WITH Queries (Common Table Expressions)
   WITH provides a way to write auxiliary statements for use in a
   larger query.  These statements, which are often referred to as Common
   Table Expressions or CTEs, can be thought of as defining
   temporary tables that exist just for one query.  Each auxiliary statement
   in a WITH clause can be a SELECT,
   INSERT, UPDATE, or DELETE; and the
   WITH clause itself is attached to a primary statement that can
   also be a SELECT, INSERT, UPDATE, or
   DELETE.
  
SELECT in WITH
   The basic value of SELECT in WITH is to
   break down complicated queries into simpler parts.  An example is:
WITH regional_sales AS (
    SELECT region, SUM(amount) AS total_sales
    FROM orders
    GROUP BY region
), top_regions AS (
    SELECT region
    FROM regional_sales
    WHERE total_sales > (SELECT SUM(total_sales)/10 FROM regional_sales)
)
SELECT region,
       product,
       SUM(quantity) AS product_units,
       SUM(amount) AS product_sales
FROM orders
WHERE region IN (SELECT region FROM top_regions)
GROUP BY region, product;
   which displays per-product sales totals in only the top sales regions.
   The WITH clause defines two auxiliary statements named
   regional_sales and top_regions,
   where the output of regional_sales is used in
   top_regions and the output of top_regions
   is used in the primary SELECT query.
   This example could have been written without WITH,
   but we'd have needed two levels of nested sub-SELECTs.  It's a bit
   easier to follow this way.
  
   
   The optional RECURSIVE modifier changes WITH
   from a mere syntactic convenience into a feature that accomplishes
   things not otherwise possible in standard SQL.  Using
   RECURSIVE, a WITH query can refer to its own
   output.  A very simple example is this query to sum the integers from 1
   through 100:
WITH RECURSIVE t(n) AS (
    VALUES (1)
  UNION ALL
    SELECT n+1 FROM t WHERE n < 100
)
SELECT sum(n) FROM t;
   The general form of a recursive WITH query is always a
   non-recursive term, then UNION (or
   UNION ALL), then a
   recursive term, where only the recursive term can contain
   a reference to the query's own output.  Such a query is executed as
   follows:
  
Recursive Query Evaluation
     Evaluate the non-recursive term.  For UNION (but not
     UNION ALL), discard duplicate rows.  Include all remaining
     rows in the result of the recursive query, and also place them in a
     temporary working table.
    
So long as the working table is not empty, repeat these steps:
       Evaluate the recursive term, substituting the current contents of
       the working table for the recursive self-reference.
       For UNION (but not UNION ALL), discard
       duplicate rows and rows that duplicate any previous result row.
       Include all remaining rows in the result of the recursive query, and
       also place them in a temporary intermediate table.
      
Replace the contents of the working table with the contents of the intermediate table, then empty the intermediate table.
    While RECURSIVE allows queries to be specified
    recursively, internally such queries are evaluated iteratively.
   
   In the example above, the working table has just a single row in each step,
   and it takes on the values from 1 through 100 in successive steps.  In
   the 100th step, there is no output because of the WHERE
   clause, and so the query terminates.
  
