (same name as the column); see for more information about the type. tableoid tableoid The OID of the table containing this row. This column is particularly handy for queries that select from inheritance hierarchies (see ), since without it, it's difficult to tell which individual table a row came from. The tableoid can be joined against the oid column of pg_class to obtain the table name. xmin xmin The identity (transaction ID) of the inserting transaction for this row version. (A row version is an individual state of a row; each update of a row creates a new row version for the same logical row.) cmin cmin The command identifier (starting at zero) within the inserting transaction. xmax xmax The identity (transaction ID) of the deleting transaction, or zero for an undeleted row version. It is possible for this column to be nonzero in a visible row version. That usually indicates that the deleting transaction hasn't committed yet, or that an attempted deletion was rolled back. cmax cmax The command identifier within the deleting transaction, or zero. ctid ctid The physical location of the row version within its table. Note that although the ctid can be used to locate the row version very quickly, a row's ctid will change if it is updated or moved by VACUUM FULL. Therefore ctid is useless as a long-term row identifier. The OID, or even better a user-defined serial number, should be used to identify logical rows. OIDs are 32-bit quantities and are assigned from a single cluster-wide counter. In a large or long-lived database, it is possible for the counter to wrap around. Hence, it is bad practice to assume that OIDs are unique, unless you take steps to ensure that this is the case. If you need to identify the rows in a table, using a sequence generator is strongly recommended. However, OIDs can be used as well, provided that a few additional precautions are taken: A unique constraint should be created on the OID column of each table for which the OID will be used to identify rows. When such a unique constraint (or unique index) exists, the system takes care not to generate an OID matching an already-existing row. (Of course, this is only possible if the table contains fewer than 232 (4 billion) rows, and in practice the table size had better be much less than that, or performance might suffer.) OIDs should never be assumed to be unique across tables; use the combination of tableoid and row OID if you need a database-wide identifier. Of course, the tables in question must be created WITH OIDS. As of PostgreSQL 8.1, WITHOUT OIDS is the default. Transaction identifiers are also 32-bit quantities. In a long-lived database it is possible for transaction IDs to wrap around. This is not a fatal problem given appropriate maintenance procedures; see for details. It is unwise, however, to depend on the uniqueness of transaction IDs over the long term (more than one billion transactions). Command identifiers are also 32-bit quantities. This creates a hard limit of 232 (4 billion) SQL commands within a single transaction. In practice this limit is not a problem — note that the limit is on the number of SQL commands, not the number of rows processed. Also, as of PostgreSQL 8.3, only commands that actually modify the database contents will consume a command identifier. table modifying When you create a table and you realize that you made a mistake, or the requirements of the application change, you can drop the table and create it again. But this is not a convenient option if the table is already filled with data, or if the table is referenced by other database objects (for instance a foreign key constraint). Therefore PostgreSQL provides a family of commands to make modifications to existing tables. Note that this is conceptually distinct from altering the data contained in the table: here we are interested in altering the definition, or structure, of the table. You can: Add columns Remove columns Add constraints Remove constraints Change default values Change column data types Rename columns Rename tables All these actions are performed using the command, whose reference page contains details beyond those given here. column adding To add a column, use a command like: ALTER TABLE products ADD COLUMN description text; The new column is initially filled with whatever default value is given (null if you don't specify a DEFAULT clause). You can also define constraints on the column at the same time, using the usual syntax: ALTER TABLE products ADD COLUMN description text CHECK (description <> ''); In fact all the options that can be applied to a column description in CREATE TABLE can be used here. Keep in mind however that the default value must satisfy the given constraints, or the ADD will fail. Alternatively, you can add constraints later (see below) after you've filled in the new column correctly. Adding a column with a default requires updating each row of the table (to store the new column value). However, if no default is specified, PostgreSQL is able to avoid the physical update. So if you intend to fill the column with mostly nondefault values, it's best to add the column with no default, insert the correct values using UPDATE, and then add any desired default as described below. column removing To remove a column, use a command like: ALTER TABLE products DROP COLUMN description; Whatever data was in the column disappears. Table constraints involving the column are dropped, too. However, if the column is referenced by a foreign key constraint of another table, PostgreSQL will not silently drop that constraint. You can authorize dropping everything that depends on the column by adding CASCADE: ALTER TABLE products DROP COLUMN description CASCADE; See for a description of the general mechanism behind this. constraint adding To add a constraint, the table constraint syntax is used. For example: ALTER TABLE products ADD CHECK (name <> ''); ALTER TABLE products ADD CONSTRAINT some_name UNIQUE (product_no); ALTER TABLE products ADD FOREIGN KEY (product_group_id) REFERENCES product_groups; To add a not-null constraint, which cannot be written as a table constraint, use this syntax: ALTER TABLE products ALTER COLUMN product_no SET NOT NULL; The constraint will be checked immediately, so the table data must satisfy the constraint before it can be added. constraint removing To remove a constraint you need to know its name. If you gave it a name then that's easy. Otherwise the system assigned a generated name, which you need to find out. The