PostgreSQL 8.4.2 Documentation
Prev | Fast Backward | Chapter 7. Queries | Fast Forward | Next

# 7.8. `WITH` Queries

`WITH` provides a way to write subqueries for use in a larger `SELECT` query. The subqueries can be thought of as defining temporary tables that exist just for this query. One use of this feature 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. 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

1. 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.

2. So long as the working table is not empty, repeat these steps:

   1. 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.

   2. Replace the contents of the working table with the contents of the intermediate table, then empty the intermediate table.

> Note: Strictly speaking, this process is iteration not recursion, but `RECURSIVE` is the terminology chosen by the SQL standards committee.

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
        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;

> Tip: 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.

> Tip: 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.

A useful property of `WITH` queries is that they are 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 less able to push restrictions from the parent query down into a `WITH` query than an ordinary sub-query. The `WITH` query will generally be evaluated as stated, 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.)

Prev | Home | Next
`VALUES` Lists | Up | Data Types