Recursive queries are typically used to deal with hierarchical or tree-structured data. A useful example is this query to find all the direct and indirect sub-parts of a product, given only a table that shows immediate inclusions:
WITH RECURSIVE included_parts(sub_part, part, quantity) AS (
    SELECT sub_part, part, quantity FROM parts WHERE part = 'our_product'
  UNION ALL
    SELECT p.sub_part, p.part, p.quantity * pr.quantity
    FROM included_parts pr, parts p
    WHERE p.part = pr.sub_part
)
SELECT sub_part, SUM(quantity) as total_quantity
FROM included_parts
GROUP BY sub_part
   When working with recursive queries it is important to be sure that
   the recursive part of the query will eventually return no tuples,
   or else the query will loop indefinitely.  Sometimes, using
   UNION instead of UNION ALL can accomplish this
   by discarding rows that duplicate previous output rows.  However, often a
   cycle does not involve output rows that are completely duplicate: it may be
   necessary to check just one or a few fields to see if the same point has
   been reached before.  The standard method for handling such situations is
   to compute an array of the already-visited values.  For example, consider
   the following query that searches a table graph using a
   link field:
WITH RECURSIVE search_graph(id, link, data, depth) AS (
    SELECT g.id, g.link, g.data, 1
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1
    FROM graph g, search_graph sg
    WHERE g.id = sg.link
)
SELECT * FROM search_graph;
   This query will loop if the link relationships contain
   cycles.  Because we require a “depth” output, just changing
   UNION ALL to UNION would not eliminate the looping.
   Instead we need to recognize whether we have reached the same row again
   while following a particular path of links.  We add two columns
   path and cycle to the loop-prone query:
WITH RECURSIVE search_graph(id, link, data, depth, path, cycle) AS (
    SELECT g.id, g.link, g.data, 1,
      ARRAY[g.id],
      false
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1,
      path || g.id,
      g.id = ANY(path)
    FROM graph g, search_graph sg
    WHERE g.id = sg.link AND NOT cycle
)
SELECT * FROM search_graph;
Aside from preventing cycles, the array value is often useful in its own right as representing the “path” taken to reach any particular row.
   In the general case where more than one field needs to be checked to
   recognize a cycle, use an array of rows.  For example, if we needed to
   compare fields f1 and f2:
WITH RECURSIVE search_graph(id, link, data, depth, path, cycle) AS (
    SELECT g.id, g.link, g.data, 1,
      ARRAY[ROW(g.f1, g.f2)],
      false
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1,
      path || ROW(g.f1, g.f2),
      ROW(g.f1, g.f2) = ANY(path)
    FROM graph g, search_graph sg
    WHERE g.id = sg.link AND NOT cycle
)
SELECT * FROM search_graph;
    Omit the ROW() syntax in the common case where only one field
    needs to be checked to recognize a cycle.  This allows a simple array
    rather than a composite-type array to be used, gaining efficiency.
   
    The recursive query evaluation algorithm produces its output in
    breadth-first search order.  You can display the results in depth-first
    search order by making the outer query ORDER BY a
    “path” column constructed in this way.
   
   A helpful trick for testing queries
   when you are not certain if they might loop is to place a LIMIT
   in the parent query.  For example, this query would loop forever without
   the LIMIT:
WITH RECURSIVE t(n) AS (
    SELECT 1
  UNION ALL
    SELECT n+1 FROM t
)
SELECT n FROM t LIMIT 100;
   This works because PostgreSQL's implementation
   evaluates only as many rows of a WITH query as are actually
   fetched by the parent query.  Using this trick in production is not
   recommended, because other systems might work differently.  Also, it
   usually won't work if you make the outer query sort the recursive query's
   results or join them to some other table, because in such cases the
   outer query will usually try to fetch all of the WITH query's
   output anyway.
  
   A useful property of WITH queries is that they are
   normally evaluated only once per execution of the parent query, even if
   they are referred to more than once by the parent query or
   sibling WITH queries.
   Thus, expensive calculations that are needed in multiple places can be
   placed within a WITH query to avoid redundant work.  Another
   possible application is to prevent unwanted multiple evaluations of
   functions with side-effects.
   However, the other side of this coin is that the optimizer is not able to
   push restrictions from the parent query down into a multiply-referenced
   WITH query, since that might affect all uses of the
   WITH query's output when it should affect only one.
   The multiply-referenced WITH query will be
   evaluated as written, without suppression of rows that the parent query
   might discard afterwards.  (But, as mentioned above, evaluation might stop
   early if the reference(s) to the query demand only a limited number of
   rows.)
  
   However, if a WITH query is non-recursive and
   side-effect-free (that is, it is a SELECT containing
   no volatile functions) then it can be folded into the parent query,
   allowing joint optimization of the two query levels.  By default, this
   happens if the parent query references the WITH query
   just once, but not if it references the WITH query
   more than once.  You can override that decision by
   specifying MATERIALIZED to force separate calculation
   of the WITH query, or by specifying NOT
   MATERIALIZED to force it to be merged into the parent query.
   The latter choice risks duplicate computation of
   the WITH query, but it can still give a net savings if
   each usage of the WITH query needs only a small part
   of the WITH query's full output.
  
A simple example of these rules is
WITH w AS (
    SELECT * FROM big_table
)
SELECT * FROM w WHERE key = 123;
   This WITH query will be folded, producing the same
   execution plan as
SELECT * FROM big_table WHERE key = 123;
   In particular, if there's an index on key,
   it will probably be used to fetch just the rows having key =
   123.  On the other hand, in
WITH w AS (
    SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;
   the WITH query will be materialized, producing a
   temporary copy of big_table that is then
   joined with itself — without benefit of any index.  This query
   will be executed much more efficiently if written as
WITH w AS NOT MATERIALIZED (
    SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;
   so that the parent query's restrictions can be applied directly
   to scans of big_table.
  