psql command \d tablename can be helpful here; other interfaces might also provide a way to inspect table details. Then the command is: ALTER TABLE products DROP CONSTRAINT some_name; (If you are dealing with a generated constraint name like $2, don't forget that you'll need to double-quote it to make it a valid identifier.) As with dropping a column, you need to add CASCADE if you want to drop a constraint that something else depends on. An example is that a foreign key constraint depends on a unique or primary key constraint on the referenced column(s). This works the same for all constraint types except not-null constraints. To drop a not null constraint use: ALTER TABLE products ALTER COLUMN product_no DROP NOT NULL; (Recall that not-null constraints do not have names.) default value changing To set a new default for a column, use a command like: ALTER TABLE products ALTER COLUMN price SET DEFAULT 7.77; Note that this doesn't affect any existing rows in the table, it just changes the default for future INSERT commands. To remove any default value, use: ALTER TABLE products ALTER COLUMN price DROP DEFAULT; This is effectively the same as setting the default to null. As a consequence, it is not an error to drop a default where one hadn't been defined, because the default is implicitly the null value. column data type changing To convert a column to a different data type, use a command like: ALTER TABLE products ALTER COLUMN price TYPE numeric(10,2); This will succeed only if each existing entry in the column can be converted to the new type by an implicit cast. If a more complex conversion is needed, you can add a USING clause that specifies how to compute the new values from the old. PostgreSQL will attempt to convert the column's default value (if any) to the new type, as well as any constraints that involve the column. But these conversions might fail, or might produce surprising results. It's often best to drop any constraints on the column before altering its type, and then add back suitably modified constraints afterwards. column renaming To rename a column: ALTER TABLE products RENAME COLUMN product_no TO product_number; table renaming To rename a table: ALTER TABLE products RENAME TO items; privilege permission privilege When you create a database object, you become its owner. By default, only the owner of an object can do anything with the object. In order to allow other users to use it, privileges must be granted. (However, users that have the superuser attribute can always access any object.) There are several different privileges: SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, TRIGGER, CREATE, CONNECT, TEMPORARY, EXECUTE, and USAGE. The privileges applicable to a particular object vary depending on the object's type (table, function, etc). For complete information on the different types of privileges supported by PostgreSQL, refer to the reference page. The following sections and chapters will also show you how those privileges are used. The right to modify or destroy an object is always the privilege of the owner only. To change the owner of a table, index, sequence, or view, use the command. There are corresponding ALTER commands for other object types. To assign privileges, the GRANT command is used. For example, if joe is an existing user, and accounts is an existing table, the privilege to update the table can be granted with: GRANT UPDATE ON accounts TO joe; Writing ALL in place of a specific privilege grants all privileges that are relevant for the object type. The special user name PUBLIC can be used to grant a privilege to every user on the system. Also, group roles can be set up to help manage privileges when there are many users of a database — for details see . To revoke a privilege, use the fittingly named REVOKE command: REVOKE ALL ON accounts FROM PUBLIC; The special privileges of the object owner (i.e., the right to do DROP, GRANT, REVOKE, etc.) are always implicit in being the owner, and cannot be granted or revoked. But the object owner can choose to revoke his own ordinary privileges, for example to make a table read-only for himself as well as others. Ordinarily, only the object's owner (or a superuser) can grant or revoke privileges on an object. However, it is possible to grant a privilege with grant option, which gives the recipient the right to grant it in turn to others. If the grant option is subsequently revoked then all who received the privilege from that recipient (directly or through a chain of grants) will lose the privilege. For details see the and reference pages. schema A PostgreSQL database cluster contains one or more named databases. Users and groups of users are shared across the entire cluster, but no other data is shared across databases. Any given client connection to the server can access only the data in a single database, the one specified in the connection request. Users of a cluster do not necessarily have the privilege to access every database in the cluster. Sharing of user names means that there cannot be different users named, say, joe in two databases in the same cluster; but the system can be configured to allow joe access to only some of the databases. A database contains one or more named schemas, which in turn contain tables. Schemas also contain other kinds of named objects, including data types, functions, and operators. The same object name can be used in different schemas without conflict; for example, both schema1 and myschema can contain tables named mytable. Unlike databases, schemas are not rigidly separated: a user can access objects in any of the schemas in the database he is connected to, if he has privileges to do so. There are several reasons why one might want to use schemas: To allow many users to use one database without interfering with each other. To organize database objects into logical groups to make them more manageable. Third-party applications can be put into separate schemas so they do not collide with the names of other objects. Schemas are analogous to directories at the operating system level, except that schemas cannot be nested. schema creating To create a schema, use the command. Give the schema a name of your choice. For example: CREATE SCHEMA myschema; qualified name name qualified To create or access objects in a schema, write a qualified name consisting of the schema name and table name separated by a dot: schema.table This works anywhere a table name is expected, including the table modification commands and the data access commands discussed in the following chapters. (For brevity we will speak of tables only, but the same ideas apply to other kinds of named objects, such as types and functions.) Actually, the even more general syntax database.schema.table can be used too, but at present this is just for pro forma compliance with the SQL standard. If you write a database name, it must be the same as the database you are connected to. So to create a table in the new schema, use: CREATE TABLE myschema.mytable ( ... ); schema removing To drop a schema if it's empty (all objects in it have been dropped), use: DROP SCHEMA myschema; To drop a schema including all contained objects, use: DROP SCHEMA myschema CASCADE; See for a description of the general mechanism behind this. Often you will want to create a schema owned by someone else (since this is one of the ways to restrict the activities of your users to well-defined namespaces). The syntax for that is: CREATE SCHEMA schemaname AUTHORIZATION username; You can even omit the schema name, in which case the schema name will be the same as the user name. See for how this can be useful. Schema names beginning with pg_ are reserved for system purposes and cannot be created by users. schema public In the previous sections we created tables without specifying any schema names. By default such tables (and other objects) are automatically put into a schema named public. Every new database contains such a schema. Thus, the following are equivalent: CREATE TABLE products ( ... ); and: CREATE TABLE public.products ( ... ); search path unqualified name name unqualified Qualified names are tedious to write, and it's often best not to wire a particular schema name into applications anyway. Therefore tables are often referred to by unqualified names, which consist of just the table name. The system determines which table is meant by following a search path, which is a list of schemas to look in. The first matching table in the search path is taken to be the one wanted. If there is no match in the search path, an error is reported, even if matching table names exist in other schemas in the database. schema current The first schema named in the search path is called the current schema. Aside from being the first schema searched, it is also the schema in which new tables will be created if the CREATE TABLE command does not specify a schema name. search_path To show the current search path, use the following command: SHOW search_path; In the default setup this returns: search_path -------------- "$user",public The first element specifies that a schema with the same name as the current user is to be searched. If no such schema exists, the entry is ignored. The second element refers to the public schema that we have seen already. The first schema in the search path that exists is the default location for creating new objects. That is the reason that by default objects are created in the public schema. When objects are referenced in any other context without schema qualification (table modification, data modification, or query commands) the search path is traversed until a matching object is found. Therefore, in the default configuration, any unqualified access again can only refer to the public schema. To put our new schema in the path, we use: SET search_path TO myschema,public; (We omit the $user here because we have no immediate need for it.) And then we can access the table without schema qualification: DROP TABLE mytable; Also, since myschema is the first element in the path, new objects would by default be created in it. We could also have written: SET search_path TO myschema; Then we no longer have access to the public schema without explicit qualification. There is nothing special about the public schema except that it exists by default. It can be dropped, too. See also for other ways to manipulate the schema search path. The search path works in the same way for data type names, function names, and operator names as it does for table names. Data type and function names can be qualified in exactly the same way as table names. If you need to write a qualified operator name in an expression, there is a special provision: you must write OPERATOR(schema.operator) This is needed to avoid syntactic ambiguity. An example is: SELECT 3 OPERATOR(pg_catalog.+) 4; In practice one usually relies on the search path for operators, so as not to have to write anything so ugly as that. privilege for schemas By default, users cannot access any objects in schemas they do not own. To allow that, the owner of the schema must grant the USAGE privilege on the schema. To allow users to make use of the objects in the schema, additional privileges might need to be granted, as appropriate for the object. A user can also be allowed to create objects in someone else's schema. To allow that, the CREATE privilege on the schema needs to be granted. Note that by default, everyone has CREATE and USAGE privileges on the schema public. This allows all users that are able to connect to a given database to create objects in its public schema. If you do not want to allow that, you can revoke that privilege: REVOKE CREATE ON SCHEMA public FROM PUBLIC; (The first public is the schema, the second public means every user. In the first sense it is an identifier, in the second sense it is a key word, hence the different capitalization; recall the guidelines from .) system catalog schema In addition to public and user-created schemas, each database contains a pg_catalog schema, which contains the system tables and all the built-in data types, functions, and operators. pg_catalog is always effectively part of the search path. If it is not named explicitly in the path then it is implicitly searched before searching the path's schemas. This ensures that built-in names will always be findable. However, you can explicitly place pg_catalog at the end of your search path if you prefer to have user-defined names override built-in names. In PostgreSQL versions before 7.3, table names beginning with pg_ were reserved. This is no longer true: you can create such a table name if you wish, in any non-system schema. However, it's best to continue to avoid such names, to ensure that you won't suffer a conflict if some future version defines a system table named the same as your table. (With the default search path, an unqualified reference to your table name would then be resolved as the system table instead.) System tables will continue to follow the convention