   An example where NOT MATERIALIZED could be
   undesirable is
WITH w AS (
    SELECT key, very_expensive_function(val) as f FROM some_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.f = w2.f;
   Here, materialization of the WITH query ensures
   that very_expensive_function is evaluated only
   once per table row, not twice.
  
   The examples above only show WITH being used with
   SELECT, but it can be attached in the same way to
   INSERT, UPDATE, or DELETE.
   In each case it effectively provides temporary table(s) that can
   be referred to in the main command.
  
WITH
    You can use data-modifying statements (INSERT,
    UPDATE, or DELETE) in WITH.  This
    allows you to perform several different operations in the same query.
    An example is:
WITH moved_rows AS (
    DELETE FROM products
    WHERE
        "date" >= '2010-10-01' AND
        "date" < '2010-11-01'
    RETURNING *
)
INSERT INTO products_log
SELECT * FROM moved_rows;
    This query effectively moves rows from products to
    products_log.  The DELETE in WITH
    deletes the specified rows from products, returning their
    contents by means of its RETURNING clause; and then the
    primary query reads that output and inserts it into
    products_log.
   
    A fine point of the above example is that the WITH clause is
    attached to the INSERT, not the sub-SELECT within
    the INSERT.  This is necessary because data-modifying
    statements are only allowed in WITH clauses that are attached
    to the top-level statement.  However, normal WITH visibility
    rules apply, so it is possible to refer to the WITH
    statement's output from the sub-SELECT.
   
    Data-modifying statements in WITH usually have
    RETURNING clauses (see Section 6.4),
    as shown in the example above.
    It is the output of the RETURNING clause, not the
    target table of the data-modifying statement, that forms the temporary
    table that can be referred to by the rest of the query.  If a
    data-modifying statement in WITH lacks a RETURNING
    clause, then it forms no temporary table and cannot be referred to in
    the rest of the query.  Such a statement will be executed nonetheless.
    A not-particularly-useful example is:
WITH t AS (
    DELETE FROM foo
)
DELETE FROM bar;
    This example would remove all rows from tables foo and
    bar.  The number of affected rows reported to the client
    would only include rows removed from bar.
   
    Recursive self-references in data-modifying statements are not
    allowed.  In some cases it is possible to work around this limitation by
    referring to the output of a recursive WITH, for example:
WITH RECURSIVE included_parts(sub_part, part) AS (
    SELECT sub_part, part FROM parts WHERE part = 'our_product'
  UNION ALL
    SELECT p.sub_part, p.part
    FROM included_parts pr, parts p
    WHERE p.part = pr.sub_part
)
DELETE FROM parts
  WHERE part IN (SELECT part FROM included_parts);
This query would remove all direct and indirect subparts of a product.
    Data-modifying statements in WITH are executed exactly once,
    and always to completion, independently of whether the primary query
    reads all (or indeed any) of their output.  Notice that this is different
    from the rule for SELECT in WITH: as stated in the
    previous section, execution of a SELECT is carried only as far
    as the primary query demands its output.
   
    The sub-statements in WITH are executed concurrently with
    each other and with the main query.  Therefore, when using data-modifying
    statements in WITH, the order in which the specified updates
    actually happen is unpredictable.  All the statements are executed with
    the same snapshot (see Chapter 13), so they
    cannot “see” one another's effects on the target tables.  This
    alleviates the effects of the unpredictability of the actual order of row
    updates, and means that RETURNING data is the only way to
    communicate changes between different WITH sub-statements and
    the main query.  An example of this is that in
WITH t AS (
    UPDATE products SET price = price * 1.05
    RETURNING *
)
SELECT * FROM products;
    the outer SELECT would return the original prices before the
    action of the UPDATE, while in
WITH t AS (
    UPDATE products SET price = price * 1.05
    RETURNING *
)
SELECT * FROM t;
    the outer SELECT would return the updated data.
   
    Trying to update the same row twice in a single statement is not
    supported.  Only one of the modifications takes place, but it is not easy
    (and sometimes not possible) to reliably predict which one.  This also
    applies to deleting a row that was already updated in the same statement:
    only the update is performed.  Therefore you should generally avoid trying
    to modify a single row twice in a single statement.  In particular avoid
    writing WITH sub-statements that could affect the same rows
    changed by the main statement or a sibling sub-statement.  The effects
    of such a statement will not be predictable.
   
    At present, any table used as the target of a data-modifying statement in
    WITH must not have a conditional rule, nor an ALSO
    rule, nor an INSTEAD rule that expands to multiple statements.