of having names beginning with pg_, so that they will not conflict with unqualified user-table names so long as users avoid the pg_ prefix. Schemas can be used to organize your data in many ways. There are a few usage patterns that are recommended and are easily supported by the default configuration: If you do not create any schemas then all users access the public schema implicitly. This simulates the situation where schemas are not available at all. This setup is mainly recommended when there is only a single user or a few cooperating users in a database. This setup also allows smooth transition from the non-schema-aware world. You can create a schema for each user with the same name as that user. Recall that the default search path starts with $user, which resolves to the user name. Therefore, if each user has a separate schema, they access their own schemas by default. If you use this setup then you might also want to revoke access to the public schema (or drop it altogether), so users are truly constrained to their own schemas. To install shared applications (tables to be used by everyone, additional functions provided by third parties, etc.), put them into separate schemas. Remember to grant appropriate privileges to allow the other users to access them. Users can then refer to these additional objects by qualifying the names with a schema name, or they can put the additional schemas into their search path, as they choose. In the SQL standard, the notion of objects in the same schema being owned by different users does not exist. Moreover, some implementations do not allow you to create schemas that have a different name than their owner. In fact, the concepts of schema and user are nearly equivalent in a database system that implements only the basic schema support specified in the standard. Therefore, many users consider qualified names to really consist of username.tablename. This is how PostgreSQL will effectively behave if you create a per-user schema for every user. Also, there is no concept of a public schema in the SQL standard. For maximum conformance to the standard, you should not use (perhaps even remove) the public schema. Of course, some SQL database systems might not implement schemas at all, or provide namespace support by allowing (possibly limited) cross-database access. If you need to work with those systems, then maximum portability would be achieved by not using schemas at all. inheritance table inheritance PostgreSQL implements table inheritance, which can be a useful tool for database designers. (SQL:1999 and later define a type inheritance feature, which differs in many respects from the features described here.) Let's start with an example: suppose we are trying to build a data model for cities. Each state has many cities, but only one capital. We want to be able to quickly retrieve the capital city for any particular state. This can be done by creating two tables, one for state capitals and one for cities that are not capitals. However, what happens when we want to ask for data about a city, regardless of whether it is a capital or not? The inheritance feature can help to resolve this problem. We define the capitals table so that it inherits from cities: CREATE TABLE cities ( name text, population float, altitude int -- in feet ); CREATE TABLE capitals ( state char(2) ) INHERITS (cities); In this case, the capitals table inherits all the columns of its parent table, cities. State capitals also have an extra column, state, that shows their state. In PostgreSQL, a table can inherit from zero or more other tables, and a query can reference either all rows of a table or all rows of a table plus all of its descendant tables. The latter behavior is the default. For example, the following query finds the names of all cities, including state capitals, that are located at an altitude over 500 feet: SELECT name, altitude FROM cities WHERE altitude > 500; Given the sample data from the PostgreSQL tutorial (see ), this returns: name | altitude -----------+---------- Las Vegas | 2174 Mariposa | 1953 Madison | 845 On the other hand, the following query finds all the cities that are not state capitals and are situated at an altitude over 500 feet: SELECT name, altitude FROM ONLY cities WHERE altitude > 500; name | altitude -----------+---------- Las Vegas | 2174 Mariposa | 1953 Here the ONLY keyword indicates that the query should apply only to cities, and not any tables below cities in the inheritance hierarchy. Many of the commands that we have already discussed — SELECT, UPDATE and DELETE — support the ONLY keyword. In some cases you might wish to know which table a particular row originated from. There is a system column called tableoid in each table which can tell you the originating table: SELECT c.tableoid, c.name, c.altitude FROM cities c WHERE c.altitude > 500; which returns: tableoid | name | altitude ----------+-----------+---------- 139793 | Las Vegas | 2174 139793 | Mariposa | 1953 139798 | Madison | 845 (If you try to reproduce this example, you will probably get different numeric OIDs.) By doing a join with pg_class you can see the actual table names: SELECT p.relname, c.name, c.altitude FROM cities c, pg_class p WHERE c.altitude > 500 AND c.tableoid = p.oid; which returns: relname | name | altitude ----------+-----------+---------- cities | Las Vegas | 2174 cities | Mariposa | 1953 capitals | Madison | 845 Inheritance does not automatically propagate data from INSERT or COPY commands to other tables in the inheritance hierarchy. In our example, the following INSERT statement will fail: INSERT INTO cities (name, population, altitude, state) VALUES ('New York', NULL, NULL, 'NY'); We might hope that the data would somehow be routed to the capitals table, but this does not happen: INSERT always inserts into exactly the table specified. In some cases it is possible to redirect the insertion using a rule (see ). However that does not help for the above case because the cities table does not contain the column state, and so the command will be rejected before the rule can be applied. All check constraints and not-null constraints on a parent table are automatically inherited by its children. Other types of constraints (unique, primary key, and foreign key constraints) are not inherited. A table can inherit from more than one parent table, in which case it has the union of the columns defined by the parent tables. Any columns declared in the child table's definition are added to these. If the same column name appears in multiple parent tables, or in both a parent table and the child's definition, then these columns are merged so that there is only one such column in the child table. To be merged, columns must have the same data types, else an error is raised. The merged column will have copies of all the check constraints coming from any one of the column definitions it came from, and will be marked not-null if any of them are. Table inheritance is typically established when the child table is created, using the INHERITS clause of the statement. Alternatively, a table which is already defined in a compatible way can have a new parent relationship added, using the INHERIT variant of . To do this the new child table must already include columns with the same names and types as the columns of the parent. It must also include check constraints with the same names and check expressions as those of the parent. Similarly an inheritance link can be removed from a child using the NO INHERIT variant of ALTER TABLE. Dynamically adding and removing inheritance links like this can be useful when the inheritance relationship is being used for table partitioning (see ). One convenient way to create a compatible table that will later be made a new child is to use the LIKE clause in CREATE TABLE. This creates a new table with the same columns as the source table. If there are any CHECK constraints defined on the source table, the INCLUDING CONSTRAINTS option to LIKE should be specified, as the new child must have constraints matching the parent to be considered compatible. A parent table cannot be dropped while any of its children remain. Neither can columns or check constraints of child tables be dropped or altered if they are inherited from any parent tables. If you wish to remove a table and all of its descendants, one easy way is to drop the parent table with the CASCADE option. will propagate any changes in column data definitions and check constraints down the inheritance hierarchy. Again, dropping columns that are depended on by other tables is only possible when using the CASCADE option. ALTER TABLE follows the same rules for duplicate column merging and rejection that apply during CREATE TABLE. Note how table access permissions are handled. Querying a parent table can automatically access data in child tables without further access privilege checking. This preserves the appearance that the data is (also) in the parent table. Accessing the child tables directly is, however, not automatically allowed and would require further privileges to be granted. Note that not all SQL commands are able to work on inheritance hierarchies. Commands that are used for data querying, data modification, or schema modification (e.g., SELECT, UPDATE, DELETE, most variants of ALTER TABLE, but not INSERT and ALTER TABLE ... RENAME) typically default to including child tables and support the ONLY notation to exclude them. Commands that do database maintenance and tuning (e.g., REINDEX, VACUUM) typically only work on individual, physical tables and do no support recursing over inheritance hierarchies. The respective behavior of each individual command is documented in the reference part (). A serious limitation of the inheritance feature is that indexes (including unique constraints) and foreign key constraints only apply to single tables, not to their inheritance children. This is true on both the referencing and referenced sides of a foreign key constraint. Thus, in the terms of the above example: If we declared cities.name to be UNIQUE or a PRIMARY KEY, this would not stop the capitals table from having rows with names duplicating rows in cities. And those duplicate rows would by default show up in queries from cities. In fact, by default capitals would have no unique constraint at all, and so could contain multiple rows with the same name. You could add a unique constraint to capitals, but this would not prevent duplication compared to cities. Similarly, if we were to specify that cities.name REFERENCES some other table, this constraint would not automatically propagate to capitals. In this case you could work around it by manually adding the same REFERENCES constraint to capitals. Specifying that another table's column REFERENCES cities(name) would allow the other table to contain city names, but not capital names. There is no good workaround for this case. These deficiencies will probably be fixed in some future release, but in the meantime considerable care is needed in deciding whether inheritance is useful for your application. In releases of PostgreSQL prior to 7.1, the default behavior was not to include child tables in queries. This was found to be error prone and also in violation of the SQL standard. You can get the pre-7.1 behavior by turning off the configuration option. partitioning table partitioning PostgreSQL supports basic table partitioning. This section describes why and how to implement partitioning as part of your database design. Partitioning refers to splitting what is logically one large table into smaller physical pieces. Partitioning can provide several benefits: Query performance can be improved dramatically in certain situations, particularly when most of the heavily accessed rows of the table are in a single partition or a small number of partitions. The partitioning substitutes for leading columns of indexes, reducing index size and making it more likely that the heavily-used parts of the indexes fit in memory. When queries or updates access a large percentage of a single partition, performance can be improved by taking advantage of sequential scan of that partition instead of using an index and random access reads scattered across the whole table. Bulk loads and deletes can be accomplished by adding or removing partitions, if that requirement is planned into the partitioning design. ALTER TABLE is far faster than a bulk operation. It also entirely avoids the VACUUM overhead caused by a bulk DELETE. Seldom-used data can be migrated to cheaper and slower storage media. The benefits will normally be worthwhile only when a table would otherwise be very large. The exact point at which a table will benefit from partitioning depends on the application, although a rule of thumb is that the size of the table should exceed the physical memory of the database server. Currently, PostgreSQL supports partitioning via table inheritance. Each partition must be created as a child table of a single parent table. The parent table itself is normally empty; it exists just to represent the entire data set. You should be familiar with inheritance (see ) before attempting to set up partitioning. The following forms of partitioning can be implemented in PostgreSQL: Range Partitioning The table is partitioned into ranges defined by a key column or set of columns, with no overlap between the ranges of values assigned to different partitions. For example one might partition by date ranges, or by ranges of identifiers for particular business objects. List Partitioning The table is partitioned by explicitly listing which key values appear in each partition. To set up a partitioned table, do the following: Create the master table, from which all of the partitions will inherit. This table will contain no data. Do not define any check constraints on this table, unless you intend them to be applied equally to all partitions. There is no point in defining any indexes or unique constraints on it, either. Create several child tables that each inherit from the master table. Normally, these tables will not add any columns to the set inherited from the master. We will refer to the child tables as partitions, though they are in every way normal PostgreSQL tables. Add table constraints to the partition tables to define the allowed key values in each partition. Typical examples would be: CHECK ( x = 1 ) CHECK ( county IN ( 'Oxfordshire', 'Buckinghamshire', 'Warwickshire' )) CHECK ( outletID >= 100 AND outletID < 200 ) Ensure that the constraints guarantee that there is no overlap between the key values permitted in different partitions. A common mistake is to set up range constraints like: CHECK ( outletID BETWEEN 100 AND 200 ) CHECK ( outletID BETWEEN 200 AND 300 ) This is wrong since it is not clear which partition the key value 200 belongs in. Note that there is no difference in syntax between range and list partitioning; those terms are descriptive only. For each partition, create an index on the key column(s), as well as any other indexes you might want. (The key index is not strictly necessary, but in most scenarios it is helpful. If you intend the key values to be unique then you should always create a unique or primary-key constraint for each partition.) Optionally, define a trigger or rule to redirect data inserted into the master table to the appropriate partition. Ensure that the configuration parameter is not disabled in postgresql.conf. If it is, queries will not be optimized as desired. For example, suppose we are constructing a database for a large ice cream company. The company measures peak temperatures every day as well as ice cream sales in each region. Conceptually, we want a table like: CREATE TABLE measurement ( city_id int not null, logdate date not null, peaktemp int, unitsales int ); We know that most queries will access just the last week's, month's or quarter's data, since the main use of this table will be to prepare online reports for management. To reduce the amount of old data that needs to be stored, we decide to only keep the most recent 3 years worth of data. At the beginning of each month we will remove the oldest month's data. In this situation we can use partitioning to help us meet all of our different requirements for the measurements table. Following the steps outlined above, partitioning can be set up as follows: The master table is the measurement table, declared exactly as above. Next we create one partition for each active month: CREATE TABLE measurement_y2006m02 ( ) INHERITS (measurement); CREATE TABLE measurement_y2006m03 ( ) INHERITS (measurement); ... CREATE TABLE measurement_y2007m11 ( ) INHERITS (measurement); CREATE TABLE measurement_y2007m12 ( ) INHERITS (measurement); CREATE TABLE measurement_y2008m01 ( ) INHERITS (measurement); Each of the partitions are complete tables in their own right, but they inherit their definitions from the measurement table. This solves one of our problems: deleting old data. Each month, all we will need to do is perform a DROP TABLE on the oldest child table and create a new child table for the new month's data. We must provide non-overlapping table constraints. Rather than just creating the partition tables as above, the table creation script should really be: CREATE TABLE measurement_y2006m02 ( CHECK ( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' ) ) INHERITS (measurement); CREATE TABLE measurement_y2006m03 ( CHECK ( logdate >= DATE '2006-03-01' AND logdate < DATE '2006-04-01' ) ) INHERITS (measurement); ... CREATE TABLE measurement_y2007m11 ( CHECK ( logdate >= DATE '2007-11-01' AND logdate < DATE '2007-12-01' ) ) INHERITS (measurement); CREATE TABLE measurement_y2007m12 ( CHECK ( logdate >= DATE '2007-12-01' AND logdate < DATE '2008-01-01' ) ) INHERITS (measurement); CREATE TABLE measurement_y2008m01 ( CHECK ( logdate >= DATE '2008-01-01' AND logdate < DATE '2008-02-01' ) ) INHERITS (measurement); We probably need indexes on the key columns too: CREATE INDEX measurement_y2006m02_logdate ON measurement_y2006m02 (logdate); CREATE INDEX measurement_y2006m03_logdate ON measurement_y2006m03 (logdate); ... CREATE INDEX measurement_y2007m11_logdate ON measurement_y2007m11 (logdate); CREATE INDEX measurement_y2007m12_logdate ON measurement_y2007m12 (logdate); CREATE INDEX measurement_y2008m01_logdate ON measurement_y2008m01 (logdate); We choose not to add further indexes at this time. We want our application to be able to say INSERT INTO measurement ... and have the data be redirected into the appropriate partition table. We can arrange that by attaching a suitable trigger function to the master table. If data will be added only to the latest partition, we can use a very simple trigger function: CREATE OR REPLACE FUNCTION measurement_insert_trigger() RETURNS TRIGGER AS $$ BEGIN INSERT INTO measurement_y2008m01 VALUES (NEW.*); RETURN NULL; END; $$ LANGUAGE plpgsql; After creating the function, we create a trigger which calls the trigger function: CREATE TRIGGER insert_measurement_trigger BEFORE INSERT ON measurement FOR EACH ROW EXECUTE PROCEDURE measurement_insert_trigger(); We must redefine the trigger function each month so that it always points to the current partition. The trigger definition does not need to be updated, however. We might want to insert data and have the server automatically locate the partition into which the row should be added. We could do this with a more complex trigger function, for example: CREATE OR REPLACE FUNCTION measurement_insert_trigger() RETURNS TRIGGER AS $$ BEGIN IF ( NEW.logdate >= DATE '2006-02-01' AND NEW.logdate < DATE '2006-03-01' ) THEN INSERT INTO measurement_y2006m02 VALUES (NEW.*); ELSIF ( NEW.logdate >= DATE '2006-03-01' AND NEW.logdate < DATE '2006-04-01' ) THEN INSERT INTO measurement_y2006m03 VALUES (NEW.*); ... ELSIF ( NEW.logdate >= DATE '2008-01-01' AND NEW.logdate < DATE '2008-02-01' ) THEN INSERT INTO measurement_y2008m01 VALUES (NEW.*); ELSE RAISE EXCEPTION 'Date out of range. Fix the measurement_insert_trigger() function!'; END IF; RETURN NULL; END; $$ LANGUAGE plpgsql; The trigger definition is the same as before. Note that each IF test must exactly match the CHECK constraint for its partition. While this function is more complex than the single-month case, it doesn't need to be updated as often, since branches can be added in advance of being needed. In practice it might be best to check the newest partition first, if most inserts go into that partition. For simplicity we have shown the trigger's tests in the same order as in other parts of this example. As we can see, a complex partitioning scheme could require a substantial amount of DDL. In the above example we would be creating a new partition each month, so it might be wise to write a script that generates the required DDL automatically. Normally the set of partitions established when initially defining the table are not intended to remain static. It is common to want to remove old partitions of data and periodically add new partitions for new data. One of the most important advantages of partitioning is precisely that it allows this otherwise painful task to be executed nearly instantaneously by manipulating the partition structure, rather than physically moving large amounts of data around. The simplest option for removing old data is simply to drop the partition that is no longer necessary: DROP TABLE measurement_y2006m02; This can very quickly delete millions of records because it doesn't have to individually delete every record. Another option that is often preferable is to remove the partition from the partitioned table but retain access to it as a table in its own right: ALTER TABLE measurement_y2006m02 NO INHERIT measurement; This allows further operations to be performed on the data before it is dropped. For example, this is often a useful time to back up the data using COPY, pg_dump, or similar tools. It might also be a useful time to aggregate data into smaller formats, perform other data manipulations, or run reports. Similarly we can add a new partition to handle new data. We can create an empty partition in the partitioned table just as the original partitions were created above: CREATE TABLE measurement_y2008m02 ( CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' ) ) INHERITS (measurement); As an alternative, it is sometimes more convenient to create the new table outside the partition structure, and make it a proper partition later. This allows the data to be loaded, checked, and transformed prior to it appearing in the partitioned table: CREATE TABLE measurement_y2008m02 (LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS); ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02 CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' ); \copy measurement_y2008m02 from 'measurement_y2008m02' -- possibly some other data preparation work ALTER TABLE measurement_y2008m02 INHERIT measurement; constraint exclusion Constraint exclusion is a query optimization technique that improves performance for partitioned tables defined in the fashion described above. As an example: SET constraint_exclusion = on; SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01'; Without constraint exclusion, the above query would scan each of the partitions of the measurement table. With constraint exclusion enabled, the planner will examine the constraints of each partition and try to prove that the partition need not be scanned because it could not contain any rows meeting the query's WHERE clause. When the planner can prove this, it excludes the partition from the query plan. You can use the EXPLAIN command to show the difference between a plan with constraint_exclusion on and a plan with it off. A typical unoptimized plan for this type of table setup is: SET constraint_exclusion = off; EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01'; QUERY PLAN ----------------------------------------------------------------------------------------------- Aggregate (cost=158.66..158.68 rows=1 width=0) -> Append (cost=0.00..151.88 rows=2715 width=0) -> Seq Scan on measurement (cost=0.00..30.38 rows=543 width=0) Filter: (logdate >= '2008-01-01'::date) -> Seq Scan on measurement_y2006m02 measurement (cost=0.00..30.38 rows=543 width=0) Filter: (logdate >= '2008-01-01'::date) -> Seq Scan on measurement_y2006m03 measurement (cost=0.00..30.38 rows=543 width=0) Filter: (logdate >= '2008-01-01'::date) ... -> Seq Scan on measurement_y2007m12 measurement (cost=0.00..30.38 rows=543 width=0) Filter: (logdate >= '2008-01-01'::date) -> Seq Scan on measurement_y2008m01 measurement (cost=0.00..30.38 rows=543 width=0) Filter: (logdate >= '2008-01-01'::date) Some or all of the partitions might use index scans instead of full-table sequential scans, but the point here is that there is no need to scan the older partitions at all to answer this query. When we enable constraint exclusion, we get a significantly cheaper plan that will deliver the same answer: SET constraint_exclusion = on; EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01'; QUERY PLAN ----------------------------------------------------------------------------------------------- Aggregate (cost=63.47..63.48 rows=1 width=0) -> Append (cost=0.00..60.75 rows=1086 width=0) -> Seq Scan on measurement (cost=0.00..30.38 rows=543 width=0) Filter: (logdate >= '2008-01-01'::date) -> Seq Scan on measurement_y2008m01 measurement (cost=0.00..30.38 rows=543 width=0) Filter: (logdate >= '2008-01-01'::date) Note that constraint exclusion is driven only by CHECK constraints, not by the presence of indexes. Therefore it isn't necessary to define indexes on the key columns. Whether an index needs to be created for a given partition depends on whether you expect that queries that scan the partition will generally scan a large part of the partition or just a small part. An index will be helpful in the latter case but not the former. The default (and recommended) setting of is actually neither on nor off, but an intermediate setting called partition, which causes the technique to be applied only to queries that are likely to be working on partitioned tables. The on setting causes the planner to examine CHECK constraints in all queries, even simple ones that are unlikely to benefit. A different approach to redirecting inserts into the appropriate partition table is to set up rules, instead of a trigger, on the master table. For example: CREATE RULE measurement_insert_y2006m02 AS ON INSERT TO measurement WHERE ( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' ) DO INSTEAD INSERT INTO measurement_y2006m02 VALUES (NEW.*); ... CREATE RULE measurement_insert_y2008m01 AS ON INSERT TO measurement WHERE ( logdate >= DATE '2008-01-01' AND logdate < DATE '2008-02-01' ) DO INSTEAD INSERT INTO measurement_y2008m01 VALUES (NEW.*); A rule has significantly more overhead than a trigger, but the overhead is paid once per query rather than once per row, so this method might be advantageous for bulk-insert situations. In most cases, however, the trigger method will offer better performance. Be aware that COPY ignores rules. If you want to use COPY to insert data, you'll need to copy into the correct partition table rather than into the master. COPY does fire triggers, so you can use it normally if you use the trigger approach. Another disadvantage of the rule approach is that there is no simple way to force an error if the set of rules doesn't cover the insertion date; the data will silently go into the master table instead. Partitioning can also be arranged using a UNION ALL view, instead of table inheritance. For example, CREATE VIEW measurement AS SELECT * FROM measurement_y2006m02 UNION ALL SELECT * FROM measurement_y2006m03 ... UNION ALL SELECT * FROM measurement_y2007m11 UNION ALL SELECT * FROM measurement_y2007m12 UNION ALL SELECT * FROM measurement_y2008m01; However, the need to recreate the view adds an extra step to adding and dropping individual partitions of the data set. In practice this method has little to recommend it compared to using inheritance. The following caveats apply to partitioned tables: There is no automatic way to verify that all of the CHECK constraints are mutually exclusive. It is safer to create code that generates partitions and creates and/or modifies associated objects than to write each by hand. The schemes shown here assume that the partition key column(s) of a row never change, or at least do not change enough to require it to move to another partition. An UPDATE that attempts to do that will fail because of the CHECK constraints. If you need to handle such cases, you can put suitable update triggers on the partition tables, but it makes management of the structure much more complicated. If you are using manual VACUUM or ANALYZE commands, don't forget that you need to run them on each partition individually. A command like: ANALYZE measurement; will only process the master table. The following caveats apply to constraint exclusion: Constraint exclusion only works when the query's WHERE clause contains constants. A parameterized query will not be optimized, since the planner cannot know which partitions the parameter value might select at run time. For the same reason, stable functions such as CURRENT_DATE must be avoided. Keep the partitioning constraints simple, else the planner may not be able to prove that partitions don't need to be visited. Use simple equality conditions for list partitioning, or simple range tests for range partitioning, as illustrated in the preceding examples. A good rule of thumb is that partitioning constraints should contain only comparisons of the partitioning column(s) to constants using B-tree-indexable operators. All constraints on all partitions of the master table are examined during constraint exclusion, so large numbers of partitions are likely to increase query planning time considerably. Partitioning using these techniques will work well with up to perhaps a hundred partitions; don't try to use many thousands of partitions. Tables are the central objects in a relational database structure, because they hold your data. But they are not the only objects that exist in a database. Many other kinds of objects can be created to make the use and management of the data more efficient or convenient. They are not discussed in this chapter, but we give you a list here so that you are aware of what is possible: Views Functions and operators Data types and domains Triggers and rewrite rules Detailed information on these topics appears in . CASCADE with DROP RESTRICT with DROP When you create complex database structures involving many tables with foreign key constraints, views, triggers, functions, etc. you implicitly create a net of dependencies between the objects. For instance, a table with a foreign key constraint depends on the table it references. To ensure the integrity of the entire database structure, PostgreSQL makes sure that you cannot drop objects that other objects still depend on. For example, attempting to drop the products table we had considered in , with the orders table depending on it, would result in an error message such as this: DROP TABLE products; NOTICE: constraint orders_product_no_fkey on table orders depends on table products ERROR: cannot drop table products because other objects depend on it HINT: Use DROP ... CASCADE to drop the dependent objects too. The error message contains a useful hint: if you do not want to bother deleting all the dependent objects individually, you can run: DROP TABLE products CASCADE; and all the dependent objects will be removed. In this case, it doesn't remove the orders table, it only removes the foreign key constraint. (If you want to check what DROP ... CASCADE will do, run DROP without CASCADE and read the NOTICE messages.) All drop commands in PostgreSQL support specifying CASCADE. Of course, the nature of the possible dependencies varies with the type of the object. You can also write RESTRICT instead of CASCADE to get the default behavior, which is to prevent the dropping of objects that other objects depend on. According to the SQL standard, specifying either RESTRICT or CASCADE is required. No database system actually enforces that rule, but whether the default behavior is RESTRICT or CASCADE varies across systems. Foreign key constraint dependencies and serial column dependencies from PostgreSQL versions prior to 7.3 are not maintained or created during the upgrade process. All other dependency types will be properly created during an upgrade from a pre-7.3 database.